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 |
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BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing | Accept (poster) | Summary: This paper proposes BLIP-Diffusion, a new subject-driven image generation model with multimodal encoder that supports multimodal control which consumes inputs of subject images and text prompts.
Strengths: The proposed method is novel and enables subject-driven generation under efficient fine-tuning and zero... | Rebuttal 1:
Rebuttal: We thank the reviewer for confirming the novelty of our approach. We address questions below.
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**Q1**: contribution of the work?
**A1**: We summarize the scientific significance of BLIP-Diffusion as below:
- **BLIP-Diffusion represents a novel approach to subject-driven generation using ... | Summary: This paper aims to solve the subject-driven text-to-image generation with a pre-trained subject representation that is derived from a vision-language encoder, the BLIP2 model. The obtained subject representation captures rich information of the visual input while being aligned with the textual space. The text-... | Rebuttal 1:
Rebuttal: We thank the reviewer for confirming the technical depth and empirical values of our work. In the following, we provide response to answer reviewer's questions.
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**Q1**: paper uses a detailed text prompt instead of a rough description like "an image of [V]" as used in Dreambooth. the featu... | Summary: The paper introduces "BLIP-Diffusion", a new subject-driven image generation model that supports multimodal control using subject images and text prompts. The model introduces a pre-trained multimodal encoder to provide subject representation and enables zero-shot subject-driven generation and efficient fine-t... | Rebuttal 1:
Rebuttal: We thank the reviewer for confirming the originality, quality, clarity and significance of our work. We provide response to reviewer's question below.
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**Q1**: Zero-shot performance and its applicability.
**A1**: As described in Section 3, BLIP-Diffusion is the very first model to unlock th... | Summary: The paper addresses the issues of lengthy fine-tuning and preserving the subject fidelity in subject-driven text-to-image generation models. Different from existing models such as Textual Inversion and Dreambooth that invert subject visuals into text embedding space, the paper introduces a new multimodal encod... | Rebuttal 1:
Rebuttal: We thank the reviewer for confirming the novelty of our approach and the comprehensive results. We address reviewer's question as below.
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**Q1**: “We get embeddings as output from CLIP Text Encoder right? How is it possible to combine them before passing as input to CLIP Text Encoder ”
**A1... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transformers over Directed Acyclic Graphs | Accept (poster) | Summary: The paper proposes an attention mechanism for directed acyclic graphs. Specifically the reachability to/from nodes is considered for a given node and the unreachable nodes are masked out. The authors use this attention mechanism in existing graph transformers for undirected graphs. Further the paper also propo... | Rebuttal 1:
Rebuttal: **Thank you for the constructive comments!**
We indeed missed to discuss the rather important Q3 in the paper and detail the actual power of our DAG attention in the global reply. There we also add details about the comparison to the reference mentioned. We are sorry for the confusion caused by t... | Summary: This paper adapts transformers to directed acyclic graphs. It restricts the receptive field of each node to its predecessor and successor nodes so that it faithfully captures the DAG structure. It also incorporates positional encodings based on the node depth. Extensive experiments show that it can improve per... | Rebuttal 1:
Rebuttal: **Thank you for the thoughtful comments!**
These are interesting points that highlight our contibution, and we included them into the paper.
**Q1 No Hyperparameter Tuning**
We indeed used the same hyperparameters as the baseline transformers, which include learning rate, weight decay, and dropo... | Summary: The paper proposed a new approach for DAG representation learning using transformer. The representation learning on DGA is significant as DAG can be adapted into many real-world problems, which is also explained in the paper. In addition, the DAG can be formed into a sequence of nodes so that it is naturally t... | Rebuttal 1:
Rebuttal: **Thank you for the constructive comments!**
We indeed missed the PACE paper, please see the global reply for a detailed discussion and experimental results. Overall, the comparison to this transformer tailored to DAGs highlights the effectiveness of our simpler and more general proposal: PACE ... | Summary: The paper proposing a new Transformer-based graph neural network for directed graphs (DAGs) that restricts the receptive field size of self-attention and adds depth-based node embeddings to improve learning from DAGs. The resulted model is more efficient than previous Graph Transformer models and at the same t... | Rebuttal 1:
Rebuttal: **Thank you for the very detailed feedback!**
Q4 raises an important point which we address in the global reply. We also added some of the discussion below to the paper since it covers interesting aspects. For results on additional datasets, please see the reply to WzDZ, Q2. We sincerely thank y... | Rebuttal 1:
Rebuttal: **We thank all reviewers for the very fair, detailed, and constructive feedback!**
We are sorry for the unnecessary confusion caused by missing **explanation about the power of our DAG attention** and hope that the below details clarify our contribution. Since the scores are borderline overall, w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | Accept (poster) | Summary: The paper presents an incentive mechanism to encourage honest data reporting in the presence of spiteful behavior aiming to harm other participants.
Strengths:
The paper considers an interesting an novel setting.
It shows that incentive schemes can in principle induce cooperative behavior.
The incentive s... | Rebuttal 1:
Rebuttal: Thank you for the review. We are glad you find our setting novel and interesting. We aim to address your concerns in the following:
**While the reward scheme in the paper has truthful reporting as an equilibrium, it is well-known that peer-prediction schemes also admit uninformative equilibria; ... | Summary: The paper considers a federated learning setting with strategic data resources. The authors assume that the entities taking part in the learning process are selfish players incentivized to get the best model but benefit if their competitors receive inaccurate models. This selfish behavior pushes players to lie... | Rebuttal 1:
Rebuttal: Thank you for the constructive and thoughtful review. We are glad you found our paper smooth to read. We aim to answer your questions in the following:
**I suspect that a mixed Nash equilibrium does exist. Also, what does the existence Nash equilibria have to do with benefits from collaboration?*... | Summary: The paper studies a centralized collaborative learning problem. Authors provide theoretical guarantees for an attack method and a defense method. Further, the paper proposes two mechanisms to incentivize honesty: a method that uses an explicit side payment method and requires transferable utility, a centralize... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful review. We are glad you found our paper easy to follow and our contributions novel. We aim to answer your questions and address your concerns in the following:
**Can these results be extended to the decentralized setting?**
In a decentralized setting w... | Summary: The authors investigate the issue of manipulation (in the form of falsifying data or model updates) among agents who mutually contribute to a shared model. Incentives for such behaviors arise when agents possess differing objectives with respect to the shared model. The authors first demonstrate that without ... | Rebuttal 1:
Rebuttal: We thank you for your positive and thoughtful review. We are glad you liked our paper and plan to incorporate your feedback in the next revision. In the following, we aim to address your concerns:
**The mechanisms proposed only induce truthfulness as a Nash Eq, implying that other non-truthful e... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and constructive feedback. We are glad that the reviewers find our setting novel and interesting ($\color{blue}hRji$), well motivated ($\color{lime}seCF$), our technical contributions novel ($\color{red}Mvyk$) and our text smooth to read ($\color{cyan}hQqh... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer | Accept (poster) | Summary: 1.The paper presents LART, a 3D Transformer framework for 3D motion transfer. One of the distinctions from previous methods is that LART does not require joint annotation or pre-defined correspondence between the source and target mesh. By preserving motion metrics and effectively controlling synthetic motions... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of the novelty of our work and the constructive feedback! We will address your questions and concerns in the following:
**Q1: The author should provide more explanation and necessary information about specific terms and blocks used in the paper. For instance, a d... | Summary: The paper presents a method to transfer motion from a dynamic input sequence to a static 3D object. There are several novel components presented in the method: a novel feature encoder with an adaptive positional encoding scheme and a novel latent geometric regularization on the transformer. The paper is evalua... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of the novelty of our work and the constructive feedback!
**Q1: The memory requirements of the method are a weakness; this is listed in the main paper itself.**
A1: Regarding the concern on the memory requirements, this memory allocation is predominantly attrib... | Summary: This paper describes a method to transfer the dynamic mesh sequences to the unseen 3D mesh target. A transformer-based model is developed to implicitly learn the correspondence. In this model, pose and identity embeddings are separately encoded from the meshes. A decoder is designed to generate mesh sequences ... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of the challenge of our work and the constructive feedback! We will address your questions and concerns in the following:
**Q: The main concern is about the experiments. I think the most attractive point of the proposed method is its generalization ability. Is th... | Summary: The paper proposes to improve the SOTA of the learned pose/motion transfer on unrigged 3D meshes. The architecture consists of a geometry adaptive feature encoder, a LART decoder, and a latent metric regularizer.
The geometry adaptive feature encoder first extracts features similar to NPT [33] by casting each... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of the novelty of our work and the constructive feedback! We address your questions and concerns in the following.
**Q1: Can authors provide videos of various results, as the paper's title is "motion transfer?" How well does the method handle temporal coherency? ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers and AC for dedicating your time and expertise to assess our manuscript thoroughly. We are glad by the positive remarks from the reviewers on various aspects of our work (the novelty of LART, the versatility of the proposed method, the robustness of handling... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MIMEx: Intrinsic Rewards from Masked Input Modeling | Accept (poster) | Summary: The paper introduces a novel method for Exploration in RL, called MIMEx (Masked Input Modeling for Exploration).
Previous approaches for exploration investigated intrinsic rewards, usually computed as a measure of a state or transition’s “novelty”, adding them to extrinsic rewards (the actual task’s rewards) ... | Rebuttal 1:
Rebuttal: Thank you for the very positive feedback. We appreciate your evaluation of our work and address the questions below.
**”What is the overall runtime / resource utilization of MIMEx compared to other baselines?”** (also **Questions [a]**)
Thank you for the suggestion. We have included tables for b... | Summary: This paper proposes to use masked autoencoding (similar to MAE) in RL and use the loss as an intrinsic reward for exploration in sparse reward domains. Their method, MIMEx, does masked reconstruction of latent observation o_t, based on the previous T observations, and assigns the loss as intrinsic reward r_t. ... | Rebuttal 1:
Rebuttal: Thank you for the feedback.
**Strengthening the masked autoencoding argument**
Thank you for the suggestion. We agree that our method only generalizes the conceptual formulation of RND or ICM as summarized in Table 1, and does not capture differences in representation learning. We will clarify t... | Summary: The paper introduces a novel approach to exploration in reinforcement learning (RL) called Masked Input Modeling for Exploration (MIMEx). MIMEx uses a masked autoencoding objective on variable-length input sequences to derive intrinsic rewards for exploration. The paper claims that MIMEx improves exploration e... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We addressed your main concern on the lack of diverse domains in our empirical study through experiments on two additional discrete-action environments. Our approach performs competitively against baselines, demonstrating its generalizability beyond continuous control t... | Summary: This work proposed a general framework for deriving intrinsic rewards called Masked Input Modeling for Exploration (MIMEx). This method starts from the observation that existing intrinsic reward approaches are special cases of conditional prediction, where the estimation of novelty can be seen as pseudo-likeli... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We addressed individual comments and questions below, in particular your main concern on the validity of our method due to the citation of a retracted paper. We clarified that the key principle our method depends on is independent of the error in the retracted paper. Pl... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback. Below, we respond to each reviewer individually.
In the PDF attached below, we include additional results on wall-clock time/GPU memory usage (reviewer mFvm, reviewer SVND) and mask distribution ablation studies (reviewer if59).
Pdf: /pdf/3... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Supported Value Regularization for Offline Reinforcement Learning | Accept (poster) | Summary: This paper studies the offline RL problem, and the authors proposes adding the Support Value Regularization (SVR) in learning Q functions, motivated from the way how CQL add value regularizations. The authors add SVR for all OOD while maintain Bellman update for ID samples. Experiments shows that the SVR-regu... | Rebuttal 1:
Rebuttal: We appreciate the time and effort that you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**[W] It is suggested to compare with other less/mild conservative method. In addition, it is suggested to compare with other density-based offline RL methods.... | Summary: This paper proposes the use of Importance sampling to distinguish between ID and OOD actions, and to operate the corresponding actions accordingly. In addition, as most of the current work is similar, it also uses model to fit behavior policy, but does not need to overly consider the accuracy of the model. It ... | Rebuttal 1:
Rebuttal: We appreciate the time and effort that you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**[W1, Q1] Importance sampling is widely used in reinforcement learning because of its unbiased nature. The differences or advantages between the proposed SVR ... | Summary: This paper proposes a new offline RL method in which the popular assumption that the new policy should be close to the behavior poplicy is abandoned and just penalizing the OOD action would be OK. Analysis show that the method has the policy improvement property. Experimental results partially verify the effe... | Rebuttal 1:
Rebuttal: We appreciate the time and effort that you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**[W1] The experimental results (Table 1) are eager to show this method is good, without illustrating how the reward is accumulated as usually done.**
We incl... | Summary: The authors propose to enforce a new squared penalization term for computing the target Q-values in offline RL. In particular, their penalty tries to only apply to target out-of-distribution (OOD) actions by taking the difference between the importance-sampled and the true estimates of a uniform distribution ... | Rebuttal 1:
Rebuttal: We appreciate the time and effort that you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**[W1] I would tone down some of the statements, e.g. lines 26-28"existing value regularization methods" -> " some of the most popular existing value...".**
T... | Rebuttal 1:
Rebuttal: ### **Global Response**
We thank all the reviewers for the insightful comments and suggestions. We are greatly encouraged by the positive comments of reviewers, e.g.,
* The paper correctly recognizes an important issue of existing popular algorithms in the offline literature. (CX3L)
* The prese... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Template-free Articulated Neural Point Clouds for Reposable View Synthesis | Accept (poster) | Summary: The authors present a method for dynamic radiance fields of subjects whose motion can be described by skeletal animation. The method automatically extracts a skeleton using medial axis transform. Further it obtains a object feature point cloud from a pre-trained NeRF and expresses their position as a function ... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! We answer your specific questions in this document. Please refer to the shared response for the experiment results and for answers to common questions.
**Human Subject Comparison**
We added additional results for our method applied to camera captured human ... | Summary: Authors presents a method to learn articulated model from multi-view video. And demonstrate an ability for efficient learning skeleton pose along as view synthesis model for dynamical structures. Moreover the suggested method drastically improve convergence compared to naive approaches. To extract the skeleto... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! We answer your specific questions in this document. Please refer to the shared response for the experiment results and for answers to common questions.
**Pre-trained Dynamic NeRF and Single Backbone**
We have added additional results showing that our method... | Summary: The method reconstructs a reposable Dynamic NeRF of an articulated object from multiview videos. This is achieved by using linear blend skinning (LBS) of an automatically extracted skeleton to represent the deformation from canonical to observation space.
Strengths: Combining LBS kinematics with NeRF appeara... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! We answer your specific questions in this document. Please refer to the shared response for the experiment results and for answers to common questions.
**Relation to Dual-Space NeRF**
Thank you for the suggestion, we will discuss Dual-Space NeRF [0] as an a... | Summary: The paper presents an approach to articulated view synthesis, introducing the concept of Template-free Articulated Neural Point Clouds. The authors utilize a structure-free point-based NeRF representation which supports forward-warping of canonical objects to any poses through Linear Blend Skinning (LBS). Such... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! We answer your specific questions in this document. Please refer to the shared response for the experiment results and for answers to common questions.
**Inconsistent Motivation & Comparison to WIM with its Original Training Iterations**
We added additional... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback, and we will use it to further improve our manuscript. We are glad that all reviewers found the description of our method clear, and that they appreciate that our method does not rely on any pre-defined skeleton (RMHs), offers better novel view sy... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper tackles the task of dynamic novel view synthesis from multiview videos and aims for the ability of reposing. It tackles the problem with a point-based rendering approach. More importantly, it does not need any pre-defined or class-specific template/skeletons and learns a per-video data-driven skelet... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! We answer your specific questions in this document. Please refer to the shared response for the experiment results and answers to common questions.
**Robustness to Initialization**
We conducted the proposed experiment, and we report the results in the share... | null | null | null | null | null | null |
Neural (Tangent Kernel) Collapse | Accept (poster) | Summary: Previous work has observed an increased alignment and emergence of an approximate block structure in a trained network's Neural Tangent Kernel (NTK) as well as the Neural Collapse (NC) phenomenon in the last hidden layer. The paper attempts to connect the two by showing that in an extreme case of perfect block... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful evaluation of our work and for raising their concerns! Below are our responses.
**W1 orthogonality:** We believe that the reviewer may have misunderstood our main assumption on the NTK structure, since we do not assume that "the gradients (of an output) are p... | Summary: The paper proposes a mechanism behind the empirical phenomenon of Neural Collapse in deep neural networks. The paper derives and analyzes the training dynamics of DNNs with MSE loss and block-structured NTK, identifying three distinct convergence rates in the dynamics.
Strengths: The strengths of the paper in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work! Below are our responses to the reviewer's questions and concerns.
**Unbalanced datasets and stochasticity:** Although we do consider only balanced datasets, we believe that our analysis could in principle be generalized for unbalanced... | Summary: The main contribution of the paper "Neural (Tangent Kernel) Collapse" is the connection of the Neural Tangent Kernel (NTK) alignment and Neural Collapse (NC) phenomenon in deep neural networks (DNNs). The authors assume that the empirical NTK develops a block structure aligned with the class labels. They deriv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work! Below are our responses to the questions.
**Cross Entropy loss:** Generalizing our theoretical results to CE loss is challenging, since the dynamics equations with CE loss are more complex than in case of MSE even with block-structure... | Summary: This work provides a theoretical connection between two related phenomena in deep learning dynamics: the structural change to the empirical NTK during training, specifically its alignment with class labels; and the neural collapse, which refers to a set of behaviors exhibited by NNs trained on multiway classif... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful evaluation of our work! Below are our responses to the reviewer's questions and concerns.
**Does NTK alignment cause NC?** We fully agree with the reviewer that we do not show a causal relationship between NTK alignment and NC but rather explore the connectio... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback! Based on the reviews, we were able to identify several improvements for the paper, which we will incorporate in the revision. Below we summarize the reviewers' concerns, our responses, and the proposed changes to our paper.
## Concerns abou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper connects NTK (neural tangent kernel) and NC (neural collapse) by assuming NTK has a block structure in the late training stage, meaning the kernel for samples within the same class is much larger than samples from different classes. The technical assumption additionally assumes that the gradients of... | Rebuttal 1:
Rebuttal: We thank the reviewer for rising the concern about the assumption of the independence between output/feature neurons, i.e., the part of Assumption 3.2 stating that $\Theta_{k,k'}(x,x')=\Theta^h_{k,k'}(x,x')=0$ for any $k\neq k'$ and any $x,x'$. While the calculations in the review do not appear fu... | null | null | null | null | null | null |
Text-to-Image Diffusion Models are Zero Shot Classifiers | Accept (spotlight) | Summary: This paper uses the text-to-image diffusion models as zero-shot classifiers. It proposes to compute a subset of the full scores matrix to be more efficient. It proves Imagen and Stable Diffusion have good zero-shot performance and are robust to misleading textural cues.
Strengths: 1. It is novel to use pre-tr... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions. We address your questions and concerns below:
- **Theoretical discussion**: Generally, the idea of using a generative model as a classifier is a fairly old and well-studied idea (e.g. “On Discriminative vs. Generative Classifiers: A comparison o... | Summary: The paper presents a new method that utilizes generative models as image classifiers and initiates explorations of notable, open-source models using this approach. Beginning with recognized image datasets, the researchers examine the models and evaluate the scores they have achieved. Several experiments are co... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions. We address your questions and concerns below:
- **Overlap with another paper**: As we state in the conclusion, “Your Diffusion Model is Secretly a Zero-Shot Classifier” is concurrent work. It was released on arxiv (but not in a peer-reviewed ven... | Summary: This study explores the potential of text-to-image diffusion models as zero-shot classifiers. The models show competitive performance with CLIP on zero-shot image classification datasets and excel in shape/texture bias tests and attribute binding. The findings suggest that generative pre-training should be con... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions. We address your questions and concerns below:
- **Runtime**: As we say in the paper, the method does not produce a very practical classifier. However, we believe it still has a lot of value for illuminating what kinds of visual knowledge diffusi... | Summary: The paper inverts pre-trained text-to-image diffusion models by using bayes rule, and evaluates them over a variety of benchmarks. For the evaluation they use two diffusion models: Stable Diffusion and Imagen. They compare these models against CLIP-L/14. They show a variety of benchmarks where the diffusion mo... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions. We address your questions and concerns below:
- **Different training datasets**: As we mentioned in the general response above, in this work, we focused on studying the capabilities of existing powerful models because training such models from ... | Rebuttal 1:
Rebuttal: ### **General response to all reviewers**
We thank all the reviewers for their helpful comments and suggestions.
Generally, reviewers (*LkNq* and *yeML*) had questions around the fairness of comparison between models trained on different datasets (such as CLIP and Stable Diffusion). In this wor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping | Accept (poster) | Summary: This paper introduced a novel and challenging task that performs dexterous grasping according to human wrist movements. This task is potentially useful for applications with prosthetic hands.
The paper further proposed a novel two-stage framework that solves the two challenging aspects of the proposed task a... | Rebuttal 1:
Rebuttal: > **Q1: From the qualitative results in the supplementary video, I noticed that for most objects, the graspings are from the same angle relative to the object. For example, with the chips can, all demonstrated graspings are from the side of the cylinder regardless of how the can is placed. This ma... | Summary: This paper introduces a novel task called human-assisting dexterous grasping, which aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task is more complex as the policy must adapt to diverse user intentions and the ... | Rebuttal 1:
Rebuttal: > **Q1: The proposed method is better suited for teleoperation settings compared to the reinforcement learning (RL) baselines used in the experiments. It is essential to include comparisons to teleoperation methods without assisted grasping, both qualitatively and quantitatively.**:
Apologies for... | Summary: This paper focuses on addressing a task called human-assisting dexterous grasping. The aim is to create a finger controller to grasp objects with the robot's wrist conditioned on a human user's wrist. The authors propose 1) a Grasping Gradient Field (GraspGF) which estimates the gradient of a synthetic graspin... | Rebuttal 1:
Rebuttal: > **Q1: This bears resemblance to teleoperation ...? ..differences and motivations between an automatic dexterous grasping method...? What is the practical application?**:
Thank you for bring this up. Due to the page limit, please refer to [Q1 of Common Response](https://openreview.net/forum?id=... | Summary: This paper proposes a new task called assisting grasping. The main difference between this task and classical dexterous grasping is the wrist movement is controlled by a human instead of by the grasping algorithm. The authors propose a two stage method to solve this problem. First, they learn the grasping skil... | Rebuttal 1:
Rebuttal: > **Q1: My major concern of this paper is whether the proposed task is more challenging than classical grasping...; As motivated by my previous argument, there should be more logical arguments on the task difficulty...; Is there a more elaborated arguments or evidences that why the proposed task i... | Rebuttal 1:
Rebuttal: ## **Common Response**:
We thank all reviewers for appreciating our ideas and experiments. “A unique dexterous grasp task **(PG5Z)**". "The proposed framework is intuitive and is properly designed for the challenges of its task **(GtH3)**". "Formulate the learning from a set of successful grasps a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sensitivity in Translation Averaging | Accept (poster) | Summary: The paper proposed a method to efficiently remove view triplets from a pose graph where the minimum angle falls below a threshold. This is relevant to global SfM algorithms, where such triplets (i.e., triangles) lead to highly uncertain translation scale estimation. The method's performance has been demonstrat... | Rebuttal 1:
Rebuttal: 1. Results with 1DSfM filter: 1DSfM filter is designed to remove outlier edges while our method removes skewed triangles, which are two distinct aspects of the problem. We apply 1DSfM and then compare the solutions without and with applying our filter on real data. In Table R1 of the rebuttal pdf,... | Summary: This paper analyzed the sensitivity problem in translation averaging. Built upon the theoretical analysis of the skew triangles in a bearing network, this paper also proposes an efficient algorithm to identify and remove edges that can make the translation averaging problem ill-conditioned. The proposed algori... | Rebuttal 1:
Rebuttal: 1. Dataset for evaluation: We use 1DSfM dataset for evaluation. We recompile the input and the ground truth using Colmap (lines 251-254 of the main paper) to get a more reliable reconstruction than the one provided using Bundler. For any dataset which is sequential in nature, like SLAM or aerial d... | Summary: The translation averaging problem is considered, i.e recover absolute translations from pairwise relative translation directions. The paper focuses on analyzing the change in solution with small changes in the input relative directions. The smallest problem (3 nodes) is initially considered which allows to und... | Rebuttal 1:
Rebuttal: 1. Important information in removed triangles: The triangles which are filtered using our method are not necessarily outliers. From Tables 1 and 3 of the main paper, it can be seen that the error of the removed nodes are high (Removed Node Errors column) compared to the errors of all the cameras, ... | Summary: The authors propose the sensitivity theory for the Translation Average problem (i.e., input is a large number of coordinate vertex point pairs in relative directions observations and the output is the absolute vertex coordinates with consistent scales), which can be used to efficiently identify the inputs that... | Rebuttal 1:
Rebuttal: 1. Fig. 1 of the main paper: We analyze the real data to understand how frequently skewed triangles occur in real data and whether skewed triangles have any relation to the presence of outliers. For this, we provide scatter plots between the minimum angle in a triangle on the x-axis (which reveals... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. In this section, we provide descriptions for the table and figures presented in the rebuttal pdf (which have the prefix ``R" in their enumeration) and address individual concerns in the individual rebuttal sections.
1. Results with 1DSfM filter (Table R1... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought | Accept (poster) | Summary: This work presents a recursive method to summarize demonstrations into programs through LLM. The idea is interesting in that it uses spec as the bottleneck to connect complex demonstrations and complex robot task code, encoded and decoded through chain of thoughts. The method is evaluated on three different b... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the questions which help us improve our paper's clarity. We are excited that the reviewer acknowledges how our approach solves a challenging problem and Robotouille as a promising benchmark. Please see below for our answers to the questions:
### Questions
#### **Q2... | Summary: This paper presents demo2code, a framework that takes as input user's language instructions as well as demonstrations, and outputs synthesized code for completing the tasks. It first iteratively summarizes given demonstrations to a compact task specification, then reasons by incorporating user preferences etc,... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer acknowledges our novelty in recursively summarizing demonstrations and hierarchically generating code. We would like to respond to the reviewer's helpful feedback and questions.
### Questions
#### **Q1: Do you have any detail on the latent task specification?**
In ... | Summary: This paper proposes Demo2Code, a new method for generating code given a natural language description and demonstrations of the task. Demo2Code recursively summarizes demonstrations using a language model (LM) to create a task specification. The task specification is concatenated to the description and then rec... | Rebuttal 1:
Rebuttal: We thank the reviewer's enthusiasm for our cooking game Robotouille and how our approach shows improvement over a wide range of tasks and domains. We also appreciate the reviewer's feedback on our assumptions. We would like to address the questions and concerns raised:
### Questions
#### **Q1: Wh... | Summary: The authors propose an LLM-based completion framework to translate natural language instructions, in addition to transcribed state sequences of demonstrations (as PDDL (or other strips-like) predicates), into code for executing the task with a robot.
The method is based on recursively summarizing the demonstr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback on how we can strengthen our work. We also thank the reviewer for seeing our approach's capability to summarize and handle learning from demonstrations!
Please find below our responses to the questions and concerns:
## Questions
#### **Q1:**
We assu... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time, energy, and helpful feedback! We are excited to see that reviewers view the problem as important and challenging *(Reviewer sxrs, ujZb, 325h, yYo2)*, find our LLM summarization framework to be novel *(325h)* and important *(sxrs, yYo2)*, and view Robotouille ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a method that can take both demo and language in and teach LLM to perform new tasks. The idea makes sense and the algorithm is easy to understand and works very well. Evaluation results and ablations show improvement over existing works.
Strengths: The paper is well written and the idea is... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer is excited about the capability of our approach and finds our paper to be clear and easy to understand. We also thank the reviewer for suggestions to make our paper clearer. We would like to answer the questions and then address the concerns.
### Questions
#### **Q... | null | null | null | null | null | null |
UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene | Accept (poster) | Summary: The paper proposed a method for large-scale scene reconstruction and rendering.
It utilizes NeRF to learn a mesh representation for the scene by optimizing the
vertex positions and neural features/MLPs using standard volume rendering.
To handle large-scale scenes, the method divides the scene into multiple b... | Rebuttal 1:
Rebuttal: A1: Thanks for your comments. Actually, our proposed method is different work from Mobile-NeRF. **For a detailed comparison, please refer to Author Rebuttal.** In our test, using NGP in the same block requires 70,000 epochs to achieve results close to our method. Mobile-Nerf training is divided in... | Summary: The authors introduce a multi-scale surface based representation for radiance field reconstruction and real-time rendering of large-scale scenes. The method involves subdividing the scene into partitioned scenes initialized with a regular octahedron mesh. Through joint optimization of the vertices' positions a... | Rebuttal 1:
Rebuttal: A1. We can use masks to exclude dynamic objects (such as people or vehicles) and prevent them from affecting the rendering results.
A2. Sincerely thank you for your suggestion.
The focus of our work is on images captured by drones in large-scale scenes with GPS information. One of our goals is t... | Summary: This work presents a system to represent a large scene using NeRF given drone-captured photos. It partitions a large scene into overlapping smaller tiles, and represent each tile with a sub-NeRF. To enable real-time rendering, it represents each sub-NeRF using meshes and neural textures (represented as a encod... | Rebuttal 1:
Rebuttal: A1.1 In UE4, you can input the command "stat fps" to show frame rate.
A1.2 Training a NGP model on low-resolution images of the entire scene aids us in better segmenting the scene. This step takes only a matter of minutes.
A1.3 Opacity is solely dependent on position and not influenced by direct... | Summary: This paper presents a method that combines NeRF and the Unreal Engine for real-time rendering of large-scale scenes. The method first partitions large scenes into sub-blocks, and represent NeRF via polygonal meshes initialized from regular octahedron. The opacity and feature vector are represented via hash-enc... | Rebuttal 1:
Rebuttal: A1: Thank you for your encouragement and advice. The description of "therefore" in line 53 of the manuscript is not entirely accurate. The impact is not solely due to the implementation in CUDA; this is merely one contributing factor, albeit not the determining one. Our approach, even without util... | Rebuttal 1:
Rebuttal: ## Comparison with Mobile-NeRF.
**Dataset** We test the performance of Mobile-NeRF in a block. It contains 239 pictures, each with a resolution of 6000x4000.
In Table 1, we see that Mobile-NeRF takes 2 days to train just one block and requires 4x3090ti GPUs. If it trains the whole scene, it ta... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces UE4-NeRF, a system that combines Neural Radiance Field (NeRF) with the Unreal Engine 4 (UE4) for real-time rendering and editing of large-scale 3D scenes. To achieve this, the system partitions scenes into sub-NeRFs and represents them using optimized polygonal meshes based on regular oct... | Rebuttal 1:
Rebuttal: - **In the submitted PDF (Figure 4), we have provided additional qualitative comparisons with MVS.** Our experiments offer comparisons in transparent objects as well as subtle details. MVS utilizes sparse reconstruction to extract feature points, which are then expanded based on morphological and ... | null | null | null | null | null | null |
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task | Accept (poster) | Summary: The paper studies compositional generation abilities of conditional diffusion models. The main contributions of the paper are the concept graph framework, which is used to examine compositional abilities in a simplified setting, and insights on the learning dynamics of diffusion models.
Strengths: The paper i... | Rebuttal 1:
Rebuttal: Dear Reviewer bYvo,
Thank you so much for carefully recognizing the strengths of our work while providing us with concrete action items to make our submission more impactful and complete. In response, we have intensively experimented over the last week to generate three new plots for you (Figs. R... | Summary: This paper proposes to empirically studied how compositional structure emerges in diffusion model. The paper proposes the abstraction of concept graphs, and illustrates how diffusion models first learn to fit the training data before compositionally generalizing. The paper illustrates how diffusion models have... | Rebuttal 1:
Rebuttal: Dear Reviewer wac1,
We sincerely appreciate the time and effort you invested in your thorough and insightful review of our submission. Your recognition of the paper's well-written aspects, alongside your positive remarks on the analysis and our approach to understanding diffusion models, has grea... | Summary: The authors try to understand the compositionality aspects of generative models by training a conditional diffusion model in a toy setting on synthetic data. They show that the models indeed learn to be compositional if we train longer. They also hypothesize that the sudden emergence of compositionality in the... | Rebuttal 1:
Rebuttal: Dear Reviewer L63E,
Thank you for the insightful review of our paper. We are pleased that you found our research question crucial, our approach novel, and our writing easy to follow. Your specific and constructive recommendations to conduct additional experiments with more than three attributes a... | Summary: This work proposes a framework for studying the compositional generalisation abilities of diffusion models (or generative models more broadly). To that end it introduces the notion of a concept graph which the authors use to manipulate simple synthetic datasets. These concept graphs arrange different combinati... | Rebuttal 1:
Rebuttal: Dear Reviewer kruN:
Thank you for your positive response! We are delighted that you find our paper "well written and easy to follow", the introduced ideas of the concept graph "clearly explained and well motivated", and the use of a hypothesis-driven approach "a welcome change to AI research." Be... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all reviewers for their diligent efforts in evaluating our submission. We are pleased by the unanimous recognition and support for our scientific approach, aimed at enhancing the understanding of diffusion models using minimal synthetic data. We would also like to thank t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Meek Separators and Their Applications in Targeted Causal Discovery | Accept (poster) | Summary: The paper focuses on applications of causal discovery, in which it is not necessary to learn the full graph. Instead, the authors propose to recover what they call the Meek separator---which consists of a set of vertices that decomposes the unoriented edges into smaller connected components when intervened on.... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer found our theoretical results to be strong. We would like to address some of the reviewer’s comments below:
> **”motivating the main concepts---i.e., why do we want to learn the Meek separator, and why is the definition reasonable? Figure 1... guide the reader thro... | Summary: This paper studies the problem of learning causal structure by learning a minimal intervention set, which is formalized as the Meek separator. The authors show that the Meek separator can orient the maximum number of edges with the minimum intervention set while limiting the size of the remaining undirected co... | Rebuttal 1:
Rebuttal: Thank you for appreciating the problem we studied, and for your recognition of our result! We’ve added illustrative examples as per your suggestion and we’d like to address your concerns below:
> **”Section 2 is poor readability due to many notions and symbols. I suggest the definition part and r... | Summary: This work explores the problem of learning the local causal structure from intervention data. Specifically, the authors introduce a novel separator called the $\alpha$-Meek Separator. Unlike traditional $\alpha$-Separators, their separator imposes bounds on the sizes of connected components in a subgraph. The ... | Rebuttal 1:
Rebuttal:
Thank you for the encouraging comments! We appreciate that you think our proposal is novel and significant. We’ve added illustrative examples for better readability and we’d like to address your comments here:
> **”Regarding readability: The majority of the article consists of descriptive statem... | Summary: * The paper provides an algorithm for finding a subset of vertices in a causal graph that, when intervened, can turn undirected edge into smaller connected components for learning a part of the causal graph.
* The proposed algorithm comes with first known average-case provable guarantees for two applications:... | Rebuttal 1:
Rebuttal:
Thank you so much for your detailed review, and for acknowledging our randomized algorithm! We would like to address your points below:
> **”add examples to show how the algorithm finds a Meek separator.”**
We thank the reviewer for this suggestion. The added examples and detailed explanations... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions.
---
In this general response, we attached a pdf of the additional figures that we will add to the manuscript. To summarize, this includes:
- A modified *Figure 1,* which now includes detailed explanations of our defined M... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Agnostically Learning Single-Index Models using Omnipredictors | Accept (poster) | Summary: The paper studies the problem of learning SIMs agnostically. The authors proposed an algorithm achieving $B\sqrt{opt}$ $\ell_2$-error under mild distributional assumptions. Their main contributions are twofold: 1) they linked the $\ell_2$ loss of a bi-lipschitz activation with its matching loss. 2) they propos... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their comments.
Regarding the significance of our Theorem 4.1, we stress that our distributional assumption (subgaussian concentration) is significantly milder than assuming the marginal to be bounded (e.g., a bounded marginal is, in particular, doubl... | Summary: The paper gives an efficient algorithm for learning Single Index Models with arbitrary monotone and Lipschitz function under the condition the marginal distribution of x has bounded variance in all directions.
The error guarantee of the algorithm is $O(B \sqrt{\lambda} \sqrt{\mathrm{opt}})+\epsilon$ ($B=\|w\|... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their suggestions and for appreciating our results. The reviewer is right that it would be interesting to have tight results (at least in the statistical query framework). However, our work not only provides the first upper bound for learning SIMs in the... | Summary: This paper studies the learning of Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations. In SIM model, labeled examples $(x, y)$ are assumed to satisfy $E[y|x] = u^{-1}(w.x)$, where $w$ is an unknown vector, and $u$ is an unknown monotone function (a.k.a. link function). Given IID-draw... | Rebuttal 1:
Rebuttal: We thank the anonymous reviewer for their comments and for appreciating our work.
You make the point in your review that ‘agnostic learning’ should mean ‘distribution-free agnostic learning,’ as the term was defined in the 1994 Kearns Schapire paper that originally defined the model. We would ... | Summary: This work studies the problem of agnostically learning single index models with arbitrary Monotone and Lipschitz activations. Compared prior work, this work establishes the existence of an learning algorithm under more relaxed assumptions. This work is based on recent work by Gopalan et al. [2023] on Omnipredi... | Rebuttal 1:
Rebuttal: We thank the anonymous reviewer for expressing their concerns about the readability of our paper.
As we stated in the global response, we are planning to make certain modifications which we believe will significantly improve the readability of our paper by researchers of diverse backgrounds. The... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewers for their constructive feedback! In this global response, we provide general responses to concerns shared by more than one of the reviewers and we provide more specific answers in the personal responses.
It is true that our upper and lower bounds on the ap... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection | Accept (spotlight) | Summary: This paper focuses on the problem of efficient adversarial contrastive learning, the authors propose Robustness-Aware Coreset selection to speed up ACL, and according to the theoretical analysis and experimental results, the proposed framework is effective while does not hurt the performance.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: Many thanks for your supportive and constructive comments! Please find our replies below.
> 1. [Reply to W1] Thanks for pointing out this challenge!
We conjecture that applying better submodular function optimization methods to solve the objective function of our proposed RCS can further improve... | Summary: This paper introduces a robustness-aware coreset selection (RCS) method without requiring label information to speed up adversarial contrastive learning. RCS selects an informative training subset that minimizes the representational divergence (RD) between adversarial and natural data. Theoretically, the autho... | Rebuttal 1:
Rebuttal: Many thanks for your positive and constructive comments!
> [Reply to Weakness] We believe this is an interesting future direction!
We conjecture that applying a better submodular function optimization method to solve the objective function of our proposed RCS can further improve efficiency. For... | Summary: This paper proposes a robustness-aware coreset selection (RCS) method, which is applied to accelerate adversarial contrast learning (ACL) in the absence of labeling information. Especially, the coreset searched by RCS minimizes the representation difference between the natural data and their adversarial exampl... | Rebuttal 1:
Rebuttal: Many thanks for your positive and constructive comments! Please find our responses below.
> 1. [Reply to W1] A larger $\lambda$ leads to more pre-training time and higher robust and standard test accuracy in downstream tasks.
We pre-trained ResNet-18 on CIFAR-10 via ACL with RCS using $\lambda \... | Summary: This paper proposes a coreset selection for efficient adversarial self-supervised learning. By selecting a coreset every epoch that can minimize the representation divergence for training, it maintains similar robustness performance despite a learning speed that is more than three times faster.
Strengths: - I... | Rebuttal 1:
Rebuttal: Many thanks for your positive and thoughtful comments! Please find our responses below.
> 1. [Reply to W1] Our RCS obtains **substantial improvement** compared to random selection (Random).
According to Figure 2, we highlight the performance gain of RCS in terms of robustness transferability fr... | Rebuttal 1:
Rebuttal: [**Rebuttal Highlights**]
Many thanks for all reviewers' supportive and constructive comments!
Following reviewers' suggestions, we uploaded extensive ***Figure G1*** and ***Figure G2*** in the "**global**" file to provide a focused discussion of the coreset.
> 1. [For Reviewer **fJND**] In **... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds | Accept (poster) | Summary: main contributions include two-folds. For heavy-tailed payoffs, design heavy-tailed linear bandits, derive the variance-dependent T-round regret; In terms of Linear MDPs, instance-dependent K-episode regret is acquired. All paper results substantially depend on Huber loss regression techniques.
Strengths: For... | Rebuttal 1:
Rebuttal: Thanks for your review.
### Some corrections of the reviewer
> For heavy-tailed payoffs, design heavy-tailed linear bandits, derive the variance-dependent $T$-round regret;
In the settings where $\epsilon<1$, the variances of reward functions do not exist. And our regret bound actually relies o... | Summary: In this paper, reinforcement learning problem is considered in the episodic setting for linear bandits and linear MDPs under heavy-tailed rewards with potentially infinite variance. Based on adaptive Huber regression and optimism in the face of uncertainty principle, the authors propose algorithms that utilize... | Rebuttal 1:
Rebuttal: Thanks for your positive comments!
### Realizable central moments assumption
In Assumption 2.6, we assume the $(1+\epsilon)$-central moments of reward functions have linear structure. This assumption is standard in current linear MDP literature where instance-dependent (variance-aware) regrets a... | Summary: This paper first addresses Reinforcement learning (RL) with function
approximation in the presence of heavy-tailed noises whose central moment is known. In
general, these online learning problems rely on the self-normalized inequality to construct a
confidence set of optimal parameter . However, the existing s... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising the concerns.
### Computational complexity
We say an algorithm is computationally efficient if the computational complexity scales polynomially to the parameters of the problem, e.g., $d,H,K$ of the linear MDP. We provide the computational complexity of Heavy-LS... | Summary: The paper considers the problem of linear bandits and linear MDPs, when the noise may be heavy tailed. The main technical tool that they use is the Huber regressor, that enables them to detect extremal noise points that are less informative, and be more robust to these. They show how this regressor can be inco... | Rebuttal 1:
Rebuttal: Thanks for your being positive to our work. We also thank you for the advice on the organization of the paper. We will make some adjustments to make it easier for reading in the next revision.
### Setup with heavy-tailed additive noise
The assumption with heavy-tailed additive mean-zero noise is... | Rebuttal 1:
Rebuttal: ### Proof of computational complexity
First, to compute $\theta_{k-1,h}$ in line 6 of Algorithm 3, we notice the loss function in (5.1) is $\lambda_R$-strongly convex and $(\lambda_R+K/\nu_\mathrm{min}^2)$-smooth, so there are plenty of convex optimization algorithms available. For example, Neste... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Data-Informed Geometric Space Selection | Accept (poster) | Summary: The paper proposes a new end-to-end distance learning method to facilitate solving downstream prediction tasks. Their core idea is to select a subset of geometric spaces from a candidate space sets that include Euclidean, projected sphere and Poincare ball types of spaces, and computes the final distance in th... | Rebuttal 1:
Rebuttal: Thank you very much for the review, especially the constructive suggestion on the writing/clarification. We will revise the manuscript as suggested and clarify some of the confusing points.
##### **For Section 3.1 and section 3.2**
We will move Section 3.1 and Section 3.2 to a separate prelimin... | Summary: The goal of this paper is to learn the geometry (manifold) underlying given data points. Rather than learning an arbitrary Riemannian manifold from the data, the paper models this manifold as a Cartesian product of manifolds with constant curvature (three prototyprs are use: Euclidean, spherical, hyperbolic). ... | Rebuttal 1:
Rebuttal: Thank you very much for the positive rating and constructive suggestions!
##### **Can one represent arbitrary manifolds using a direct product of these prototypes?**
We cannot represent arbitrary manifolds in this way. We mainly focus on three popular manifolds (spherical, hyperbolic, and euclid... | Summary: Data representation is an important problem in today's deep learning world. Representation beyond Euclidean geometry, such as spherical or hyperbolic spaces can provide additional flexibility and benefits in capturing underlying properties of data. For example, hyperbolic space can better capture data that has... | Rebuttal 1:
Rebuttal: Thank you very much for the positive feedback and reconfirming the importance of the explored problem.
##### **Q1**: While the matrix completion and link prediction seems like good applications, it is not clear whether the proposed techniques can be extended to some of the other mainstream learn... | Summary: In many applications (especially those involving discrete data structures), choosing the right geometry for the embedding space, matching the structure of the data, can lead to significant performance gains. Extant approaches often make an ad hoc choice or use heuristics for the type of geometry applicable glo... | Rebuttal 1:
Rebuttal: Thank you very much for confirming the novelty as well as the potential of the proposed approach. Also, the suggestions can certainly help us improve the manuscript.
Below we answer the questions and make some clarifications.
##### **Why do we use CNN layers (appropriateness of algebra involvin... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their suggestions and we answer corresponding questions under each review's rebuttal. The pdf contains some figures to show the stereographic projection models for hyperbolic & spherical spaces. Mobius sum is also demonstrated in this pdf.
Pdf: /pdf/07cf220f7b4ae6cb8... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Permutation Equivariant Neural Functionals | Accept (poster) | Summary: The paper introduces and evaluates permutation equivariant neural functional networks (NFNs).
Neural functionals are models which take weights of other neural networks, or more general weight-space features, like gradients or sparsity masks, as inputs.
Their permutation equivariance addresses the issue that an... | Rebuttal 1:
Rebuttal: Thank you for your review and detailed analysis of the technical aspects of our work.
> It should be mentioned that the proof assumes a specific choice of group action w.r.t. which the layer is equivariant, namely first order permutation actions.
We will make it clear at the beginning of the pro... | Summary: This paper proposes the NF-Layer that maps the weight space of a deep neural network (DNN), including MLP and CNN, to another weight space, possibly with a different number of channels. Neural Functional Networks (NFNs) are then constructed using the NL-Layers to process the weight space of a DNN. NF-Layer is ... | Rebuttal 1:
Rebuttal: Thank you for your review and interesting question.
> A network architecture with equivariance based on parameter-sharing is not original and has been proposed in [51]
To clarify, [51] (Equivariance through parameter sharing) provides general strategies for developing layers equivariant to a giv... | Summary: This paper studies the problem of defining linear layers (and by extension, neural networks) that operate on neural network weight spaces. The core idea of this work is to take into account weight permutation symmetries, similar to Navon et al., ICML’23. In particular, the weights of certain feedforward archit... | Rebuttal 1:
Rebuttal: We appreciate the detailed and insightful review, and agree that indepedently developed frameworks can be useful contributions to the community.
> I found the explanations in section B.4. a bit hard to follow.
We will aim to improve the exposition in Section B.4. We also welcome any feedback on ... | Summary: The authors proposed an architecture for processing other networks’ weights and implicit neural representations (INRs). The generalization abilities of this architecture are enhanced and the number of parameters is reduced by leveraging the symmetries of deep networks.
The authors claim the following contribu... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and questions--we aim to clarify our contributions and strengthen the experiments with the suggested baselines.
> explain what is the novelty of the current work over [DWS].
We agree that DWSNets are a very relevant recent work with significant overlaps and notable... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and thoughtful comments and questions. Reviewer suggestions have helped us improve the writing and pointed us towards additional experiments that significantly strengthen the paper. A brief summary of changes and new experiments:
* We will update the intr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the design of architectures whose inputs are the parameters of neural networks. They propose an equivariant weight-sharing scheme based on the permutational symmetries of neural networks: one can permute at least the internal neurons (the “HNP” case), and sometimes the input/output neurons... | Rebuttal 1:
Rebuttal: Thank you for your insightful review, and for highlighting aspects of our contribution such as the NP setting and application to CNNs.
> it is important to have a more in-depth discussion of how [DWS (Navon et al) and NFN] fit together and the novelty of this work, and for this discussion to appe... | null | null | null | null | null | null |
Are Diffusion Models Vision-And-Language Reasoners? | Accept (poster) | Summary: The paper introduces Diffusion-ITM, a new method that directly adapts diffusion-based models to image-text matching tasks without retraining. Additionally, the authors collected a new benchmark called Generative-Discriminative Evaluation Benchmark (GDBench), which includes seven complex vision-and-language tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and effort to provide their detailed feedback on our submission! We are happy to note all the positive comments from the reviewer including:
- “... finding of the relative difference between with and without text conditions (Fig. 3) is pretty interesting."... | Summary: Recently diffusion-based text-to-image generation models have evolved rapidly, but it’s still challenging to evaluate them quantitatively in an efficient way.
This paper smartly converts evaluations of Stable Diffusion based image generation tasks as simpler image-text matching tasks (e.g. image-text retrieva... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your kind and insightful comments! We are thrilled by your comment that the paper is "Well written and easy to read".
We are glad you recognized that we are **"tackling a challenging problem, i.e., efficient quantitative evaluation of image generation models"**, with... | Summary: This paper studies how to use a pre-trained text-to-image generative diffusion model to do discriminative tasks like image and text matching. Building upon previous works [Li et al. 2023, Clark and Jaini 2023], this paper introduces two technical contributions: 1) Unconditional normalization largely improves i... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and effort, showing that you engaged with our work.
We are glad you found our “two technical contributions (unconditional normalization & tuning on MS COCO) effective and well ablated" and that our study contributes to drawing attention to bias.
Similarly, oth... | Summary: This paper studies the discriminative capabilities of diffusion models measured by image-text matching. A new matching score computation enables text-based image retrieval beyond simply text retrieval in existing works. A new benchmark, augmented from existing image-text benchmarks, is proposed for researchers... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and effort, showing that you engaged with our work! We are happy to note some of your positive feedback such as our method working with “minimal changes” and how “light-weight GDBench covers diverse phenomena”.
Similarly other reviewers noted:
- “Tackling a cha... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
First we would like to thank all reviewers for writing detailed and thoughtful responses. It raised interesting discussions among the authors and will make it an overall stronger paper. In particular, we are grateful that Reviewer JbKR gave us a a score of 7 with h... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes novel techniques for improving the performance of text-to-image diffusion models on zero-shot image-text matching tasks. They first propose subtracting the unconditional denoising error $\|\epsilon - \epsilon_\theta(x_t, t)\|_2^2$, which reduces the problem in image retrieval where one imag... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We are thankful for your detailed and extremely thorough review that shows you have engaged with the work and are very confident with the subject area!
Thank you as well for highlighting strengths of the paper such as:
- "The hard negative finetuning method in particular is intuiti... | null | null | null | null | null | null |
Prototypical Variational Autoencoder for 3D Few-shot Object Detection | Accept (poster) | Summary: This paper proposes a novel approach for few-shot 3D object detection by combining prototype learning and variational autoencoders. To address the weak geometry regularization and data imbalance issues of the existing methods, it proposes a novel VAE specifically designed for prototype learning named Prototypi... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our submission carefully and for the insightful suggestions. We address the reviewer’s comments below. Also, we will release code upon the publication of this work.
**Q1: How to obtain the number of instances $N_{ins}$?**
A1: Given the per-point features $\\{z'... | Summary: This paper studies a challenge task called FS3D. They first presents that the previous work on FS3D lacks fine-level supervision, as the intermediate features are simply averaged to update the prototypes, which are then used to augment features for sequential detection. In order to solve this problem, they lev... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our submission and for the insightful comments. We address the reviewer’s comments below.
**Q1: Survey on 3D FSL.**
A1: Thank you for pointing this out. Our work focuses on FS3D, we believe a more careful review on FSL for point cloud can lead to high-level insi... | Summary: The paper proposes an approach to enhance Few-Shot 3D Point Cloud Detection (FS3D) through prototype learning with VAEs. The authors leverage VAEs to learn prototypes represented by GMM-like distributions. Two VAEs are specifically designed to preserve geometric information and refine instance features. The e... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for the valuable comments. We address the reviewer’s comments below.
**Q1: Why are the prototypes learnt by previous methods less geometric-informative? More detailed discussions about the limitations of previous works.**
A1: ... | Summary: The paper introduces Prototypical Variational Autoencoder (P-VAE) for Few-Shot 3D Point Cloud Object Detection. It tackles the preservation of geometric information and data imbalance through learning distribution parameters. The authors propose two extensions, GP-VAE and CP-VAE, focusing on geometry and class... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and reviewing our paper. Overall, most of the review comments request clarifications and minor revision on the paper. We will carefully revise the paper accordingly. Also, we will release code upon the publication of this work.
**Q1: Effectiveness and ge... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data | Accept (poster) | Summary: The authors introduced the concept of sparsified MDP. Based on this concept, they proposed a new algorithm that takes as input a dataset, uses it to design and deploy a non-reactive exploratory policy, and then outputs a locally near-optimal policy. A nearly minimax-optimal upper bound for the sample complexit... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on our paper! We will try to answer your questions below.
1. Q: The algorithm is based on tabular setting, which limits its application.
A: Indeed our paper is a first step for exploration with a non-reactive policy which is computed with the help of... | Summary: The paper explores reinforcement learning applications where a pre-existing dataset of collected experience is available, and suggests the possibility of obtaining additional online data to enhance policy quality. To avoid the costs associated with switching policies, the authors propose utilizing a single non... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on our paper! We will try to answer your questions below.
1. Q: Firstly, from a theoretical research perspective, this assumption narrows down the problem to a very specific setting, so even with rigorous mathematical proofs, the generalizability of th... | Summary: This paper proposes an algorithm for policy fine-tuning in reinforcement learning using a dataset of pre-collected experience. The algorithm leverages the dataset to design a non-reactive exploratory policy and outputs a locally near-optimal policy. The paper makes theoretical contributions in analyzing the qu... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on our paper! We will try to answer your questions below.
1. Q: Can you give more details on the non-reactive property of a policy? It seems that most RL policies in an MDP will be non-reactive, as long as they take only the current state s_t as input.... | Summary: The paper proposes an algorithm that, given a previously collected dataset of transitions from an MDP, produces a non-reactive policy that can effectively collect additional data that enables a near-optimal policy to be obtained for any possible reward function. The algorithm is model-based and combines elemen... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on our paper! We will try to answer your questions below.
1. Q: I’m curious if it is known that better sample complexity can be obtained if you allow the policy to be adaptive? (Although I understand the engineering-related reasons for not doing so.) I... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The work proposes a method to create a non-reactive exploratory policy from an initial input dataset. Then, leveraging the new data, the algorithm generates a locally near-optimal policy.
The relevance of the algorithm is in the low-switching algorithms, where it is assumed there is a cost to changing a deplo... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on the paper! We will try to answer your questions below.
1. Q: The weakness of the paper is the need for empirical. For example, it would be helpful to see how well the algorithm performs given initial data sets of different sizes and coverage.
A: We... | Summary: The paper considers the setting where it is possible to leverage a dataset of transitions, together with the possibility of deploying a policy to collect additional information. The question then lies into what kind of policy should be deployed and what kind of data should be gathered. The authors argue that d... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions on our paper! We will try to answer your questions below.
1. Q: Why is learning from the generated experience such a bad idea in practice? It seems like a slowly changing deployed policy (where changes perhaps happen through a trust region) would be a... | null | null | null | null |
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences | Accept (poster) | Summary: This paper introduces a novel algorithm, the Bootstrapped Training of Score-Conditioned Generator (BOOTGEN), to optimize the design of biological sequences. BOOTGEN overcomes the challenge of high-cost evaluations and vast search space by training a score-based generator using rank-based weights and a bootstra... | Rebuttal 1:
Rebuttal: Thanks for providing a valuable review.
**W1: About the paper "Deep Extrapolation for Attribute-Enhance Generation"**
Thank you for pointing out the relevant literature. GENhance [1] and our method share the common goal of extrapolating from offline datasets using generative models. BootGen is... | Summary: This paper proposes to solve the problem of generating novel objects (in this case biological sequences) by learning weighted MLE models, and augmenting the training set of those MLE models with virtual data whose score is based on extrapolations from a proxy.
This method is tested on standard biological sequ... | Rebuttal 1:
Rebuttal: Thanks for providing a constructive review.
**
**What can we learn from this paper?**
Focusing on fundamental and rigorous approaches is more important in offline design optimization tasks than relying on fancy techniques.
Offline design optimization is inherently challenging as it prohibits a... | Summary: The paper proposes BootGen, a model-based sequence optimization algorithm, the author apply to the task of biological sequence design. BootGen has two stages where the first stage trains multiple sequence generators to give higher probability to sequences predicted to have higher scores from a proxy score mode... | Rebuttal 1:
Rebuttal: Thanks for the constructive review and feedback. We provide responses for addressing the concerns below.
### Response for Novelty
We made a novel combination of well-known techniques. Our novel high-level algorithm involves (a) propelling the generator to discover novel data points beyond the ... | Summary: The authors propose a novel algorithm: bootstrapped training of score conditioned generators (BOOTGEN), for the offline design of biological sequences. The key idea is to enhance the score-conditioned generator by suggesting a novel variation of the classical ensemble strategy of bootstrapping and aggregating... | Rebuttal 1:
Rebuttal: ### Comparison with Biological Sequence Model-specific Baselines
Thank you for your recommendation. We have already compared it with the recent state-of-the-art method GFN-AL ("Biological Sequence Design with GFlowNets") [1] in the main text, which is an optimization method for biological sequenc... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for providing valuable and constructive feedback on our work. We sincerely appreciate the time and effort you have dedicated to reviewing our manuscript. In response to each reviewer's comments, we have provided a detailed explanation and made necessary revisions to ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Generative Noisy-Label Learning by Implicit Dicriminative Approximation with Partial Label Prior | Reject | Summary: This paper introduces a novel approach to tackle the challenge of noisy label learning through a generative framework. Firstly, it presents a new model optimization technique that establishes a direct association between the data and clean labels. Secondly, the generative model is implicitly estimated by lever... | Rebuttal 1:
Rebuttal: We thank Reviewer jVwM for the insightful comments.
> Z and Y cause X seems more reasonable
This is a reasonable concern. In principle, the latent feature $Z$ is important for generating $X$ in a causal relationship because the generative model would need $Z$ to "anchor" the $P(X|Y)$ modeling as... | Summary: This paper discusses the solution of learning with noisy labels by directly optimizing P(X|Y) relying on associating the data with clean labels directly. A informative label prior is derived with experimental results on several benchmark datasets.
Strengths: The author derive a solution for generative noisy l... | Rebuttal 1:
Rebuttal: We disagree with most points from Reviewer rUmF and provide details below to support our position.
> Strong but unrealistic assumptions to directly optimize $P(X|Y)$
To explain the assumptions to optimise $P(X|Y)$, we first need to consider its intractability, which is in part due to the infinit... | Summary: This paper focuses on improving the efficiency of the generative model in the context of learning noisy labels. To achieve this, the authors first introduce a generative framework whose loglikelihood given a variational posterior can be extended into a label transition term and two KL-divergence terms. Then, t... | Rebuttal 1:
Rebuttal: We thank Reviewer 6r3h for the insightful comments.
> Modeling P(X|Y) contributes to the informativeness of noisy labels.
As shown in Eq.5, $P(X|Y)$ is modelled with the variational posterior $q(Y|X)$. In turn, $q(Y|X)$ depends on the modelling of the latent clean label $Y$. Inspired by partial ... | Summary: Most previous works address learning with noisy labels with discriminative models while this paper takes the generative approach which maximizes directly on associated data and clean labels. This generative model is implicitly estimated with a discriminative model, making it computationally more efficient.
S... | Rebuttal 1:
Rebuttal: We thank Reviewer 9Aeh for the insightful comments.
> Hard for the reviewer to understand why generative is better than discriminative models for noisy label problems.
It is still unclear which method is more suitable for noisy label learning, generative or discriminative. In fact, generative me... | Rebuttal 1:
Rebuttal:
- **Reviewer 9Aeh, Fig.2 is difficult to read**. We have uploaded a new figure to describe our proposed framework more clearly.
- **Reviewer jVwM, result from other kinds of dataset**. we have uploaded new results from two public NLP news topic classification benchmarks. The baselines are select... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rotating Features for Object Discovery | Accept (oral) | Summary: CAEs promise to resolve some of the concerns of slot-based representation. They bring the promise of flexible object granularity, the promise of extracting part-whole hierarchies as per need, and faster training speeds. However, the original CAE was tested with grayscale images and on a rather small number of ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We are taking this opportunity to address the concerns and inquiries raised.
### Strengths
**2) The way weights and biases were applied seems to have been simplified (a welcome change). An ablation experiment highlighting this specific change for the case of n... | Summary: This work proposes a novel approach to unsupervised object discovery that does not depend on slots. Instead, the model uses an extra set of dimensions to code object assignment based on rotation, potentially allowing for a more flexible distributed form of object discovery than in standard slot-based approache... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and constructive feedback. We welcome the opportunity to address the questions and concerns that have been brought up.
**1 / 2) How would the model perform when tested on a greater number of objects than it was trained on? / Controlled experiments could be per... | Summary: This work addresses the problem of unsupervised object discovery. It seeks to remedy some limitations (primarily object storage capacity) of the recently introduced synchrony-based approach, CAE [1], and scales it to more visually complex scenes compared to CAE [1] which was only applied to simple grayscale (S... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. We would like to take the opportunity to respond to the questions and concerns that you have posed.
### Weaknesses
**1) How novel is this evaluation procedure compared to the one used by CAE?**
While our proposed evaluation method closely resembles the CA... | Summary: The paper presents a new approach for extracting objects from distributed representations, based on a binding mechanism called 'rotating features' that extends previous phase-based binding notions to a much higher dimension binding space, and avoids the use of separate slots for individual objects, showing pro... | Rebuttal 1:
Rebuttal: Thank you for the feedback from your thoughtful review. We would like to take this opportunity to address the two questions you posed:
**I found it a bit difficult to be sure I was seeing fair comparisons in table 1. If I understand correctly, DINOSAUR MLP is much simpler than the Rotating Featur... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback. We are delighted to see the reviewers recognize that the exploration of alternatives to slot-based schemes as studied in our paper is important (e8db, r4Rq) and interesting (cH5o), and that our work may stimulate many interesting directions f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes | Accept (spotlight) | Summary: It has been hypothesized that the brain builds an internal model of its environment, and uses this model to make inferences and plan actions. This works aims to understand the neural mechanisms that are the basis of such computations. To this end, the authors construct several classes of artificial neural netw... | Rebuttal 1:
Rebuttal: - *Not really a weakness, but details about the best fitting model layers would be useful to add. It would also be interesting to see some analysis comparing the best fitting latent layers across different models.*
Absolutely agree. We actually compared the latent layers in Figures 2-4 across dif... | Summary: The authors compare "foundation models" of vision for mental simulation. They consider several large models including models trained on static scenes and dynamic scenes. It was found that the models optimized with self-supervision on dynamic scenes yielded the best neural predictivity.
Strengths: The work is... | Rebuttal 1:
Rebuttal: - *Since all the video foundation models are trained with Ego4D and the image foundation models are trained with ImageNet, it is not straightforward to disentangle the effect of dataset vs role of dynamics itself.*
We wanted to understand this question too, and as a result, we included the latent... | Summary: The paper compares a rather large variety of DL models that are able to “future predict” environmental states, including pixel-based deep networks, compositional approaches (e.g., slot-wise processing objects), as well as image and video foundation models. In the latter case, the latent space of the foundation... | Rebuttal 1:
Rebuttal: - *…the paper focuses on only two tasks, which are not very well-motivated.*
We thank the reviewer for suggesting the need for this clarification, and will add it to the Introduction. Specifically, the OCP task (Bear et al. 2021) tests realistic simulations of a wide range of everyday physical ph... | Summary: This manuscript compared several classes of deep-learning based sensory-cognitive models in their ability to predict human behavior and monkey neural responses in tasks that require reasoning about physical relationships based on visual inputs. They find that the models that match best to **neural data** are t... | Rebuttal 1:
Rebuttal: - *is there any pattern among the stimuli that the models turn to make a wrong judgment? …maybe some examples to put in supplementary material, or some subjective summary from the authors' observation can be helpful, or a more fine-grained comparison between different scenarios (are some scenarios... | Rebuttal 1:
Rebuttal: **Global Response:**
We thank the reviewers for their thorough reviews, helpful suggestions, and overall positive enthusiasm about our work.
**For common reference, the core contributions of our work are:**
**1. Dense neurophysiological data strongly constrains hypotheses:** Overall, we find th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion | Accept (poster) | Summary: This paper proposes **RS-Del** -- a novel certified defense that provides guarantees w.r.t. insertion, deletion, and substitution of bytes within a variable length input. As its name indicates, RS-Del is based on randomized smoothing. However, unlike classic randomized smoothing, the authors cannot certify r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback.
### RE: fit for NeurIPS
We believe our paper is aligned for NeurIPS (see below), and note that this was not a concern raised by other reviewers. Our paper advances certified robustness for generic sequence classifiers, providing... | Summary: This paper tackles the issue of applying randomized smoothing to discrete sequences under the Levenshtein edit distance. Because the underlying sequence is discrete, it necessitates new mathematical approaches to proving the robustness. Enticingly, the edit distance is bounded by employing only the delete oper... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing such encouraging feedback. We are pleased the reviewer recognized the novelty of our work in extending randomized smoothing to new threat models for discrete domains. We also appreciate the reviewer's pragmatic assessment of the limitations.
### W1: Missing ref... | Summary: The paper proposes a general randomized smoothing approach for certifying robustness concerning arbitrary perturbations defined in Levenshtein distance. The critical challenges of proposing the randomized smoothing approach are
1) how to design the smoothing distribution? The paper uses a deletion distribut... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoroughly engaging with our work and providing detailed feedback.
### W1: High deletion >90% doesn't harm accuracy
We offer an explanation in Appendix E.1 (lines 922-929). In short, it's important to realize that a deletion probability of 90% does not mean 90% of the se... | Summary: This paper aimed to design a certified defense for discrete sequence classifiers against edit distance-bounded adversaries. This method exploited randomized smoothing mechanism to consturct the defense and proposed RS-Del to confer robustness against adversarial delection, insertion and substitution edits.
St... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and appreciating our contribution to certification for discrete modalities.
We respond to specific feedback below.
### W1: Why can't defense mechanisms for continuous fixed-dimensional inputs be used
A key reason why certified defenses for continuous fixed-dimensi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive reception of our paper and for providing us with constructive feedback.
We would like to draw the reviewers' attention to the attached rebuttal PDF, which contains updated figures/results in response to Reviewer MXYp. For the benefit of the other reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Analysis of Variance of Multiple Causal Networks | Accept (poster) | Summary: This paper proposes a single structural model to simultaneously construct multiple networks so as to identify causalities varying across multiple cohorts while identifying stable ones. Each causal network is represented via a directed cyclic graph, and the authors propose an analysis of variance (ANOVA) algor... | Rebuttal 1:
Rebuttal: “The svd based technique employed for correspondence analysis to detect key responder drivers pairs, is not clear. It would be helpful if the authors could elaborate further on this while also providing the intuition behind this. Bootstrapping is used to achieve this- what sort of a bootstrap meth... | Summary: In this paper, the authors proposed NetANOVA, an algorithm that simultaneously constructs multiple causal networks and infer their disparities. Theoretical justification of the proposed method is also derived. The paper further proposes measures for variable’s contribution to receivers and responders. Overall,... | Rebuttal 1:
Rebuttal: “The paper is not really good to read. Section 2 starts deriving the method directly without further explanation and formulation of the problem. The authors should consider adding a subsection before the method to formally describe the problem.”
The main purpose is to construct and compare mult... | Summary: The paper introduces NetANOVA, an algorithm designed for parallel computation to construct a unified structural model for multiple causal networks, or DCFs. NetANOVA utilizes analysis of variance (ANOVA) to identify causalities that differ across networks, as well as important drivers and responders. It is sca... | Rebuttal 1:
Rebuttal: “In NetANOVA, most computations are matrix products and inversions. Can you briefly estimate the computation cost required when n and k scales?”
Assuming bar{n}=sum_{k=1}^K n^{(k)} the average sample size, we can break down the computational complexity as follows: The complexity associated wit... | Summary: This paper presents a unified structural model that describes multiple DCGs in one model and develops a limited-information-based method to simultaneously infer networks and their disparities. Furthermore, it provides robust non-asymptotic theoretical properties. And it is applied to synthetic and real dataset... | Rebuttal 1:
Rebuttal: “… fail to see the benefits of proposing such a "unified" model, nor any mathematical novelty in model construction. … no relation between or information shared by K networks.”
We disagree with the reviewer’s claims on the benefit and novelty. To the best of our knowledge, we are the first to s... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for providing constructive comments, which provide us a chance to clarify some confusions and improve the quality of our work. We have carefully been through each comment and done our best to address each. While we have addressed each reviewer’s points in ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an algorithm, called NetANOVA, for constructing a unified structural model for multiple causal networks with cycles. The algorithm is designed for parallel computation and is scalable to data size and network complexity. It is able to infer causal networks beyond directed acyclic graphs (DA... | Rebuttal 1:
Rebuttal: “Instrumental variables-based identification of causal effect are proposed for DAGs. There is a lack of discussion on how this extends to causal structures that contains cycles. It is unclear to me if the identification results in 2.3 is correct when cycles exists.”
We agree that IV-based metho... | null | null | null | null | null | null |
Representation Learning via Consistent Assignment of Views over Random Partitions | Accept (poster) | Summary: The authors propose a new method for self-supervised learning based on cluster assignments. The method is based on a consistent assignments approach that assigns the same prototype to different views of the same image. To overcome issues of the previous methods that are not well scalable, a divide and conquer ... | Rebuttal 1:
Rebuttal: > W1: would be interesting to see how the methods work with transformer-based architectures?
We thank the reviewer for the suggestion. It is in our plans to explore how the proposed random partition pretext task would behave with other architectures, such as ViTs. However, we chose ResNets as the... | Summary: This work extends the existing work about consistent assignment for representation learning by introducing random partitions. Specifically, when the number of prototypes is large in clustering-based self-supervised learning, the work shows that CARL [36] cannot handle the loss and the regularization well. To m... | Rebuttal 1:
Rebuttal: > [...] my major concern is about the limited contribution compared to CARL.
The main difference between CARL and CARP is the random partition pretext task and its positive effects on training SSL models. As described in Section 3.1, training a system like CARL is difficult due to stabilities, re... | Summary: This paper works on self-supervised representation learning. Under the setting of consistent clustering assignment between augmented views (SwAV-like), the authors found that when the number of prototypes is significantly larger than the batch size, the commonly used technique for avoiding trivial solutions fa... | Rebuttal 1:
Rebuttal: > Why do we need so many prototypes during pre-training? [...]
Based on our practical experience, **the optimal number of prototypes highly depends on the number of hidden classes of the dataset.** Due to ImageNet's high number of classes (1000), in practice, a higher number of prototypes produ... | Summary: This paper addresses a collapsing problem that arises in clustering-based contrastive learning. To resolve the problem, the paper proposes an improved version of consistent assignment in CARL, utilizing a strategy of random partitioning. In particular, the original consistent assignment loss exhibits a stabili... | Rebuttal 1:
Rebuttal: > The discussion and comparison to other related baseline methods [1, 2, 3], [...], seem to be missing.
To address the concerns regarding a proper comparison to the other suggested methods, we took the pre-trained models from their respective official repositories and ran the same benchmark used ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time reviewing our work and their valuable feedback. We will incorporate the additional results presented in this rebuttal as well as the suggestions in the final version of our manuscript.
In summary, our main contributions are the proposal of a stochastic partit... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LEPARD: Learning Explicit Part Discovery for 3D Articulated Shape Reconstruction | Accept (poster) | Summary: The paper studies the research problem of shelf(DINO)-supervised articulated 3D shape reconstruction. The key idea of the paper is to factorize shapes into different primitives, and to model the shape primitives using both global and local deformation parameters. The parameters of the model are optimized in a ... | Rebuttal 1:
Rebuttal: >Q1: Motivation of kinematics-based optimization approach adopted in this paper.
A1: Thanks for the comment! We realize that we may have made presumptions regarding the reader's familiarity with PDMs, and we appreciate the feedback. Let's break down the approach and the intuition behind our choi... | Summary: The paper introduces LEPARD, a framework for reconstructing the 3D shape of animals from single images. LEPARD reconstructs 3D shapes as parts, which are parameterized primitive surfaces with global and local deformations. LEPARD is trained using off-the-shelf deep features without the need for 2D or 3D annota... | Rebuttal 1:
Rebuttal: Thanks for your detailed comments! We will release the code for reproduction. We hope the following responses can address your concerns.
>Q1: The model parameters are not defined precisely. It’s unclear how $q_c$ and $q_\theta$ are defined.
A1: $q_c = c \in \mathbb R ^3$ represents the 3D transl... | Summary: The paper presents LEPARD, a framework for reconstructing the 3D articulated shape of animals from a single in-the-wild image. It explicitly represents the parts as parameterized primitive surfaces (superquadrics) with global and local deformations in 3D. The authors employ a kinematics-inspired optimization t... | Rebuttal 1:
Rebuttal: Thank you for your strong recognition of our work! We hope the following responses can address your concerns.
>Q1: There are a limited number of categories in the datasets evaluated. Can the authors qualitatively evaluate their methods on held-out images from other sources for the same training c... | Summary: The paper describes a method for fitting K superquadric geometric primitives (enhanced with tapering, bending, and diffeomorphic local deformations) to a set of images of an animal category (e.g. elephant). The main contribution is that the method requires no supervision, and uses 2D feature correspondence to ... | Rebuttal 1:
Rebuttal: Dear reviewer, we greatly appreciate your recognition of our work, and thank you for your valuable comments! We hope the following responses can address your concerns.
>Q1: Animal bodies are kinematic chains, where one limb affects another. This approach does not take that into account. How would... | Rebuttal 1:
Rebuttal: Dear AC and reviewers, we are grateful for the strong recognition and valuable comments of our work. In the following, we will first answer the common concern of all reviewers, followed by answers to each reviewer's comments.
The common concern is mainly the limitation of our approach.
We share ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a part-based method to reconstruct 3D shapes in a category-specific manner. Compare against its baseline LASSIE, the paper uses an elegant primitive part representation that could capture both global and local deformations to increase the fidelity of reconstruction. The method also does not ... | Rebuttal 1:
Rebuttal: Dear reviewer, we greatly appreciate your recognition of our work, and thank you for your valuable comments! We hope the following responses can address your concerns.
>Q1: The motivation of introducing image force in the training objective is not well explained. As LASSIE adopts simple silhouett... | null | null | null | null | null | null |
A fast heuristic to optimize time-space tradeoff for large models | Accept (poster) | Summary: This paper proposed an algorithm for the rematerialization problem. The proposed algorithm is based on simulated annealing.
Strengths: Please see the questions section.
Weaknesses: Please see the questions section.
Technical Quality: 2 fair
Clarity: 2 fair
Questions for Authors: - As far as I know, Checkm... | Rebuttal 1:
Rebuttal: We really appreciate your feedback emphasizing the importance of comparing FastSA results to Checkmate ILP. In our original paper, we relied solely on Checkmate LP due to licensing issues and the large sizes of the models. However, we now understand the necessity of a comparison with Checkmate ILP... | Summary: This paper proposed a new method for optimizing recomputation in neural network training. It formalizes the recomputation as a sequence of nodes that indicate the computation schedule. It then uses simulated annealing with segment tree to find a sequence that optimizes throughput with a certain memory budget. ... | Rebuttal 1:
Rebuttal: Thank you for your review.
Acknowledging that our problem formulation shares resemblances with existing work, it's pivotal to highlight that, based on our knowledge, our algorithm is the first algorithm able to perform recomputation in arbitrary computational graphs and can be applied to real-wor... | Summary: This paper proposes the Fast Simulated Annealing Algorithm (FastSA), based on the Add-max segment tree and simulated annealing, to optimize memory usage and training time. Furthermore, FastSA introduces grouped nodes to aid the convergence of simulated annealing and effectively reduce the peak memory. It can a... | Rebuttal 1:
Rebuttal: Thank you for your review.
We acknowledge and appreciate the reference to Moccasin, which was unavailable at the time of our paper's submission, but is indeed an important comparison to make. Consequently, we have conducted further comparisons between Moccasin and FastSA, which we will include, a... | Summary: This paper introduces a fast simulating annealing combined heuristics approach for gradient checkpoint/recomputation.
The solution achieves a significant memory reduction of 73% with an average recomputation cost of 18%.
It outperforms the state-of-the-art MILP-based technique Checkmate in terms of runtime b... | Rebuttal 1:
Rebuttal: Thank you for your review. In our original experiments, we compared our algorithm to Checkmate LP, instead of the exact ILP, using open-source LP solver OR-Tools due to the prohibitive cost of commercial ILP/LP solvers. However, we acknowledge that the evaluation of the solution quality of our alg... | Rebuttal 1:
Rebuttal: Dear reviewers
We appreciate the insightful feedback. The review comments have been proven to be very valuable and help to increase the quality of the paper. Here, we provide additional results regarding two common concerns; (1) Comparison with Moccasin [Bartan et al.], a rematerialization algori... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Multi Time Scale World Models | Accept (spotlight) | Summary: The paper addresses learning predictive world models that operate at multiple (i.e. 2) time scales. At the slower time scale, the belief over the “task” (i.e. the high-level state) is updated at every H time steps by aggregating the influence of the low-level observations and actions received over that period.... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for posing these critical questions about our model and the suggestions given. We would attempt to answer these with the following paragraphs and global comment (and attached rebuttal pdf document).
**Weakness:** It is unclear whether the experimental results s... | Summary: The paper proposes a formalized multi-scale world model, which works at two timescales: a fast-timestep module that predicts individual timesteps, and a slower one that is only updated over fixed number of steps. The slower module defines a "task" that controls how the fast module functions, and the slower mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. Here are our replies to some of the weaknesses and questions listed.
**Weakness:** Transformers used were rather small (4 layers, ~100 dimensions), testing the scalability of the different methods in terms of parameters to train. **Questions... | Summary: Looking to tackle the lack of temporal granularity in existing world models, the paper proposes a multi-time scale linear Gaussian state space model (MTS3). The model uses an efficient closed-form inference scheme on multiple time scales for highly accurate long-horizon predictions and uncertainty estimates ov... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and valuable suggestions. We write our replies to the weaknesses/questions listed by the reviewer below.
**(Weakness) "However, how dramatically could results ... wrong discretization step chosen. Is there a way to systematically infer this through ... | Summary: The authors introduce the Multi Time Scale State Space (MTS3) model in this work. The model uses closed-form equations derived using exact inference, spread across two time-scales, to produce long-horizon predictions and uncertainty estimates. They demonstrate the superiority/competitiveness of their inference... | Rebuttal 1:
Rebuttal: We thank the reviewer for looking at our submission in a positive light. We would like to address a few questions raised by the reviewer below:
**Question: How were the hyperparameters for MTS3 tuned? How were the baselines tuned?**
All hyperparameters for MTS3 and baselines including Transforme... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable suggestions / insightful questions and comments. We would like to post answers here to some common questions/weaknesses raised by multiple reviewers.
**1. Questions on whether the strong experimental results are out of learning at multiple time scales... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sample based Explanations via Generalized Representers | Accept (poster) | Summary: The paper proposes a unifying framework for sample-based explanation methods via generalised representers. The framework proposes to approximate a general nonlinear predictive function using a surrogate from the Reproducing Kernel Hilbert Space (RKHS) (Surrogate function $f(x)=\sum_{i=1}\phi(x_i,x)=\sum_{i=1}\... | Rebuttal 1:
Rebuttal: We are grateful for the review, and we truly value the time you dedicated to reading our paper. Our point-to-point responses to your comments are given below.
**Code release**: We are working on reorganizing the code, and plan to release it when the paper is published.
**Additional insights fr... | Summary: In this study, the authors conducted an axiomatic analysis of a measure that quantifies the influence of a given training data on predictions.
Under several axioms, the authors demonstrated that an effective measure of influence is limited to the form of a suitable coefficient multiplied by a continuous and po... | Rebuttal 1:
Rebuttal: Thank you for the review. We sincerely appreciate your time in reading the paper and we are grateful for your feedback! Our responses are given below.
**Validity of axioms**: The axioms of the generalized representers encompass both practical and mathematical implications for what an explanation... | Summary: This paper studies a new framework for generating sample-based explanations for black-box machine learning algorithms. To explain a black-box model, the basic idea of sample-based explanations is to quantify how each training data is influencing the prediction of certain test data. The main contribution of thi... | Rebuttal 1:
Rebuttal: Thanks for your encouraging words and constructive comments. Your questions are answered below.
**Seemingly Inconsistent experimental results of Influence function kernel**: When dealing with language data, we calculate the influence function kernel using the last-layer embeddings [5,53]. This ch... | Summary: This paper studies and proposes a set of desirable axioms for sample based explanations. This further demonstrates that the only solution satisfying the set of desirable axioms has the form of $\alpha_i K(x_i,x)$ (i.e., the product of two components: a global sample importance, and a local sample importance th... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper! We are grateful for your feedback. Please see our responses below.
**Regarding whether we propose new approaches or explain existing approaches**: One of our key goals was to provide an axiomatic framework for a large class of sample bas... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules | Accept (poster) | Summary: In this work, the authors investigate tokenization and decoder in masked graph modeling (MGM), and present Simple GNN-based Tokenizer (SGT) as well as stacking GINE and GraphTrans (GTS) as an efficient decoder. SGT uses non-trainable linear graph aggregation to get tokenization of graphs. It also introduces a ... | Rebuttal 1:
Rebuttal: > **Q1.** The performance on the quantum mechanics benchmarks, e.g., QM9, MD17.
**Response:** Thanks. We have included the results on the QM7, QM8, and QM9 datasets in our updated Table 3. This experiment reuses the model checkpoints and settings in Table 5. We observe that SimSGT consistently o... | Summary: The paper examines the effectiveness of tokenizer and decoder in the self-supervised representation learning of molecular graph following masked auto-encoding framework. Specifically, the paper adopts GraphTrans architecture for its encoder and a smaller GraphTrans for its decoder. A simple GNN-based architect... | Rebuttal 1:
Rebuttal: > **Q1.** The proposed method utilizes a simple GGN-based architecture to learn the feature embeddings. However, these feature embeddings only serve as target for masked auto-encoding. I think it is misleading to call it tokenizer.
**Response:** Thank you for your insightful comment on the module... | Summary: This paper mainly revisits the graph tokenizers and the graph autoencoders in Masked Graph Modeling (MGM) frameworks. Authors examine the roles of different tokenizers as the MGM’s reconstructions targets and propose a simple GNN-based tokenizer method and a decoding strategy. The experiment results show the e... | Rebuttal 1:
Rebuttal: > **Q1.** The motivation ... is somehow unclear and the novelty is limited ... There’s no explicit insight drawn from the revisiting results, which can be helpful for designing the proposed method.
**Response:** We appreciate your insights, but wish to respectfully emphasize our contribution and... | Summary: The authors attempt to categorize existing approaches for pretraining neural networks on molecular graphs and assess their contributions to pretraining quality. They then propose a new strategy for pretraining molecular graphs and compare it to existing results.
Strengths: I was very impressed by this pape... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We're genuinely pleased to hear that you found the research well-motivated and the approach novel. Your positive feedback on the paper's clarity and informativeness means a lot to us. The constructive feedback provided will undoubtedly help us fur... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers' efforts for reviewing this submission. Our submission has received diverse ratings, including one strong accept (8), one weak accept (6), one borderline accept (5), one borderline reject (4), and one reject (3).
We would like to thank all the reviewers for provid... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a masked graph modeling framework called Simple GNN-based Tokenizer (SGT) for molecular graph analysis. Extensive experiments show the performance of the proposed method.
Strengths: 1. The article is well-written and easy to understand.
2. The proposed framework is attractive to this resea... | Rebuttal 1:
Rebuttal: > **Q1.** The limitations of the previous methods are not explained clearly.
**Response:** Thanks for your comments. Although the limitations of prior methods have been elucidated (as outlined in Lines 51-54 and Table 1, and supported by findings in Sections 4.1 and 4.2), we will clarify more:
... | null | null | null | null | null | null |
Learning Domain-Aware Detection Head with Prompt Tuning | Accept (poster) | Summary: 1. Proposed a novel framework for domain-aware object detection with
a) a vision-language model-based backbone to extract highly generalized features
b) a domain-aware detection head by prompt tuning
2. Design the prompt includes domain-invariant tokens, specific tokens, the token for class, domain-related t... | Rebuttal 1:
Rebuttal: Comment:
We sincerely appreciate the reviewer for the constructive feedback. We are encouraged that the reviewer finds our idea is evident and reasonable. We will explain your concerns point by point.
**Q1: I see that the box head is frozen when tuning the prompts, so the box head is trained wit... | Summary: This paper proposes a new domain adaptive object detection (DAOD) method named DA-Pro. Unlike previous methods, which ignore the domain bias in the detection head, DA-Pro applies the learnable domain-adaptive prompt to generate the dynamic detection head for each domain. To do so, the prompt is designed to be ... | Rebuttal 1:
Rebuttal: Comment:
We appreciate the reviewer for the valuable comments. Our response to the reviewer’s questions is as follows.
**Q1: The novelty of the paper is a little weak. I do acknowledge that the paper has certain novelty in a sense that it reasonably integrates [4] into the DAOD task and the auth... | Summary: Most existing methods incorporate a visual encoder (detection backbone) to mitigate the shift across the domain. This paper leverages domain adaptive prompts comprises of domain invariant tokens, domain-specific tokens, and domain-related textual description with class label. These domain adaptive prompts intr... | Rebuttal 1:
Rebuttal: Comment:
We appreciate the reviewer for the valuable comments. We are pleased to see our idea being regarded as interesting. We will explain your concerns point by point.
**Q1: Authors need to consider Pascal to Clipart, watercolor, and comic experiments. That allows the method to be evaluated i... | Summary: This paper designs a novel Domain-Aware Detection Head with Prompt Tuning (DA-Pro) framework for domain adaptive object detection. The motivation is learning the discriminative detector for each domain instead of reducing the domain bias as in the traditional DAOD methods. Specifically, the authors leverage th... | Rebuttal 1:
Rebuttal: Comment:
We sincerely thank you for your comprehensive comments and constructive advice. We are pleased to see our work being regarded as reasonable and addressing a crucial problem. We will explain your concerns point by point.
**Q1: One major concern of this paper is the differences between th... | Rebuttal 1:
Rebuttal: **Comment:**
We thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find that:
1. The idea of learning domain-aware detection head is **reasonable** (Reviewer eDFv, Reviewer p2gs, Reviewer A2TR), **evident** and **interesting** (Reviewer 85mu)... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces VLM to domain adaptive object detection. To be specific, this paper uses highly generalized VLM as detection backbone, and adapts detection head instead. To learn the domain-invariant and domain-specific knowledge, this paper extends the prompt to domain-invariant and domain-specific on... | Rebuttal 1:
Rebuttal: Comment：
We sincerely thank you for the valuable comments. We are encouraged to see that our work is recognized as a promising direction. We will explain your concerns point by point.
**Q1: Source and Target classifier in Figure 2 means similarity calculation, and drawn entity is misleading.**
... | null | null | null | null | null | null |
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods | Accept (poster) | Summary: This paper shows mainly two things:
(a) SGD suffers from an exponential dependence on the initial stepsize if it is not tuned to be smaller than the learning rate. This exponential dependence is unavoidable.
(b) Methods with gradient normalization and running gradient sum normalization, such as Normalized SGD,... | Rebuttal 1:
Rebuttal: Thanks for the comments.
> **The result on the exponential dependence on the smoothness constant is a known consequence of another result in the literature...**
We thank the reviewer for pointing out the two relevant references and we will add more discussions about them in the revision. Howeve... | Summary: The authors investigate the behavior of untuned SGD in the smooth nonconvex setting and show a new result on the convergence rate of SGD w.r.t. to gradient norm, where there is an exponential dependence on the smoothness constant. They further argue that the exponential dependence is unavoidable through a cons... | Rebuttal 1:
Rebuttal: Thanks for the recognition of our work.
> **The current state of numerical experiments seem preliminarily and is only done on one dataset MNIST on a small-network. I would like to see a more comprehensive investigation into larger practical networks, perhaps from [6, 24, 54].**
To complement ou... | Summary: This paper analyzes the complexity of finding an $\epsilon$-stationary point for untuned SGD and compares that with three families of adaptive methods - NSGD, AMSGrad and AdaGrad. Compared to previous convergence analysis results for tuned SGD and Adaptive methods: this work gets rid of several assumptions tha... | Rebuttal 1:
Rebuttal:
Thanks for the recognition of our work.
> **the constructed function $f(x)$ in Figure-2 does not look so. Is there an extension of the function outside the segment-1. If so, the author should mention that. Although the equation for sregment-4 and segment-1 are still provided, segment-2 and 3 are... | Summary: This work analyses the rate of SGD and other adaptive SGD methods in reducing $\\mathbb{E}\\|\\nabla f(x_t)\\|$, where $f$ is non-convex $L$-smooth, and shows that SGD has an exponential dependence on $L$ when the stepsize is not properly tuned, while other adaptive methods do not incur this exponential depend... | Rebuttal 1:
Rebuttal: Thanks for the recognition of our work.
> **At a high level, one can argue that the result was "known" in the following sense... I think it would be worthwhile for the authors to discuss the view that the exponential constant corresponds to the amount of catch-up SGD needs to make.**
We agree th... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and the overall positive evaluation of our work.
As requested by Reviewers CaFA and vyoV, we conducted additional experiments with deep neural networks to demonstrate the gradient explosion effect on a more practical (large scale) experiment. I... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The article under review presents results to show that untuned SGD may be less adapted than normalized versions of it when solving smooth non-convex problems.
In order to prove this, the authors present several results, from upper to lower bounds on smooth non-convex problems to find a critical point, without ... | Rebuttal 1:
Rebuttal: Thanks for the comments!
> **The main phenomenon ... Maybe some experiments on non-toyish problem ...**
Thank you for suggesting additional experiments. We have now included a large-scale experiment on a widely-used 50-layer ResNet trained on the CIFAR-10 dataset and observed similar phenomenon... | null | null | null | null | null | null |
Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling | Accept (poster) | Summary: This paper studies asynchronous proportional response dynamics (PRD) in linear Fisher markets under adversarial scheduling. The authors proposed an associated game with specific player utilities which admits an exact potential function. Then, the authors show that the set of pure NE of the associated game is t... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Regarding the question about price convergence in non-generic markets: We believe that you are correct and prices do converge in general for PRD (as we did show for best-reply dynamics). However, we were not able to prove this with our current techniques and so the sta... | Summary: In this paper, the authors examined Proportional Response Dynamics (PRD) in linear Fisher markets in a setting where participants act asynchronously. In particular, they considered a setting where at each step, an adversary selects a subset of players to update their bids. The paper showed that in the generic ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address below the specific points raised in the review.
Motivation: Natural dynamics in markets (such as the PRD that we study) can be viewed as the aggregate emergent outcome of joint simple learning strategies of the participants. We believe that studying the em... | Summary: The paper studies the convergence of Proportional Response Dynamics in linear Fisher Markets.
Fisher Markets are markets consisting of m divisible goods that should be shared among n agents with a linear utility function on items. The market not only must to decide the allocation, but it must also assign a pay... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address below the specific points raised in the review.
Relevance: While we agree of course that our analysis is theoretical, we believe that the paper is relevant to NeurIPS: Natural dynamics in markets (such as the PRD that we study) can be viewed as the aggrega... | Summary: This paper studies the problem of convergence in Proportional Response Dynamics (PRD) in linear Fisher markets when participants update in the dynamics under adversarial scheduling, i.e. an adversary specifies which subset of agents update their dynamics in a given round, subject to the constraint that each ag... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Regarding the question about intermediate asynchrony models, we agree that intermediate levels of asynchrony with information delays are an interesting avenue to explore which can be useful for making progress in (or at least gaining insights on) the analysis of full a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback, we will use it to improve the paper. We reply to specific points of each reviewer separately. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Summary: The paper studies the Fisher market model, where there is a set of m sellers and n buyers. Each seller brings a unit of a divisible commodity for sale and each buyer brings a budget. The vendors value the money while the buyers value the commodities (goods). A substantial amount of research has been ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Yes, the strategy space is compact, it forms the full polytope where each buyer allocates its budget arbitrarily among the items. While you are correct that the best-reply function with respect to the standard utility function is formally undefined at zero, what we nee... | null | null | null | null | null | null |
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First | Accept (poster) | Summary: This paper proposes a novel curriculum learning method that progressively includes edges into training based on their difficulty level, starting from easy to hard. The difficulty level is determined by the expected model performance in predicting the edges.
Strengths: (1) Graph representation learning is a ve... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
Q1. 'Since the initial node embedding heavily relies on the quality of the encoder and the effectiveness of reconstruction loss, it is advantageous to initialize the training process using a pre-trained GNN encoder...'
A1. Your suggestion aligns with the app... | Summary: The paper presents a curriculum learning strategy that works in the node classification setting. The key property in the node classification setting is that edges are not necessarily independent. The paper proposes a way to perform curriculum learning for this task, including the edges from easy to hard based ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and insightful suggestions.
major 1. We present the results of PNA on synthetic and real-world datasets in global reply point 2 (Table 2 in PDF), which illustrate that our curriculum learning approach consistently improves the performance of PNA backbone by 2.... | Summary: This study addresses the challenge of varying learning difficulties among edges in a graph and proposes a curriculum learning approach that gradually incorporates more difficult edges. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in improving ac... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
W1. `The improvements on real-world datasets are not substantial.'
A1: We would like to highlight, in line 323-330 and line 331-335, that our model: (1) Tops performance in 26 out of 27 tasks across nine real-world datasets, signifying its effectiveness; (2... | Summary: Summary
This work explores continual learning on data that is not independent, but has dependencies, such as graph edges. Three issues are raised when transferring continual learning techniques to learning on graphs: 1. there is no simple way to evaluate how easy/hard an edge is; 2. the curriculum should inclu... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable comments and acknowledgement of our work.
Q1. `My understanding is that, based on the fact that edges are selected based on similarity in embedding space, the proposed approach might struggle with heterophilic graphs. While I appreciate the synthetic experi... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for your efforts in providing critiques and suggestions to our work. We summarize the newly expanded experiments and frequent questions as below:
1. We have included new experiments on six real-world heterophilic datasets. As shown in PDF Table 1, our method c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a curriculum learning (CL) method for graph neural networks on the node classification task. Existing CL strategies are mostly designed for indepedent data samples, and cannot trivially generalize to graphs that contain data dependencies. The proposed solution, termed as Relational Curricul... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed assessment and valuable suggestions.
R1: The term `Relational' in our title is intended to emphasize our research on integrating inter-node relationships into Curriculum Learning (CL) strategies for GNN models.
`why traditional CL strategies are insufficien... | null | null | null | null | null | null |
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks | Accept (spotlight) | Summary: This paper studies the feature learning capability of a 3-layer neural
network, where the bottom layer is random and fixed, the middle layer is
trained for only one step from zero, and the upper layer is trained in
the rest of the gradient descent steps. The paper characterizes the
richer feature learning capa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful review, and address specific comments below.
> “Can the authors comment on other possible features that this approach can learn effectively”
- We hypothesize that our results here can be extended to learn arbitrary polynomial features, and t... | Summary: The paper theoretically studies the feature learning in three layer neural networks. For the analysis, it considers layer-wise GD; more precisely, the first layer is not trained, the second layer is trained for one step, and then there is the training for last layer. Particularly, they show that three layer ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful review, and address specific comments below:
> “However, it would have been interesting to also run experiments with the more common setting (e.g., training all parameters together) for both two-layer and three-layer NNs…”
- We agree that th... | Summary: In this work the authors show that there is a three layer neural network setup with better provable learning guarantees than the current best bound for two layer setups. The setup involves a randomly initialized layer with frozen weights, which feeds into a two layer network where first the hidden layer weight... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful review, and address specific concerns of yours below.
> “While the bounds show great improvement over the quoted bounds in the two hidden layer case, it is not clear how tight these bounds are in the various scenarios discussed.”
- For the q... | Summary: This paper analyzed the features learned by a three-layer network trained with layer-wise gradient descent as existing analyses are largely restricted to two-layer networks. It presented a general purpose theorem that upper bounds the sample complexity and width needed to achieve low test error when the target... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful review, and address specific comments below.
> “It will make the problem that the paper solved more clear if that point is made clear in an earlier part of the paper.”
- Thank you for this feedback; we will add more details about what speci... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer | Accept (poster) | Summary: This paper considers the problem of learning to defer, and propose a softmax-based surrogate loss for the task, which is consistent and can be well-calibrated. The paper theoretically proves that one needs asymmetric losses for a bounded probability estimate---which in turn leads to a better calibration proper... | Rebuttal 1:
Rebuttal: **Q1. Please provide more experimental results on other datasets.**
A1. Thank you for your constructive advice! We have added more experiments on datasets with real-world expert annotations, including Hatespeech [1] and ImageNet-16H [2] (2 tasks: “080” and “095”). The experimental results are pro... | Summary: In this paper, the authors investigate the task of probability forecasting in multi-class classification with an expert deferral option (L2D). They address the issue of unbounded and invalid estimates of experts' accuracy that often arises in probability estimation for L2D. Furthermore, the authors highlight t... | Rebuttal 1:
Rebuttal: **Q1. An estimation error bound should be given. The Lipschitzness of the asymmetric softmax w.r.t. g should also be clarified.**
A1. Thank you for your helpful advice! We have derived the estimation error bound of the ERM with our proposed risk and will update it in the revised version of our ma... | Summary: This paper studies the learning to defer setup where you have to defer to an expert if the classifier is likely to be wrong. It has been shown that softmax based consistent estimators for the learning to defer losses do not provide calibrated probability estimates for the likelihood of deferring. One other wor... | Rebuttal 1:
Rebuttal: **Q1. The presentation should be improved. It is encouraged to add a separated section to introduce the used notations.**
A1. Thank you for your constructive advice! We will summarize the used notations in and add an extra section in the revised manuscript.
**Q2. The R^c loss is not defined in D... | Summary: This paper shows that the miscalibration of the softmax-based surrogate loss for learning to defer is due to its symmetry. Instead, an asymmetric softmax-based surrogate loss is proposed and proved to be both calibrated and consistent. More generally, they reveal the connection between miscalibration and the s... | Rebuttal 1:
Rebuttal: **Q1. Can the proposed asymmetric softmax-based surrogate be generalized to the setting that an additional constant cost will be triggered when the model choose to defer to experts? If such generalization is available, will it have the same issue of underfitting as shown in [1]?**
A1. Thank you f... | Rebuttal 1:
Rebuttal: ## General Response
We thank all the reviewers for their valuable comments and devoted time. We are glad that all the reviewers praise the insight and theoretical contribution of this work. We are also encouraged that the reviewers find this work easy to use (Reviewers Ztxz, r3aW), and appreciate... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the learning to defer (L2D framework), where one can defer to an expert decision when unsure about the model’s prediction, and a cost is incurred when either the prediction is wrong or when one defers to the expert and the expert makes a mistake.
The paper builds on top of prior work that sh... | Rebuttal 1:
Rebuttal: **Q1. The authors should add the comparison/discussion with [1] or any possible other asymmetric variation of softmax.**
A1. Thank you for raising this concern! In this work, the asymmetric softmax is introduced to directly map the scoring function into the desired region $\Delta^{K}\cup[0,1]$ wh... | null | null | null | null | null | null |
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks | Accept (spotlight) | Summary: The authors study a finite width correct to the Dynamical Mean Field Theory (DMFT) of finite depth neural networks in the feature learning regime. While I will be the first to admit that I am not an expert on the DMFT calculations, the authors did produce very convincing simulations capturing interesting prope... | Rebuttal 1:
Rebuttal: We thank their reviewer for their support and good questions. We hope to make our methods more understandable and self-contained in the paper. Below we provide some more explanations about how we solve our self-consistent equations.
### Response to Questions
1. This is a great question that we... | Summary: Building on past work which set up a DMFT (Dynamical Mean Field Theory) for fully connected networks in the infinite width limit (where the width of each layer tends to infinity), this paper reasons about the *fluctuations* around the infinite width limit. This is important because for finite sized neural netw... | Rebuttal 1:
Rebuttal:
We thank the reviewer for appreciating the strengths of our approach and its applicability to wide DNN dynamics. Below we provide some responses to the weaknesses, questions and limitations.
**Responses to Weaknesses**
We agree that the paper is spread a bit thin at times. Based on the detaile... | Summary: The paper addresses the problem of analytical description of the rich (feature learning) dynamics of neural networks. To achieve this, the authors use previously introduced dynamical mean field theory (DMFT), which identifies several key characteristics of the problem - order parameters - defines their probabi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and supportive comments. Below we address the weaknesses pointed out and attempt to answer the reviewer's questions.
**Response to Weaknesses**
The reviewer is correct to point out that the DMFT equations are difficult to solve numerically and that t... | null | null | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their detailed reading and comments. We appreciate the general support for this paper and the comments on the paper's strengths and weaknesses. Many concerns were shared among reviewers which has caused us to make the following updates to the paper
1. We spend mor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Reduced Policy Optimization for Continuous Control with Hard Constraints | Accept (poster) | Summary: This paper proposes RPO to handle general hard constraints (equality and/or inequality constraints per step) for RL. The framework consists of construction and projection stages with a penalty loss for end-to-end training. Finally, the authors validate the effectiveness of their approach on 3 test benchmarks w... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading and valuable suggestions. Below we will answer your concerns point-by-point.
> **Q1**: This paper misses the recent literature [R5], which also deals with hard safety constraints for RL.
**A1**: Thank you for your valuable suggestion. We have ca... | Summary: Inspired by the GRG algorithm, this paper proposed a new reduced policy optimization (RPO) algorithm to handle hard equality and inequality constraints that must be satisfied by any learned policies for continuous control. The algorithm consists of two separate phases. Phase 1 involves the training of the poli... | Rebuttal 1:
Rebuttal: We thank you for your thorough reviews to help us improve the quality of our work. We will answer all the questions that you concern about.
> **Q1**: What is the real technical contribution of RPO?
**A1**: Thank you for your constructive suggestion. The real technical contribution of RPO is to ... | Summary: The paper solves the RL problem with equality and inequality hard constraints with a reduced policy optimization (RPO) algorithm, which combines RL with the generalized reduced gradient (GRG) algorithm. RPO partitions actions into basic actions and nonbasic actions following the GRG method, outputs the basic a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback on our paper. Here we address your detailed questions as below:
> **Q1**: In Equation (4), it seems that there is an implicit assumption that $J^F_{:, m:n}$ is invertible. However, this may not always be true.
**A1**: Thank you for raising the ... | Summary: The authors introduce a policy optimization methodology suitable for continuous control problems with hard constraints. The optimization framework, named Reduced Policy Optimization (RPO), utilizes mathematical tools such as Generalized Reduced Gradient (GRG) and Lagrangian relaxation to address hard (equality... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable feedback and constructive comments! We itemize the weaknesses or comments you mentioned and answers to them.
> **Q1**: The number of experiments and the complexity is insufficient to back up the algorithm's robustness and versatility for complex tasks.
**A... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of considerate and meaningful suggestions to help us improve our paper. We sincerely appreciate that the reviewers find our work "innovative" (RHA4), "interesting" (u5yD, ixcf) and "novel and well-motivated as the first attempt to introduce GRG to ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC | Accept (poster) | Summary: The paper presents a new training and sampling procedures for learning energy based generative models. The method is compared to earlier (few-step) MCMC based approaches and to diffusion models. The procedure is evaluated on computer vision tasks.
Strengths: The paper presents an original algorithm, with a ... | Rebuttal 1:
Rebuttal: ### Thank you for your detailed comments
We sincerely thank you for your time and detailed comments. Below, we provide point-to-point responses to hopefully address the concerns you have.
- > Additional context in goals of this direction and the limitations of previous approaches would be helpful... | Summary: This paper proposes the DAMC sampler (Diffusion-Amortized MCMC) and develops a new learning algorithm for LEBM (Latent-space Energy-Based Model) based on it. Theoretical and empirical evidences are provided for the effectiveness of our method.
Strengths: The paper is generally well-written. The idea of amor... | Rebuttal 1:
Rebuttal: ### Thank you for your insightful comments
We sincerely thank you for your time and thoughtful comments! Below, we provide point-to-point replies to your comments that hopefully would address the concerns you have.
- > Methodology connection and experiment comparison with existing methods using a... | Summary: The authors propose DAMC, an amortization of MCMC sampling, via a scheme based on diffusion models, as an alternative to pure MCMC sampling, which usually suffers from either long mixing time or from being short and biased,for priors and posteriors in energy based models. The method is theoretically sound, an... | Rebuttal 1:
Rebuttal: ### Thank you for your insightful comments
We sincerely thank you for your kind words and thoughtful comments! Below, we provide point-to-point responses to hopefully address the concerns you have.
- > Are there some constraints that the latent space needs to satisfy for the proposed method to ha... | Summary: This paper proposed a diffusion-based amortised method to address the short-run MCMC samplers issues in the latent-space energy-based models. One interesting part is that it interleaves the distill T-steps of Langevin dynamics and KL divergence minimisation to sample from the target distribution $\pi$. Regardi... | Rebuttal 1:
Rebuttal: ### Thank you for your constructive comments
We sincerely thank you for your kind words and thoughtful comments! Below, we provide point-to-point responses to hopefully address the concerns you have.
- > p_{uncond} may need more explanation, rather than a short word in the input of Algorithm. $z_... | Rebuttal 1:
Rebuttal: ### Summary of our response
We thank the reviewers for their insightful and constructive comments and careful reviews of our paper! We appreciate that the reviewers consider our submission "well-written and well-motivated", "clearly stated", "new" and "interesting" and provide "convincing", "diver... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a diffusion-based amortized MCMC method for sampling the prior and posterior in latent space energy-based models. The paper provides some theoretical evidence using directly the result from Li et al., 2017. The paper shows the effectiveness of the proposed method throughout an extensive camp... | Rebuttal 1:
Rebuttal: ### Thank you for your detailed and insightful comments
We sincerely thank you for your time and constructive comments! Below, we provide point-to-point replies to your comments that hopefully would address the concerns you have.
- > The idea of amortizing the short-run prior and posterior MCMC s... | null | null | null | null | null | null |
Entropic Neural Optimal Transport via Diffusion Processes | Accept (oral) | Summary: The paper proposes to solve dynamic entropic optimal tansport (EOT), also known as Schrödinger bridge problem, with nerual solver. Specifically, the authors propose a saddle-point, maximin, formulation of EOT, yielding a GAN-resemble algorithm that can be trained in an end-to-end fashion. Experiments are condu... | Rebuttal 1:
Rebuttal: Dear Reviewer KyaP, thank you for your comments. Here are the answers to your questions.
**(1) Connection to OT for unregulated case $\epsilon=0$.**
**We emphasize that our work focuses on developing a new algorithm for solving entropic OT and the equivalent SB problem. This implies that $\epsil... | Summary: Inspired by how Sinkhorn duals are derived the authors adapt said derivation to the path measure and via the disintegration theorem they derive a novel unconstrained min-max objective for solving the Schrodinger bridge problem, the authors then proceed to showcase their method in eOT based tasks, introducing a... | Rebuttal 1:
Rebuttal: Dear Reviewer WYHi, Thank you for your comments. Here are the answers to your questions.
**(1) Comparison with MLE-SB.**
Comparing entropic OT methods is difficult because they are based on different principles: IPF-based (MLE-SB, DiffSB, FB-SDE), dual form based (LSOT, SCONES), semi-dual form b... | Summary: This paper focuses on neural optimal transport and more particularly through a "dynamic schrodinger bridge" approach. I am not an expert in this particular topic, but I must say that the authors manage to make it quite readable and a good introduction to the methodology.
As far as I can tell, the particulari... | Rebuttal 1:
Rebuttal: Dear Reviewer LQN6, thank you for your comments. Here are the answers to your questions.
**(1) p3 "Hence, one may optimize (5) over processes $T$ for which $T_{|x,y}=W_{|xy}$ for every $x, y$ and set the last term in (6) to zero". How do you actually do that ? By enforcing gaussian dynamics ?**
... | Summary: The main idea of the paper is to estimate a stochastic map for the entropic optimal transport problem using its connection to the dynamic Schrödinger bridge (SDB) problem. The authors formulate the SDB as a saddle point problem of an associated Lagrangian. Then they recover the transport plan as the joint dist... | Rebuttal 1:
Rebuttal: Dear Reviewer TSgQ, thank you for your comments. Here are the answers to your questions.
**(1) In line 223, it is mentioned that the negative entropy is not strongly convex. This is false as the function $p\mapsto x\ln(x)$ has second derivate $x \mapsto \frac{1}{x}$ which is bounded from below by... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for taking the time to review our paper.
Your valuable feedback and constructive comments are greatly appreciated. We are particularly pleased that all reviewers found our paper well-written and easy to read (TSgQ, LQN6, WYHi, KyaP). We are also pleased that you find our... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents | Accept (spotlight) | Summary: This manuscript introduces the concept of calibrated forecasts in repeated Stackelberg games (SG) and proposes two concepts: the calibrated Stackelberg games (CSG) that generalizes the standard SGs and the adaptive calibrated forecast. The technical contribution is as follows: First, a principal's learning alg... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback on our paper. We’re happy to see that the reviewer recognizes the importance of addressing Stackelberg games through calibration, moving beyond traditional assumptions. We address the specific questions in the subsequent paragraphs:
**Stackelberg sec... | Summary: In this work, the authors consider a problem of Calibrated Stackelberg Games (CSG), which is a generalization of the Stackelberg Games. These framework differ from the standard online learning problems as in the SG framework instead of only having a single learner entity, there is a principal and an agent. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of our work.
**On whether Nonasymptotic guarantees are possible**
Yes, absolutely! While we state our results in asymptotic forms in the main body, we have already provided non-asymptotic guarantees for finite time horizon in the appendix. Specifically,... | Summary: The paper defines and studies a new Stackelberg games setup. Rather than making some standard assumptions --- e.g. that the principal and/or the agent exhibit specific types of play (e.g. agent playing no regret), or assuming access to the agent’s best response oracle, etc. --- this paper only assumes that the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful comments. Below we address the specific questions.
**The principal needs to know the agent’s calibration rate**
We will add a more detailed discussion to our paper.
On the one hand, the principal does *not* need to know the agent's exact c... | null | null | Rebuttal 1:
Rebuttal: Please refer to the attached PDF for the added figures.
Pdf: /pdf/d47477467891d068fd3532dfd46daae32d9e5910.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Switching Autoregressive Low-rank Tensor Models | Accept (poster) | Summary: This paper introduces a new unsupervised probabilistic model for time series analysis: the Switching Autoregressive Low-rank Tensor (SALT). It combines a Low Rank Tensor parametrization of autoregressive (AR) models with switching Dynamics. The two main contributions are:
1) The SALT model itself. Although t... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4hZ1
We thank the reviewer for the time to review our submission; for their detailed and insightful review; and for highlighting the contributions and the clarity of our submission.
## R.E. Weaknesses:
**1. Section title:**
>Title 3.3 is confusing. I agree that the theor... | Summary: The paper introduces a new model for time-series called SALT (switching autoreg. low-rank tensor). The goal with SALT is to offer a "best of both worlds" alternative to AR-HMMs and switching linear dynamical systems (SLDS).
SALT's relative advantages are:
* enjoys closed-form parameter estimation (unlike SLDS... | Rebuttal 1:
Rebuttal: # Response to Reviewer 6MVR:
We thank the reviewer for taking the time to read our submissions and for their detailed and insightful feedback. We especially appreciated the description of our method as “elegant”!
The main theme of your review seems to be centred on the experimental utility of S... | Summary: This paper proposes a new time-series model called Switching Autoregressive Low-rank Tensor Model (SALT) that combines the advantages of autoregressive hidden Markov models and switching linear dynamical systems while addressing their weaknesses. SALT allows for longer range dependencies without overfitting an... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for their detailed and insightful feedback. The five strength points were particularly heartening. We now provide more detailed feedback.
## RE Weaknesses
**1. Theoretical analysis:**
>Lack of theoretical analysis: While the... | Summary: The paper proposes Switching Autoregressive Low-Rank Tensor (SALT) models, a variant of an Autoregressive Hidden Markov Model (ARHMM) in which the model’s temporal dynamics are captured by a low-rank tensor approximation, thus combining the parameter efficiency of Switching Linear Dynamical Systems (SLDS) with... | Rebuttal 1:
Rebuttal: # Response to Reviewer BTJL
We thank the reviewer for taking the time to read our submission and for their detailed and insightful feedback. The strengths you outline really neatly encapsulated our objectives, and so that was great to hear! We will now provide some more detailed feedback beyond... | Rebuttal 1:
Rebuttal: Thank you to all four reviewers for taking the time to read our submission and provide insightful and constructive feedback. We presented Switching Autoregressive Low-Rank Tensor (SALT) Models, which combine the benefits of ARHMMs and SLDS models (such as parameter efficiency, fast exact inference... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning | Accept (poster) | Summary: This paper proposed Multi-Task Diffusion Model, a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multitask offline settings. The performance of the proposed model on Meta-World and Maze2D benchmarks was shown.
Strengths: This pa... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and a positive assessment of our work! We are glad that you find our paper easy to follow, well-written, and the first to achieve both effective planning and data synthesis for multi-task RL. To address your concerns, we have added additional experiments on MuJ... | Summary: The paper studies the use of diffusion models in offline multi-task reinforcement learning for planning and synthetic data generation. Both approaches use prompting to encode task-specific conditions for the generative model along with a transformer backbone. In the multitask setting, the approach outperforms ... | Rebuttal 1:
Rebuttal: >1. ... custom settings with large amounts of data ... minimum data needed to be effective is.
We downsample the "near-optimal dataset" to 0.1$\times$/ 0.2$\times$/ 0.3$\times$ the size via random selection. We observed that the performance of *MTDiff-p* decreases with dataset reduced, dropping a... | Summary: - The paper investigates the effectiveness of learning a diffusion model for modelling multi-task offline data. To do so, the paper introduces two variants of using a learned diffusion model: (a) by planning over a sequence of actions, (b) generating data, and using the generated data for offline policy optimi... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for a positive assessment of our work! We carefully address your concerns as follow:
>1. For Table H in appendix, it will be useful to report results for other baselines too.".
Thanks for this good suggestion! Considering the space limitation during this ... | Summary: This paper extends diffusion-based planners to multi-task settings by combining prompt learning. Specifically, a few segments from expert demonstrations are used as task prompts to distinguish different tasks and guide the diffusion model to generate task-specific trajectories. A classifier-free guidance appro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and detailed review and welcome suggestions for improvement. Here, we address your concerns as follows:
>1. The experiment section needs further revision: Figure 5 x-label is not correct.
We apologize for mistaking the x-label of Fig. 5. This figure quan... | Rebuttal 1:
Rebuttal: ### General Response
We thank all of the reviewers for their time and insightful comments. Furthermore, we are very glad to find that reviewers generally recognized our key contributions and clear presentation of our paper:
#### Contributions:
* **Method:** "This paper is the first to achieve b... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The author propose a diffusion model MTDIFF for multi-task RL. The goal is to leverage diffusion process and transformer backbone to have a sota generative model for RL. The author demonstrates its effectiveness with generative planning on meta world and data augmentation.
Strengths: Combing transformer and d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and a positive assessment of our work! We are glad that they found that our paper provides an interesting analysis and executes a straightforward idea very well. To address your concerns, we would polish our paper in the next version to make it cle... | null | null | null | null | null | null |
Sequential Memory with Temporal Predictive Coding | Accept (poster) | Summary: This paper proposes to use Predictive Coding Networks for temporal association of sequences.
Strengths: The paper is well-structured and easy to follow. The motivation is clear: a deep network model with biologically plausible learning algorithms for sequence learning.
Weaknesses: 1. This paper lacks of nove... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments on our paper. Specific responses are provided below and we kindly request that the reviewer consider reevaluating their score in light of our responses:
***
> “This paper lacks of novelty and is in fact a trivial extension of [1]. In [1], the s... | Summary: The paper presents work on (relatively) biologically-plausible neural networks for remembering sequences of inputs, extending work on temporal predictive coding nets (a simple architecture of a layer of neurons for feature values and a layer for prediction error, with some interneurons) and asymmetric modern h... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments on the additional references and possible extensions of our model. Specific responses are given below:
***
> “The sequential memory solutions considered here use changes to connection weights to store the sequence, these might be compared with ... | Summary: The authors propose a temporal predictive coding model that can memorize and recall sequences. The model performs better than a model based on asymmetric Hopfield networks. The authors provide a theoretical evaluation end explain the reasons for better performance. This work is inspired by neuroscience results... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments on connecting our models to behavioral data and additional references. Specific responses are provided below and we kindly request that the reviewer consider reevaluating their score in light of our responses:
***
> “In its current form, the pa... | Summary: The paper generalizes predictive coding as a method of training neural networks to Hopfield networks, giving a model of temporal predictive coding (tPC). tPC proves itself able to memorize discrete sequences at a level competitive with Asymmetric Hopfield Networks in experiments, and provides an intriguing hi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments on generalization and connection of our model to cognitive maps. Specific responses are provided below and we kindly request that the reviewer consider reevaluating their score in light of our responses:
***
> “The authors overclaim about biol... | Rebuttal 1:
Rebuttal: **We performed additional experiments as requested by the reviewers and presented the results in the attached PDF file. Since experiments in Fig 1 and 3 are related to the comments from multiple reviewers, we include descriptions of them here for reference:**
> Description of Figure 1:
In this e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution | Accept (poster) | Summary: The key idea behind the proposed Bi-Sampling 12 Parameter Attribution (BSPA) method is to reconcile the unbalanced inherent data bias, namely the heavy-tailed distribution are visually more important than smooth areas. Similar observation has been extensively observed in the literature of image restoration and... | Rebuttal 1:
Rebuttal: Response to Reviewer xmJz (denoted as R5)
*Q5-1: The proposed bi-sampling framework in Fig. 2 seems to be based on heuristics.*
A5-1: It is **unreasonable** to say the proposed framework is heuristics. We aim to propose a simple yet effective bi-sampling parameter attribution method for accurat... | Summary: The uniform sampling of the data, with flat regions occupying most of the training samples, can impair the accuracy of the reconstruction. Therefore, the authors enhance the model representation from the perspective of data sampling and propose a simple and effective Bi-Sampling Parameter Attribution (BSPA) me... | Rebuttal 1:
Rebuttal: Response to Reviewer xmJz (denoted as R4)
*Q4-1: The significance of the experimental results is not sufficient.*
A4-1: We have already supplemented more experiments about integrating the proposed method on the latest model, the effect of parameter K, and the additional time costs. Thanks for as... | Summary: This work focuses on studying the unbalanced distribution of the SR training data. The authors propose a bi-sampling strategy with parameter attribution. The bi-sampling consists of uniform sampling and inverse sampling, which pay more attention to hard samples. Moreover, integrated gradient is introduced to m... | Rebuttal 1:
Rebuttal: Response to Reviewer 4W2B (denoted as R3)
*Q3-1: The motivation is not so novel. The data unbalance of SR training data has been widely mentioned in previous works. Many sampling strategies have also been proposed to solve this problem.*
A3-1: **Motivation**. Most of the existing SR models only ... | Summary: Observing the issue of uneven distribution of image contents, the author proposed to utilize inverse data sampling to resolve the inherent unbalanced data bias. In the proposed BSPA method, SR model is alternatively updated with uniformly and inversely sampled image data. For the latter, only part of the trivi... | Rebuttal 1:
Rebuttal: Response to Reviewer tDMi (denoted as R2)
*Q2-1: A few notations in the paper are inconsistent and confusing.*
A2-1: We are sorry about this. It should be $(x^{us},y^{us})$. We will revise it in the new version.
*Q2-2: The experimental results of ablation studies presented in Tab. 1, 2, 3 only ... | Rebuttal 1:
Rebuttal: In this uploaded PDF, we mainly provide more visual results on benchmark datasets and the histogram distribution.
Pdf: /pdf/0f08835aec9f03b5912b6c6ae1555f4346f81717.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of data imbalance in single image super-resolution (SISR) training, where the majority of training samples contain flat regions while only a small percentage represents sharp regions with rich texture details. The authors propose a Bi-Sampling Parameter Attribution (BSPA) metho... | Rebuttal 1:
Rebuttal: Response to Reviewer zt8x (denoted as R1)
*Q1-1: In Table 1-3, the authors perform evaluations on Set14, which contains only 14 images. This may not help draw robust conclusions to more general scenarios under limited data size. The authors are suggested to conduct ablation study on larger datase... | null | null | null | null | null | null |
Provable convergence guarantees for black-box variational inference | Accept (poster) | Summary: This paper offers the first convergence results for black-box VI, a widely used and popular framework for Bayesian problems. Under assumptions on the log model, $\log p$, and given a Gaussian variational family of distributions, convergence rates are established by utilizing recent advances in the field. The a... | Rebuttal 1:
Rebuttal: ### C is a matrix but C=0
Yes, in this context, of writing something like w=(m,0) then 0 means a matrix of zeros. If φ(z)=-log p(z,x), then the gradient noise is bounded in terms of how far the parameters w=(m,C) are from representing a delta function centered at the MAP solution. (Note this is d... | Summary: This paper offers a convergence proof for the stochastic optimization problem inherent in full-rank Gaussian variational inference when the log-density of the target is concave. The primary challenge of the convergence proof lies in managing the non-smoothness present in the entropy term of Gaussian VI. This i... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
### Convergence result when data subsampling
Our proofs can indeed address subsampling with minor technical difficulty, namely bounding the variance of a slightly changed gradient estimator. Note that the main optimization results only depend on (1) the stru... | Summary: The paper proves 1/sqrt(T) respectively 1/T convergence rates for black-box variational inference methods, when implemented with a proximal stochastic gradient method. Such rates were not available in the literature until now, due to difficulties in bounding the gradient noise. The main contribution of the pa... | Rebuttal 1:
Rebuttal: Thank you for your review.
### Natural gradient algorithms
We agree this should be discussed. We will also mention that extending theory to address these algorithms remains an open problem.
### Related work by Diao et al.
We agree we should mention these recent works. (We note that they make u... | Summary: The paper addresses the lack of provable convergence guarantees for black-box variational inference (VI) and proposes convergence guarantees for two stochastic optimization algorithms applied to Gaussian variational families.
The authors identify challenges in analyzing VI as a standard stochastic optimizati... | Rebuttal 1:
Rebuttal: Thank you for your review. We will respond to two points.
### Examples
We ask for some consideration for the constraints imposed by a 9-page limit. Given that this is a theoretical paper whose goal is to provide guarantees for algorithms already commonly used in practice, we prefer to focus enti... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper analyzed the convergence of Black-box VI, which has been widely used in variational inference in recent years. Under the assumption that the target joint distribution is convex or strongly convex and the variational posterior distribution is Gaussian, the convergence of the variational parameter obt... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
### Novelty of the analysis
Given the current state of knowledge, the convergence of Prox/Proj-SGD is not apparent, because the convexity of the objective function alone is not enough. A second essential hypothesis must be satisfied by the estimator of the gr... | null | null | null | null | null | null |
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery | Accept (poster) | Summary: The paper addresses the research question of unsupervised 3D object location detection from LIDAR data in autonomous driving scenes.
The proposed method, DRIFT, improves upon the MODEST baseline by incorporating heuristics for judging objectness likelihood and using them as rewards within a reinforcement lear... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and appreciation of our contributions! We address individual questions below:
> Include context within the well-studied objectness literature
We thank the reviewer for pointing out the comprehensive works [1, 2] and the explanation of the concept of objectness... | Summary: The proposed DRIFT framework is an approach to realize object discovery without labels. DRIFT first extracts foreground proposals based on the PP-score method, and then leverages common-sense heuristics including shape prior, box alignment, and background point filtering, to reward proposed boxes. Reinforcemen... | Rebuttal 1:
Rebuttal: Thank you for your positive reviews and thoughtful feedback. We address the individual points below:
>Missing related works
Thank you for pointing to these papers. Unfortunately we did not find code for them, and thus could not compare with them during our rebuttal. We will instead include discu... | Summary: This paper proposes a new reinforcement-learning-based framework for unsupervised 3D object detection that uses these common-sense heuristics directly as a reward signal. Avoid handcrafting training examples for each object detector. Furthermore, under the premise of greatly accelerating the convergence speed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments, and will incorporate all of the edits/analysis that they suggest. Regarding the mention of LLMs, our method draws inspiration across many different fields (Reinforcement Learning, Object discovery, RLHF) and we had hoped to showcase an example of ... | Summary: This paper proposes DRIFT, a novel reward fine-tuning method for unsupervised object discovery with point cloud input. Specifically, three reward methods are proposed to identify good bounding boxes. First, shape prior reward prefers bounding boxes with similar sizes to the prototypes. Second, an alignment re... | Rebuttal 1:
Rebuttal: > About using the Gaussian distribution as an approximation
We agree with your point that the Gaussian distribution approximation might not be the most accurate one, but from our observation the hood or front window of vehicles only contribute to a small fraction of the point cloud (Figure 2 in t... | Rebuttal 1:
Rebuttal: We express gratitude to the reviewers for their constructive feedback on our work and appreciate their acknowledgment that the writing is "well written" and "easy and intuitive" to follow [iC9C, ZfQG, vhou, Baoq]. To reiterate, our work introduces a novel adaptation of Reinforcement Learning (RL)-... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work contributes to unsupervised object discovery in LIDAR point clouds. It defines a few typical properties of customary bounding boxes in LIDAR point clouds and then develops Rewards for a Reinforcement Learning algorithm to learn this, lacking a gradient for direct optimization.
It is based on Persist... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We address the individual points:
> Novelty: compared to MODEST
We highlight that although MODEST and DRIFT both use commonsense properties, MODEST uses them only when generating seed labels and filtering between self-training rounds. In contrast, DRIFT directly inc... | null | null | null | null | null | null |
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models | Accept (poster) | Summary: The authors propose a sampling method for diffusion probabilistic models by solving an alternative SDE with the same marginal distribution. Approximation techniques are applied in constructing the computationally efficient solver and corrector . The results show a superior FID with less function evaluations.
... | Rebuttal 1:
Rebuttal: First, we would like to thank you for taking the time to carefully review our paper, acknowledgment of our novel contributions, and the insightful questions. Below we respond to the questions:
Q1: The paper content seems not well organized yet.
A1: Thank you for your sincere advice. The paper is... | Summary: This paper extends UniPC to a stochastic manner to obtain a faster sampler called SA-Solver for diffusion models. Specifically, the authors start from the formulation of diffusion SDE and derive the SA-Predictor and SA-Corrector. Extensive experiments on CIFAR10, ImageNet, LSUN, etc demonstrate the effectivene... | Rebuttal 1:
Rebuttal: First, we would like to thank you for taking the time to carefully review our paper, acknowledgment of our novel contributions, and the insightful questions. Below we respond to the questions:
Q1: It is questionable whether the novelty of SA-Solver is enough.
A1: As we have claimed in lines 38-3... | Summary: The paper proposed a stochastic Adam solver for solving diffusion SDEs in an efficient way with a convergence guarantee. Authors adapt the stochastic Adam from numerical literature and use Lagrange interpolation to predict unknown terms. They show strong convergence for both predictor and corrector. Numerical ... | Rebuttal 1:
Rebuttal: First, we would like to thank you for taking the time to carefully review our paper, acknowledgment of our novel contributions, and the insightful questions. Below we respond to the questions:
Q1: I don't think I fully understand how to choose the parameter tau(t) in the experiments. Please comme... | Summary: The paper proposes a new solver for diffusion SDEs, termed SA-Solver, combining the ideas of a predictor-corrector scheme and the stochastic Adam solvers. The predictor and corrector utilize the Lagrange polynomials for extrapolation to lower the approximation error at future time stamps. Experimentally, SA -S... | Rebuttal 1:
Rebuttal: First, we would like to thank you for taking the time to carefully review our paper, acknowledgment of our novel contributions, and the insightful questions. Below we respond to the questions:
Q1: I think some theoretical analysis in the main text to make the paper more self-contained. A concurre... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of fast sampling of diffusion models. Since standard DDPM sampling is slow, this is a very important topic of late, with many competing methods. Many methods reformulate as a ODE solving problem, which makes it easier to do few-step sampling. However, it has been noted that sampl... | Rebuttal 1:
Rebuttal: First, we would like to thank you for taking the time to carefully review our paper, acknowledgment of our novel contributions, and the insightful questions. Below we respond to the questions:
Q1: Seems to be missing quantitative evaluation (e.g. CLIP score on stable diffusion) for text-to-image ... | Summary: The paper presents a multistep SDE solver for diffusion models instead of ODE solvers. The main goal is to have diverse and high-quality samples while reducing the number of solver step required. To do this, the paper proposes a new SDE that includes an additional term, ensuring the marginal distribution uncha... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and carefully reviews. Below are our responses to the raised questions:
Q1: The proposed method may be considered incremental. This is because the key factor leading to good results, such as predictor-corrector, and multi-step have been well-established in ... | null | null | null | null |
Norm-guided latent space exploration for text-to-image generation | Accept (poster) | Summary: This paper proposes a novel method for interpolating between two seeds and demonstrates that it defines a new non-Euclidean metric that takes into account a norm-based prior on seeds. This paper describes a simple yet efficient algorithm for approximating this metric and using it to further define centroids i... | Rebuttal 1:
Rebuttal: Thank you for finding our approach effective with good theoretical support. We address your comments below.
#### **Q1: Core path optimization and centroid method did not elaborate enough.**
**A1:** We value the reviewer’s feedback to improve our paper. Due to lack of space, we provide more detail... | Summary: The paper observed that the seed (noise) for the trained diffusion model has a property that the norm of the seed, which follows the $\chi$ distribution, is concentrated around a certain positive number, $\sqrt{d}$ where $d$ denotes the seed dimension. Based on this observation, the paper proposed a way to exp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback, acknowledging the broad usability of our approach and the well-designed, easy-to-follow experiments. We address your comments below.
#### **Q1: It is unclear how many piece-wise linear paths are used.**
**A1:** For few-shot learning benchmarks we ... | Summary: This paper investigates a new method for interpolating in the seed space of diffusion models, which is the Gaussian distribution used to initialize the generation process. Experiments demonstrate that diffusion models struggle with generation when the norm of the input differs from the distribution of norms of... | Rebuttal 1:
Rebuttal: Thank you for finding our approach effective with compelling results. We address your comments below.
#### **Q1: Comparing with Asyrp [1].**
**A1**: We value the reviewer's suggestion to conduct a comparison between interpolation in the input space (our approach) and interpolation in a feature sp... | Summary: This paper makes the observation that current training procedures make diffusion models biased toward inputs with a narrow range of norm values. To address this issue, the authors propose a novel method for interpolating between two seeds and demonstrate that it defines a new non-Euclidean metric that takes in... | Rebuttal 1:
Rebuttal: Thank you for finding our approach inspiring and interesting. We address your comments below.
#### **Q1: The proposed approach only works well with seed optimization.**
**A1:** Let us explain where and why NAO works well without seed optimization and the relation between the two methods. First... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We were happy to see that the reviewers have found our approach **"interesting and inspiring"**, **"well-motivated" (R1)**, and recognized its **potential to benefit various tasks related to diffusion models' applications (R1, R3)**. Additionally, they have acknowledged... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Learnability of In-Context Learning | Accept (poster) | Summary: The authors propose a PAC framework to analyze the expressiveness power of in-context learning in a finite sample complexity scheme. The framework consists of largely two parts, first is the initial pretraining of the next token prediction phase, and the second is the in-context learning phase. Regarding the p... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and supportive feedback.
1. Regarding the model size, intuitively the learning algorithms in Assumption 1 is a large language model that fits the pre-training distribution well enough. By assuming a scaling law behavior with respect to the model size, one might g... | Summary:
This paper attempts to formalise the few-shot in-context learning phenomenon observed in large language models. To that end, they make a set of assumptions about the underlying data-generating distributions, pretrained models, etc and try to formalise the task of in-context learning in the PAC framework.
Th... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and supportive feedback.
1. We agree that in real world data there is an additional distribution shift which is caused by the fact that tasks in the pre-training mixture distribution usually use more soft labels and more flexible input formats. That being said, t... | Summary: The paper presents a theoretical framework for in-context learnability. The framework is grounded in the Probably Approximately Correct (PAC) learning theory and provides the first-ever finite sample complexity results for the in-context learning setup. The authors' approach involves a pretraining phase follow... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and supportive feedback.
1. Regarding the transition between the pretraining phase and the in-context learning phase, please note that we analyzed vanilla in-context few-shot learning as introduced in the GPT-3 paper [1], which does not have an instruction-tuning... | Summary: This paper studies the PAC-learnability of in-context learning when the pretraining distribution is a mixture of latent tasks, and the downstream task belongs to one of them. In addition to this mixure-of-tasks assumption, the other non-trivial assumptions the authors make include: the pretrained model can app... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and supportive feedback.
1. We agree that pre-training data in the real world is often messy, and we will acknowledge that as a limitation in the camera-ready paper. Note that we already discussed some limitations of our work in the last paragraph of Section 5. S... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful feedback. We apologize for the broken links to the appendix. The link in line 266 should point to section 1 in the appendix, while the link in line 305 should point to section 2 in the appendix.
Pdf: /pdf/fdd0525b646b645ea401f2208117ba4ac58bd6cb.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to explain the learnability of in-context learning. The main idea is that the pretraining tasks learn multiple downstream tasks and the prompt specify a particular task.
Strengths: 1. The paper addresses the learnability of the in-context learning. It's a hot topic and very few theories are co... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback.
1. Increasing the number of in-context examples has been shown to be beneficial in practice. Evidence for this can be traced back to the GPT-3 paper [1], in which Figure 1.2 clearly demonstrates that performance improves as the number of in-context examp... | Summary: In-Context Learning (ICL) allows large language models (LLMs) to be easily specialized to natural language downstream tasks. When users input a concatenated string of examples of a particular downstream task, modern LLMs often perform successfully without changing their weights, providing an effective new angl... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback.
### Regarding Assumption 2
Our PAC-style guarantees are worst-case in nature and do not depend on the sampled $s_1$ and $s_2$. That being said, your suggestion is a promising direction for extending our framework to input-dependent bounds, which will el... | null | null | null | null |
Collaborative Alignment of NLP Models | Accept (poster) | Summary: This paper frames operationalizing concept as a solution to enumerate all possible concepts and developed a corresponding framework CoDev, that starts by collecting text and labels from users, then have GPT-3 generates text and labels. When there are disagreement among users and GPT-3, users would be asked to ... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments and constructive feedback.
W1) As mentioned in the general rebuttal we agree that literal disagreements and multiple possibilities is a very important question but even in the absence of literal disagreements, the problem of ML interference still remains and that is... | Summary: This paper proposes a new framework to debug NLP models. Specifically, while debugging a global model, it starts with training some local models on specific concepts. Then new data used for model improvement is labeled if the global model disagrees with the local model.
Experiments indicate that the proposed... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive feedback.
- W1) Unfortunately providing theoretical results for deep neural nets (and transformers in our case) is an open problem however, previous work (reference 10,11,12,13) consider overparameterized linear regression as a way to provide some insights ... | Summary: This paper describes a multi-user collaborative model alignment framework that teaches certain desired concepts (behaviors, rules) to large language model (LLM).
The authors train a global model that intergrates the original data and all concepts, and a local model for each concept. The LLM is guided to genera... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback. As mentioned in the general rebuttal, we will release code and data in the camera ready, and we will make the experiment section more clear. Hopefully this information removes this weakness, leading to an improved score.
---
Rebuttal Comment 1.1:
Comment: I... | Summary: The paper proposes a method based on data augmentation and instance selection for training a supervised model to be aligned with "concepts", where concepts dictate specific model behavior on certain inputs. In the proposed setup users illustrate a concept with a training example, this is followed by generating... | Rebuttal 1:
Rebuttal: Thanks a lot for your constructive feedback.
W1) In this work we focused on post-training adjustment to enforce business rules, rectify undesired behavior, or align with user values. A concept relates a set of inputs to desired behaviors, e.g. “religion does not connote sentiment”. For teaching... | Rebuttal 1:
Rebuttal: We like to thank the reviewers for their constructive feedback, and stating that the problem we are considering is “important” and “beneficial to the community”, also stating that our “approach is novel”, our “theory provides some insights”, the paper is “well written” and “experiments show the ef... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a framework for collaborative NLP development (CoDev) that enables multiple users to align a model with their beliefs.
The proposed work CoDev aids users in clarifying their concepts (an area humans often struggle with) and assists ML models to handle conflicts between concepts (an area ML ... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive feedback.
- W1) We chose only positive and only negative cases to showcase that even in a very extreme case of bias in the seed data, CoDev can still generalize to the whole concept. We will add extra experiments with different distributions such as other... | null | null | null | null | null | null |
Privacy Auditing with One (1) Training Run | Accept (oral) | Summary: The paper improves the computational efficiency of auditing differentially private machine learning systems by connecting differential privacy and statistical generalization. The authors propose the first 1-round scheme compared to the standard solutions with hundreds of training rounds.
The auditing procedu... | Rebuttal 1:
Rebuttal: Thank you very much for your review!
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Rebuttal Comment 1.1:
Comment: Thanks for the reply!
I have read all contents on this page. I will keep my rating. | Summary: The paper gives a simple version of a differential privacy (DP) auditing, and the proposed method is related to the recent works (G. Andrew, P. Kairouz, S. Oh, A. Oprea, H. B. McMahan, and V. Suriyaku- mar. “One-shot Empirical Privacy Estimation for Federated Learning”, 2023 and S. Zanella-Beguelin, L. Wutschi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, which we respond to now.
**On comparison with previous works:** Nasr et al. [NHSBTJCT23] uses multiple runs and, as a result, they achieve tighter bounds than we do for the same algorithm; however, we are not sure if there is any meaningful comparison th... | Summary: The authors propose a scheme for auditing differentially private machine learning models with a single training run (instead of thousands as have been used so far).
Strengths: 1. limitations clearly explained and illustrated
2. paper is really well structured (except for related work; see weaknesses)
3. impor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, particularly the suggestions regarding the related work section.
**1. Line 123:** We will clarify that there may be overlap between the design of attacks and our analysis. But, conceptually, most auditing attack design considerations are the same in our s... | Summary: This paper presents a one shot approach for auditing privacy. Their approach is as follows: given n independent input samples and a (dp) algorithm to audit, they divide the $n$ input samples into two groups $X$ and $Y$ of size $m$ and $n-m$ respectively. Then they randomly select a partition of the first part ... | Rebuttal 1:
Rebuttal: Thank you for your time and comments. We respond below.
**Not small delta:** Our approach works well for reasonable values of delta. E.g. delta=10^-3 in Figures 1 & 2. Handling larger delta is difficult even with multiple runs.
**Conceptual roadblock:** The fact that multiple examples was previo... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the problem of auditing differentially private machine learning systems. They propose a procedure which does so in one training run -- the key is the ability to include/exclude multiple data items in the run, as well as a novel analysis via leveraging connections between DP and generalization... | Rebuttal 1:
Rebuttal: Thank you for your time in reading and reviewing our submission!
**Writing:** We will edit the paper, and especially Section 6 for clarity.
**Why this score function:** This is the same score function as used in the prior work, previous works showed it achieves tight results. Our approach works ... | null | null | null | null | null | null |
Iterative Reachability Estimation for Safe Reinforcement Learning | Accept (poster) | Summary: This paper proposed an iterative reachability estimation method for safe RL. The reachability is estimated by the probability of future trajectories entering unsafe state sets. Compare to previous reachability-based methods, the proposed method could handle stochastic dynamics and also improved the performance... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and the detailed suggestions.
>The problem formulation, equation (4) should be emphasized better... The notation system is a bit messy ... You should improve it to highlight the differences between your algorithm and the previous ones
Thank you, we will fur... | Summary: This paper presents Reachability Estimation for Safe Policy Optimization (RESPO), for safety-constrained RL in general stochastic settings. The authors extend the previous RCRL approach into stochastic settings and push the agent to (re)enter the feasible region. They formulate a safe RL problem with REF and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and detailed suggestions.
> The writing could be further improved, especially the comparison with RCRL. The reviewer acknowledges that there is some explanation of the difference between the proposed approach and RCRL, still, it would be much better to add ... | Summary: Previous approaches to safe reinforcement learning used the constrained MDP formulation where there is a constraint imposed on the cumulative sum of costs to minimise violations. This framework is not applicable very easily where there is a need for hard constraint satisfaction. The previous approach (RCRL) wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and detailed suggestions.
> it looked like RESPO was achieving a different point in the trade-off curve compared to the other methods.
Our approach consistently achieves higher rewards and lower costs compared to the other safety baselines. Particularly, R... | Summary: The paper proposes a new algorithm that may handle hard and soft constraints, in which the policy optimization and Hamilton-Jacobi reachability are leveraged to ensure safety. Moreover, experiment results on safety gym, safety PyBullet, and safety MuJoCo also show the good performance of their algorithm.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and detailed suggestions.
>Paper writing quality needs to be improved a lot, I am confused about the paper notation, e.g., $V\_h$ and $V\_c$.
We have provided the definitions of the notations in lines 108-165 of the main paper, as well as a summary of nota... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed suggestions and feedback. Our novel algorithmic contributions in this paper include 1) providing a reachability-based hard constraint satisfaction approach for stochastic and deterministic settings and 2) the ability of (re)entrance into the feasible set w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection | Accept (poster) | Summary: This paper proposes a two-phase learning framework called HubRouter for global routing in chip design. Different from previous works that directly generate routes from chip images, which potentially cause inconnectivity, this paper proposes to generate hubs representing tiles in the first phase and then constr... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback, as well as your positive comments and interest in our paper. According to your constructive comments, we make some replies to the questions.
> **W1:The overflow still exists for those generated global routing.**
Yes, overflow still exists but HubRou... | Summary: The paper focuses on the generative global routing tasks and mainly ensures the connectivity of generated routes via a two-stage framework. In the first phase, the approach involves a typical generative task, which exploits multi-task learning to promote the generation quality and utilizes a trick called strip... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback, as well as your positive comments and interest in our paper. According to your constructive comments, we make some replies to the questions.
> **W1: Displaying time overhead in either phase.**
The time overhead of the two phases is shown in Table 2 ... | Summary: This paper investigates the issue of global routing in VLSI systems and introduces HubRouter, a method that initially generates hubs and subsequently connects them to pins. In the first phase, the authors explored different generative models. In the second phase, the authors employs an actor-critic model to ge... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. According to your constructive comments, we make some replies to the questions.
> **W1: The two phases are independent, potentially leading to suboptimal result./Q1: Why the authors did not design the two phases as a loop?**
We do not design an end-... | Summary: This paper presents a new two-phase learning approach, called HubRouter, to address the issue of unconnectivity in the generated routes of global routing (GR) tasks in VLSI systems. It has two steps. Firstly, a deep generative model generates a 'hub,' which acts as a key point in the route; then secondly, HubR... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. Our replies to the questions are as follows.
> **W1/Q1: The background on the challenge of connectivity is unclear.**
Thanks for your suggestion.
Existing generative global routing methods adopt an end-to-end model and suffer from the connectivity ... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
We appreciate the reviewers’ time, valuable comments, and constructive suggestions. From an overall perspective, we are happy to see that the reviewers approve of the novelty (Co1X, Vi96), originality (Co1X, GN8b), and generality（Co1X, 2ZCa, Vi96) of our approach. ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders | Accept (poster) | Summary: The paper presents a new SSL representation learning framework remote sensing and earth observation data. The presented framework combines a contrastive objective with a reconstruction objective working on single or multi-modal inputs i.e. multispectral satellite data and synthetic aperture radar data. Cross-m... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work. We appreciate you recognizing the strengths of our work: (i) the introduction of X- and 2D- ALiBi, (ii) the optionally multimodal nature of CROMA, (iii) the extensive evaluation across methods, tasks, and datasets, and (iv) the thorough ablation.... | Summary: This paper presents a CROMA, a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multi-modal representations. CROMA separately encodes masked-out multispectral optical and synthetic aperture radar samples and performs cross-modal contrastive learning. ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work. Please see our replies below:
**Q**: *Clarification of main contributions.*
**A**: Please see our comments to all authors that clarify our contributions.
**Q**: *What is FFT?*
**A**: The term FFT does not appear in our document; perhaps the r... | Summary: This paper proposes CROMA to align optical and SAR modal images via contrastive learning and reconstruction. Comprehensive experiments on three datasets have demonstrated the effectiveness of CROMA.
Strengths: 1. This paper introduces a multi-modal representation using contrastive learning and reconstruction.... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work. We appreciate the recognition of the strengths of our work: the introduction of learning multimodal representations by jointly leveraging reconstruction and contrastive learning and the superiority over the current SoTA. We believe we can answer ... | Summary: The paper presents a self supervised representation learning model for multimodal sentinel images. The model learns from geographically aligned optical and radar (sentinel-2 and sentinel-1, respectively) representations that are then used for downstream tasks, such as classification and segmentation. In the pa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work. We believe we can address all the concerns you raise.
**Q**: *Optionally multimodal.*
**A**: CROMA is indeed optionally multimodal. In sections 4.1 & 4.2, we only use the optical encoder (Sentinel-2-only tasks). In section 4.3, we use all three... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. Before replying to reviewers individually we will restate our contributions:
* We leverage reconstructive and contrastive objectives to learn joint multimodal representations. This is not only novel for Earth Observation (EO) but is novel for multimodal ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
$\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning | Accept (poster) | Summary: This work proposes a simple yet effective temporal contrastive learning approach for encoding the high-dimensional observations and inputs for reinforcement learning. The authors propose a loss function (TACO) related to the mutual information between representations of current states paired with action sequen... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and review! Below we address the concerns and questions that you have raised. We are encouraged that you appreciate TACO's significant empirical performance and recognize the comprehensiveness of our experiments in both online and offline RL, alongside the ab... | Summary: This paper introduces an auxiliary objective based on contrastive learning to learn action and state representation for continuous control benchmarks. The auxiliary objective is called TACO. The main idea behind the objective is to maximize the mutual information between the current state s_t, current and futu... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and review! We are encouraged by your recognition of the broad applicability of our approach, as manifested in our application of TACO for both model-free and offline visual RL settings.
---
To address your question of CURL and reward prediction loss, below w... | Summary: The paper introduces TACO, a framework that learns state and action representations simultaneously in visual reinforcement learning for continuous control tasks. TACO optimizes mutual information between current state-action pairs and future state representations. It additionally optimizes 2 auxiliary losses. ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and review! We are encouraged that you recognize TACO's simplicity, flexibility, outstanding performance, and theoretical analysis, all of which contribute to the strength of our approach. Below we address the concerns and questions that you have raised.
---... | Summary: This work introduces TACO, a novel state-action representation learning technique based on contrastive learning. Empirically, TACO outperforms both model-free and model-based visual RL baselines in both online and offline settings.
Strengths: 1. The work studies joint state-action representation learning, whi... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and review! We are encouraged that you recognize the novelty of our approach in joint state-action representation learning and appreciate the promising empirical results of TACO. Below we first address your two concerns.
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**Offline RL ablation**:
We conduct... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful questions and valuable feedback. We are encouraged that reviewers recognize the importance of our tackled problem in state-action representation learning (gh9U). They also appreciate the flexibility and applicability of our proposed approach, TACO, in bo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Continual Learning for Instruction Following from Realtime Feedback | Accept (spotlight) | Summary: Training of a continuously learning, instruction following agent from feedback provided by users during collaborative interactions. The problem is that humans often give noisy reward and at irregular intervals. The method formulates the learning scenario as a contextual bandit problem and alternates between tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are looking forward to answering any follow up questions during the discussion period.
Assumption of feedback alignment: real time feedback follows patterns of human response, including its delays. So this assumption follows the role of h... | Summary: The authors propose a method for online continually training an instruction-following agent based on user realtime feedback gathered for a collaborative game CerealBAR. The agent need to follow the human's instructions and complete the task. The paper utilizes the contextual bandit learning approach, with im... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are looking forward to answering any follow up questions during the discussion period.
Model design: We provide the design of the policy model in the Appendix. In short, it is a modification of the architecture used by previous work (Suhr... | Summary: This work demonstrated a simple yet effective framework for continual learning in instruction following task utilizing human feedback. Using CEREALBAR as testbed, this work demonstrated the framework in abundant details, and show effectiveness through experiment results. This work also conducts various analysi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are looking forward to answering any follow up questions during the discussion period.
Q1, Figure 3 x axis: The x-axis represents a proportion of interactions (so it sums to 100), rather than the exact number of interactions. Each differe... | Summary: This strong work presents a systems contribution in a fully-fledged system for continual learning from language feedback, in the context of situated human-to-robot instruction following tasks. Using the CerealBar environment (roughly inspired the card game SET, with an embodied flair), this work starts by lear... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are looking forward to answering any follow up questions during the discussion period.
Other methods: We agree that experimenting with different learning methods is a good direction for future work; in this case, we opted for simplicity e... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and questions! We look forward to continuing the discussion here during the discussion period. Below are responses to general comments.
Experimenting with different learning paradigms: While we use an objective based on the popular policy gradient REINFOR... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors work on the CEREALBAR setting, where two agents (one human and one computer) cooperate using natural language to achieve a shared goal. Specifically, the authors propose a new setting where the human agent can provide binary feedback to the computer agent. In their new setting, the authors follow t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are looking forward to answering any follow up questions during the discussion period.
Contributions: As the reviewer suggests, indeed, learning from real-time human feedback in embodied interactions is certainly an underexplored area. Th... | null | null | null | null | null | null |
An Adaptive Algorithm for Learning with Unknown Distribution Drift | Accept (poster) | Summary: The paper proposes an algorithm for environments with changing distributions without assuming a priori knowledge about the change in distributions. The proposed algorithm provides error bounds that decrease with the number of time steps.
Strengths: The idea of considering independent distributions with distri... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for your feedback.
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**Question**: The idea of considering independent distributions with distribution shift is unexplored.
**Answer**: We study the classical setting where we have a sequence of drifting distributions, but the samples from those distribut... | Summary: This paper under considers the theoretical problem of determining the sliding window size for empirical risk minimization in the presence of unknown distribution changes. The proposed method aims to enable the learning of a classifier comparable to approaches that have prior knowledge of the distribution chang... | Rebuttal 1:
Rebuttal: Thank you for your work in reviewing our manuscript, and for providing thorough feedback.
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**Question**: The paper relies on computability assumptions for key variables, such as the distribution discrepancy (e.g., $|P^r_T - P^r_T|_{\mathcal{F}},
C{\mathcal{F},1}
C_{\mathcal{F},2}$). However, ... | Summary: The author propose a general algorithm to learn a family of functions with respect to the current distribution at time T. This algorithm achieve a drifting-instance-dependent bound without any prior knowledge of the drift. Based on this, the author further analyze a tractable algorithm on binary classifier.
S... | Rebuttal 1:
Rebuttal: Thank you for your close reading of our paper and for your feedback.
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**Question**: The error bounded is only measured on windows that end at T. Instead, a better goal would be to select a window from t1 to t2. As an example of why this problem could be more relevant, there might be large dri... | Summary: This research paper presents a straightforward algorithm designed to facilitate adaptive learning of models in the presence of distribution drift. The algorithm is specifically designed to adapt to changing data patterns without requiring any prior knowledge of the drift. Moreover, the paper provides a proven ... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our paper and for your feedback.
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**Question**: "The algorithm proposed is to adaptively find r that can achieve the best trade-off between the statistical and drift error. The proposed algorithm might get a competitive average accuracy compared to ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Model Spider: Learning to Rank Pre-Trained Models Efficiently | Accept (spotlight) | Summary: This paper investigates how to select the most suitable PTM given a target task efficiently and accurately.
A novel approach called Model Spider has been proposed. It learns to encode both PTMs and tasks into vectors and measures their similarity, which is further used to rank the PTMs. It can also incorporate... | Rebuttal 1:
Rebuttal: **Thank Reviewer mKeC for the valuable insights and thoughtful questions**. The feedback enhances our work's clarity and robustness. Here is our response:
**Question 1**: Generalization ability and dependency on frozen encoder $\psi$. The new task differs from the training ones.
**Answer 1**: **... | Summary: This paper introduces Model Spider, a unique method to efficiently and accurately rank Pre-Trained Models (PTMs) for a specific task within a model zoo. Model Spider innovatively creates tokens for both PTMs and tasks, encapsulating their characteristics in a manner that facilitates an efficient selection proc... | Rebuttal 1:
Rebuttal: **We deeply appreciate Reviewer 1u8a's insightful queries and constructive input**. The engagement has undoubtedly enhanced the quality and coherence of our paper. Here are our responses:
**Question 1**: Hyperparameter $k$ in Figure 1.
**Answer 1**: The hyperparameter $k$ corresponds to **the nu... | Summary: This paper introduces a very interesting approach named "model spider", to address the challenging problem of selecting suitable Pre-Trained Models (PTMs) from a large number of options to fit the target tasks. Instead of relying on time-consuming and computationally heavy forward or backward passes over all P... | Rebuttal 1:
Rebuttal: **We sincerely appreciate Reviewer iJup's perceptive suggestions and valuable feedback**. The suggestions have been instrumental in enhancing our paper. For some questions, our responses are as follows:
**Question 1**: Whether the proposed method can be generalized to tasks in other modalities.
... | Summary: This paper proposes a method to select the "best" pre-trained model for a given task. This problem is important given the large number of available pre-trained models. The key behind Spider relies on tokenizing both the models and the tasks by summarizing their characteristics into vectors. More specifically, ... | Rebuttal 1:
Rebuttal: **We sincerely value the insightful suggestions** provided by Reviewer PpFQ. **Within our General Response PDF file**, we have provided **concrete examples of model-task tokens**, demonstrating with pre-trained models like Food, SUN397, Caltech101, and Dogs datasets. This presentation effectively ... | Rebuttal 1:
Rebuttal: **Dear Reviewers:**
We would like to express our sincere gratitude to Reviewers Gb1N, PpFQ, iJup, 1u8a, and mKeC for **their insightful reviews of our submission**. We are heartened by the constructive feedback and valuable suggestions each reviewer provides. We acknowledge that **all reviewers**... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a method called MODEL SPIDER for selecting the most suitable pre-trained models (PTMs) for a given downstream task. The proposed method aims to maintain a balance between efficiency and accuracy in the selection of PTMs. To achieve this, the authors tokenize all PTMs and tasks into vector re... | Rebuttal 1:
Rebuttal: **Thank Reviewer Gb1N for the insightful review** and for recognizing the strengths of our paper and the Model Spider method for pre-trained model selection. **We're grateful for Reviewer Gb1N's recognition of our novel approach**, which leverages tokens representing both pre-trained models and ta... | null | null | null | null | null | null |
Learning Dictionary for Visual Attention | Accept (poster) | Summary: This paper presents a new architecture that can be applied for various tasks, including image classification, point cloud classification and image segmentation. The key idea is to leverage a learnable dictionary module to replace the attention model in the transformer architecture. The model is able to achieve... | Rebuttal 1:
Rebuttal: We sincerely thank for your valuable suggestions on this paper. The following responses might help address your questions and concerns about this paper:
#### 1. **Weakness 1. (Major) Confusion on method...**
**Q(1)** "...The key motivation seem..."
**Answer to Q(1):** Thank you for your car... | Summary: This paper is about a new attention module called Dic-Attn, which is based on dictionary learning and sparse coding in the human visual perception system. The module can extract nonlinear structural information in visual data and reconstruct attention maps. The paper emphasizes the potential of leveraging spar... | Rebuttal 1:
Rebuttal: We appreciate your careful review and feedback on this paper.
The following responses might help address your questions about this paper:
**Question & Weakness .** "Experiments on CIFAR10 for image classification are not convincing. Please conduct experiments on ImageNet dataset to compare Swin ... | Summary: This paper proposed a novel dictionary learning-based attention (Dic-Attn) module, The proposed Dic-Attn module can be plug-and-play and stacked layer by layer to form a deep attention encoder. Extensive experimental results on computer vision tasks, e.g., image classification and point cloud classification, d... | Rebuttal 1:
Rebuttal: We appreciate your careful comments and constructive suggestions. The followings are detailed responses to your questions/concerns:
1. **Weakness 1.** Writing: This is not a well-organized paper. It is more like an application paper that pursues SOTA results rather than introducing an interpretabl... | Summary: This paper introduces a new attention mechanism, dictionary learning-based attention (Dic-Attn), to replace existing attention modules (e.g., self-attention) in deep networks (e.g. Vision Transformer, ViT). The proposed Dic-Attn comes from the combination of dictionary learning and sparse coding, and sparse vi... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive suggestions. The following responses might help address your concerns and questions about this paper:
1. **Weakness 1.** How does the backward process work in Algorithm 1? The sparse representation $\phi$ in Eq. 3 is not differentiable.
**An... | Rebuttal 1:
Rebuttal: # More Experimental Results
## **Answer** to Related Questions including:
- Weakness 2 by Reviewer 2tL6
- Weakness 2 by Reviewer Vec1
- Weakness 2 by Reviewer nmhX
- Weakness 1 by Reviewer 1dqN
- Weakness 2 and Question 1 by Reviewer q2Dg
We appreciate your comments and your expectation for mo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel attention mechanism called Dic-Attn that enhances the performance of deep vision models on various computer vision tasks. Specifically, the Dic-Attn module allows for the disentanglement of underlying nonlinear structural information in visual data, providing an intuitive and elegant... | Rebuttal 1:
Rebuttal: We appreciate your careful review, positive feedback and constructive suggestions. The following responses might help address your questions about this paper:
1. **Weakness 1.** "**Inadequate referencing...**
**Answer.** Thank you for your feedback. We have indeed reviewed the two papers you m... | null | null | null | null | null | null |
Geometry-Aware Adaptation for Pretrained Models | Accept (poster) | Summary: In this paper, the authors explore the adaptation of pretrained models from a geometric perspective. Specifically, this paper leverages the label information as a metric space and proposes a simple approach to predict new classes based on the pretrained models’ zero-shot prediction without any further training... | Rebuttal 1:
Rebuttal: Thank you for your review! We have responded to your points below.
**On the usage of the Fréchet mean estimator**
We made the choice to use the Fréchet mean because we have information about the relationships between classes. To ground our rationale in a different type of problem, consider a re... | Summary: This paper explores the concept of geometry-aware adaptation for label spaces. The paper introduces a method called "LOKI" that allows pretrained models to make predictions for classes that were not observed during training. LOKI utilizes metric space information to adapt the model's predictions to unobserved ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for **noting the originality, clarity, quality, and significance of our work!**
**On comparative analyses**
We are unaware of other approaches that operate in our setting: adapting a pretrained classifier to enable the navigation of metric spaces without... | Summary: The authors consider the problem of predicting examples from unseen but "known" classes. Taking inspiration from structured prediction, the paper intends to exploit the knowledge of structure in the full label space. The authors propose an alternative to the popular "argmax-over-logits" prediction, called Loki... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and for **noting the significance, theoretical contributions, and scalability of our work!**
**On the true label space metric vs. approximations**
Excellent question! We find that there is not a single “correct” metric space for a given problem, and that many... | Summary: This paper considers the zero-shot model adaptation to testing tasks that include classes not seen during training. The authors propose a post-hoc method called LOKI, which applies a class-graph-based transformation to make predictions on these unseen classes. The authors also provide the theoretical analysis ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and for noting the significance of the problem considered in our work! We have clarified points about our experimental results and the metric spaces that we use below.
**On prior knowledge of the classes**
Metrics relating the classes are often readily availa... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments! We particularly appreciate reviewers **praising the significance of our work** (reviewers cqEn, 4Uud, and 2m87) and **praising the simplicity of our method** (reviewers zgY7 and dZLD).
Given the novelty of our problem setting and approach, reviewers had... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a method capable of adapting a pre-trained model to a larger label space. To achieve this, it leverages the information of geometric distances between labels within the target larger space and replaces the common argmax operation with the Frechet mean. The paper also includes several theo... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments, and for praising the simplicity of our method and the thoroughness of our theoretical analysis!
**On evaluation fairness:**
This is an excellent point – we have added experimental results that address it in two ways.
- First, in our new calibration results, ... | null | null | null | null | null | null |
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation | Accept (poster) | Summary: The paper proposes an efficient conditional 3D generation via voxel-point progressive representation. More specifically, a voxel semantic generator and a point upsampler have been created to achieve efficient generation on multi-category objects. To verify the effectiveness of the method, extensive experiments... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**W1: The capability of VPP for partial completion task.**
Thank you for your suggestions, and we will make the necessary revisions to the pertinent statement. Furthermore, in **Figure 5** of the global response PDF, we show the partial generation results, whi... | Summary: This work proposes to use a voxel-point progressive representation for efficient 3D generation, and it proposes a few architectures for different applications, including generation, editing, upsampling, and pre-training. Based on the reported results, the proposed method could generate various 3D shapes and co... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**W1: Unseen categories generation.**
- Differing from NeRF-based approaches like DreamFusion, although they can achieve open vocabulary zero-shot generation, the high computational time and training costs make practical utilization challenging. Our method str... | Summary: Authors propose an approach to generate 3D point clouds of objects with an image or text description as input. Authors use a pre-trained CLIP model to generate text/image embeddings and use this to first generate features in voxel space (Voxel Semantic Generator). These voxel features are then decoded into a c... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**W1: Difference between Voxel Semantic Generator and CLIP-Sculptor?**
- Our goal is to share the representation advantages of both voxels and points. The objective of the Voxel Semantic Generator is to provide positional encoding for the Point Upsampler. In c... | Summary: The VPP proposes a model for 3D generation. It utilizes both point-based and voxel-based representations. Voxel-based representations are used to generate the coarse tokens, and the point-based one further improves the result. Both of which are pretrained with MAE-like self-supervised method.
The proposed met... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**W: Some presentation suggestions. The unclear explanation of tokenizer and 3D VQGAN.**
- We appreciate your suggestions regarding the presentation. In the revised version, we include **the exact time in Table 1**, incorporate **symbols** into the process di... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable feedback that significantly contributed to our work. VPP achieves efficient, multi-category, high-quality conditional generation through voxel-point progressive representation, and is capable of performing various tasks such as editing, completion... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed a text-driven point cloud generation model that can be used for various downstream tasks such as generation, editing,
completion and pretraining, while being very efficient. The method largely follows Muse [2], but adapted it to 3D point clouds. The model consists of multiple components, w... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**W1: Are the components of the proposed method new?**
We have introduced some novel structures or strategies to adapt to 3D generation. Such as the **3D VQGAN with occ loss**, the **Grid Smoother** to smooth the gap between two representations, and the **Cent... | null | null | null | null | null | null |
Causal Discovery from Subsampled Time Series with Proxy Variables | Accept (poster) | Summary: The authors suggest a method of causal discovery for multivariate time series under the regime when the data is being sampled at constant skips in the time dimension. Under mild assumptions, they prove that their method works asymptotically.
Strengths: The paper's contribution is clear, the methods are intere... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment and valuable suggestions on our paper. We will modify the manuscript accordingly.
**About related works.** The existing methods have been discussed in lines 30-36. To summarize, identifiability is only achieved in linear data. As for nonlinear data, only a s... | Summary: # Summary
In this paper, the authors address the problem of inferring causal structures from subsampled time series data, where the frequency of measurement is much lower than that of causal influence. This presents challenges in identifying the causal structure, as hidden variables at unobserved time steps ca... | Rebuttal 1:
Rebuttal: Thank you for the highly constructive feedback and thoughtful suggestions on our paper. We address your concerns below.
**About assumptions**:
We first would like to point out that our assumptions, such as the first-order SVAR assumption and the self-causation assumption are commonly adopted in ... | Summary: This paper proposed a non-parametric constraint-based algorithm that can identify the entire causal structure from subsampled data, which leverages the proxy variable to adjust the bias induced by the hidden variable.
Strengths: - Concise and clear theoretical derivation. Introduce the proposed method by dis... | Rebuttal 1:
Rebuttal: Thank you for your efforts on our paper. We address your concerns below.
**Q1.** The paper is rather incremental as the core of the method is based on [19] to use proxy variables to detect and eliminate the confounding effect brought by the subsampling.
**A**: First note that our paper solves a ... | Summary: In this paper, the author(s) propose a new technique to learn the summary graph of time-series data. As a motivation for their work, the author(s) discuss the interesting application of learning causal pathways in Alzheimer’s disease. The time series model studied in this work is quite general, and suitable fo... | Rebuttal 1:
Rebuttal: Thank you for your efforts on our paper. We address your concerns below and hope this can help you re-evaluate our paper.
**Q1.** My main concern pertains to the faithfulness assumption. Faithfulness is useful for getting identifiability results, and it allows for recovery of the causal structure... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their efforts and valuable comments. We are particularly pleased to hear that our work addresses an important and challenging problem (3AR7,Y9xG, GTBD, XP2K) and that our method is considered novel, solid (3AR7,Y9xG,7zAn), and well-pr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of subsampled time series in causal discovery, in which the unobserved time steps may lead to the existence of latent confounders. To this end, this paper proposes a constraint-based algorithm by leveraging proxy variables to remove the bias induced by latent confounders. The exp... | Rebuttal 1:
Rebuttal: Thank you for your efforts and valuable suggestions on our paper. We address your concerns below.
**Q1.** In Theorem 2.8, extra assumptions are required for testing conditional independent relations in related literature, but they are not discussed in this paper.
**A**: These assumptions are men... | null | null | null | null | null | null |
Doubly-Robust Self-Training | Accept (poster) | Summary: This paper presents a very simple approach to semi-supervised learning that utilizes both labeled and unlabeled datasets. When there is a large amount of unlabeled data available, following the same distribution as the labeled dataset, the most effective method to leverage this unlabeled data for training is s... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We have corrected all the typos suggested. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> In the experiments (sec. 3.1), the authors use curriculum-based loss in each epoch. With $\alpha_t<1$, the proposed me... | Summary: The paper proposed a doubly robust loss for self-training. The proposed loss is analysed and shown to have preferable theoretical properties.
Strengths: 1. The idea is interesting: a simple change from 1/(m+n) to 1/n (line 51 - 53) lead to a doubly robust loss function for self-training.
2. The writing is c... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We have corrected all the typos suggested. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> While the proposed doubly robust loss for self-training enjoys theoretical advantages, directly minimizing the loss du... | Summary: The authors propose a very simple yet effective modification to the original loss for self-training by re-weighting terms of the loss function making. This change effectively balances between using the pseudo-labels when the predictor is strong and learning to not use it when it is unreliable, making it doubly... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We have corrected all the typos suggested. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> The analysis provided is for very simplistic settings of linear predictor or mean-predictions and it’s unclear how muc... | Summary: This paper proposes a pseudo-labeling approach that balances out the supervised signal between the labeled and incorrect pseudo-labeled datapoints during the training process. The aim is to only account for the pseudo-labels when they are correctly labeled, which may happen when the covariate distribution of t... | Rebuttal 1:
Rebuttal:
Thank you for your valuable comments and suggestions. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> Novelty and missing prior work: Pseudo-labeling is the defacto method for entropy regularization techniques in semi-supervised learning problems. This is a pape... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the valuable comments and suggestions, which help us greatly improve our paper. Besides individual responses, we summarize the revision and new experimental results in the one-page PDF uploaded. We also include the markdown table for your reference, which a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Supply-Side Equilibria in Recommender Systems | Accept (poster) | Summary: The paper presents a theoretical study regarding the equilibrium of supply-side competition in recommender systems. In particular, the analysis examined when and how the recommender system influence producers' creation of online contents and will specialization occur under the effect of recommender systems.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and respond to their questions below. We present new results that address some of their concerns: in particular, **a new empirical analysis on the MovieLens-100K dataset** that validates and goes beyond our theoretical findings (see the General Response).
... | Summary: This paper aims to understand the equilibria of the digital content producer side competition in the recommender platforms. It specifically studies the potential of specialization, where different producers create different type of content. The paper proposed to model value of product as the inner product of u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and respond to their questions below. We present new results that address some of their concerns: in particular, **a new empirical analysis on the MovieLens-100K dataset** that validates and goes beyond our theoretical findings (see the General Response).
... | Summary: This work studies the supply-side equilibria in content recommender platforms. The authors proposed a game-theoretic model to describe content creators' competition and derive necessary and sufficient conditions under which the specialization over genres occurs or does not occur at the equilibrium.
Strengths... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and respond to their questions below. In response to the reviewers, we also showed **a new empirical analysis on the MovieLens-100K dataset** that both validates and go beyond our theoretical findings (see the General Response).
**“What if we generalize t... | Summary: In this paper, the authors investigate the supply-side equilibria in personalized content recommender systems. They propose a game-theoretic model that captures the multi-dimensional decisions of producers and the heterogeneous preferences of users. They analyze the conditions for specialization to occur and t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and respond to their questions below. We present new results that address some of their concerns: in particular, a **new empirical analysis on the MovieLens-100K dataset** that validates and goes beyond our theoretical findings (see the General Response).
... | Rebuttal 1:
Rebuttal: Thanks to the reviewers for their feedback. We provide a **new empirical analysis of our theoretical findings on the MovieLens-100K dataset**. We then clarify the nature of our contribution of proposing and analyzing a mathematical model to study an economic phenomenon. (We respond individually to... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding and Improving Feature Learning for Out-of-Distribution Generalization | Accept (poster) | Summary: The paper studies OOD generalization in the presence of spurious correlation. First, it provides a theoretical analysis showing that during ERM training, both spurious and invariant features are learned but at different rates, which, in turn, influences the performance of the following optimization with OOD ob... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper and your positive feedback! We hope our response below would make you more confident in supporting our work.
> W1.1 Axis in Fig.2.
We have revised the caption of Fig.2 to include the details. As mentioned in Appendix C.1, the invariant and spurio... | Summary: The paper consists of two parts. The first part provides a theoretical analysis of the training dynamics for a simple model and data distribution under ERM and IRM. Specifically, the authors explore the questions of feature learning, when one of the features changes its correlation to the target between enviro... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our work. Please see our detailed responses to your comments and suggestions below where we use references in our draft due to the token limit.
> W1.1 The linear activation in the CNN model
We respectfully disagree with the point:
- First, we need to clarify ... | Summary: This work aims to understand and compare feature learning in ERM and certain OOD generalization objectives. Additionally, it proposes an approach to enhance feature learning for improved OOD generalization.
First, the authors examine data consisting of invariant and spurious features. They theoretically show ... | Rebuttal 1:
Rebuttal: Thanks for your support and constructive comments! Please see our detailed responses to your comments and suggestions below where we use reference numbers in our manuscript due to the character limit:
> Q1.1 The effects of longer ERM training epoch.
Thank you for the insightful question. **In fa... | Summary: This paper explores the relationshp between the ERM training and OOD generalization in feature learning. The authors analyze the corresponding learned features by ERM and OOD objectives. To answer the question, they conduct the investigation of feature learning in a two-layer CNN network training with ERM and ... | Rebuttal 1:
Rebuttal: Thank you for your time and your positive feedback! Please see our detailed responses to your comments and suggestions below.
> W1 Some superscripts and subscripts are redendunt: L_S in Eq.(2), \ell‘ in Eq.(5), o_d in Thm 4.2. Please double-check these symbols and make them clear.
Thanks for yo... | Rebuttal 1:
Rebuttal: Dear reviewers,
We thank the reviewers for their many helpful comments and suggestions.
Most reviewers agree that our theoretical findings are interesting, important and useful (KJZ8, NB5i, qcQH). The insights we obtained deepen the understandings of feature learning under distribution shifts ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Black-Box Differential Privacy for Interactive ML | Accept (poster) | Summary: This work proposes a novel method to apply DP to the context of interactive ML.
Strengths: The method itself is an interesting proposal, well-supported with proofs and theory. This work also extensively describes prior advances in the field in great detail.
Weaknesses: The paper has numerous issues with the ... | Rebuttal 1:
Rebuttal: **> there should not be any citations in the abstract**
This is a minor issue, and we are willing to remove the citations
**> this paper lacks a proper conclusion; there are no clear contributions; it is incredibly difficult to identify the merits; the manuscript does not do the method any just... | Summary: The paper addresses the problem of privacy preserving interactive learning. In this problem, a recommendation algorithm improves its model by answering private queries performed by a set of parties in sequential rounds. The responses to users queries should adapt to the query made by each party. Therefore, in ... | Rebuttal 1:
Rebuttal: **> The space dedicated to the main contributions is a bit short**
While our algorithms are relatively simple, the proofs are non-trivial and could not fit the main body at its current form. We are open to reorganizing the paper, and will do our best (within the page limit) towards providing mor... | Summary: This paper studies privacy in the setting of interactive machine learning processes. Challenge DP is presented as a new relaxation of DP that is satisfies many of the desirable properties of DP and any non-private online prediction algorithm can be constructed into a Challenge DP online prediction algorithm. ... | Rebuttal 1:
Rebuttal: **> The motivating example is with a chatbot... is there an example that is more related to private online classification?**
1. A hospital conducting a study on a new disease might use private online classification to predict the risk of an individual having this disease (based on available tests... | Summary: The authors propose a new differential privacy definition with desirable online learning properties. In this new variant named Challenge Differential Privacy, the adversary can observe the output of a sequence of online queries, except a "challenge query". In this query, two possible pairs of inputs are picked... | Rebuttal 1:
Rebuttal: **> heavily reliant on the appendix**
While our algorithms are relatively simple, the proofs are non-trivial and could not fit the main body at its current form. We are open to reorganizing the paper, and will do our best (within the page limit) towards providing more proof details and insights. ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks | Accept (poster) | Summary: This paper proposed a novel pre-training method for interpretable graph neural network. Interpretable GNN is currently an important research issue and has attracted rising research attention recently. However, exiting methods are generally designed for some special types of datasets, and thus are hard to gener... | Rebuttal 1:
Rebuttal: >Q1: As the constructed synthetic graphs with ground truth labels may significantly affect the performance of the pre-training model. How to construct good synthetic graphs should be clearly explained. Currently, it is not clear enough on how to construct the data. More details on the synthetic da... | Summary: This paper presents a novel method for GNN explainability. The key innovation of this method is that of relying on synthetic graphs with known explanations to pretrain the model. The pretraining helps to learn general explainability patterns, introducing an inductive bias. Such patterns are then aggregated and... | Rebuttal 1:
Rebuttal: >Q1: The main weakness in my opinion is that the dependency between the test tasks and the synthetic pre-training dataset is not well investigated. However, the synthetic dataset is probably one of the most important hyper-parameters, which could be difficult to optimize in real-world settings. I ... | Summary: The paper proposes a generalizable GNN interpretation model, aiming to learn the universal structural patterns of graphs so that it can be applied any downstream applications.
Strengths: (1) The problem that the paper studies is very interesting, a model trained to identify the universal explanatory subgraph ... | Rebuttal 1:
Rebuttal: >Q1: The motivation of the hypergraph refining module and its advantages.
A1: For GNN explainers, the edges and the edge interactions are more essential compared with nodes and node interactions [1,2]. Therefore, we need a more expressive edge representation learning paradigm. To capture the edge... | Summary: The authors propose a pre-trained interpretable GNN named \pi-GNN that can distill universal graph structural patterns. \pi-GNN is pre-trained on a newly constructed synthetic graph datasets with ground-truth explanations and then able to generalize across different graph datasets and tasks. Technically, a str... | Rebuttal 1:
Rebuttal: >Q1: It seems like some related works about interpretable GNNs are missing in section 5, such as CAL [1] and OrphicX [2].
A1: Thanks for your suggestion. We will add the suggested references in the final version.
>Q2: The subgraph selection process is a little obscure to me. The authors may want ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and constructive feedback. We have made point-to-point response to the comments of each reviewer.
Moreover, we report two supplemental experiments in the attached file.
Finally, we once again thank all reviewers for their insightful comments which... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design | Accept (poster) | Summary: Scaling laws in LLMs have typically been used to derive compute optimal model sizes. In fact one of the initial scaling laws papers in language modeling has indicated that as long as the model size is kept constant, the model shape (corresponding to embedding dim, mlp ratio, number of heads, depth) are not as ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and careful review. Please see our response below:
- We disagree that our work lacks originality. We provide an approach based on scaling laws for inferring compute-optimal model shapes, which has not been done in the literature before. In addition, we intro... | Summary: The authors study the recent empirical insight that test performance follows a predictable power-law structure in terms of (optimally-allocated) compute and extend this notion to take into account the “shape” parameters of underlying model such as width, depth etc. They demonstrate that power-law behaviour can... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and careful review. Please see our response below:
- Compute is defined in terms of FLOPs throughout the paper. However, when the architecture is fixed, compute becomes proportional to the number of seen examples. That’s why in Line 127, we refer to infinite... | Summary: This paper proposes a novel and empirical take on the design of large vision transformers (ViTs), in the continuity of a previous paper aiming at optimizing the training of transformers.
Whereas the previous paper was aiming to optimize a single parameter (optimal model size) given a fixed training budget, thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and careful review. Please see our response below:
- In Figure 1, we use different axes for different dimensions. This may make it difficult to see how the MLP dimension is scaled faster than the others. Note, for example, that going from 1T to 100T GFLOPs c... | Summary: This paper introduces an efficient approach to investigate the scaling laws for compute-optimal model shapes, such as model width and depth. It proposes a shape-optimized vision transformer called SoViT.
A comprehensive evaluation across various tasks highlights the effectiveness of the proposed architecture.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and careful review. Please see our response below:
- In Figure 3, each dot corresponds to a model architecture pretrained on 600M examples and evaluated on one downstream metric.
The metrics from left to right are: 5-shot, 10-shot, and 25-shot (all in Image... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections | Accept (poster) | Summary: The paper proposes a novel method for improving zero-shot classification of VL models, in an unsupervised manner. This is done without additional visual labeled examples, yet with additional unlabeled examples. They rely on LLM to generate a dataset of text describing the desired classes. Then, they train a te... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent in reviewing our paper. In the following, we provide a response to the questions raised in the review.
**Performance of CLIP-PR baseline.** The CLIP-PR reported image classification results only on CIFAR-10 and ImageNet.
We used the official codebase prov... | Summary: This paper proposes a finetuning approach for Vision-Language models that does not require any labels. It begins by demonstrating the transfer of information between different modalities and training a classifier using natural language inputs. This classifier achieves the successful classification of visual da... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent in reviewing our paper. In the following, we provide a response to the questions raised in the review.
**Performance on fine-grained datasets.** On datasets like Flower-102 and SUN-397 our method improves the base
CLIP model by $4.4$% and $3.7$% respective... | Summary: This paper proposed a new approach to improve zero-shot vision recognition capability which leveraged the common embedding space for image and text. Specifically, with a pre-trained VLM (vision-language model), the authors leveraged LLM to automatically generate multiple language prompts for training a text-ba... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent in reviewing our paper. In the following, we provide a response to the questions raised in the review.
**Unsupervised finetuning naming convention.** Thank you for pointing it out. In our manuscript, we refer to the second
stage of LaFTer (Section 3.2 of t... | Summary: This paper proposes a new method to improve the zero-shot classification accuracy for a pre-trained vision-language (VL) model. By leveraging the shared embedding space between the vision and language modalities, and relatively accessible text data generated by large language models (LLMs), the proposed method... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent in reviewing our paper. In the following, we provide a response to the questions raised in the review.
**Failure of Llama needs more explanation.** While comparing the descriptions of CIFAR-10 classes generated by Llama and GPT respectively, we found the fo... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their efforts to review our paper and for
providing insightful feedback.
We are happy to see that they found our work: **novel** `(YPJ1, zXQC, bBjN, o7C2)`,
**interesting** `(YPJ1, o7C2)` and **theoretically sound** `(bBjN)`.
Furthermore, we also thank them for h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization | Accept (poster) | Summary: The paper investigates the challenge of embedding multiple continuous attractor manifolds within a single RNN, with a focus on hippocampal place cells. The issues arise due to the presence of discrete steady states, visualized as minima on an abstract energy landscape, which disrupt the continuity of network a... | Rebuttal 1:
Rebuttal: >While the manuscript is generally well-written, some areas could benefit from improvement. A diagrammatic illustration could help elucidate the issue of discretized states in the context of the energy landscape.
We agree that it will be helpful to include a schematic illustration to clarify the ... | Summary: The paper studies the storage of multiple continuous attractors in a recurrent neural network. Specifically, the authors tackle the interference between attractors and its effect on activity bump drift. By using a perturbative approach, they compute a correction to the connectivity that reduces the drift drama... | Rebuttal 1:
Rebuttal: >First, as the authors note, the resulting connectivity is extremely fine-tuned. This is a known problem with continuous attractors that is not addressed here.
We agree (please see also our 7'th response to reviewer MLg2, regarding fine-tuning).
>Second, the problem and solution are highly relat... | Summary: A new method is proposed to allow the simultaneous embedding of multiple attractors in an RNN through minimization of the energy function corresponding to the dynamics.
Two different methods to achieve this are considered, the first one based on the linearized energy function and the second on constrained opt... | Rebuttal 1:
Rebuttal: >There is ... landscape.
Existence of the Lyapunov function rigorously guarantees that network dynamics will converge to stationary states which are local minima of the energy (lines 96-97). We have precisely characterized these minima, and carefully verified our numerical scheme by checking that... | Summary: This work tackles the problem of interference between continuous attractors when they are held in a single RNN. The authors adopted a Lyapunov function as a depiction of energy of the network and tried to flatten the energy landscape of attractors by adding a modulation term to the original connection matrix.... | Rebuttal 1:
Rebuttal: >The recent experimental data actually showed that in the remapping of cognitive maps in hippocampus, place cells encoding different maps actually have little overlap, i.e., the hippocampus recruits different groups of neurons to form different continuous attractors. In other words, the interferen... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of our submission and for their insightful comments. Please see our point-by-point responses to each review. We will highly appreciate any additional comments or requests for clarification that may arise in response to our answers to the questions. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a new technique for embedding multiple attractor manifolds into RNNs. To do so, they first randomly choose a number of attractor manifolds, embed these into an RNN, and then make weight adjustments to smooth out the interference created by multiple manifolds. The authors propose two strateg... | Rebuttal 1:
Rebuttal: >While the results are very strong ... task complexity?
In order to reduce the computational cost, we explored our schemes in 1D, but it is straightforward to extend our approach and implement it in 2D. Conceptually, we do not expect a qualitative difference when doing so. One notable difference ... | null | null | null | null | null | null |
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis | Accept (poster) | Summary: The paper tackles the problem of multiple-choice learning (MCL) in the regression setting with specific focus on the overconfidence problem and hypothesis collapse problem found in previous approaches for MCL where predictions from the heads corresponding to rare events are overestimated. The proposed rMCL mod... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the remarks and the feedback on the paper.
The reviewer is indeed correct in that the EMD (Earth mover's distance or Wasserstein-1 metric [A]) considers all the hypotheses predicted and their associated scores. When it comes to the oracle metric, it evaluat... | Summary: The authors propose Resilient Multiple Choice Learning (rMCL), a modification of the Multiple Choice Learning (MCL) approach, for conditional distribution estimation in regression contexts where each input can have multiple target samples. While MCL is a straightforward strategy for multimodal density estimati... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful comments.
> Based on the weakness it would be helpful for authors to provide comments on how the rMCL approach might work in case of noise in the data and whether there is any comparative analysis of WinnerTakesAll type approaches that tak... | Summary: This paper proposes a technique for resilient Multiple Choice Learning (rMCL), which extends the vanilla Muliple Choice Learning (MCL) paradigm to conditional distributions for regression where multiple targets maybe sampled for each training input. It is known that MCL uses multiple scoring heads to score mul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and comments, as well as for the suggestions for extending the experimental results of the paper. We provide here a detailed answer to the raised concerns.
**The metrics interpretation**
> “The authors present too much emphasis on just the multimodal EMD ... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their remarks and suggestions, which will allow us to improve the quality of the paper.
We summarize here the main changes that will be made to the submission in a next revision, in accordance with the reviewers inputs. Please refer to the individual respo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning and processing the ordinal information of temporal sequences in recurrent neural circuits | Accept (poster) | Summary: In this manuscript the authors use a custom training regime to force simple recurrent neural networks (RNNs) to learn the ordinal structure of sequential inputs. Specifically, they train the network to learn the order of sequences by presenting the elements of a sequence with variable durations and variable in... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, which are very helpful for us to improve the paper. Below are our detailed replies.
Weaknesses
**On the contribution of tree-structured attractors**
Thank for raising this concern. To address this concern, we conducted an additional experiment (see Fig.R2-C in... | Summary: This paper investigates how recurrent neural circuits learn to represent the abstract order structure of temporal sequences and how the disentanglement facilitates sequence processing. The main objectives were better understand the brain's mechanisms for representing temporal sequence ordinal information and c... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, which are very helpful for us to improve the paper. Below are our detailed replies.
Weaknesses
**On the scalability of the model**
Thanks for raising this important issue. As the first step of presenting the framework, we only evaluate the model with short se... | Summary: This paper describes a method for training RNNs that is used to extract ordinal sequences. There are two variations on the training that make this possible. First, the network is trained on sequences with a wide range of temporal delays, so that only ordinal position is relevant. Second, the training signal... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, which are very helpful for us to improve the paper. Below are our detailed replies.
Weaknesses
**On the prior of the ordinal structure**
Thank for raising this concern, but we would like to point out that this is not a problem for the brain, although it may be... | Summary: The canonical biological neural circuit model, described by equation (1) in this work, primarily relies on attractor dynamics to perform cognitive tasks involving temporal sequences. Facilitating the emergence of appropriate attractors during training is a difficult task that challenges neuroscientists even to... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, which are very helpful for us to improve the paper. Below are our replies the comments point-by-point.
On weaknesses:
1. Thank for raising this important issue. Shortly speaking, reusing an existing tree-structured template for a new task in our model does not n... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from all reviewers, which are very helpful for us to improve the work. We have addressed all concerns of the reviewers point-by-point.
Attached please find the supplementary figures to answer the concerns of reviewers.
Pdf: /pdf/15b198b189886101d56fdebb1b92add... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics | Accept (poster) | Summary: The paper works on trajectory generation for soft body manipulation with differentiable simulation. To address the key challenge of representing task goals for optimization, the authors propose to use natural language descriptions to lower the barrier for annotation, where a framework that utilizes LLM for tra... | Rebuttal 1:
Rebuttal: We thank you for your thorough assessment and positive feedback concerning our paper. It is genuinely rewarding to learn that you recognize the significance of our distinctive task representation, the novel method to integrate LLM, and the overall clarity evident in our writing. Your insights are ... | Summary: This study focuses on soft-body manipulation problems such as flattening dough using a rolling pin, cutting deformable objects, and more. It introduces a method that engages non-expert users to provide detailed annotations and identify sub-goal states within key frames of the task video. Despite requiring addi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comprehensive evaluation and positive feedback on our paper. It's truly gratifying to know that you found value in our unique task representation, the novel manner in which we incorporated LLM, and the clarity throughout our writing.
> Imposes constrains on policy str... | Summary: This paper presents a novel approach to soft body manipulation employing the strengths of the Large Language Model (LLM). The key innovation is viewing tasks as data, with each data point consisting of an initial scene and an optimization objective. To tackle the challenge of task representation, the authors i... | Rebuttal 1:
Rebuttal: We're thrilled about your positive feedback on our work. Your acknowledgment of our innovation, robustness, and solid foundation is truly gratifying. Your encouraging words inspire us to pursue excellence in all our efforts. Thank you.
> the amount of human labor required for dataset collection
... | Summary: This paper demonstrates curating a set of 100 soft-body manipulation tasks and provides expert policies for them by using a mix of: annotators that provide supervision in the form of keyframes and/or natural langauge annotation, translating the annotations into programs via an LLM, and solving an optimization.... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful evaluation. We genuinely appreciate your recognition of the significance of our soft body manipulation dataset and the challenges it presents. Your criticism is invaluable to us, and we would be more than happy to discuss your concerns in greater detail.
> I personal... | Rebuttal 1:
Rebuttal: # General response
We thank all reviewers and ACs for their time and effort in reviewing the paper. We are glad that the reviewers generally recognized the following contributions.
**Problem and dataset** The paper curated a hard and valuable dataset (`kids`, `xC3q`), which is beneficial to the ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes DiffVL, a novel framework that tackles soft-body manipulation, which consists of a GUI for users to specify tasks easily and a large language model (LLM) for translating text instructions to programs for policy learning and execution.
Strengths: - An intuitive user interface for task spec... | Rebuttal 1:
Rebuttal: We genuinely thank you for your thorough assessment and encouraging remarks regarding our manuscript. We are pleased to learn that you appreciated the intuitiveness of our user interface and the novelty of our approach to integrating LLM. Additionally, we are grateful for your recognition of our e... | null | null | null | null | null | null |
Semantic HELM: A Human-Readable Memory for Reinforcement Learning | Accept (poster) | Summary: This paper addresses the problem of partial observability by proposing a semantic-enhanced method. This approach converts environmental observations into human-readable language tokens and incorporates them into the hidden state embeddings. Relative to other methods, this technique exhibits resilience against ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and address the following concerns:
**Rudimentary approach:**
We agree with the reviewer that our approach is simple and builds on existing works. However, we disagree that SHELM is only suitable for simplistic environments. Our semantic memory per... | Summary: This work introduces a a memory mechanism for RL agents that relies on foundation models but does not require training for correct functioning. The main contribution of this memory architecture is that memory tokens are preserved in a "natural language" space, making easier for humans to interpret the decisio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and adopted the following changes:
We have updated Figure 2 of the paper to make the differences to HELMv2 more explicit (see Figure 1 in accompanying one-page pdf). Along with the updated figure we elaborate on the following points more in-dept... | Summary: This study presents Semantic History-Embedded Language Model (SHELM), a new interpretable memory mechanism for reinforcement learning (RL) agents in partially observable scenarios. Current memory methods are often uninterpretable for humans, which hampers their use in critical areas like autonomous driving or ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the mentioned weaknesses as follows:
- Thank you for pointing out the typos, we will correct them.
- Thank you for pointing out the confusion about the Avalon results. Indeed this claim is wrong, since we observed performance on-pa... | Summary: This paper introduces Semantic HELM, a memory mechanism for encoding an agent's past observations into a semantically meaningful space and recalling from the memory. It is an extension/modification of HELM, a previous work that uses a language model to encode past observations, but unlike prior methods, Semant... | Rebuttal 1:
Rebuttal: We greatly appreciate the positive feedback from the reviewer.
**Weaknesses:**
- We agree with the reviewer that some of our claims about interpretability were a bit unfortunate. Therefore, we have revised the claims w.r.t. interpretability of the decision-making process and user-trust. Specifi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the time they invested to give constructive feedback which helps us to greatly improve our manuscript.
We are glad the reviewers found our paper clear and well-written (xnPR, FbDD), relevant (xnPR), found our empirical analysis thorough (a9Pk, f2kZ) and ou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper looks at using a compressed representation of recent observations in the form of top-k CLIP-extracted text entities. They look at training policies in a few different simulated environments with this representation. They cast this as an extension of the prior "HELM" method, although their key idea ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and address the concerns as follows:
**First Weakness:**
We agree with the reviewer that the downstream policy itself remains a black box, but we would like to point out that we explicitly specify that our work only focuses on enhancing interpretab... | Summary: Paper presents a method to augment a feed-forward RL policy with an “interpretable” memory. In general, it is based off GTrXL and HELM, which uses transformer-XL (TrXL) to encode current visual observation (or further map it to some embedding through embedding look-up, CLIP encoder, etc) while paying attention... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the raised points as follows:
**Baselines:**
We have **added the missing baselines**, specifically, Dreamerv2 on Avalon (these were originally shown only in Table 3 in the appendix), and PPO on MiniGrid and MiniWorld.
Dreamerv2 is S... | null | null | null | null |
Graph-Structured Gaussian Processes for Transferable Graph Learning | Accept (poster) | Summary: This work proposes a generic graph-structured Gaussian process framework (GraphGP) to investigating the knowledge transferability between homophilic and heterophilic graphs. GraphGP uses a structure-aware neural network to encode local node representation and global graph representation (domain-level) simultan... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful comments and valuable suggestions. Hereafter, we present responses addressing the concerns and queries raised by the reviewer.
**Q1:** There are many hyperparameters in the GraphGP algorithm, and whether the performance of the model is sensit... | Summary: This paper deal with the ttransferable graph learning problem, especially between the homophily and heterophily graphs. To solve this problem, they propose a graph Gaussian process (GraphGP) algorithm, which is derived from a structure-aware neural network encoding both sample-level node representation and dom... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's constructive comments. We would like to address the concerns and questions as follows.
**Q1:** More explanations on “most existing works followed the IID assumption”?
**A1:** In the introduction section, we started by introducing several transfer learning app... | Summary: The paper studies transferable graph learning over non-IID graph data. In order to adapt the knowledge from source graphs to target graphs, the paper proposes a graph-structured Gaussian Process (GraphGP). The GraphGP is derived from a structure-aware neural network and due to the flexibility of the hyperparam... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful reviews and constructive questions about our paper. We appreciate the strengths you highlighted regarding our motivation and theoretical results on transferable graph learning. Here are our answers regarding the concerns.
**Q1:** It is not very clear from t... | Summary: This paper studies the problem of transferable graph learning involving knowledge transfer from a source graph to a relevant target graph. To solve this problem, the authors propose a graph Gaussian process (GraphGP) algorithm, which is derived from a structure-aware neural network encoding both sample-level n... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments and suggestions. In the following, we present our responses addressing the raised concerns.
**Q1:** Related work is inadequate. Graph transfer learning has also been studied in some important literature [1,2,3], but they are not di... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection | Accept (poster) | Summary: This paper proposes a novel cross-model 3D detector BiProDet, which leverages the information from image domain in two ways. First, it proposes point-to-pixel bidirectional propagation strategy to boost the representation ability of the point cloud backbone. Second, it introduces NLC map estimation as an auxil... | Rebuttal 1:
Rebuttal: ### Response to Reviewer DmuL
We sincerely appreciate the reviewer for your time and effort in reviewing our paper. In the following, we will comprehensively address your concerns.
### **Comment 1:** *The motivation is weak. As the proposed method point-to-pixel is just a fusion strategy in cros... | Summary: This paper studies 3D object detection with multi-modal inputs (image and point cloud). This study uses a two-stage 3D object detection pipeline and proposes two approaches for further performance improvements. Firstly, the authors propose a bidirectional feature module to fuse point cloud and image features. ... | Rebuttal 1:
Rebuttal: ### Response to Reviewer s1rE
We sincerely appreciate you raising insightful points that helped improve our work. The comments have helped us better articulate the key contributions and value of our work.
### **Comment 1:** *The technical novelty is somewhat limited. Bidirectional feature fusion... | Summary: This paper propose a multi-modal usion-based 3d object detector named BiProDet. BiProDet adopts a bidirectional feature propagation mechanism, i.e., point-to-pixel module and pixel-to-point module. Besides, BiProDet propose a new auxiliary task called Normalized Local Coordinate (NLC) map.
Strengths: The pap... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 5jwD
We sincerely appreciate the time and effort you have dedicated to providing such insightful and comprehensive feedback on our work. The comments you have raised help us identify areas for improvement in our work. The review process has been informative in refining ou... | Summary: This work addresses the task of 3D object detection from LiDAR and cameras. Their main contribution is developing a joint 2D and 3D stream architecture, with simple bidirectional feature flow in the backbones. To improve this, they propose to predict NLC maps in the image stream. The proposed components demons... | Rebuttal 1:
Rebuttal: ### Response to Reviewer gAXu
We sincerely appreciate the time and effort in evaluating our manuscript. Your meticulous review and thoughtful critiques truly reflect your deep domain expertise and diligence as a reviewer. In what follows, we will address your remaining concerns comprehensively and... | Rebuttal 1:
Rebuttal: ### General Response
We thank all reviewers for your time and constructive comments. Here we want to summarize a few key clarifications concerning the contributions of our work again:
**(1) The novelty of our work.** The key motivation of our point-to-pixel module is not to propose structurally n... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a method for multimodal (image and LiDAR) 3D object detection. The main purpose of the proposed method is to enhance the image branch. The authors design the task of NLC Map estimation, which is to predict the normalized local coordinates of points within a ground-truth box. The prediction ... | Rebuttal 1:
Rebuttal: ### Response to Reviewer bgiL
We sincerely appreciate the reviewer's time and effort in reviewing our paper. Thanks for your valuable comments and recognition of our work. In the following, we will comprehensively address your concerns.
### **Comment 1:** *The experiments are only conducted on t... | null | null | null | null | null | null |
Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference | Accept (poster) | Summary: To alleviate the dramatic computation and memory overhead in HE-based GCN inference, this paper proposes a new HE-based ciphertext packing technique named Penguin. Penguin focuses on a sequence of matrix-matrix multiplications which is the bottleneck during private GCN inference. Thus, it employs an effective ... | Rebuttal 1:
Rebuttal: **1. Response to Weakness 1-Missing Comparison:**
Thanks for your constructive feedback. We have conducted additional experiments and reported the comparison results with Gazelle [1] in Table 1 below. As Table 1 shows, our proposed solution outperforms that of Gazelle across all three datasets. T... | Summary: The paper introduces Penguin, a novel HE-based ciphertext packing technique for accelerating GCN inference on encrypted graph data while ensuring data privacy. By exploiting the unique computation pattern of GCN layers, Penguin reduces computation and memory overhead associated with HE operations. The techniqu... | Rebuttal 1:
Rebuttal: **Response to Weakness- More comparisons with other existing works:**
Thanks for your comments. In addition to the baselines E2DM[3], uSCORE[4], We have conducted additional experiments and reported the results comparisons with other existing relevant approaches (e.g. Gazelle [1] and HElayers [2]... | Summary: This paper proposed an efficient data-packing method for cryptographically-secure inference on GCN, where the feature matrix and adjacency matrix are encrypted using homomorphic encryption (HE). The problem statement is interesting as the GCNs typically exhibit a significant sparsity level, increasing the numb... | Rebuttal 1:
Rebuttal: **1. Response to Weakness 1-Comparison with SOTA data packing method:**
Thanks for your constructive comments. We have conducted the experiments and reported the comparison results with HElayers in Table 1 below. As Table 1 shows, our solution–Penguin beats HElayers consistently across the datas... | Summary: The paper proposes a framework for optimizing latency in secure inference of GNNs. The authors propose a CKKS based packing scheme that is tailored for the structure of operations applied in Graph Convolution Networks. This appears to be the first work that considers both adjacency matrix and the feature matri... | Rebuttal 1:
Rebuttal: **1. Response to Weakness 1-if a fair evaluation would include CryptoGCN:**
Thanks for your comments. Our experiments have included a fair comparison with CryptoGCN. For CryptoGCN, the key idea is to use Adjacency Matrix Aware (AMA) ciphertext encoding technique, followed by the patterned sparse ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Flat Seeking Bayesian Neural Networks | Accept (poster) | Summary: This paper proposes modifying the loss used for Bayesian neural networks (BNNs) to take into account the sharpness/flatness of the loss with respect to the model parameters. Theory, based on that of sharpness-aware minimization (SAM), is developed to propose this loss modification. Making BNNs sharpness-aware ... | Rebuttal 1:
Rebuttal:
We sincerely appreciate your constructive comments. We are dedicated to addressing all the questions listed below to the best of our capabilities.
**Is it possible to have results on ImageNet for more of the studied methods?**
We acknowledged the limited number of experiments on ImageNetWe and ... | Summary: This paper introduces a method called Sharpness-Aware Bayesian Neural Networks (SABNN) that aims to improve generalization performance on test datasets. The key idea is to replace the negative empirical loss function with the negative SAM (Sharpness-Aware Minimization) [1] loss function, enabling consideration... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. Based on your suggestion, we conduct some more experiments (report in the attached pdf file) and hope that we can address some of your points as presented below.
**Largest Hessian eigenvalue**
We report the log scale of the largest eigenvalue of the Hessi... | Summary: This paper extends SAM -- sharpness aware minimization framework of Foret et al, who seeks parameters of Neural Networks in the regimes of flat loss landscape. The current understanding is that a model that exhibits a flat loss landscape exhibits better generalization performance. Particularly in this paper, s... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We carefully address all the questions listed below to the best of our capabilities and improve our paper based on that.
**Presentation could perhaps improve a bit more.**
Thank you for pointing this out. We will enhance the clarity and motivat... | Summary: The paper proposes a new approach to posterior inference for Bayesian neural networks that takes into account the sharpness/flatness of deep learning models, leading to better generalisation ability. The authors introduce the Sharpness-Aware Posterior (SA-Posterior), which allows the sampling of a set of flat ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We carefully address all the questions listed below to the best of our capabilities and improve our paper based on that.
**The experiments could be extended to include more datasets and models.**
Thanks for this comment. In this paper, we condu... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' constructive comments. We would like to report additional experiments on both ImageNet and CIFAR datasets, the computation cost, the sharpness scores, and the eigenvalues of the Hessian matrix in the attached pdf.
Pdf: /pdf/40ab1969ffae241a5f360a8366c22d635e281059.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduce theories in Bayesian settings and propose variational inference for the sharpness aware posterior in the context of Bayesian Neural Network. The proposed approach is incorporated with existing state of the art Bayesian Neural Networks and experiments were conducted to show the effectivene... | Rebuttal 1:
Rebuttal:
We sincerely appreciate your constructive comments. We carefully address your question to the best of our capabilities and improve our paper based on that.
**Computational head**
We further report the computational cost of several experiments in Tables 1 and 4 in the attached pdf. Similar to SA... | null | null | null | null | null | null |
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models | Accept (poster) | Summary: This paper focuses on solving inverse problems using diffusion based probabilistic models. More precisely, it is only assumed that one has access to a diffusion model for the prior distribution and a likelihood, so that no additional training is needed. The aim of the present paper is to provide an asymptotica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and are glad they found the paper to be original, well-written and to have sound theoretical and numerical support. We believe that our new experiments and clarifications below thoroughly addresses the weaknesses noted.
**Computational cost.** ... | Summary: The paper proposes an SMC algorithm to draw conditional samples form a diffusion model. Specifically, they wish to draw samples p(x0|y) given a diffusion model p(x0) and likelihood p(y|x0). Existing techniques to do so rely on expensive training of conditional diffusion models or heuristics which do not sample... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and are glad they found the paper to be theoretically well-founded, and that they appreciate the state-of-the-art results. We believe we thoroughly address the noted limitations in the below.
**VDM as a baseline:** As we noted in our high-level reply, the... | Summary: This paper proposes a practical approach for achieving asymptotically exact inference from diffusion models through exact conditional sampling in terms of Sequential Monte Carlo (SMC) . This discovers the connection between SMC and diffusion models, and one of the key feature is to approximate the optimal twis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and are glad they appreciate the inspiration for the method. We hope the new clarifications and benchmarks described in the below provide improved support for the progress we have made in this direction.
__Weaknesses:__
1. The first comment on weak... | Summary: This paper addresses the challenge conditioning in unconditionally-trained diffusion models. The most successful approaches often require explicitly training on conditional data. This paper frames sampling from such conditionals as an SMC procedure and proposes Twisted Diffusion Sampler (TDS), a method derived... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very careful and detailed review. We are glad they found it to be significant and interesting. We found the questions and weaknesses noted helpful and believe we have addressed them below.
**Improving the experimental validation:** As noted in the response to all... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed comments and suggestions. We are pleased that the reviewers found our method to be “practical”, “original”, “theoretically well-founded”, and to give “state-of-the-art” results in protein design. We believe we have addressed all suggested weaknesses, and h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline | Accept (poster) | Summary: The authors propose a simple method to perceive the length of an LLM response by asking the LLM. Then, the authors propose to groups queries with similar response lengths into micro-batches, which are then allocated to different GPU nodes and processed in parallel . The authors show empirical gain in terms of ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments. We are pleased to see that the reviewer acknowledges our contribution. The questions are answered below.
**Weakness-1**: The foundation of our method is not based on the assumption that individual requests can be "reordered," but rather... | Summary: >**Rebuttal:** The provided details satisfy my concerns. I think this paper should be accepted after applying the agreed changes.
>**TL;DR:** The paper presents a new technique to reduce the inference time of LLMs under intensive usage. This is an important problem that can reduce wasteful computations. Howev... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments. We are pleased to see that the reviewer acknowledges our contribution. The questions are answered below.
**Weakness-1**: One significant strength of our method is its compatibility with various existing toolkits. In [2], the proposal in... | Summary: This paper comes up with the technique of using LLM to help LLMs’ inference to be more efficient. It predicts the queries’ response length, and group the those with similar response length into the same micro-batch, so that the inference efficiency can be effectively improved.
Strengths: In the experiments, t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments. We are pleased to see that the reviewer acknowledges our contribution. The questions are answered below.
**Question-1**: No matter how extreme length is predicted (we can also assume a worst case: this sample actually gets a very short ... | Summary: The authors propose to improve the throughput of the LLM inference systems by correctly predicting the length of the response.
Method summary:
1. Predict the length of the response (Binning length for prediction modules to learn better)
2. Use the prediction to batch the queries with similar prediction to i... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments. We are pleased to see that the reviewer acknowledges our contribution. The questions are answered below.
**Question-1**: We appreciate the reviewer's clarification on the 'Avg. length' metric. Indeed, the metric does not means the avera... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Inferring the Future by Imagining the Past | Accept (spotlight) | Summary: This paper presents an efficient Monte-carlo algorithm to infer goals from single snapshots. The problem the authors consider is the same as in work by Lopez-Brau (2020, 2022) but previous solutions are slow as they apply rejection sampling. In this work, the authors use the insight that one can sample a valid... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments about our paper. We address your major concern about the lack of a linking theory in the **common response**. Here, we address your additional questions:
**Is our method applicable to continuous domains?**
Yes, it is: the cart-pole example in the supplemen... | Summary: This papers proposes a new approach to the inference problem of inferring an agent's goal state from information about a single state. This approach is based on the bidirectional Monte Carlo sampling of trajectory sequences, both from the agent's given state $x$ to the goal state $g$, and from an initial state... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our paper. We address your questions below, and in the **common response**.
**Did you compare to Lopez-Brau et al?**
Yes, we indeed show these comparisons in Tables 1/2/3, under the heading of "Rejection." We apologize for the confusion and will revise ... | Summary: This paper presents an algorithm for efficiently inferring the goal of an RL agent from just observing its current (single) state $x$. The method improves substantially upon rejection sampling based prior work by 1) only sampling paths through $x$ by separating the path into past and future 2) sampling the pa... | Rebuttal 1:
Rebuttal: Thank you for your careful feedback on our work. Please see the **common response** for our remarks on the role of A-star. We respond to the rest of your questions below.
**Table 3 ground truth**
Thank you for raising this concern, which we will address as follows:
1. Mention the correctness ch... | Summary: This paper deals with the problem of inferring the goal state $g$ of an agent given only a single state $x$ in a trajectory, i.e., inferring $p(g|x)$.
For this, the authors claim that we need to integrate all possible initial states, and thus, sampling past trajectories is necessary for Monte Carlo estimation.... | Rebuttal 1:
Rebuttal: Thank you for raising several important points about our work. We believe we can address all of your concerns — we respond to them at the beginning of the **common response**.
---
Rebuttal Comment 1.1:
Comment: Thank you for the response.
I admit that I was wrong about Eq.(2). Paths passing thr... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful feedback. We address some key concerns below, and the rest in individual responses.
**Is sampling really necessary? Why not fit a neural network? (iHfx)**
Thank you for raising this important point. We realize that our paper's motivation was not f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
XAGen: 3D Expressive Human Avatars Generation | Accept (poster) | Summary: The paper proposes a generative model for 3D expressive human avatars. Based on the backbone of the recent works, tri-plane 3D feature representation, volumetric rendering, transformation from pose space to canonical space, parametric body models and GAN, the authors propose to improve the expressive of the mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and recognition that 1) our method achieves state-of-the-art results; 2) our work is solid and its overall quality is good; 3) our experiments are extensive and convincing. We respond to each of your comments one by one in what follows.
> **Weakness... | Summary: The paper proposes a method for the generation of high quality, articulable 3D avatars of humans. The method proposed builds on top of the 3D GAN framework which has been used to learn to generate 3D articulable human bodies from collections of 2D images of humans. Additionally, the method adopts the proposed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and recognition that 1) our method has SOTA results; 2) our method is ablated well. We respond to each of your comments one by one in what follows.
> **Weakness 1.1**
Thanks for the suggestions, we have computed these metrics for ENARF and EVA3D. T... | Summary: The authors proposed novel part-aware sampling and feature parametrization strategies to improve the fidelity of the avatar model, especially for smaller body parts. These techniques s enable the efficient learning of diverse fashion shapes with a focus on the hand and facial details. Through experimental eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive suggestions. We respond to each of your comments one by one in what follows.
> **Weakness 1**
Our motivation of separating face and hands from body features is to increase the resolution of Tri-planes and improve the model's capaci... | Summary: The paper presents a framework for human 3D avatar generation. The whole model is trained on a set of 2D images, thus can support large variations in terms of shape. The framework is based on EG3D with several important modifications:
1) Incorporation of the inverse LBS, that will deform the canonical represen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and helpful feedback. We respond to each of your comments one by one in what follows.
> **Weakness 1.1**
First, we agree with the reviewer that our presentation could be further improved. However, there do exist several significant differences between our... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for their constructive and insightful feedback. We appreciate the reviewers' time and efforts spent on our submission.
Please check our rebuttal PDF files uploaded here for the additional figures and tables. We have sent the video results on AMASS data to AC fo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work addresses the problem of 3D full body avatar generation, going beyond prior work primarily on more detailed hand and face generation quality and controllability.
The pipeline comprises a 3D-aware GAN where the generator generates tri-plane feature maps from noise vector, followed by a 3D decoder tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and recognition that 1) our pipeline is effective, and it makes a good technical contribution to the avatar generative modeling; 2) our results are expressive and show a clear performance edge. We respond to each of your comments one by one in what f... | null | null | null | null | null | null |
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing | Accept (poster) | Summary: This paper examines the role of gradient descent in inducing implicit regularization for tensor optimization, within the lifted matrix sensing framework. The authors show that with sufficiently small initialization, gradient descent applied to the lifted problem results in rank-1 tensors and critical points wi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and constructive suggestions. The following are our responses to the review comments:
(1) We agree with the reviewer's observation that a two-layer neural network with quadratic activation alone is not a widely used model in modern day... | Summary: Studies the implicit regularization of gradient descent for a certain tensor optimization problem, obtained through the “lifted” matrix sensing framework. Lifting matrix sensing problems is a technique for transforming the original (non-convex) landscape into a new (still non-convex) landscape with favorable p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and keen suggestions.
(1) Due to space constraints, some explanations were placed in the appendix. We apologize for any confusion and will address each concern:
- Points 1 and 3: Theorem 1 outlines a relationship between ratio $\kapp... | Summary: This paper presents a new GD algorithm that is suitable for the problem of matrix sensing. Within this algorithm a 1-rank approximation of the corresponding tensor is made. A distinctive feature of this algorithm is that some points of local minima are turned into saddle points, which improves the convergence ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and keen suggestions. The following are our responses to the review comments:
(1) We express our gratitude to the reviewer for bringing up this concern. To enhance the accessibility of our work, we intend to streamline the main text by... | Summary: Gradient descent induces implicit regularization for tensor optimization. Specifically, it has a bias towards approximately rank-1 solution in the lifted matrix sensing framework. In Theorem 1, the authors show that the ratio between the second v-eigenvalue and the first v-eigenvalue exponentially decays to 0 ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and keen suggestions. The following are our responses to the review comments:
(1) The reviewer raises a nice point regarding the specific example given. It is important to emphasize that the condition in (11) represents a sufficient co... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks | Accept (poster) | Summary: The authors proposed ASPEN, an opportunistic parallelism method that breaks the synchronization barriers of each operator presented in a DNN graph so that parallel compute resources can tranverse and execute multiple data-paths independently with much less synchronization overhead in a **shared memory** system... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback! In this response, we will address and clarify your individual concerns one by one.
___
> **Issue 1** Applications of ASPEN are limited.
* GPUs don't have high threading overheads and are bottlenecked by memory bandwidth
Overcoming the threadi... | Summary: When we run a deep neraul network, a sequence of operators are executed. The existing deep learning framework/compiler would wait for the completion of the prior operator (A) before launching the subsequent one (B). The authors of this paper observe that some computation in operator B only depends on part of t... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback! In this response, we will address your individual concerns one by one in addition to the general response. We hope this clarifies your concerns about our work.
___
> **Issue 1** More case study is needed to justify where the speedup comes from.
... | Summary: DNN is composed of multiple computational blocks, each using different tensor operators. However, due to the nature of the computational graph, there are internal dependencies among the blocks and operators. Consequently, the synchronization barrier results in considerable overhead for modern high-parallelism ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback! In this response, we will address your individual concerns in addition to the general response. We hope this clarifies any concerns about our work.
---
> **Issue 1** The optimizations/algorithm appear to be ad-hoc and heuristic.
Our work aims to... | Summary: In this paper, authors proposed ASPEN, a parallel computation solution for DNNs, which utilizes a new class of parallelism for DNNs, namely opportunistic parallelism, to dynamically locate and execute any parallel computation opportunities during runtime. More specifically, the authors have presented three mai... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback! In this point-to-point response, we will address and hopefully clarify your concern with our work.
---
> **Issue 1** The hardware platform is limited to CPUs.
As explained in detail in the general response, ASPEN is not necessarily limited to C... | Rebuttal 1:
Rebuttal: General Response
---
Thank you for taking your time to review our paper! In this response, we will explain the essential value of ASPEN and the reasons behind our selection of evaluations. Then, we will clarify individual questions and concerns one by one.
As an exploratory work on fine-grained ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Inference performance is one of the key metrics driving the commercial adoption and integration of modern DNNs into user-facing applications. In the paper, the authors propose a framework, ASPEN, to improve the inference performance of DNNs by exploiting a novel strategy to extract maximal parallelism, called ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback! In this response, we will address your individual concerns one by one in addition to the general response. We hope this clarifies your concerns about our work.
---
> **Issue 1** The evaluation only focuses on CPUs and does not contain GPU result... | null | null | null | null | null | null |
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee | Accept (poster) | Summary: This paper considers the problem of sampling from a Gibbs distribution $p(x) \propto e^{-U(x)}$ using discretized Langevin dynamics. Since the approximation error of such methods usually depends on $\mathrm{Tr}(\nabla^2 U)$, the proposed algorithm splits $U$ into a quadratic part $g(x) = \frac m2 ||x||^2$ and ... | Rebuttal 1:
Rebuttal: ● Neither of those methods require such a complicated step-size scheme, which seems to be the main novelty of the paper, but the need for it is unclear.
We introduce the random step size to bound the error such as $\|\int_0^t x_n(s) - x_n^*(t)\mathrm{d} s\|^2$ in the discretization analysis. The... | Summary: The paper suggests a novel version of the Unadjusted Langevin Algorithm with sample complexity in Wasserstein-2 distance scaling with the effective dimension of the problem (trace of the potential's Hessian) instead of the ambient space dimension in case of strongly log-concave distributions. This result compl... | Rebuttal 1:
Rebuttal: ● First of all, the suggested Algorithm 1 (DRUL) does not seem to be really an implementable one, […] It is not clear if the control of this discretization error would not yield an explicit dimension dependence in the stepsize $h$ in Theorem 4.2.
We apologize for the possible misunderstanding in... | Summary: The paper adapts the Randomized Midpoint Method to the composite optimization context considered in Freund et al, and consequently improves the dependence from $O(tr(H)/\epsilon)$ to $O((tr(H)/\epsilon)^{1/3})$.
Strengths: The application of randomized midpoint in this composite sampling is novel, and there i... | Rebuttal 1:
Rebuttal: ● The primary contributions of this paper are not particularly original and mostly stem from combining the framework in Freund et al. with the known analysis for the randomized midpoint in Shen and Lee. This in my view is the primary weakness of the paper.
We would like to emphasize that our meth... | Summary: In this paper, the authors propose a Langevin-type algorithm for sampling a strongly log-concave distribution with a composite structure. Their method can be viewed as a variant of the randomized midpoint method, with two key modifications: (i) they only discretize the smooth convex part of the negative log li... | Rebuttal 1:
Rebuttal: Page 9, Lines 282-292: It is also unclear to me why "the randomized step size makes it possible to consider the averaged effect".
● In particular, the explanation in Section 5 is not very helpful [...] better explain how they remove the dimension dependence.
In short, the introduction of the ran... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods | Accept (oral) | Summary: This paper provides a theoretical framework based on generalized variational inference [1] and Wasserstein gradient flows (WGF) for analyzing deep ensemble methods and their regularized versions. The authors demonstrate that deep ensembles and other variational Bayesian methods can be cast as instances of an i... | Rebuttal 1:
Rebuttal: #### Details on experimental section
As the reviewer notes themselves, the paper is already rather densely packed. Because of space limitations, we were not able to include all relevant experimental details in the paper. We chose to focus on methodological details, but will move as many details i... | Summary: The paper established theoretical connections between ensembling an old and established method of deriving uncertainty estimates They use theory from iteraction of particles in a thermodynamic system to generalise and connect seemingly different ways of ensembling and Variational Bayes(Inference) methods. Thi... | Rebuttal 1:
Rebuttal: #### Questions
1. This is indeed an excellent question. It is true that in practice, we will need to replace full gradients by mini-batch versions and for example the kernel mean embedding with its monte carlo estimator. The reviewer correctly observes that the theoretical results in Section 4 do ... | Summary: The authors propose to unify existing theory on Bayesian (variational) inference (VI) by addressing a generalized objective, which is obtained from standard parameterized loss minimization by “probabilistic lifting” (re-casting in a space of probability measures over the parameter) and “convexification” (ensur... | Rebuttal 1:
Rebuttal: #### Weaknesses
1. One of the aims of this paper is to show that the naive strategy of train-and-repat can be understood as Wasserstein gradient flow of the probabilistic lifting of the loss function. Which strategies are covered depends on a case-by-case basis. Weight-decay for example is covere... | Summary: To improve the accuracy of the uncertainty quantification, the authors aim to provide a mathematically rigorous link between Bayesian inference, Variational Bayes methods and ensemble methods. In this work, methods s.a. variational inference, Langevin sampling and deep ensembles can be seen as particular cases... | Rebuttal 1:
Rebuttal: #### Weaknesses
[This is meant as a response to the first 2 bullet points]
We thank the reviewer for their helpful suggestions. We will include the suggested references and give a thorough discussion in the final version of the manuscript. It allows us to contrast infinite-dimensional gradient-fl... | Rebuttal 1:
Rebuttal: ### General Response:
We want to thank all the reviewers for taking the time to read our manuscript so carefully and for providing valuable feedback that we believe will significantly improve the manuscript further. Overall, we have obtained a median score of 8, which is a wonderful reward for t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper offers a viewpoint on deep ensembles as a (unregularized) Wasserstein gradient flow in the space of probability measures. This viewpoint enables new algorithms for deep ensembles (Langevin and repulsive via MMD), which are evaluated on some small datasets.
Strengths: 1) The paper is technically so... | Rebuttal 1:
Rebuttal: #### Weaknesses
1. We understand the reviewer’s concern and agree that the paper does not provide a full analysis of empirical performances. One reason for this is the limited amount of space that 9 pages allow in order to comprehensively present our firmly grounded theoretical framework and a nu... | null | null | null | null | null | null |
Direct Training of SNN using Local Zeroth Order Method | Accept (poster) | Summary: This paper proposes a Local Zeroth Order method, which can fit arbitrary surrogate functions by sampling a group of variables from a certain distribution. Experiments have verified the superiority of the proposed scheme.
Strengths: 1. The authors‘ idea about fitting arbitrary surrogate functions by sampling i... | Rebuttal 1:
Rebuttal: **W:** If time permits, I suggest that the authors can supplement their experiments on large-scale datasets (e.g. ImageNet).
**A:** Due to the time constraint, we were not able to run our experiments on the full ImageNet dataset, but we could run them on the Imagenet-100 dataset, which has the sa... | Summary: The authors propose a new method for training spiking neural networks
(SNNs). To estimate the gradient of the step function for spike
generation, they propose to directly estimate the gradient by local
sampling around the point of derivation and averaging the linearly
calculated slopes, a method that is known ... | Rebuttal 1:
Rebuttal: **W:** While the theoretical connection is interesting it could be argued that the existing SparseGrad method performs similarly in practice, so that there is not much of an improvement of the state-of-the-art. However, still, the accuracy seems to be slightly improved due to the introduced rando... | Summary: This paper proposes a new direct training algorithm for SNN, combining the standard surrogate methods and zeroth order method together. The algorithm applies the 2-point zeroth order method on the Heaviside function to generate a surrogate gradient, which is more efficient. The author applied his method to var... | Rebuttal 1:
Rebuttal: **W1:** This paper has limited novelty. It seems simply to be a combination the forward gradient method [1] and sparse gradient together [2].
**A1:** We try to elaborate on the motivation and technical implementation of our work, hoping to be more clear why our method is not simply a combination ... | Summary: This paper presented a direct SNN training algorithm that alleviates the loss of the gradient information and improves the performance of the SNN on multiple datasets, including both static image datasets and dynamic vision datasets.
Strengths: - Rigorous theoretical and empirical analysis to justify the nece... | Rebuttal 1:
Rebuttal: **W1:** The proposed method is only verified on small-scale datasets such as CIFAR-10/100 and DVS-CIFAR-10. Since the proposed method shows improved performance on simple vision tasks, it is necessary to further verify the performance on large-scale datasets such as ImageNet-1K or ImageNet-100.
*... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning | Accept (poster) | Summary: The paper proposes learning a contrastive learning based joint embedding space to align gene expressions and histology. This expression space is used to generate expression predictions for queried patches from the histology modality. The paper shows improved correlations in predicting gene expressions as well ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these comments. We summarize the major points below along with our rebuttal to each point.
Correlation Measure:
- We used the Pearson correlation coefficient for measuring both prediction-GT correlation and gene-gene correlation.
Clarification on Figure 2:
- E... | Summary: The authors present, BLEEP, a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide H&E stained histology images. This work stems directly from spatial transcriptomics, where we have spatially map gene expression profiles with H&E images. A well descripte... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these comments. We summarize the major points below along with our rebuttal to each point.
Additional experiments:
- While our paper focuses on a single organ system, this choice was made to deeply explore and validate our approach to demonstrate the effectiven... | Summary: The authors introduce a method, called BLEEP, of imputing the (aggregate) gene expression profile of cells in patches of histology images. Inspired by CLIP, BLEEP trains image and profile encoders to jointly embed paired images and expression profiles, except in replaces the typical CLIP loss with a novel loss... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these comments. We summarize the major points below along with our rebuttal to each point.
CLIP loss w/ smooth vs. w/o smooth comparison:
- Please see rebuttal addressed to all reviewers for ablation studies and discussion
Choices made for imputation and thei... | Summary: The authors have developed the model titled BLEEP, which is a contrastive learning implementation, trained on data from the 10x Genomics Visium platform, a common spacial genomics platform. Spacial genomics generates high dimensional data that includes both images and RNA expression on small patches on tissue... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these comments. We summarize the major points below along with our rebuttal to each point.
There is no guarantee that the signal that is being derived from given methods is in learning from the data presented. While the presentation of improved variance predict... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their comments and their acknowledgment to the strengths of our paper including:
- “significantly improved performance” and “not losing out on the heterogeneity information.” (J9t4).
- “first use of a CLIP-like joint embedding objective for learning to predict... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of gene expression profiling using histology images. They propose a bi-modal embedding framework BLEEP (Bi-modaL Embedding for Expression Prediction), which is capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained his... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these comments. We summarize the major points below along with our rebuttal to each point.
Weak comparisons. This work only compares with two deep learning methods HisToGene and ST-Net:
- HisToGene and ST-Net are state of the art methods for the task of express... | null | null | null | null | null | null |
Strategyproof Voting under Correlated Beliefs | Accept (poster) | Summary: Post rebuttal: I improved my scores and encourage the authors to include the information from the rebuttal in the final text.
The authors study strategy-proofness in voting under the assumption that the voters do not have, as is usual, full knowledge about the votes/preferences of the other voters, but rather... | Rebuttal 1:
Rebuttal:
**Reviewer comment:**
> In short, the assumed belief models mean that from the perspective of the considered agent, she is winning. Indeed, if the central ballot agrees with the current vote, the the top choice candidate is expected to have the highest plurality score. Then, strategy-proofness ... | Summary: This paper explores a probabilistic form of strategy-proofness for voting rules referred to as Ordinally Bayesian Incentive Compatible (OBIC) in a setting where voters believe that other voters have correlated votes. The paper considers both the situation when voters believe others have preferences similar to ... | Rebuttal 1:
Rebuttal: **Reviewer Comment:**
> Theorem 2 goes on to rely on "sufficiently large n" which seems weakened given the motivation. Is that a reasonable criticism and does that weaken the impact of the result?
**Response:**
This requirement is theoretically necessary, because it is possible to define posit... | Summary: This paper presents several results related to strategy proof voting rules when the set of agents has correlated beliefs. The classic results in social choice theory assume that a manipulating agent has access to the entire set of preferences of all agents, while in the setting discussed in the paper we assume... | Rebuttal 1:
Rebuttal: **Reviewer Comment:**
> My biggest comment here is fit for NeurIPS. This is a pretty straight social choice paper. While there are 2 references to ICML papers the bulk of the paper is a pretty straight statistical analysis/bounds paper (AI Stats?). This isn't all bad but it would be nice to inclu... | Summary: The paper considers a typical social choice problem: to design a voting rule that has desirable properties (e.g., it is onto and non-dictatorship) and it does not enable voters to misreport their vote for achieving outcomes that she prefers more. It is known that this is impossible in general, even if one allo... | Rebuttal 1:
Rebuttal: **Reviewer Comment:**
> I believe that there is an error in the last two equations on page 6 (even if they do not affect the final result). Indeed it should be $1/(|C|+1) (u(C) - |C|u(a)) \geq |C|/(|C|+1) (u(c) - u(a))$, for $c$ with minimum $u(c)$ among all $c$ in $C$ (similar for the last equat... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the voting problem where agents' ranking preferences are correlated. Roughly speaking, voters do not know exactly each other's preferences; when a voter knows his/her own preference, (s)he can "infer" others' preferences. Strategyproofness is then defined with expected utilities. This is a p... | null | null | null | null | null | null | null |
Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach | Accept (poster) | Summary: The paper is concerned with binary classification, when the data comes from two point clouds that are superposition of Gaussian distribution. This model allows for data distribution with fat tails. The authors analyse the performance of empirical risk minimization in the high-dimensional regime where the numbe... | Rebuttal 1:
Rebuttal: We thank the referee for her/his positive comments on our paper. We summarise our answers to her/his questions below.
* As foreseen by the referee, the method can indeed be applied to the study of an estimator obtained by minimisation of a proper convex function, and in particular, to the study of... | Summary: This paper investigates the asymptotic behavior of Generalized Linear Models (GLM) when the number of training samples $n$, and the dimension of the feature-space $d$ both go to infinity, but the ratio $n/d$ is fixed to some known bounded value $\alpha$. Moreover, authors assume the training data points are dr... | Rebuttal 1:
Rebuttal: We thank the referee for her/his time and comments. Here are some observations concerning the points raised in the report:
* We agree on the fact that the non-asymptotic behavior is also an interesting problem to consider. In this paper, we have worked in line with a large body of literature that ... | Summary: The paper is focused on the non-Gaussian mixture model and asymptotical investigation of the asymptotic characterization of the statistics of the empirical risk minimization estimator. The paper takes under consideration the models with two clusters and applies their analysis to the convex loss functions and r... | Rebuttal 1:
Rebuttal: We would like to thank the referee for her/his time in reading and evaluating the manuscript. As a general comment, we would like to stress that the goal of our work was to provide, for the first time, a theoretical model to analytically handle the asymptotic properties of classification estimator... | Summary: The paper derive a theory for training and generalization error when classifying a large number of points from a non-Gaussian high-dimensional data distribution. The data model is a double-stochastic process where a parameter is sampled from a scalar distribution and then a sample is taken from a Gaussian dist... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for her/his remarks and positive evaluation of our paper, and for capturing the spirit of our contribution very well. We are grateful to the referee for her/his suggestions about improving the clarity of the manuscript, which we implemented in the new version. W... | Rebuttal 1:
Rebuttal: *General remarks* We thank the reviewers for their helpful feedback which helped us improve the readability and clarity of our work, and better express the importance of our contribution. To take into account their comments, we have prepared a new version of our manuscript, in which, beyond addres... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Combating Bilateral Edge Noise for Robust Link Prediction | Accept (poster) | Summary: This paper focuses on the robustness of graph neural networks (GNNs) in the presence of edge noise during link prediction on graphs. The authors empirically investigate the impact of edge noise on both the input topology and target labels, revealing significant performance degradation and representation collap... | Rebuttal 1:
Rebuttal: We thank the reviewer 4XLr for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!
**Q1**. *If the SSL and REP versions are parallel methods, could the authors discuss the guidelines on method sel... | Summary: This paper proposes to tackle the bilateral edge noise via mutual information. The authors start from empirical observations that existing GNNs are vulnerable to bilateral edge noises. To tackle this issue, the authors propose a robust graph information bottleneck which is information-theory guided. In practic... | Rebuttal 1:
Rebuttal: We thank the reviewer Z7rx for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!
**Q1**. *In RGIB-SSL, the authors introduce hybrid graph augmentation which shows superiority over other contrast... | Summary: This paper focuses on the robustness of GNNs under the edge noise. The authors disclose the influence of bilateral edge noise and the corresponding robustness issue via a series of empirical studies on edge noise. Based on the observations of bilateral noise, the authors propose an information-theory-guided pr... | Rebuttal 1:
Rebuttal: We thank the reviewer cjox for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!
**Q1**. *There is a lack of more comprehensive studies on other graph representation learning tasks, such as node... | Summary: The authors extend the Graph Information Bottleneck (GIB) to "bilateral" structural noise and label noise. That is, both the adjacency matrix and the labels are being randomly perturbed. The authors observe that the bilateral noise leads to "poorer alignment and a worse uniformity". To handle this noise, the a... | Rebuttal 1:
Rebuttal: We thank the reviewer 4Q8x for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!
**Q1**. *The method seems to rely heavily on the assumption that the node features are clean. It would be good to... | Rebuttal 1:
Rebuttal: ### A General Response by Authors
**We would like to thank all the reviewers for their valuable comments on our work.**
**We have received five reviews with positive ratings 7,6,7,7,7. We appreciate that all the reviewers have good impressions on our work**, including **(1)** interesting proble... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper tackles the challenge of link prediction on graphs in the presence of edge noise, a topic that has seen little exploration despite the advancements in graph neural networks (GNNs). Through an empirical study, the authors reveal that edge noise can adversely affect both input topology and target labe... | Rebuttal 1:
Rebuttal: We thank Reviewer Fboz for the valuable feedback and the positive support of our work.
Any further comments and discussions are welcomed! | null | null | null | null | null | null |
TRIAGE: Characterizing and auditing training data for improved regression | Accept (poster) | Summary: This paper present a data characterization method for regression task. The method leverages conformal predictive systems literature and proposed to estimate training data scores by thresholding percentile of calibration data points given their conformity measure. The method is interesting in terms of leveragi... | Rebuttal 1:
Rebuttal: Dear ``R-N4d8``.
Thank you for your thoughtful comments which have helped improve the paper. We provide answers (A)-(E) & highlight updates to the paper
# (A) Evaluation over all training steps vs looking at higher iterations [Design motivation]
TRIAGE aims to analyze the behavior of different... | Summary: The task of data characterization aims to address variations in individual-level performance despite achieving good average performance. Existing methodologies have predominantly focused on classification, leaving a gap in data characterization approaches for regression. In this paper, the authors propose the ... | Rebuttal 1:
Rebuttal: Dear ``R-pNo8``.
Thank you for your thoughtful comments which have helped improve the paper. We provide answers (A)-(C) & highlight updates to the paper
# (A) Many appendices - include discussion in the main text
Thank you for suggesting better flagging our numerous appendices' contents.
**UPD... | Summary: The authors introduce a new data characterization framework, TRIAGE, for regression models. The method utilizes conformal predictive distributions to compute the training examples' scores. To compute TRIAGE scores, the authors use predictive distributions and conformal prediction. A proper training set is used... | Rebuttal 1:
Rebuttal:
Dear ``R-pEXt``.
Thank you for your thoughtful comments which have helped improve the paper. We provide answers (A)-(E) & highlight updates to the paper
# (A) Computational time
Thank you for bringing up this point. We agree that analyzing the time cost to compute TRIAGE scores is important. W... | Summary: The problem studied in this work is the following: Given a dataset $\lbrace (x_i, y_i ) \rbrace_{i=1}^M$ and a regressor $f_\theta$ trained on this dataset, assign a group label $g_i$ to each sample that specifies whether the regressor under or overestimates on the sample. Such group labels can be used to iden... | Rebuttal 1:
Rebuttal: Dear ``R-oVcJ``.
Thank you for your thoughtful comments to improve the paper. We provide answers (A)-(D) & highlight updates to the paper.
# (A) Comparing TRIAGE to Ref [23]
We discuss differences & similarities to illustrate TRIAGE’s contribution.
**Differences:**
1. **Objective:** TRIAGE per... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and positive feedback!
We are encouraged that they found the "problem being studied and data-centric perspectives attractive and important” (**R-ckq8**) for "real applications" (**R-oVcJ**) and that TRIAGE is a principled (**R-pNo8**) and “novel” (**R... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates the problem of training data characterization for regression problems. The authors noted that existing research on training data characterization mostly focuses on classification problems and there remains an absence of research for regression problems. This work proposes TRIAGE, a nove... | Rebuttal 1:
Rebuttal: Dear ``R-ckq8``.
Thank you for your thoughtful comments to improve the paper. We provide answers (A)-(D) & highlight updates to the paper
# (A) Additional comparisons
Thanks for suggesting an empirical & conceptual comparison to valuation methods (e.g. Shapley-based & LAVA) to strengthen the re... | null | null | null | null | null | null |
Contextual Gaussian Process Bandits with Neural Networks | Accept (poster) | Summary: The paper proposes extension of the Gaussian Process (GP) based contextual bandits to the contextual case, where the reward function is time and context dependent. The proposed method models the context dependency via a multi-output GP and its relationship to the actions via a neural network. The inner product... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the valuable time, questions and comments. We find them very helpful. Please find our response to the comments below.
$\textbf{Comment 1: }$Algorithm 1 has some novel aspects such as the way mu and sigma are calculated but the methodological novelty is rather incrementa... | Summary: This work studies the contextual bandit problem with the Gaussian process. Authors tried to use neural networks to learn reward functions and introduce one algorithm NN-AGP. Then conduct empirical evaluation and theoretical analysis for it.
Strengths: Recently, the bandit community paid more and more attentio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable time, questions and comments. We find them very helpful. Please find our response to the comments below.
$\textbf{Comment 1: }$This work is an extension of [37] but very similar is very high. The key difference from [37] is changing the reward function to th... | Summary: This paper proposes a reward model, called the neural network-accompanied Gaussian process (NN-AGP) for solving contextual bandit problems where the space of contexts and the space of decision variables may be continuous. This model is an inner product of a neural network and a multi-output GP. The neural netw... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable time, questions and comments. We find them very helpful. Please find our response to the comments below.
$\textbf{Comment 1: }$As defined, the reward model is an inner product of a neural network (NN) and a multi-output GP. This reward structure seems not na... | Summary: This work proposes a NN accompanied GP model. It leverages NN to approximate the unknown reward function regarding the context variable and maintains a GP with the decision variable. By introducing NN, the proposed method offers a better approximation accuracy. Theoretical implications, including maximum infor... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the valuable time, questions and comments. We find them very helpful. Please find our response to the comments below.
$\textbf{Comment 1: }$Potential limitations of the model: Although NN could help enable a better approximation accuracy on the reward function regardin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their review and for providing us with valuable comments to improve our work. We have treated every comment to our best efforts. In addition to the point-to-point responses to each of the reviewer, we also attach a document in this global response, containi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diversify \& Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement | Accept (poster) | Summary: This paper introduces a novel curriculum learning algorithm (D2C) in the context of reinforcement learning. Unlike previous approaches, D2C does not rely on prior knowledge of the environment's structure, including the distance measure between states. Instead, it leverages a goal-conditioned binary classifier ... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. The D2C algorithm relies on ...
→ We appreciate your insightful comment about the extension to the high-dimensional spaces. As you have rightly pointed out, directly applying the bipartite matching problem (section 4.3) in high-dimensional spaces may not work. However, we can ... | Summary: This paper proposes a new reinforcement learning algorithm called Diversify for Disagreement & Conquer (D2C).
The idea of the algorithm is to divide the search space and let conditional classifiers explore these subspaces.
With a set of experiments, the authors verify the effectiveness of D2C compared to var... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. Again I need to admit that I am entirely new to this field. At the same time, the other baseline experiments all do explore the search undirected. Is it a fair comparison when your approach can explore many directions situationally?
→ First of all, it is slightly unclear the m... | Summary: This paper propose training multiple classifiers on a data set of "desired" and "reached but undesired" states, motivating these classifiers to be different off distribution, and using this divergence as a metric for exploration.
Strengths: The approach is simple and easy to implement in many domains.
Weakne... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. Since the approach is very similar to model-disagreement, I would expect a simple comparison to such an approach like exploration via disagreement. Moreover, given that random-network distillation works so well for exploration, its unclear if the objective of the classifier her... | Summary: Focusing on goal-conditioned RL, the author propose Diversify for Disagreement & Conquer (D2C) to form outcome-directed exploration by generating a sequence of curriculum goals, given some desired outcome examples. By ensuring multiple classifiers disagree on unseen states, D2C uses bipartite matching to creat... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. It is not clear for me how to interpolate the curriculum distribution from the initial state distribution through minimizing Eq 4.
→ Question section.
2. Some related work and its comparison missing. Diversity has been considered in Curriculum RL. For example, Curriculum-guide... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for reviewing our work and providing constructive feedback. We hope that our response has adequately addressed your comments. If you have any remaining questions (existing or new ones) that we can address in our follow-up response to improve your opinion about ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed Diversify for Disagreement & Conquer (D2C), which is an outcome-directed curriculum RL method. D2C performs diversification of the goal-conditional classifiers to identify similarities between visited and desired outcome states and ensures that the classifiers disagree on states from out-of-... | Rebuttal 1:
Rebuttal: **Weakness:**
1. The writing of the paper needs some polishing to improve its readability.
→ Thank you for your suggestion. Since there is no way to revise the current manuscript in the rebuttal phase, we will try to rewrite some phrases and fix some grammatical errors as much as possible after t... | null | null | null | null | null | null |
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