title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Self-supervised video pretraining yields robust and more human-aligned visual representations | Accept (poster) | Summary: The research question addressed by the paper is whether self-supervised training on
videos leads to image features that are better aligned with human perception. To that
end, the authors propose a contrastive learning method that leverages natural
changes over time as different views, beyond adapted image augm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and address the concerns below.
**“none of the other video based self-supervised methods is tested and none of the ablation studies considers alignment with human vision.”**
We agree this was a limitation of our current results and have addressed this by gr... | Summary: This paper introduces a new video dataset (VideoNet), and architectual adjustments (VITO) that enables allows pretraining on video datasets to improve performance on downstream transfer tasks, such as segmentation, object detection, and generalization. The VITO network uses a ResNet backbone, and architectural... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and now address the weaknesses/questions.
**Weakness 1: lower ImageNet validation numbers**
Yes VITO does underperform supervised ImageNet pretraining on ImageNet classification by ~10% and other SSL ImageNet pretraining methods by 5-7%. This how... | Summary: The paper proposes a self-supervised method for learning image representations from videos.
Towards this end, a procedure to curate video datasets most suitable for such pre-training is proposed by selecting videos that best match the distribution of visual classes found in ImageNet.
Secondly, this dataset is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and address the concerns below.
**Weakness 1: More extensive ablations**:
See our global response for a detailed discussion of more extensive ablations we have now performed. Notably, we have verified that all components (dataset, contrastive attention poo... | Summary: This paper studies how to take advantage of natural temporal distortions in video to learn image spatial representations. The paper first proposes a VideoNet dataset that filters the video data from common video datasets by an ImageNet classifier. The paper further proposes to multi-scale attention pooling to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and address the concerns below.
**Weakness 1: VideoNet curation**
We realize that the VideoNet curation procedure does involve utilizing the class distribution or implicit labels. However, most of the high-performance SSL methods we compare to were trained ... | Rebuttal 1:
Rebuttal: We thank all reviewers for the feedback and acknowledging our work's clear presentation of a novel step towards learning more general human-aligned visual models. We will address global concerns here and individual comments separately. Please see the rebuttal PDF for new results.
**Ablations:** ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a SSL method using video pre-training to produce general visual representations for both image and video tasks. The proposed VITO pipeline includes a data curation process and a video SSL technique based on MoCLR. The authors conduct a comprehensive evaluation of VITO's performance on dive... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and direct them to the global response for our summary. We will address each cited weakness point by point.
**Weakness 1: technical novelty**
We agree with the reviewer that the basic idea of applying an image-based SSL framework to a video datas... | Summary: The paper proposes VITO, a new method for self-supervised video pretraining that learns general and human-aligned visual representations. It made several modifications over existing contrastive learning frameworks, including larger crop sizes, improved temporal sampling scheme, and multi-scale attention featur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Regarding the human alignment evaluations, we agree that more evaluations should be done; however, we focused on saliency and shape-bias tasks as these have been recent and prevalent tasks where many deep image models fail to capture important aspects of h... | null | null | null | null |
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data | Accept (poster) | Summary: This paper studies the machine learning problems on tabular data and convert the problem into node classification. Then, they formally analyze the Cross-Class Neighborhood Similarity (CCNS) and validate the performance on benchmark datasets.
Strengths: S1. The studied problem is very important.
S2. The pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing that the problem we study is important, and that we managed to present the theoretical ideas in a clear form.
We address the questions/concerns of the reviewer below.
**W1:** Long-tail node classification baselines deal, depending on its definition, with pr... | Summary: The paper studies quantitative measures to evaluate the usefulness of k-NN graphs when performing classification in datasets that do not originally come with a known graph topology. The authors argue that the use of k-NN graphs is not useful.
Strengths: A study of applying k-NN to construct a graph and then ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing constructive feedback and the opportunity to clarify unclear points.
**W1**: As we better discuss in our answers below, we rely on Gaussian **mixtures** that are known to be universal approximators. In addition, despite not covering all use-cases, assumption 2... | Summary: This paper shows that using a k-NN graph in tabular data classification is not beneficial compared to MLP. Specifically, based on Cross-Class Neighborhood Similarity, the authors theoretically induce the analytical forms of the squared error distance (SED) of two approaches. In the experiment, they demonstrate... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing constructive feedback. Below, we address the reviewer questions, each of which is related to a specific weakness.
**Q:** Following [W1], could you provide the correlation between attributes in the real-world datasets?
A: Please have a look at the PD... | Summary: This submission provides a theoretical framework to explore the benefits of Nearest Neighbor (NN) graphs in the absence of a predefined graph structure. The Cross-Class Neighborhood Similarity (CCNS) is formally analyzed to evaluate the usefulness of structures within nearest neighbor graphs. The study also in... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read and assess our work. Below we provide an answer to the questions posed.
**Q1:** Equation (2) is not clear. For example, how to derive the equivalent between two absolute values?
**A:** The equivalence of Eq. 2 follows from the linearity of expec... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive feedback. We did our best to answer all questions and we look forward to a fruitful discussion.
Attached you can find a PDF with additional results to answer the reviewers' questions. Please note that the Table is partially complete due to r... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The main contributions of this paper are as follows:
1. The authors of the paper study Cross-Class Neighborhood Similarity (CCNS) for nearest neighbor graphs and provide its first lower bound.
2. They apply a Deep Graph Network (DGN) to an artificial k-NN graph and study how it affect the class separability ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the merits and strengths of our work. Below, we will address the comments and questions that have been raised.
**On the independence assumption**
The conditional independence assumption has been widely used in the graphical models literature because it is pa... | Summary: This paper proposes to investigate the practice of using nearest neighbour graphs build upon tabular data in order to classify samples (then relying on a node classification model). The authors note that this practice has been shown to be relevant in the semi-supervised case, but not with full supervision - an... | Rebuttal 1:
Rebuttal: We are glad that the reviewer found our work well presented and motivated. Below, we comment on the points raised by the reviewer, hoping that it will better clarify the scope of this work.
**Q:** While the paper clearly discusses the limitations brought by the assumptions it makes, it would be i... | null | null | null | null |
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation | Reject | Summary: This paper proposes a method called FedLGD that utilizes distilled virtual data on both clients and the server to train FL models. To address the synchronization issue and class imbalance, the authors use iterative distribution matching to distill the same amount of local virtual data on the clients for local ... | Rebuttal 1:
Rebuttal: > Privacy concern on sharing the local dataset statistics
Thank you for the comments. However, we would like to point out that FedLGD aims to reduce local privacy leakage *by training and sharing gradients w.r.t. local virtual data*. The global virtual data is designed for *regularizing local tr... | Summary: This work proposes a method to address data heterogeneity from the perspective of dataset distillation, named FedLGD. Specifically, the proposed iterative distribution matching and federated gradient matching strategies are used to iteratively update the local balanced data and the global shared virtual data, ... | Rebuttal 1:
Rebuttal: > Explanation of the notations and symbols
In Sec. 3.1, we started with the classical FL setting and derived to FVL setting, so we used $\widetilde{D}\_{i}$ to represent each client’s virtual data. Beginning Sec. 3.2, we introduced the global and virtual data, so we used $\widetilde{D}^c_i$ and $... | Summary: To solve the challenges of synchronization, efficiency, and privacy, this paper presents a local-global distillation mechanism for FL (FedLGD). In FedLGD, an iterative distribution matching scheme is proposed to distill global virtual data to alleviate the heterogeneous problem. Experiments have shown superior... | Rebuttal 1:
Rebuttal: > Justification of the client heterogeneity considered in FedLGD
We studied on both *label and feature shift* among clients as we stated in the last paragraph of our Introduction(line 64-76). Intuitively, generating the same Images Per Class (IPC) can balance label shift. Particularly, we showed ... | Summary: This paper introduces an approach on Federated learning using dataset distillation techniques
Strengths: 1. The idea of using dataset distillation for FL is interesting
2. The solution is reasonable
3. The experimental results show the effectiveness of the proposed approach
Weaknesses: 1. In a few equation... | Rebuttal 1:
Rebuttal: > Explanation of Eq. 3 and Eq. 5
We stated that $L_{CE}$ is the cross-entropy loss in line 197 and $L_{Dist}$ is defined the same as that in [*46*] in line 219. Apologize for the confusion, following your suggestion, we have added the detailed equation of $L_{CE}$ and $L_{Dist}$ in our revised ve... | Rebuttal 1:
Rebuttal: > Appreciation to the reviewers
We thank the reviewer for the positive comments about our FedLGD design(5ytS, DZFG, zf9v), FedLGD's effectiveness through comprehensive experiments(5ytS, DZFG, zf9v, TJpJ), the presentation of FedLGD (DZFG), and the privacy-preservation mechanism of FedLGD(TJpJ).
W... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Algorithmic Reasoning Without Intermediate Supervision | Accept (poster) | Summary: The paper addresses neural algorithmic reasoning without the supervision of intermediate steps of reasoning. Typically, neural algorithmic reasoning requires supervision on the intermediate steps of reasoning. The paper proposes a method that does not require intermediate supervision and achieves competitive r... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful review!
We agree that adding a more detailed explanation of the Hint-ReLIC method to the background section would make the paper easier to follow, we will update this section. However, we are happy and open to clarifying something during the discussion period if nee... | Summary: This paper addresses the challenge of developing neural networks capable of performing algorithmic reasoning. The paper discusses disadvantages about the use of intermediate hints during training of algorithmic reasoners. The authors propose a regularisation term to ensure that meaningful representations are l... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thoughtful review of our paper!
First, we would like to emphasize one of our contributions - the modification of the no-hint mode towards being more similar to the hint-based one - that is not discussed in the review. While this contribution is technically simple, we b... | Summary: The paper is about neural algorithmic reasoning, which is the task of building models that can execute classical algorithms. The paper focuses on learning algorithms without intermediate supervision, which means using only input-output pairs and not the steps (hints) of the algorithm. The paper proposes two im... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thoughtful review! Let us address the raised concerns and questions.
> Can the authors give a more detailed description of the proposed “no hint” architecture?
The main difference between the original no-hint version and the proposed one is the presence of an encoding... | Summary: This paper proposes a novel method for Algorithmic Reasoning without intermediate supervision. The core idea is to use a self-supervised objective that regularize the internal computations. The authors evaluated the proposed method with CLRS algorithmic reasoning benchmark and achieve state-of-the-art performa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and positive feedback!
Following your suggestion, we conducted several ablations (please, see the details in the [global response](https://openreview.net/forum?id=vBwSACOB3x¬eId=UUd9q6foXC)):
- Different augmentations count (Table 3 from the global respons... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback and many constructive suggestions and comments.
We have incorporated additional clarifications and experiments in the attached pdf, which contains:
**Table 1.** Parameters of the model and training procedure.
**Table 2.** Ablation on the steps count... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper focuses on neural networks that learn to execute algorithms, using the CLRS benchmark. It tackles the case of learning to execute when there are no hints available (no access to intermediate steps of the algorithm's trace). To do that, it proposes two modifications: the first is architectural, which ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and constructive comments!
We would like to additionally support our motivation towards investigating no-hint reasoners with an experiment (Fig. 1(a) from the global response), which demonstrates the execution trajectories for sorting models. We discuss our ... | null | null | null | null | null | null |
An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits | Accept (poster) | Summary: The paper addresses the challenge of selecting an algorithm suited to the environment type, which is often unknown in real-world applications. This paper introduces the concept of best-of-both-worlds (BOBW) linear bandit algorithms that perform well in both stochastic and adversarial environments. Previous BOB... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers' thoughtful and comprehensive comments and feedback.
The reviewers' insights have greatly contributed to the improvement of our work, and we sincerely appreciate the time and effort you have invested in this review.
We hope our response below addresses your conce... | Summary: This paper considers the best-of-both-worlds problem for linear bandits. They investigate an Exploration-by-Optimization approach for this problem and show O(d^2\logT) regret for stochastic setting and O(d\sqrt{T}) regret for adversarial setting.
Strengths: This paper investigates a new approach for Best-of-... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper.
We hope the following answers address your questions.
> Given existing works on BoBW problem for linear bandits, the convergence results provided by this paper are not optimal.
As the reviewer pointed out, our bounds are not tight and w... | Summary: The paper establishes $\log T$-style instance-dependent regret upper-bounds for linear bandit algorithms built upon the FTRL framework and appropriate exploration-by-optimization style update steps.
Strengths: The $\log T$-style instance-dependent regret upper-bounds can be achieved on general convex action s... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We hope that our response below addresses your concerns.
> Similar to most existing works on linear bandits that mainly discusses sample complexity instead the actual computational cost, the paper by default assumes one can effectively and accura... | Summary: This paper studies the "best-of-both-world" problem for linear bandits, meaning that designing a single algorithm that simultaneously achieves a $O(\sqrt{T\log T})$ regret in the adversarial case and $O(\log T)$ regret in the stochastic case. They propose to use the Exploration-by-Optimization approach to obta... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have invested in this review.
> The analysis relies on the unique optimal action assumption, and the bound in the stochastic environment can be arbitrarily worse than the instance-dependent bound by Lattimore and Szepesvari (2017). However, these ar... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Reflexion: language agents with verbal reinforcement learning | Accept (poster) | Summary: This paper presents a method by which LLMs can improve task performance through a form of learning in language: distilling unsuccessful attempts down to a "lesson learned" via a self-reflection process. The work shows that this idea leads to improved performance within a variety of task domains, and includes f... | Rebuttal 1:
Rebuttal:
Thank you for your helpful suggestions!
### 1. Related work
Thank you for pointing out these, which we will cite and discuss!
### 2. Evaluation for closed-source large language models
- This is a general issue for all works that use closed-source LLMs, and motivated us to create our own Leetc... | Summary: The paper introduces a novel framework, Reflexion, to improve the learning capabilities of language agents. Instead of traditional reinforcement learning methods, Reflexion uses linguistic feedback, where agents verbally reflect on task feedback and store these reflections in an episodic memory buffer to enhan... | Rebuttal 1:
Rebuttal:
### 1. the specific design details may only work on one task and may fail on the other ones
Please see the General Response.
### 2. Accuracy comparisons
You are right. The Reflexion performance on HotpotQA is not meant as a SoTA claim. Rather, it is intended to show how performances can be imp... | Summary: This paper proposes a novel framework to do RL style learning through LLM. The idea is interesting. The authors have also done evaluation to show this technique/framework can work better than baseline methods w/ a few different tasks.
Strengths: The author proposed a novel framework to let LLM learn new task... | Rebuttal 1:
Rebuttal:
### 1. different heuristic/tuning/LLM/VLM for each task
- In this work, there is no finetuning or VLM used.
- We only used OpenAI GPT models as LLMs in the paper. In **General Response (2)**, we show it's also possible to use other LLMs, or use different LLMs for different roles (actor, evaluat... | Summary: The paper presents Reflexion, a framework to reinforce language agents by using language feedback. Reflexion agents verbally reflect on task feedback signals, then store their own reflective text in a memory buffer to improve their decision-making in future trials. Reflexion is flexible and effective across di... | Rebuttal 1:
Rebuttal: Thank you for the great comments!
### 1. Other models
Please see **General Response (2)**, where we run additional experiments on closed and open source LLMs.
### 2. Short vs long-term memory
In this work, we define short-term memory as the current trajectory and long-term memory as the reflec... | Rebuttal 1:
Rebuttal:
We appreciate all of the great feedback from our reviewers.
### 1. Related Work, Ablations, and Baselines
Reflexion is a general framework for reasoning, decision-making, and programming problems. A typical Reflexion loop consists of a generation, evaluation, and feedback function. Thus, the se... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces Reflexion, a framework for in-context reasoning and decision-making for LMs. It allows the LM to iteratively attempt to solve a task and then reason about the results of its attempts and plan improved courses of action. The authors evaluate Reflexion on a diverse array of tasks (navigating... | Rebuttal 1:
Rebuttal:
Thank you for your feedback!
### 1. Token requirements and context
Reflexion computation scales linearly with respect to the number of episode attempts.
However, with regard to context sizes, Reflexion only keeps last N reflections in the context ($N=1$ for coding and $N=3$ for HotpotQA an... | null | null | null | null | null | null |
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion | Accept (spotlight) | Summary: This paper proposes a new method to generate multi-view images, ensuring pixel-to-pixel multi-view consistency.
The multi-view consistency is guaranteed by the correspondence aware module, which is utilized in the latent diffusion multi-image generation process.
The proposed method outperforms previous works f... | Rebuttal 1:
Rebuttal: We thank Reviewer VE1c for constructive suggestions.
---
**W1:** Our current method is restricted to the generation of multi-view images where one-to-one correspondences are readily available. Unfortunately, this condition isn't fulfilled in casual multi-view image generation scenarios, and thus ... | Summary: This paper introduces MVSDiffusion, a diffusion framework that generates multi-view images with content consistency. The problem setting is interesting and essential in practice. The proposed correspondence-aware attention mechanism provides cues for multi-view consistency.
Strengths: 1. The paper proposes a ... | Rebuttal 1:
Rebuttal: We thank you for the constructive feedback and suggestions. We reply to the questions/concerns in the following:
---
**W1.** While our experiments are currently with the ScanNet dataset, we believe that the most pragmatic usage is the creation of textures for handcrafted scene meshes. These meshe... | Summary: This paper presents MVDiffusion, a new diffusion model to generate consistent multi-view images, e.g., panorama. The authors propose a novel correspondence-aware attention mechanism in order to enforce pixel-level correspondence and cross-view consistency. More specifically this mechanism is used in three modu... | Rebuttal 1:
Rebuttal: We thank Reviewer x9w4 for the valuable questions and suggestions. We address the comments in the following:
---
**W1:** Thanks for your suggestion. For the final version, we plan to enhance Figure 2. We will illustrate a pipeline flow to show that images are initially created by the generation ... | Summary: The paper proposes a multiview latent diffusion model that is aware of the correspondence between views. Equipped with correspondence-aware attention blocks, the proposed generation module, interpolation module and the super-resolution module help MVDiffusion outperforms existing works.
Strengths: The propos... | Rebuttal 1:
Rebuttal: We thank Reviewer 2y4s for the constructive suggestions and feedback. We answer the questions as follows:
---
**1. Is the PSNR computed between generated image and ground truth image?**
No. The PSNR is calculated between two consecutive generated images in their overlapping regions, L219 in the ... | Rebuttal 1:
Rebuttal: We thank all reviewers and appreciate the constructive comments and the recognition of novelty, and we are grateful for all the positive initial ratings (two accept, one weak accept, one borderline accept).
This general response provides updated figures and accompanying discussions to answer sev... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model | Accept (spotlight) | Summary: The paper introduces an extension to the standard unconstrained features model in neural collapse theory by incorporating multiple layers above the unconstrained features. The study demonstrates that, under certain conditions and at optimality, all of the top layers exhibit all properties of neural collapse.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review and positive feedback. We address all the points raised in the review below.
**The rationale behind asserting that SGD converges to a solution satisfying neural collapse solely based on optimality is not entirely clear**
*Response.* We fully agre... | Summary: The paper studied Deep Neural Collapse (DNC) which extends the structure of Neural Collapse of the last layer to multi-layers in deep neural networks. Theoretically, this paper generalizes the established unconstrained features model to the deep unconstrained features model (DUFM) of multi-layer non-linear mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive, insightful and engaging review, and for pointing out the strengths of our work. We address all the points raised in the review below.
**The paper only considers the binary classification and the bias-free case**
*Response.* For the extension to multiple c... | Summary: The paper proposes a novel approach to investigating deep neural collapse in deep neural networks by presenting a generalization of the analytical framework for neural collapse (NC) to multiple non-linear layers. The paper introduces the deep unconstrained features model to demonstrate that the unique global o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and insightful assessment. We address all the points raised in the review below.
**Extension to multi-class classification problems**
*Response.* This issue was raised by multiple reviewers. Hence, we have opted to respond to it in the global response above... | Summary: This paper introduces the concept of deep neural collapse (DNC), extending the understanding of neural collapse to earlier layers of deep neural networks. It proposes the deep unconstrained features model (DUFM) as a theoretical framework to analyze DNC. The authors demonstrate that for binary classification, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful assessment and for the positive evaluation of our work. We address the points raised in the review below.
**Theorem 3 cannot explain the progressive neural collapse**
*Response.* We agree that there is a gap between the predictions of the DUFM and the pr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the positive feedback on our work. We reply to reviews separately and we address here one point raised by all reviewers.
**The paper only considers the binary classification case. Are the results generalizable to the multi-class classification and if yes, ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion | Accept (poster) | Summary: This paper proposes a distributional RL algorithm, PQR, that uses distributional return estimates for exploration by updating with a greedy distributional Bellman operator for time-varying, random risk measures. The paper demonstrates that, under a simple concentration condition on the random risk measures ove... | Rebuttal 1:
Rebuttal: We would like to thank you for the time and effort in reviewing our paper. Please find below our response to the main points raised in the review.
## The proposed algorithm itself may not be substantially novel.
Of course, any scheduling that is sufficiently close to the QR-DQN can yield a risk-n... | Summary: The authors propose a new exploration strategy that is more principled than epsilon-greedy or DLTV of Mavrin et al. (based on truncated variance). The proposed exploration strategy is to based on a novel perturbed distributional Bellman optimality operator, where the goal (Eqn. 1) is to minimize the distributi... | Rebuttal 1:
Rebuttal: We would like to thank you for the time and effort in reviewing our paper. Please find below our response to the main points raised in the review.
## How does the perturbed Bellman operator is a principled way to modeling epistemic uncertainty?
We are not trying to model epistemic uncertainty thr... | Summary: This paper investigates the exploration problem in distributional reinforcement learning (DRL). It proposes the Perturbed Distributional Bellman Operator (PDBOO) as an extension of the distributional Bellman operator, which introduces non-directional noise to the target return distribution. This extension orig... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for taking the time to review our submission. Contrary to your concerns, we want to emphasize that we've already designed PDBOO in the direction you're thinking, and we have provided detailed answers below.
## Misconceptions about how PDBOO is designed
We will addr... | Summary: In this work, the authors address the issue of biased exploration caused by a one-sided tendency on risk in action selection by proposing the method perturbed quantile regression (PQR). PQR selects actions by randoming risk criterion while retaining a risk-neutral objective. The authors also derive a sufficien... | Rebuttal 1:
Rebuttal: We would like to thank you for the time and effort in reviewing our paper. Please find below our response to the main points raised in the review.
## Provide more context to back up the importance of this challenge.
We will add a few more papers in line 50 that attempt to use distributions for ex... | Rebuttal 1:
Rebuttal: # Global Response from Author
We thank all the reviewers for their valuable comments on our paper.
First, we added a revised **description of the pseudocode** and **additional experimental results for the schedule of $\Delta_t$** in the PDF, based on feedback from some reviewers.
The additional... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses exploration via distributional RL which has, to date, been most recently performed under an _optimism in the face of uncertainty_ (OFU) paradigm. This paper loosely characterizes this OFU approach as problematic as it results in biased exploration, resulting in sub-optimal value distributi... | Rebuttal 1:
Rebuttal: First of all, we appreciate the time and effort you put into our paper. We will revise our paper based on all feedback to improve the quality of the paper. We have responded to the key comments below.
## Clarity of “Without losing the risk-neutral objective”.
Previous work has focused on learnin... | null | null | null | null | null | null |
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics | Accept (poster) | Summary: The paper introduces a diffusion model based approach to tackle inverse problems. The method is then applied to High Energy Physics in order to reconstruct kinematic quantities.
Strengths: The paper is well written with clear structure, definitions, and figures. The paper includes testing of the proposed meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their king comments and detailed questions. We address the limitations comment in the global rebuttal. We answer the questions here.
1. & 2. We thank the reviewer for noticing these points. We have done a pass through the paper and fixed these presentation issues along w... | Summary: This paper proposes a unified framework, which combines the latent diffusion model and variational diffusion model, and applies the method to the inverse problem in the field of High Energy Physics. The loss terms for VAE and variational diffusion model are combined to achieve end-to-end training. The proposed... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful questions.
1. We respectfully disagree with this characterization. Many important contributions in machine learning, such as weight regularization and the original VAE, may be characterized in this reductive manner if one overlooks the reasoning behind t... | Summary: The paper benchmarks several network architectures on a real-life problem in particle physics, i.e. the problem of inverting the effect of limited detector resolution and guess the features of a given collision from what is actually observed in the detector (unfolding). Considering several metrics to assess th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind comments and support of our paper. | Summary: Thanks for the author's rebuttal. I have read all of the rebuttals and reviews and decided to keep the current rating.
Current work proposed an extension of the variational diffusion model, called the variational latent diffusion model. It is applied to inverse problems in the high energy physics field and te... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful questions and comments.
1. We agree with the reviewer that Figure 1 could benefit from additional context. The intent of this figure was to offer a visual illustration of parton configurations and detector observations, as subsequent sections describe th... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their detailed comments and questions. We begin with a discussion of the limitations as requested by two of the reviewers and then answer the remaining questions in individual responses. We also include a document with updated figures.
Limitations
--------------
... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive Algorithms for Relaxed Pareto Set Identification | Accept (poster) | Summary: This paper studies a less common setting that arms are multi-variate distributions. In the expiration period, the agent seeks to identify the arm which is Pareto optimal.
This problem is formulated as a fixed-confidence identification problem. The most novel setting in that, the fixed confidence identificati... | Rebuttal 1:
Rebuttal: Thank you for your review.
* We would like to clarify from your summary that our algorithm is not meant to identify "the arm which is Pareto optimal" as there might be more than one Pareto optimal arm. We propose a sampling rule called Adaptive Pareto Exploration that when combined with differen... | Summary: The primary objective of the paper is to address the challenge of identifying a set of arms, consisting of at most $k$ arms, where each arm is either Pareto optimal or close to Pareto optimal. The paper explains the concept of Pareto optimality within the context of bandit problems. To tackle this problem, the... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns and answer your questions below.
* We will add a paragraph about the computational complexity of $\varepsilon_1$-APE-$k$ in Section 5, with some details in Appendix H. The time complexity of $\varepsilon_1$-APE-$k$ is $\mathcal{O}(K^2D)$ and its... | Summary: This paper extends the best arm bandit problem to a multi-objective version to identify the arms that are in the Pareto front.
Strengths: This problem is new and the author provides a clear practice motivation of this problem.
The theory study seems valid and complete.
Weaknesses: The math notation seems a ... | Rebuttal 1:
Rebuttal: Thank you for your review. We comment about each weakness mentioned and answer your question below.
* Due to the multi-dimensional setting and the fact that we consider three relaxations simultaneously, we agree that the notation can be a bit heavy. We will double check if some simplification is... | Summary: This paper studies a relaxed problem of Pareto set identification, in which the learner is required to identify a subset of the optimal arms. A single sampling strategy APE is proposed and then combined with various stopping rules to realize different relaxations of the original problem. In theory, this paper ... | Rebuttal 1:
Rebuttal:
Thank you for your review. We address your two concerns about weaknesses below.
* Our motivation for the $\epsilon_1$-PSI-$k$-relaxation comes from possible applications to early stage clinical trials, e.g. in vaccinology as in our real-world scenario. In this context, the constraint to identif... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Equilibrium Based Neural Operators for Steady-State PDEs | Accept (poster) | Summary: The paper proposes a method combining Fourier Neural Operator (FNO) with the Deep Equilibrium Model (DEQ) for more efficient operator learning under noisy conditions. Overall, the paper is well-written with clear methods and theory, and some effective experimental results. It's a decent piece of work, and I re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are encouraged to see that the reviewer finds the paper to be well-written with positive experimental results.
Please find our replies to some of your comments and concerns:
**Re: Lacking references to attention based operator learning.**
We apologiz... | Summary: The paper proposes two weight-tied neural network architectures for solving steady-state partial differential equations (PDEs) using the universal approximation capabilities of neural networks. The first architecture is a weight-tied version of Fourier Neural Operators (FNO), while the second architecture is a... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive review! Please find our responses to the concerns you raised below.
**Re: Proposed architectures not applicable to all PDEs.**
There are a multitude of different forms of PDEs, each with their unique characteristics that may or may not be efficiently modele... | Summary: This research examined the solution of steady-state Partial Differential Equations (PDEs) using Fourier Neural Operator (FNO) based architecture. The author introduced a fix-point iteration mechanism into the FNO framework, leading to the proposal of weight-tied FNO and FNO Deep Equilibrium (FNO-DEQ) models. C... | Rebuttal 1:
Rebuttal: Thanks for the encouraging review and feedback. We are glad that the reviewer finds our paper as novel and captivating!! Please find our replies to some of your comments below. We promise to make the corresponding changes to the camera-ready version of our draft to incorporate your suggestions.
... | Summary: The paper tackles the problem of solving steady-state PDEs with weight-tying FNOs. The authors argue that instead of stack multiple FNO layers with different parameters, repeatedly applying one FNO layer computation is a better choice. This hypothesis is motivated by the fact that steady-state PDEs are solved ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and response! We are glad that the reviewer finds that our paper is well structured and likes the empirical results. We address some of the concerns raised by the reviewer below:
**Re: A discussion on the convergence of the fixed point.**
Increa... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and the detailed comments, and are encouraged to see the overall positive response for our paper! We hope that we have sufficiently addressed all the questions and comments posed by the reviewers in their individual responses.
Furthermore, we have also a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization | Accept (poster) | Summary: This paper presents a multi-decoder (population) neural network structure and training method to solve the combinatorial optimization problem. In particular, the paper presents a model update method in which multiple decoders can specialize in different types of problem instances. The method proposed in this p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and appreciating the novelty, simplicity and applicability of our method. We hope our answers address the reviewer’s remaining concerns.
> Q1. In Algorithm 1, reward of the result of the second best agent $R(\tau_{i^{\ast\ast}})$ was used as the basel... | Summary: This paper proposes a construction method that learns a population of agents to improve the exploration of the solution space of combinatorial optimization problems. Experiments demonstrates that the proposed method improves the solving efficiency of four popular NP-hard problems.
Strengths: 1. The paper is w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We believe our answers address the concerns raised and hope these enable the Reviewer to reconsider their assessment.
> W1. [The motivation] would be more convincing if the authors could provide an example of combinatorial optimization problems [in Fig. 1... | Summary: The paper proposes a new training procedure that allows to train a diverse set of policies for solving combinatorial optimization problems. Most existing approaches for these problems train a single policy/agent and aim to construct a solution in a single shot (or by sampling multiple solutions). In contrast, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and outlining the strengths of our work (novelty, performance, applicability, clarity). While there are no questions, we are happy to provide some feedback on a comment made in the review.
> Overall, the additional training of a population with th... | Summary: The paper proposes that populations of agents can produce better results than using single agents. This leads to a new "Poppy" algorithm which performs policy gradient updates only on the agent which produced the highest reward. This is then applied to combinatorial optimization problems using attention-based ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and appreciating the presentation and clarity of the work. We hope our answers address all concerns and are sufficient to reconsider the assessment.
> Could the proposed Poppy RL algorithm be more generally applicable to any MDP? If so, which problems (out... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback on our manuscript. We summarize some common points across reviewers:
* *Not having an explicit objective for diversity is an advantage, not a weakness.* We empirically show that Poppy attains diversity as a by-product of optimizing the proposed p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Improving Adversarial Robustness via Information Bottleneck Distillation | Accept (poster) | Summary: This paper proposes Information Bottleneck Distillation (IBD) to improve adversarial robustness from the perspective of information bottleneck principle. Specifically, two distillation strategies are proposed to boost information bottleneck. Different from the existing works, this paper utilizes the prediction... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! Here is our response to the concerned questions.
#### **Q1: Some derivation details in this paper are overly simplified and jumpy**
**A1**:Thank you for your comment and sorry for the confusion. To clear up this, we further provide the detailed derivation of Eq.(... | Summary: This paper takes a closer look at the information bottleneck principle and show that specially designed robust distillation can boost information bottleneck, benefiting from the prior knowledge of a robust pre-trained model and presents the Information Bottleneck Distillation (IBD) approach. What’s more, this ... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! Here is our response to the concerned questions.
#### **Q1: The main contributions of our work.**
**A1**: Thank you for your comment and fruitful advice. The main contributions of our work include as follows:
1) **Theoretically**, we utilize conditional variatio... | Summary: This paper draws inspiration from prior studies that suggest robust models can offer strong prior information, thereby enhancing both the robustness and uncertainty of the model. Accordingly, we propose a new Information Bottleneck (IB) objective, which is designed to distil robustness in the context of a Vari... | Rebuttal 1:
Rebuttal: We thank you very much for your valuable and encouraging comments on our work! Thanks! | Summary: This paper proposes the Information Bottleneck Distillation (IBD) method to enhance adversarial robustness, derived from revisiting variational information bottleneck from the perspective of robustness distillation. IBD leverages two distillation strategies to perform the optimization processes of the informat... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! Here is our response to the concerned questions.
#### **Q1: The impact of the hyperparameters $\alpha$**
**A1**: Thanks for your comment. The $\alpha$ is a trade-off the adversarial robustness and natural accuracy.
We conduct ablation experiments to verify the... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful and constructive comments on our work. During the rebuttal period, we carefully addressed all the comments and suggestions raised by all reviewers. We hope that our response has properly addressed the comments of the reviewers and that its overall contri... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to improve the adversarial robustness of deep neural networks. From the perspective of the Information Bottleneck, a knowledge distillation method is proposed. It makes use of intermediate features and logits from a robust teacher to get priors for guidance in training of the student model.
Ex... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! Here is our response to the concerned questions.
#### **Q1: From the perspective of knowledge distillation, feature-based methods have already been explored by previous methods.**
**A1**: Thanks for your comment. Our approach indeed resembles the feature-based di... | null | null | null | null | null | null |
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild | Accept (poster) | Summary: The paper proposes a unified diffusion framework, UnitControl for a more fine-grained controllable generation based on input visual conditions, utilizing two modules mixture-of-experts adaptor that extract features from different visual conditions, and a task-aware HyperNet that extracts language-based task em... | Rebuttal 1:
Rebuttal: We sincerely appreciate your suggestions and questions of our paper. Your concerns are addressed as follows.
**Q1: UniControl vs ControlNet**
Components and #params of whole UniControl Model and Multi-ControlNet
| | Stable Diffusion | ControlNet | MoE-style Adapters | TaskHyperNet | Total|
|-... | Summary: UniControl is a diffusion-based image generation model that can condition on natural language input as well as multiple types of visual inputs (e.g. edge map, depth map). The framework is built upon components of Stable Diffusion Models, an MOE adapter and a ControlNet modulated by a task-aware HyperNet. The m... | Rebuttal 1:
Rebuttal: We are grateful for your suggestions to enrich our paper. Your concerns and questions are addressed as follows.
**Q1: Complexity of UniControl**
Components and #params of whole UniControl Model and Multi-ControlNet
| | Stable Diffusion | ControlNet | MoE-style Adapters | TaskHyperNet | Total|... | Summary: This paper introduces UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework. UniControl enables pixel-level-precise image generation, where visual conditions primarily influence the generated structures and langua... | Rebuttal 1:
Rebuttal: Thank you for your valuable time to review this paper. Your concerns are addressed below.
**Q1: More comparison with the single-task-controlled methods**
Thank you for your valuable suggestion. In response, we've expanded our quantitative analysis to include classic single-task-controlled metho... | Summary: This paper presents a method for controlling the output of a diffusion
model with multiple modalities of reference images, e.g. edges,
segmentation, depth, etc. It can be seen as proposing a multi-task
version of ControlNet. Experiments in the paper show the multi-task
approach outperforms the single-task, and... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments and suggestions for our submission.
**Q1: Missing Ablation Study**
Thank you for pointing this out. We've conducted an ablation study with FID scores as follows. Our experimental setup adheres to Sec. 4.3 of the main paper, employing DDIM as the ... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for your valuable time in reviewing this paper. We sincerely appreciate your constructive comments and questions to make this paper better. Below, we respond to each concern in the order.
Pdf: /pdf/0e6ce502ea59837a78d0dcf97bcc75ff12c2bcdc.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents UniControl, that unifies multiple visual controlling condition into a single unified model. To achieve this, the authors introduce a task-aware HyperNet to modulate the diffusion models, enabling adaptation to different condition-to-image (C2I) tasks simultaneously. UniControl is trained on ... | Rebuttal 1:
Rebuttal: Thanks for your valuable time to review this paper. We sincerely appreciate your constructive comments. Your concerns are addressed below.
**Q1: More quantitative evaluations for the alignment of the generated content and conditional inputs**
Thank you for highlighting this. We agreed with your... | null | null | null | null | null | null |
You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization | Reject | Summary: The paper first characterizes the 'zero-gradient' issue---a challenge associated with learning a model in the 'predict-then-optimize' paradigm---in terms of the number of active KKT constraints of the optimization problem. It then proposes a surrogate optimization problem for which the zero-gradient does not a... | Rebuttal 1:
Rebuttal: **1. The zero-gradient theorem is not novel**
It is indeed well known that differentiating through linear programs is impracticable due to zero/undefined gradients. However, to the best of our knowledge, the zero-gradient problem for *nonlinear* convex optimization was not known before.
The main ... | Summary: This paper identifies the zero-gradient problem in Predict and Optimize (P&O) for convex optimization and proposes a method to address it. The method is based on using a Quadratic Programming (QP) approximation for computing decisions, smoothing the feasibility region around the current solution to reduce the ... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our work.
To the best of our knowledge, differentiation through convex programs [2] is considered to be the ultimate solution for convex non-linear P\&O problems, as it computes the true gradient. We are not aware of other works studying approximations for the P\&... | Summary: This paper studies predict and optimize problem which utilizes machine learning to predict unknown parameters of optimization problems. The paper identifies the zero-gradient problem and proposes a method to solve this issue. Additionally, the paper conducts an experimental study to verify the proposed method.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to evaluate our work!
In the paper, Figures 3a and 4 show that the performance of the $r-$smoothing approach is better than that of the standard algorithm. We believe that this happens due to the zero-gradient problem, but we also agree that the performance plots ar... | Summary: Predict+Optimize (P+O) is an emerging paradigm that lies in the intersection of classical optimization and machine learning. Specifically, it considers the setting where a parameterized optimization problem:
$$x^{\star}(w) = \operatorname*{argmin}_{x} f(x,w) \text{ subject to } x \in \mathcal{C}$$
must be s... | Rebuttal 1:
Rebuttal: *1. Insufficient benchmarks*\
Thank you for providing us with these references. We have not included more benchmarks in the submission since all the papers we are aware of focus on linear/combinatorial problems. The reason behind that is simple -- the zero-gradient problem was not noticed before, ... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing us with valuable feedback. To address the comments related to benchmarks and experiments, we conducted some additional experiments. We provide detailed responses individually for each reviewer, and in this text, we describe the new Figures that can be found in ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focus on the topic of "predict and optimize" and identify the zero-gradient problem. This issue is characterized by a situation where the gradient related to the best decision concerning parameters in machine learning models might be zero. This can occur even when assuming convexity, smoothness, and... | Rebuttal 1:
Rebuttal: Thank you for identifying the strengths of our work as well as highlighting some important drawbacks.
1. There is indeed a typo in Algorithm 1, thank you for pointing that out. As $f_x, \hat{f}_x$ are the gradients of the objective function, they are vectors of size $n.$ Then, the definition of $... | null | null | null | null | null | null |
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues | Accept (poster) | Summary: This work focuses on the monocular 3D object detection task and proposes a unified 3D detection framework for both vehicle and infrastructure sides. In particular, to unify the diversity of pitch angles and focal lengths of multiple cameras, the authors propose a unified optimization target named normalized de... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the quality, clarity, and effectiveness on infrastructure side of our paper. As you have stated, our method is easy-to-follow, and achieves SOTA performance on the infrastructure side and competitive results on the vehicle side. We also thank you for providing insightf... | Summary: The paper proposes a new approach called MonoUNI for monocular 3D object detection of both vehicle and infrastructure sides in autonomous driving. The approach addresses the challenge of constructing algorithms for the two sides based on different prior knowledge, by taking into account the diversity of pitch ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the significance of the unify problem in autonomous driving. As you have stated, our MonoUNI achieves state-of-the-art performance on all three benchmarks without introducing any extra information. We also thank you for providing insightful suggestions. We will try to ... | Summary: This paper proposes an optimization target which unifies 3D detection problems for vehicle and infrastructure sides, by taking into account the diversity of camera pitch angles and focal lengths. Furthermore, the paper develops 3D normalized cube depth of obstacle to promote the learning of depth information.
... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the motivation, novelty, rationality and effectiveness of our method. We especially thank you for supporting our insight on learning a normalized depth that decouples depth from the focal length and the camera’s optical axis orientation. As you have stated, our normaliz... | Summary: This paper proposes a unified architecture for vehicle and infrastructure-based monocular 3D object detection network. At its core, the paper puts forth the concept of normalized depth that is independent of camera intrinsic focal length and extrinsic pitch angle w.r.t the ground plane. As such, the network is... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the motivation, significance, and potential of our method. We are pleased that you support our insight on the problem of monocular 3D object detection under varying focal length and pitch mounting angle. As you have stated, our network firstly simultaneously avoids the ... | Rebuttal 1:
Rebuttal: **Global Rebuttal**
**Table 1: Monocular 3D detection performance of Vehicle category on Waymo val set**
|$\mathbf{IOU_{3D}}$ |Difficulty|Method|Reference|Extra|$\mathbf{AP_{3D}}$(all) | $\mathbf{AP_{3D}}$(0-30m) | $\mathbf{AP_{3D}}$(30-50m) | $\mathbf{AP_{3D}}$(50m+) | $\mathbf{APH_{3D}}$(a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection | Accept (poster) | Summary: This paper proposes to exploit the reflection symmetry as a new supervision to HBox-supervised oriented object detection. Several modifications are made to adapt the new self-supervised (SS) branch, including removing the angle subnet in the weakly-supervised (WS) branch and a CircumIoU loss for box regression... | Rebuttal 1:
Rebuttal: # To Reviewer Pshp
We sincerely appreciate your valuable suggestions. We hope our clarification could help to make a more informed rating to our work.
**Q1 It would be more rational that the converse proposition holds for symmetric axis prediction and flip/rotate consistency**
We think our (emp... | Summary: This paper proposes an advance solution for using horizontal bounding boxes as supervision to learn oriented object detectors.
The proposed method H2RBox-V2, which is a modification of the recent work H2RBox, has some technique novelty and contribution as it jointly uses the weakly- and self-supervised branch... | Rebuttal 1:
Rebuttal: # To Reviewer tfHb
Thank you for the time and nice suggestions. We humbly point out that there may exist misunderstandings in the review, and we hope our clarification could help on a more informed rating to our work.
**Q1.a Eq. 2 and Eq. 4 conflict: When k is an odd number, the nets learn the o... | Summary: This paper introduces H2RBox-v2, an innovative approach to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. It seeks to address the limitations of the original H2RBox model, which required high-quality annotations and large training datasets, and was incompatible wi... | Rebuttal 1:
Rebuttal: # To Reviewer W3Ay
Thanks for your positive comments and constructive suggestions. Your endorsement of our method gives us significant encouragement.
**Q1 The computational overhead and the time taken for the model training/testing process**
**Test phase:** In our submission, we only gave FPS f... | Summary: This paper proposes a new horizontal box-supervised rotation object detection detector. The proposed detector consists of two modules: a self-supervised regression branch for angle regression and a weakly supervised branch for horizontal box regression. This method is more simpler and shows clear improvement o... | Rebuttal 1:
Rebuttal: # To Review k1cY
Thank you for the nice comments and valuable suggestions. By revising accordingly, the article is now clearer and more complete!
**Q1 Robustness to inaccuracies in horizontal bounding box annotations**
Thanks. We add some random noise to the annotation and record AP50/AP75 unde... | Rebuttal 1:
Rebuttal: General Response:
We thank the reviewers for their time and constructive suggestions. And the reviewers give appreication in a few points:
1. writing/presentation (**k1cY**:The paper is well written, and the method is clear and well described; **W3Ay**: The paper is well-structured and clearly p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Egocentric Planning for Scalable Embodied Task Achievement | Accept (poster) | Summary: The paper introduces Egocentric Planning, alternating exploration and task solving symbolic planning for long-horizon object-oriented POMDP in environments with deterministic action effects. The presented method is used as the planner in a hybrid agent, SOTA in the 2022 ALFRED benchmark, with neural perception... | Rebuttal 1:
Rebuttal: Thank you for your comments. We appreciate the insight into our work in the multi-agent setting.
> **Strengths:**
>
> The primary contribution is that our method enables generalized solutions for new task types, using the same set of objects and relationships.
>
> **Weaknesses:**
>
> Your poin... | Summary: The authors propose an approach combining symbolic planning and object-oriented POMDPs for symbolic planning, which gets extremely strong performance on the ALFRED benchmark and won the CVPR ALFRED challenge. Their approach uses PDDL, but extends it with a set of exploration-focused actions. They use a combina... | Rebuttal 1:
Rebuttal: Thank you for your comments. We've addressed some of them in the general response.
> **Strengths:**
>
> - A lot of great ideas; some explanations are very good
> - Great to see a new approach to solving ALFRED, not just building off FILM/HLSM
We look forward to elaborating on these ideas.
> **... | Summary:
The paper presents a modular approach that combines symbolic planning and object-centric Cost POMDP for solving ALFRED tasks. The proposed method demonstrates improvements compared to previous end-to-end and modular approaches, such as FiLM. Unlike methods like FILM, HLSM, and Prompter, EPA utilizes a semanti... | Rebuttal 1:
Rebuttal: Thank you for your comments. Some of your comments were addressed in the general response.
## Strengths
> The paper demonstrates:
> - The use of preconditions and effects through PDDL to improve the overall success of long-horizon tasks, especially unseen success rate.
> - The combination of sym... | Summary: This paper studies the problem of embodied tasks in which the agent needs to plan over long task horizons given natural language instruction. Current methods that use end-to-end training leads to entangled representation, which makes it hard to solve the task. On the other hand, planning methods such as PDDL c... | Rebuttal 1:
Rebuttal: Thank you for your comments. The connection with SayCan is interesting, as our work complements theirs.
> **Weaknesses:**
> The proposed method might be too specific to the ALFRED task, in the sense that the method is optimized solely for this task. For example, there's no abstraction of the act... | Rebuttal 1:
Rebuttal: ## Introduction
We would like to extend our heartfelt gratitude to the reviewers for their insightful comments and constructive criticism. The feedback has been encouraging and instrumental in helping us understand the significance of our work in the following areas:
- Our **modular approach** (... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a hybrid approach leveraging neural perception models and symbolic planners for egocentric planning and task completion in embodied environments. They demonstrate their approach in ALFRED benchmark winning the 2022 CVPR challenge. Their central idea is the use of symbolic planners, which ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review.
There are indeed issues in the manuscript.
We apologize for the lack of clarity.
As we stated, we have already improved part of the issues in the manuscript and will certainly further improve it thanks to your feedback.
The general response addre... | null | null | null | null | null | null |
Temporal Continual Learning with Prior Compensation for Human Motion Prediction | Accept (poster) | Summary: The paper proposes a novel multi-stage training framework called Temporal Continual Learning (TCL) for Human Motion Prediction (HMP) to address the challenges of short-term and long-term predictions and the incorporation of prior information from past predictions into subsequent predictions. The Prior Compensa... | Rebuttal 1:
Rebuttal: **Part 1 (Part 2 is in global rebuttal)**
Thanks to the reviewer for the constructive comments. We have carefully addressed your concerns and provided detailed responses for each review.
**Q1:Some issues with the wording.**
Re: Thank you for pointing out them. We will correct them.
**Q2: Some ... | Summary: This paper introduces the continual learning insight into human motion prediction. By analysis of the performance relationship between the short and long-term prediction, a compensatory method is proposed in a multi-stage learning setting. On several widely-used benchmarks, the proposed method is cooperated wi... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the constructive comments. We have carefully addressed your concerns and provided detailed responses for each review.
**Q1:What is the additional cost of using the proposed method? Please discuss the efficiency and the other possible cost.**
Re: The extra cost only occ... | Summary: This paper proposes to train human motion prediction networks by gradually increasing the prediction horizon.
This encourages the network to learn short-term predictions first and then leverage the learned to predict longer horizons.
The easy-to-hard curriculum makes the network learn more efficiently, as evid... | Rebuttal 1:
Rebuttal: **Part 1 (Part 2 is in global rebuttal)**
Thanks to the reviewer for the constructive comments. We have carefully addressed your concerns and provided detailed responses for each review.
**Q1: The comparison is a bit weak. Shall compare with more recent methods, for example, "Back to MLP: A Simp... | Summary: This paper aims to enhance human motion prediction. The main contributions of this paper are:
1. The paper presents a multi-stage training strategy named Temporal Continual Learning to incorporate the learning of both short-term prediction and long-term prediction.
2. The paper introduces Prior Compensation Fa... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the constructive comments. We have carefully addressed your concerns and provided detailed responses for each review.
**Q1:** **The paper could benefit from a more thorough discussion of related work on Human Pose Prediction, such as in L23-24, transformer architectures... | Rebuttal 1:
Rebuttal: **(Part 2 for reviewer GqjD)**
**reference:**
[1] Guo, W., Du, Y., Shen, X., Lepetit, V., Alameda-Pineda, X., & Moreno-Noguer, F. (2023). Back to mlp: A simple baseline for human motion prediction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4809-4819... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the common trade-off between the short- and long-term prediction quality of 3D human motion prediction models. The proposed training technique improves the performance of the underlying models both on short- and long-term prediction horizons, where the models generally prioritize one and su... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the constructive comments. We have carefully addressed your concerns and provided detailed responses for each review.
**Q1:** **There is a potential problem with lemma 3.1. It expects b to be between 0 and 1, a likelihood value from a probability mass function. For the ... | null | null | null | null | null | null |
Online robust non-stationary estimation | Accept (poster) | Summary: This paper considers an online estimation setting where the learner observes a sequence of samples, which are drawn from a (previously determined but unknown) sequence of probability distributions, and with some fraction of samples arbitrarily corrupted. In each round, the learner makes a decision, and regret ... | Rebuttal 1:
Rebuttal: **Excess risk regret is a direct corollary of our result:**
As the loss function $\mathcal{L}$ is M smooth (Assumption 1 in our draft), we have that $\mathbb{E}[\mathcal{L}(Z,\theta_t)−\mathcal{L}(Z,\theta^*_t)]\leq M \|| \theta_t− \theta_t^* \||^2$. Thus, a regret bound on the norm $\||\theta... | Summary: This works studies robust sequential estimation under a non-stationary environment. A loss function is fixed in advance. The data generating process is non-stationary over time, and hence the optimal parameter \theta^*_t, which minimizes the expected loss over the distribution at time t, is changing over time.... | Rebuttal 1:
Rebuttal: **Fundamental improvements in the problem setting and results compared to Besbes et.al. :**
We thank the reviewer for pointing out missing a reference and comparison to Besbes et.al. which we will add in the revision. Here, we highlight two conceptual contributions we make in the paper compared t... | Summary: The paper studies the problem of online estimation in a setup that generalizes the stochastic i.i.d. input assumption. The authors consider a setting where the input distribution is allowed to change over time (a certain number of times), and furthermore the input is allowed to be adversarially corrupted (a ce... | Rebuttal 1:
Rebuttal: We thank the reviewer for making a thorough read and providing feedback on the paper. Part of the reviewer's questions are also addressed in the table in the attached pdf. Below here, we respond in text with the reviewer’s questions highlighted in ***bolded italics*** with our response below.
**... | Summary: The paper studys online estimation problems on data stream exhibits challenging properties, including distribution drift, heavy tails, and outlier/anonmalies corruptions. Formally, at each time step, (given all the data that has arrived) the algorithm needs to output an estimation on certain unknown parameter ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough read and providing feedback on the paper.
**Distinguishing between drift and corruption:** Indeed, the reviewer’s intuition is spot on. Our lower bound in Proposition 2.6 and in the sketch in the attached pdf is based on the fact that drift and corruption in... | Rebuttal 1:
Rebuttal: Here, we address a common questions asked by multiple reviewers -
***"What is the best known upper and lower bounds for the various settings of online estimation in the presence and absence of distribution shifts and corruptions?"***
We answer this in the table attached in the pdf, which we wil... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the online robust estimation problem in possibly non-stationary environments. Under the assumption that the loss function is strongly convex, the authors propose an online clipped stochastic gradient descent algorithm with tunable clipping parameter that is able to achieve both adaptiveness ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough read and providing feedback on the paper.
**Regarding model definition:** The unknown vector $\theta^{*}_{t}$ is the minimizer of the expected loss function $\mathcal{L}$, where the expectation is with respect to the random vector $Z_t$. Concretely, $\thet... | null | null | null | null | null | null |
Neural McKean-Vlasov Processes: Inferring Distributional Dependence | Reject | Summary: The submission considers McKean-Vlasov Stochastic Differential Equation models, which are a generalization of the more-familiar Ito processes. The difference is that the former additionally feature the evolution of the law of the process $X_t$ (denoted $p_t$) as well as $X_t$ itself. Such processes are the lim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and invaluable comments.
We also appreciate the positive remarks.
We address the individual concerns below.
1. (Where MV-SDEs may be of interest)
The reviewer makes a good point, we will include additional discussion on the applications of MV-SDEs and th... | Summary: This paper considers the problem of parameter estimation from data when the underlying dynamical system is modeled by the MV-SDE. To represent the target MV-SDE, the authors propose two strategies: (i) expressing a layer in a neural network as an expectation with respect to a density and (ii) using generative ... | Rebuttal 1:
Rebuttal: We regret that the reviewer felt so negatively towards the work and could not find a single strength within the entire manuscript.
1. (Poor presentation)
We regret the reviewer found the presentation poor.
We will address the reviewer's points.
- Both $f$ and $\varphi$ are learned.
We would... | Summary: The authors proposed two new methods of modelling McKean--Vlasov SDEs using a neural network, and studied its empirical performance.
Since I'm not an expert this exact topic, I would like to ask the authors some questions first. I would be happy to raise my score further once I understand the paper better.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments, questions, suggestions for improvement, and the time spent reviewing the paper. We respond to individual points below.
1. (Linear factorization of the drift)
We added a discussion of this in the response to all reviewers.
2. (Different forms of $... | Summary: This paper proposes a methodology for simulating McKean-Vlasov (mean-field) equations using standard function approximation techniques, e.g. neural networks. It provides mathematical intuition for these algorithms and evaluates them on a broad suite of benchmarks.
Strengths: The paper is very well written wit... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the well-thought feedback and comments.
We address these individually below.
- (Simplicity in the ideas)
We hope that in addition to the methodological contributions, the motivations behind studying distributional dependence within the context of stoch... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their helpful feedback and all the comments in helping improve the paper. Here we include the common responses to all reviewers:
### Mean-field layer exposition
Let us first define the measure of the particles at time $t$ to be $P_t$ and an arbitrary base... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers | Accept (spotlight) | Summary: This paper proposes an efficient and interpretable context-based dynamic pruning method. They use additional query / key layers to generate dynamic attention masks and sparsify self-attention maps. They also introduce a sparse sigmoid and regularization term to control the sparsity. In experiments, they demons... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the interesting questions. We are glad that the reviewer found the method easy to follow, and we hope our work sparks future work in the direction of efficient and more interpretable inference. In the following, we address comments made.
> Quantitative c... | Summary: The paper introduced a novel inference strategy for transformer models that focuses on inference efficiency. Instead of retaining all context tokens throughout the entire inference process, they gradually eliminate tokens as they move deeper into the layers. To determine which tokens to drop, they trained some... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the interesting points raised. We are glad to hear that they found our method description clear and easy to follow. In what follows ahead, we would like to take the opportunity to address the primary concern.
> Choice of downstream tasks and eva... | Summary: The authors propose a modified dynamic masking operation to the traditional multi-headed attention in transformers in order to allow models to learn to drop tokens at specific layers during training. In order to facilitate learning, they use a sparse sigmoid that is annealed to interpolate from a traditional s... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of efficient inference and the novelty of dynamically pruning the context as a successful alternative. In the following, we take the opportunity to address the comments made.
> How well this adaptive sparsity can be used when training from scra... | Summary: Given the trend in large language models, it is a pretty important problem to search to efficient architectures. In this direction is the line of work to make the attention component efficient by introducing sparsity in the attention block and allowing every token to attend only a subset of the previous tokens... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and for acknowledging the impact of efficient inference in autoregressive models. Here, we discuss the comments raised.
> Was there any analysis done on which set of NLP tasks get affected the most with such sparsity?
Such an analysis is indeed int... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking the time to review our paper and for the valuable feedback. Here we address common points raised and present more experiments that we believe further strengthen our findings.
**How sparsity affects specific NLP tasks (@65eF, @LhSf, @uWWw)**
Evaluat... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function | Accept (poster) | Summary: This work introduces a novel and orthogonal approach by exploring the potential of using random weights network as a loss function. The authors have carefully designed the random weights network with theoretical constraints based on mathematical manifolds. To validate the proposed solutions, extensive experime... | Rebuttal 1:
Rebuttal: **1,detailed information.**
Thanks for pointing out this issue. Initially, due to page constraints, we've presented the relevant methodologies and procedures in the supplementary materials. Furthermore, we will share the source code to elucidate the experimental setup details. Lastly, we'll thoro... | Summary: This paper seeks to explore the untapped capabilities of random weights networks as a loss function. Inspired by mathematical manifolds, the authors propose innovative and straightforward solutions for random weights networks based on rigorous mathematical properties. Extensive experimental results across vari... | Rebuttal 1:
Rebuttal: **1,visual comparison.**
Thanks for pointing out this issue. As you suggested, we will highlight the best results for a clear illustration. In addition, we will provide the more visual comparison in the main body to enrich this work.
**2,experimental configuration.**
Thanks for pointing out thi... | Summary: This paper introduces the idea that random weight networks can be used as loss functions for training image restoration networks. The paper proposes to use Taylor’s Unfolding Network, Invertible Neural Network, Central Difference Convolution, and Zero-order Filtering as random weight networks. The analysis and... | Rebuttal 1:
Rebuttal: **1, technical flaw.**
1) Since our proposed loss functions are used alongside pixel loss, there is no added computational burden. Pure pixel loss optimization can lead to local optima and oscillations. Conversely, our approach mitigates local oscillation and enhances model convergence. For insta... | Summary: This paper explores the notion of using random weight networks as a constraint during the training process for image restoration. This approach aims to encourage the network to learn more robust features and produce better results, addressing the limitations of traditional optimisation methods and deep learnin... | Rebuttal 1:
Rebuttal: **1, typos.**
Thanks for highlighting the issue! We'll thoroughly review the entire paper and correct all typos and grammar errors.
**2, differences to other works.**
1) Our method centers on designing the loss function, initially employing a random weights network with a strict mathematical ma... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Can semi-supervised learning use all the data effectively? A lower bound perspective | Accept (spotlight) | Summary: this paper investigates whether improvements in the rates of sample complexities are possible for semi-supervised learning compared with supervised learning and unsupervised learning (up to a change in sign) for the specific case of a classification problem where each class is a symmetrical Gaussian. it is fou... | Rebuttal 1:
Rebuttal: We thank you for appreciating the importance of the problem as well as the thorough theoretical and experimental analyses in our paper. We are grateful for the feedback that you have provided and hope that our answers address the points raised in your review.
> 5 for Algorithm 3, how does it make... | Summary: The paper presents a detailed analysis of semi-supervised learning (SSL) algorithms. Specifically, the authors establish lower bounds for 2-Gaussian mixture model distributions, revealing that no SSL algorithm can improve the sample complexities of optimal supervised or unsupervised learning. However, SSL can ... | Rebuttal 1:
Rebuttal: We would like to thank you for appreciating the clarity of our manuscript and the importance of the contribution presented in it. In the “General comments” we address your question regarding possible extensions of our results to more general distributions, beyond GMMs. In what follows, we answer t... | Summary: This paper analyzes the effect of SSL methods to improve the error bound. The results suggest that SSL cannot improve over the statistical rates of both SL and UL at the same time, but it is possible to improve the errors by a constant factor. Simple experiments on synthetic and small-scale real-world data val... | Rebuttal 1:
Rebuttal: We would like to thank you for appreciating the importance of the problem that we study as well as the significance of the insights that follow from our theoretical analysis. We are also grateful for bringing up the important observation that in the SSL setting, model selection based on only the u... | Summary: The paper provides a tight lower bound for semi-supervised learning for the 2GMM model. It compares the minimax rate with supervised and unsupervised learning. The authors also provide supporting experiments on real-world and synthetic datasets."
Strengths: 1. The paper is very well-written. The authors have ... | Rebuttal 1:
Rebuttal: Thank you for appreciating our results and empirical validation as well as the clarity of our writing! We are grateful for the points that you have brought up in the review! We addressed your comment on the 2-GMM distribution in the “General comments” and now answer your remaining specific questio... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to the reviewers for their thorough review of our manuscript and for the constructive feedback provided. We are heartened by the acknowledgment of the importance of our research question: whether SSL algorithms can enhance performance over optimal SL and UL algorithms.... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation | Accept (poster) | Summary: This submission proposes a semi-supervized pseudo-label-based method for 3D point cloud segmentation method with sparse label annotations. Instead of thresholding on the confidence of pseudo labels, the authors propose to use all unlabelled points and encourage high-confidence pseudo labels by regularizing wit... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and your acknowledgment of our extensive experiments and the provided theoretical analysis. In the following, we address your concerns carefully.
---
### S1. Motivation and swapping contents.
We very much appreciate your confirmation of our idea.
Besides the ... | Summary: This paper proposes two losses for point cloud semantic segmentation. The first one is Entropy Regularization (ER) loss, which makes the pseudo-labels have low entropy and thereby be confident (like one-hot vectors). The other one is Distribution Alignment (DA) loss, which is a KL divergence between the pseudo... | Rebuttal 1:
Rebuttal:
We sincerely thank you for your positive comments and the overall acknowledgment of our deceptively simple yet effective method. In the following, we address your concerns carefully.
---
### Q1. Visualization of training process.
We thank you for your advice on inspecting the training process t... | Summary: This paper proposes a novel learning strategy to regularize the generated pseudo-labels and narrow the gaps between pseudo-labels and model predictions. It introduces an Entropy Regularization loss and a Distribution Alignment loss for weakly supervised learning in 3D segmentation tasks. The approach can bette... | Rebuttal 1:
Rebuttal: We sincerely thank you for your acknowledgment of the performance gain of our method and its potential to improve future stronger baselines. In the following, we address your concerns carefully.
---
### Q1. Entropy Regularization (ER) on high-frequency predictions.
We would like to mention that,... | Summary: This paper considers the task of weakly supervised 3D scene semantic segmentation, where only a limited number of points in each training scene are given labels. Assuming a baseline system that operates within a pseudo-label paradigm, the paper proposes a new set of regularizing loss terms, that aim to (1) red... | Rebuttal 1:
Rebuttal: We sincerely thank you for your acknowledgment of both our theoretical and empirical analysis, as well as the potential broader impact. In the following, we address your concerns carefully.
---
### Q1. Application to other tasks.
We also agree that our method could be extended to other tasks, s... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers time and effort in providing feedback.
Here, we provide more experiments and visualization for better analysis and understanding of our paper, including the table **R1**, **R2**, **R3**, and Figure **R4** mentioned below.
Pdf: /pdf/152264468c63757bbae346a2b907f23... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions, which introduces an Entropy Regularization loss and a Distribution Alignment loss for weakly supervised learning in 3D segmentation tasks, resultin... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts and we are grateful for your confirmation of the novelty and effectiveness of the proposed method. In the following, we address your concerns carefully.
---
### Q1. Descriptions in Figure 1:
Thanks for your advice.
For your question **a)**, we wo... | null | null | null | null | null | null |
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications | Accept (poster) | Summary: This work presents a novel graph mixup strategy, which focuses on synthesizing a 'midpoint' graph between two graphs based on the graph structure-signal product metric space. The authors utilize the Fused Gromov-Wasserstein (FGW) distance to achieve this and also propose a method to accelerate the computation ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and suggestions. We made every effort to address all the concerns. In the following, we quote your comments and then give our detailed response point-by-point.
> **W1. Please provide the computation cost for the vanilla model and other baselines to better und... | Summary: Authors study the problem of graph data augmentation for graph-level classifications. They propose a mix-up strategy based on the computation of Fused Gromov-Wasserstein(FGW) “mid-point” (or barycenter) between a pair of graphs from the training dataset. To solve these optimization problems, they adapt a recen... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and suggestions. We made every effort to address all the concerns. In the following, we give our detailed response point-by-point. W denotes Weakness, and Q denotes Questions.
- **W1:**
- a) We want to point out the fact that G-Mixup does not apply GW metr... | Summary: This paper proposes a new graph data augmentation method for graph-level classifications. To address the limitation of existing methods, the authors consider the joint interaction between the graph structure and node features by finding an optimal inter-graph node matching strategy. Furthermore, the authors in... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and suggestions. We made every effort to address all the concerns. In the following, we quote your comments and then give our detailed response point-by-point.
> **W1. Many existing works change both graph structure and node features to generate augmented gra... | Summary: This paper addresses a gap in graph data augmentation for graph-level classifications, where existing methods mainly focus on augmenting graph signal space and graph structure space separately, overlooking their mutual interactions. The authors formulate the issue as an optimal transport problem that considers... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and suggestions. We made every effort to address all the concerns. In the following, we quote your comments and then give our detailed response point-by-point.
> **W1/Q2. Analysis and comparisons of computational complexity:**
We provide runtime comparisons ... | Rebuttal 1:
Rebuttal: Thanks for all the constructive suggestions and comments concerning our paper from five nice reviewers. Here we provide our responses to some questions that are commonly asked by the reviewers.
> **Q1. Can you present the runtime comparison between your methods and compared baselines? Can you fur... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors study a new method "FGWMixup" for graph data augmentation.
For the two input graphs $G_1, G_2$, they propose to construct a synthetic graph $\tilde G$ through optimizing the weighted distance sum (2). To further improve the efficiency of the algorithm, they relax the polytope const... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and suggestions. We made every effort to address all the concerns. In the following, we quote your comments and then give our detailed response point-by-point. The references given in numbers correspond to the reference id in our paper, and those given in lett... | null | null | null | null | null | null |
Optimal approximation using complex-valued neural networks | Accept (poster) | Summary: The paper studies approximation rates of shallow complex-valued neural networks (CVNN) with general non-polyharmonic activation functions. First, the paper establishes upper bounds for the error of approximation of polynomial and general smooth functions by CVNNs. Then, various aspects of the optimality of the... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and in particular for the positive comments on the quality and clarity of the paper.
In the following, we individually answer your comments:
> The usual real-valued neural networks are important both mathematically and practically. They represent simple and... | Summary: The paper studies approximation error bounds for complex-valued neural networks based. The authors relied on several techniques from the work by Mhaskar (1996) and proved several theorems in the paper. I will summarize two important results here.
1. Given a function from $C^k$, an optimal error bound is proved... | Rebuttal 1:
Rebuttal: We are glad that you enjoyed reading our paper!
In the following we address each of the points that you raised.
# Point 1
> The hypothesis of continuous weight selection is not a practical assumption. The optimal choice of the network weights is discontinuous in general (see the reference below).... | Summary: This paper studies the approximation power of complex-valued neural networks (CVNNs). They derive that the approximation error is with the order $m^{-k/2n}$, where m is the number of neurons, k is the smoothness of the target function, and n is the input dimension of the neural network. They also show that thi... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback for our paper.
Your main criticism of our paper is that
> It will be more convincing if they can show results for general activation functions without the assumption of the continuity of weight selection.
We agree that it would be interesting to establish l... | Summary: The paper studies the expressive power of complex-valued neural networks. It is shown that depth 2 networks with non-polyharmonic (the complex equivalent of non-polynomial) activations can approximate any continuous function on a compact domain to arbitrary accuracy which decays polynomially with the width and... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback. We agree that CVNNs are an interesting model to study.
# Weaknesses
> Most results seem to take existing proofs and just apply them to the complex setting.
We do not think that this is a fair assessment of our work. There are several novel results and idea... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning | Accept (poster) | Summary: This paper presents a method to enhance the random sampling component of hyperband. The authors propose replacing it with a combination of random sampling, prior-based sampling, and incumbent-based sampling. They also suggest adjusting the proportion of these samplers based on the current state in the hyperpar... | Rebuttal 1:
Rebuttal: We thank you for your comments.
We would request you elaborate on your thoughts on the situations in which our work could be useful. Understanding this could give us perspective on how to address your concerns.
---
> The motivation for this work may appear artificial...the paper lacks a detailed... | Summary: This paper proposes PriorBand, an extension of HyperBand that adds expert priors and a novel sampling technique to replace random sampling in HB, called the Ensemble Sampling Policy (ESP). The ESP allows the algorithm to lean on the expert prior, but also use the current incumbent in case the prior is non-opti... | Rebuttal 1:
Rebuttal: We are glad to hear that the reviewer appreciated our presentation and found a favorite part too! Thank you for your review and comments.
---
> My major concern is in the use of average relative rank to demonstrate efficacy. While a lower rank indicates better performance, it does not indicate wh... | Summary: In the paper "PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning" the authors propose an extension to the well-known HPO methods Hyperband by adapting the way how candidate hyperparameter settings are sampled. To this end, the authors propose to use a weighting mechanism to balance be... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for appreciating the strong performance of PriorBand, our paper presentation and our efforts to make research open-source and reproducible.
---
> What is the individual effect of incumbent-based and prior-based sampling?
---
1.
**a)** We apologize th... | Summary: This paper presents PriorBand, a hyperparameter optimization (HPO) algorithm designed specifically for deep learning models. PriorBand fulfills six key requirements and tackles the shortcomings of existing HPO methods that are unsuitable for DL. It leverages cheap proxy tasks while considering expert input. Th... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewer for their kind remarks of appreciation for the method itself and we are delighted to read that the effort put into the presentation, structure, and motivation made the paper clear.
---
> Though the experiments conducted in the paper are quite comprehensive, I found... | Rebuttal 1:
Rebuttal: To all reviewers and chairs,
We upload the permitted extra PDF with plots addressing a few points raised across the reviews.
**Figure 1)** shows the influence of a "good" prior on PriorBand, per-dataset under the reproducible protocol for prior generation in our experiments (_Appendix D.3_). Thi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees | Accept (poster) | Summary: This paper presents an incremental minimax risk classifier (imrc) to effectively utilize forward and reverse learning and explain evolutionary tasks. Furthermore, the performance improvements provided by forward and backward learning can also be described analytically based on the expected quadratic changes of... | Rebuttal 1:
Rebuttal: **The assumption in the paper can better describe datasets with evolving tasks than the usual i.i.d. assumption**
The paper's assumption describes scenarios in which the underlying distributions change as a random walk with independent increments. This type of assumption is often used to describe ... | Summary: IMRCs (Incremental Minimax Risk Classifiers) are a technique for incremental learning of a growing sequence of classification tasks. The key feature of IMRCs is their ability to exploit the similarity between consecutive tasks by leveraging both forward and backward learning.
Forward learning utilizes informat... | Rebuttal 1:
Rebuttal: **Complexity of IMRCs and running time in comparison with other methods and for different backward steps**
In the final version of the paper, we will show that the running time of IMRCs is similar to other state-of-the-art techniques and increases moderately with the number of backward steps and ... | Summary: This paper presents a novel method to tackle continual learning, called IMRCs which is able to exploit forward and backward learning to account for evolving tasks. The authors provide theoretical guarantees on the performance of IMRCs in terms of the tasks' expected quadratic change and the number of tasks. Au... | Rebuttal 1:
Rebuttal: **Computational complexity of IMRCs**
In the final version we will further discuss the computational complexity of IMRCs, the running time for different number of backward steps and number of tasks, and the running time in comparison with the state-of-the-art methods, as described in the general ... | Summary: The papers studies a specific case of continual learning over a sequence of tasks. Authors extend the typical i.i.d. assumption of task meta-distribution to the case where only the differences between subsequent task distributions are assumed to be independent and zero-mean. Under this assumption, they devise ... | Rebuttal 1:
Rebuttal: **Table with main notations used in the paper**
In the final version of the paper we will include Table 2 in the attached pdf that shows the main notations used. Specifically, such table shows the notation used for the mean vector, the confidence vector, the MSE vector, the ESS, the uncertainty s... | Rebuttal 1:
Rebuttal: In the responses below we are confident to have addressed all the comments and questions made by the reviewers. Please let us know if you have any additional inquiry so that we can completely clarify any aspect in the paper during the rebuttal period.
We plan to use the extra page allowed to clar... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Though class-incremental learning has been studied the most as a default setting for continual learning, task-incremental study could be crucial for other areas. This paper focuses particularly on the case where tasks being introduced over time are interrelated under specific conditions. Under the supervised l... | Rebuttal 1:
Rebuttal: **Explanations about methods and datasets**
In the final version of the paper, we will further describe the methods and datasets used in Table 1 to make the main paper more self-contained as suggested by the reviewer. Specifically, we will include the following comments on the datasets and method... | null | null | null | null | null | null |
Sample-efficient Multi-objective Molecular Optimization with GFlowNets | Accept (poster) | Summary: Molecule generation involves the optimization of multiple potentially competing objectives simultaneously. As evaluating these objectives can be a time-consuming and costly task, sample efficiency is paramount. This work proposes a multi-objective Bayesian optimization approach leveraging GFlowNets to tackle t... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> It is not directly evident to me how this work is different from Pareto GFN proposed in GFlowNet Foundations.
While the concept of Pareto GFN was theoretically discussed in GFlowNet Foundations, we are among the fi... | Summary: A GFlowNet for molecular optimization conditioned on the preference weights of multiple objectives is proposed. To be precise, the model is trained to sample molecules from a target space with probability proportional to some combination of reward functions (weighted sum or Chebyshev scalarization) and trained... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> There is the paper "Multi-objective GFlowNets" [MO; arXiv:2210.12765, ICML 2023], which also studies GFlowNets for multiobjective Bayesian optimization by conditioning on scalarization weights. Can you comment on th... | Summary: This paper proposes the use of GFlotNets to address the problem of sample-efficient multi-objective molecular optimization, an important problem in various scientific discovery application - such as materials design and drug discovery.
The key idea proposed in this work is to leverage hypernetwork-based GFlowN... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> Although the batch size may significantly affect the overall computational cost as well as the optimization performance, there is no discussion on the impact of selecting a specific batch size nor any empirical eval... | Summary: This work addresses molecular design with GFLOWNET, an algorithm learning a sampling policy $\pi$ proportional to the reward function, i.e. where $\pi(x) \propto R(x)$. In particular, this work tackles the essential yet under-adressed multiobjective optimization setting. They do so using a hypernetwork (condit... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the insightful and constructive comments!
> How are the objectives computed? Is it “physics” based or does it rely on learning a surrogate model? Then, how are the generated molecules evaluated? Are the generated molecules evaluated on the objective using an ... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
We greatly appreciate the reviewers' time, valuable comments, and constructive suggestions. Overall, the reviewers deem our paper as "well-organized" (9YCN, BkDd, hutP), studying "an important problem" (9YCN, BkDd, Dc63), acknowledging our methodology novelty and s... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Energy-Based Sliced Wasserstein Distance | Accept (poster) | Summary: This paper introduces a new distribution of slices of the Sliced-Wasserstein distance based on the energy-based model framework. The authors study the theoretical properties and conduct numerical experiments on EBSW.
Strengths: - The study of the idea is organized and clear.
- Theoretical quantities of inter... | Rebuttal 1:
Rebuttal: We appreciate the review's feedback and comments and want to express our thanks. Our responses are as follows. We remain open to additional questions and further discussion.
**Q19**: The method is not compared with the vanilla Distributional Sliced-Wasserstein method which should be the baseline ... | Summary: This paper proposes an extension to Sliced Wasserstein Distance (SW), an approach for measuring distances between distributions by computing the average of the energy of the 1-d Wasserstein distances between 1-d projections. The authors argue that moving towards non-uniform ways of sampling the projections is ... | Rebuttal 1:
Rebuttal: We'd like to express our gratitude for the constructive feedback. We're here to provide responses as outlined below. We are eager to participate in further discussions.
**Q10**: On Lipschitz constrained $f$
**A10**: Regarding the Lipschitz energy function, the polynomial function is Lipschitz an... | Summary: This paper has proposed an energy-based slicing distribution that maps original distributions into one-dimesion space to compute the Wasserstein distance.
Strengths: This paper has proposed an energy-based slicing distribution that maps original distributions into one-dimesion space to compute the Wasserste... | Rebuttal 1:
Rebuttal: We begin by expressing our gratitude for the insightful feedback and comments provided in the review. In response, we offer the following explanations and answers to your inquiries. We welcome any further questions and are open to engaging in a more in-depth discussion on the matter.
**Q5**: An i... | Summary: This paper proposed a new variant of sliced Wasserstein distance that is inspired from Energy-Based Model called Energy Based Wasserstein Distance (EBWD). The proposed method models the energy function of the distance, from which the slices can be sampled. Three sampling techniques are proposed. The paper eval... | Rebuttal 1:
Rebuttal: We wish to extend our appreciation for the valuable feedback and comments provided in the review. In response, we would like to address your questions as follows. We are readily available to address additional inquiries and to engage in further discussion.
**Q1**: It would be reasonable to evalua... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewers for their time and feedback. We would like to summarize some additional experiments in the rebuttal PDF and address common questions from the reviewers. Other questions are addressed in the corresponding rebuttal of reviewers.
1. **Additional experiment... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Interpretable Characteristic Kernels via Decision Forests | Reject | Summary: The authors show the random forest induced kernel is characteristic, and they empirically study the validity of the kernel for independence and k-sample testing.
Strengths: The authors show the connection between the characteristic property of the kernel and random forest. The topic is interesting and importa... | Rebuttal 1:
Rebuttal: Thank you very much for the thorough review!
**Weakness and Limitation:**
We will incorporate more background information on random forest kernels and existing works related to hypothesis testing. Moreover, we will include an updated conclusion to emphasize the main contribution of the paper, i.... | Summary: The paper show how decision forest can be used to induce a kernel for k-sample testing.
Strengths: Better-than-SOTA results.
Weaknesses: The paper is a extremely hard to follow, particularly Section 3, which I was unable to follow without going deep into the referenced papers. The same goes for related works... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper!
**Additional Comments 1-3:**
Thank you for pointing out the typos, we fixed them and also changed notation for the indicator matrix.
**Additional Comments 4:**
Figure 4 exclusively showcased two competitors, HHG and HSIC, for specific reasons:... | Summary: In this paper, the authors introduced a new method called KMERF, which employs random forest for kernel construction. Through their algorithm, they were able to establish that the kernel they created has certain properties, namely being positive definite and asymptotically characteristic. The authors also dem... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable comments and questions. Below is the response to all the weaknesses and questions.
**Weakness 1:**
As shown in Figure 1 and Figure 2, the test power equals the type 1 error under independence. This supports the validity of the test, which means the test will ... | Summary: In this paper, random forest induced kernel/proximity is combined with distance correlation and a recently developed chi-square test method to form a hypothesis testing method that is useful for independence testing and k-sample testing. The authors prove that the kernel is asymptotically characteristic and th... | Rebuttal 1:
Rebuttal: Thank you very much for the review and the questions. Indeed, the main innovation is not about the random forest kernel itself, as the proximity matrix is well-established within the random forest framework. Our main contribution is the direct utilization of this proximity matrix as a valid and co... | Rebuttal 1:
Rebuttal: Thank you all very much for taking time to thoroughly reviewing our paper and providing valuable feedback. Attached is a one page document containing figures we will refer to in each of our rebuttals.
Pdf: /pdf/35ae1f2b90103c050d359aa5186e9682ebd2a2f8.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this study, the authors proposed a new kernel KMERF for independence testing.
In KMERF, multiple decision trees are constructed similar to Random Forest, and the number of trees in which two data points belong to the same leaf node is calculated.
This count is used as the kernel value between the two points... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing the paper and the valuable suggestions!
**Question 1:**
Thank you for pointing out the limited coverage of existing research on random forests. We will expand the background section to include more works of random forest and existing connections to kernel and h... | null | null | null | null | null | null |
Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork | Accept (poster) | Summary: This paper presents a Data-free Subnetworks (DSN) approach for task incremental learning. With a random initialized neural network, DSN learns a task-specific neuron-wise mask to find an optimal subnetwork for a new arriving task, and performs data-free replay for transferring the knowledge to the past tasks. ... | Rebuttal 1:
Rebuttal: ## 1. Response to "*backward knowledge transfer*"
Thanks for this concern. After training a new task and task similarity measurement, our data-free replay is to produce impression crafts of the most similar task. For backward knowledge transfer, as we claimed that we treated the subnetwork as the... | Summary: In this work, the authors explore task-incremental learning through the lens of the Lottery Ticket Hypothesis (LTH). They contend, primarily from an LTH perspective, that a distinct task necessitates merely a sparse collection of neurons, hence using only a compact sub-network for its operation. Subsequently, ... | Rebuttal 1:
Rebuttal: ## 1. Response to "*WSN*"
Thanks for this suggestion.
(1) DSN devises a neuron-wise mask mechanism to select neuron-affiliated weights for new task learning.
(2) DSN enables positive knowledge transfer in both forward and backward directions. In particular, the data-free replay mechanism in DSN... | Summary: The paper proposes a novel method for task incremental learning that uses a subnetwork for each task. The method is based on neuron masking obtained by learnable embedding. The method also finds the most similar task from the past tasks and allows for backward transfer. The method is tested on four benchmarks:... | Rebuttal 1:
Rebuttal: ## 1. Response to "*multiple previous tasks*"
Thanks for this concern. We consider that transferring new knowledge to multiple old tasks can result in significant time costs (as well as memory costs) that outweigh the benefits. Thus, we only make backward knowledge transfer to the most similar tas... | Summary: This paper focuses on task continual learning and attempts to enhance the elastic knowledge transfer across the tasks that sequentially arrive. With the help of masks, achieve forward and backward knowledge transfer.
Strengths: This paper is well written and easy to follow. The proposed method can achieve bac... | Rebuttal 1:
Rebuttal: ## 1. Response to "*mask & knowledge transfer*"
*(1) Forward Knowledge transfer*: Thanks for this question. Our solution is to use the mask mechanism to determine the subnetwork architecture of each arrived task from the hypernetwork $\mathcal{H}$. Thus, any subnetwork is a subset of $\mathcal{H}... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Building upon the principles of the Lottery Ticket Hypothesis, this paper introduces a hyper network model embedded with a series of competitive "ticket" sub-networks. Each of these sub-networks is designed to excel at their corresponding tasks, with a particular emphasis on knowledge transfer. Furthermore, th... | Rebuttal 1:
Rebuttal: ## 1. Response to "*complex processes*"
*(1) Similarity measurement*: Thanks for this concern. First, the mask similarity measurement is a simple vector-based computation that uses the cosine distance to obtain the similarity scores between different tasks. Second, our masks are neuron-level, whi... | null | null | null | null | null | null |
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation | Accept (poster) | Summary: This work benchmarks several point selection approaches for label-efficient LiDAR point cloud semantic segmentation. Their proposed criterion leads to better results than existing selection approaches and good generalization with few annotations. They use several settings depending on the accessibility of auxi... | Rebuttal 1:
Rebuttal: We appreciate the comments from the reviewer KrDz. We have answered all the questions and sincerely hope they can address the concerns.
**Q1: the entire contribution boils down to the "voxel confusion degree" selection criterion. Yet the authors present the entire field of active learning as a co... | Summary: This paper introduced a baseline for active learning called Annotator for LiDAR point cloud semantic segmentation. The paper includes an analysis of various active selection strategies, including random selection, entropy-based selection, margin-based selection, and a novel strategy called voxel confusion degr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer a35W for the very constructive comments. We are glad that the reviewer acknowledge that the task is valuable and important, the idea is simple but effective, and the investigation is novel. Here we address the biggest concern raised by the reviewer, i.e., the fundam... | Summary: This work proposes a general and efficient data annotation pipeline, namely Annotator, to label LiDAR data for semantic segmentation. Specifically, the proposed method introduces a voxel-centric online selection strategy to determine which voxel should be annotated by humans. Voxel confusion degree (VCD) is th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer dFZX for the very valuable comments. We are glad that the reviewer acknowledges our new criterion can achieve better performance with only very few times of annotations. Here we address the concerns of our paper, and hope our response can address the concern.
**Q1:... | Summary: This paper presents Annotator, a general and efficient active learning baseline for LiDAR semantic segmentation, which can adapt to different settings and scenarios with minimal annotation cost. Annotator consists of a voxel-centric online selection strategy that exploits the local topology and structure of po... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer N28Y for the appreciative and constructive comments. We are encouraged that the reviewer found our three original ideas, thorough experiments, sufficient background, and well-written supplementary material. Here, we would like to respond to the issues and hope that ... | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We sincerely thank all the reviewers for their positive comments and helpful feedback that have certainly helped improve the quality of this paper. We have uploaded the responses w.r.t. each reviewer together with `the one-page PDF`.
In response to the comments, we have c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a voxel-centric online active learning baseline that efficiently reduces the labeling cost of enormous point clouds and effectively facilitates learning with a limited budget. The contribution of this paper can be summarized in three aspects:
1. A voxel-centric online active learning baseli... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer zYAC for the detailed summary and constructive comments. We are glad that the reviewer acknowledges that the problem has great practical significance, the method is novel and generally applicable, and the experiments are very impressive. Here we answer all the quest... | null | null | null | null | null | null |
Mechanic: A Learning Rate Tuner | Accept (poster) | Summary: This paper focuses on a method (MECHANIC) for tuning the learning rate of any given base optimizer. This is compatible with any given base optimizer along with a learning rate schedule*. Let u_1, u_2,..u_t be the steps of the base optimizer up until now, then MECHANIC chooses a s_t such that the neural network... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and work reviewing our paper! We have included answers to your questions below, and we’d be happy to elaborate further.
Intuition for scaling by the sum of the steps: At a high level, the sum of the steps is a more “stable” value because it changes slowly. Ind... | Summary: This paper proposes a parameter-free technique to tune the learning-rate scale factor automatically, which can be applied to any given base optimization algorithm to match its performance given carefully tuned hyper parameters. This approach is mainly empirical in nature, though grounded in reduction from rece... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed comments! Your feedback will be very helpful to us in revising the paper. In the below we provide some more detail that we hope will address your main concerns.
**Main concern about missing empirical information**
We provide a lot more detail in the appendi... | Summary: The paper proposes a scheme to automatically tune the learning rate scale factor for any gradient-based optimization algorithm. The method can be viewed as a practical realization of recent theorertical results in online convex optimization (OCO), which reduces the problem to minimizing the regret of a one-di... | Rebuttal 1:
Rebuttal: Thanks very much for your detailed review and feedback on the presentation - making sure everyone can follow the paper is a priority for us. Unfortunately, we cannot provide an updated revision, so below we’ve copied in some changes that will ameliorate the issue. We would be happy to hear your fe... | Summary: This paper develops a method to automatically determine the learning rate of an optimization algorithm. Their approach, referred to as Mechanic and motivated by theoretical advances in online convex optimization, determines a new learning rate for iteration t using an update that resembles an adagrad update pl... | Rebuttal 1:
Rebuttal: **Main concern about baseline hyperparameter tuning**
We provide a lot more detail into the appendix section B. However, to specifically answer your questions:
```How baseline was tuned and Is it the best learning rate and if so out of how many searched```: For all baselines we either grid swee... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution | Accept (spotlight) | Summary: In this work, the authors propose a new genomic foundational model that can be pretrained on the human reference genome. This model, called HyenaDNA, is built upon the previous model Hyena. The advantage of this model is that it can train on ultra-long sequences (up to 450k) at single nucleotide resolution wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review! We’re glad you appreciate the importance of foundation models for DNA sequences, and the clarity of writing of the manuscript. Below we address concerns and clarifications the reviewer raised. We’re happy to answer any further questions the review... | Summary: This paper presents HyenaDNA, an advanced genomic foundation model leveraging the Hyena language model's capabilities, which are based on implicit convolutions. The authors highlight the limitations of prior Transformer-based genomic models, which have been constrained by token lengths and therefore impeded ac... | Rebuttal 1:
Rebuttal:
Thank you for the thoughtful review. We’re glad the reviewer is excited about the contribution of Hyena to DNA sequences, as well as the novelty of soft prompting for long-context. Below we address concerns the reviewer raised, and clarifications to technical details that were asked. We’re happy ... | Summary: This paper introduces HyenaDNA, a genome foundation model based on the Hyena architecture that replaces attention layers with implicit convolutions. Though being 2500 times smaller, it achieves better performance than the state-of-the-art model.
Strengths: - The paper is clearly written.
- The proposed metho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review of our work. We’re glad they appreciate the writing clarity, strong empirical results and the exploration of in-context learning and tunable prompting in genomics. Below we address the weaknesses and questions the reviewer described. We’re happy to f... | Summary: The authors train the Hyena operator model on the human genome and adapt it to downstream tasks in computational biology.
Strengths: Lots of clever things about this work that I really like:
Very good use case of Hyena model with very long-range dependencies
Curriculum learning is very clever and makes sens... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review of our work and are glad they appreciate many of the contributions including the long-range capabilities, single nucleotide resolution, and the curriculum learning introduced. Below, we address the concerns the reviewer made about the GenomicBenchmar... | Rebuttal 1:
Rebuttal: ### Common Response
We thank the reviewers for their time and in-depth reviews. We believe that addressing the reviewer’s feedback and questions has helped greatly improve the quality of our manuscript.
We are happy to hear the reviewers appreciated the strong empirical performance of HyenaDNA o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This manuscript applies Hyena, a neural operator based on implicitly parametrized long convolutions and data-controlled gating, to the domain of DNA modelling.
The subquadratic complexity of Hyena enables scaling to context lengths of up to 450,000 at single nucleotide resolution.
This represents a significa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review of our work. We’re glad they appreciate the strength of the results, the originality and significance of the application, and the clarity of the manuscript. Below, we address the reviewer's concerns about the contributions, design, performance, and t... | null | null | null | null | null | null |
Uncovering Meanings of Embeddings via Partial Orthogonality | Accept (poster) | Summary: This paper aims to uncover the semantic meaning of embedding vectors
within a given space. The basic idea is to determine
a generalized Markov boundary by computing the cosine similarity of the
orthogonality projected vectors within a subspace. The top K
candidates are then selected. Furthermore, the authors p... | Rebuttal 1:
Rebuttal: Thank you for your questions! We're glad you found the ideas novel and the paper well written.
**Experiments**
We completely agree with the reviewer that the paper is more focused on theory and definitions. The reason we consider pre-trained image-language model CLIP is that we found it tends to ... | Summary: This paper investigates the relationship between the semantics and linear algebraic structure of token embeddings. It proposes utilizing partial orthogonality to define the "Markov boundary" of token embeddings. Given that token embeddings have limited dimensions and the Markov boundary can consist of numerous... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and suggestions!
**“Although the authors aim to study the independence relationship between word embeddings, they do not provide an evaluation metric to substantiate the effectiveness of the proposed method.”**
The experiment section is divided into two parts. T... | Summary: The central question (quoting the paper) is "How to make sense of an embedding vector in relation to other embedding vectors?" For that
purpose, the authors propose to generalize the idea of the Markov boundary to embeddings, with a relaxed adaptation of this notion to
cope with word embeddings peculiarity. T... | Rebuttal 1:
Rebuttal: Thanks for your review!
**“The scientific goal”**
We apologize for the confusion. In this paper, we hypothesize that semantic meanings have an independence structure. We use the abstract “independence model” to formalize this idea. On the other hand, embeddings, which are vector representations ... | Summary: This paper presents some theory and a method for reasoning about information gain in embedding space via a relaxation of conditional independence, as well as some theory on independence preserving embeddings.
As information gain is inherently linked to independence, the paper focuses on defining a generalizat... | Rebuttal 1:
Rebuttal: **“The method and experiments for the generalized Markov boundary only involve a single target embedding.”**
The goal of this paper is to find good descriptions/explanations of given embeddings with other embedding vectors. We feel like it does not make too much sense to try to describe/explain m... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews and for recognizing the ideas presented in the paper as novel and appealing. In addition, we ran some quick experiments to additionally verify the effectiveness of our method and theory as suggested by reviewers. Tables and figures are included i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds | Accept (poster) | Summary: This paper propose a system of model compression methods for text-to-image models such as stable diffusion. Authors propose to apply robust training (i.e. stochastic depth training) to train a network robust to architecture change and propose CFG-free knowledge distillation during the compression of the u-net.... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive feedback and valuable comments. We appreciate that the reviewer acknowledges our paper studies a hot and interesting topic, and includes good evaluation and experimental results. We are glad to know the reviewer likes the video in the supplementary material... | Summary: This work develops a lightweight Stable Diffusion model with architecture compression and step reduction. For architectural compression, an efficient UNet architecture is obtained from evaluating the importance of individual residual and attention blocks, and an efficient image decoder is obtained via channel ... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive feedback and thoughtful comments. We appreciate that the reviewer acknowledges that our successfully compressed two-second text-to-image model can achieve significant attention in academia and industry, our step distillation is novel and can be broadly appl... | Summary: This paper introduces a novel framework that significantly reduces the inference speed of text-to-image diffusion models to under two seconds on mobile devices. The authors propose two key techniques. Firstly, they introduce Efficient U-Net which enhances inference speed by eliminating redundant architectural ... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive feedback and valuable suggestions. We appreciate that the reviewer acknowledges our paper introduces a novel framework with efficient Unet and novel step distillation to significantly reduce the inference speed of the text-to-image models, and stands as the... | Summary: Text-to-image generation (T2I) is getting popular and has a vast application value. This paper explores efficient T2I by reducing computational redundancy. They learn an efficient U-Net via data distillation and then decrease the required diffusion steps. They can achieve T2I on an iPhone 14 Pro in 2 seconds.
... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive feedback and thoughtful comments. We appreciate the reviewer's acknowledgment that the paper proposes a valuable efficient U-Net for research and practical usage, is easy to follow, and provides an attractive demo video.**
***
**Q1. About using ViT for enc... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you all for your positive rating and valuable comments. We upload a one-page PDF to include additional figures and algorithms. All other questions are addressed in the following individual responses. We sincerely look forward to having further discussions with you during Rev... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Domain Agnostic Fourier Neural Operators | Accept (poster) | Summary: This paper presents a novel method of extending Fourier Neural Operators (FNO) to irregular geometries using an indicator function to represent the shape of the geometric area. This area is then extended to a larger, regular area, thereby allowing FNO to handle irregular geometries. This is indeed an innovativ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
**References on transformer-type neural operators and F-FNO**: We thank the reviewer's kind suggestions, and have added F-FNO [4] as a baseline in our comparison. As shown in the tables below: in both the hyperelasticity and airfoil design pr... | Summary: The paper explores the extension of Fourier Neural Operators (FNOs) to irregular geometries and topology changes. To leverage the computational speed benefits of the fast Fourier transform (FFT) employed by FNOs, they enclose the physical domain with a period box. They then adapt the FNO kernel by multiplying ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable suggestions.
**Comparison with additional baselines**: We have added F-FNO as an additional baseline in both the elasticity and airfoil problems. On the other hand, since INR focuses on learning a time-continuous dynamics model of the underlying flow, and ... | Summary: The Fourier Neural Operator (FNO) is a model in the field of neural operators that has successfully interpreted various physical phenomena. However, one of the issues with FNO is its limitation to learn only on rectangular domains. In Geo-FNO, this problem is addressed by lifting irregular domains to the laten... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
**Mathematical motivation behind the domain characteristic function**: In order to obtain a truly domain-independent operator, we aim to hard-code domain information into the architecture. The challenge is to maintain the applicability of FFT... | Summary: In this paper, the authors propose the Domain Agnostic Fourier Neural Operator (DAFNO), an FNO that can deal with irregular boundaries. While the classical FNO is limited by construction to irregular domains, DAFNO simply includes a smoothed function $I(\cdot)$ of the characteristic function of the domain on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable suggestions.
**High-dimensional problems**: DAFNOs are readily applicable to more complex settings and higher-dimensional problems, as neither the characteristic geometric encoding nor the smoothening technique is constrained to a specific dimension. It wi... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments, for recognizing the importance/usefulness of our work (reviewers FUid, UPhM, dnax), the novelty and elegance of DAFNO's architecture (reviewers FUid, fNa3, dnax, BzBv), DAFNO's role as the first neural operator that can handle dynamically chang... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Fourier Neural Operators (FNOs) are a popular method for modeling physical systems such as different types of PDEs. However, in order to use the FFT to make FNO efficient, the input needs to be a regular grid. The authors study the question of irregular grid inputs for FNO, as well as problems with changing to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable suggestions.
**Highly irregular topologies or topologies with fine-grained features**: DAFNO can be readily combined with the grid mapping technique in Geo-FNO, to handle non-uniform grids. No modification on the NN architecture is required, and one just n... | null | null | null | null | null | null |
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness | Accept (poster) | Summary: This paper deals with the CBwK problem with a fairness constraint of equalized average costs between groups. The authors propose a dual algorithm: PGD for CBwK (with adaptive stepsize) and provides regret bounds.
Strengths: The problem statement is pretty clear and the related work is relatively thoroughly pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the reading but would respectfully disagree with the evaluation.
For the PGD algorithm (and the proofs) being standard: Yes, the PGD approaches in the CBwK litterature are standard, as we acknowledge and as other rewievers point out (see, e.g., Agrawal and Devanur 2016).... | Summary: The problem studies the contextual bandit with knapsack problem with contexts coming from a continuous set, signed costs, and under the assumption that expected reward and cost functions can be uniformly estimated. The learner aims at maximizing their cumulative rewards while guaranteeing constraints of the fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and fully agree with the evaluation.
For the statements of the paragraph starting at line 158, including twice the word "typically": We agree that this paragraph will benefit from some rewriting. For the total spendings B_total, we had in mind the exa... | Summary: This paper studied contextual bandit problems with knapsacks (CBwK). Under this setting, at each time step, a scalar reward is obtained and vector-valued costs are suffered. The agent aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and generally agree with the evaluation.
For the weakness raised: We only provide (very) preliminary simulations (see pages 33-35), rather illustrating how to successfully deal with the fairness constraints. What these simulations do not illustrate, h... | Summary: The paper considers the problem of general contextual bandits with knapsacks in the regime where Omega(sqrt{T}) <= B <= O(T^{3/4}). The paper provides a new algorithm that is based on prior methodologies of primal-dual algorithm, but instead of relying on them as black-boxes, updates the dual variables using a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and generally agree with the evaluation.
For the fairness example: It was our own motivating example for developing a CBwK theory able to handle small total-cost constraints. (Chohlas-Wood et al., 2021, who first introduced this example, could not pro... | Rebuttal 1:
Rebuttal: We generally agree with the evaluations by Reviewers aT3N - EATd - Hs7n but respectfully disagree with the evaluation by Reviewer 3rBq. We explain below in detail the main two issues we disagree with:
- The main algorithmic contribution is not the PGD approach per se, but its adaptive step-size t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation | Accept (poster) | Summary: The paper introduces CL-NeRF, an approach for efficiently adapting Neural Radiance Fields (NeRFs) to real-world scene changes over time. CL-NeRF focuses on continual learning and requires only a few new images to adapt to changes while retaining the memory of unaltered areas. It consists of two main components... | Rebuttal 1:
Rebuttal: Dear Reviewer Fafa,
Thank you for appreciating our approach. We will address your comments below.
**W1: The assumption that continual learning is necessary for NeRFs and the relevance of this assumption to the problem.**
Thank you for the comment. We understand your concerns regarding continual... | Summary: This paper tackles the task of continuing learning of NeRF, which aims to adapting NeRFs to real-world scene changes over time using a few new images. To prevent the forgetting problem during adapting, the authors propose CL-NeRF. The CL-NeRF consists of two key components: an expert adaptor for adapting to sc... | Rebuttal 1:
Rebuttal: Dear Reviewer tKPz,
Thank you for appreciating our approach. We will address your comments below.
**W1: Experimental results are not very well represented.**
1) We regret the omission of the analysis for Table 1 and appreciate your comments. Table 1 uses the Backward Transfer Metric (BTM) and F... | Summary: This paper proposes a challenge of how to reduce the data and time cost of retraining NeRF when the scene changes. To this end, the paper proposes two key components: a trainable network for the changing part of the scene, and a conflict-aware network for the unchanged part of the scene. With these two compone... | Rebuttal 1:
Rebuttal: Dear Reviewer f2t1,
Thank you for appreciating our approach. We will address your comments below.
**W1: Evaluate the method on more real scenes.**
We conduct experiments on two more challenging scenes (real-world indoor and outdoor scenes) containing various objects and environments to demonstr... | Summary: The authors propose CL-NeRF, which tries to solve the problem of rendering scenes that evolve over time using a few images of the altered scene while retaining information about the unaltered regions. The proposed method contains two key components - 1. an expert adapter, to adapt to new regions, and 2. a conf... | Rebuttal 1:
Rebuttal: Dear Reviewer fqk8,
Thank you for appreciating our approach. We will address your comments below.
**W1: Evaluate [1] in the proposed benchmark.**
Thank you for the insightful advice. Unfortunately, due to the limited rebuttal time and the unavailability of MEIL-NeRF's [1] code, it is challengin... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs:
Thank you so much for your time and efforts in assessing our paper. Hope our rebuttal has addressed your concerns. We are happy to discuss with you further if you still have other concerns. Thanks for helping improve our paper.
Best regards, Paper 1565 Authors
Pdf: /pdf/e... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work aims to tackle the challenge of efficiently adapting NeRFs to real-world scene changes in a continual learning setting. To achieve this, it develops two techniques, including an expert adaptor for adapting to new changes and a conflict-aware knowledge distillation scheme for memorizing unchanged part... | Rebuttal 1:
Rebuttal: Dear Reviewer QJef,
Thank you for appreciating our approach. We will address your comments below.
**W1: Limited contribution and novelty, compared to [1,2,3].**
1) Existing methods in continual learning primarily deal with image classification tasks that involve adding new classes incrementally... | null | null | null | null | null | null |
Dual control variate for faster black-box variational inference | Reject | Summary: The authors introduce a "dual" control variate for reducing gradient variance in black-box variational inference in the context of models that admit data subsampling (i.e. exhibit the required conditional independence). The dual control variant is joint in that it simultaneously addresses the two sources of Mo... | Rebuttal 1:
Rebuttal: Thank you for your careful review and suggestions.
- Usage of "M": This notation comes from the SAGA paper, but we agree it likely causes confusion here. We plan to replace it with $g(w)$ or $\bar{g}$ in later revisions.
- Local latent variables: We agree that this is a limitation. In our PPCA e... | Summary: This paper proposes a new control variate for black-box variational inference. In particular, the proposed "dual" control variate attempts to reduce the subsampling noise and Monte Carlo noise at the same time. For this, the paper utilizes an incremental gradient-like scheme. The performance of the new control... | Rebuttal 1:
Rebuttal: Thank you for your positive review and detailed suggestions. Please see below for our response.
- Naming: We agree that the connotation of Lagrangian duality is an unfortunate clash of terminology and are certainly open to changing the name. Our reason for not using "doubly control variate" is th... | Summary: The paper presents a method for variance reduction in stochastic gradient estimation in doubly stochastic variational inference
where there exists two sources of variance: (i) Monte Carlo noise when sampling from the variational distribution and (ii) gradient
variance due to the minibatch sampling. The autho... | Rebuttal 1:
Rebuttal:
Thank you for your careful review and suggestions.
- Choice of models: We would like to clarify that the models we experiment with have rather high latent dimensionality. The models presented in Fig. 4 have 7840, 12544, and 5525 latent variables, respectively. Certainly, larger models exist, but... | Summary: Existing stochastic methods for black-box variational inference only attempt to reduce the noise either due to data subsampling or Monte-Carlo sampling of the expectation. This paper proposed a new "dual control variate", which addresses both types of noise at the same time. In experiments, the proposed contro... | Rebuttal 1:
Rebuttal: Thank you for your careful review and suggestions.
- Experiments and models used: Our experiments focus on Bayesian inference on mechanistic models, a well-recognized area. Many of the models we consider are high-dimensional, with latent dimensions going from 5000 to 12000 for the larger models (... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their careful reviews.
In the PDF file, we provided the following additional content:
- UG9s has concern regarding the fairness of the experiments, in particular, since dual requires extra gradient calls at each iteration, the baseline estimators should ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the drawback of the black-box variational inference framework for Bayesian posterior inference by proposing dual control variate that is capable of jointly reducing the variances from both data subsampling and Monte Carlo sampling. The experimental evaluations on various datasets demonstrat... | Rebuttal 1:
Rebuttal:
Thank you for your careful review and suggestions.
- Presentation/clarity: We agree that the presentation could be improved. We will aim to better clarify technical terms and impact, as well as incorporate suggestions from reviewers SSK5 and NBCj.
- Approximation function: We appreciate that th... | null | null | null | null | null | null |
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors | Accept (poster) | Summary:
This paper tackles the problem of 3D scene reconstruction from posed RGB-D images by learning an implicit occupancy function.
Unlike previous methods [58, 3, 65, Ref_DS] that directly use the depth values (obtained either from depth images, SfM pipelines, or from depth prediction networks) of individual rays... | Rebuttal 1:
Rebuttal: 1. Misunderstanding in summary
* “...a particular point on a sampled ray for supervision. ”
We do not use the interpolated occupancy to supervise the inferred occupancy due to the inaccuracy or incompleteness of TSDF.
* “... TSDF will explain such points the best”
Fig.3 in our manuscript show... | Summary: This paper proposed a simple yet effective approach of neural implicit surface reconstruction from RGB-D seuqences that leverages the reconstructed TSDF grids as a prior which effectively improve the reconstruction quality of fitting a neaural implicit surface from multi-view RGB-D images directly. The idea of... | Rebuttal 1:
Rebuttal: 1. Experimental settings for comparing with multi-view reconstruction methods
Since our method focuses more on SLAM applications which require both camera tracking and mapping two procedures, it is very hard to conduct completely fair comparisons with multi-view reconstruction methods. The compar... | Summary: The paper proposes a pipeline for estimating scene geometry with TSDF represented with neural implicit function, based on multi-view RGBD inputs. The main novelty is a fusion mechanism which utilized fused depth geometry as prior, and fuses geometry prior with estimated geometry using attention-based weighting... | Rebuttal 1:
Rebuttal: 1. Attention network justification
We did report ablation studies on attention network justification in our manuscript. We reported numerical and visual comparisons with different attention modeling alternatives in Tab.10 and Fig.8, respectively, where our current attention network produces the b... | Summary: This paper aims to improve the performance of 3D reconstruction from multi-view RGB-D images. The key innovation is the attentive depth fusion prior, which allows the networks to directly use the depth fusion prior with the inferred occupancy as the learned implicit function. Experiments show that the proposed... | Rebuttal 1:
Rebuttal: 1. Low-quality completion
As described in L226-228, all completed meshes in the right column in Fig.2 are reconstructed in the context of SLAM. Under this setting, we do camera tracking on each frame, render RGB and depth images every 5 frames for reconstruction by minimizing rendering errors, fu... | Rebuttal 1:
Rebuttal: We thank reviewers for their valuable comments and highlighting our novel and interesting idea (REVA x87y, 3uRZ), extensive evaluations and analysis (REVR QXhf, x87y, 3uRZ, sZZA), well-motivated and sound technical method (REVR QXhf), clear presentation (REVR QXhf), and well-written manuscript (RE... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift | Accept (poster) | Summary: This paper proposes to combine contrastive learning and self-training for unsupervised domain adaptation. Experimental results on UDA benchmarks demonstrate the empirical effectiveness of the approach and a thorough study demonstrates the theoretical benefits.
Strengths: - The results on UDA tasks are consist... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and for their thoughtful feedback. We will improve the exposition of the final version as per your suggestions (e.g., a bulleted list of contributions, algorithmic description of STOC).
> **The proposed method STOC is vaguely desc... | Summary: The paper explores the synergy of self-training and contrastive learning in semi-supervised learning (SSL) and unsupervised domain adaptation (UDA). It discovers their complementary effect in UDA. Furthermore, it proposes Self-Training Over Contrastive learning (STOC) to combine the benefits of the two approac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and positive feedback. In the next revision, we will **add discussion on Zhai et al. [1]** and correct typos. We hope our responses address any outstanding concerns. Please let us know if there are any additional questions.
> **The combination of... | Summary: This paper empirically explores and theoretically studies the complementary benefits of using self-training (ST) and contrastive learning (CL) for unsupervised domain adaptation (UDA), where unlabeled data is available from source and target domains, whereas labels are only available from the source domain. Fi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and positive feedback. In our revision, we will elaborate on the setup in Sec 5, and move the discussion on related work from App B to the main paper. We **add experiments with two additional combinations of CL+ST** algorithms (see general response... | Summary: This paper investigates the complementary benefits of combining self-training and contrastive pretraining for domain adaptation under distribution shifts. Through an empirical study on 8 benchmarks, the authors demonstrate that applying self-training (FixMatch) after contrastive pretraining (SwAV) yields subst... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and their detailed and constructive feedback. For the rebuttal, we **added new results with Barlow Twins** as the pretraining algorithm and some **preliminary results with Imagenet pretrained networks**. Please let us know if this ad... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their thoughtful feedback and are glad to see all of them recommending acceptance. Per their feedback we have **added experiments on more combinations of CL+ST** where we find our empirical findings on SWaV+FixMatch continue to hold. In the general response, we... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception | Accept (poster) | Summary: This work proposes a solution to train multimodal multi-task training and demonstrate the model trained via this strategy outperforms other approaches in zero-short learning problems. The proposed solution mainly relies on current solutions including AGD, JAX library, DropToken, etc. it seems that this work ca... | Rebuttal 1:
Rebuttal: Thanks for providing a review of our paper, here we try to answer some of the concerns and comments about the paper.
>If this work focuses on the training strategy, more studies on downstream tasks are needed.
What kind of downstream tasks are you looking for? We provide evaluation of zero-shot ... | Summary: This paper proposes Integrated Multimodal Perception, which can use image, video, and audio datasets for multimodal and multitask training. The method is scalable, benefiting from the alternating gradient descent as it can alternate diverse modalities, loss functions, and datasets to perform gradient descent. ... | Rebuttal 1:
Rebuttal: Thank you for a very detailed review, we will try our best to address your comments.
>Ablation studies show that alternating between the objectives on each step is better than combining them by summing them. Is this true for all cases or only for large-scale pretraining?
This is a good point, an... | Summary: This paper proposes a scalable multimodal multitasking approach. It combines alternating gradient descent and mixture-of-experts to train a unified model. The extensive experiments verify the effectiveness of the proposed method. By scaling up the model, this method sets up a new state-of-the-art in zero-shot ... | Rebuttal 1:
Rebuttal: Thanks for providing a detailed review of our paper, here we try to address some of the comments of the paper.
>Using alternating gradient descent for efficient multimodal multitasking learning is not new, which has been explored in PolyViT. It seems that the proposed method is an extension with ... | Summary: The paper proposed Integrated Multimodal Perception (IMP) for multi-modal multi-task learning. IMP consists of a shared Mixture-of-Experts (MoE) Encoder as well as modality-specific embedding layers and heads re-projecting representations to modality-specific space. Optimizing towards both supervised classific... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, we will try to address any concerns encountered in the paper.
>One critical problem of IMP is that it seems to suffer from performance drop when incorporating new modalities as per to Fig. 5
We would like to note that this problem of performance drop when inte... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Supervised Reinforcement Learning that Transfers using Random Features | Accept (poster) | Summary: The authors propose a self-supervised method to learn approximate multi-step Q-estimates by leveraging random features as bases for a reward function in the target task. This approach addresses some limitations of model-based and model-free RL. In particular, model-based RL typically suffers from compounding e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We respond to your comments below.
> The main weakness of this approach lies in the experiments and poor framing of how this work fits in the context of meta-RL. meta-RL is not mentioned, … beforehand and adapting to the target task.
Thanks for the sugg... | Summary: The paper introduces RaMP, an approach for fast adaptation to unseen reward functions given offline data collected with arbitrary behavior policies under the same dynamics. RaMP learns a set of basis multi-step Q-functions, each corresponding to a random reward defined as the accumulation of random state actio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> The algorithm described in the main paper seems to be improved by several "implementation details" ... I believe a rigorous analysis of their impact would be necessary to assess the method's potential and workings.
Thanks for the suggestion. We will mo... | Summary: The paper proposes an approach for unsupervised pre-training of RL agents, ie pre-training on offline agent experience without rewards. The approach generates a set of random reward functions and then learns a successor representation of the state-action space for predicting cumulatives of these rewards on fix... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We respond to your comments below.
> Relation to prior work not sufficiently explained.
Thank you for the great suggestion. We will add a preliminary section to summarize the idea of successor features, and emphasize our novelties in the section, in our ... | Summary: The paper tackles the problem of
In the training phase, they create $H$ randomly initialized neural networks that serve as reward functions during the purpose of pretraining. The Q-function learned online is a linear combination of these random features: $w^T\sum_{h\in [H]}\phi(s_h,a_h)$.
Strengths: This is... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We respond to your comments below.
> The paper would benefit from more baselines: for example doing something like IQL with a reward of 1 at the end of each trajectory during offline training.
Thanks for the great suggestion. We have added new experimen... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and suggestions. We highlight our main experimental additions here and then address individual reviewer concerns in each reviewer response:
- **Meta-RL baseline (Reviewer 8qBK)**: We conduct a comparison with a meta-RL baseline, RL2 [1] that performs rec... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a Random Features for Model-Free Planning (RaMP) algorithm to solve the problem of learning generalist agents that are able to transfer across platform where the environment dynamics are shared, but reward function is changing. The proposed algorithm leverages diverse unlabeled offline data ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We respond to your comments below.
> The paper is not well-written and very hard to follow. For example, Line 13-15 and Line 59-61 is very hard to follow.
Thanks for the comment. Line 13-15 simply means our method can be trained “without” reward labels, ... | null | null | null | null | null | null |
Sampling weights of deep neural networks | Accept (poster) | Summary: This article introduces an alternative to random features for sampling weights of neural networks. Their method relies on data points (both inputs and outputs) and activations to build iteratively the weights and biases of one layer after the other, in opposition with data-agnostic/purely random methods. It pr... | Rebuttal 1:
Rebuttal:
**Regarding weaknesses:**
* [W1] We hope that an additional page after - potentially - accepting the paper can help to make Algorithm 1 more readable, and we can also add more explanations to the theoretical section.
* [W2] We also commented on a similar question in our answer to reviewer KCHD:... | Summary: The paper proposes a novel approach and analysis to sample weights of neural networks that can potentially address backprop limitations.
The method is based on computing differences between data points and can be scaled to deep networks (by computing the difference of data point activations). The paper introd... | Rebuttal 1:
Rebuttal: **Regarding weaknesses:**
* [W1] In both papers, "Gradients without Backpropagation" and "Learning by Directional Gradient Descent", the authors propose methods to compute the gradient of the network with respect to its parameters (weights and biases). In contrast, we propose to choose data point... | Summary: This study proposes a sampling learning method for deep ReLU networks. The proposed distribution is data dependent and the sampled network is shown to have universality.
Strengths: The idea of sampling parameters is well-investigated, for example, such as Bayesian NNs, sampling-based dimension reduction for ... | Rebuttal 1:
Rebuttal: **Regarding the sampling distribution:** Equation 2 uses both the map $\Phi^{(l-1)}$ (ReLU) in the denominator, and the target function values $f(x)$ in the nominator. Based on this review, we refined the definition of our probability density and now assume the following:
* Compactness of the dat... | Summary: This paper presents an approach to training deep neural networks by introducing a probability distribution for weights and biases, which significantly reduces the necessity for iterative optimization or gradient computations. The proposed sampling scheme is data-driven, factoring in the input and output traini... | Rebuttal 1:
Rebuttal: Regarding weaknesses:
* [W1] Indeed, comparisons to non-iterative methods are still missing, e.g. particle-based or simulated annealing. We expect that these methods sometimes may lead to more accurate solutions (if they find a global solution that we did not), but are probably still orders of m... | Rebuttal 1:
Rebuttal: We very much appreciate all the constructive criticism, feedback, and suggestions. We replied to every review individually. Several questions were raised on (a) the loss function we use for the last layer, and (b) the interpretability of the sampled networks, so we would like to comment on these h... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work provides a method to sample weights of a neural network in a data-driven manner, such that expensive iterative gradient calculations and optimization can be avoided. The proposed sampling technique sets weights and biases of fully connected networks with ReLU and tanh activations based on pairs of tr... | Rebuttal 1:
Rebuttal: Weaknesses:
* [W1] Please see our general answer on the topic of choosing different loss functions (multiple reviewers asked for this).
* [W2] Different optimizers other than Adam should not drastically change the results in our chosen experiments, as long as they are iterative and gradient bas... | Summary: This work presents a method for sampling the parameters of a deep neural network, including the final task-specific layer. In contrast to prior work that leverages Bayesian deep learning or generative models to learn the sampling distribution, this work presents a sampling algorithm that defines a data-depende... | Rebuttal 1:
Rebuttal: This high-level explanation of the paper may help: instead of training all parameters of hidden layers with gradient-based methods, we construct each combination of weight and bias using pairs of data points from the training set, i.e., $(x_1,x_2)$. As there are usually many more pairs of points i... | null | null | null | null |
A Logic for Expressing Log-Precision Transformers | Accept (poster) | Summary: In this submission, the authors generalize an expressivity result of transformers from Chiang et al. (2023), who showed that finite-precision transformers can be equivalently expressed in a counting generalization of first-order logic. In the paper at hand, the authors generalized this result that log-precisio... | Rebuttal 1:
Rebuttal: Thank you for your review and questions!
> Would you consider moving Lemma 2 to the main part of the paper? What are your opinions on that?
We appreciate this point and agree that having some discussion of the Lemma 2 proof in the main paper could give more intuition about why the result at larg... | Summary: This paper provides two contributions which extend previous work showing that finite-precision transformers may be expressed in FOL.
* Firstly, the authors prove that finite-precision transformers can only uniformly attend to bounded-length windows over their input sequence, with the constraints on previous t... | Rebuttal 1:
Rebuttal: Thank you for your review! We appreciate your suggestions for making the paper less dense. If accepted, we will add a high-level overview of the proof toward the end of Section 4, as you suggest. A figure illustrating the Algorithm from 6.1 would also be nice if space permits - we will consider wh... | Summary: The paper presents a theoretical analysis on the expressiveness of transformer-based models. In particular, the authors have managed to prove that the any log-precision transformer is equivalently expressible as normal first-order logic plus majority-vote quantifiers, FO(M). This yields the tightest known uppe... | Rebuttal 1:
Rebuttal: Thanks for your review! We appreciate that you found our work to be of theoretical interest. As you suggest, we will clarify the notation used in Figure 1 in the caption so that it is more self-contained. | Summary: In this paper, the computational power of transformers is studied subject to floating point precision and related to that of first-order logic. In particular, the authors show that (1) fixed-precision is not sufficient to compute uniform attention for arbitrary context lengths and (2) log-precision can be simu... | Rebuttal 1:
Rebuttal: Thank you for your review! We’d like to address some of your comments and suggestions around the role of positional encodings, different notions of uniformity, and other aspects of the paper.
## Positional encodings
> The role of positional encoding is not discussed in the analysis. Yet, fixed-p... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Data Selection for Language Models via Importance Resampling | Accept (poster) | Summary: This paper aims to improve the samples to pre-train language models.
It propose a novel method called "Data Selection with Importance Resampling (DSIR)" to select better pre-training samples and found superior fine-tuning performance compared to various other approaches, including random-sampling.
The author... | Rebuttal 1:
Rebuttal: We thank LQ7v for the feedback. Overall, LQ7v felt that the paper was “well written” and “addresses a crucial issue”. We answer specific questions below:
> “we do not know whether the authors use the (GLUE) dev set to find the best-performing epoch. From the paper, we know that RoBERTa default hy... | Summary: The work presents a novel framework for effectively selecting a representative document subset during the pre-training of Large Language Models. Pre-training such models incurs significant costs, prompting efforts to minimize the subset size and associated expenses. The authors commence by highlighting the imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Overall, znTv notes that we provide **“a strong conceptual framework with a commendable novelty factor”, along with “extensive experimental work and promising results”**. We respond to specific questions below:
> “There are strong evidences of the superiori... | Summary: The authors present a novel framework for selecting examples from a large and diverse dataset which are most relevant to a specific target domain. They apply this towards data selection for 'continued pretraining' in which a pretrained LM is trained further on a domain-specific dataset, in order to improve its... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Reviewer rytZ felt that the problem is **“important and especially relevant”** and the paper provides a **“tractable and approachable means of solving a quite general problem, as well as presents many compelling directions for future work”**. We answer speci... | Summary: This paper proposes a simple method for selecting pretraining data for language modeling based on the downstream fine-tuning tasks. It uses ngrams as features for a corpus, and weighs the pretraining data based on importance sampling, which estimates how similar the pretraining data is to the task specific fin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Reviewer NB9k notes that the method “seems like a simple/scalable approach” and the paper included comprehensive experimental settings, as it “tested the method on both domain adaptive continued pretraining and training from scratch”. We answer specific qu... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors begin by highlighting an important issue related to data quality in (encoder only) LMs, motivating the need for an improved way to filter large datasets (e.g. The Pile) for samples which are in distribution to a held out target sample. The authors propose a metric, KL-reduction, which quantifies ho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. LSWj notes that the paper is **“highlighting an important issue” and “shows strong performance improvements on existing datasets/baselines”**. We answer specific questions below:
> “The authors only consider n-gram based features, it’s mentioned in the limi... | null | null | null | null | null | null |
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback | Accept (poster) | Summary: The paper introduces Human Guided Exploration (HUGE), a system designed to integrate human feedback within the Goal-Conditioned Reinforcement Learning (GCRL) environment, offering a cost-effective and straightforward method for learning diverse robotic tasks through actual human interaction. The authors' prima... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for taking the time to review our work. Please find answers/additional experiments to address your concerns below
> “GCSL paired with Go-Explore could have operated independently without human feedback”
Go-Explore + GCSL suffers from the issues of undirected front... | Summary: This paper focuses on the exploration problem in decision-making tasks. Previous works try to leverage human guidance with constant synchronous high-quality human feedback, which is expensive and impractical to obtain. In this paper, the authors propose Human Guided Exploration (HUGE), which is able to leverag... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for taking the time to review our work. Please find answers/additional experiments to address your concerns below
> Figure 1 provides limited information.
We will improve this as suggested to include comparisons with prior methods. Thank you for your suggestions!... | Summary: This paper introduces HUGE, a Reinforcement Learning algorithm that makes use of human preferences to guide the selection of partial goals.
HUGE expands upon Goal-Conditional Supervised Learning (GCSL) by improving the goal selection method using noisy labels from humans to form a model, $f_\theta$, of distan... | Rebuttal 1:
Rebuttal:
Thank you for your feedback and for taking the time to review our work. Please find the answers to your comments and concerns below.
> “More representative to choose PEBBLE”
We implemented PEBBLE (using author provided GitHub repository) and provide the new curves with this implementation in Fi... | Summary: Broadly, the paper addresses the challenge of learning multi-stage robotic navigation and manipulation tasks in simulation.
The paper frames several benchmark tasks as goal-conditioned RL (GCRL), and they present a novel method ("HUGE") for leveraging human preferences collected during learning. In particula... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for taking the time to review our work. Please find answers/additional experiments to address your concerns below
> “Poorly-tuned reward function”
A key point of our paper is the robustness to noisy human feedback, which means that even with a simple guiding human... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your constructive feedback. In response to reviewer concerns, we have conducted a number of new experiments and analyses. We describe these briefly below and refer reviewers to individual responses for detailed clarifications:
**[DISCLAIMER 1]**: In the following ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling | Accept (poster) | Summary: This work focus on theoretical analysis on two main score-based approaches. The first one is the score-based casual discovery, where the authors give the sample complexity error bounds for score matching using ReLU NNs as well as an upper bound of the error rate. The second one is the score-based generative mo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the feedback and suggestions provided by reviewer LFXr. Regarding the issues raised by the reviewer concerning the relationship between causal inference and the generative model aspects of the paper, as well as the convincing of assumption 2, we have addressed these matters... | Summary: The paper provides sample complexity bounds on score function estimation when (1) the score function is estimated by using SGD to minimize a denoising score matching objective, (2) the probability distribution is induced by a structural causal model (SCM) with additive Gaussian noise, and (3) the score functio... | Rebuttal 1:
Rebuttal: We deeply appreciate the feedback and suggestions from reviewer FzFv. Regarding the issues raised by the reviewer regarding the relationship between causal inference and the generative model aspects of the paper, as well as the lack of experimental validation, we have provided responses in the [ge... | Summary: This study aims to investigate the sample complexity associated with score-matching and its applications in the field of causal discovery, providing valuable theoretical bounds. The authors present theoretical evidence that training a conventional deep ReLU neural network using stochastic gradient descent enab... | Rebuttal 1:
Rebuttal: We greatly appreciate the comments and suggestions provided by reviewer RLDi. Regarding the convincing of Assumption 2 and the lack of validation experiments in the paper, we have addressed these concerns in the [general response](https://openreview.net/forum?id=uNnPWR66b8¬eId=VWjUWDv2Kl). In t... | Summary: This paper presents error bounds (sample complexity and convergence rates) for the problems of score based generative modelling and score-order based causal discovery. It is primarily a theory paper, and one of the first few works to present error bounds for a causal discovery algorithm that is not conditional... | Rebuttal 1:
Rebuttal: We sincerely appreciate the input and suggestions provided by reviewer Ndh6. Regarding the issues raised by the reviewer concerning the relationship between causal discovery and the generative model of the paper, as well as the lack of experimental validation, we have addressed these concerns in t... | Rebuttal 1:
Rebuttal: # General response:
We extend our gratitude to the reviewers for their valuable feedback. In response to recurring issues highlighted by multiple reviewers, we offer a consolidated response as follows:
**Q1: Relationship between two parts.**
A1: The theoretical foundation of this paper spans t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides detailed theoretical results for causal discovery and score-based generative modeling. For causal discovery, it first provides a sample complexity analysis for the estimation of the score function for nonlinear additive Gaussian noise models, then it proves that the error rate of a score-ma... | Rebuttal 1:
Rebuttal: We greatly appreciate the comments and suggestions from reviewer wqRq. Regarding the questions raised by the reviewer about the relationship between causal discovery and the generative model of the paper, as well as concerns about strong assumptions, we have provided responses in the [general resp... | null | null | null | null | null | null |
Adaptive Linear Estimating Equations | Accept (poster) | Summary: The authors propose a general method for constructing debiased estimator called Adaptive Linear Estimating Equations (ALEE) estimator, which achieves asymptotic normality even in sequential data collection.
To obtain valid statistical inference, the online debiasing concept is used. The online debiasing proce... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewer for dedicating the time to reviewing our paper and offering us valuable feedbacks. We truly appreciate your comments and suggestions, and believe they can make our work better.
In the following, we are going to take this opportunit... | Summary: This paper considers the problem of least squares when the data is collected sequentially. It proposes a form of weighted least squares where the weights are designed to lead to estimates that are asymptotically normal and nearly optimal variance. The appropriate weights are derived for the multi-arm bandit, a... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewer for dedicating the time to reviewing our paper and offering us valuable feedbacks. We truly appreciate your comments and suggestions, and believe they can make our work better.
In the following, we are going to take this opportunit... | Summary: This paper proposes an estimator (ALEE) for adaptively collected data generated from adaptive linear models, describes its construction such that asymptotic normality holds for practically relevant examples, and demonstrates its desirable properties in numerical experiments.
Strengths: I enjoy reading this pa... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewer for dedicating the time to reviewing our paper and offering us valuable feedbacks. We truly appreciate your comments and suggestions, and believe they can make our work better.
In the following, we are going to take this opportunit... | Summary: This paper introduces a general method for constructing debiased estimator within the context of sequential data collection. The proposed methodology is applied explicitly to multi-arm bandits, autoregressive time series, and contextual bandits. Experiments are conducted in these three domains to verify the ap... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewer for dedicating the time to reviewing our paper and offering us valuable feedbacks. We truly appreciate your comments and suggestions, and believe they can make our work better.
In the following, we are going to take this opportunit... | Rebuttal 1:
Rebuttal: Due to the page limit, we only include part of the updated simulations. Please see attached pdf file for simulations regarding different distributions for the noise variables and varying dimension $d$ for the contextual bandit example.
We plan to add limitation in the discussion section.
**Limit... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models | Accept (poster) | Summary: This paper adapts the matrix pencil method to estimate parameters in models with prescribed subgraph counts, and to simulate from them. A prime example are edge counts and the stochastic blockmodel.
Strengths: The idea to use the matrix pencil method for parameter estimation is interesting and the paper addr... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. The discussions they spurred have been useful for clarifying the paper, as there were some key misunderstandings.
Weaknesses:
The main concern, about Equation (6), is a misunderstanding by the reviewer, but suggests an excellent way that we c... | Summary: The paper studies how to make inference sampling from a stochastic blocking model (SBM) given its corresponding subgraph densities. They first estimate the normalized degrees and the relative sizes of the latent blocks of a SBM, and then infer the connection properties of the SBM using generalized Prony's meth... | Rebuttal 1:
Rebuttal: Thank you so much for your time and comments.
Strengths:
It is nice that you found the paper easy to follow! We really try to make it easy on the reader, and with the restructuring/moving parts to dedicated Appendices, we think it will be even more accessible.
Weaknesses:
The typos are mu... | Summary: Given the subgraph densities of the stochastic block model (SBM), the authors consider the problem of obtaining SBM's parameters (node and edge probabilities). The authors observe a connection between this estimation problem and estimating parameters of an exponential signal. So, they cleverly teleported the c... | Rebuttal 1:
Rebuttal: Thank you very much for the thoughtful review.
Summary:
Your summary was super accurate and succinct. And we very much appreciate the "cleverly teleported". Your suggestions are really helping the readability our paper, primarily with continuity of the narrative, and an inclusion of a pedago... | null | null | Rebuttal 1:
Rebuttal: Thank you so much to all the reviewers for taking the time to read our paper. We really appreciate it being easy and fun to read, so the comments on how to improve the narriative were particuarly appreciated.
The main issues seem to be the mention of Exponential Random Graph Models in the begi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fair Allocation of Indivisible Chores: Beyond Additive Costs | Accept (poster) | Summary: This paper studies the allocation of m indivisible chores among n agents with non-additive preferences. The authors show that, for the case of approximate MMS, the best approximation factor is super constant, and specifically they give a lower bound of min{n, log m/ log log m } for submodular costs, and an upp... | Rebuttal 1:
Rebuttal: We thank the reviewer for your appreciation of the problem we have studied and the results we have obtained. We also thank the reviewer for the constructive and helpful comments.
**Question 1: combinatorial problems considered in the case of goods**
**Response:** We appreciate this question an... | Summary: This paper studies the MMS fair allocation of combinatorial tasks (indivisible chores) problem where the cost function is submodular or subadditive. For submodular functions, they prove that no algorithm can ensure better than min{n, log m/log log m}-approximation. For more general subadditive cost functions, ... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for the dedicated effort in reviewing our paper. We are especially appreciative of the reviewer's acknowledgment of the theoretical results and techniques presented in our paper. We also understand the reviewer’s concern about the relevance of our paper to ... | Summary: The paper deals with problem of allocating indivisible chores to agents whose valuation functions for bundles of chores are subadditive or submodular, where an allocation that guarantees every agent their maximin share (MMS) may not exist. Here, an agent's maximin share is the agent's worst-case disutility fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper again. We are pleased to hear that the reviewer acknowledges the technical contribution of our work and the improvements made in the revised manuscript. We will incorporate all reviewers’ suggestions to further enhance and refine... | Summary: The paper studies the classical fair allocation of indivisible items setting with two twists: (1) Items correspond to *tasks* instead of *goods*, i.e., any agent would prefer receiving no item at all. (2) Valuations are not additive but may be subadditive. The paper also considers some special cases of subaddi... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our paper’s contribution to the literature on fair allocation, as well as the technical depth and the potential to stipulate subsequent research. We also thank the reviewer for pointing out our paper’s weaknesses and offering many constructive and helpful sug... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exploring Geometry of Blind Spots in Vision models | Accept (spotlight) | Summary: The authors explore the output space of vision models. Specifically, they propose an algorithm (Level Set Traversal, LST) which, starting from a "source" input image transforms it into an image that looks completely different (e.g. an image from a different class) while still confidently predicting the "source... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer found that the proposed method is original, explained clearly, and is a significant contribution to the field. We respond to the questions raised below:
> The authors state that the algorithm takes 200-400 iter... | Summary: The paper studies the under-sensitivity of such deep neural networks, where it is possible to find large perturbations in the input space (such as transforming one image to another) without significant changes to the activations/predictions. Towards this goal, the paper proposes Level Set Traversal, LST, which... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer found that the proposed method is explained clearly and intuitively, is effective in practice and supported by theoretical insights. We respond to the questions raised below:
>The experiment details mention 10... | Summary: The authors propose a new adversarial attack on the discriminative vision models called Level Set Traversal (LST). Contrary to the previous attacks, this new algorithm exploits the orthogonal component of the network's gradient to produce samples that can bypass existing adversarially-trained classification ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer found the paper well-motivated, clear, convincing and exciting. We respond to the questions raised below:
- *Connections to model ensembling:* We thank the reviewer for the suggestion. The existence of piece-wi... | Summary: This paper introduces the idea of level sets for image classification models. Image classification models are said to be ‘under-sensitive’ when two visually distinct images produce the same output (class). The authors propose a method to compute ‘equi-cconfidence’ level sets such that two images belonging to t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. We are glad that they found our contributions novel, significant, and insightful. We answer the questions raised below:
**Analysis and implications of level set**
- We would like to emphasize that the level set itself is primarily a property of... | Rebuttal 1:
Rebuttal: **A note to all Reviewers**
We sincerely thank the reviewers for their time and valuable feedback on our work. We are glad that the reviewers appreciate the presentation and motivation of the proposed Level Set Traversal (LST) algorithm, its effectiveness in identifying the extent and geometry of... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors aim to systematically study specific shortcomings (called blind spots) of vision models caused by their under-sensitivity.
For this purpose, they devise an algorithm that given an arbitrary pair of images (called source and target) can produce inputs that result in the same prediction output as the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments, and address the specific aspects raised below:
**Contributions**
- We present a novel Level Set Traversal (LST) algorithm that iteratively uses orthogonal components of the local gradient to identify the extent and geometry of equi-confidence sets ... | null | null | null | null | null | null |
FairLISA: Fair User Modeling with Limited Sensitive Attributes Information | Accept (poster) | Summary: This paper aims to achieve fair user modeling with limited sensitive attribute information and propose a general framework, FairLISA, which efficiently utilizes data with known and unknown sensitive attributes to facilitate fair model training. The authors also provide theoretical guarantees from a mutual info... | Rebuttal 1:
Rebuttal: >**A1. How does FairLISA perform on classic fairness metrics?**
Q2: We have already conducted this experiment in our paper, and the details can be inferred from Experiments RQ4 and Appendix C.6. To provide a clearer presentation of these results, we report the performance of all methods on class... | Summary: The authors investigate the problem of fair user modeling in a setting with limited sensitive attributes. Due to the lack of such attribute information, they propose a general framework called FairLISA, which efficiently applies unlabeled data to facilitate fair model training. Compared to previous works, Fair... | Rebuttal 1:
Rebuttal: >**A1. Can fairness be extended to settings without sensitive attributes?**
Q1: I sincerely appreciate your question. Currently, our methods cannot be extended to settings without sensitive attributes due to the necessity of labeled data to train the discriminator. However, it is essential to not... | Summary: This paper propose FairLISA that learns fair user modeling using limited sensitive attribute information. Specifically, for users with known sensitive attribute information, FairLISA maximizes the cross-entropy of predicting the sensitive attribute using user representations; for users with unknown sensitive a... | Rebuttal 1:
Rebuttal: >**Q1. The authors may better illustrate why statistical parity is important in recommender system. To my understanding, many recommendation tasks is related to sensitive attribute as well. For example, a recommender system don't want to recommend feminine care items to male users. (L1)**
A1. Sor... | Summary: This paper proposes a novel adversarial learning method for fairness with limited demographics.
Strengths: Pros:
1. This paper focus on an important and practical problem. Fairness with limited demographics is a practical and important problem.
2. This paper provides a theoretical-driven perspective on fairne... | Rebuttal 1:
Rebuttal: >**Q1. The datasets used in this paper seem not very common. So it is a little bit hard to evaluate the effectiveness of the proposed method. It is suggested that authors also conduct experiments on commonly used fairness datasets such as ADULT and COMPAS.**
A1:We greatly appreciate your suggesti... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' time and efforts in reviewing our paper. We would like to thank all of them for providing constructive and valuable feedback which we will leverage to improve this work. Meanwhile, we are encouraged by the positive comments from reviewers, including:
**Motiv... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed an algorithm to train fair user modeling (e.g recommender systems) when given limited sensitive attributes. The idea is to factorize out the effect of sensitive attributes from the model's fair training objectives, and isolate its impact in training.
Strengths: 1. Studied an important probl... | Rebuttal 1:
Rebuttal: >**Q1. The proposed algorithm only applies to generative-based user modeling, which is limited. Many user modeling methods are prediction-based.**
A1. Sorry for the confusion! I quite agree that currently many user modeling methods are prediction-based. In fact, I'd like to clarify that the use... | null | null | null | null | null | null |
Focus Your Attention when Few-Shot Classification | Accept (poster) | Summary: This paper proposes to directly adapt the large-scale pretrained model to the downstream classification task via fine-tuning on few-shot examples, therefore yielding a novel few-shot learning paradigm. Different from common few-shot classification methods, their paradigm is featured for 1) utilizing large-scal... | Rebuttal 1:
Rebuttal: Please check the reference rules in the global response first to help following reading.
**1.many-hot presentation**
For an input image, we obtain its position prompts, i.e., the position index set *O* of the key patches. For a vector *z* of length *N*, where *N* is the patch number, we set its ... | Summary: This paper introduce a method called FORT, aiming to adapt pre-trained vision transformers to few-shot image classification task. This method contains two steps. 1) Utilizing attention and the gradient information to locate important entities, which is denoted as position prompts. 2) A new loss is defined to e... | Rebuttal 1:
Rebuttal: Please check the reference rules in the global response first to help following reading.
**1.fixing or updating the position prompts along fine-tuning**
The position prompts are obtained before fine-tuning and fixed as supervision signals during fine-tuning process. We find that updating them al... | Summary: This paper addresses few-shot learning by resorting to pre-trained large vision models. A new prompt strategy, termed position prompt, is proposed during fine-tuning to encourage the model to focus on class-relevant patches. This is realized by an attention-based token selection module and an optimization obje... | Rebuttal 1:
Rebuttal: Please check the reference rules in the global response first to help following reading.
**1.vector *G* in Eq.6-main**
Yes, the vector *G^T* with shape of (1, *N*) is repeated row-wise to add to the attention matrix with shape of (*N*, *N*).
**2.motivation to reserve the first principle compone... | Summary: - The paper adapts pre-trained vision transformers for few-shot classification.
- Full/parameter-efficient fine-tuning using only a few examples may harm performance due to spurious correlations.
- The proposed method uses an additional auxiliary loss to guide the attention of the top layer to focus on the c... | Rebuttal 1:
Rebuttal: Please check the reference rules in the global response first to help following reading.
**1.difference between the attention visualization in Fig.3-main and Fig.4-main**
Fig.3-main visualizes the attention scores where the different colors represent different values, while Fig.4-main chooses th... | Rebuttal 1:
Rebuttal: We really appreciate all the reviewers for taking your precious time to make the valuable comments. Overall, **our work has the following strengths**:
1. **the proposed new setting is more realistic and has great value. [nhXH]**
2. **reasonable and interesting idea. [8eth], [SFDV], [nhXH]**
3. **s... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to force the pre-trained model to focus on class-related entities for few-shot image classification. To achieve this, it proposes position prompts that use attention and gradient information to automatically locate the positions of key entities. An attention enhancement loss is used to be t... | Rebuttal 1:
Rebuttal: Please check the reference rules in the global response first to help following reading.
**1.attention maps with sufficient training samples**
We follow the setup of Fig.4-main, except fine-tuning on the 20-way 50-shot task from CUB and 20-way 200-shot task from Pets without attention enhancemen... | null | null | null | null | null | null |
Static and Sequential Malicious Attacks in the Context of Selective Forgetting | Accept (poster) | Summary: This paper investigates the potential and viability of malicious data update requests in the context of the unlearning process. The authors put forward a malicious selective forgetting attack in a static scenario and present a framework for sequential forgetting attacks. And the framework is formulated as a st... | Rebuttal 1:
Rebuttal: We really appreciate your thoughtful comments and valuable suggestions. Below, we provide our response to the questions and concerns.
**1: Clarifying the attack goal (e.g., diminishing the fairness of the unlearning model, or misclassifying specific samples?) of the proposed approach. Giving a hi... | Summary: This paper studies the malicious data update in machine unlearning. The authors consider two strategies. The first is static selective forgetting attack framework, where the adversary exploits vulnerabilities in the unlearning systems by submitting a set of carefully crafted data update requests at once. The s... | Rebuttal 1:
Rebuttal: We really appreciate your thoughtful comments and valuable suggestions. Below, we provide our response to the questions and concerns.
**1: Providing more experiment details: (1) What is the target class in the targeted setting? (2) What is the class that the machine unlearning aims to forget? The... | Summary: This paper explores the malicious forgetting issue in model unlearning and proposes two attack strategies: static attack and dynamic sequential attack. The authors also present a theoretical framework for selective forgetting attacks. Experimental results on multiple benchmark datasets demonstrate that the pro... | Rebuttal 1:
Rebuttal: We really appreciate your thoughtful comments and valuable suggestions. Below, we provide our response to the questions and concerns.
**1: Discussions on whether this topic has been previously studied, and the technical standpoint.**
Thank you for your helpful suggestions regarding our work's no... | Summary: This paper identified a novel class of machine learning attacks, i.e., ML models can be manipulated with malicious data update requests during the machine unlearning process.
The authors study two threat scenarios: (1) selective forgetting attacks and (2) sequential selective forgetting attacks.
Specifically... | Rebuttal 1:
Rebuttal: We really appreciate your thoughtful comments and valuable suggestions. Below, we provide our response to the questions and concerns.
**1: Discussions on training the substitute model in the black-box setting where the adversary does not have prior knowledge about training data.**
Thank you for ... | Rebuttal 1:
Rebuttal: We would like to express our great gratitude to all the reviewers for their valuable time, comments, and questions. We highly appreciate all the feedback and suggestions, which further help us improve our paper. We are greatly encouraged that they found our ideas and contributions to be novel and... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs | Accept (poster) | Summary: The paper proposes a new method for Bayesian optimization under uncertain inputs, where the distribution of an input can be complex and unknown but can be sampled from. The paper proposes an MMD based kernel between probability distributions and the use of the Nystrom approximation to make the computations tra... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments, especially regarding the constructive suggestions on the kernel validity, ablation test, and experiment.
### 1. Validity of kernel
> When designing a new kernel, it is important to prove that it is a valid kernel.
> $\eta$ is said t... | Summary: This work focuses on the situations where input uncertainty arises and the input values are unobservable, and introduces to measure the distance of uncertain inputs through MMD when training the Gaussian process surrogate. The authors theoretically and empirically demonstrate the effectiveness of the proposed ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments.
### 1. Sampling issue
> MMD-GP needs to query m times more samples than GP, which can restrict its application since BO is usually applied to tasks where evaluating a query can be time-consuming. What about the time cost of this work co... | Summary: The paper proposes a novel Bayesian Optimization (BO) algorithm that explicitly tackles input uncertainty by introducing a new integral probabilistic metric (IPM)-based kernel. The algorithm also utilizes an efficient and stable Nystrom estimator to approximate the Maximum Mean Discrepancy (MMD), which serves ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments.
### Theoretical bound
> The theoretical bound is arguably sound. Due to the numerical approximation of the Maximum Mean Discrepancy (MMD), it may potentially result in pseudo-metric or even worse outcomes in practice. Therefore, it re... | Summary: The paper tackles the problem of robust Bayesian Optimization (BO) with uncertain inputs, i.e., the input values are deviated from the intended value before evaluation. The paper proposes a new technique, namely AIRBO (Arbitrary Input uncertainty Robust Bayesian Optimization), that can model the uncertain inpu... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments. **The reference is listed in the global rebuttal**.
### 1. Motivation
> I think the application of this problem setting should be motivated much better as it’s unclear on the significance of the problem tackled in this paper.
Thank... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their insightful comments and constructive suggestions. In this global rebuttal, we mainly provide the updated version of our theoretical results and a discussion on the limitations, followed by an attached pdf for the supplementary experiments (The other d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability | Accept (poster) | Summary: This paper analyzes the implicit biases of GD and SGD on two-layer diagonal linear networks, specifically with large step sizes. This paper shows that SGD converges to a limit that is determined by the trajectory, and specifically by a certain effective initialization. Moreover, while both GD and SGD are close... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and the feedback. We would first like to point out a misunderstanding in the review (this is most certainly only a typo between batchsize and stepsize): *it is pointed out that while larger batch sizes generally hurt the performance of GD, they may actually i... | Summary: This is a theoretical work on implicit bias in diagonal linear networks. It proves the empirical observation by Pesme et al that SGD with large learning rates can recover the sparse signal while GD or small learning rates cannot. Technically it utilizes mirror descent with time-vary potentials.
Strengths: 1.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall very positive feedback, all the helpful remarks and questions, that could lead to additional valuable discussions in our paper.
**1.** The quantity $\tilde \gamma_{max}$ is not robust to the sampling of the random inputs $x_i$, as it is defined conditionally ... | Summary: This paper studies the impact of stochasticity and step size on the implicit regularization of GD and SGD, in the setting of two-layer diagonal linear network. It is show that large step size can benefit SGD, but hinder the performance of GD. Both the implicit bias and convergence are proved for (S)GD, providi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing, the questions, and for the positive feedback.
**Technical novelty.**
Our technical tools are in fact very different from the cited references: our underlying process (SGD on the neurons) is **discrete** while theirs are **continuous-time** proce... | Summary: This paper studies the implicit bias of 2-layer diagonal linear networkss.
The authors show the convergence of GD and SGD with macroscopic stepsizes and characterise their solutions through an implicit regularization problem.
Moreover, the theoretical results reveal the difference between the generalization ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing, the thoughtful comments and reference.
We first answer the ‘weakness’ part of the review.
Our paper indeed generalizes existing results: references [48,50,61] provide the implicit bias of gradient flow and stochastic gradient flow over DLNs, wh... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper analyzes 2-layer diagonal linear networks and explains the different characteristics of the solutions received through GF, GD, and mini-batch SGD.
Strengths: The paper analyzes the impact of the different step sizes and batch sizes and explains the differences between the solution received from trai... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough reviewing, the helpful remarks, and the noticed typos.
As rightfully noticed and as acknowledged in our paper (lines 41-42), part of our results do not apply for large step sizes at the edge of stability. Nonetheless our main result (the implicit bias result... | null | null | null | null | null | null |
ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints | Accept (poster) | Summary: This paper introduces ReDS for offline reinforcement learning (RL) where the variability of demonstrated behaviours changes non-uniformly across the state space. Unlike prior offline RL methods, e.g., CQL and AWR, that directly constrain the learning policy by distribution matching with the behaviour policy, R... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful response. Regarding novelty, we would like to clarify that while prior works exist that utilize re-weighting with offline RL algorithms, in this work, we not only develop a method for re-weighting distributional constraint methods but also analyze challenges with distr... | Summary: The paper has identified the presence of heteroskedasticity in realistic offline RL datasets, which negatively impacts the performance of existing offline RL methods that rely on distributional constraints. To tackle this issue, the authors introduce a new approach called ReDS, which transforms distributional ... | Rebuttal 1:
Rebuttal: Thanks for the feedback and positive assessment of our work. We address your concerns below and will update the paper to clarify each of the questions. Please let us know if your questions are resolved, we are happy to discuss further if any questions are remaining.
***Inconsistent of the differ... | Summary: The paper introduces ReDS, a novel offline RL method designed to handle heteroskedastic datasets. ReDS incorporates support constraints by reweighting the data distribution based on conservative Q-learning (CQL). This allows the learned policy to deviate from the behavior policy within its support while maximi... | Rebuttal 1:
Rebuttal: Thank you for the feedback and positive assessment of our work. To address your concerns, we add 2 comparisons to the support-constraint methods InAC and Hong et al 2023. We also explain how we took statistical errors into account when we compute the statistics for the didactic example.
**Please... | Summary: The paper focuses on heteroskedastic datasets, where the distribution of actions may not be uniform in certain states but close to uniform in other states. The authors propose the ReDS method, which re-weights the actions to penalize "bad" actions that perform poorly on the dataset but have a higher probabilit... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive assessment of our work. We address your concerns below and will update the paper to clarify each of the questions. **Please let us know if your questions are resolved, and if so, we would appreciate it if you are willing to upgrade your score. We are happ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models | Accept (poster) | Summary: The paper studies the problem of differentially private prompting, i.e. the scenario where a prompt-augmented LLM is exposed to users, which should be able to interact with the model, but should not be capable of extracting the private prompt prepended to their queries. The authors first show, that a membershi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find our detailed response in the following:
>**1. The novelty of PromptDPSGD and PromptPATE compared with existing work.**
- PromptDP-SGD: Our work is the first one to show the good utility of soft prompt tuning with DP-SGD. Compared wit... | Summary: This paper investigated the privacy leakage in prompted large language models (LLMs), and have proposed methods to protect the privacy of potentially sensitive data used for prompt engineering. The authors first demonstrated high membership inference leakage in existing prompted LLMs and then proposed PromptDP... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback! Please find our detailed response in the following:
>**1. One might still prefer parameter-efficient LLMs based on their needs on the utility.**
We want to emphasize that the main advantage of prompt learning with DP is its flexibility and appli... | Summary: This paper studies privacy preservation in the context of prompt learning for large language models (LLMs). It highlights the vulnerability of prompting data to membership inference attacks (MIAs) and proposes differential privacy (DP)-based defense methods for both soft and hard prompt learning. For soft prom... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find our detailed response in the following:
>**1. PromptDPSGD is somewhat of a direct application of the original DPSGD to the soft prompt learning setting.**
Our work is the first one to show the good utility of soft prompt tuning with ... | Summary: This paper discusses the potential privacy risks associated with prompting data in large language models, which can be exposed through a membership inference attack. To address this issue, the authors propose two methods, PromptDPSGD and PromptPATE, for achieving private prompt learning. PromptDPSGD involves o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find our detailed response in the following:
>**1. Could you provide more details of the membership inference attack?**
We thank the reviewer for their question and are happy to provide more details on the membership inference attack. As... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their feedback which has greatly helped us improve the paper. We are glad that the reviewers recognize our work to “present a timely study” (reviewer 96HV) on the private adaptation of LLMs “with real world deployment scale and on black-box commercial APIs” (revi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents differentially private techniques for optimizing continuous and discrete prompts for solving text classification tasks using Large Language Models (LLMs). The need for differentially private prompt learning is motivated by showing that examples used in few shot prompts for classification ta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find our detailed response in the following:
>**1. The susceptibility of few shot learning to membership inference attacks has been studied before at least for image classification**
Thank you for pointing out this paper. We will add it t... | null | null | null | null | null | null |
Real-World Image Variation by Aligning Diffusion Inversion Chain | Accept (spotlight) | Summary: This paper proposes a framework RIVAL to generate variation of the real image without any tuning. It has two key components, the first one is a cross-image self-attention injection, where it first inverts the real image using DDIM, and then sample another random chain but uses a mix of real inverted chain’s va... | Rebuttal 1:
Rebuttal: Dear reviewer 5k69,
Thank you for your valuable feedback and constructive comments. We will address your comments below and in the revised paper.
### Q1 A Clarification of Inference Time
We want to clarify the point on inference time:
After the inversion process, the overall inference time is no... | Summary: This paper works on the design of diffusion model to generate image variations given an image examplar as the source image. The basic idea is to align the image generation process to the image inversion process of source image. This is achieved by designing an cross-image self-attention injection for feature ... | Rebuttal 1:
Rebuttal: Dear reviewer SLMY,
Thank you for your valuable feedback and constructive comments. We will address your comments below and in the revised paper.
### Q1 Clarification of Cross-Image Self-Attention Injection
We employ the pre-trained Stable Diffusion model in RIVAL without adding trainable paramet... | Summary: The authors propose a tunning-free pipeline called RIVAL(Real-world Image Variation by Alignment) for generating diverse and high-quality variations of real-world images.
In previous works, some models also generate images with novel concepts and styles but require additional training stages and data, and oth... | Rebuttal 1:
Rebuttal: Dear reviewer aMYr,
Thank you for your valuable feedback and constructive comments. We will address your comments below and in the revised paper.
### Q1 Comprehensive Quantitative Experiments
We agree that comprehensive quantitative evaluations are vital to support our claims. Please refer to **r... | Summary: Generating real-world image variations is an important research task with practical applications such as image editing, image synthesis, and data augmentation. Past approaches include texture synthesis, neural style transfer, and generative models, among others. This study proposes a method called "Real-world ... | Rebuttal 1:
Rebuttal: Dear reviewer mFBx,
Thank you for your valuable feedback and constructive comments. We will address your comments below and in the revised paper.
### Q1 Utilizing Capability of the Base Model
We respect your comments on the trend of recent works leveraging mature models. While we agree these base... | Rebuttal 1:
Rebuttal: Dear all reviewers,
We want to express our gratitude to all reviewers for their thorough reviews and constructive comments. In the global rebuttal, we aim to summarize and address representative questions raised by the reviewers.
**We encourage all reviewers to refer to the global response for Q... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates the domain gap between generated images and real-world images, as it is challenging in generating high-quality variations of real-world images. It mainly contains two contributions, the first one is the cross-image self-attention injection for generating images that correspond to the giv... | Rebuttal 1:
Rebuttal: Dear reviewer 4HxC,
Thanks for your valuable feedback and constructive comments. We will address your comments below and in the revised paper.
### Q1 Shuffle Operation
In Eq. 8, the shuffle operation $X_G^T=\text{shuffle}(X_R^T)$ is designed to rearrange latent elements within the spatial dimens... | null | null | null | null | null | null |
MADG: Margin-based Adversarial Learning for Domain Generalization | Accept (poster) | Summary: This paper aims to ease the distribution problem from a theoretical perspective, which uses margin loss and a scoring function to describe the relationship between domains, and the generalization bound in terms of functional class complexity is subsequently analyzed. Based on their theoretical analysis, a marg... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> The motivation in the introduction is problematic. The literature is happy to see inspiring theory, but the theory itself is not the purpose. Rather, the paper should emphasize... | Summary: The paper proposes a new adversarial learning objective using a margin based approach for domain generalization. The goal of domain generalization broadly is to build classifiers that are trained on one or more source domains, and are expected to generalize to an unseen target domain. The key idea is to levera... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and suggestions. We respond below to each of their concerns/suggestions.
> I found the paper to be hard to read in general, at its core, the paper is proposing to model error on the unseen target using a convex hull of the source domains — which i... | Summary: - The paper presents a margin-loss based analysis of domain generalization.
- First, the paper derives a bound on margin disparity discrepancy (MDD) of any unseen domain within the convex hull of source domains in terms of the margin loss on source domains, ideal margin loss, and max MDD between any two source... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> It is not clear if the single optimization step for the adversarial models f’ is sufficient for tightly approximating MDD....this may require many steps for the adversarial mod... | Summary: This paper is commendable for its innovative use of a margin-based theoretical framework to solve domain generalization problems, which contrasts with the largely heuristic and empirical approaches adopted by existing methods. By grounding their approach in a theoretical foundation, the authors provide more in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> While the average performance of the proposed method is the highest, the performance gap across different tasks is quite significant.....
**Response:** We agree and in Sec 6 (... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive feedback: proposed method is well designed [Ru5h1, RFjmm]; the method is well supported by theoretical analysis as well as extensive ablation studies [Rwf8A, RFjmm, RMRuq]; the development of a theoretical framework for DG problem not only enhaces the comp... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates domain generalization (DG) using a margin-based theoretical framework. The authors first formulate the generalization upper bound by leveraging the margin disparity discrepancy (MDD). Then, an adversarial learning strategy (MADG) is devised to minimize the empirical MMD between source d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> 1. The primary concern of the reviewer is in its effectiveness. While MADG outperforms most baselines in the DomainBed benchmark, the improvement is marginal/minor. This made it... | null | null | null | null | null | null |
Graph Denoising Diffusion for Inverse Protein Folding | Accept (poster) | Summary: The paper proposes discrete diffusion for the task of inverse protein folding (IPF). The many-to-one mapping of sequence to structure warrants a generative model of sampling multiple possible sequences that fold into a given structure. Nearly all prior works use autoregressive generative models so the explorat... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the novelty and contribution of our work. Your insightful comments helped us enrich the analysis a lot. By the response below we hope we address your concerns properly.
**weaknesses**:
1. *comparison to alternative diffusion methods*: The question of choosing... | Summary: The paper highlights that "Existing discriminative models struggle to capture the diverse range of solutions, while generative diffusion probabilistic models offer the potential for generating diverse sequence candidates." They propose to use the denoise diffusion model together with the prior information from... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback on our paper and recognizing its contribution and performance. We have taken your remarks into careful consideration and offer the following responses to your concerns and questions:
**weaknesses**:
- *measurement of diversity, and additional comparison wit... | Summary: The authors of the manuscript present GraDE-IF, a diffusion model based method for inverse protein folding given the backbone of the structure. The denoising network is a graph neural network that's equivariant to rotations and translations. Moreover, a biologically relevant inductive bias is incorporated into... | Rebuttal 1:
Rebuttal: Thank you for your meticulous feedback, as well as your recognition of the novelty of our work. Below we respond to your concerns and questions point-by-point.
**Weaknesses** :
1. *Quantitative Results on Diversity*: We have incorporated quantitative evaluations of generated results using the `d... | Summary: This work presents a denoising diffusion model for protein inverse folding: predicting the amino acid sequences that fold into the given 3D protein structure. The proposed method leverages a discrete denoising diffusion model with respect to the graph structure representing the protein backbone. This work prop... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback on our work, particularly your kind recognition of the quality of our presentation, the originality of our work, and its significance. We would like to address your questions and concerns as follows:
**Weaknesses**:
- *secondary structure represent... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed comments and insightful suggestions. We incorporated additional experiments and analyses as per recommendations. Here, we present a concise overview of the major enhancements that have been universally implemented, focusing on aspects such as diversity... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Uncovering motifs of concurrent signaling across multiple neuronal populations | Accept (spotlight) | Summary: This work extends an established line of previously proposed methods for Gaussian Process factor analysis-based models of neuroscience data. In comparison with previously published methods, it extends the factor model to include correlations and time delays between different (matched) latents underlying differ... | Rebuttal 1:
Rebuttal: > **Weaknesses**
Thanks to the reviewer, we realize we could have been clearer about the independence structure of the latents *a priori* and *a posteriori*. We have added the following text:
- Section 2: "Under equation 7, latents are independent and identically distributed across trials."
- Sup... | Summary: This paper develops a new approach to analyze multi-region neural populations recordings. This approach extends DLAG to analyze communication across more than 2 regions. The main technical novelty lies in tractably extending the model definition of DLAG to multiple regions through the incorporation of the ARD ... | Rebuttal 1:
Rebuttal: > **Weaknesses**
> 1. The main question that I found myself struggling with...
We thank the reviewer for the opportunity to clarify this point. The ordering of latents across regions is preserved by definition of the model. Let us write out the observation model (Eq. 1) more expansively:
$$y^m_... | Summary: The recent developments in neural recording technologies allow recording from large populations of neurons from multiple brain regions simultaneously. Latent space models are often used to analyze these datasets, but they are generally limited to the study of single or two populations of neurons. This work exp... | Rebuttal 1:
Rebuttal: > **Weaknesses**
> While the authors show the promise of their method to understand multi-area signals, they overlooked other existing methods...
We thank the reviewer for pointing out these alternative methods. We agree that they are relevant, and we cite several review papers that mention such... | Summary: This paper extends the delayed latents across groups (DLAG) to a recurrent general form (mDLAG), which allows analyzing the contribution of each latent dimension for multiple observational groups. Besides, the newly proposed mDLAG is able to identify the complicated directions of the information flow between g... | Rebuttal 1:
Rebuttal: >**Weaknesses**
We realize we could be more explicit in defining our notation for the latents, $\mathbf{x}$. We have added the following text throughout Supplementary Section S1:
- "we define latent variable $j$ (out of $p$) in population $m$ at time $t$ on trial $n$ as $x^m_{n,j,t} \in \mathbb{R... | Rebuttal 1:
Rebuttal: ## General Response to Reviewers
We thank the reviewers for their constructive comments, which helped to strengthen our submission. Here, we provide responses to comments shared by multiple reviewers.
### Amount of data needed
Reviewers qoeh and DL1P inquired about the amount of data needed to ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Data Valuation with Weighted Banzhaf Values | Accept (poster) | Summary: The paper proposes that 1) the semi-values obtained from fitting Kronecker noise to the data robustly rank data more consistently across runs and 2) we can efficiently estimate these weighted Banzhaf values with the maximum sample reuse principle. The extensive experiments illustrated the advantages of the con... | Rebuttal 1:
Rebuttal: Thank you for pointing out our insufficient presentation for the context as well as your helpful suggestions. The suggested reference is useful, and will be added in our revision. Please view our global response for clarification, and below we address other comments, and we will polish our paper a... | Summary:
This paper extends the notion of Banzhaf values to weighted Banzhaf values to improve the robustness of data ranking. The authors show under Kronecker noises, when minimizing the worse-case entropy, the most robust parameters belong to the family of weighted Banzhaf values. Similarly, as implemented in Data B... | Rebuttal 1:
Rebuttal: Thanks for the detailed review and feedback! Please refer to our global response for clarification on the context. Below we address other specific comments.
**Q: How can you ensure exact Shapley value, if each evaluation is noisy?**
A: To clarify, each noisy utility function $v$ can be written a... | Summary: This work focuses on data valuation with weighted Banzhaf values, which seems to be effective, particularly in cases where the dataset or the data valuation process is noisy. Toward that, the authors introduce and utilize a Kronecker noise model to calculate robust values* and moreover to do it in an efficien... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and feedback. Please refer to our global response for clarification on our context. We will release our code after acceptance to ease the replication of all our results. Below we address other specific comments.
**Q: How does the Kronecker noise model behave for la... | Summary: The paper looks at the standard data valuation problem, in case of noisy estimation of the value of a coalition. It proposes a model of noise, Kronecker noise, and shows that under this noise a weighted Banzhaf value (with weight that depends on parameters of the noise) is semi-value that maximizes a notion of... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and helpful feedback. Some comments are addressed in our global response, and below we address with the remaining ones. We have elaborated more on our proofs to ease the verification from readers. At the very beginning, we first noticed weighted Banzhaf values are... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the constructive feedback. Below we address the questions most reviewers are concerned with, which will be included in our revision.
**Q: Clarify the setting for theorem 1**
A: For each noisy utility function $v$, *its randomness is due to stochasticity during trai... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PlanE: Representation Learning over Planar Graphs | Accept (poster) | Summary: The paper proposes PLANE: a graph neural network which is complete on planar graphs. The idea behind this is very natural and well explained in the paper: while graphs in general are difficult to separate, and as a result standard GNNs cannot separate them, planar graphs can be separated in polynomial time, an... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We respond to your comments below:
> Main question: The part about why we want GNNs complete for planar graphs is explained very well. What is not explained so well, I believe, is: given that there exist `graph isomorphism algorithms' for planar graphs, ... | Summary: The paper deals with supervised learning on the graphs. Conventional models of message passing Graph Neural Networks are known to be restricted in their expressivity by the 1-WL test for isomorphism. Although more expressive models like higher-order GNNs have been proposed, they are inefficient and also, are n... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns below.
> Novelty: The major problem with this paper is the lack of novel ideas or insights….This paper leverages the KHC algorithm in a straightforward manner.”
Our work generalizes the classical, KHC algorithm to a *learnable neural* model whi... | Summary: This work focuses on enhancing the representation power of GNNs in terms of distinguishing non-isomorphic graphs. Inspired by the classical planar graph isomorphism algorithm, the paper designs architectures within the proposed PLANE framework for learning complete invariants of planar graphs. The proposed fra... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We respond to your points below:
> While the synthetic datasets used in the study provide some insights into the theoretical power of the proposed framework, they may not fully capture its potential. Notably, the clustering coefficient and EXP datasets ca... | Summary: This paper improves the graph isonophism inspired GNN design for a particular type of graph, planar graphs.
It utilizes KHC algorithm to generate a symbolic code for each graph. Follow the sequence, they apply GNN to recursively get the whole graph representation, which shall have the good property to be inva... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We respond to your main points below:
> My major concern for this papaer is that the presentation makes it really hard to understand the algorithm.
Thanks for raising this point. We have now included a figure visualising the full decomposition and encodi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. We have responded to each concern in detail in our individual responses. In this global response, we include a response file, *response.pdf*, containing the results of additional experiments for your reference.
The changes made during the rebuttal can be... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Message passing based neural network is well-known for its limited expressivity of identifying graphs, while higher order neural network like IGNs and SetGNNs are not scalable. Instead of trying to solve complete invariant for any graphs, this paper focuses on finding complete invariant for planar graphs. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, and respond to their main points below:
> The presentation is not clear enough, the author better provide a good figure to illustrate the hierachical steps clearly to help the reader understand the main idea in a minute.
Following your sugge... | null | null | null | null | null | null |
Knowledge Distillation Performs Partial Variance Reduction | Accept (poster) | Summary: This work explores knowledge distillation, a technique used to improve the performance of smaller "student" models by leveraging the knowledge of more powerful "teacher" models. The authors analyze knowledge distillation from an optimization perspective and reveal that it can be seen as a stochastic variance r... | Rebuttal 1:
Rebuttal: Thank you for your comments and positive evaluation of our work.
- *There is no explanation why LBFGS teacher is better than SGD teacher.*
***Response.*** This is simply because LBFGS as an optimization algorithm can train the teacher to achieve almost zero train loss, while the SGD teacher conv... | Summary: This paper gives a new interpretation of the logit-based knowledge distillation algorithm. In particular, for simple one-layer models, authors show theoretically that the knowledge distillation can be viewed as diminishing the scale of the student model gradient by some amount proportional to the teacher model... | Rebuttal 1:
Rebuttal: Thank you for your comments and positive evaluation of our work
- *It would have been better if ...*
***Response.*** We acknowledge this suggestion, and in fact this is one of the key aspects of our theory which is discussed in lines 269-277 (Importance of the results) after the proof overview o... | Summary: This paper examines the benefits of Knowledge Distillation (KD) from an optimization perspective. They show that, under certain assumptions, KD performs partial variance reduction on SGD noise and that the amount of reduction depends on the quality of the teacher model. Their analysis suggests that the distill... | Rebuttal 1:
Rebuttal: Thank you for your comments and evaluation of our work.
- *The core proposition of the paper (Prop. 1) does not apply to deep neural networks, and the empirical evidence presented to support the claim that it’s a good approximation is quite limited (one scenario on MNIST with one hidden layer FF... | Summary: This work analyzes Knowledge Distillation (KD) using an optimization point of view.
By recasting the KD problem as a standard learning problem with a custom loss, the authors analyze the convergence of SGD on such loss and identify the variance-reducing properties of KD, through the bias induced by the teacher... | Rebuttal 1:
Rebuttal: Thank you for your comments and positive evaluation of our work.
- *The biggest weakness of this work is its ambivalence on the validity of the results for deep learning. ...*
***Response.*** We acknowledge the fact that the focus of our work is analytical: we identify a first non-trivial inter... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
Thank you for the time and effort you put into evaluating our work. We have responded to all comments in your reviews by providing additional discussions/clarifications and numerical validations (please find the attached PDF response for the additional plots).
Plea... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GEX: A flexible method for approximating influence via Geometric Ensemble | Accept (poster) | Summary: This work studies approximations methods for data influence. The work identifies a common theoretical drawback behind popular approximation methods to Influence Function (IF) which suppresses their expressive power and affects their performance.
This work proposes a novel interpretation of existing IF approxi... | Rebuttal 1:
Rebuttal: We appreciate your careful review and constructive comments! We give point-to-point replies to your comments in the following. Official comments will address questions that could not be answered due to the character limit.
* Q1. [**Terminology**] Influence Function (IF) refers to the celebrated m... | Summary: The paper provide a novel connection between IF approximations and LA, and introduces a new IF approximation method. The motivation takes advantage from two observations, the removal of linearization can alleviate the bilinear constraint and, the utilization of Geometric Ensemble is advantageous. Empirical re... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and suggestions! We provide a detailed reply to your questions in the following.
* Q1. [**Scalability of GEX**] W1. The method is only justified on small dataset such as MIST and SVHN. Is it possible to verify the effectiveness of the method on larger dataset... | Summary: This paper proposes a new method for approximating influential examples. It treats losses as non-linear functions, addresses the singularity problem of hessians and does not require multiple checkpoints or JVP computations for the influence. The authors also highlight a connection between Influence Functions (... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper so thoroughly. We appreciate your feedback and would like to provide point-to-point replies to your questions in the following.
* Q1. [**Bilinearity used in IF approximations**] The authors emphasize bilinear approximation but they do not explain what bilinearity ap... | Summary: The paper identifies that existing influence functions suffer from a fundamental drawback due to their bilinear form. To address, this they propose an influence calculation that is nonlinear .
Strengths: The GEX influences seem to outperform all baselines wh... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our paper. We appreciate your feedback and would like to provide point-to-point replies to your questions in the following.
* Q1. [**Lack of details in main text**] There is a lot of information pushed to the appendix that is reference in the main text, to th... | Rebuttal 1:
Rebuttal: * G1. [**Additional experiments: Relabeling**] Our first additional experiment is the relabeling task presented in Section 5.2 on the ImageNet-1K environment with ViT and MLP-Mixer. To this end, we follow the relabeling process in Section 5.2 with the estimated influence in Table 2. The following ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors point out that standard approximations of Influence Function (IF) suffer from performance degradation due to oversimplified influence distributions caused by their bilinear approximation, suppressing the expressive power of samples with a relatively strong influence. Therefore, they ... | Rebuttal 1:
Rebuttal: We appreciate your careful review and constructive comments! We give point-to-point replies to your comments in the following.
* Q1. [**Scalability issue of other IF approximations**] In table 2, the gap between GEX and the other settings is very large. It seems that the other settings are not sp... | null | null | null | null | null | null |
InstanT: Semi-supervised Learning with Instance-dependent Thresholds | Accept (poster) | Summary: This paper introduces a new thresholding methodology for Semi-Suervised Learning. This paper proposes InstanT, which uses an instance-dependent threshold for each unlabeled data (Fig1). This algorithm shows the improvement on multiple semi-supervised benchmark datasets.
Strengths: Pros.
- This paper is well-... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contributions. We are grateful for your constructive feedbacks and positive remarks about our work. We hope we can address you questions and concerns:
> **W1: Are the classes from CIFAR10/100 a subset of pre-trained dataset?**
Thank you for this very insightful co... | Summary: This paper focuses on semi-supervised learning (SSL) and proposes the study of instance-dependent thresholds to make incorrect pseudo-labels have higher thresholds.
Strengths: This paper studies an important problem in SSL, and tries to propose a dynamic and adaptive threshold to guarantee the pseudo-label qu... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully going over our proof and pose challenges to them, we strongly agree that the theoritical part of the paper should be rigorous, and believe that your questions and suggestions will further refine our paper.
> **W1: Proof regarding to Theorem 2.**
For simplicit... | Summary: This paper presents a new approach for selecting confident examples called InstanT, which uses instance-dependent thresholds for assigning pseudo-labels to unlabeled data. Unlike existing methods that apply the same threshold to all samples, InstanT considers the instance-level ambiguity and error rates of pse... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contributions and posing a wide range of valuable questions & suggestions, all of which have been very inspiring to us. We sincerely hope that our response can address your questions and concerns:
> **W1 & Q3: Relationship between Quality-Quantity trade-off and eff... | Summary: This paper proposes a semi-supervised method with instance-dependent thresholds (InstanT), which can assign different thresholds to individual unlabeled data based on the instance-dependent label noise level and prediction confidence. Also, this paper provides a theoretical analysis of the proposed InstanT. Ex... | Rebuttal 1:
Rebuttal: We appreciate your invaluable feedbacks and suggestions, all of which will significantly contribute to the enhancement of our work. Please find our response addressing your concerns:
> W1: Lack of comparsion with SOTA (FreeMatch, SoftMatch etc.)
Thank you for raising this question, we have cond... | Rebuttal 1:
Rebuttal: Dear reviewers, please find our general responses to some of the commonly asked questions, due to character limits in some of the individual rebuttal, we kindly refer you to see our response here:
> **GA1: Comparsions with more recent baselines**
We have included the comparsions with two recent ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: - Assumption for instance-dependent threshold setting and theoretical proof based on it
- Transition matrix modeling (estimator design) to reduce label error through instance-dependent threshold function
- This paper shows good performance on various datasets (CIFAR10, 100, STL-10)
Strengths: 1) A new approac... | Rebuttal 1:
Rebuttal: Dear reviewer 1e9N, we are grateful for your comprehensive review and the substantial range of questions you have raised.
> **W1: Computational complexity**
As suggested in Table 2 of our paper, InstanT brings a minimal increase in terms of training time. Here we present you with more run-time a... | null | null | null | null | null | null |
Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | Accept (poster) | Summary: The paper introduces a novel Federated F-Divergence-Based Aggregation (Fed-FA) algorithm to enhance defense against backdoor attacks in federated learning within NLP tasks. Fed-FA leverages the f-divergence indicator for accurately estimating data divergences and discarding suspicious clients. Experimental res... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for positive comments. Here are our responses.
[Q1] The paper assumes that the data across clients are independent and identically distributed (IID), which may not be true in practical scenarios. The defense performance in non-IID cases was not as satisfactory as... | Summary: The paper proposes a new algorithm called Federated F-Divergence-Based Aggregation (Fed-FA) to defend against backdoor attacks in natural language processing (NLP) tasks. Backdoor attacks are launched by malicious clients in federated learning algorithms, which train neural network models across multiple decen... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments. Here are our responses.
[Q1]The Fed-FA method depends on the assumption that |S| = n/2 + 1, and limits its defense against most clients are malicious clients.
[A1] Theoretically, if most clients are malicious, the server can not defend against the... | Summary: This paper proposes a defense method against backdoor attacks in federated learning for NLP tasks. In essence, the paper suggests estimating the f-divergence of the model parameters uploaded by different clients utilizing a constructed few-shot dataset, and assigning smaller weights to models with anomalous f-... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments. Here are our responses.
[Q1] The authors argue that, compared to CV tasks, the difference in distance between malicious and non-malicious models in NLP tasks is less pronounced and this motivates the following research. However, this viewpoint lack... | Summary: The paper proposes a new defense method against backdoor attacks in federated learning for NLP tasks. Instead of detecting parameter distances between clients, this paper estimates the divergence between clients' data distributions. The authors derive a theoretical lower bound on the f-divergence between two d... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s helpful comments. Here are our responses.
[Q1] Since it is unknown if any client in the federated system has been injected backdoors, measuring false positive rates on a system with only clean clients is also crucial for backdoor defense methods [1]. A practical defe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their helpful comments. We respond to the concerns of reviewers respectively. Here are the References and they are named [algorithm name]:
Reference
[Krum] Blanchard, P., Mhamdi, E.M.E., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzanti... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary:
This paper studies how to identify the backdoor in Federated Learning (FL) which is achieved by 'explicitly modeling the data divergence among clients'. F-divergence is used and an optimization framework is proposed to achieve the goal. The method is verified on NLP FL experiments based on GRU, LSTM, and CNN ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments. Here are our responses.
[Q1] Because Eucleadian distance is a special case of f-divergence, the success of f-divergence is quite predictable.
[A1] The success of the Euclidean distance lacks theoretical evidence, and our theoretical analysi... | null | null | null | null | null | null |
DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning | Accept (poster) | Summary: The authors propose a self-supervised speech representation that combines masked language modeling, online clustering, and self-distillation. They apply these techniques using jointly-trained transformer-based teacher and student models, where the student has to guess the cluster assignment of masked input. Ev... | Rebuttal 1:
Rebuttal: We thank Reviewer RFky for taking the time to review our paper and providing constructive suggestions to improve the paper. Below we answer the question raised in the review.
---
> Section 4.6 is well done, although the “mapping phones to codewords” section could be made more compelling by utili... | Summary: The paper proposes a self-supervised paradigm combining the methods of self-distillation and online clustering. Specifically for each frame, a teacher model's activations are clustered based on initialized codebooks. The codebooks are then updated using a momentum based method. Then a student model is trained ... | Rebuttal 1:
Rebuttal: We thank Reviewer UMJ9 for taking the time to review our paper and recognizing our contribution. Below we answer the question raised in the review.
---
> No explanation given for hyper-parameter tuning experiments especially why we see the huge change as the top N layers for clustering is change... | Summary: The paper introduces a method called DinoSR for improved speech representation learning. DinoSR combines three existing key concepts: masked language modeling, self-distillation and on-line clustering. The authors demonstrate that these components complement each other and lead to a better model for speech rep... | Rebuttal 1:
Rebuttal: We thank Reviewer Z4mQ for taking the time to review our paper and expressing explicit concerns. Below we answer the question raised in the review.
---
> Small dataset evaluation
> Generalizability to other sizes or model architectures
We would like to emphasize that the model size and dataset... | Summary: The paper introduces DinoSR, a method that combines masked language modeling, self-distillation, and online clustering for self-supervised speech representation learning.
The authors demonstrate that these concepts complement each other and result in a strong representation learning model for speech. DinoSR ... | Rebuttal 1:
Rebuttal: We thank Reviewer Jtdv for taking the time to review our paper and recognizing our contribution. Below we answer the question raised in the review.
---
> Limited Discussion of Limitations: The paper does not explicitly discuss the limitations of DinoSR. It is crucial to acknowledge any potential... | Rebuttal 1:
Rebuttal: Attached pdf file contains figures that display $P(phone|code)$ for different layers per reviewer UMJ9's request.
Pdf: /pdf/37dc6768eac72972448a3d1bd047573827eaef51.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes DinoSR, a self-supervised training method with self-distillation and online clustering. The key contribution of this work is the online clustering which is a gradient-free method to learn acoustic unit representations. The authors demonstrate that this approach outperforms previous state-of... | Rebuttal 1:
Rebuttal: We thank Reviewer Maey for taking the time to review our paper and providing constructional feedbacks. Below we answer questions raised in the review.
(quotes from the original review are trimmed to save space)
---
> The contributions of this paper are somewhat limited...
We would like to highl... | Summary: This paper proposed DinoSR, a novel self-supervised learning (SSL) approach for speech representations that combines the idea of the masked language model, self-distillation, and online clustering. It improves the data2vec method by using online clustering to obtain discrete targets from the teacher model and ... | Rebuttal 1:
Rebuttal: We thank Reviewer zY5D for taking the time to review our paper and providing the detailed feedback. Below we answer concerns/questions raised in the review.
---
> The major weakness to me is originality when compared to data2vec. Although the main idea of the paper is presented as combining the ... | null | null | null | null |
The Bayesian Stability Zoo | Accept (poster) | Summary: This paper aims to establish the equivalences among various definitions of distribution independent stability and different definitions of distribution dependent stability. Furthermore, the authors propose a stability-boosted interpolating learning rule that exhibits logarithmic expansion of KL-stability with ... | Rebuttal 1:
Rebuttal: **The paper is not well-written and is lack of self-containment. For example, the abbreviation "PAC" is used throughout the main text without providing its full name when it is initially mentioned, despite it being a well-known concept in machine learning.**
PAC learnability is defined on line 1... | Summary: The paper is a thorough collection of relations between different notions of stability used in learning theory literature. The authors propose a systematization of the many relations by proposing a bifurcation of the notions into two classes -- distribution-independent stability and distribution-dependent stab... | Rebuttal 1:
Rebuttal: **I found the order of the sections to be weird -- section 3 on preliminaries should be before section 2 for example.**
The current order was chosen so that the main contributions of the paper will appear as early as possible (this is common practice in some conferences). However, we agree that i... | Summary: This paper categories different definitions of stability (approximate/pure DP, replicability, global stability, perfect generalization, TV indistinguishability, mutual information stability and KL divergence stability) in the literature into two families (distribution-dependent stability and distribution-indep... | Rebuttal 1:
Rebuttal: **This paper introduces a series of definitions without any literature, for example, $\mathsf{D}_\alpha$-Stability, etc. Without any backgrounds/intuitions on the definitions and any future direction section, I am not convinced enough on how there results can be applied to other topics. Some discu... | Summary: **Post-rebuttal**
I thank the authors for their response. I will keep my score as is.
This work aims at building a comprehensive taxonomy of stability definitions showing that many definitions of stability are equivalent to each other.
Strengths: This paper studies the interrelations between different type... | Rebuttal 1:
Rebuttal: **TV indistinguishability (alluded to in the abstract) is not defined in the main text or supplementary. How is TV indistinguishability related to TV stability?**
They are the same. [1] used the term TV-indistinguishability and we preferred to use the term TV-stability, we believe that it e... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper shows that many definitions of the "stability" in learning theory are equivalent (according to a specific definition of equivalence).
They prove this is the case for various distribution-independent definitions of stability as well as compile the same result for distribution-dependent definitions fro... | Rebuttal 1:
Rebuttal: **I'm having some trouble understanding the motivation behind wanting the difference between the prior and the posterior to be small in the description of stability on lines 33–34.**
Considering the "distance" between a prior distribution and the posterior is very common. (The word "distance" is ... | null | null | null | null | null | null |
Secure Out-of-Distribution Task Generalization with Energy-Based Models | Accept (poster) | Summary: This paper studies the intersection of OOD generalization and meta-learning, which is rather new. The main claim is that existing meta-learning algorithms may fail to generalize well in OOD settings since they are not specifically designed to solve OOD tasks. To this end, authors proposed a general framework t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments to improve our paper. We detail our response below point by point. Please kindly let us know if our response addresses the questions you had for this paper.
##### A lack of theoretical analysis using the derived Bayesian framework. I assume a gen... | Summary: In this paper, the authors address the generalization problem of meta-learning methods on out-of-distribution tasks in the wild. However, existing Bayesian meta-learning methods suffer from incomplete convergence of the feature distribution shifts and insufficient expressiveness of meta-learning priors. The au... | Rebuttal 1:
Rebuttal: Thank you a lot for the constructive comments. You may find our corresponding explanations below for your concerns. We would really appreciate it if you could let us know if you have any further concerns.
##### Q1: On what $\theta$ and $\phi$ represent in EBML
> Lines 33 in the Introduction is a h... | Summary: This paper proposes EBML, an energy-based meta-learning which models the joint P(X,Y) with two energy functions - one for E(X,Y,phi) that models task-specific joint P(X_i,Y_i|phi) and E(phi) that models task-specific latents P(phi). The motivation is twofold: 1. completeness: energy-based models can naturally ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments on this paper. You may find our response below for your major and minor concerns. We would appreciate it if you could let us know if you have any further concerns.
##### Technical contribution
> - First, we respectfully point out the misunderstandings in the a... | Summary: The paper deals with the problem of detecting and adaptation of out-of-distribution (OOD) tasks in the meta-learning algorithms. Recent solutions adapt Bayesian meta-learning methods which have certain limitations in terms of complete coverage of OOD tasks and based on known probability distribution that may n... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and comments to improve our paper. We detail our response below point by point. Please kindly let us know if our response addresses the issues you raised in this paper.
##### Additional results for EBML on meta-datasets.
> We will include URL [31] (wh... | Rebuttal 1:
Rebuttal: ### Global Response to Reviewers
##### Why (11) should be seen as "adapting to ID"; the goal of (11) is to adapt $q_\psi$ to the OOD task
> As we explain below, our original description of Eqn. (11) in Line 221, i.e., "adapts this inadequate meta-learned prior back to the ID region" **is not con... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of meta-learning on out-of-distribution (OOD) tasks and proposes a solution to improve the generalization capability of meta-learned prior knowledge in safety-critical applications. The paper introduces an energy-based coherent probabilistic model that enables both detection an... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments to improve our paper. We detail our response below point by point. Please kindly let us know if our response addresses the questions you had for this paper.
##### On how EBML uniquely addresses the challenges inherent to OOD tasks in meta-learnin... | null | null | null | null | null | null |
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning | Accept (poster) | Summary: This paper presents a groundbreaking Disentangled Counterfactual Learning (DCL) approach for physical audiovisual commonsense reasoning. The main objective of the proposed method is to infer objects' physics commonsense based on both video and audio inputs, effectively mimicking human reasoning abilities. To a... | Rebuttal 1:
Rebuttal: # Response to Reviewer PAiU
Thank you for your time and valuable comments, below are our responses to your concerns:
## Q1: Applications in diverse scenarios.
This is a question worthy of exploration. Our module is equally applicable to other scenarios. Below are our results on the Visual Common... | Summary: The work proposes an approach to separate object and action information from videos to improve the model's reasoning capabilities. The proxy-task of audio-visual question answering is used to train the model commonsense concepts of the physical world. Their contribution focuses on learning from introduced coun... | Rebuttal 1:
Rebuttal: # Response to Reviewer FCQF
Thank you for your time and valuable comments, below is our response to your review:
## Q1: K-fold cross validation.
Thank you for your suggestion. We conducted the experiments on the PACS-Material subset using the K-fold cross-validation approach you mentioned. The re... | Summary: This paper proposes a novel Disentangled Counterfactual Learning (DCL) method for physical audiovisual commonsense reasoning. DCL consists of two main modules: Disentangled Sequential Encoder and Counterfactual Learning Module(CLM). Disentangled Sequential Encoder decouples videos into static (time-invariant) ... | Rebuttal 1:
Rebuttal: # Response to Reviewer j4kk
Thank you for your time and valuable comments, below is our response to your review:
## Q1: Some typos.
We will revise the "causal learning module" on line 11 of the paper to "counterfactual learning module", and make sure that the entire paper is consistent.
## Q2: Wh... | Summary: The paper introduces a novel approach for physical audiovisual commonsense reasoning by Disentangled Counterfactual Learning (DCL). The authors propose a disentangled sequential VAE to separate static and dynamic factors in the visual latent space with an additional contrastive loss term. In addition, a causal... | Rebuttal 1:
Rebuttal: # Response to Reviewer QHKe
Thank you for your time and valuable comments, below is our response to your comments:
## Q1: The impact of more parameters.
Thank you for your appreciation of our model. Audiovisual physical commonsense reasoning is indeed a formidable challenge (we will elaborate on ... | Rebuttal 1:
Rebuttal: # Our rebuttal material
Pdf: /pdf/c93ad97e21f58c15e7537ef0cc437dc0e3516836.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a disentangled counterfactual learning (DCL) approach to solve physical audio-visual commonsense reasoning. This approach first decouples videos into static and dynamic latent features, and then uses a causal learning module to augment the model's reasoning ability. Authors show that this m... | Rebuttal 1:
Rebuttal: # Response to Reviewer ZZdS
Thank you for your time and valuable comments, below is our response to your review:
## Q1: Marginal Performance of our method.
Merlot Reserve utilizes a large-scale private dataset (YT-Temporal-1B) and a specialized device (v3-1024 TPU) for training. Due to this limita... | null | null | null | null | null | null |
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks | Reject | Summary: Large language models lack expertise skills and this is reflected in their limited capability for arithmetic etc.
The paper proposes a method to integrate a CoNN (compliled neural networks) into an LLM via gating. Such integrations allow better performance for rule intensive tasks such as symbolic reasoning, ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and providing constructive feedback. Below are our rebuttals to address your concerns. We hope this helps clarify any misunderstandings.
---
**Review:** The novelty of the paper concretely is using the rule based trigger to combine a CoNN w... | Summary: Authors have proposed a novel way to augment large language model called Neural Comprehension to improve symbolic reasoning in tasks where rule-based execution is required by design such as numbers summation. The core idea behind their method is to augment the LM with compiled NN (CoNN) for a specific task is ... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work's novelty and significance in improving symbolic reasoning tasks.
---
**Review:** Hardcoded structure of CoNNs under consideration
**Response:** You correctly pointed out that in the current implementation, \beta and CoNNs are hardcoded and manually sp... | Summary: While Large Language Models show promise for a wide swath of tasks, they are lacking when applied to symbolic reasoning tasks. To overcome this limitation, the authors propose to employ Compiled Neural Networks (CoNNs). They create networks specialised to arithmetic and symbolic tasks and propose a mechanism b... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and constructive feedback.
In fact, as shown in the supplementary code, the process of AutoCoNN constructing CoNN requires Instruct (describing specific rules) and Example.
```python
from NeuralCom.AutoCoNN import AutoCoNN
INSTRUCT = 'Create an SOp that is ... | Summary: The paper shows a strategy to improvise ICL by including CoNNs in the learning pipeline, which enable the LM to learn symbolic operations in addition to standard autoregressive LM generation. The resulting model is trained by a hand designed gradient accumulation technique and results are compared on symbolic ... | Rebuttal 1:
Rebuttal: We appreciate your detailed and constructive feedback very much. We hope this response helps address your concerns about the design and applicability of the method. We will update our documentation accordingly to make it more clear, and will consider your other suggestions.
---
**Review:** It is... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for their constructive feedback and recognition of our paper's contribution.
## Strengths
In the review, our paper was praised for its "**novel approach**", "**correct motivation**", "**high-quality experiments**", and "**superior performance**":
- We appreciate Rev... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
NetHack is Hard to Hack | Accept (poster) | Summary: This paper seeks to study and better understand the large performance gap between neural and symbolic agents in the NeurIPS 2021 NetHack Challenge. The main hypothesis is that symbolic agents advantage derives from hierarchical reasoning, which was not an element in participating neural agents. To test this hy... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for the time and effort that you have taken to review our submission. We are delighted that you found our paper to be well-written and the insights generated by our experiments to be valuable as a basis for future work on neural approaches to learning in complex, long-ho... | Summary: # Problem Statement
The paper addresses the challenge of neural policy learning methods struggling in long-horizon tasks, particularly in open-ended environments with multi-modal observations, such as the game NetHack. It was observed that symbolic agents significantly outperformed neural approaches in the Neu... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your thoughtful and detailed feedback on our submission. We look to address your remaining concerns about our paper below.
> ...while this approach is interesting, it is unlikely to exceed the performance of experts that generate demonstrations, not to mention t... | Summary: The paper improves the existing solutions in the NetHack Learning Environment (NLE). This is done by taking earlier solutions from a competition around NLE, collecting more data with the best available (symbolic) agent, and using that data to improve a neural only solution. The paper provides experiments with ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your detailed and thoughtful feedback on our paper, which has greatly helped to further strengthen our work. We hope to address the concerns and questions that you raise in your review below.
> If a new method is proposed to generally improve RL/IL performance, ... | Summary: This is an emergency review, and I regret that the paper is out of my expertise, which is why my review will rather stay at the surface level.
The paper is concerned with the NetHack challenge, a complex AI challenge that in 2021 reached headlines, because symbolic agents considerably outperformed neural agen... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We are very grateful for the time you have taken to provide thoughtful feedback and comments on our paper.
> The paper appears to be missing a link to the dataset.
We will publicly release the HiHack dataset upon revision of our submission, at which time we will include a link... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive and insightful feedback. We are glad that you found our analysis to be comprehensive (qyRc), our experimental insights impactful (eWjQ, Ub8t, 83YB), and our submission to be well-written (Ub8t, qyRc, 83YB).
However, in this general response we would l... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper explores reasons for this performance gap between neural and symbolic methods in NetHack:
Symbolic agents use hierarchical policies and parsers to extract high-level features
Symbolic agents have handcrafted heuristics and error correction
Neural agents lack inductive biases like hierarchy that may b... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your feedback and comments on our submission. We are glad that you found our paper to be clear and insightful.
> The model is not so representative, can switch a more popular model
The transformer-LSTM policy architecture that we employ in this paper is indeed... | null | null | null | null | null | null |
Derandomized novelty detection with FDR control via conformal e-values | Accept (poster) | Summary: This paper applied the derandomized e-value to the conformal novelty detection, which reduces the randomness of original approach using conformal p-value. The authors also refined the method by adaptively weighting the conformal p-values based on an estimate of the out-of-sample accuracy of each underlying mac... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback. We are addressing your comments below.
### Novelty and data-driven weights
Our data-adaptive weighting method involves technical innovations and is different from the use of “side-information” weights discussed in Ren and Baber [2022... | Summary: The paper employs conformal e-values, as opposed to p-values, to quantify statistical significance during outlier testing under FDR control. This approach enables the principled aggregation of results from mutually dependent tests, thereby providing a solution to de-randomize (split) conformal inferences.
Str... | Rebuttal 1:
Rebuttal: Thank you for your review; we appreciate the effort and honest feedback. We are sorry to hear you found the paper a bit hard to understand, but we hope we can answer your questions here.
- Clarity. Other reviewers found the paper to be clear, but we can try to make it even more accessible. It wou... | Summary: The main limitation of conformal prediction lies in its inherent randomness. However, this paper presents an innovative solution by introducing a derandomized version of conformal prediction, specifically applied to the field of novelty detection. Through the incorporation of conformal e-values, the proposed m... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback.
We also appreciate your suggestion of including an additional experiment utilizing more complex synthetic data or real data; if space permits we will insert these results in the revised manuscript. | Summary: This papers proposes a way to reduce the randomness in novelty detection methods (detecting out-of-distribution points) that are based on the split-conformal inference paradigm. This is done by ensembling over several ($K$) train-validation splits of the dataset. The main technical point is to aggregate the ev... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback.
### Broader relevance of our work
It is true that this work focuses explicitly on de-randomizing conformal inferences for novelty detection tasks, which may seem like a relatively narrow scope compared to the broader range of possi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations | Accept (poster) | Summary: This paper presents an offline safe imitation learning algorithm called SafeDICE. A unique point of SafeDICE is that a safe policy is learned by non-preferred and unlabeled demonstrations in the imitation learning framework. Based on the formulations studied in DICE family, this paper formulated the safe IL pr... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and comments. Please feel free to ask any additional follow-up questions.
**[Responses to Weaknesses]**
**(mathematical contributions)**
The mathematical contribution of our paper is to present a well-defined mathematical formulation for a new problem s... | Summary: Learning safe behaviors from a dataset of demonstrations is a challenging problem. The work presents SafeDICE, an algorithm which learns a safe policy using preference-based imitation learning. The method leverages non-preferred demonstrations in the space of stationary distributions in contrast to prior metho... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and comments. Please feel free to ask any additional follow-up questions.
**[Responses to Weaknesses]**
**(1) (Choice of Baselines)**
Please note that the setting we consider in the paper is **offline safe imitation learning, not constrained RL**, and *... | Summary: This paper proposes an offline safe imitation method to avoid non-preferred demonstrations. The mixture coefficient is alpha, and the paper provides an effective way to obtain hyperparameter instead of hyperparameter search techniques. The paper uses scarce but labeled non-preferred demonstrations from the non... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and comments. Please feel free to ask any additional follow-up questions.
**[Responses to Weaknesses]**
**(1.1) (Problem Setup & The need for labeled non-preferred demonstrations)**
In our work, we focus on solving safe imitation learning in an offline ... | Summary: This paper focuses on offline safe imitation learning (IL) setting. There are several unique properties of the problem setting: 1) there exists non-preferred (constraint violated) demonstrations, 2) there exists massive unlabelled data where you don't know whether they are preferred or non-preferred, 3) you do... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and comments. Please feel free to ask any additional follow-up questions.
**[Responses to Weaknesses]**
**(1) (Different settings with non-preferred demonstrations)**
Thank you for your suggestion for the additional experiment varying the amount of **la... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback and comments. Below we restate the main clarification and experiments of our rebuttal. If you have any additional questions or concerns to our response, we are happy to provide additional responses during the rebuttal period.
**[Clarifica... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Dream the Impossible: Outlier Imagination with Diffusion Models | Accept (poster) | Summary: The paper proposes a learning framework to generate outliers in the pixel space by way of diffusion models with only the in-distribution data. By learning a text-conditioned latent space based on in-distribution data, the methods further sample outlier latents in low-likelihood regions. The empirical result s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough comments, which we address below:
**A1. Clarification on our contribution**
We summarize the non-trivial differences between NPOS and DREAM-OOD below:
- First, our key contribution is to enable the generation of high-resolution outliers for OOD detection, ... | Summary: The paper tackles OOD detection in the image space and proposes to generate outliers with diffusion models. Specifically, the authors find embeddings in the text-conditioned latent space that are on the boundary of in-distribution embedding clusters. These embeddings are likely to be those of strong outliers. ... | Rebuttal 1:
Rebuttal: We are encouraged that the reviewer found our approach novel and the paper well-written. We address comments in detail below:
**A1. Clarification on adding simple Gaussian noise to the latent space**
Thanks for acknowledging our ablations on different outlier image synthesis methods! We agree w... | Summary: This paper proposes DREAM-OOD.
It constructs a text-conditioned latent space by learning an image encoder $h_\theta$ with a pretrained text encoder $\mathcal{T}$ and a contrastive loss.
During OOD generation, DREAM-OOD first generates outliers in the latent space according to the k-NN distance in Eq. (5), and... | Rebuttal 1:
Rebuttal: We are happy to see that the reviewer finds our work easy to follow with appropriate visualizations & ablations and that our paper is organized. We thank the reviewer for the thorough comments and suggestions, which we address below:
**A1.Discussion on the computation cost**
As suggested, we su... | Summary: This paper focuses on utilizing auxiliary outliers for the OOD detection task. Specifically, different from other works using the collected outliers, this work studies generating photo-realistic outliers in the high dimensional pixel space. This paper proposes a new framework, namely, DREAM-OOD, which utilizes... | Rebuttal 1:
Rebuttal: We are glad that the reviewer finds our work new, owns high originality, and is easy to understand, with promising results and analyses. We thank the reviewer for the thorough comments and suggestions, which we address below:
**A1.Technical differences between DREAM-OOD and related work**
Great ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable comments. We appreciate that reviewers find our approach DREAM-OOD **novel** and **effective** (Qkax, 94Wq, 3rTL), and the results are **comprehensive**, **extensive** and **promising** with **detailed** and **sufficient** ablations (Qkax, 94W... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes DREAM-OOD, a new diffusion-models-based framework to enable the generation of photo-realistic high-resolution outliers for OOD detection. DREAM-OOD works by training a text-conditioned latent space using ID data and then samples the outliers in the low-likelihood region of the latent space.... | Rebuttal 1:
Rebuttal: We are encouraged that you recognize our method to be new, effective, and reasonable, and with comprehensive and extensive empirical results. We thank the reviewer for the thorough comments and suggestions, which we address below:
**A1. Discussion on DREAM-OOD versus feature-based outlier synthes... | null | null | null | null | null | null |
Add and Thin: Diffusion for Temporal Point Processes | Accept (poster) | Summary: The submission derive a probabilistic diffusion model for TPPs. By proposing the Add-Thin framework, the proposed method can naturally handles the continuous and discrete nature of point processes and directly models the whole event sequences. Compared with the traditional autoregressive approaches for the tem... | Rebuttal 1:
Rebuttal: Thank you for your review and appreciation of our work.
---
Rebuttal Comment 1.1:
Title: Thank you for your response
Comment: Thank you for your response. I think this is an interesting work and keep my original score. | Summary: The forward process adds points from homogeneous poisson process (HPP) into the sequence and removes points from original sequence ($\mathbf{t}^{(0)}$). The goal is such that $\mathbf{t}^{(n)}$ is HPP. The neural network is trained to approximate missing information about $\mathbf{t}^{(0)}$, e.g. figure out wh... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback. In the following, we address the specific concerns and questions.
> W.1a. Obervations
Generally a realization of a TPP can be represented as a sequence of strictly increasing arrival times: $\mathbf{t} = (t_1, \dots, t_K)$, $0 < t_1 < \dots < t_K \leq T... | Summary: The paper proposes a probabilistic diffusion model for TPPs, ADD-THIN, that naturally handles the continuous and discrete nature of point processes and directly models whole event sequences. While autoregressive methods are expressive in modeling event sequences, ADD-THIN does not suffer from the accumulation ... | Rebuttal 1:
Rebuttal: Thank you for the extensive review, feedback, and questions. In the following, we address the raised concerns and questions.
> W.1,4 Readability and understandability (3, 3.2)
Considering the character limit, we refer to both our response to all reviewers, where we outline suggested enhancements ... | Summary: The paper introduces a novel probabilistic diffusion framework for temporal point process. Its significance lies modeling a whole event sequence directly, overcoming common limitations of autoregressive models.
Strengths: Originality: This paper is very novel. It connects diffusion models with TPPs and model... | Rebuttal 1:
Rebuttal: Thank you for your thorough review, appreciation of our work, and suggestions to further improve our paper.
> W.A Comparison to Shchur et al.
We want to point out that the intensity-free model by Shchur et al. is a very strong baseline, and both our model and this baseline achieve near-perfect me... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and appreciation of the contribution and originality of our novel diffusion-based TPP model. We have attached a PDF with the model's sampling runtime and would further like to highlight parts of our answers to reviewer 1d9q (ELBO), reviewer 1xQ2 (... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | Accept (poster) | Summary: This paper introduced a novel method to evaluate the participant contribution under the setting of federated learning. In the beginning, the author raised two challenges of recent works related to the participant contribution of FL: Multi-round training and dataset dependency, which are actually about the comm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> The author should pay attention to the writing style. For example, more focus on key points, like how to reduce computational complexity. Otherwise, it is hard to follow.
Thanks for poi... | Summary: This paper studies to evaluate the contribution of each client during federated training efficiently. It proposes a framework named SPACE, which trains a student model in the server with one communication round to measure the similarity between local datasets and the validation dataset in server. Finally, exte... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We have answered all your concerns. In the following, we respond point by point.
> One important assumption in this paper is the server holds validation dataset, which is rare in reality. What if the validation dataset is hold by distributed clients rather than... | Summary: This paper proposes a single-round participant contribution evaluation method for FL. The novel and interesting part is using the sample embedding similarity between client data and (server) validation data to indicate contribution, thus avoiding the time-consuming model retraining step.
Strengths: 1. Very us... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We have answered all your concerns. In the following, we respond point by point.
> No theoretical analysis of why the embedding similarity can lead to an excellent approximation to the original Shapley-based contribution evaluation with re-training.
Please s... | Summary: The paper introduces a novel approach named Single-round Participants Amalgamation for Contribution Evaluation (SPACE) for efficiently evaluating the contribution of participants in Federated Learning (FL). Accurately evaluating participant contribution has been a challenge in current FL, especially considerin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and summary of our paper. We have addressed all your questions in the following.
> I believe additional experiments with more extensive datasets, or an additional discussion on how this method can be applied to real-world scenarios might help.
Please see "A... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive comments. Common questions asked by multiple reviewers would be replied here in a unified manner.
> Theoretical support of why SPACE leads to an excellent approximation to the original Shapley-based contribution evaluation with re-t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the client contribution evaluation problem under federated learning settings. The goal is to achieve more computational and communicational efficient contribution evaluation. The paper proposes Federated Knowledge Amalgamation and Prototype-based Model Evaluation technique for the goal. Fede... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> While the paper proposes an interesting and empirically effective technique for improving the efficiency of participant contribution evaluation in federated learning, there is no theoret... | null | null | null | null | null | null |
Exponential Lower Bounds for Fictitious Play in Potential Games | Accept (poster) | Summary: This paper studies fictitious play in potential games and in particular, two-player identical payoff games. It is shown that fictitious play, under arbitrary tie-breaking, needs super-exponential time with respect to the number of actions to find an approximate Nash equilibrium. The lower bound is proved throu... | Rebuttal 1:
Rebuttal: We thank the reviewer for all its effort, the insightful comments and the carefully reading of our work. We start by answering the reviewer's questions.
*Questions*
1. Thanks for spotting the typo.
2. The reviewer is right we missed the assumption in Theorem 3.1. In fact Lemma 3.6 requires $\ep... | Summary: This paper examines the convergence rate of Fictitious Play (FP) in potential games. The paper proves that FP for potential games can take exponential time to reach a Nash equilibrium. The research has yielded a recursive rule for constructing payoff matrices, demonstrating that fictitious play, regardless of ... | Rebuttal 1:
Rebuttal: Thank you for your work and the valuable comments. We would like to address comment 1 and 3 together, as they are closely related. Additionally, we want to clarify that when referring to [1], we assume the paper the Reviewer nmF9 mentioned is titled "On the Exponential Rate of Convergence of Ficti... | Summary: This paper constructs a common-payoff two-player game for which fictitious play must take a number of iterations exponential in the number of actions to converge to an $\epsilon$-equilibrium. The paper closes with some empirical demonstrations of the behavior of fictitious play on the game.
Strengths: The co... | Rebuttal 1:
Rebuttal: Thank you for your work and your feedback on our paper. We appreciate your valuable comments.
1. Indeed, A should have been B; thank you for pointing it out.
2. The equality sign does indeed get confusing for the reader; we will change it to either $\coloneqq$ or $\rightarrow$ to signify assignm... | Summary: This paper studies the convergence rate of Fictitious Play (FP) in potential games. While prior work shows that FP asymptotically converges to a NE in the case of $N$-player potential games, the current paper proves that FP can take exponential time to do so. Specifically, the paper recursively constructs a tw... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer of all the work. We sincerely appreciate the reviewer's valuable comments. Related to reviewer's questions/concerns:
1. $[1]$ is for diagonal zero-sum games and moreover the tie-breaking rule is fixed in advance (not adversarial as in [2]).
2. Indeed, $A$ sh... | Rebuttal 1:
Rebuttal: We thank the reviewers for their hard work. We have attached a pdf with experimental evaluations on the stochastic version of fictitious play, to address a question asked by reviewer nmF9 (what happens in our constructed game if each agent has some exploration). The experiments suggest that a more... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond | Accept (poster) | Summary: This paper characterizes the generalization of neural networks, and prove efficient sample complexity guarantees in the mean-field region with the presence of feature learning. It presents a general framework to establish sample complexity of MFLD for binary classification problem. Such framework can be used t... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments.
**Q:** *While I understand the 2-sparse parity problem is a well studied problem with a rich body of literature, I still do not quite understand the significance or the motivation of studying this particular problem.*
**A:** The parity setti... | Summary: This paper considers the problem of learning the k-sparse parity problem with a two-layer network in mean-field regime. The main results are in two folds: 1. the authors proposed an annealing method to obtain a better rate of convergence. 2. the authors compute the classification error via computing the local ... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments. We address the technical points below.
**Q:** *It seems to me that annealing is added for the purpose of controlling the loss properly, rather than fundamentally speeding up the dynamics (in fact, annealing does not improve the convergence rate... | Summary: The paper conducts a theoretical study of the generalization error and sample complexity of two layer neural network training with Mean Field Langevin Dynamics (MFLD) or (informally a version of )noisy gradient descent. Specifically the paper specializes the generalization error for subset parity problem to de... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our paper and giving insightful comments.
**Q:** *The paper appears to be spend a lot of real estate with preliminaries.*
**A:** Thank you for the suggestion. Our goal is to make the main text as self-contained as possible; hence we introduced the basics of t... | Summary: This paper proves optimization and generalization guarantees for MFLD for the $k$-sparse parity problem. It proves that if the network is sufficiently overparameterized (width > $e^{\Omega(d)}$) then $n = d$ samples suffice, which is independent of $k$. Furthermore it proves an exponential convergence rate whe... | Rebuttal 1:
Rebuttal: Thank you for your supportive comments. We address the technical comments below.
**Q:** *How difficult would it be to adapt the techniques in this paper to the case of label noise (i.e. flip each label with probability $p < 1/2$)?*
**A:** We believe that extending our result to a situation wit... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models | Accept (poster) | Summary: This paper comprehensively evaluates three efficient training algorithms for transformer language models: layer stacking, layer dropping, and selective backpropagation. Such an evaluation is crucial due to the extensive computational resources needed for training transformer-based models.
The authors highlig... | Rebuttal 1:
Rebuttal: > _1. Limited Scope_
Thanks. Based on this feedback we have added three more recent efficient training algorithms. Please see the global response.
> _2. Limited Novelty_
Thank you for bringing this up. We argue that the observation that none of the popular recently-proposed efficient training ... | Summary: Many algorithms have been proposed to make the training of ever larger models more efficient.
The authors present a critical empirical study of three selected training algorithms (layer stacking, layer dropping, and selective backpropagation) with fixed training budgets and find that these algorithms often do ... | Rebuttal 1:
Rebuttal: > I agree on the limitations pointed out by the authors in the section "Limitations and Future Work", including evaluation of small subset of efficient training algorithms only, language model pre-training only. Overall, the paper might be felt to be too simple and straight forward, the more as it... | Summary: The paper reevaluates several training algorithms aimed at enhancing the efficiency of Transformer-based models, such as layer stacking, layer dropping, and selective backpropagation. The authors effectively manage the training resources by employing a metric called reference system time. However, the key resu... | Rebuttal 1:
Rebuttal: > While revisiting these methods undoubtedly holds value for the research community, it is important to note that the obtained results may be somewhat trivial and lack significant insights. The paper might be better suited for more specialized venues.
Thanks for this. We have added three more rec... | Summary: This paper studies three efficient pertaining techniques for Transformer models (layer stacking, layer dropping, and selective backpropagation). At variance with previous works on the subject, the authors adequately control for the learning rate schedule by evaluating performance at fixed compute budgets (as d... | Rebuttal 1:
Rebuttal: > _W1. Limited significance_
Thank you for pointing this out. Based on this feedback, we have added three more recent efficient training algorithms, see the global rebuttal response.
> _W2. unclear how the proposed Reference System Time approach improves_
We believe there is a small confusion ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their insightful and encouraging reviews:
* **b1c1** - _“the goal of this work is very welcome and in my humble opinion useful to the community_”;
* **DsDW** - “_allowing the community to take a step back and retrospectively analyse the validity of previo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents an analysis of 3 algorithms for training transformer models with a focus on efficiency. The authors present a way to measure wall clock time irrespective of the underlying hardware and make a principled comparison of the 3 algorithms by predefining a compute budget and adapting each algorith... | Rebuttal 1:
Rebuttal:
> _The main weakness of the work is possibly the choice of algorithms to be analyzed. There are only 3 while there could be a plethora of others as mentioned in the paper's related work. Moreover, the 3 methods evaluated are not particularly widespread (possibly because they don't work as well as... | null | null | null | null | null | null |
GlyphControl: Glyph Conditional Control for Visual Text Generation | Accept (poster) | Summary: This work proposes an approach to generating images with visual text. The main approach consists of a generalized version of ControlNet with rendered text as control guidance. To facilitate the training and evaluation, a benchmark dataset called LAION-OCR is proposed. Experiments show that the proposed approac... | Rebuttal 1:
Rebuttal: ## Response to Reviewer Yxcw
We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
> "It would help to add some failure cases analysis in order to better understand when models may fail."
A: Thanks for your suggestion. We have added addi... | Summary: This paper addresses the development of diffusion-based text-to-image generative models for generating coherent visual text. They propose GlyphControl, which augments textual prompts with glyph conditional information to encode shape details and improve accuracy. They introduce the LAION-OCR benchmark dataset ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviews and suggestions.
> Q1
A: Great point!
👉 For font sizes, our GlyphControl supports users to control the font size of the rendered text by modifying the width property of the text bounding box. We further report the generation results of various font sizes u... | Summary: The paper add glyphcontrol to diffusion model by adding controlnet that takes rendered whiteboard images as inputs. Texts in the whiteboard images are extracted by OCR engine during training and then rendered by glyph renderer. During inference, glyph renderer renders the whiteboard images based on the instruc... | Rebuttal 1:
Rebuttal: ## Response to Reviewer kTnz
We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
> "The proposed method requires additional training. The method may be seen as an extension of ControlNet and may appear to involve the combination of mult... | Summary: In this paper, a glyph-conditional text-to-image generation model named GlyphControl is proposed for visual text generation. In addition, the authors introduce a visual text generation benchmark named LAION-OCR by filtering the LAION-2B-en. The results show that method of this paper outperforms DeepFloyd IF an... | Rebuttal 1:
Rebuttal: ## Response to Reviewer wGfp
We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
> "The contribution is limited. The whole model is the same as ControlNet. The LAION-OCR dataset is just filtered out from an open-source dataset LAION-2B-... | Rebuttal 1:
Rebuttal: ## To AC and All Reviewers
We would like to express our gratitude to all the reviewers for their careful reviews and constructive suggestions. We appreciate the positive comments, such as "the task of visual text generation is interesting" (Reviewer Cb1X), "the visualization results shown in thi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes GlyphControl for visual text generation by augmenting textual prompt with additional glyph conditional information. A benchmark of LAION-OCR is built for evaluating this model.
Strengths: ++The task of visual text generation is interesting.
++Good results are shown in experiments.
++A new... | Rebuttal 1:
Rebuttal: ## Response to Reviewer Cb1X
We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows.
> "The main concern is the limited technical contribution. The whole architecture of GlyphControl is a simple extension of ControlNet by using additional c... | null | null | null | null | null | null |
Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes | Accept (spotlight) | Summary: The authors demonstrate a novel approach to decoding behavioral variables from neural activity recording using dense silicon probes. Most approaches utilize a sort-then-decode line of attack – first channels are spike sorted, then decoders are trained on unambiguously identified single-units. Here, the authors... | Rebuttal 1:
Rebuttal: We thank reviewer Cpfb for their thorough and thoughtful review. We appreciate that the reviewer recognized the contribution of our method to the neuroscience community.
**Major Weakness:**
We performed motion correction (registration) during the preprocessing step, ensuring that motion artifacts... | Summary: This paper presents a way to decode behavior from neural recordings, bypassing an explicitly spike sorting step. They model individual spikes as coming form a mixture of gaussian distribution, and the assignment probability to different mixtures is used instead of a 'hard' assignment of each spike to a cell. R... | Rebuttal 1:
Rebuttal: We thank Review C56U for the thoughtful review and useful comments. We are glad that the reviewer felt that the manuscript was clearly written and appreciate that the reviewer recognizes that our work makes an important contribution to the literature.
**Weakness 1:**
Although our decoder is mainl... | Summary: This paper proposed to decode animal behavior with a spike-sorting-free method that is well-suited for high-density recordings, by modeling the distribution of extracted spike features using a mixture of Gaussians (MoG) on uncertainty of spike assignments, and decoding using a generalized linear model (GLM). T... | Rebuttal 1:
Rebuttal: We thank reviewer Fmkq for their thorough and thoughtful review. We appreciate their valuable feedback and would like to address their main concerns.
**Weakness 1:**
Please refer to the global response for the novelty of our method.
**Weakness 2:**
It is important to clarify that the baseline d... | Summary: The paper develops a decoding method directly on ‘spike features’, without going through spike sorting. The authors model spike assignment uncertainty using a mixture of Gaussians model, and then perform variational inference to model the relationship between the spike features and behavior.
Strengths: The pr... | Rebuttal 1:
Rebuttal: **General:**
We thank Reviewer tDhN for carefully reviewing our manuscript, and appreciate the opportunity to address the concerns raised. We want to emphasize the novelty of our method, which involves conditioning of the data generating process on external variables, allowing for improved predict... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback on our manuscript. We were encouraged that the reviewers recognized our paper's empirical robustness and its solution to the challenging task of decoding behaviors from highly overlapping neural signals. To address the concerns, we conducted the following expe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models | Accept (poster) | Summary: Authors create SheetCopilot that takes input as a natural language task and then controls spreadsheet to perform the task. To provide an interface between natural language and the spreadsheet, author use a set of atomic actions that abstract spreadsheet functions. SheepCopilot uses state machine to create spre... | Rebuttal 1:
Rebuttal: Thank you for a thorough review that will help us improve the work. Please see below for answers to your questions.
**W1: Paper contributions are moderate.**
We believe our work contributes to the field of tool-augmented LLM, for three primary reasons:
- We propose a novel framework enabling mo... | Summary: This paper introduces an LLM based spreadsheet manipulation task system from high level human language to spreadsheet manipulation tasks. The work is in the area of tool augmented LLM systems where LLM systems are used to create a chain of actions representing a complex manipulation task in a spreasheet system... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and insightful comments. Below, we address the concerns:
**W1: a better discussion on the task collections, the Q&A pair collection, how complex these tasks are, their categorization, why they are multi-categorized, what do practical realworld spreads... | Summary: The paper proposes a benchmark and a framework based on observe-propose-revise-act for tackling spreadsheet problems.
Strengths: - The paper is very well written and the ideas are neatly presented with figures and tables. It was a pleasant read!
- The ablation studies are quite interesting. It is interesting... | Rebuttal 1:
Rebuttal: Thank you for the insightful review and feedback. Please see below for answers to your questions.
**W1: hard to understand the generalization capability … discuss any deduplication efforts**
Thank you for this constructive comment.
A direct way to test generalization is to split our dataset in... | Summary: This paper introduces SheetCopilot, a model that aims to generate step-by-step executable command sequences for software control according to the natural language description. Besides, a benchmark dataset for evaluating software control tasks is collected. Experimental results based on the dataset are reported... | Rebuttal 1:
Rebuttal: Thank you for the constructive and insightful comments. Thank you for pointing out the strengths of our paper. Your concerns are addressed in detail below:
**W1: Reporting the stability test results through the line chart may be clearer.**
Thanks for the advice. We have conducted extra experimen... | Rebuttal 1:
Rebuttal: **Global Response**
_The authors would like to thank all reviewers for their appreciation and instructive suggestions!_
The authors are encouraged to hear that the reviews commented that
- the studied spreadsheet automation task is **valuable** (bHxV) and **challenging** (TF2z)
- the proposed S... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stable Diffusion is Unstable | Accept (spotlight) | Summary: This paper finds some vulnerabilities of stable diffusion model, and proposes an auto-attack model to generate attack prompts.
Strengths: This paper is well written and is easy to understand.
This paper discusses some vulnerabilities of the stable diffusion model, and does many experiments to verify.
The meth... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their diligent review of this paper and for providing constructive feedback.
**RE Purpose and Motivation:** Stable diffusion along with other text-to-image generative models (e.g., Midjourney and DALL-E 2) have wide-ranging applications and implications in... | Summary: The paper introduces Auto-attack on Text-to-image Models (ATM), a method to efficiently generate attack prompts that closely resemble clean prompts.
The method modifies text prompts by replacing or extending words, using a Gumbel Softmax distribution for differentiability.
It further applies a binary mask to ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their diligent review of this paper and for providing constructive feedback.
**RE Replacing and Extending:** The two types of modifications can be automatically selected by our sampling mechanism and can be optimized using gradients. In the process of prom... | Summary: This paper proposes an adversarial attack against text-to-image models that can generate adversarial prompts to prevent the stable diffusion models from generating the desired subjects. The attack is gradient-based by utilizing the Gumbel Softmax to make the work embedding differentiable. Then, the authors pro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their diligent review of this paper and for providing constructive feedback.
**RE Objective and C&W:**
Our objective differs from that of C&W from three perspectives:
1. **The type of target models:** C&W attacks commonly target classification models, aim... | Summary: In this paper, the authors use Gumble softmax as well as the gradient based method to learn the distribution of an attack text prompt, defined as an attack text prompt that enables a text-to-image generation model to generate images that do not match the text description without changing the category keywords ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their diligent review of this paper and for providing constructive feedback.
**RE Generation Speed with Different Random Seeds:** We prefer to focus on the relative generation speed difference of different categories under the same initial noise since the ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their diligent review of this paper and for providing constructive feedback.
This PDF includes a Violin plot illustrating the generation speed of the same class with different initial noises. Additionally, there is a table that details the classification a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy | Accept (poster) | Summary: In order to distinguish between human-generated text and machine-generated text, the authors propose the use of the periodicity of cross entropy for discrimination. More specifically, they suggest analyzing cross entropy through the Fourier transform.
Strengths: 1. This paper is well-written and easy to follo... | Rebuttal 1:
Rebuttal: Regarding Weakness 1:
Please read our general response where we addressed the motivation issue.
Regarding Weakness 2:
Actually applying FFT to CE sequences is our biggest innovation. First of all, as we discussed in detail in the general response, applying Fourier analysis on cross-entropy seq... | Summary: This paper proposes a new measure of natural language generation (NLG) quality based on similarity between the spectrum of cross-entropy in natural vs. generated text. Fourier Analysis of the Cross-Entropy of language (FACE) is inspired by NLP and psycholinguistic studies suggesting that surprisal is not unifo... | Rebuttal 1:
Rebuttal: Regarding Weakness:
Thank you for your review. If needed, we can include examples in our paper once it is accepted. It is important to note that our approach considers the distinctions between human and model-generated languages in terms of cross entropy and periodicity, which sets it apart from ... | Summary: This paper proposes a set of metrics based on Fourier Analysis of the estimated Cross-Entropy (FACE) of language. The main idea is to compute the similarity between the spectra of cross-entropy in model-generated texts and human-written texts. Experimental results show that FACE as a computationally efficient ... | Rebuttal 1:
Rebuttal: Regarding Weakness 1:
Indeed, in our preliminary research, we conducted a comprehensive analysis of the spectrum of cross entropy and observed its ability to effectively reflect the periodic patterns of high/low entropy words.
It is important to note that our work is not just an empirical trial;... | Summary: This paper proposes a new language generation evaluation metric.
Prior work in psycholinguistics has shown that surprisal changes periodically in natural language, with natural utterances displaying moments of high and low surprisal.
This paper thus proposes to evaluate natural language generation models by qu... | Rebuttal 1:
Rebuttal: Regarding Weakness 1:
Regarding the results from various model sizes, there are indeed some inconsistent cases where small models out-perform big ones. The data from our current experiments are insufficient to explain this inconsistency, but we are planning a more complete future study to address... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for providing the useful feedback for further improving our paper. We notice that some reviewers suggest us to strengthen the motivation part, especially the reasons of using Fourier analysis on the cross-entropy sequence of language. We believe that the mo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a set of metrics to measure the distance between model-generated and human-written languages. Specifically, this paper uses FFT to analyze the cross-entropy sequences of the language data.
Strengths: 1. This new metric is efficient. Given the fact that our models are getting exponentially ... | Rebuttal 1:
Rebuttal: Regarding Weakness 1: The related work on psycholinguistic motivation is limited. Entropy is also a popular metric in computational linguistics, which is probably worth citing.
Thanks for pointing this out. We will read through our paper in detail to include more comprehensive citations once our ... | null | null | null | null | null | null |
FATE: Fairness Attacks on Graph Learning | Reject | Summary: This paper proposes to attack different fairness definitions for a variety of graph learning models. The task is formulated as a bi-level optimization problem, which is solved in a meta learning manner. The major advantages of the proposed framework include 1) it is feasible to any fairness notion and graph le... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and valuable feedback for further improving our work. We summarize the main concerns and the point-to-point responses as follows.
**Q1. Justification of learning rate.**
Thank you for your comments. We would like to clarify that Eq. (6) discusses continuo... | Summary: The authors propose an attacking framework called FATE. Existing research in algorithmic fairness aims to prevent bias amplification but neglects fairness attacks. This paper fills this gap by formulating the fairness attack problem as a bi-level optimization and introducing a meta-learning-based attack framew... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and valuable feedback for further improving our work. We summarize the main concerns and the point-to-point responses as follows.
**Q1. Necessity of maintaining utility for deception.**
Thank you for your comments about the necessity of mataining utility.... | Summary: The paper proposes a novel framework named Fate, which is capable of attacking any fairness definition on any graph learning model, as long as the corresponding bias function and the task-specific loss function are differentiable. Fate is equipped with the ability for either continuous or discretized poisoning... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and valuable feedback for further improving our work. We summarize the main concerns and the point-to-point responses as follows.
**Q1. More evaluation on different graph learning tasks and datasets.**
Thank you for your suggestions. We choose the task as... | Summary: This paper presents a novel approach for introducing fairness attacks in graph learning, which is impressive. To address this issue, the article proposes an attack framework for graphs and conducts experiments on the classic GCN model. Compared to two baseline methods, DICE and FA-GNN, the proposed method is m... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and valuable feedback for further improving our work. We summarize the main concerns and the point-to-point responses as follows.
**Q1. Sub-optimal content organization.**
Thank you for your suggestions in content organization. In the revised version, we ... | Rebuttal 1:
Rebuttal: To all reviewers,
We sincerely thank your time and efforts in evaluating our work. In our rebuttal, we have provided point-to-point responses to your concerns. Meanwhile, in this thread, we provide this one-page PDF that includes additional experimental results on a new dataset named NBA and an n... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies an interesting problem, attacking fairness on GNN. Specifically, the authors aim to amplify the unfairness while maintaining the performance of the downstream tasks. They propose a bi-level optimization scheme with a meta-gradient poisoning attack to achieve this goal. Experiments on both st... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and valuable feedback for further improving our work. Please see our point-to-point responses as follows.
**Q1. How to group/Rawls fairness by attacking a specific demographic group or best/worst accuracy group.**
Thank you for your comments in adding mor... | null | null | null | null | null | null |
CQM: Curriculum Reinforcement Learning with a Quantized World Model | Accept (poster) | Summary: - The proposed method addresses the challenge of learning a curriculum in goal-conditioned policies, which is a significant problem. Previous research on curriculum in goal-conditioned policies has often overlooked the importance of learning the underlying semantic goal space. This paper builds upon a recently... | Rebuttal 1:
Rebuttal: Dear reviewer vggh
We sincerely appreciate your constructive and insightful comments. We found them extremely helpful. We prepared our response below:
---
**Q1. It seems restrictive to the reviewer to use single-code representations with VQ-VAE so would be interesting to see how the results cha... | Summary: The method works as follows. Graphs are built by (1) quantizing visual observations to create a goal space & (2) creating temporal relations over goal space vectors. Curriculum goals towards a "user"-specified goal are made using this graph.
A VQ-VAE is used to create the goal space where goals are decodings o... | Rebuttal 1:
Rebuttal: Dear reviewer eib8
We sincerely appreciate your constructive and insightful comments. We found them extremely helpful. We prepared our response below:
---
**Q1. The paper could strongly benefit from some figures that describe the details of the method. Another figure would strongly improve the ... | Summary: This paper proposes a curriculum RL approach using a VQ-VAE to learn a goal space, and then construct a graph with the VQ-VAE codes as nodes, and a temporal distance estimate of the Q-value as weighted edges. The curriculum is then constructed by doing frontier-based exploration on this graph, by sampling goal... | Rebuttal 1:
Rebuttal: Dear reviewer uT8t
We sincerely appreciate your constructive and insightful comments. We found them extremely helpful. We prepared our response below:
---
**Q1. Regarding the term “world model”**
**A1.** Thanks for your valuable comment. We understand your concern regarding the term "world mod... | Summary: This paper introduces Curriculum RL with Quantized World Model (CQM), a novel approach that leverages a VQ-VAE to create a discretized goal space and constructs a graph structure over it. CQM further proposes a curriculum strategy based on uncertainty and temporal distance to guide the learning process. The au... | Rebuttal 1:
Rebuttal: Dear reviewer paJb
We sincerely appreciate your constructive comments. We found them extremely helpful. We prepared our response below:
---
**Q1. Lacks clarity on why using the representation from VQ-VAE is suitable for graph-building. VQ-VAE does not appear to take into account temporal distan... | Rebuttal 1:
Rebuttal:
Dear reviewers,
We sincerely appreciate your constructive and insightful comments.
We have prepared our responses at the bottom of each review you provided. This global response includes:
- [Additional Results] In this global response, **we attached a PDF containing the experimental results** ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed a new curriculum reinforcement learning method, CQM, that uses VQ-VAE to learn a quantized goal space, constructs a graph on the quantized goals to propose curriculum goals by distance, and learns a goal-conditional policy.
Strengths: - The proposed method pioneers in "auto" curriculum RL t... | Rebuttal 1:
Rebuttal: Dear reviewer G8ip
We sincerely appreciate your constructive and insightful comments. We found them extremely helpful. We prepared our response below:
---
**Q1. Differences between ‘Vector Quantized Models for Planning’ [1] and our research.**
**A1.** Thank you for your helpful suggestions on... | null | null | null | null | null | null |
Tracr: Compiled Transformers as a Laboratory for Interpretability | Accept (spotlight) | Summary: This submission builds on the RASP language proposed by Weiss et al. It proposes a way to compile RASP program into real Transformer weights. It also comes with a case study showing the use case of Tracr to study a phenomenon called superposition which is widely known in Mechanistic Interpretability field. It'... | Rebuttal 1:
Rebuttal: > On the second use case: It feels to me an overclaiming that Tracr could serve as a ground-truth for evaluating interpretability methods. This is an understandable imagination but such idealism isn't correct. An interpretability algorithm can discover very different underlining algorithms and if ... | Summary: The paper presents Tracr, which is a compiler from RASP programs (a language designed to showcase a possible computational model used by Transformers) to Transformer architectures and weights. The paper first details the operation of the compiler, then discusses examples of several simple RASP programs and the... | Rebuttal 1:
Rebuttal: > How precisely do the compiled Transformers implement the original algorithms?
Tracr can compile any algorithm to a finite model that implements it exactly, i.e., with zero approximation error. This is because we know the full (discrete) input vocabulary for the model at compile-time. So, while ... | Summary: This paper presents Tracr, a compiler that can take 'program' specifications and translate them into GPT (decoder only) style transformer models. Tracr is built on the RASP 'programming' language introduced by Weiss et. al., and translates a RASP program into model weights via an intermediate representation te... | Rebuttal 1:
Rebuttal:
> How different are tracr model weights from those that gradient descent learns?
Your description is accurate: Tracr models are significantly sparser than real trained transformers, and, in particular, small Tracr models tend to be easy to interpret. There are straightforward ways to "obfuscate"... | Summary: In this submission, the authors present Tracr, a compiler for RASP (a DSL for transformer computations) into transformer weights. The authors introduce their compilation approach, which includes an “assembly” language called Craft, which is used to represent the transformer weights agnostic to explicit impleme... | Rebuttal 1:
Rebuttal: > How do the authors try to approach the mentioned limitations (in their last section) in future work? What is the path from here?
There are technical limitations of Tracr that mostly come from design choices we made for simplicity (e.g., Tracr models embed different variables orthogonally, whic... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments. We are glad the reviewers found our paper clear and appreciated the contribution of Tracr to studying the computational models of transformers (Reviewers XH5L, ACvV), to advancing interpretability research (Reviewers 767m, PkEE), and as a didac... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators | Reject | Summary: This paper introduces a new integration method (mean inverse integrator) for learning dynamics from noisy data. Experiments on Hamiltonian systems show the effectiveness of the proposed method.
Strengths: - The problem of learning physical dynamics from noisy data is an interesting one.
- It combines techniqu... | Rebuttal 1:
Rebuttal: We are grateful for the helpful comments and suggestions provided by the reviewer. Below are our responses.
### Assumptions on noise (Q1 - Q2)
- The reviewer points to one of the advantages of MII, which we will make more clear in the revised version: that it puts no assumption on the noise / da... | Summary: The paper investigates mono-implicit Runge--Kutta (MIRK) methods for learning dynamical systems from data. In particular, MIRK methods can be made explicit by introducing the external data into the solver step itself, leading to a more efficient integrator while keeping favorable stability, symmetry, and sympl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments, which we will use to improve our paper in its revised version. Below are our responses.
### Our method and baselines, and relations to other methods (Q1 - Q3)
The RK$4$ baseline is defined as in Eq. 6, meaning that the vector field is trained ta... | Summary: The authors present a novel method, Mean Inverse Integrator (MII), used to aggregate data generated through numerical integration of the vector field characterizing Hamiltonian systems. In particular, the objective is to improve the training of Hamiltonian Neural Networks (HNNs) when the data used is noisy.
Th... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the encouraging feedback, and are happy to argue for the relevance of our paper to NeurIPS. The paper builds largely on the ideas of Hamiltonian neural networks, first presented in [1], and is related to several NeurIPS papers where numerical analysis and deep learning i... | Summary: The presented work considers a novel class of integrators that are used to train Hamiltonian Neural Networks (HNNs). This class is called mean inverse integrator and it averages the trajectories from mono implicit RK methods (MIRK) to obtain higher accuracy. The authors provide theoretical results on how MIRK ... | Rebuttal 1:
Rebuttal:
We are grateful for the detailed review and suggestions for how to improve this work.
### Backward passes (Q1)
Since forward-mode automatic differentiation outperforms the adjoint sensitivity method for computing derivatives of ODE solutions for smaller-sized systems ($n<100$, and we have $n=4$... | Rebuttal 1:
Rebuttal: Here we attach a PDF with four additional numerical experiments, responding to specific questions posed by the reviewers. The figures are referenced in the rebuttals below.
Pdf: /pdf/efc20a80f16854ee3897ca30271c05562a475ba4.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel method aimed at learning the vector field of a dynamical system. The proposed approach is called the mean inverse integrator, which utilizes a neural network (e.g., SRNN) to accurately estimate the integrator in the presence of noisy data. The authors provide theoretical insights ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and would like to provide the following response.
- The idea of the theoretical analysis behind Theorem 5.2 is precisely to provide an understanding of why the mean inverse integrator provides more accurate estimates, namely because the averaging over multi... | null | null | null | null | null | null |
Ignorance is Bliss: Robust Control via Information Gating | Accept (poster) | Summary: This paper empirically investigates a few approaches to modulating the amount of information used by a neural network learner in a variety of control-adjacent learning problems. The proposed approach, InfoGating, learns an input-conditioned continuous-valued mask that is applied to the same input or features t... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive feedback on our paper. We appreciate the time and effort you have put into evaluating our work.
**W1: Evaluations on noiseless environments and W2: Generalizations of masking network**
> Yes, “noise-free observation space” indicates the default DM Contr... | Summary: The authors hypothesise that gating information propagation in neural networks will lead to better generalisation. To achieve this they propose a system that performs gates information using a multiplicative differentiable operation, which they call InfoGating. To show the validity of their approach they compa... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive feedback on our paper. We appreciate the time and effort you have put into evaluating our work.
**Question 1: relation between lambda and the overall performance / reliance on the lambda parameter**
> We illustrate the effect of lambda on performance in... | Summary: The authors proposed a mutual information-based encoder that generates masks to gate inputs in order to either pass on minimal information for downstream tasks, or to remove any useful information that could be used to optimize the downstream loss in an adversarial setting. This is a general model that can be ... | Rebuttal 1:
Rebuttal: Thank you for your review and positive feedback on our paper. We appreciate the time and effort you have put into evaluating our work.
**Minimax objective details**
> We did not use nested loops or different numbers of gradient updates for optimizing the image and mask encoders in the (minimax) ... | Summary: This paper introduces a novel masking technique called InfoGating for learning masks in contrastive loss settings with InfoNCE. The proposed approach is simple, well-motivated, and is evaluated in several RL setting including inverse dynamics models, Q-learning, and behavior cloning. Some ablation studies on... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive feedback on our paper. We appreciate the time and effort you have put into evaluating our work.
**Comparison with Masking-based methods**
> We ran new tests using mask-based latent reconstruction (MLR). Even after extensive hyper-parameter tuning, we we... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback. We have individually responded to points made by each reviewer and provided further experiments in support of our response (please refer to the attached rebuttal PDF). Below is a list of additional experiments we have included:
1. **Evaluations of all ba... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose InfoGating as a way to learn parsimonious representation that could achieve better generalization by being robust to noise and spurious correlations. Representations that identify the minimal information required for certain task. Those representations attempt to be robust to... | Rebuttal 1:
Rebuttal: Thank you for your review and positive feedback on our paper. We appreciate the time and effort you have put into evaluating our work.
**Benchmarking on vision-based datasets**
> Since we focus on RL settings, we did not test on vision-based domain generalization, but this could be interesting i... | null | null | null | null | null | null |
Language Semantic Graph Guided Data-Efficient Learning | Accept (poster) | Summary: This paper proposed a general framework for exploiting semantic information within labels in classification tasks to improve the performance of deep nueral networks. The framework is both task-agnostic and model-agnostic, making it applicable to a wide range of classification tasks and modalities. The framewor... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and comments on our submission. Please find our responses your questions below.
**Q1. Scenarios where label semantics are weak.**
A1. Thanks for your insightful comment. It is a solid concern, as there exists certain scenarios like defect detection where ... | Summary: This paper employ a language semantic graph to cature the relationship among different class, with the hope to alleviate the requirement of extensive training data and, in particular, human supervision. Generally, this paper first build the language semantic graph with the pre-trained language models. After t... | Rebuttal 1:
Rebuttal: Thanks for your questions. We'll do our best to explain our method here and will thoroughly revise the paper to improve its clearity.
**Q1. Code for better understanding.**
A1. Our code is now provided to AC, please refer to it for better understanding.
**Q2. About the effect of loss $\mathca... | Summary: The paper addresses the importance of labels’ semantic meanings when training models. First, the framework constructs an LSG graph. Node features are text embeddings generated by language models, and the similarity matrix constructs edges. After that, a GCN is trained to aggregate node features of the LSG with... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and comments on our submission. Please find our responses your questions below.
**Q1. Quality analysis of LSG.**
A1. We provide two analysis to thoroughly evaluate LSG.
- We show the T-SNE visualization of the initial node embeddings and the GCN refined... | Summary: The paper introduces the Language Semantic Graph (LSG), a novel approach to data-efficient learning that leverages semantic information from labels. The LSG is used to train an auxiliary graph neural network, which then guides the primary model's training, enhancing the utilization of label knowledge. This met... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and comments on our submission. Please find our responses for your questions below.
**Q1. Performance evaluation on low quality labels.**
A1. Thanks for the good comment. To investigate the effectiveness of LSG on low quality label scenarios, we stimulate... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
We extend our utmost gratitude for your dedicated commitment in meticulously assessing our manuscript and for providing us with your profound insights. Your considered evaluations serve as a testament to the rigor and importance of our research.
We are heartened by... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The typical supervised training ignores the semantic information in the labels. This paper proposes to use the label semantic information during fine-tuning. 1) A label semantic graph is constructed by calculating the sentence embedding similarity of the label descriptions; 2) a GNN is trained on the semantic ... | Rebuttal 1:
Rebuttal: We thank you for your endorsement of our idea and the insightful feedback. Please find our responses for your questions below.
**Q1. Motivation of the two-step approach with GNN.**
A1. We take vision model as an exemplar of the primary encoder $F$ to explain the idea. Our main goal is to ali... | null | null | null | null | null | null |
ForecastPFN: Synthetically-Trained Zero-Shot Forecasting | Accept (poster) | Summary: The work present the ForecastPFG a zero-shot forecasting method trained using only synthetic dataset and it is evaluated on several real world dataset.
Strengths: The paper is well written and the steps well described.
Moreover, I think that it could be an interesting approach when you have very few data.
We... | Rebuttal 1:
Rebuttal: Thank you for the excellent and detailed feedback. We are glad to see your positive view of our work. Your comments and suggestions will greatly improve the final version of our paper. We reply to each question below.
**Q1 Repetition of text.**
Thank you for catching this. We have now added more ... | Summary: The paper proposes to pre-train a deep learning model on synthetic data which follows characteristics of real world time series data. This model can then be used for any downstream forecasting dataset. The authors performed experiments to show that the proposed method performs well compared to existing classic... | Rebuttal 1:
Rebuttal: Thank you for the excellent feedback. We are very glad to see that you view this direction as exciting and of huge significance, and that you found the paper well-written, neatly organized, and with comprehensive experiments. We very much appreciate your comments, which we believe will substantial... | Summary: This paper proposes a zero-shot prior-data fitted network (PFN) for time-series forecasting. Existing works have challenges in designing a general and flexible PFN for general time-series distributions, and tuning an architecture and training scheme. This work overcomes these by designing a novel synthetic tim... | Rebuttal 1:
Rebuttal: Thank you for the excellent feedback. We are glad to see that you are positive towards our work, including our novel methodology and our results. We also find that your comments will substantially improve the final version of our paper. We reply to each question below.
**W1.1. Multiplicative data... | Summary: ForecastPFN is a zero-shot forecasting model designed to overcome the limitations of traditional forecasting methods when dealing with data-sparse applications. Unlike most approaches, ForecastPFN is trained solely on synthetic data, which captures diverse time series patterns and incorporates multi-scale seas... | Rebuttal 1:
Rebuttal: Thank you for the excellent feedback. We are glad to see that you find our idea innovative and exciting, and that you find the execution commendable. We also find that your comments will substantially improve the final version of our paper. We reply to each question below.
**W1: No tables.** Than... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback and suggestions. Our work introduces the first synthetically-trained, zero-shot, universal forecasting model, which performs particularly well in low-resource settings (small amount of in-distribution data, and/or low inference time budget), which... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Human-Guided Complexity-Controlled Abstractions | Accept (poster) | Summary: This paper proposes a method for learning discrete representations whose complexity can be smoothly annealed. Experiments demonstrate that, at the appropriate level of complexity, these representations can be useful for downstream classification tasks involving abstract categories.
Strengths: - The work propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their continued engagement with our work!
>This is a compelling motivation, but unlike humans, the proposed approach does not involve any method for autonomously selecting the appropriate level of abstraction for a given task.
The reviewer is correct that we do not prov... | Summary: This paper proposes a framework for human-in-the-loop training of machine learning models where humans select among pretrained models with different complexity levels based on prototypes. The authors demonstrate that finetuning performance is significantly impacted by representation complexity in the experimen... | Rebuttal 1:
Rebuttal: >The authors only show transfer performance within single benchmarks rather than across tasks or in realistic pretraining and finetuning settings such as ImageNet or LLMs.
While we do not consider LLMs (we did not wish to expand to language domains), we do use feature extractors pre-trained on Im... | Summary: The authors observe that the downstream tasks for pretrained models can rely on representations of varying level of complexity: as a running example, a birdwatcher relies on significantly more complex representations of images to classify bird species, relative to a child who may want to identify the color of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful review. They understood the paper quite well and provided useful suggestions for strengthening baselines (which we implemented).
# Significance
>[T]he benefits of the idea only occur when n <= 3 and k <= 5… classifiers do not perform particularly well, get... | Summary: This paper explores an interesting premise -- how does the level of abstraction captured by a discrete (visual) representation dictate downstream task performance, where downstream tasks can be at arbitrary levels of abstraction. Specifically, the running example from the work that I really like is that of bir... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review!
# Clarifications
The reviewer raises several questions about how we generate “degraded representations.” Here, we seek to clarify our approach with a brief summary and address specific questions later.
First, we train a VQ-VIB$_\mathcal{C}$ model to high ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments. We have replied to specific questions in individual responses. Here, we briefly highlight results that address some common themes:
# Regularization baselines:
Reviewers 99tN and TMXo asked for stronger finetuning baselines, with regularized models trai... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.