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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach | Accept (poster) | Summary: The paper addresses that there are hidden confounders in recommendations and existing methods cannot handle them well. \
The paper proposes a unified multi-task learning approach to tackle that problem. \
Specifically, they devise a residual network to calibrate the propensity and the imputed error by using un... | Rebuttal 1:
Rebuttal: We sincerely thank you for the helpful suggestions. **Below, we hope to address your concerns and questions to improve the clarity and quality of our paper.**
> **W1:** Motivation is weak.
- Hidden confounders are assumed to exist. There is no theoretical or experimental evidence for that.
**R... | Summary: This paper presents a critical examination of prevalent debiasing methods in recommendation systems and their limitations in addressing hidden confounding factors. The authors underline that current methods—propensity-based, multi-task learning, and bi-level optimization—fail to mitigate selection bias when un... | Rebuttal 1:
Rebuttal: We sincerely thank you for the helpful suggestions. **Below, we hope to address your concerns and questions to improve the clarity and quality of our paper.** Below we categorized the reviewers' concerns into **Methodology**, **Experiments**, and **Clarity**.
### **Methodology**
> **W4:** Equatio... | Summary: This paper highlights the prevalent issue of selection bias in recommender systems, emphasizing the often-overlooked aspect of hidden confounding. Existing approaches and their limitations are discussed, with a special focus on hidden confounders. The authors then introduce a unified multi-task learning approa... | Rebuttal 1:
Rebuttal: We sincerely thank you for the helpful suggestions. **Below, we hope to address your concerns and questions to improve the clarity and quality of our paper.**
> **W1:** The theoretical result is weak. It states that if the consistency loss is zero then the calibrated loss is unbiased. While the r... | Summary: This paper studies unbiased learning in recommendation systems in the presence of hidden confounding. The authors first theoretically analyze the limitations of previous MTL methods and those combine some unbiased data and then design a unified MTL debiasing method by calibrating the learned nominal propensiti... | Rebuttal 1:
Rebuttal: We sincerely thank you for the helpful suggestions. **Below, we hope to address your concerns and questions to improve the clarity and quality of our paper.**
> **W1:** The novelty of this work lies in the proposed consistency loss that utilizes unbiased data to calibrate the learned nominal pro... | Rebuttal 1:
Rebuttal: We sincerely thank you for all helpful suggestions. We add statistical significant results and more detailed results of the unbiased data ratio in the attached PDF. We welcome any further technical advice or questions on this work and we will make our best to address your concerns.
Pdf: /pdf/bae1c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper shows that existing approaches that are based on multi-task learning or take advantage of unbiased data have theoretical limitations in the problem of recommendation debiasing when hidden confounding is present. The paper proposes to address these limitations by a unified multi-task learning approach... | Rebuttal 1:
Rebuttal: We sincerely thank you for the helpful suggestions. **Below, we hope to address your concerns and questions to improve the clarity and quality of our paper.**
> **W1:** It is not fair to claim that the paper is the first to "perform theoretical analysis to reveal the possible failure of prev... | null | null | null | null | null | null |
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games | Accept (poster) | Summary: This work proposes a cyclic coordinate descent method to solve the two-player zero-sum extended form game (EFG) and derives the convergence.
Strengths: To me, solving problems with non-separable constraints by coordinate-descent-type methods is novel and interesting. Therefore, I believe that this is indeed a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We provide responses to your concerns below:
> Possibly expensive computational cost per iteration. Following the literature survey in the manuscript, I read reference [1] which studies coordinate descent for solving optimization problems with no... | Summary: This paper combines the local prox update technique with the extrapolated cyclic algorithm and proposes the ECyclicPDA algorithm. While the local prox update technique is well understood, the authors reinterpret it as a coordinate method (CM). Then, the method is combined with a new extrapolated method. Theore... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We provide responses to your concerns below:
> It is hard to understand why we want to reinterpret the well-known local update rules of OMD with dilated DGF to CM. As we know, people already compute the strategy in a bottom-up fashion in previous... | Summary: This paper introduces ECyclicPDA, a new first order method for solving extensive form games. The main idea is to implement something in the spirit of coordinate descent where improving directions can be found by considering a part of the current iterate in isolation. Empirical results show it generally outpe... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We are glad that you found our contributions interesting. We provide responses to your concerns below:
> As far as I can tell, the details of how restarting is implemented are never clearly explained. The clearest explanation I can find is in the... | Summary: The draft considers solving the extended-form game (EFG) with extrapolated block-coordinate descent methods, proving that it achieves O(1/T) convergence. The authors further show that with a restarting strategy, the proposed algorithm may be comparable sometimes to state-of-the-art algorithms like CFR+. In my ... | Rebuttal 1:
Rebuttal: > However, I would like to claim that the original CODER method may already cover the blockwise updates. [...] So the draft's theoretical result (independence on the number of variables) is not surprising. And the proposed method does not relax the "separability assumption" since the 2-player tre... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for taking the time to provide valuable feedback for our paper. Here we provide clarifications for a couple of points raised by multiple reviews. Please note that we have attached a PDF containing tables that we refer to in the rebuttal.
### Theoretical... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a novel method for solving Extensive-Form Games based on a block-coordinate approach. The authors motivate and explain their idea and experimentally evaluate its performance in terms of primal-dual gap in four different games. The method features a favorable theoretical convergence rate bu... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We provide responses to your concerns below:
> It seems that in empirical studies the method is consistently worse than PCFR$^+$ (Fig 2 and 4). I have a difficulty to find a complete justification for this in the paper.
As we have discussed in o... | Summary:
This paper develops a cyclic block-coordinate-descent-like method for two-player zero-sum extensive-form games (EFG). Such methods for EFG are difficult due to non-separable nature of block structure of the problem. The decision problem for a player in a EFG can be formulated using Treeplex, for which regula... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We provide responses to your concerns below:
> line 105-``As discussed before RCMs are not applicable to our setting''. It is not discussed anywhere in the paper why randomized coordinate methods are not applicable.
We address this in our top-le... | Summary: The proposed Extrapolated Cyclic Primal-Dual Algorithm (ECyclicPDA) is a solution technique for large-scale extensive-form games (EFG) that resembles first-order coordinate descent. To enable pseudo block-wise updates, it takes use of the recursive nature of the proximal update caused by dilated regularizers. ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We provide responses to your concerns below:
> The execution times [...] comprehensive evaluation is still lacking.
We address this in our top-level comment (5), the pdf attached to it (containing numerical evidence that ECyclicPDA scales inde... | null | null |
On the Exploitability of Instruction Tuning | Accept (poster) | Summary: The authors propose AutoPoison, an automated data poisoning pipeline.
They demonstrate two types of attacks: content injection (e.g, brand names), and over-refusal attacks.
The authors demonstrate how AutoPoison can change a model’s behavior by poisoning only a small fraction of data while maintaining a high l... | Rebuttal 1:
Rebuttal: > Evaluation of more recent models.
Thank you for the suggestion. We are working on extending the evaluation to more recent models. Poisoning experiments often require training a model many times on a range of poison ratios. The cost of using newer models is high since recent models like Llama d... | Summary: This paper analyzes the threat model for poisoning the data for instruction tuning to steer a language model towards certain behaviors. It proposes a data poisoning pipeline AutoPoison, that uses an oracle LLM to generate adversarial clean labels from perturbed adversarial instructions. AutoPoison is evaluated... | Rebuttal 1:
Rebuttal: > LLM-based evaluation.
Thank you for the constructive feedback. We agree that conventional text quality metrics can be limited in evaluating instruction-tuned models.
We have adopted your suggestion and conducted LLM-based evaluations on a recent benchmark developed by the Vicuna team: MT-Bench... | Summary: This paper proposes AutoPoison, an approach that automatically constructs poisoning data for instruction tuning. AutoPoison replaces training responses with poisoned responses obtained by querying an oracle LM with poisoned instructions. AutoPoison is evaluated on two tasks, content injection and over-refusal ... | Rebuttal 1:
Rebuttal: > Evaluation of LM's ability on more comprehensive benchmarks.
Thank you for the constructive feedback. We agree that it is important to maintain the model's ability on general tasks; otherwise, users will stop using it. Therefore, we adopt your suggestion and conduct additional evaluations on th... | Summary: This paper proposed AutoPoison, an automated data poisoning pipeline to showcase two example attacks: content injection and over-refusal attacks over the instruction-tuned models.
Overall, this is a nice paper, I appreciate the authors for tackling this research problem. Authors proposed a sound methodology t... | Rebuttal 1:
Rebuttal: > More metrics to evaluate the attack's stealthiness.
We agree that perplexity is a limited metric to evaluate the attack's stealthiness. We define stealthiness mainly via text quality, and perplexity is a commonly applicable metric of text quality [1]. We agree that machine-generated texts tend ... | Rebuttal 1:
Rebuttal: ### Global response
We thank the reviewers for their constructive feedback. We appreciate the positive comments about our proposed method and the writing of the paper. We acknowledge the potential safety issues raised by the ethics reviewer and have greatly revised and extended the discussion on ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Direct Diffusion Bridge using Data Consistency for Inverse Problems | Accept (poster) | Summary: This paper focuses on the diffusion model-based inversion problem. The paper first analyzes the current works and unifies them with the Direct Diffusion Bridges. Then, the authors point out that the data consistency is ignored by current works and propose CDDB. Experiments show that the proposed CDDB works wel... | Rebuttal 1:
Rebuttal: We appreciate your honesty and the best efforts to give a constructive feedback. While we are unsure if the method will be applicable to the case of personalization (e.g. textual inversion), please see general comment 3, where we discuss potential applications that are beyond the setting of invers... | Summary: In this paper, the authors propose Consistent Direct Diffusion Bridge (CDDB) a modification of the Direct Diffusion Bridge (DDB) procedure [1,2] that includes a data consistency term similar to [3,4]. The authors propose two ways to introduce the consistency: the first one forgets about the Jacobian term which... | Rebuttal 1:
Rebuttal: **W1. Weak background for DDS, theory over-claimed**
**A.**
Thank you for your encouraging comments. In the revised manuscript, we will spend more effort on the discussion of [1]. Moreover, we agree that we cannot guarantee the convergence of CDDB. The statement will be removed from the manuscrip... | Summary: This paper studies the existing works about Direct Diffusion Bridges (DDB) with a unified scheme and limitations, and proposes a modified inference procedure that imposes data consistency without the need for fine-tuning, called data Consistent DDB (CDDB), as a new diffusion model-based inverse problem solvers... | Rebuttal 1:
Rebuttal: **W1. Why would DDB outperform DIS?**
**A.**
The main reason that DDB often outperforms DIS (DDS falls into this category) is that DDBs are trained *specifically* for a given task with paired datasets, rather than being a *general* solver. Moreover, it learns to directly start the diffusion proce... | Summary: The paper introduces a unified view of several existing diffusion-model-based methods for solving the inverse problem (i.e., DPS and $\Pi$GDM), and proposes a new approach for this problem called Direct Diffusion Bridge (DDB) which is derived from the DDPM ancestral sampling, and to some extent, links to the I... | Rebuttal 1:
Rebuttal: After reading the comments, we would like to respectfully stress that there seems to be a **clear misunderstanding** of the paper. 1) Our work aims for the unified view of DDB, not DIS; 2) We do not introduce a new way to compute $\hat{x}_0$, which is directly estimated from the neural network. On... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive and thorough reviews. We are encouraged that the reviewers think that our paper “provides an interesting unified view, with support of experimental results” (7H1C), is “well written, organized, with comprehensive experiments” (TLjQ), and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Lightweight Vision Transformer with Bidirectional Interaction | Accept (poster) | Summary: The paper proposes a new lightweight ViT structure called FAT. They use a fully adaptive self-attention mechanism for vision transformer to model the local and global information as well as the bidirectional interaction between them in context-aware ways. In addition, the paper introduces a fine-grained downsa... | Rebuttal 1:
Rebuttal: ### **On motivation & novelty, explanation of Eq.5 and Comparison With SOTA**
**Q1:** Motivation & Novelty
**R1:** Thanks. The key novelty of our paper is bidirectional interaction, which is simple to implement and has negligible costs. This module is inspired by existing research on the human b... | Summary: This paper presents a new family of lightweight vision transformers that are built on the Fully Adaptive Self-Attention module. The key innovation of these transformers lies in their bidirectional interactive module, which enhances both local and global features. The experiments primarily focus on image classi... | Rebuttal 1:
Rebuttal: ### **On scale-up capability, efficiency, motivation, and training stability**
We thank the reviewer for recognizing the positive aspects of our paper, and we will address the reviewer's concerns in the following parts.
**Q1:** scaling behavior of the method.
**R1:** Thanks, we scale up our FAT... | Summary: This paper presented an efficient vision transformer backbone for several tasks including classification, segmentation and detection. The key idea of this paper is on a new design of considering the interaction between local and global features. The experiments on imagenet, AD20K, and COCO have shown that th... | Rebuttal 1:
Rebuttal: ### **On comparison, efficiency, ablation study and scale-up capability**
We thank the reviewer for recognizing the positive aspects of our work. We will address the reviewer's concerns in the following parts.
**Q1:** Comparison in the paper.
**R1:** Thanks. The number of parameters and the com... | Summary: This paper proposes a new family of light-weight vision transformers named FAT, which enhances the local and global feature fusion by a bi-directional interaction between them. Experiments on image classification, object detection, and semantic segmentation are conducted.
Strengths: 1. The paper provides a go... | Rebuttal 1:
Rebuttal: ### **On Novelty, Difference with MixFormer and Efficacy of Bi-directional interaction**
We thank the reviewer for recognizing the positive aspects of our paper, and we will address the reviewer's concerns in the following parts.
**Q1: Novelty.**
**R1:** Thanks. We want to highlight that the ke... | Rebuttal 1:
Rebuttal: The global response contains two things:
1. Updated version of Table 1 in the paper.
2. Plots to show the training stability.
Pdf: /pdf/e18276811ccb6398f7a0a33d4c3dee8a2ec667c3.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Accept (poster) | Summary:
The authors tackle the problem of nondifferentiable nonconvex locally constrained federated learning (Eq. $FL_{nn}$), and the bilevel variant (Eq. $FL_{bl}$). The minimax settings (Eq. $FL_{mm}$) is a special case of the latter.
The authors provide error, iteration and communication complexity for these set... | Rebuttal 1:
Rebuttal: We thank you for your valuable suggestions and detailed comments on improving this work. Below, please see our response to each comment.
$\textbf{Your comment:}$ Contrary to the reasoning for obtaining the algorithm, the results of Thm. 1 and 2 are not discussed. Doing so would help understand t... | Summary: The paper considers the federated optimization problem with the non-smooth non-convex target function. The authors use a zero-order oracle, which allows to approximate the gradient. This approximation is related not only to the original target function, but also to its smoothed version (an additional theoretic... | Rebuttal 1:
Rebuttal: Thank you for bringing this paper to our attention. We will
include this work in our references and comment on it in section 2. With respect, this paper has significant differences in scope and treatment with our work. We clarify these in the following.
(i) First, note that this paper studies zer... | Summary: **Summary:** The paper develops zero-th order methods for solving non-smooth and non-convex federated problems. In addition, the paper also develops zero-th order methods for solving bilevel and minimax optimization problems in a federated setting. The authors develop a randomized smoothing-based approach and ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the comments point by point as follows.
$\textbf{Response to weakness 1:}$ Note that [R1] considers smooth, but possibly nonconvex, problems while [R2] considers smooth and strongly convex problems. Several distinctions persist. (I) While some FL me... | Summary: Existing federated optimization algorithms usually rely on the assumption of differentiability and smoothness, which may fail to hold in practical settings. To this end, this paper employs randomized smoothing approach and zeroth-order optimization techniques for the development of FedRZO algorithm to address ... | Rebuttal 1:
Rebuttal: Thank you for your helpful suggestions and comments on improving this work.
$\textbf{Response to weakness 1:}$ Thank you for pointing this out. To be clear, the three formulations are all nondifferentiable nonconvex FL problems. The motivation for the three formulations is mentioned in lines 112... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their time and thoughtful reviews on improving this paper. The following is a summary of major changes that would appear in the revision if accepted.
1. In response to reviewer "Lumm" and "LNaU", we will add the following note to emphasize the signif... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation | Accept (poster) | Summary: In this paper, the authors developed an energy-based model for unsupervised binding affinity prediction. The energy-based model was trained under SE(3) denoising score matching where the rotation score was predicted by Neural Euler’s Rotation Equation. Experiments on protein-ligand binding and antibody-antigen... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please read our response below and let us know if you have more questions.
**Q1**: The authors fail to consider the flexibility of proteins and ligands. The side-chains of proteins are also ignored. However, these factors are quite important... | Summary: The authors propose an energy-based model for unsupervised binding affinity estimation, which is trained by SE(3) denoising score matching (DSM). Different from standard DSM, they add noise by random rotations and translations on the ligand. Utilizing Euler's rotation equations, the rotation matrix can be deri... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please read our response below and let us know if you have more questions.
**Q1**: Under the docked setting, the RMSD is quite large for the generated antibody-antigen complexes (median 19.4), however, the correlation is similar to the cryst... | Summary: The authors address the problem of protein-ligand binding. Specifically, the authors reformulate binding energy prediction as a generative modeling task: they train an energy-based model on a set of unlabelled protein-ligand complexes using denoising score matching and interpret its log-likelihood as binding a... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please read our response below and let us know if you have more questions.
**Q1**: NERE DSM relies on co-crystal structures for training and co-crystal/docked structures for binding prediction of a new protein-ligand / Ab-Ag pair, which can ... | Summary: The paper introduces an unsupervised learning approach for predicting protein-ligand binding energy, called NERE (Neural Equivariant Rotation Estimation). The authors employ SE(3) denoising score matching to train an energy-based model on a dataset of unlabeled protein-ligand complexes. The model's effectivene... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please read our response below and let us know if you have more questions.
**Q1**: The proposed method only considers rigid transformations (translations & rotations) in the diffusion process, neglecting the degrees of freedom in small molec... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their valuable comments and suggestions. We would like to summarize three main results here in response to some common questions/suggestions. The results are included in the attached PDF file (rebuttal Table 1 and Figure 1a-h).
### Additional metrics
As suggest... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces NERE, an unsupervised method for predicting protein-ligand binding affinity. The authors propose a generative modeling approach that utilizes an energy-based model (EBM) to capture the characteristics of protein-ligand complexes. The EBM is trained by maximizing the log-likelihood of crys... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please read our response below and let us know if you have more questions.
**Q1**: The paper lacks visualizations of the correlation between the predicted energies and binding affinities.
In rebuttal Figure 1a, we visualize the correlation ... | null | null | null | null | null | null |
Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism | Accept (poster) | Summary: The paper focuses on enabling training on memory-constrained platforms. Instead of using conventional backpropagation-based methods, the proposed approach is based on forward gradient descent (FGD), which approximates the gradients through only forward passes. The paper claims that a brute-force utilisation of... | Rebuttal 1:
Rebuttal: Dear Reviewer bhPv,
Thank you for your comprehensive review and insightful comments. We have carefully considered your feedback and offer the following responses:
**Weaknesses:**
**Q1:** "Does idle worker really exists when training on Edge device? If so, why not batching inputs to parallel com... | Summary: ThIS paper introduces AsyncFGD, a forward-only based training method. The key idea behidn this paper is integrating asynchronous updates with Forward Gradient Descent (FGD). The authors test their method on several small-scale datasets.
Strengths: - The proposed proof of the convergence guarantee of AsyncFGD-... | Rebuttal 1:
Rebuttal: Dear Reviewer yH2P,
Thank you for your comprehensive review and the valuable insights you provided. We genuinely appreciate your feedback, as it offers us a clear direction for refining our work. We have taken the time to address each of your concerns in detail.
**Weaknesses:**
**Q1:** "Unclear... | Summary: This paper proposes an asynchronous version of the forward gradient descent (FGD) method, in order to alleviate the forward locking exhibited with FGD and enable more efficient implementation. Specifically, AsyncFGD decomposes a network into K decoupled subnetworks that work as a delayed pipeline and enable as... | Rebuttal 1:
Rebuttal: Dear Reviewer Kesv,
Firstly, we would like to express our gratitude for the meticulous review and valuable feedback you provided on our paper. Your insights are instrumental in refining our work, and we have made concerted efforts to address each of the concerns you highlighted.
**Weaknesses:**
... | Summary: This paper proposes a novel forward gradient descent method named AsyncFGD to decouple dependencies between layers and thus maximize parallel computation. The authors demonstrate that their method can reduce memory consumption and enhance hardware efficiency through empirical evaluations on AGX Orin.
Strength... | Rebuttal 1:
Rebuttal: Dear Reviewer empf,
We sincerely thank you for your comprehensive review and the constructive feedback you provided. Your insights are invaluable, and we have made efforts to address each of the concerns and questions you raised.
**Weaknesses:**
**Q1:** "Unclear Relevance to Machine Learning Ve... | Rebuttal 1:
Rebuttal: ### Sincere Gratitude
Dear All Reviewers and Program Chairs of NeurIPS 2023,
We would like to extend our heartfelt gratitude to each of you for the time, effort, and expertise you have invested in reviewing our manuscript. Your constructive feedback, insightful comments, and valuable suggestions ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Unified Framework for U-Net Design and Analysis | Accept (poster) | Summary: This paper proposes a formal definition of U-nets, a crucial building block of modern deep learning pipelines such as diffusion models.
Thanks to this definition it is possible to both get theoretical results explaining some of the u-nets behaviours, and generalize u-nets to settings more exotic than the 2D im... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed review. We are thankful for the appreciation of the formalism of our theoretical framework which contributes to an understudied area, among other strengths. We respectfully would like to point out that we disagree with the concerns on Theorem... | Summary: This paper proposes a framework for designing and analyzing general UNet architectures. Theoretical results are presented that can characterize the role of encoder and decoder in UNet and conjugacy to ResNets is pointed out via preconditioning. Furthermore, this paper proposes Multi-ResNets, UNets with a simpl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review, and for highlighting various components and insights of our unified framework as strengths of our work. Below, we would like to focus on the two weaknesses the reviewer mentions.
> “The presentation of the paper is not smooth and clear, which needs signific... | Summary: The paper presents a unified framework for U-net design, by generalizing the overall structure of U-nets into different components. The framework is then investigated from a theoretical point of view. At its core, the paper presents Multi-Resnets which is a novel class of U-nets, which is then tested rigorousl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our work. We are delighted that you appreciate the approach of "taking a step back" in our research and our analysis of the role of the encoder in U-Nets (Sec 5.1), as well as the originality and quality of our theory, experiments, and Appendix.... | Summary: The work proposes a unified mathematical framework to analyze and design U-Net. Authors highlight the importance of preconditioning and provides several highlights to design unet. Authors propose a new parameter-free encoder based on wavelet space.
Strengths: 1. Sec 2 presents a unified mathematical framework... | Rebuttal 1:
Rebuttal: We are glad to hear the reviewer appreciated our submission.
> “Though it shows improvement in other tasks, there are minimal improvements in diffusion model tasks. Authors should report performance of Multi-ResNet for diffusion models and compare it with existing U-Nets [...]”
It is important ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and insightful comments and questions. Motivated by the reviews, we make several changes towards a potential camera-ready version of the submission, using the one additional page, which we list below.
### Summary of key changes towards a camera-ready ver... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A database-based rather than a language model-based natural language processing method | Reject | Summary: This paper proposes a new method for natural language processing. Instead of using language corpus, authors suggest to use database. Sentence generation is a linear schematization of a database-based representation. It is indeed an interesting idea.
Strengths: Authors propose a brand new NLP approach that is... | Rebuttal 1:
Rebuttal: **Q11: Database (R4)**
The “database(s)” is used to store information (knowledge). It is equals to the memory of the human being. The database factually describes the information (knowledge) in the real world and how they are stored and organized in the human brain. Please refer to the reply of Q... | Summary: This paper aims to take a novel standpoint with respect to all the neural network architectures working on natural language (NN-NL). According to the authors, these NN-NLs work on the surface of the language, disregarding that the language is encoding information. In their opinion, information should be repres... | Rebuttal 1:
Rebuttal: **Q6: Large corpus and Neural network (NN) (R3)**
Large corpus are the sample sets for training NN models; the NN models are a statistical inference model. See the reply in Q1; the statistical inference models are unsuitable for NLG and NLU problems. _The research object of my work is the informa... | Summary: This paper studies a database-based natural language processing method and proposes a tree-graph hybrid model based on three types of spatial relations. The model is further applied to both natural language generation and natural language understanding tasks.
Strengths: The insight of borrowing human cogniti... | Rebuttal 1:
Rebuttal: **Q4: Typos (R2)**
Thanks for your kind reminder; I will thoroughly check and fix them in the revised version.
**Q5: Literature review (R2)**
I tried to find some literature at the beginning of my work, but unfortunately, it was unavailable. After that, I throw myself into my work. | Summary: This paper advocates the separation of language and knowledge. It proposes a database-NLP method. The knowledge is contained in one or more databases whose internal structures can be a tree, a graph, or a hybrid. There are associated methods to query and retrieve from the database(s) the information. Finally, ... | null | Rebuttal 1:
Rebuttal: Thank you for reading and commenting on my work. I look forward to more opportunities to communicate in the future.
**Q1: Lack of Experimental Section (All)**
I apologize for the operating error in the checklist on the initial submission. For the experiments option, I selected “yes”; actually, i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance | Accept (spotlight) | Summary: This paper introduces a meta-learning algorithm which compresses a training set $D$ of size $|D|$ into a smaller training set $T\subset D$ of size $|T|$. The algorithm can be defined for any *base learner*, i.e. any training strategy which outputs a trained hypothesis $h$ when given a training set. The set $T$... | Rebuttal 1:
Rebuttal: Many thanks for the positive review and constructive comments. In addressing them, we have produced additional material (see attached pdf and below) that we believe will improve the quality of the manuscript.
**Weaknesses**
* 2.1. We concur that our sentence on PAC-Bayes methods in regression p... | Summary: This work elaborates on the recent breakthroughs of Campi&Garatti 2023, by exploiting compression theory results to design a novel meta-algorithm, namely the Pick-To-Learn (P2L) algorithm. This algorithm aims to compress the dataset to a smaller, truly impacting one, this notion of impact being defined through... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the Reviewer for their valuable time and feedback on the work, which we found useful and to the point.
**Questions**
* “To perform their experiments…”: Considering the small-data regime was motivated both by applications and by the fact that, in these setting... | Summary: In this paper, the authors present a novel framework called P2L, which aims to derive generalization guarantees for black-box supervised learning algorithms. P2L operates as a meta-algorithm that utilizes a learning algorithm to induce a compression scheme. The algorithm relies on two main components: a *crite... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their positive review and constructive comments. We include our responses below, which we hope can help better understand and position our paper.
**Weaknesses**
* “In contrast…”: while it is true that our bound cannot be directly optimized, the size of the... | null | null | Rebuttal 1:
Rebuttal: We would like to take this opportunity to thank all the Reviewers and PCs for their valuable time and feedback. Below, we address each of the reviews individually, while we attach here a pdf containing additional figures used in the responses.
Pdf: /pdf/3db9681c4a51af7716dd350ec5327c26614d9db5.p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback | Accept (poster) | Summary: The paper studies the problem of designing uncoupled learning dynamics that provably converge to Nash equilibria in two-player zero-sum Markov games. As a preliminary result, the paper introduces the first dynamics that converge last iterate in self play in matrix games with bandit feedback. Then, the paper sh... | Rebuttal 1:
Rebuttal: Thank you for your comments! We address your concerns below.
*Q: A major weakness that I see is that the results presented in the paper seem minor adaptations and different analysis of already known techniques/tools. The authors should focus more on discussing the novelty of their approach.*
A :... | Summary: The paper introduces a new algorithm for learning in two-player zero-sum Markov games based on prior work that is uncoupled (agents only need their own reward as feedback), convergent (to NE) and rational. The result is an algorithm that is similar in concept to a single agent RL algorithm, but guarantees con... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive feedback! We will add more discussions on the optimism technique in Algorithm 3 in the revised version. We address your question below.
*Q: Have any experiments been run using Algorithms 1, 2 and 3 to compare them to current SOTA methods? If so, how do... | Summary: The paper studies the last iteration convergence of uncoupled learning in two player zero-sum markov game, with bandit feedback. The paper provide the first finite last-iterate convergence guarantee under the bandit feedback.
The paper studies the problem of two-player zero-sum Markov game and designs a new u... | Rebuttal 1:
Rebuttal: Thank you for your very positive comments! We address your questions below.
*Q1: Can you provide some intuition on the number of $T^{-1/8}$ or $T^{-1/9}$, ideally, it would be nice if one can written down a short explanation on how these magic exponent comes from.*
A: These exponents are a resul... | Summary: This paper studies the problem of learning a Nash equilibrium in two-player zero-sum Markov games with bandit feedback.
The proposed algorithm introduces the entropy regularization technique into online mirror descent.
First, the author proves that the proposed algorithm converges to an equilibrium in two-play... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive comments. We will include a more detailed proof sketch of Theorem 3 in the revised version. Your questions are addressed below.
*Q1:How much computational cost will it take to update the strategy (e.g., line 6 in Algorithm 1)?*
A: To update the strat... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies algorithms for two-player zero-sum Markov games that are uncoupled, convergent, and rational. Previous attempts at designing such algorithms failed short in one aspect or the other. This work uses recent advances in entropy-based regularization to design new algorithms that overcome the inhe... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. We address your questions below.
*Q1:How tight are the obtained convergence rates?*
A: The obtained convergence rates in this paper may not be tight. For example, Algorithm 1 achieves a high-probability $O(t^{-1/8})$ last-iterate convergence rate under band... | null | null | null | null | null | null |
Polynomial Width is Sufficient for Set Representation with High-dimensional Features | Reject | Summary: Summary: The paper proves that for symmetric neural networks, specifically DeepSets, there exist exact representations for symmetric functions, where the symmetric embedding layer width can be chosen to be polynomial in the set size and input dimension, rather than exponential as shown in stricter settings.
S... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer S2JM for appreciating our proof technique and its deeper implication. We are also grateful for constructive suggestions to improve this manuscript. Please see our responses as below:
**1. A more robust discussion of the tradeoffs of this parameterization.**
We agree w... | Summary: This manuscript proves that an embedding with polynomial width in the set size and feature dimension is sufficient to precisely reconstruct a set function, under some constraints on the embedding layer architecture. The main contribution is on the upper bound, which removes assumptions of previous studies, and... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer gdbo for acknowledging the depth and significance of our work. Please see our responses as below:
**1. The proof is an existential argument and the convergence of training is not guaranteed.**
This work focuses on expressive power analysis for DeepSets and the main go... | Summary: The paper studies the representative properties of DeepSets models, which are networks for length-$N$ sequential modeling that apply identical neural networks to each $D$-dimensional input $x^i$ to obtain $L$-dimensional features, sum up their outputs, and apply an additional neural network to the output. Whil... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer zB8R for your careful examination of our work and raise meaningful discussion. We have corrected all the flaws in our results and proofs, and all the modifications are summarized in our **general response**. Please read our detailed response as below:
**1. Errors of Le... | Summary: This paper studies the required neural network width for representing permutation invariant functions on sets. Existing works either focus on the case where the set elements are scalars, or require exponentially large neural network width with respect to the dimensions of the set elements. This work proves tha... | Rebuttal 1:
Rebuttal: We thank reviewer YiWd for acknowledging the significance of our results compared with prior arts. We will carefully proofread our paper and fix all the typos. Per your questions, please read our responses below:
**1. Practical implication of the results (e.g., in GNN, PointNet).**
The LP layer ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their time and efforts reviewing our paper. However, we have to apologize for the misplacements in the current submission due to a tight timeline when we prepared this manuscript, which led to an imprecise statements. After we submitted our paper, we n... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Estimating Noise Correlations Across Continuous Conditions With Wishart Processes | Accept (poster) | Summary: The goal of this paper is to compute the covariance of recorded neurons in a given stimulus condition with a low number of samples. Although the conditions are different, some aspects are shared which justifies the fact that the covariance for a specific condition should depend on the covariance in other condi... | Rebuttal 1:
Rebuttal: > **While the smoothness part is new, exploiting similar conditions [...].**
Thank you for the pointer to the PoSCE paper. We had overlooked it since it is a neighboring field (none of us have experience with fMRI data).
Roughly speaking, PoSCE can be adapted to our problem by estimating the cov... | Summary: This work proposes to use Wishart Processes, originally proposed by Wilson and Ghahramani, to estimate the covariance structure of neural activity in experiments where there are parametric variations linking stimulus conditions. By pooling estimates appropriately across task conditions, the limited number of e... | Rebuttal 1:
Rebuttal: > ***This is a relatively straightforward application of an existing method***
This is one of the most important points to discuss so we have laid out a detailed response in our “general rebuttal." To summarize our arguments:
* “Application” papers of all stripes are requested in the “NeurIPS Cal... | Summary: The estimation of noise covariance in neuroscience is limited by the experimental difficulty of obtaining large numbers of trials for the same neurons, but also the desirability of large numbers of neurons. Here the authors begin by recognizing this need for lower-variance estimators of the noise covariance, a... | Rebuttal 1:
Rebuttal: > **Although I see the benefits of model simplicity [...].**
We agree that accurately inferring the mean is an important aspect of accurately inferring the covariance. Using a GP to enforce some smoothness in the mean response across conditions is indeed reasonable. See, for example: Wu et al. ("... | Summary: - Exhibits good performance, especially for recordings of large neural populations with very few trials per condition.
- Enables dense sampling of conditions with only a single trial per condition, unlike standard estimators that require a large number of trials per condition (although based on empirical assum... | Rebuttal 1:
Rebuttal: > **Computationally more expensive than standard covariance estimators**
We have clarified that the computational burden is reasonable: it takes 80 seconds to fit a dataset with 100 neurons, 80 conditions, 32 trials per condition. Notice that we run 10,000 iterations of our optimization algorithm... | Rebuttal 1:
Rebuttal: # General Response
We thank the reviewers for their thoughtful and productive critiques which did not identify any major technical errors. We have done our best to incorporate all reviewer feedback and requests for more details (see individual responses to each reviewer).
Below we summarize the t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Quantization Model of Neural Scaling | Accept (poster) | Summary: This paper proposes a possible mechanism that explains both the phenomenon where the cross-entropy loss of large language models (LLMs) decreases as a power law with respect to the training corpus size, and the phenomenon in which certain capabilities of LLMs emerge spontaneously as the loss becomes low enough... | Rebuttal 1:
Rebuttal: Thank you for your feedback and question/suggestion! We agree that more work could be done in evaluating to what extent scaling on real-world datasets satisfies the Quantization Hypothesis.
**Could you provide a practical demonstration of the Quantization Hypothesis in a controlled environment th... | Summary: This paper proposes a hypothesis that there exists a universal and crucial discrete set of computations (quanta) for reducing loss in various prediction problems, and the model's performance is determined by successfully learning these computations. Through this hypothesis, it demonstrates the relationship bet... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and questions, and for highlighting the importance of clarifying our definitions in the paper! We think the clarifications we’ll make, prompted by your review, will significantly improve the paper. We’ll respond point by point:
### Weaknesses:
**1. The paper l... | Summary: This paper proposes a new way of understanding scaling laws. Namely, it shows that capabilities can be thought of as being broken into discrete units (quanta) which themselves follow a power law and have an inherent ordering--called the Q Sequence. This combined with the fact that 1) an increasing subset of qu... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We’re glad you found the paper compelling!
**While the paper shows that the model scaling trend agrees with theory on real data, it does not empirically validate whether the same alpha of 0.083 matches the data scaling theory. Also, it would be interesting to show thi... | Summary: The authors investigate neural scaling laws and propose an explanation for the power law scaling that is observed in addition to the emergence of new behaviours.
Strengths: The ideas are interesting and I found some of the experiments such as per token losses on the language models to be quite interesting.
... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions! We’re glad you found our ideas and some of our experiments interesting. Before responding to your questions point by point, we’d like to apologize for not making our most interesting contribution more clear, namely our notion of quantization and of quanta... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes that the capabilities of a neural network are composed of discrete “quanta” that each help reduce loss on a subset of training examples, and that are learned in approximately decreasing order of frequency. If the quanta are present in a Zipfian (power-law) frequency distribution in the train... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions! We’re glad you found the paper to be interesting and valuable. We’ll comment on the weaknesses and respond to your questions below:
**The multitask parity setting seems too obviously likely to lead to quanta, so it doesn’t seem to prove very... | null | null | null | null | null | null |
From One to Zero: Causal Zero-Shot Neural Architecture Search by Intrinsic One-Shot Interventional Information | Reject | Summary: This paper proposes a 0-shot NAS method. The key point is that there are latent factors that can influence the architecture search procedure, making the validation accuracy of one-shot NAS unreliable. The method adopts Gaussian intervention to the data and evaluates each operation's performance to reduce the b... | Rebuttal 1:
Rebuttal: Thanks for reviewing this manuscript. Here are some questions:
1. What do you mean by 'definitions'? The validation accuracy is the accuracy of the validation set.
2. What do you mean by 'straightforward;? As far as I know, straightforwardness is a strength in writing. Besides, our proof of the Ga... | Summary: This paper formulates zero-shot NAS as a causal-representation-learning. Further, it uses the high-level interventional data from one-shot NAS to facilitate zero-shot NAS to refine the imperfectness. Extensive experiments achieved comparable performance results on multiple benchmarks.
Strengths: 1) This paper... | Rebuttal 1:
Rebuttal: Thanks for reviewing this manuscript. Here are some questions:
1. We do not use Zen-NAS as the baseline but build it on our own. Please could you explain why our work is related to Zen-NAS? And why 'the novelty is incremental'?
2. The search cost is significant in NAS. Have you ever read the relat... | Summary: This paper presents a causal definition of zero-shot NAS and facilitate this with interventional one-shot knowledge data. The paper theoretically demonstrates the validation information of either a neuron or a neuron 60 ensemble obeys a Gaussian distribution given a Gaussian input. It then uses high level inte... | Rebuttal 1:
Rebuttal: Thanks for reviewing this manuscript. We will consider the dataset you mentioned. However, there are too many zero-shot neural architecture search datasets. I don't think it is a must. Anyway, thank you for this suggestion.
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Rebuttal Comment 1.1:
Comment: I have read the response and most of ... | Summary: This paper proposes a causal zero-shot neural architecture search (NAS). The NAS problem is decomposed into two components: ensemble selection and neuron selection. By employing the Gaussian intervention to approximate validation accuracy, the authors adapt the perturbation-based approach from DARTS+PT to sea... | Rebuttal 1:
Rebuttal: Thanks for reviewing this manuscript. I am very pleased that you mentioned some related works in Zeo-shot NAS. Here are some questions:
1. Please could you explain the way our work is related to DARTS+PT? We have not found a clue yet.
2. Does ZiCo a common theoretical approach or just an increment... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models | Accept (poster) | Summary: Recent methods have mainly utilized the reconstruction property of in-distribution samples to detect OOD by diffusion models. However, they often suffer from detecting OOD samples that share similar background information to the in-distribution data. Based on the observation that diffusion models can project a... | Rebuttal 1:
Rebuttal: Dear Reviewer SZQH,
We sincerely thank you for your helpful feedback and insightful comments. In what follows, we address your concerns one by one.
___
**[Q1]** From the introduction, the projection cannot change the background information too much, which seems to be harmful on near-OOD detection... | Summary: This paper discusses a method in machine learning known as Novelty Detection, which is used to identify abnormal or out-of-distribution (OOD) samples. The authors suggest that diffusion models, a popular generative framework due to their strong generation performance, has recently become an attractive tool for... | Rebuttal 1:
Rebuttal: Dear reviewer 9KBe,
We sincerely thank you for your helpful feedback and insightful comments. In what follows, we address your concerns one by one.
___
**[Q1]** Could you provide more details on the computational requirements of PR? For instance, what is the time complexity, and how does it scale... | Summary: This paper presents a novel approach to detecting and handling novelty in data using a generative diffusion model, with a specific focus on addressing biased backgrounds. The proposed method aims to transform noisy samples into perfect ones by leveraging the capabilities of the diffusion model. The central ide... | Rebuttal 1:
Rebuttal: Dear Reviewer ZGie,
We sincerely thank you for your helpful feedback and insightful comments. In what follows, we address your concerns one by one.
___
**[Q1]** One weakness of this paper is that the main idea of using projection and reconstruction error for novelty detection is not novel. There ... | Summary: This paper proposes Projection Regret (PR) to mitigate the bias of background information for novelty detection. As an effective perceptual distance, it is able to detect abnormality by reducing the effect of dominant background using recursive projections. Experimental results show the effectiveness of the pr... | Rebuttal 1:
Rebuttal: Dear Reviewer mygo,
We sincerely thank you for your helpful feedback and insightful comments. In what follows, we address your concerns one by one.
___
**[Q1]** About the detection running time. Diffusion models are used in the proposed method, which leads to an increase of inference time when de... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We sincerely appreciate your valuable time and effort spent reviewing our manuscript.
As reviewers highlighted, our work aims at an important problem **(Reviewer ZGie, 9KBe, SZQH)** with an interesting/novel method **(Reviewer ZGie,9KBe)**, strong empirical results **(Rev... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence | Accept (poster) | Summary: This paper proposes a dual-coordinate descent solver for a class of ERM (Empricial Risk Minimization) problem with general linear inequality constraint, which is of linear convergence rate and efficient coordinate-update cost of O(n) (n is the number of samples). The main contribution is it extends existing me... | Rebuttal 1:
Rebuttal: ## Weaknesses
> The paper completely missed discussion on existing works of dual-coordinate ascent on general ERM problem. There is a thread of works such as:
>
>> Shalev-Shwartz, Shai, and Tong Zhang. "Stochastic dual coordinate ascent methods for regularized loss minimization." Journal of Machi... | Summary: This paper proposes a new optimization algorithm for convex piecewise linear-quadratic objectives with $L_2$ regularization and linear constraints, which achieves the best known iteration complexity simultaneously with the best known per-iteration computational cost, resulting in a smaller total computational ... | Rebuttal 1:
Rebuttal: ## Weaknesses
> The experimental evaluation is missing a few details that need clarifying. For example, line 212 states that the BenchOpt framework is used "to implement optimization benchmarks for all the SOTA solvers". I find this statement a little unclear. Did the authors implement all of the... | Summary: This paper studies the Empirical Risk Minimization(ERM), which is an important framework of machine learning, and focus on a general regularized ERM based on a convex PLQ loss with linear constraints. According to the authors, the existing algorithms are faced with the problem of slow convergence or high compu... | Rebuttal 1:
Rebuttal: ## Weaknesses
> Actually, rather than those solvers tested in the experiment part, there are a number of general optimization solvers, including CPLEX, GUROBI, SCIP, etc. Those optimization solvers have exhibited great success in solving linear/nonlinear continuous/discrete optimization problems.... | Summary: This paper introduces an algorithm, ReHLine, which has a linear convergence rate on minimizing convex piecewise linear-quadratic loss functions. Experiments on several tasks show great performance gains over existing algorithms.
Strengths: General: The paper is clean well-written and easy to follow. Definitio... | Rebuttal 1:
Rebuttal: ## Weaknesses
> The main contribution of this paper is proposal of ReLU-ReHU decomposition and its resulting formulation. Its convergence result is shown by classical result. I feel the contribution is limited and marginally above the borderline.
**Reply**: Thanks for the comments. We would like... | Rebuttal 1:
Rebuttal: # To All Reviewers
Thank you all reviewers for the encouraging and insightful comments. We appreciate the time and effort the reviewers have dedicated to providing valuable feedback on our manuscript. In this round, we have made every effort to address all the comments of the reviewers. **The poi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors consider tackling the optimization fo finite sum of piecewise linear quadratic functions (PLQ) arising from e.g. robust empirical risk minimization by means of a reformulation of PLQ functions as sums of ReLU and smoothed ReLU functions combined with a stochastic dual ascent algorithm. The authors ... | Rebuttal 1:
Rebuttal: ## Weaknesses
> (related work)
**Reply**: Thanks for the comments. We emphasize that our contribution is not only applying CD (and its extensions such as SDCA) to a specific optimization problem, but also to understand the class of problems such that the CD variant can be implemented with linear... | Summary: This paper introduced a new function class called composite ReLU-ReHU which is shown to be equivalent to the class of convex PLQ functions. Based on the ReLU-ReHU decomposition of convex PLQ functions, the authors then formulated a new box-constrained quadratic programming optimization problem, named ReHLine o... | Rebuttal 1:
Rebuttal: ## Weaknesses
> While authors claimed that the simplification of Canonical CD updates is highly non-trivial, it seems to be a straight application of [18] to the ReHLine optimization problem.
**Reply**: Thanks for the comments. When the ReHLine optimization problem (1) and (3) is given, the pape... | null | null | null | null |
DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models | Accept (poster) | Summary: Imprecisions
- “According to the completeness requirement” add reference
- line 75-76 PADE-> PDAE
- line 270-271. Typo: compare with disco. dissdiff -> compared with disco, disdiff
Strengths
- Achieving disentangled representations in diffusion models is an interesting and useful problem.
Weaknesses
- Missin... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We appreciate your feedback and have made several changes to address your concerns. Please find below our responses to each point you raised.
We have thoroughly revised Section 4 of our paper. We have made significant changes in the presentat... | Summary: This paper proposes to disentangle a pre-trained diffusion probabilistic model (DPM) in an unsupervised way to improve interpretability. The author designed two constraints, invariant condition and variant condition, to guide the disentanglement. The proposed method was evaluated on three synthetic datasets an... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We appreciate your feedback and have made several changes to address your concerns. Please find below our responses to each point you raised.
We have thoroughly revised Section 4 of our paper. We have made significant changes in the presentati... | Summary: This paper proposes a new task of unsupervised disentanglement of diffusion probabilistic models (DPMs) and presents an approach named DisDiff to achieve disentangled representation learning in the framework of DPMs. The authors connect disentangled representation learning to DPMs to take advantage of the rema... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions and the encouraging words. We appreciate the time and effort you took to review our paper. Please find below our responses to your concerns and the changes we have made to address them.
We have thoroughly revised Section 4 of our paper. We hav... | null | null | Rebuttal 1:
Rebuttal: # Section 4
We assume that a dataset $\mathcal{D} = \lbrace x_0\sim p(x_0)\rbrace$ is generated by $N$ underlying ground truth factors $\mathcal{C} =\lbrace f^c|c=1,\dots,N\rbrace$, where $p(x_0)$ is the data distribution, i.e., each sample is generated by the underlining factors, $h: (f^1, \dots,... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations | Reject | Summary: The authors propose EquiformerV2, which is an improvement over the original Equiformer architecture. The main improvement is using a more efficient parameterization of the tensor products used in Equiformer which is computationally expensive for higher order representations. The more efficient parameterization... | Rebuttal 1:
Rebuttal: > 1. [Weakness 1] Statistic significance of numbers in Table 1(a).
We empirically find that training the same models twice with identical settings results in almost the same force MAE with differences less than 0.05 meV/Å.
In comparison, increasing the number of epochs from 12 to 20 and from 20 ... | Summary: This paper provides a new equivariant graph neural networks named EquiformerV2 to enhance the original Equiformer performance. It uses four new modules. The first module is to use the convolution in the eSCN (https://arxiv.org/abs/2302.03655) to replace the depth-wise tensor production accelerating the speed. ... | Rebuttal 1:
Rebuttal: > 1. [Weakness 1] As a suggestion, it will be better if efficiency study includes both training time and inference time. Computational complexity metric such as FLOPs or MACs can help measure the inference complexity.
We provide the comparison in **General Response 6**.
> 2. [Weakness 2] The de... | Summary: In this paper, the authors proposed EquiformerV2, which is an equivariant network for 3D molecular modeling. The EquiformerV2 is built on the Equiformer with several architectural modifications: 1) replace SO(3) convolutions (tensor product operations) with efficient SO(2) counterparts from eSCN; 2) Attention ... | Rebuttal 1:
Rebuttal: > 1. [Weakness 1] The novelty of integrating the eSCN convolution and S^2 activation into the Equiformer is limited.
We would like to clarify that the contribution of this work is to investigate whether the design choices of previous equivariant Transformers, which consider only lower degrees, c... | Summary: This paper propose EquiformerV2, a advance verison based on Equiformer and eSCN structure extend to higher degree representations, which achieve better performance in force and energy tasks.
Strengths: This paper is well-written and organized, presenting a clear and coherent structure throughout. The introduc... | Rebuttal 1:
Rebuttal: > 1. [Weakness 1] A few spelling errors. For instance, in Section 6, the word "acknolwdge" etc. Along with any other mistakes found throughout the manuscript.
Thanks for finding this. We will double check the paper and correct spelling mistakes if we find one.
> 2. [Weakness 2] The experiments c... | Rebuttal 1:
Rebuttal: Thank you for all the constructive feedback! We are glad reviewers found the writing clear (3XDb, GdTP, 9yvD) and the empirical results on OC20 and AdsorbML impressive (6dKw, GdTP, 9yvD, 3XDb). We address general questions here:
> 1. Smaller EquiformerV2
We trained a smaller version of Equiforme... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Accept (poster) | Summary: In this paper, the authors propose RECESS, which is a proactive defense against untargeted model poisoning attacks. Specifically, RECESS proactively detects malicious clients with test gradients and robustly aggregates gradient with a new trust scoring based mechanism. For the former, the key insight is that t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We provide our responses and additional evaluations below to address the concerns.
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**Q1:** Limited capacity of defense.
**R1:** We appreciate the comment that our method primarily handles untargeted model poisoning.
This submission does ... | Summary: This paper proposes a new defense against model poisoning attacks in FL. The idea is that the server sends some perturbed aggregation gradient to clients in some selected training rounds, and based on the responses, the server adjusts trust scores for the clients. Experimental results show the effectiveness of... | Rebuttal 1:
Rebuttal: Thanks to the reviewers' insightful suggestions, which are helpful to strengthen the completeness of this work.
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I didn't see severe weaknesses. I think the paper is above the bar. I have three suggestions:
**S1:** Since the paper talks about detection. I would suggest also comparing with de... | Summary: The paper presents RECESS, a backdoor defense method in federated learning. The central server keeps querying each client and tries to estimate the trust score of each client to determine if it is malicious or not. The underline intuition of the anomaly detection method is that the malicious client will push t... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. Please find our response below.
___
**Q1:** The paper does not validate its assumption. The goal of the adversary in poisoning attacks is to mislead the output with the trigger, and there is no need to manipulate the malicious gradient so that it is far away from... | Summary: To defend the untargeted poisoning attack on the federated learning, the authors proposed a new defense called as RECESS, which exploits the outlier detection to analyze the gradients returned from clients. Once the gradient from one client is judged as outlier, this client will be considered as malicious clie... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable comments. Please find our responses below.
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**Q1:** The main weakness of this work is its trivial contribution compared to STOA [11, 12]. The intuition for outlier detection mentioned in the Summary has been considered. Compared to STOA, the new 'trust sco... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Beyond probability partitions: Calibrating neural networks with semantic aware grouping | Accept (poster) | Summary: The paper proposes a novel method for dealing with the well-known problem of miscalibration in deep learning models (and machine learning models more generally). The proposed approach is to partition the input space and fit a calibration function to each set. The paper shows that the idea of partitioning the i... | Rebuttal 1:
Rebuttal:
We express our gratitude to the Reviewer mMe6 for conducting a careful evaluation of our paper and offering valuable suggestions concerning the representation of formulas. We agree with the reviewers' perspectives, acknowledging that in certain equations, we have overloaded symbols without provid... | Summary: This work focuses on improving calibration (e.g., measured with Expected Calibration Error) using partitions of the feature space. This contrasts with most well-established recalibration techniques (isotonic regression, histogram binning, temperature scaling...) that solely use the estimated probabilities of t... | Rebuttal 1:
Rebuttal: I appreciate your provision of recent relevant research. Below, we will expound on the distinctions between our approach and these methods. Moreover, we intend to incorporate an analysis of these references in the revised version of the paper.
**Q1**: [1] also proposes an algorithm for learning a... | Summary: This paper proposes a method to calibrate neural networks more effectively. It introduces a general framework called PCE, which aims to provide an explanation for existing calibration methods. The proposed method partitions the input space into groups and minimizes the discrepancy between the predicted probabi... | Rebuttal 1:
Rebuttal:
I express gratitude for the insightful suggestions you've provided. We will improve the paper based on your suggestions.
**Q1**: The term ETS is used without definition (l171)
**A1**: We will add the full name of ETS(Ensemble Temperature Scaling) and corresponding reference.
**Q2**: It took so... | Summary: In this paper, the authors address the model calibration problem and propose a generalized definition of calibration error called Partitioned Calibration Error (PCE). Previous calibration methods mainly bin the data by the prediction probabilities, while PCE utilizes groups and partition functions to partition... | Rebuttal 1:
Rebuttal: I express gratitude for the valuable suggestions you have offered to elevate the quality of our manuscript. We shall duly incorporate the corresponding modifications into the article.
**Q1**: For the grouping function, it is parameterized as a set of weights and biases, which is claimed as semant... | Rebuttal 1:
Rebuttal: The support information of A6 and A7 to Reviewer yhZK (performance on class-wise ECE, and the reason of performance drop of TS in SWIN model), and performance measured by NLL are in the PDF file.
Pdf: /pdf/400bbc2b6179be0ceadf59c20070c54e1a440072.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a generic form of a calibration error metric. Partitioned Calibration Error (PCE) evaluates calibration errors across the group of partitions to alleviate data uncertainty. Furthermore, the paper presents experimental results on CIFAR-10, CIFAR-100, and ImageNet using several backbone model... | Rebuttal 1:
Rebuttal: I extend my gratitude for the queries and suggestions you've raised. We will duly enhance the relevant sections in the revised version of the article.
**Q1**: The confidence calibration performance is on par with ETS.
**A1**: *Our method has achieved statistically significant performance improve... | null | null | null | null | null | null |
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis | Accept (poster) | Summary: The paper proposed a novel graph-based program representation, PerfoGraph, which is based on the current state-of-the-art method PrograML and aims to address its limitation by providing numerical awareness, introducing new tactics for handling local variables, and supporting aggregate data types. By conducting... | Rebuttal 1:
Rebuttal: - **(W1) Unconvincing experiment setup in comparison with PrograML:**\
In the downstream tasks of sections 5.3, 5.4, 5.5, and 5.6, we used the RGCN encoder with ProGraML and compared it with PerfoGraph. For each of these downstream tasks, we used an RGCN model with the same architecture as describ... | Summary: The paper presents a graph-based representation for LLVM IR programs for processing with graph neural networks. The representation is based on ProGraML, an established technique for such graph representations, but adds several features to the graph: collapsing nodes that refer to the same variable, adding addi... | Rebuttal 1:
Rebuttal: - **(W1.a, Q2) The computation cost PerfoGraph:**\
We conducted an experiment in terms of the time it takes to train the GNN model using *PerfoGraph* versus *ProGraML*. We found out that *PerfoGraph* takes less training time than *ProGraML*, and *PerfoGraph* also has better performance than *ProGr... | Summary: This work proposes Perfograph, a program graph representation based on LLVM-IR and an extension to ProGraML. This graph representation is designed for the purpose of performance optimization and program analysis applications based on graph neural networks (GNN). This work made three contributions to the existi... | Rebuttal 1:
Rebuttal: - **(W1) Differences and Contributions that differentiate PerfoGraph from ProGraML:**\
While we acknowledge the similarities between *PerfoGraph* and *ProGraML*, as both represent programs as graphs using LLVM Intermediate Representations, *PerfoGraph* differs itself from *ProGraML* by:\
A) A more... | Summary: The research identifies limitations in the current state-of-the-art program representation PROGRAML, in capturing features of numerical values and composite data types.
To address these limitations, this work introduces an enhanced GNN-based program representation for LLMV-IR with modifications to the nodes a... | Rebuttal 1:
Rebuttal: - **(W1) Unknown numbers during the testing phase:**\
Here, by unknown numbers, we mean numeric tokens not encountered by the model during the training phase but present in the testing phase. Digit Embedding allows us to generate embedding for those unknown numbers. Because it breaks the numbers i... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive feedback and comments.
Hereby, we address some of the common concerns.
- **Moving the Ablation Study to the main paper:**\
Due to the lack of space in the paper, we currently have the ablation study results in the Supplementary file. We w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Interpretable Low-dimensional Representation via Physical Symmetry | Accept (poster) | Summary: This paper presents an approach to interpretable representation learning based on ''physical symmetry''. The core idea is to learn to predict the temporal evolution of a latent variable which is additionally encouraged to be equivariant under some transformation (e.g. translation or rotation). An autoencoder-l... | Rebuttal 1:
Rebuttal: Thanks for the detailed feedback and for raising those important questions. In the below chain of comments, we respond in a breakdown format.
> **How important is L_rec, relative to L_prior? That is, can we turn off L_rec and still get similar results?**
**Reply 1**:
No, we can’t. L_rec is ... | Summary: The paper presents a new way of training a self-supervised system that includes a data augmentation module in the latent space that leverages physical symmetry. This introduction of a transformation covariance in the latent space while training helps learn interpretable, robust and data-efficient representatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the feedback and raising important questions and concerns. We answer these questions here and also like to take the opportunity to address some concerns raised in weaknesses/limitations.
> **Why could physical symmetry be seen as a counterfactual inductive bia... | Summary: This paper utilizes the concept of physical symmetry as a self-supervised constraint within an auto-encoder framework to enhance the learning of interpretable and disentangled representations. The authors validate their approach through experiments conducted on unlabelled monophonic music audio and monocular v... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review! We really appreciate the concise summarisation on both the strengths and the current limitation of the paper. We hereby respond in a breakdown format.
> **Could you explain what a random-pooling layer does and the details of how you split the style ... | Summary: This paper proposes a symmetry equivariance constraints on the transition dynamics of latent representations for time-indexed data. The claim is that imposing these certain symmetries create interpretable model representations that correspond to popular domain-specific representations in the audio and video do... | Rebuttal 1:
Rebuttal: Thank you for giving the insightful feedback and raising such important questions. Below we respond in a breakdown format.
> **Is it possible to frame the training objective (Equation 1) as a proper probabilistic loss? See [1] (the static case) and [2] (the time-indexed case) for the probabilist... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This study introduces a new methodology to learn interpretable representations from data by incorporating physical symmetry as a self-consistency constraint in the latent space. It addresses a key challenge in machine learning: how to learn interpretable low-dimensional factors from unlabeled data without rely... | Rebuttal 1:
Rebuttal: Thank you for the review. We sincerely appreciate your comprehensive understanding of this paper’s vision and strengths as well as its current limitations. Additionally, thank you for the in-depth questions. We’d like to address them here to the best of our knowledge.
> **How do the authors prop... | null | null | null | null | null | null |
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes | Accept (poster) | Summary: The paper proposes the "importance weighted samples gradient (IWSG)" estimator and describes its integration into a "mixture density particle filter (MDPF)" for state space architectures. Similar to regularized particle filters, the MDPF framework represents the continuous state posterior with a continuous mix... | Rebuttal 1:
Rebuttal: Thank you for your thorough comments and helpful feedback, which we will use in future revisions.
We concede that the presentation in the paper can be improved, and based on feedback we intend to move some technical details that are present in the supplement into the main paper text (see global... | Summary: This paper discusses the limitations of traditional particle filters in representing multiple posterior modes and their applicability to high-dimensional observations like images. Instead, the authors propose a method that leverages training data to learn particle-based representations of uncertainty using dee... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback. We address various comments and reviewer questions below.
We would like to highlight Appendix A.4 (supplement) which gives additional details about the use of smaller neural networks and non-learned operations when constructing the dynamic and measurement... | Summary: The paper studies the problem of sequential state estimation using discriminative particle filters. Particle filters (or SMC) methods are widely used in this setting owing to their flexibility. Recently, inspired by the success of deep learning enabled by end-to-end differentiable methods, there has been inter... | Rebuttal 1:
Rebuttal: Thank you for your praise and feedback. We are grateful for the careful review of our work and appreciate your highlights to the broad applicability of our IWSG estimator beyond particle filtering and the simplicity and effectiveness of our MDPF method. Our intention is to release the code publicl... | Summary: The paper consider the problem of particle filter (PF)-based state estimation in nonlinear models with unknown dynamics and (discriminative) observation models. The key challenge they address is the typical inability of traditional gradient learning approaches, applied to PF, to deal with (backprop through) t... | Rebuttal 1:
Rebuttal: Thank you for the positive comments and constructive feedback. We agree that some of the discussion of baseline methods in the main text is not sufficiently clear, and will shift material from Appendix A to the main paper to address this issue.
Please see our global response to all reviewers for... | Rebuttal 1:
Rebuttal: Thank you all for your feedback and helpful suggestions. We want to address a few topics that were referenced by multiple reviewers.
First, for additional technical details about our methods as well as baselines, please refer to Appendix A of the supplement. When drafting the paper, we put thes... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an unbiased and low-variance gradient estimator for differentiable particle filters, where resampling steps incurs discrete changes that are non-differentiable in previous methods. The proposed method solved the problem by representing posteriors as continuous mixture densities, which is si... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback. To answer your specific questions:
1. Classical particle filters assume known dynamics and measurement models, and thus also implicitly assume the latent state has been manually defined; typically it has an interpretable real-world meaning such as the po... | null | null | null | null | null | null |
QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution | Accept (spotlight) | Summary: This manuscript is devoted to pushing the super-resolution (SR) models to ultra-low bit-width (2-4 bits). It proposes two methods and pushes the bit-width to ultra-low 2/4 bits with little accuracy loss. Meanwhile, the proposed methods not only boost the accuracy performance but also reduces the parameters and... | Rebuttal 1:
Rebuttal: > **Q1a**: The functions in RLQ should be further clarified. …, but it is not clear whether it plays a role in the forward. The author needs to clarify its function and discuss whether it would reduce the inference efficiency of the quantized model.
**A1a**: We clarify that the $\phi$ function c... | Summary: This paper proposes a novel quantization method for SR models, including two new technologies to improve the unit representation and architectural potential of quantized SR models and push low-bit SR models to full-precision performance. The results on CNN and Transformer models on SR tasks show that QuantSR a... | Rebuttal 1:
Rebuttal: > **Q1**: One notable issue is there are some places in the paper that should be revised and clarified, including but not limited to the following: [...]
**A1**: Thank you for pointing them out. We will carefully revise them in our final revision.
> **Q2a**: In RLQ, …, so why it needs to be use... | Summary: The paper proposes a new quantization scheme for super-resolution (SR) networks. The paper starts with a claim that weight quantization results in the homogeneity of parameters, leading to the loss of gradient information during backward pass. To resolve the claimed issue, the paper introduces Redistribution-d... | Rebuttal 1:
Rebuttal: > **Q1**: Missing discussions and quantitative comparisons against related works. What is the major difference between DQA and DropConnect [A] and AIG [B]? Also, why limiting to variants 100%, 50%, and 25% in comparison to these two related works?
**A1**: We will incorporate the related work sugg... | Summary: This paper propose a new quantization method for single image super resolution, including Redistribution-driven Learnable Quantizer (RLQ) and Depth-dynamic Quantized Architecture (DQA). The previous one diversifies the representation and gradient information of quantized values by redistribution in quantizers,... | Rebuttal 1:
Rebuttal: > **Q1**: The two main reasons for this performance degradation is much common, ... Thus the method proposed in this paper is not SR-optimal.
**A1**: We clarify that in this work, the techniques in QuantSR focus on the SR task since it possesses certain attributes not found in other high-level ta... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for the constructive reviews and positive feedback to our QuantSR. Your expertise and insightful comments help us to further improve our paper.
The attached PDF includes:
* Figure 1: Visual comparison of weights in QuantSR with and without learnable quantizer paramet... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper propose an low-bit quantization method for efficient image super-resolution by introduce a Redistribution-driven Learnable Quantizer (RLQ). Besides, the proposed Depth-dynamic Quantized Architecture (DQA) allows for the trade-off between efficiency and accuracy during inference through weight sharin... | Rebuttal 1:
Rebuttal: > **Q1**: … Are there any special designs for SR tasks? If so, it is suggested to provide more discussion to highlight the contributions.
**A1**: We clarify that in this work, the techniques in QuantSR focus on the SR task since it possesses certain attributes not found in other high-level tasks,... | null | null | null | null | null | null |
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals | Accept (poster) | Summary: Recent research works have revealed that the over-smoothing issue, a prevalent challenge in Graph Neural Networks, similarly plagues Transformers. Contrary to expectations, the performance of a Transformer model does not invariably improve with increased depth of the self-attention layers. In fact, a deeply la... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Q1**. The theoretical explication put forth in this paper applies accurately only to Transformers with single-head self-attention mechanisms. However, given that the prevailing Transformers utilize mult... | Summary: This work shows that self-attention layers in transformers minimize a funcitonal which promotes smoothneess, thereby casuing token uniformity. The work also proposes a novel regularizer to preserve fidelity of the tokens. The work empirically shows that NeuTRENO outperforms baseline transformers in reducing th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Q1**. Some use of the wording could be improved, for example in line 220s, the authors like to use words like "significantly", "addresses" etc. In my opinion, some of the experimental results are not str... | Summary: This paper analyses the over-smoothing problem of transformer architecture by showing that self-attention layers minimize a functional that causes over-smooth. To address this problem, the authors introduce a regularizer that penalizes the norm of the difference between the smooth output tokens and input token... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Q1.** Current experiments are mostly conducted on small transformer models( e.g., DeiT-tiny). Given that the transformer architecture is crucial for large pre-trained language models, it remains unclear ... | Summary: This paper studies the oversmoothing problem in transformers. Roughly speaking, it was observed that embeddings start to converge when the network is deeper. The authors built a model to explain this phenomenon. Roughly speaking, they relate having deeper architecture with making progress towards minimizing a ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Q1**. I feel I was not able to properly parse some math texdt. My most unsure part is whether the amount of hand-waiving is too much even under today’s standard. For example, the authors feel very comfor... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
Thanks for your thoughtful reviews and valuable comments, which have helped us improve the paper significantly. We are encouraged by the endorsements that: 1) Our paper's variational denoising framework for self-attention is novel and interesting (Reviewer qvvE, h3ak, qcqa... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper analyzes the reason of over-smoothing in Transformer based on Self-attention as a Gradient Descent Step to Minimize a Nonlocal Function and random walk analysis. The paper then proposes a novel regularizer that penalizes the norm of the difference between the output tokens from self-attention and th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Q1**. The empirical evaluations missed some important details and analyses. In Table 1, the configuration of NeuTRENO Adaptation is not explained.
**Reply:**
Thank you for your comment. In our NeuTRENO ... | null | null | null | null | null | null |
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams | Accept (poster) | Summary: The authors point out that model may suffer from non-interest samples while TTA. Existing TTA methods are not robust to these samples. To address these issues, the authors proposed a methods called SoTTA with two key components, input-wise robustness via high-confidence uniform-class sampling and parameter-wis... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive comments. We respond to your question in what follows. Please also refer to the *global response* we have posted together.
---
**Weakness 1. It would be better for the authors to clarify the differences between the propos... | Summary: This paper studies a practical problem of test-time adaptation where non-interest testing samples may appear and mislead the adaptation. This problem is quite serious in practical applications, and the problem setting is relatively novel. To address this problem, the authors propose the SoTTA method, which sol... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive comments. We respond to your question in what follows. Please also refer to the *global response* we have posted together.
---
**Weakness 1.**
In our current manuscript, the CIFAR100 dataset acted as one of the non-intere... | Summary: This article presents a new Test-Time Adaptation (TTA) scenario, wherein the model is adapted to noisy test streams. To address the challenges posed by this scenario, the paper introduces the Screening-out Test-Time Adaptation (SoTTA) algorithm, which leverages input-wise and parameter-wise robustness. The eff... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive comments. We respond to your question in what follows. Please also refer to the *global response* we have posted together.
---
**Weakness 1.**
We appreciate the reviewer's insightful comment. Regarding the memory bank con... | Summary: This paper proposes screening-out test-time adaptation which is claimed robust to non-interest samples. It filters out the impact of non-interest samples with a high-confidence uniform-class sampling. It proposes entropy-sharpness minimization to deal with large gradients. Experiments are completed on CIFAR10-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive comments. We respond to your question in what follows. Please also refer to the *global response* we posted together.
---
**Weakness 1. The problem is interesting but the proposed two methods are straightforward. I am a li... | Rebuttal 1:
Rebuttal: ## Global Response
Dear Reviewers,
We sincerely appreciate your efforts and time in reviewing our manuscript.
The contribution of our work lies in investigating the crucial yet unexplored challenge of test sample diversity in real-world scenarios. Notably, we unveil that existing TTA algorithm... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | Accept (poster) | Summary: In this paper, authors propose a new approach to perform class-incremental learning in federated setting. Their approach uses a generative model trained at the server side which generates synthetic images to be used as a replacement for data corresponding to old classes. The authors claim through empirical res... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the details of our paper and appreciate their comments. We are grateful for their insightful questions and try to address their concern in detail:
---
> FedCIL compared to MFCL and other methods
We have briefly discussed the differences between FedCIL and MFCL ... | Summary: This paper introduces a framework for federated class incremental learning, which employs a generative model trained at the server. This model is then utilized by the client to generate samples from previous distributions to mitigate catastrophic forgetting.
Strengths: This paper addresses the challenging pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the details of our paper and appreciate their comments highlighting the novelty of our method and our extensive empirical results as the strengths of the paper. We are grateful for their insightful questions and try to address their concern in detail:
---
> Pract... | Summary: The work proposes using a server-learned generator for synthetic data replay for federated Class-IL. The method saves client-level compute while preserving data-privacy. The authors show their method outperforms existing methods on 2 existing benchmarks, plus a proposed larger-scale ImageNet benchmark protocol... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the details of our paper and appreciate their comments highlighting our design choices, superior performance, clarity, and writing as the strengths of the paper. We are grateful for their insightful questions and try to address their concern in detail:
---
> Nov... | Summary: This work introduces MFCL, a method to primarily alleviate the catastrophic forgetting problem that arises in (more realistic) FL settings framed under the continual learning paradigm. In MFCL, the model is split into a generator (only trained on the server side) and a discriminator (only trained by clients). ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the details of our paper and appreciate their comments highlighting the strengths of our method and finding the setting more realistic and practical. We are grateful for their insightful questions/suggestions and try to address their concerns in detail;
---
> Imp... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insights and comments on the paper. Here we have attached a PDF that includes the following;
* A more clarified version of Figure 3.(a) of the paper for reviewer 4Enn.
* More experiment results with increased memory sizes for reviewer p34V.
Pdf: /pdf/adb855bf... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On Formal Feature Attribution and Its Approximation | Reject | Summary: The paper proposes and studies a notion of feature attribution in which features are scored for a given instance according to the proportion of minimal explanations for that instance in which they participate. Although an exact computation of this scores can be computationally unfeasible, the paper exploits th... | Rebuttal 1:
Rebuttal: Thank you for the comments! In the following, we do our best
addressing them, which hopefully convinces you that our work has
enough merits to get accepted.
### On Missing Proofs
Note that the proof of Proposition 2 is really only a single line,
making use of results in the cited work [22]:
**P... | Summary: This paper introduces a novel approach to XAI called Formal Feature Attribution. The authors address the limitations of existing model-agnostic methods and formal XAI approaches by proposing FFA as a solution for feature attribution.
The FFA method leverages formal explanation enumeration to define feature att... | Rebuttal 1:
Rebuttal: Thank you for the positive view on our work! Please find our response
in regard to the weaknesses and limitations you identified in it.
### On Axiomatic Analysis and Approximation Guarantees
We agree that some initial axiomatic analysis would be nice to have
for FFA and we will add a couple of c... | Summary: The authors propose formal feature attributions, a novel type of local feature attribution method for
explaining the predictions of black box models. Their approach builds on the notion of abductive explanations (AXp's), a type of minimal sufficient subsets. One issue with AXps is that there are a potentiall... | Rebuttal 1:
Rebuttal: Thank you for the positive comments. As the weaknesses you identified
directly correlate with the questions asked, let us address them by
answering the questions down below:
### Answers to Questions
**Q1.** Please see one of the general comments on the use of FFA
and the adequacy of its comparis... | Summary: This work proposes a new approach called formal feature attribution (FFA), inspired by successful FXAI methods, to compute feature attribution scores. FFA is defined as the proportion of explanations where a feature occurs. Experiments try to demonstrate the effectiveness of FFA compared to SHAP and LIME under... | Rebuttal 1:
Rebuttal: Thank you for the comments. We try to address them below, which will
hopefully convince you that our work has merits justifying acceptance.
### Answers to Questions
**Q1.** We would like to clarify that by no means we claim that
Shapley values have no formal definition. On the contrary, as Shapl... | Rebuttal 1:
Rebuttal: We thank the reviewers for the thorough and helpful comments.
### Why FFA?
Several reviewers raise concerns regarding the use of FFA as a "gold
standard" in feature attribution and also regarding the validity of
its comparison with other feature attribution measures. Hence, we
would like to give... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Volume Feature Rendering for Fast Neural Radiance Field Reconstruction | Accept (poster) | Summary: This work improves grid-based NeRF on both quality and training speed. To predict view-dependent color, they proposes to condition MLP on the volume rendered voxel features instead of each original point features. As a result, the MLP only run once for each pixel instead of the original dozen of MLP evaluation... | Rebuttal 1:
Rebuttal: Thanks for your encouraging feedback. We respond to your concerns one-by-one as follows.
**Weaknesses:**
__Videos are not provided:__ Thanks for your suggestion. We will upload the rendered videos to showcase the improvement on view-dependent effect in our final version of this paper.
**Questio... | Summary: The paper proposes a method for fast NeRF reconstruction. The main contribution of the paper
is that instead of integrating radiance along the camera ray which requires evaluation of the
color MLP at each point, the paper proposes to integrate features along the ray and only
evaluate a single MLP on the int... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments for our manuscript. We respond to your concerns one-by-one as follows.
1. __How the method integrates the features along the ray:__ We apologize for the missed details of how to obtain the integration weights. The weights are calculated from densities of sam... | Summary: The authors propose to perform feature accumulation first at each pixel location, and then pass the accumulated feature to a MLP to get the rendered color in NeRF. They show improved rendering quality over baseline methods on NeRF synthetic dataset and object-centric 360-degree captures.
Strengths: 1. Idea s... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments on our manuscript. We address your concerns as follows.
1. __Jittering problem:__ We understand jittering is the problem of 3D inconsistency when rendering video with changing viewpoints. We do not observe the consistency difference between our rendered videos an... | Summary: In the paper, a novel method for view synthesis is proposed. More specifically, for given posed RGB input images, multi-resolution hash grid features are trained which are rendered to the image plane via volume rendering. The rendered feature is that passed through an MLP, processed with Spherical Harmonics (S... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments on our manuscript. We address your concerns as follows.
* __Time comparison:__ In light of your suggestion, we report on the training times of various comparison methods measured on the same GPU RTX 3090. As shown in Tables 2 and 3, we can draw the same conclusio... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their precious time and thoughtful review of this manuscript. The comments and suggestions raised are extremely valuable and constructive, and very helpful for improving the quality of the manuscript.
Please see the detailed... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep learning with kernels through RKHM and the Perron-Frobenius operator | Accept (poster) | Summary: The authors combines RKHM and Perron-Frobenious Operator to deep RKHM, a deep learning framework for kernel methods. By virtue of $C^*$-algebra, they manage to get a better bound on Rademacher generalization error and provide a clear connection with CNNs. Their theoretical analysis provides a new lens for deep... | Rebuttal 1:
Rebuttal: ### More efficient methods specific to deep RKHM
As we stated in Rem. 5.6, we can apply random Fourier features to reduce the computational cost, but as you point out, methods specific to deep RKHM should be investigated as future work.
We will study more about this topic and conduct some experime... | Summary: The authors introduce a generalization of RKHS for $C^*$ algebra valued kernels, called RKHM; they build networks by composing sequentially elements taken from a collection of RKHMs, one RKHM per layer. They prove generalization bounds for those networks. These networks output matrices at each layer.
Strength... | Rebuttal 1:
Rebuttal: ### Relation between RKHM and vvRKHS
RKHM is a generalization of vvRKHS in the sense that we can reconstruct vvRKHS using RKHM (Please refer Thm. 4.13 of [2]).
Since the output space of the functions in a vvRKHS is not necessarily a $C^*$-algebra, the connection between RKHM and vvRKHS is a little... | Summary: This paper proposes deep Reproducing kernel Hilbert $\mathcal{C}^*$-module (RKHM), a deep learning framework for kernel methods， which generalizes RKHS by means of $\mathcal{C}^*$-algebra. In this setting, a map as the composition of functions in RKHMs is constructed. Theoretically, the authors develop a new R... | Rebuttal 1:
Rebuttal: ### Dependency on the input dimension
The input dimension depends on the generalization bound through the $\mathcal{A}$-valued kernel $k$.
In Thms. 4.1 and 4.5, the bound depends on $\mathrm{tr}(k(x_i,x_i))$.
Therefore, the dependency of the input dimension on the bound is determined by the depend... | Summary: The paper establishes properties on the composition of functions belonging to a RKHM, a generaliztion of RKHSs. The authors compute the Rademacher complexity of this function class and establish a representer theorem.
Strengths: - The objects studied in the paper are well introduced. The writing is generally... | Rebuttal 1:
Rebuttal: ### Derivations of the results
Our Rademacher complexity analysis and representer theorem for RKHM use Cauchy-Schwarz, Jensen inequalities, and orthogonality arguments, as most of the results in the RKHS case. However, it is important to be precise that there are specificities and technical diffic... | Rebuttal 1:
Rebuttal: ## To all the reviewers
Thank you very much for your constructive comments. We address your questions and concerns below. We will revise our paper based on your comments and the response below for the camera-ready version.
We attached a PDF file to support some of our responses here. Also, we pro... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction | Accept (poster) | Summary: The paper presents a learning-based framework on the well-studied neural surface reconstruction problem. The key contribution of this paper is to take the complex photon-particle interaction into account and present a more generalized pipeline rather than relying on volume rendering. The proposed framework rel... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful evaluations.
**Q1. Hybrid resolution is not new, and justification of the proposed hybrid extractor with the original one.**
Our main contribution is to rectify the oversimplification in traditional volume rendering by introducing a generalized fo... | Summary: This paper proposes ReTR, a new architecture that leverages transformer to replace the traditional volume rendering process. The insight of the paper is that: the traditional volume rendering equation is oversimplified to model photon-particle interaction. Moreover, the color compositing function highly relies... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful evaluations.
**Q1. More related work should be discussed in Section 2.**
Thanks for the feedback. NeuRays(CVPR22) leverages neural networks to model and address occlusions, enhancing the quality and accuracy of image-based rendering. GPBR(ECCV22) ... | Summary: The paper proposes a new framework for generalizable neural surface reconstruction, which utilizes the mechanism of transformers to model the rendering process. The authors first derive a general form of generalizable volume rendering based on existing methods and identify its limitations. They then suggest im... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful evaluations.
**Q1. Reconstruction’s description appears incomplete and confusing.**
**1.1. how does equation 6 (and its improvement in equation 10) fit into the general framework proposed in equation 5?**
Thanks for the feedback. Since our render... | Summary: This works focus on generalizable asset reconstruction: given a few posed images, predict the 3D representations using a network.
Instead of using volume rendering to compute the transmittance, the authors propose to use transformer on the sampled points to compute the weight of each point.
Besides, the author... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful evaluations.
**Q1. Incremental work of SparseNeus and Volrecon.**
We respectively disagree with this. Our main contribution is to address the intrinsic limitation of the extensively-used volume rendering (line 25-37, $Fig. 1$ in the main paper), p... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers for their thoughtful feedback and time invested in evaluating our work. We're heartened by Reviewer DVpn's commendation of our solution as "interesting in generalizable neural surface reconstruction" and by Reviewer TPgh's acknowledgment that our "results are sol... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a interesting solution for volume renderings in generalizable neural surface reconstruction by leverage Transformers to predict depths and colors from feature volumes. The results on sparse view reconstruction prove its useness.
Strengths: The authors identify the limitation and derive a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful evaluations.
**Q1. If our learned transformer can replace volume rendering for optimization-based methods.**
Yes, we've conducted qualitative experiments, as shown in Figure 1 of the rebuttal PDF, using optimization-based methodologies. Rather th... | null | null | null | null | null | null |
Learning Large Graph Property Prediction via Graph Segment Training | Accept (poster) | Summary: This work uses graph segment training to reduce memory requirements in order to address the scalability concerns of training with large graphs. Additionally, to reduce computation time, historic embeddings for graph segments are stored. These historic embeddings are updated once for all embeddings at the end o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and insightful comments. We appreciate the confirmation that the proposed method is important and our proposed method is successful. We hope we are able to address the review’s concerns, and respectfully ask to consider increasing the score.
**Q1. S... | Summary: This paper aims to predict properties of very large graphs, by segmenting the large graph into multiple subgraphs with the existing graph partitioning algorithm and then learning over segmented subgraphs where gradients are calculated on some of them for memory-efficient training. Also, to further efficiently ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and insightful comments. We appreciate the reviewer for confirming that our paper is novel, extremely well-written and highly valuable in the graph community.
**Q1. How to fetch the embeddings of subgraphs from the embedding table, if the embedding... | Summary: This paper studies an important problem on large graphs.
The authors propose a new Graph Segment Training (GST) method for large-scale prediction of properties. The proposed method utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint. GST div... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and insightful comments. We appreciate the confirmation that the problem we studied is important and experimental design is well-conducted. We hope we are able to address the review’s concerns, and respectfully ask to consider increasing the score.
... | Summary: This paper deals with large graph learning tasks via graph segmentations. More specifically, in each training step, the author sample nodes from graph segmentations and only update parameters related to the selected nodes. To optimize memory consumption, the author further introduced a historical embedding tab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and insightful comments. We appreciate the reviewer for confirming that our paper is technically sound and easy to follow. We respectfully ask the reviewer to consider increasing the score if our clarification has addressed the concerns raised by the... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A General Framework for Equivariant Neural Networks on Reductive Lie Groups | Accept (poster) | Summary: The paper presents a novel and highly general Equivariant Neural Network (ENN) architecture that is capable of respecting the symmetries of the finite-dimensional representations of any reductive Lie Group G. The proposed approach generalizes the successful ACE and MACE architectures for atomistic point clouds... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper. We appreciate
that you find our paper well-organized and well-written and addresses a
significant and challenging problem. You will find our response to your
remarks and questions here. We respectfully hope that our responses will
be satis... | Summary: The paper proposes a class of G-equivariant neural networks for reductive lie groups that generalizes the previous ACE and MCAE models (which are designed to be equivariant with respect to the orthogonal group O(3)) to more general irreducible Lie groups. A software library (Lie-NN) has also been developed an... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper. We
sincerely appreciate your thoughtful review. We appreciate that you have
found our paper to be novel and well-written. You will find here-after
our responses to your questions and remarks, including the changes we
made to the paper f... | Summary: The authors generalize MACE, a point cloud network that uses higher-order interactions via tensor products of basis expansions of the features, to being equivariant to arbitrary reductive Lie groups. The paper shows that this setup inherits universality properties from MACE. A generic method to compute the a b... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review and excellent comments. We
appreciate that you find our work interesting and well written. Below we
respond to your questions and suggestions.
## Clarification on $E(3)$
> Figure 1 of the paper appears to suggest that the method works on
> E(3), but t... | Summary: This paper proposes a framework for building equivariant neural networks on reductive lie groups. The proposed method first constructs a linear model for multi-set functions which is then symmetrised to generate a complete basis of equivariant multi-set functions. The model is also extended to a multi-layer ar... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate that you find our work
novel and our open-source library interesting to the community.
Below we respond to your questions and suggestions to further improve
the paper.
## Formatting of tables
> Table 2 caption should be on top of the table. Tables... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort in reviewing our paper.
We are glad you think that our work is "new" (R1) and that our "proposed method is an elegant generalization of previous methods" (R2), addressing "a significant and challenging problem" (R4) of the field of equivariant neura... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Birth of a Transformer: A Memory Viewpoint | Accept (spotlight) | Summary: The paper studies in detail two-layer transformers and extend the setting of pure associative recall based on data in-context with mixing these tasks with tasks coming from a global birgram model. They then study these two-layer Transformers by freezing layers and probing.
Strengths: The paper studies an int... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and for the interest to try out our code! We hope our response can provide more perspective about the motivations behind our work, and may help you reconsider your score.
*freezing layers*
See our general response. In particular, we chose to simplify the archite... | Summary: This paper provides a detailed analysis of how in-context learning behavior emerges in a simplified version of the transformer architecture on a toy task. This work can provide important insight into how in-context learning emerges in LLMs. The toy task is this: given a sequence of tokens of the form $\ldots, ... | Rebuttal 1:
Rebuttal: Thank you for the very detailed and encouraging review.
*"The transformer architecture used in this paper is drastically simplified ... the paper requires a much more detailed, readable discussion ..."*
Thanks for raising this point. We hope that our general response to all reviewers provides us... | Summary: Given the blackbox that large language models are, this paper tries to use a small simplified view of a 2 layer transformer model, and a synthetic task to understand how global and in-context language statistics are learned by the transformer model.
By freezing specific layers, The authors show how the memory... | Rebuttal 1:
Rebuttal: Thank you for the helpful review and for the positive assessment of our work.
*“The results are still very preliminary…” "Transformer models have a lot of other components..."*
This is the main topic of our general response post, which we hope provides a valid justification of our approach, as w... | Summary: This paper studies the dynamics of how induction heads emerge in LLMs during training. The authors describe a simple synthetic task to test their hypotheses on, outline a plausible implementation of an induction head based on associative memory, and describe both empirical and theoretical observations.
Streng... | Rebuttal 1:
Rebuttal: Thank you for the encouraging review and helpful suggestions. We are glad you found our work interesting, well written, and that you liked our methodology!
*“Can you better explain the significance of the results? …”*
Thanks for asking this, it is definitely something we should have discussed mo... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their insightful feedback and valuable comments.
We are happy that most reviewers found our work relevant and significant. Indeed, we believe that our insights on the internals of transformers can pave the way for improved methods in several aspects of LLMs... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Authors perform an in-depth study of the toy case of learning associate recall task using causal Transformers with the aim to understand the emergence of in-context learning abilities during training. Informally, they propose a modified bigram distribution where after sampling a sequence, for a special set $Q$... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
*“The associative recall task has been studied… novel contributions”.*
Indeed, several works have looked at similar tasks. Nevertheless, to our knowledge we are the first to have a precise picture of (pre-)training dynamics and as a result, a precise understa... | null | null | null | null | null | null |
Learning via Wasserstein-Based High Probability Generalisation Bounds | Accept (poster) | Summary: This paper focuses on studying PAC-Bayes generalization bounds based on the Wasserstein distance. It introduces novel high-probability bounds applicable to both batch learning (i.i.d. setting) and online learning (non-i.i.d. setting). Unlike previous PAC-Bayes bounds, their bounds are not limited to bounded or... | Rebuttal 1:
Rebuttal: We thank you for your careful review, and we answer your concerns below.
**Weaknesses**
As noted in the general answer, the 'distance to initialisation' is a particular case of our Eq. (5) when $K=1$. This particular case has been plotted in Table 3 of Appendix C.4. More precisely, this table sh... | Summary: This paper introduces Wasserstein-based PAC-Bayes bounds both for offline (batch learning) and online learning.
These bounds are obtained with a clever use of the Kantorovich-Rubinstein duality together with a control of some additional terms appearing in the proof that only depend on the prior. To use the K... | Rebuttal 1:
Rebuttal: Thank you for the positive review and for pointing out the 'multiple strengths'.
**Your major concern**
Thank you for spotting this - see also our general response. A Wasserstein is defined on a Polish space w.r.t a distance $d$. Indeed our Algos in Eqs. (5-7) must be described w.r.t $d$ and not... | Summary: This work propose PAC-Bayesian learning with the KL divergence replaced by the Wasserstein distance on the metric space of hypotheses $(\mathcal{H}, d)$. Denoting the data distribution by $\mathcal{D}$, the authors study the following learning problems:
1. Batch learning. Under the assumption that the loss fu... | Rebuttal 1:
Rebuttal: We thank you for your enthusiastic review. We are thrilled to read that you see this work as a 'strong result in the field of PAC-Bayes learning'. We answer your questions below.
**About the batch size**
This is indeed a fair question, and unfortunately at this stage we found no particular rea... | Summary: The paper builds on recent advances in PAC-Bayesian learning and derives new Wasserstein distance-based generalization bounds. Besides batch learning for iid data, a first set of results are derived for online learning (with non iid data). The authors also provide tight bounds for the case of heavy tailed loss... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful review. We are encouraged to read that 'the introduction and main results are well-placed in the context of recent literature' and that you appreciated the goal of our work, even though 'this reviewer has not worked on the topic of PAC-Bayes learning', as one of ou... | Rebuttal 1:
Rebuttal: We warmly thank all reviewers for their insightful reviews of our work. We are encouraged to see that our work generated enthusiasm and we answer thoroughly to all of the concerns raised by the reviewers. We address, in this general response, remarks raised by at least two reviewers.
**New exper... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? | Accept (poster) | Summary: This paper presents an in-depth theoretical examination of Sharpness-Aware Minimization (SAM) in the context of feature learning. The authors identify the challenge of overfitting in the training of these networks, a problem that becomes more prominent as the model size increases. The traditional gradient-base... | Rebuttal 1:
Rebuttal: Thank you for your strong support. Below, we provide answers to your comments and questions, and we will ensure that the revisions are made in the final draft.
**Q1**. Further research could be needed to determine if SAM has similar benefits for different architectures.
**A1**. Thank you for you... | Summary: This paper studies the question of why SAM generalizes better than SGD on a specific binary classification task. The task looks like a special case of the sparse coding model where the relevant parameter is a signal-to-noise ratio (SNR). The authors present theoretical results on the performance of SAM and SGD... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! Due to space limits, we answer your major comments and questions as follows.
**Q1**. It would be helpful to motivate the model by presenting a connection to the well known sparse coding problem.
**A1**. Thank you for bringing our attention to the sparse codi... | Summary: The paper aims to provide a theoretical basis for the superiority of SAM over SGD. Different from former explanation based on Hessian information, the authors firstly discuss the loss landscape of non-smooth neural networks like two-layer convolutional ReLU networks. Notably, the paper proves that under condit... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We provide our responses below and will make the corresponding adjustments in the final version.
**Q1**. I suspect the learning rate 0.1 is too high … It might be better to add an experiment and discuss the influence of the learning rate.
**A1**. Thank you f... | Summary: This paper presents two theoretical contributions regarding benign/malign overfitting of two-layer convolutional ReLU neural networks. For an idealized data distribution, it i) gives the conditions (with respect to the dimension of the data and to the signal strength) under which benign/malign overfitting occu... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. Due to space limits, we will address your major comments and questions as follows. We will revise the corresponding parts accordingly as well as address the minor points in the final version.
**Q1**. The abstract misses to state Contribution i).
**A1**. ... | Rebuttal 1:
Rebuttal:
We want to thank all the reviewers for their valuable comments. In the uploaded pdf, we include additional empirical results to address reviewers' concern including Figures 1 and 2.
+ **Figure1**: To address Reviewer TY4G’s concern that our synthetic experiment is only performed with gradient ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning bounded-degree polytrees with samples | Reject | Summary: The paper describes an approach for learning bounded in-degree polytrees that a family of Bayesian networks. More precisely, given the skeleton of the polytree $P$ from which the samples are from, their algorithm learns a $d$-polytree whose distribution is likely to be close to $P$ (with respect to KL divergen... | Rebuttal 1:
Rebuttal: **Motivation and practicality**
The structure learning problem for high-dimensional distributions has been widely studied in the machine learning community for the last four decades (e.g., Chapters 16-20 of [KF09]). In particular, learning polytree Bayes nets is of great interest, because polytre... | Summary: This paper considers the number of samples to learn a particular class of distributions: bounded-degree polytrees (Bayesian networks whose skeleton is a forest). Recent work has shown that tree-structured Bayesian networks (1-polytrees) are learnable with finite samples; this work makes progress on the natural... | Rebuttal 1:
Rebuttal: **Typos regarding $\hat{I}$ and $C \cdot \varepsilon$**
Thank you for pointing this out, we indeed compare $\hat{I}$ with $C \cdot \varepsilon$. We will fix these in our revision.
**Other typos and writing suggestions under minor remarks**
Thank you. We will fix the typos and incorporate your w... | Summary: This paper introduces an efficient learning algorithm for bounded degree polytrees and establishes finite-sample guarantees. Explicit sample complexity and polynomial time complexity are provided. An information-theoretic lower bound is provided, which shows that the sample complexity of the algorithm is nearl... | Rebuttal 1:
Rebuttal: **Tightness and violation of Assumption 11**
You are right that the skeletal assumption is crucial in our algorithm and analysis. The sufficient condition is a useful proxy check for the applicability of our methods. If one believes that the sufficient conditions hold in a dataset of interest, th... | Summary: The paper gives an efficient PAC-learning algorithm for learning graphical models called "bounded polytrees". These are distributions where 1) the undirected skeleton of the graph is a forest and 2) the in-degree of every node is bounded by some constant $d$. This extends a recent result [1] for directed trees... | Rebuttal 1:
Rebuttal: **Citation in proof of Theorem 1**
We agree that a reference for Theorem 1.4 in [BGPV21] should be given here as it is the same proof idea: given a good enough graph, one can apply the parameter learning algorithms referenced in our submission; see line 223.
**Lines 225-227**
We will add th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for providing valuable feedback on our paper. In this global response, we address one of the issues raised by multiple reviewers about the known skeleton assumption, sufficiency of the Assumption 11, and the intuition behind the skeleton-based approach.
--... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization | Accept (poster) | Summary: This paper presents DP-HyPO, a pioneering framework for adaptive private hyperparameter optimization. Privacy risks are often neglected in private ML model training. DP-HyPO employs a comprehensive differential privacy analysis to bridge the gap between private and non-private optimization methods (non-adaptiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback!
We totally agree that the current experiment settings like MNIST are indeed overly simplistic, and think that could be the underlying cause why our experiments have not shown a significant gain. To address this, we conducted new experiments on ... | Summary: This paper introduces DP-HyPO, a framework for “adaptive” private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization, that can allow practitioners to adapt to previous runs and focus on potentially superior hyperparameters. Besides, the arbitrary a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for valuable feedback. While we respectfully acknowledge the input, we do have different viewpoints on certain aspects raised by the reviewer.
**Empirical demonstration is important**: While our intention is to provide a rigorously theoretically-backed privacy accountin... | Summary: Hyperparameter optimization (HPO) is an important step for enhancing the performance of private model training methods such as DP-SGD. Currently, most advanced HPO algorithms (e.g., Bayesian optimization) require to adaptively select the hyperparameters, but existing private HPO methods are non-adaptive. To fi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for valuable feedback.
**Structure and flow**: We will restructure the paper to be of a better flow.
**Advantage and practicality of Theorem 1**: The statement of Theorem 1 involves two RDP guarantees that **can be the same**, that is, we can have $\alpha = \hat{\alph... | Summary: In the paper "DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization" the authors propose a framework for differential privacy HPO turning adaptive aka. model-based HPO methods into privacy preserving HPO methods. In their empirical study, the authors demonstrate that despite restrictions and c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback!
**For general ML problems**: We clarify that our results hold for much broader HPO problems beyond deep learning. The requirement of “an ML algorithm run can be repeated to achieve a certain degree of privacy” is not an assumption of our framew... | Rebuttal 1:
Rebuttal: We acknowledge that the current experiment settings like MNIST are indeed overly simplistic, and think that could be the underlying cause why our experiments have not shown a significant gain. To address this, we conducted new experiments on CIFAR10 (a much more challenging task), and revealed a s... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a differentially private (DP) adaptive hyperparameter optimization algorithm called DP-HyPO, which encompasses several existing DP non-adaptive hyperparameter optimization algorithms. DP-HyPO is able to deal with hyperparameters that come with infinitely many values and by leveraging the pre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback!
**MNIST is simplistic**: We acknowledge that the current experiment settings like MNIST are indeed overly simplistic, and hypothesize that could be the underlying cause why our experiments have not shown a significant gain. To address this, we ... | null | null | null | null | null | null |
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces | Accept (spotlight) | Summary: The paper describes a system to perform pixel-based interaction with human-taylored screens based on GUIs. The proposed system relies on a transformation to a text-based, structured representation of the screen and the screen tasks performed using a larger, pre-trained model of almost 300M parameters. The prop... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Ethical considerations
We agree there are important considerations for responsibly developing and deploying models that can interact with websites. While we attempted to identify some of these concerns (e.g. breaking CAPTCHAs) in the “Broader Impact” subsection of Se... | Summary: This paper investigates to create agents that interact with the UIs based on pixel-based screenshots and a generic action space corresponding to keyboard and mouse actions. Extensive experiments on simulated environments, i.e., MiniWob++, WebShop, evaluate the effectiveness of proposed agents, show the benefit... | Rebuttal 1:
Rebuttal: Thank you for your review!
> “The biggest problem of this work is about the novelty of its proposed method. It's kind of difficult for readers to catch up with the core differences compared with the existing pixel-based agents, e.g., Pix2Act, and understraning its advantages.”
We are confused by... | Summary: One of the main goals of intelligent agents is to interact with the internet in the same way that humans do. Prior state-of-the-art models relied on both the DOM structure and the graphical user interface (GUI) to achieve good performance on web browsing tasks that involve following instructions. In this paper... | Rebuttal 1:
Rebuttal: Thank you for your review!
> WebShop details and hyperparameters
While some prior work (e.g. Humphreys et al. 2022) evaluated only on MiniWob, we believe it was important to also evaluate on WebShop to better understand the generality and limitations of our approach. We will provide some additio... | Summary: This paper presents PIX2ACT, a method that interacts with GUIs using pixel-level visual representations and generic low-level actions, emulating how humans interact with these interfaces. Unlike previous approaches, it doesn't rely on structured text-based data sources, but rather processes pixel-based screens... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Limitations of tree search in real-world environments
We tried to address some of the tree search limitations you mentioned in section 7, as well as some potential directions towards applying such an approach more broadly (e.g. generative models of potential instruct... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their comments and suggestions! We tried to address any questions in the individual responses.
We would also like to respond to the ethical reviewers, as we did not see a way to respond individually to ethics reviews.
__Ethics Reviewer Vxcz__
Than... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces PIX2ACT, a model designed to interact with GUIs using only pixel-level visual representations. Unlike most prior works that depend on structured interfaces (like HTML or DOM trees), PIX2ACT relies solely on what it visually sees. This approach is motivated by the way humans interact with i... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Discrete bins for coordinates and scrolling
We utilized discrete coordinate bins primarily for simplicity. We agree with the limitations you mentioned. While some prior work has also used coordinate bins (e.g. Humphreys et al. 2022), other work has used regression o... | Summary: This work explores the possibility of building an agent that can complete tasks for users solely based on pixel-level visual representations of the GUI state and generic low-level actions, without relying on structured or task-specific representations. The authors demonstrate the effectiveness of their approac... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Bottleneck is vision encoder or text decoder?
The text decoder is only responsible for decoding short strings corresponding to the closed set of actions shown in Figure 1. Therefore, it seems reasonable to assume that the ViT encoder is the “bottleneck” towards achie... | null | null | null | null |
Undirected Probabilistic Model for Tensor Decomposition | Accept (poster) | Summary: The authors propose a new Probabilistic Tensor Decomposition (TD) method. By modeling the joint probability of the data and latent tensor factors using an Energy Based approach, they make no (possibly restrictive) structural and distributional assumptions on the generative process linking latent and observatio... | Rebuttal 1:
Rebuttal: ## W1: Code availability.
We send an anonymous link to the AC via the official comment.
## W2: Motivation of using probabilistic methods over traditional ones.
There are several advantages of using probabilistic models, such as,
1. Using probabilistic models, we can deal with different data type... | Summary: This paper proposes a probabilistic tensor decomposition model called EnergyTD that integrates a deep energy-based model (EBM) in tensor decomposition. The EnergyTD does not model the values in a tensor as the conditional probability conditioned on the latent factors based on the predefined models but models t... | Rebuttal 1:
Rebuttal: ## W1: Time/space complexity.
The time complexity of training should be $\mathcal{O}(\nu B ( D R H + L H^2))$, where $B$ is the batch size, $\nu$ is the number of conditional noises, $H$ is the number of hidden units per layer, $L$ is the number of layers and $D$ is the tensor order. The space co... | Summary: This paper uses the Energy Based Model (EBM) framework to capture the joint probability of the data and latent tensor factors to learn as much information from data as possible, which discards the structural and distributional assumptions and thus avoid picking an inappropriate TD model. To further flexibly l... | Rebuttal 1:
Rebuttal: ## W1: Directly modeling $\phi(x; \theta)$ and the choice of $m$.
Thanks for the question. Apart from the data distribution $\phi(x; \theta)$, in tensor decompositions, we aim to infer the latent factors, i.e., $z$ in the manuscript. If we directly model $\phi(x; \theta)$, we cannot obtain the in... | Summary: The paper proposes an innovative approach for non-linear tensor decomposition. It utilizes the deep energy-based model (EBM) to model the joint energy function of tensor observations and latent factors. This design enables a more flexible decomposition without the need for explicitly defining the interaction b... | Rebuttal 1:
Rebuttal: ## W1: Comparison with THIS-ODE.
Thanks to the reviewer for pointing out this reference, which is very relevant to our model. We would like to add some discussions about it in the manuscript and compare it with our model in experiments. However, its official implementation runs very slow and we a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive comments and suggestions. Below we respond to them respectively.
Pdf: /pdf/dc05a80dd475f1ad9adb270c51adf05e9e88c605.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Borda Regret Minimization for Generalized Linear Dueling Bandits | Reject | Summary: The authors consider the linear dueling bandit problem, where the regret is measured in terms of Borda regret. The Borda regret function was adopted as that used in Saha et al. 2021a. Different from conventional bandit settings, there is a mismatch between the reward function and the regret function definition... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your comments and questions as follows:
---
**Q1**: The experiment results are not very convincing. The setup for experiments is confusing. The two algorithms are for single context and adversarial settings, respectively. It is not clear why they can be u... | Summary: This paper studies the problem of minimising Borda regret for dueling bandits in the generalised linear setting where each pair of arms has a context vector associated with it.
The paper considers both the stochastic setting where the parameter $\theta^*$ used for generating rewards is fixed and the adversari... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate that you recognize our work as “filling an important research gap”. We address your comments and questions as follows:
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**Q1**: The algorithms and their analysis have limited novelty. While I understand why Borda reduction does not trivially work,... | Summary: This paper discusses Borda regret minimization in a contextual dueling bandits scenario, where the context is given in a generalized linear form.
It provides a worst-case lower bound for the stochastic and adversarial learning scenario. The authors develop for both scenarios algorithmic solutions whose asympto... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your comments and questions as follows:
---
**Q1**: While reading, I've collected the following typos/minor suggestions: … \
**A1**: Thank you so much for the suggestions! We will fix them accordingly.
---
**Q2**: Does the hard instance in Rema... | Summary:
In dueling bandits, each pair of arms corresponds to some unknown probability $p_{i,j}$ where $p_{i,j}$ is the probability arm i is ranked higher than $j$. The learner sequentially chooses pairs of arms and receives a noisy result as to the ordering of the pair, i.e. Bernoulli$(p_{i,j})$. This paper considers... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and strong support. We address your comments and questions as follows:
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**Q1**: I do not see why $\delta$ is given as input to the algorithm, it is not taken as a parameter but rather passed to the exploration phase $\tau$. \
**A1**: Thanks for pointing t... | Rebuttal 1:
Rebuttal: Dear reviewers,
Based on the feedback of Reviewer ECQz, we conducted an additional experiment to examine the performance of BEXP3. Please find the figure and description we provided in the uploaded PDF file.
In this experiment, the number of items is $K = 64$, and the feature dimension is $d = ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society | Accept (poster) | Summary: This paper introduces CAMEL, a role-playing framework involving two LLMs (an AI user and an AI assistant) communicating with each other to finish a specific task prompted by a human. The two agents are supposed to give instructions and provide answers respectively. On complex tasks (including AI society, code,... | Rebuttal 1:
Rebuttal: Dear Reviewer Z1bn,
Thank you for your careful review and valuable feedback. We appreciate your recognition of the novelty of our proposed framework and your insightful observations.
**Responses to Strengths:** We're pleased to note your agreement on the potential of our inception prompting meth... | Summary: # Summary
## Motivation
Completing tasks by human-in-the-loop is time-consuming. An alternative is to let autonomous agents cooperate to solve tasks.
## Approach
This paper propose to let two LLM agents *role-play* a user and an assistant to solve tasks.
Their data collection approach follows the following s... | Rebuttal 1:
Rebuttal: Dear Reviewer 6oPa,
Thank you for your thoughtful review and positive feedback on our paper. We're pleased to hear the originality and significance of our work is recognized. Below, we address your questions and concerns:
**Strengths:**
> 1. **Originality** - Using a team of LLMs or generally ... | Summary: This paper attempts to address a dilemma in leveraging large-language models (LLMs) for solving complex tasks in a collaborative setting: the question of oft-needed human intervention in the equation. More specifically, the authors have come up with an intuitive and novel cooperative agent framework called rol... | Rebuttal 1:
Rebuttal: Dear Reviewer wYFm:
**Response to Strengths:**
We appreciate your positive comments on the novelty, motivation, and potential benefit for future work.
**Weaknesses:**
1. **Response:** We acknowledge the reviewer's observation about the role of the AI user (stock trader). The decision to use the... | null | null | Rebuttal 1:
Rebuttal: To all esteemed reviewers of our NeurIPS 2023 submission,
We express our sincere gratitude for your thorough and insightful reviews of our manuscript. The detailed feedback from each of you provides us with clear guidance on how to refine and improve our work.
**Reviewer wYFm:**
Thank you for r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CoVR: Learning Composed Video Retrieval from Web Video Captions | Reject | Summary: This paper focuses on the task of Compositional Video Retrieval (CoVR) in which given a video and a text which modifies the video aims to rank and retrieve the modified video. In this work, a new large-scale dataset for pre-training (named WebVid-CoVR) is developed with generated modifications from a Large Lan... | Rebuttal 1:
Rebuttal: **1. Has any analysis been performed into the types of modifications within the dataset?**
We analyze the number of words in the modification text in Figure A.2 of the supplementary material, but in Figure R.3 of the rebuttal PDF we provide further analysis on the distribution of noun/adjective/ve... | Summary: The paper addresses the challenge of composed video retrieval, which involves querying a video and a modification text to find videos that exhibit similar visual characteristics with the desired modification. The main challenge is the lack of data for composed video retrieval. To overcome this, the paper propo... | Rebuttal 1:
Rebuttal: **1. Dynamic vs static content in CoVR.**
We acknowledge that, as the reviewer pointed out, some videos can be retrieved by only looking at one frame. This is a common problem in video datasets as highlighted by Jie et al. [“Revealing Single Frame Bias for Video-and-Language Learning”, ACL 2023]. ... | Summary: This paper proposes a scalable approach to automatically generate composed visual retrieval training data. Specifically, based on the WebVid2M dataset, the authors generates a WebVid-CoVR training dataset with 1.6M CoVR triplets.
Strengths: The data augmentation strategy is scalable.
Weaknesses: The overhead... | Rebuttal 1:
Rebuttal: **What is the dataset augmentation overhead?**
We outline the detailed computation time for each step of the dataset generation. The computation times below are obtained using a *single* NVIDIA RTX A6000, but it is important to note that most of the processes can be parallelized, which would signi... | Summary: This paper automatically constructs a new dataset called WebVid-CoVR by applying a scalable automatic dataset creation procedure that generates triplets from video-caption pairs to a large-scale WebVid2M collection, resulting in 1.6M triplets. Moreover, this paper introduces a new benchmark for composed video ... | Rebuttal 1:
Rebuttal: **1. Incorporating visual similarity between videos.**
We agree that considering visual similarity between videos is important for the triplet generation process. However, in this work, we indeed rely only on caption similarity because, as mentioned in L114, we rely on the assumption that caption ... | Rebuttal 1:
Rebuttal: We thank all four reviewers (`#k4Sb`, `#xqD8`, `#4ap2`, `#xJ3a`) for constructive feedback.
It is encouraging to see that our automatic triplet generation pipeline has been well-received, particularly for its careful design and multiple phases (`#k4Sb`, `#xJ3a`), as well as its scalability (`#xqD8... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | Accept (poster) | Summary: The overall theoretical framework of the paper can be regarded as an extension of the FedFTG theory. Building upon the server-side learning of challenging samples, it incorporates the generation of data from the previous time step by the generator to prevent catastrophic forgetting in the global model. The exp... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer deo9 for positive comments and precious feedback on our work. We respond to specific comments below.
Q1: The impact of knowledge transfer on synthesizing different datasets lacks theoretical support and experimental evidence, as demonstrated by the decision boundary ... | Summary: The paper considers learning a robust global model in heterogeneous federated learning (FL). It aims to support scenarios of both data-heterogeneous, where data distributions among clients are non-IID (identical and independently distributed), and model-heterogeneous, where the model architecture among clients... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer i49s for positive comments and precious feedback on our work. We respond to specific comments below.
Q1: For the data heterogeneity considered in this work, what distribution the final test dataset follows? Is it the global distribution over all clients’ local data d... | Summary: This paper studies how to learn a robust global model in the data-heterogeneous and model-heterogeneous FL, by proposing a data-free knowledge distillation method. The proposed method is evaluated on six image classification datasets and outperforms compared methods.
Strengths: - This paper studies data heter... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer XBHz for positive comments and precious feedback on our work. We respond to specific comments below.
Q1: It is not clear how to calculate the term $\tau_{i,y}$, which is important to adjust the synthetic data.
R1: We delve into the calculation of $\tau_{i,y}$ in the... | Summary: In the presented paper, the authors lay out a strategy for fine-tuning a global model, specifically within the Heterogeneous Federated Learning setting. To transfer knowledge from the individual local models (identified as 'teachers') to the global model (or 'student'), the authors construct and train a genera... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer 1PRp for positive comments and precious feedback on our work. We respond to specific comments below.
Q1: The manuscript offers a promising exploration into mitigating catastrophic forgetting in the context of Federated Heterogeneous Learning. However, as a reviewer, ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful, constructive, and positive review of our manuscript. We are encouraged to hear that the reviewers found the DFRD method we present to be interesting and practical (Reviewers i49s, 1PRp), and thoroughly-evaluated (Reviewers Yxnp, 1PRp, XBHz, i49s, deo9).... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new method called DFRD for robust and privacy-constrained Federated Learning (FL) in the presence of data and model heterogeneity. DFRD uses a conditional generator to approximate the training space of local models and a data-free knowledge distillation technique to overcome catastrophic ... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer Yxnp for positive comments and precious feedback on our work. We respond to specific comments below.
Q1: This work is well-engineered, comprising multiple components and hyperparameters. Although stability testing was performed on the same dataset, the optimal hyperp... | null | null | null | null | null | null |
Post Hoc Explanations of Language Models Can Improve Language Models | Accept (poster) | Summary: This paper proposes an alternative prompting framework to Chain-of-thoughts, by using post-hoc explanations from proxy smaller LLMs. Specifically, given a query question and few-shot examples, the method firstly uses a proxy model to get post-hoc explanations on key input words. Then the key input words are co... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments, and for recognizing the novelty of our work. In the subsequent sections, we will address the specific questions and comments raised by the reviewer. Additionally, we are committed to incorporating all of our responses and discussi... | Summary: This paper demonstrates how post hoc explanations through a small LM could assist the performance of LLMs. The process is divided into 4 steps: Proxy Model Selection, Few-shot sample selection, compute explanations, and formatting prompts for LLMs. This technique automatically generates few-shot demonstrations... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable thoughts and suggestions. We appreciate the reviewer's acknowledgement of the novelty and analysis provided in our work. In the subsequent sections, we will address the specific questions and comments raised by the reviewer. Additionally, we are committed to ... | Summary: The paper presents AMPLIFY, an approach that uses post-hoc explanations from a proxy model to improve the prompting performance of large language models. For a given dataset, the approach assumes access to a set of labeled validation data that is used for crafting a prompt. First, the approach selects k exampl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and for acknowledging the novelty and insights presented in our work. In the subsequent sections, we will address the specific questions and comments raised by the reviewer. Furthermore, we intend to incorporate all our responses and discussions ... | Summary: This paper proposes AMPLIFY framework that leverages post-hoc explanations to generate rationales automatically for chain-of-thought prompting. The framework consists of four stages: (1) adopt a light-weight model as the proxy to compute explanations, (2) select few-shot samples misclassified by LLM, (3) use a... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their insightful comments and suggestions. We are pleased to know they acknowledge our novel approach of using smaller models to augment the decision-making capabilities of larger ones. In the following sections, we address specific questions and comments raised... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their valuable and insightful comments. It is appreciated that the reviewers found our work to be well-written and well-motivated, and considered our analysis to be novel and insightful in improving LLM performance. In this section, we would like to address the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning | Accept (spotlight) | Summary: The paper proposes a framework, called HUME, for providing human labels without any supervision. The method uses an assumption that classes derived from human labelling are linearly separable regardless of the representation space used to represent the dataset, ie invariant to sufficiently strong representatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the simplicity of our methodology and the novelty of the generalization perspective for unsupervised learning. We are also glad that the reviewer appreciates the overall quality and soundness of our paper as well as thoroughness of the related work section. ... | Summary: This paper proposes a novel unsupervised learning algorithm called HUME.
HUME aims to minimize the disagreement between two linear models. The two linear models consume features from different backbones, and thus they are iteratively optimized to map difference feature spaces into the same label space.
HUME ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive evaluation of our work and acknowledging the fundamentally new perspective on unsupervised learning which our work proposes. We are glad that the reviewer appreciates the extensiveness of our experiments and finds our results very promising. We are also ... | Summary: This work proposes HUME, an unsupervised framework for inferring labels of an image dataset. Based on the assumptions of linear separability and model invariance, the authors propose an objective that utilizes two self-supervised models, one on the target dataset and another using large-scale pre-training. Spe... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and for appreciating the coherence of our presentation, strong justification of each component of our framework and extensiveness of our experiments.
*The biggest concern of the reviewer is the assumption of linear separability of pretrained represent... | Summary: The work presents a new method for inferring human labeling without supervision. The main idea of the method is that human labels for a dataset should be linearly separable for all good representations, and relatively invariant to the representation space. The method uses bi-level optimization with an inner lo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work and for acknowledging the originality of the proposed method as well as its significance for future research. We are glad that the reviewer finds the results impressive and our paper well written.
>*HUME uses DINO and MOCO while other... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and positive evaluation of our work. We appreciate fruitful suggestions of the reviewers that helped to improve the overall presentation of our work. In the response to the reviewers’ feedback, we have conducted additional experiments shown in the... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a clever method that exploits recent findings about the effectiveness of linear probing across diverse representation spaces in order to discover semantically-meaningful clusters (i.e. ones that correspond to human labeling) by seeing which linearly-separable clusters are preserved across m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work and for acknowledging the methodological novelty of our method. We are glad that the reviewer finds HUME to be *a very clever method that beautifully exploits recent findings about the effectiveness of linear probing* as well as our pap... | null | null | null | null | null | null |
Addressing Negative Transfer in Diffusion Models | Accept (poster) | Summary: This paper discusses the use of diffusion-based generative models for various generative tasks, such as image, video, 3D shape, and text generation. The authors argue that negative transfer, which refers to competition between conflicting tasks leading to decreased performance, should be investigated and addre... | Rebuttal 1:
Rebuttal: We appreciate the insightful feedback provided by reviewer sPtA. We will address the raised concerns and revise the paper accordingly, as these comments contribute significantly to improving our work.
---
### **W1: Lack of experiments on different datasets and diffusion models.**
Thank you for ... | Summary: This paper analyzes diffusion training from an MTL perspective and observes the negative transfer in diffusion training. Several multi-task learning algorithms are employed to address the negative transfer problem, leading to improved performance.
Strengths: 1. The paper presents a detailed analysis of task a... | Rebuttal 1:
Rebuttal: We deeply appreciate the insightful comments by reviewer 8Ptq. The comments are very helpful in making our work more complete from an MTL perspective. We will address all raised concerns by the reviewer and revise the paper accordingly.
---
### **W1: Lack of comparison of time and GPU memory co... | Summary: This work explores the phenomenon of negative transfer in the diffusion training procedure, where different time steps or signal-to-noise ratios may conflict with each other and degrade overall performance. The authors propose a solution to this problem by introducing internal clustering and implementing sever... | Rebuttal 1:
Rebuttal: We are grateful to reviewer 7mPc for providing constructive comments, which are very helpful in improving our work through experimental results. We will address all raised concerns by the reviewer and revise the paper accordingly.
---
### **W1: Experiments are only conducted on small datasets.*... | null | null | Rebuttal 1:
Rebuttal: # Global Response
Dear reviewers,
We sincerely thank you for dedicating time and effort to review our manuscript. In an attached PDF file, we have provided the results of all conducted experiments during the author response period for addressing concerns and questions raised by reviewers. In thi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions | Accept (poster) | Summary: The paper proposes to parametrize frailty models with neural networks and provide results on learning rates for these models.
Experiments demonstrate the efficacy of the proposed models.
Strengths: 1. The theoretical analysis is interesting and cool.
Weaknesses: 1. The paper states that: "[56] used a neura... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments. Below we address your specific points:
### Q1: On evaluation with proper score metrics
Please kindly refer to the first part of the general response for a detailed explanation. In our opinion, while right-censored log-likelihood (CLL) is a p... | Summary: The authors propose a neural architecture to estimate the survival function for observations of survival times and censored survival times. The authors describe a methodology to include heterogeneity among the population by including the frailty component. The authors then theoretically and empirically illustr... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments. Below we address your specific points:
### Q1: Evaluation of idealized settings
According to our understanding, if the true surival time is always observed (i.e., no censoring effects), then the problem boils down to an ordinary regression p... | Summary: The frailty model is one of the popular models in survival analysis, and it is an extension of the classical Cox proportional hazard model. This paper extends the frailty model by using neural networks, and this paper provides the theoretical analysis of the proposed models.
Strengths: + This paper proposes... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments. Below we address your specific points:
## Q1: The motivation of frailty and its advantage
Firstly, we would like to emphasize that random effect (called frailty), which serves as a principled tool to model *unobserved heterogeneity*, has pla... | Summary: The authors propose a framework for survival regression called neural frailty machine. They have shown that most of the existing methods are a special case of the proposed framework. Also, they have drawn statistical convergence guarantees for the proposed model. The experiments show marginal improvement compa... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments. Below we address your specific points
## Q1: The intuition of proportional frailty model
Firstly, we would like to emphasize that random effect (called frailty), which serves as a principled tool to model *unobserved heterogeneity*, has pl... | Rebuttal 1:
Rebuttal: We'd like to thank all the reviewers for providing insightful comments, we will integrate some of the suggestions into the camera-ready version.
We have found that there are several common issues raised by different reviewers:
- The motivation of frailty and its advantage.
- IBS/IBNLL/Cindex are... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GLIME: General, Stable and Local LIME Explanation | Accept (spotlight) | Summary: This work is embedded in the research on model-agnostic explanations, i.e., to provide the user an understanding on the outputs of otherwise black-box prediction methods without knowing about the model's internals. While LIME is a popular approach to solve this problem, prior work has demonstrated LIME to suf... | Rebuttal 1:
Rebuttal: We thank Reviewer jFSL for reviewing our paper and for the insightful comments. We hope our answers will address your concerns.
> **Q1:** What are GLIME's short-comings and what are plans to improve on the method in the future?
**A1:** From our perspective, the following may be promising for fut... | Summary: This paper proposes a new explanation method, GLIME, which provides more general, stable and local LIME explanations over ML models. Specifically, the authors demonstrate that small sample weights cause the instability of LIME, which results in dominance of regularization slow convergence and worse local fidel... | Rebuttal 1:
Rebuttal: We thank Reviewer sX7G for reviewing our paper and for the insightful comments. We hope our answers will address your concerns.
> **Q1:** The experiments are only conducted on one dataset, i.e., ImageNet dataset. It would be better if the authors could show more results on more benchmark datasets... | Summary: In this paper, the authors present GLIME an approach for explainable ai that generalizes LIME. Here, the authors present a framework that encompasses different explainability methods as instantiations of different aspects such as loss function, sampling function, etc.
The authors also present an analysis of ... | Rebuttal 1:
Rebuttal: We thank Reviewer DXPw for reviewing our paper and for the insightful comments. We hope our answers will address your concerns.
> **Q1:** You mention two main issues with LIME being the interaction of the weighting with regularization and sub-par sampling. However, it would seem like ALIME does n... | Summary: The paper introduces GLIME as a solution to tackle the issues of instability and diminished local fidelity encountered in the original LIME method. To address the problem of instability, GLIME employs a novel sampling scheme that guaranteed to have a faster sampling rate. The diminished local fidelity problem ... | Rebuttal 1:
Rebuttal: We thank Reviewer NTaD for reviewing our paper and for the insightful comments. We hope our answers will address your concerns.
> **Q1:** One weakness would be limited applicability of the proposed GLIME. The paper only demonstrates it can only be applied to the image domain. As other features fr... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
An Efficient Tester-Learner for Halfspaces | Reject | Summary: The paper introduces an efficient algorithm for learning halfspaces in the testable learning model in which the tester-learner first applies a test on the training data and if the test succeeds the algorithm produces a hypothesis which is guaranteed to be near-optimal. It is required that if the data comes fro... | Rebuttal 1:
Rebuttal: We thank the anonymous reviewer for their constructive comments and suggestions, as well as for alerting us to a number of typos (including one within Algorithm 1) and relevant references. We also thank the reviewer for pointing to an interesting open direction regarding whether our techniques wou... | Summary: This papers gives an efficient algorithm for learning halfspaces under the testable learning framework of Rubinfeld and Vasilyan (STOC'23) and facing either Massart or agnostic noise. In this setting, the algorithm is given some reference marginal distribution $D^*$ (which is assumed to be isotropic and strong... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their constructive feedback and for appreciating our results!
The problem of designing efficient tester-learners for non-homogeneous halfspaces is an interesting open question. Our approach does not immediately apply to this problem, because we cruciall... | Summary: Learning halfspace is a very important problem in machine learning which has been studied extensively. However, generally some distributional assumptions like gaussianity are assumed which in general is difficult to verify. To address this issue, recently Rubinfeld and Vasilyan (STOC 23) have introduced Testab... | Rebuttal 1:
Rebuttal: We thank the anonymous reviewer for their feedback and comments. While our results are indeed of theoretical nature, we view the testable learning framework as an important step towards bridging theory with practice, since it removes a significant part of the modeling assumptions typically require... | Summary: This work provides an efficient algorithm for testably learning halfspaces, extending the frontier of the recently introduced testable learning which does not assume anything about the given data distribution. Specificially, the setting is as follows: the target distribution is standard Gaussian (or any fixed ... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their feedback and for appreciating our work!
Regarding the reviewer’s question, prior work (e.g., [1], [2], [3], [4]) has provided evidence (in terms of statistical-query or cryptographic lower bounds) that achieving $\mathrm{opt}+\epsilon$ for the pro... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release | Accept (poster) | Summary: The paper proposed to protect data privacy in collaborative inference settings. Specifically, the paper presents a formal way to capture the obfuscation of an image using adversarial representation learning, a well-studied technique. The paper does so by using a metric-based differential privacy notion. Since... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We respectfully disagree with several points mentioned by the reviewer and have tried our best to address those by quoting them inline.
First, we would like to clarify “collaborative inference” as studied in several existing papers[1-5] including ours.
In co... | Summary: A method is proposed to encode inputs to a server that performs inference, to limit the ability of the service provider to infer detailed information about the input. A formal privacy guarantee, inspired by differential privacy, quantifies the amount of leakage. This metric is related to the local Lipschitz co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We agree that tasks which require granular information from data such as translation would not be possible with collaborative inference approaches. However, plenty of use cases exist where all the information is not necessarily required to produce an answer.... | Summary: This paper proposes a method for evaluating the privacy guarantees provided through adversarial representation learning. More precisely, the framework proposed is built on the Propose-Test-Release paradigm (PTR) as well as the d_x-privacy metric. The primary objective is to be able to characterize the privacy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and address the concerns as follows -
**LDP mechanisms and limits of differential privacy** - We note that our LDP experiment is only for the purpose of illustrating the gap in utility. The protection by LDP is so high that it is unreasonable to expect any u... | Summary: This paper is concerned with the development of privacy-preserving collaborative inference. A collaborative inference framework allows users to create a privacy-preserving encoding of their data, which in turn can be used in place of real private inputs when interacting with machine learning-based services. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the feedback. We address the three main discussion points brought up by the reviewer below -
**Application to tabular data** - Our framework is agnostic to the data modality. From the point of view of privacy, the input needs to be a vector so that it can be pr... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception | Accept (poster) | Summary: This submission introduces a carefully-crafted transformer model family (models with varying speed-accuracy trade-off) for 3D detection from multi-view camera data. The adopted model design methodology prioritises hardware-efficiency, customising the proposed architecture to the target GPU. For this purpose, a... | Rebuttal 1:
Rebuttal: **ReW1:** Thank you for this question to give us a chance to clarify our motivations.
Currently, the pre-fusion family, such as BevFusion, is very expensive to deploy in practice. The current in-vehicle market is dominated by camera-only solutions, such as Tesla, or post-fusion of camera and lida... | Summary: This paper proposed a latency-aware design strategy to search for an efficient network structure for BEV perception. To make this happen, this paper proposed a convolutional modulation layer for replacing native self-attention, and proposed to use BN to replace LN to make the network inference faster. Based on... | Rebuttal 1:
Rebuttal: **ReW1:** Thank you for highlighting areas in our article's expression that need improvement.
(a) The detailed structure of the modules in equation 1 is provided in Figure 9 -11 of the appendix, also our searched structures are provided in Table 13-15 of the appendix. We will mention this in the ... | Summary: The paper proposes a novel hardware-efficient transformer-based framework called HotBEV for camera-only 3D detection tasks. The framework is designed to achieve high-speed inference on multiple devices, including resource-limited ones, by considering hardware properties such as memory access cost and degree of... | Rebuttal 1:
Rebuttal: **ReW1:** We have not updated the abstract of the paper on the OpenReview interface. We sincerely apologize. The title of our submitted paper is “HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception”. So our proposed method is ‘HotBEV’. We appreciate your valuable ... | Summary: This paper presents HotBEV, a new model developed for 3D detection tasks. By prioritizing actual on-device latency and considering key hardware properties, HotBEV achieves impressive reductions in computational delay. This optimization allows for real-time decision-making in self-driving scenarios, making it a... | Rebuttal 1:
Rebuttal: **ReQ1:** For a fair comparison with SoloFusion, we tested a 4-frame version of SoloFusion, as shown in Table A. Across multiple benchmarks, our HotBEV consistently outperforms SoloFusion, with the exceptions of mATE and mAVE. Furthermore, HotBEV achieves a 35% faster FPS compared to SoloFusion.
... | Rebuttal 1:
Rebuttal: We first sincerely thank every reviewer for your insightful and constructive feedback. Then, we will answer the specific questions from each reviewer. We upload a pdf file with figures (Figure A,B) which we will present in our rebuttal.
Pdf: /pdf/6f567aee476f88c7450b9193ddf6ec097bed0d47.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails | Accept (poster) | Summary: This paper considers the problem of unconstrained min-max optimization with bilinear structure. Specifically, this paper considers the setting where the payoff matrix $A_t$ changes over time. The two changing dynamics of $A_t$ considered in this paper is the periodic game and the converging perturbed game. Thr... | Rebuttal 1:
Rebuttal: Thanks a lot for your support and interests to our results, especially we thank you for proposing important and challenging question to strengthen current results. Please see our itemized responses below:
1. Whether the current result can be extended to a generalized linear case with $f_t(x,y) = ... | Summary: This paper studies the last-iterate behavior of three different algorithms on two types of time-varying zero-sum games: periodic and convergent perturbed games. For periodic games, the authors prove that EG will converge while OGDA and the negative momentum method could diverge. For convergent perturbed games,... | Rebuttal 1:
Rebuttal: Thank you for your supportive comments, suggestions and questions on intuitive explanation and experiments. Please see our itemized responses below:
1. Could the authors provide some examples of periodic and convergent perturbed games?
Please refer to part 2 of the global rebuttal for an explana... | Summary: This paper studies the problem of learning Nash equilibria in two-player zero-sum bilinear games where the payoff matrix varies with time.
When the payoff matrix is a periodic function, it is proven that the extra gradient algorithm converges to a Nash equilibrium, whereas the optimistic gradient descent ascen... | Rebuttal 1:
Rebuttal: Thank you very much for your support, careful reading and helpful suggestions. Please see our itemized responses below:
1. What is the definition of $\ker(\cdot)$ ？
For a payoff matrix $A \in \mathbb{R}^{n \times m}$, $\ker(A)$ is defined to be the set $\\{ x \in \mathbb{R}^m |\ Ax = 0 \\}.$
*... | Summary: Over the last few years an extensive literature has studied the last iterate convergence of learning dynamics in zero-sum games, particularly those using optimism and extra-gradient approaches. This paper extends that approach to two classes of unconstrained non-stationary game (periodic and decaying noise) a... | Rebuttal 1:
Rebuttal: Thank you for your support, interest to current work, and inspiring question for future work. Please see our itemized responses below:
1. Is there a reason to focus on these classes of games other than technical convenience? Do they have important applications? What more general but harder class ... | Rebuttal 1:
Rebuttal: We appreciate the efforts of all reviewers, thanks for your constructive questions and critical suggestions! The PDF file contains additional experimental results as requested by reviewers. If you have questions about the experimental parts of the paper, please refer to this file. In the followin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work investigates the problem of last-iterate convergence in time-varying zero-sum games. Specifically, the authors study the last-iterate convergence of three kinds of algorithms (OGDA, EG, and negative momentum method) considering two kinds of time-varying games with specific structures, i.e., periodic ... | Rebuttal 1:
Rebuttal: Thank you for your careful reading, supportive comments, and helpful suggestions in improving the paper. We will clarify the issues in revision. Please see our itemized responses below:
Questions part：
1. What is the range of $t$ in Theorem 3.1 ?
Here $t$ denotes the number of rounds that ... | null | null | null | null | null | null |
Schema-learning and rebinding as mechanisms of in-context learning and emergence | Accept (spotlight) | Summary: The authors attempt to analyze the reason for the success of In-context learning (ICL) for few-shot learning regimes. They apply ICL to clone-structured causal graphs (CSCGs) that can be used to interpret how ICL works in LLM. The CSCG is constructed as causal graph model where for the task of next token seque... | Rebuttal 1:
Rebuttal: We appreciate the review for distilling the essential concepts we have tried to convey in the paper. Our goal here is indeed to elucidate a general framework for in-context learning behavior, leveraging the interpretability of CSCGs. A mechanistic understanding of ICL in transformers is still a wo... | Summary: This paper implements Cloned Structured Causal Graphs (CSCG), a model that was previously used in Neuroscience to explain Hippocampal cognitive maps, on language tasks that require in-context learning (ICL). They show the success of CSCG's across a variety of language benchmarks and come up with a theory on th... | Rebuttal 1:
Rebuttal: We are heartened by your emphasis on the strengths of the CSCG approach and the novelty of its application in this setting. We are similarly excited to leverage the interpretability of CSCGs to elucidate by analogy a general framework for in-context learning behavior. You might also find interesti... | Summary: The paper aims to replicate the in-context learning (ICL) phenomenon in large language models (LLM) with clone-structured casual graphs (CSCG), which is roughly trying to learn hidden states in PODMP such that the transition matrix is invariant new environments, and only the emission matrix needs re-learning.
... | Rebuttal 1:
Rebuttal: Indeed, it is the combination of context-dependent latent representation and transitive generalization capability that drives the power of CSCGs. Since the thrust of this paper is on in-context learning behavior, page constraints limit us from elaborating on CSCG details. We are happy to add to th... | Summary: The authors propose the clone-structured causal graph (CSCG) with rebinding as a model of in-context learning in language models. The authors conduct several experiments showing that CSCGs can learn latent graphs corresponding to meaningful concepts seen in the training data, while also generalizing to instant... | Rebuttal 1:
Rebuttal: We are glad that you consider novel and interesting our application of CSCGs towards a framework for understanding ICL. We will elaborate on the challenges of scaling CSCGs to large datasets in the discussion section, and some potential directions for progress.
To recap how we leverage CSCG inter... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and thoughtful comments. As the reviews have identified, we have used CSCGs as a sequence model and leveraged their interpretability to deconstruct in-context learning (ICL) behavior into a combination of schema-learning (at training time) and sc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Time-uniform confidence bands for the CDF under nonstationarity | Accept (poster) | Summary: The authors propose a method for constructing time-uniform and value-uniform bounds on the CDF of the averaged historical distribution of a real valued random process. They propose a design principle AVAST (always valid and sometimes trivial), implying that their bounds always have coverage, but may converge t... | Rebuttal 1:
Rebuttal: > The one point that is not clear to me is the instance-adaptivity of Theorem 3.3.
Thanks for pointing this out, we'll add more commentary, since this is an important and desirable property of the technique which should be highlighted.
The width guarantee is dependent upon the smoothness of the ... | Summary: The paper considers the problem of estimating the CDF of a sequence of random variables obtained sequentially according to some distributions.
Many classical results are known for this problem when the random variable sequence is obtained in an i.i.d. manner following a certain distribution, along with its con... | Rebuttal 1:
Rebuttal: > The contents of the Introduction seem to be insufficient
Great point, the current introduction is abstract and a concrete example will be helpful. We will discuss the scenario of continuous monitoring for software regression detection, which combines the desire to do inference beyond the mean ... | Summary: This paper proposes a new construction of time-uniform confidence bands for CDF, where the standard toolkit such as Glivenko—Cantelli cannot be applied due to nonstationarity.
At a high level, the proposed method combines confidence sequence for a certain subset of values via a union bound.
Despite the impossi... | Rebuttal 1:
Rebuttal: > The paper is not very self-contained. For example, Table 1 needs to be elaborated further. For example, there is no description on “$w_{\max}$-free” in the main text or in the caption.
We will expand section 5 to explicitly define the six properties we identify in the columns of Tab... | Summary: The paper presents the time and value uniform bounds on the CDF of the running averaged conditional distribution of a real valued random variable. The new bounds do not require iid setting and always achieve non-asymptotic coverage. The converge speed depends on the smoothness of distribution against the refer... | Rebuttal 1:
Rebuttal: > This is not my research area and, I found the paper was hard to read and follow.
Thanks for the honest feedback. We are trying to promote confidence sequences within the machine learning community (they are better known within the statistics literature) and it is challenging to properly calibr... | Rebuttal 1:
Rebuttal: We thank for reviewers for helping to improve the paper.
To improve the intelligibility of Algorithm 1, we propose adding the attached figure. Further, in the appendix, we will manually describe the execution of Algorithm 1 step-by-step until termination on a simple dataset of five items.
Pdf: /p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Im-Promptu: In-Context Composition from Image Prompts | Accept (poster) | Summary: This paper presents a comprehensive study of in-context learning of compositionality in images. It introduces a simple benchmark of compositional language-driven visual transformations and explores under what conditions a model can perform in-context analogies from image pairs.
Strengths: Very interesting stu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and positive feedback.
---
Rebuttal Comment 1.1:
Comment: ack on rebuttal | Summary: This paper focuses on the problem of using composable elements of visual modality to perform in-context referring for analogical reasoning like LLMs do. Firstly, the authors constructed three benchmarks to evaluate the generalization capacities of visual in-context learner. Then, the authors unified the formul... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and positive feedback. We address the various concerns below.
> Just one question: The proposed three benchmarks are very different from actual scenes, are there any plans to introduce more real-scene images to the benchmark?
We are experimenting with photo... | Summary: This work proposes a framework for in-context image generation/composition from image prompts. The in-context learner resembles analogy completion and is simply optimized with a reconstruction objective. Several variants in terms of visual representations (pixels/latent vectors), the compositionality of the ve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive feedback. We respond to the questions and concerns below.
> The main concern comes from the confusing contribution clarification of this paper. I do not see which specific/major contributions the authors want to emphasize. Text-free in-conte... | Summary: This work investigates whether analogical reasoning can enable in-context composition over composable elements of visual stimuli. First, they introduce a suite of three benchmarks to test the generalization properties of a visual in-context learner. They formalize the notion of an analogy-based in-context lear... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and positive feedback. We address the various concerns below.
> Since the number of in-context demonstrations is one important parameter in in-context learning fields, is there any ablation study for the number of demonstrations?
We agree with the reviewer ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for raising a number of great questions, adding detailed comments, and drawing our attention to related works. We will incorporate the feedback in the next iteration of the manuscript. We address some common concerns below and introduce additional results.
###... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates whether analogical reasoning can enable in-context compositional generalization over visual entities. The authors construct three benchmarks to test the compositional generalization on visual analogy-making, including 3D shapes, BitMoji Faces, and CLEVR. The authors also present a visual... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive feedback. We respond to the questions and concerns below.
> What is the fundamental difference between the proposed Im-Promptu learning in Section 4 and visual analogy-making?
We agree that our work bears similarity to visual analogy-making... | null | null | null | null | null | null |
Fairness Aware Counterfactuals for Subgroups | Accept (poster) | Summary: The authors identify various aspects of fairness that require consideration when assessing recourse bias between subgroups. They propose FACTS (Fairness Aware Counterfactuals for Subgroups), in an attempt to lay foundations for a framework that can be used to audit subgroup fairness through counterfactual expl... | Rebuttal 1:
Rebuttal: **W1 "The assumption that while cost is not exactly quantifiable, a given action's cost is uniform across the input space, and can thus be compared between instances, does not always hold. One simple example would be changing salary by amount X. Individuals commanding higher initial salaries are l... | Summary: The paper presents a framework called FACTS (Fairness Aware Counterfactuals for Subgroups) for auditing subgroup fairness through counterfactual explanations. The authors aim to formulate different aspects of the difficulty individuals face in achieving recourse, either at the micro level (individuals within s... | Rebuttal 1:
Rebuttal: **W1 "more detailed description of the methodology used"**
We acknowledge that some details about the experimental methodology description are missing in the main text, and probably not adequately covered in the supplementary material. We intend to remedy this should the paper be accepted. We sho... | Summary: This paper considers the problem of fairness of machine learning based decisions. The setting is as follows: there is a set of features X in R^n where X_n denotes the demographic group, and a classifier h: X to {0,1} and we have access to a dataset D of individuals with h(X)=0. Each individual can take actions... | Rebuttal 1:
Rebuttal: **W-i "There are two limitations of the algorithm: the form of the predicates (subgroups) and the form of the actions. In particular the subgroups are conjunctions of multiple features (does not allow arbitrary subgroups) and second actions are also conjunctions of feature values. This will lead t... | Summary: In this paper, the authors explore the fairness of recourse in detail and distinguish between the micro and macro viewpoints. Moreover, they propose an efficient, interpretable, model-agnostic, highly parameterizable framework, called FACTS, to audit for fairness of recourse and provide an interpretable summar... | Rebuttal 1:
Rebuttal: **W "The experiments section could be improved by including more datasets and comparisons with other fairness metrics and definitions."**
We would like to point out that due to lack of space in the main text, we have only included a single dataset Adult with sex as the protected attribute. In the... | Rebuttal 1:
Rebuttal: We are thankful to the reviewers for their insightful and constructive comments. In this global response, we would like to address some misunderstandings about the focus of our work and how we handle numerical attributes, and also discuss limitations.
### Global Explainability vs Auditing for Fai... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a framework (termed as FACTS) for analysing the recourse fairness of a machine learning model. The work introduces multiple metrics for quantifying recourse fairness - both at micro level (individuals considered separate) and macro level (individuals considered together). Some proposed recou... | Rebuttal 1:
Rebuttal: **W1a "experiments using toy datasets to create a biased model and then demonstrated that FACTS can uncover the recourse bias"**
Please note that we have provided a toy example in Fig, 1 where we show how burden is not nuanced enough to capture other notions of recourse bias.
Most importantly, w... | null | null | null | null | null | null |
Explore to Generalize in Zero-Shot RL | Accept (poster) | Summary: ---
I have raised my score based on the answers and results provided by the authors during the rebuttal.
---
This paper proposes an algorithm named ExpGen that can selectively exhibit a maximum entropy exploration behavior at test time by measuring epistemic uncertainty through ensemble of policies. In order... | Rebuttal 1:
Rebuttal: We include an evaluation of ExpGen+IDAAC (described in the general comment and accompanied by Rebuttal Figure 1), showcasing state-of-the-art performance on all ProcGen environments.
Regarding memory and computational inefficiencies:
This is a valid point, which we address in the general comment... | Summary: The paper is based upon the key insight, that maxEnt exploratory policies exhibit a much smaller generalization gap than usual reward seeking policies.
Previous work introduced the framework of epistemic POMDPs, where a random action is chosen until the uncertainty of the policy is low enough again (quantified... | Rebuttal 1:
Rebuttal: L2/L0: We used L0 with an intuition that pixel changes are what matters for games like ProcGen, where the agents are localized. This is indeed an approximation, but one that we found to work well. We started an experiment with L2, but did not yet get the results. We remark that L2 is also an appro... | Summary: This paper studies zero-shot generalization in RL. They first make an interesting observation: that intrinsic novelty-based rewards corresponding to maximum entropy exploration exhibit a smaller generalization gap than extrinsic environment rewards on ProcGen games. This suggests that MaxEnt rewards are in som... | Rebuttal 1:
Rebuttal: Methodological issue - sample size:
Thank you for raising this point. We address it in the general comment (accompanied with Rebuttal Figure 2).
Combining ExpGen+IDAAC:
This is a valuable insight, one that is expressed by the other reviewers as well. In the experiments described in the general c... | Summary: This work studies generalization on unseen similar tasks, in a zero-shot manner, and discusses how invariance based approach to overfitting might not work all the time. The algorithm proposed, called ExpGen, has one part that explores the space, while a ensemble of agents are trained to do the reward optimizat... | Rebuttal 1:
Rebuttal: Training progress:
ExpGen combines already trained exploration driven policy with an ensemble of (trained) reward policies at test-time. Therefore the evaluation targets test environments and does not produce figures of the training progress.
We will clarify this point, as well as clarify Figure 4... | Rebuttal 1:
Rebuttal: Thank you for your valuable insights and suggestions.
This paper is the first to incorporate exploration driven behavior at test-time towards generalization in RL, and in doing so, achieves state-of-the-art performance in environments that are widely regarded as challenging by all the leading alg... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Representational Strengths and Limitations of Transformers | Accept (poster) | Summary: This paper investigates the representation power of attention layers in transformer networks and compares them with other neural network architectures. The authors establish both positive and negative results on the benefits and limitations of attention layers, focusing on intrinsic complexity parameters such ... | Rebuttal 1:
Rebuttal: > While the paper provides rigorous mathematical analysis and proofs, it lacks empirical evaluation of the proposed tasks and architectures. Including experiments using real-world datasets could provide practical validation of the theoretical findings and further strengthen the paper's conclusions... | Summary: The paper presents the representational strength and some limitations of the transformer architecture.
1. Strength: separation between a unit of self-attention and a one-hidden layer neural or a recurrent neural network.
The authors present a task where the complexity of the latter networks scale with N (numbe... | Rebuttal 1:
Rebuttal: > Minor comment: When reading Section 1.1 for the first time, I could not follow the authors' intentions/message. Only after carefully reading the rest of the text and definitions could I understand it. As written now, I think it does not convey information properly for someone just interested in ... | Summary: This paper focuses on the representational capabilities of attention layers in transformer models and showcases both the strengths and limitations. On one hand, the paper proves that attention layers excel at the presented sparse averaging task compared to RNNs and FNNs. On the negative side, they have complex... | Rebuttal 1:
Rebuttal: > As said previously, this is not within my area of expertise. I am however interested in how relevant/connected are the proposed tasks (e.g. sparse averaging and triple detection) to empirical studies such as language or visual data modeling. I think the work would be improved if more insights co... | Summary: This paper mainly investigates the inductive biases of attention-based models. They propose three computational tasks that show the limitations of Transformers, namely, sparse averaging, pair matching, and triples-matching. Specifically, they analyze the representational power of embedding dimensions and show ... | Rebuttal 1:
Rebuttal: > Initially the notation is a bit confusing, especially in section 1.1 that details the contributions. Variables and should be more clearly defined and explained when mentioning results of the theoretical analysis.
Upon a closer reading, we see that we use a large amount of condensed English and ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and thoughtful feedback on our submission. We are grateful that the reviewers largely appreciated the strength and value of our fundamental theoretical contributions while identifying areas of improvement. We agree that some aspects of the presentation of ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors study the representational power of transformers. They quantify how the transformers are superior to other neural network architectures, as well as the limitation of the transformer architecture. They focus on the $q$-sparse averaging (qSA) task which amounts to averaging $d$-dimensional input vec... | Rebuttal 1:
Rebuttal: > The fixed precision (Theorem 2) result still uses precision that needs to grow with the data size and approximation parameters. Although it is a good first step, it leaves open the approximability with constant precision (which is used in most practical cases).
We agree that constant precision ... | null | null | null | null | null | null |
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics | Accept (poster) | Summary: This paper introduces an E(3)-invariant temporal attention scheme, calculated with the help of discrete Fourier transform, within the E(3)-equivariant GNN framework. The overall idea of considering higher-order temporal effects in physics is sound, and the formulation appears to be correct.
There are a few ty... | Rebuttal 1:
Rebuttal: Thanks! Your feedback is instrumental in strengthening our paper.
>Q1: There are some typos in the paper, such as the missing year in reference [16]. I recommend thorough proofreading.
Thank you very much for the mentioned typos, and we will fix them and proofread our paper carefully.
>Q2: —La... | Summary: This paper addresses the Markov limitation of previous methods in simulating physical dynamics by treating it as a spatio-temporal prediction task. The authors propose Equivariant Graph Neural Networks (GNNs) to account for the non-Markovian nature of the systems. Additionally, they design three components to ... | Rebuttal 1:
Rebuttal: We are grateful for your positive and constructive comments, and provide the answers to your questions below.
>Q1: Lack of clarity regarding EDFT: The paper does not provide sufficient explanation of how EDFT improves prediction accuracy. It is important to clarify the underlying mechanisms and ... | Summary: This paper studies the non-Markovian dynamics that often appear in physical systems and proposes a spatio-temporal E(3) equivariant graph network that moves beyond the simple frame-to-frame prediction task.
The authors introduce an equivariant feature extraction method based on Fourier Transform, as well as se... | Rebuttal 1:
Rebuttal: We are grateful for your positive and constructive comments, and provide the answers to your questions below.
>Q1: Following the weaknesses above, a comparison with other spatio-temporal equivariant graph networks would further enhance the credibility of the proposed method.
Thank you very much ... | Summary: This work proposes a novel architecture for predicting physical dynamics. This architecture first extracts the frequency feature of the input dynamics using a new technique. The frequency feature is then processed by spatial-temporal networks to generate predictions for the future dynamic. Through evaluating t... | Rebuttal 1:
Rebuttal: We are grateful for your positive and constructive comments, and provide the answers to your questions below.
>Q1：In the MD17 dataset, the visualization seems to be really hard to differentiate the ESTAG model and the STEGNN model. But why is the MSE difference so big? Is the observed MSE differ... | Rebuttal 1:
Rebuttal: ## General Response
We sincerely thank all reviewers and ACs for their time and efforts on reviewing the paper. We are glad that the reviewers recognized the contributions of our paper, which we briefly summarize as follows.
- **Novelty**. "The architecture proposed in this work (ESTAG) is novel"... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to simulate the physical dynamics with a spatio-temporal attentive graph network. The major contribution is to integrate the concept of spatio-temporal graph neural network with DFT to capture the data dependencies. The proposed model is evaluated on three datasets regarding the molecular-, pro... | Rebuttal 1:
Rebuttal: Thank you for your comments! We provide the following responses to your concerns:
>Q1: My primary concern is technical novelty. This paper adapts STGNN for physical dynamic simulation without significant contributions or novel designs. The paper claims that it is important to relieve from the Mar... | null | null | null | null | null | null |
Idempotent Learned Image Compression with Right-Inverse | Accept (poster) | Summary: The paper shows how to achieve a truly idempotent image compression method, where f(x) = f(f(x)). The paper shows that it's sufficient to have E(D(y)) == y. This only requires a surjective E.
Strengths: Interesting derivation of the required conditions for idempotence. Also nice to see this problem studied.
... | Rebuttal 1:
Rebuttal: # To reviewer fD4t
Thanks for your advices. We address your concerns as follows.
**weakness-1 & limitation**: We test replacing GDN with residual blocks (as suggested by ELIC[1]), and report as follows (also shown in **[rebuttal fig.4]**):
| framework | BD-rate (RB $\times$ 1 v.s. GDN) | BD-rat... | Summary: The paper argues that invertibility is sufficient but not necessary for achieving idempotent codecs, and proposes a framework for achieving idempotent learned image compression (LIC) with right-inverse, which allows more flexible and expressive transforms. This paper details the expressive and right-reversible... | Rebuttal 1:
Rebuttal: # To reviewer 4J8x
Thanks for your advices. We address your concerns as follows.
**weakness-1**: We provide the flops of various components of the proposed idempotent framework (for a $256 \times 256 \times 3$ input) as follows.
| components | GFLOPs |
| -------- | -------- |
| blocked convolut... | Summary: This paper introduces a learned image codec with a right-inverse transform. The task is to ensure that an image can be re-compressed multiple times without significant quality degradation, while the compression is still lossy. This work is one of the few early attempts along this line of research. Its applicat... | Rebuttal 1:
Rebuttal: # To reviewer H19p
Thanks for your advices. We address your concerns as follows.
**weakness-1**: Yes, and we have mentioned it at Line 43. We are the first to apply null-space decomposition to LIC task. More importantly, we propose several novel designs to enable efficient idempotent LIC as summ... | Summary: In this work the authors address the problem of stability of codec re-compression (idempotence). In particular the paper points out the difficulty of achieving idempotence in learned image compression, and existing methods rely on invertible models such as normalizing flows which limits their performance.
The... | Rebuttal 1:
Rebuttal: # To reviewer FGoX
Thanks for your advice. We address your concerns as follows.
**weakness-1**:Thanks for the kind advice. We will improve Sec.2 and Sec.3.1 in order to give a better presentation for these two points.
Specifically, to better explain the transition from idempotence to right inve... | Rebuttal 1:
Rebuttal: To all reviewers:
please kindly reach to the pdf file below for **[rebuttal fig.x]**.
Pdf: /pdf/1c6f3fe8b2a7eddbe912e259fecd1700837dd5da.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Enemy is Inside: Alleviating VAE's Overestimation in Unsupervised OOD Detection | Reject | Summary: The paper first mathematically examines the unsupervised (without training label) OOD detection performance using VAE, decomposing the expected ELBO into two components: (i) entropy $\mathcal{H}(x)$ of a dataset, (ii) KL divergence $D_{KL}(q(z)||p(z))$ between the estimated $z$ and the prior. It's theoreticall... | Rebuttal 1:
Rebuttal: **For weakness 1:** Thanks for your careful reading and we will correct the inconsistent notation.
**For weakness 2 & question 1:**
We pretty much appreciate this thoughtful comment. In the beginning, please allow us to point out a factual error in your comments that the entropy of a dataset wil... | Summary: The paper discusses the phenomenon of overestimation, the allocation of higher likelihoods to out-of-distribution data points, in deep generative models. It analyses two factors which may cause the overestimation problem specific to VAEs from a reformulation of the ELBO. These two factors are posterior collaps... | Rebuttal 1:
Rebuttal: **For weakness 1:**
Thanks for this helpful suggestion. As shown in Lines 180~183 in our paper, we have summarized the answer to the stated question after enumerating several typical cases. Then we highlight that the analyzed case in Fig.3 is targeted at non-linear VAEs (note the activation funct... | Summary: The paper studies unsupervised OOD detection (i.e., training data contains no labels) using deep generative models. DGMs model the probability distribution of the inputs, and can be an ideal candidate for unsupervised OOD detection. The authors study one specific class of DGMs, namely VAEs. They show that VAEs... | Rebuttal 1:
Rebuttal: **For weakness 1:** Thanks for your suggestion and we will add these citations.
**For weakness 2:** We're sorry for the non-self-contained organization and we will add the limitation to the main paper.
**For weakness 3:** Thanks for your comment on the notation and we will improve it as you sugg... | Summary: In the context of VAE, the authors identified two factors that potentially cause VAE to assign higher likelihood to OOD data than ID data. They propose a new scoring mechanism that improves upon VAE's overestimation of the likelihood on OOD samples.
Strengths: - Decomposing the ELBO carefully is interesting. ... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, the following is our response.
**For weakness 1:** We absolutely agree with your point that the model distribution can hardly converge to the data distribution in practical VAEs, and there does exist a third term in ELBO to affect the performance of ELBO-based... | Rebuttal 1:
Rebuttal: **For all reviewers: Introduction to additional experiments in the attached "one-page rebuttal pdf"**
First of all, we would like to extend our sincere gratitude to all the reviewers for their meticulous reviews, thoughtful comments, and valuable suggestions. Their feedback has greatly contribute... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Evolving Connectivity for Recurrent Spiking Neural Networks | Accept (poster) | Summary: This study presents an application of a previously developed approach, NES, to train RSNNs formulated based on connection probabilities. However, there are concerns regarding the ethical aspect of this work, specifically the reproducibility and proper citation of related works (see Weaknesses). Due to these re... | Rebuttal 1:
Rebuttal: **[W1] Code availability**
**[Response]**
Thanks for your advice, but we cannot agree on code availability as the main reason to reject.
According to the conference policy in Call for Papers, NeurIPS strongly **encourage** accompanying code and data to be submitted with **accepted papers** when... | Summary: Facing on the inaccurate and unfriendly limitation of current surrogate gradient-based learning methods for recurrent spiking neural networks (RSNN), this study develops the evolving connectivity (EC) framework for inference-only training. The EC framework reformulates weight-tuning as a search into parameteri... | Rebuttal 1:
Rebuttal: **[Q1]** *The novelty of the weight-based parameterization method and the NES methods in EC framework should be explained further.*
**[Response]**
Thank you for your advice.
Our work introduces a novel approach to the NES framework by utilizing a 1-bit discrete search space formulation, which d... | Summary: The paper describes an evolutionary training algorithm to optimize the binary weight matrix of a recurrent spiking neural network. The optimization algorithm is derived using natural evolutionary strategies assuming that the weights follow a Bernoulli distribution with parameter $\rho$. The resulting weight up... | Rebuttal 1:
Rebuttal: **[S1]** *The gradients of the EC training algorithm are unbiased.*
**[Response]**
Thank you for your affirmation and suggestion.
We have also pointed out that "the surrogate gradient leads to inherent inaccuracy in the descent direction" in line 37.
We will further emphasize the contribution ... | Summary: The authors present a new evolutionary algorithm, evolving connectivity (EC) to train recurrent spiking neural networks (RSNN). The algorithm alleviates the gradient estimation problem of surrogate gradient-based (SG) training methods which are often hard to implement in neuromorphic hardware. Further, by focu... | Rebuttal 1:
Rebuttal: **[Q1]** *Describe the RSNN architecture and how it compares to the RNNs used in Experiments in terms of number of layers/params/model size/precision?*
**[Response]**
Thanks for your valuable question.
The table below outlines the similarities and differences between RSNN and baseline models in... | Rebuttal 1:
Rebuttal: Thanks for all your suggestions. In light of them, we gathered commonly asked questions and ran a comprehensive set of additional experiments, as detailed below. The result figures are shown in the supplementary PDF. All our additional experiments are configured using the same settings and hyperpa... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mutual Information Regularized Offline Reinforcement Learning | Accept (poster) | Summary: A long withstanding problem in offline RL is the distribution shift issue, i.e., the query of the action values for out-of-distribution state-action pairs.
This paper proposes to consider the mutual information (MI) between states and actions.
Specifically, the authors view state and action as two random varia... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and valuable feedback and suggestions for further improvements. We would like to address the concerns as follows.
> why does a better estimation of MI lead to better policy performance?
Intuitively, estimating the mutual information $I(S; A)$ of the datase... | Summary: The authors propose a new offline RL method called MISA. Similar to prior work in offline RL, MISA constrains the learned policy to lie within the offline data manifold and does so by maximizing a lower bound on the mutual information between states and actions in the dataset. The authors consider three differ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognising the novelty and contribution of our work. We would like to address the concerns below.
> it would be nice to show a plot of numerical values of the different mutual information estimates (BA, MISA-$f$, MISA-DV, MISA) to see if the bounds in line 274... | Summary: This paper integrates two distinct methods in the offline RL domain: the KL regularized method and the conservative Q-learning method. It achieves this by incorporating mutual information in both the value loss function and the policy loss function. To accurately approximate the mutual information between stat... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognising the novelty and contribution of our paper, and giving us valuable suggestions. We would like to clarify the confusion below.
> BA outperforms MISA-f and MISA-DV in most cases within the MuJoCo medium-replay environments.
We extend our thanks to the reviewer ... | Summary: The authors of this paper introduce a novel framework called MISA, which aims to optimize the lower bound of mutual information between states and actions in the dataset to direct the policy improvement. They provide a theoretical explanation for MISA's superior performance over CQL and empirically demonstrate... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for acknowledging the novelty and contributions of our work, as well as for providing us with valuable suggestions. We would like to address and provide clarification on the questions as follows.
> It appears that there is a confusion between the true $Q$ ... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all the reviewers for acknowledging the novelty and contributions of our work, and for providing valuable questions for discussion along with constructive suggestions.
We wish to draw your attention to the additional experiment results, as suggested by Reviewer... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Diffusion-Model of Joint Interactive Navigation | Accept (poster) | Summary: The paper deals with the problem of generating vehicle trajectories at the scene level conditioned on a map and some known observations (e.g. past or future states). They propose a diffusion model based on SceneTransformer that is trained with observation conditionings with random masking to reflect multiple d... | Rebuttal 1:
Rebuttal: Consistency with input observations: We agree that in some examples, DJINN produces samples which are inconsistent across time. We also agree that these inconsistencies may be reduced by utilizing test time guidance. Specifically, classifier-free guidance as outlined in section 5.1 and demonstrate... | Summary: The paper proposes a generative model for producing synthetic traffic scenarios. The proposed method uses a diffusion model and learns to predict multiple agent trajectories jointly in a scene. The main contribution to previous methods is the increased flexibility of conditioning which can use different observ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to consider our work, and for providing positive, and constructive feedback. We address their concerns below:
**Demonstration of increased variability**: We agree that such a comparison should be considered in the evaluation of our model in comparison to ... | Summary: The paper proposes a diffusion model-based method for generating joint or conditioned predictions of traffic agents. The proposed method combines the EDM diffusion and SceneTransformer model architecture and provides different guidance for conditional predictions. The experiment results indicate comparable res... | Rebuttal 1:
Rebuttal: Thank you to reviewer TiFK for their thoughtful review of our work and their praise of our paper’s clarity. Below, we have responded to the two areas of weakness which the reviewer has highlighted.
**Novelty**: In regards to the novelty of our approach, we accept that our method combines the trai... | Summary: DJINN (Diffusion-based Joint Interaction Network) is an innovative generative model that creates, edits, and forecasts multi-vehicle traffic scenarios in a stochastic manner. Leveraging diffusion models, DJINN also addresses the challenge of generating traffic scenes conditioned on a flexible configuration for... | Rebuttal 1:
Rebuttal: We would like to thank reviewer 1Qe5 for taking the time to consider our work, as well as for their overwhelmingly positive and constructive feedback. We consider the areas which the reviewer believes that our work can be further improved below:
**Detailed Analysis of Limitations**: We agree that... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their time in considering our work and their thoughtful comments which we believe will help to improve the quality of our submission.
In response to requests for additional qualitative results, we have provided an attached pdf which contains a compo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Compressed Video Prompt Tuning | Accept (poster) | Summary: This work studies the video classification task in the compressed video. With the motion vector and residual as the prompt, this work proposes selective cross-model complementary prompter idea to enhance the cross-model interactions, achieving promising results while maintaining a small number of trainable par... | Rebuttal 1:
Rebuttal: W1:The evaluation benchmarks (UCF, HMDB, and SSv2) are all small scaled. One concern is the scalability of the proposed method.
A1:In fact, SSv2 is one of the largest datasets used in compressed video, containing a substantial **193,690** videos. Our approach also shows performance improvement on... | Summary: The authors present a way to adapt pre-trained raw video models to compressed videos. They utilized the existing concept of prompt tuning from NLP and repurposed it for the compressed video domain. Their findings indicate that by fine-tuning just 0.1 percent of parameters for a downstream task such as video cl... | Rebuttal 1:
Rebuttal: W1:Only video classification is shown as a downstream task.
A1:Our designed modules are tailored to address a wide spectrum of tasks pertaining to compressed video. We follow [6] and provide experiments on video classification as a downstream task. Nonetheless, it is necessary in extending our ... | Summary: The paper presents one alternative way of finetuning to work on compressed videos. Specifically, it designs a specific data flow within three modalities (RGB, residual, and motion vector). It also presents the way to make the model adapt to new compressed videos and provide a fair comparison. It demonstrates S... | Rebuttal 1:
Rebuttal: W1: The motivation of residual gating motion vector and gating I-Frame information is still weak to me. It would be better if the author can provide more evidence (exps and visualization).
A1:In compressed video, motion vectors capture motion displacement between preceding and subsequent frames v... | Summary: This paper proposes an efficient fine-tuning method based on the prompting concept in the compressed video domain. The intuition is to freeze the backbone pre-trained on raw videos and use the proposed prompting techniques to query the required information for the compressed videos. To address the multi-modali... | Rebuttal 1:
Rebuttal: W1:Some operations in the proposed method are confusing:
In Eq. 6, what is the physical meaning of adding an attended motion vector to image embeddings/tokens? The motion vectors represent the relative movement information for each of the spatial blocks (movement in x and y directions in the form... | Rebuttal 1:
Rebuttal: We are appreciative of the valuable insights shared by the reviewers. In response, we have thoroughly addressed each comment, providing individualized responses to each reviewer. The supplied PDF includes visualizations corresponding to Weakness 1, as referenced by Reviewer Eouy.
Pdf: /pdf/f89cccb... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores how to transfer pretrained RGB models to compressed videos with a parameter-efficient paradigm and introduces a prompt-tuning method named Compressed Video Prompt Tuning (CVPT). In CVPT, the learnable prompts are replaced with encoded compressed modalities that are refined in each layer. To... | Rebuttal 1:
Rebuttal: W1&Q1:The computational cost (GLOPs) of the proposed method may be higher than that of some previous works based on ViT. According to (1), the tokens of I-frames, motion vector prompts and residual prompts are all sent to each layer of pre-trained ViT, so the number of input tokens may be larger t... | null | null | null | null | null | null |
Training on Foveated Images Improves Robustness to Adversarial Attacks | Accept (poster) | Summary: This paper studies the effect of foveation via adaptive gaussian blurring and color modulation in training on an image. Their goal is not computer vision driven, nor ML based, but rather to shed light on the physiological nature of the retina and spatially adaptive computation in humans -- that may prove usefu... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback on our paper, and asking thoughtful questions. We are glad and encouraged to find that the reviewer liked our work and found it to be unique and interesting. We hope that in our responses below we will be able to fully address the reviewer's ou... | Summary: In their paper the authors introduce R-Blur as a foveation technique for biologically inspired defense against adversarial attacks on DNNs.
With their approach they try to simulate the human visual field by blurring and desaturating the image depending on the distance to a given fixation point. They continue t... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback on our paper. We find it encouraging that the reviewer will earnestly consider improving their rating if their questions are answered.
**In Figure 5 [...] A comparison with adversarial training would be more insightful**
The goal of Figure 5... | Summary: The paper presents a biologically inspired approach that improves the robustness of DNNs against samples with adversarial perturbations or common corruptions. In the proposed approach the models are trained using the images transformed using the proposed R-Blur (Retina Blur) transformation. The proposed R-Blur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and hope that our responses address their concerns
**[...]The paper fails to highlight the role of noise [...].**
We will highlight the role of adding noise in writing. Figure 1 in the global response shows that under moderate adversarial perturbat... | Summary: This paper proposes a data augmentation technique named R-Blur to improve the robustness of vision classifiers against adversarial perturbations and other non-adversarial image corruptions. The method is inspired by human visual systems where the perceived scene consists of varying levels of fidelity. As such,... | Rebuttal 1:
Rebuttal: We hope that in our responses below we will be able to fully address the reviewer's outstanding concerns and that the reviewer will consider increasing their score of our paper.
**verify that the adaptive filtering from R-Blur is indeed improving the robustness of the model beyond simple Gaussian... | Rebuttal 1:
Rebuttal: We are very thankful to the reviewers for taking the time to read our paper carefully and providing valuable feedback that will undoubtedly help strengthen the paper and increase its impact. We also thank the reviewers for asking thoughtful questions and raising important concerns.
We have respo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
What Do Deep Saliency Models Learn about Visual Attention? | Accept (poster) | Summary: This paper proposes a new framework, which can decompose the learned features of a saliency model into trainable bases (using [42]), those bases are combined to formulate the final saliency map, and the weight of the combination indicates the contribution of each basis. The semantic meaning of each basis can b... | Rebuttal 1:
Rebuttal: **1. Q**: What if other datasets are used, will the bases also match other concepts? Should one exhaustively match all concepts to find the best match?
**R**: We appreciate the thoughtful questions raised about the potential limitations of matching bases to a single dataset. We agree that the ma... | Summary: This paper examines the problem of predicting visual saliency in images. Unlike many other works, it focuses on determining what leads to the predictions made, including underlying features that are learned, and formulating the prediction as a combination of bases. Through this, one is able to garner an unders... | Rebuttal 1:
Rebuttal: **1. Q**: It would be nice to see some quantitative results on how well the model performs.
**R**: We thank the reviewer for the suggestion and include the results in the global comment, which shows that our model achieves state-of-the-art performance.
**2. Q**: Can you comment on how this forma... | Summary: This paper attempts to decompose the learned representation of a data-driven saliency model into a constituent set of bases that are mapped onto semantic concepts, thereby providing insight into what is driving the model's representation of saliency. This method is applied to three different saliency models of... | Rebuttal 1:
Rebuttal: **1. Q**: The relative importance of elements to saliency is contextual. These attributes change from model to model and dataset to dataset; what conclusions are to be drawn from this? Is the technique shedding light on dataset composition, model bias, or some deeper aspect of relative aspects of ... | Summary: The paper presents a novel analytic framework that provides a principled interpretation and quantification of the implicit features learned by deep saliency models, which are used for predicting human visual attention. The framework decomposes these features into interpretable bases aligned with semantic attri... | Rebuttal 1:
Rebuttal: **1. Q**: The work has a lot of novelty with good empirical analyses, but has missed comparing the performance of their model.
**R**: We acknowledge the importance of performance comparison with existing methods using standard metrics for measuring saliency, and have added comparisons in the gl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback. We are encouraged that they recognize our work as solving a fundamental, important, challenging, and open-ended problem, which approaches explainability and takes a step towards truly understanding the success of deep learning models (ZMfD, srb... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PromptCoT: Align Prompt Distribution via Adapted Chain of Thought | Reject | Summary: This paper introduces PromptCoT, an enhancer that automatically refines text prompts for diffusion-based generative models, improving their capability to produce high-quality visual content. The system is based on the idea that prompts that resemble high-quality image descriptions from the training set lead to... | Rebuttal 1:
Rebuttal: We sincerely thank you for participating in the review of our work and providing meticulous reviews.2
Regarding your questions, we strive to provide as comprehensive answers as possible.
## Q1: Replace finetuning by prompt lerning or LoRA.
Indeed, the fine-tuning process for full-parameter LLaMA... | Summary: In this manuscript, the authors proposed a simple yet effective framework to improve the generation quality of pretrained generative models. Generally, to align the prompt distribution with large language models, the authors present three individual solutions to align and enhance the original textual inputs, ... | Rebuttal 1:
Rebuttal: We sincerely thank you for participating in the review of our work and providing meticulous reviews. Regarding your questions, we strive to provide as comprehensive answers as possible.
## Q1: Intuitively, providing details improves the generation quality. Clarify the novelty.
Adding details to ... | Summary: This paper aims to improve the images generated by the off-the-shelf diffusion model, such as Stable Diffusion. This is done by fine-tuning a large language model (LLaMA) using text continuation on more high-quality prompts, which are collected by hand-crafted rules on, for example high CLIP similarity and tex... | Rebuttal 1:
Rebuttal: We sincerely thank you for participating in the review of our work and providing meticulous reviews. Regarding your questions, we strive to provide as comprehensive answers as possible.
## Q1: Technical contribution is unclear.
Expanding the text input does not guarantee a definite improvement i... | Summary: The main motivation of this paper is to better align prompts to the textual information of high-quality images within the training set. The authors propose datasets, instruction templates and use CoT to finetune LLM to achieve this goal. Adapters are also use adapters to facilitate dataset-specific adaptation.... | Rebuttal 1:
Rebuttal: We sincerely thank you for participating in the review of our work and providing meticulous reviews. Regarding your questions, we strive to provide as comprehensive answers as possible.
## Q1: Evaluate effectiveness of CoT aligner datasets besides LAION.
The most prominent open-source generative... | Rebuttal 1:
Rebuttal: # To ALL
We sincerely thank all reviewers for your comments. After reading all theomments carefully, we add more corresponding results and image examples to te rebuttal PDF file. We hope this rebuttal can address your concerns. If you have other concerns, we will reply as soon as possible.
Pdf: /... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Double Auctions with Two-sided Bandit Feedback | Accept (poster) | Summary: This paper studies double auctions, where a set of $N$ buyers interacts with a set of $M$ sellers to trade some goods. This is a fundamental problem in economics that has been studied extensively. The specific mechanism that is studied in this paper is the average mechanism; it sorts the bids of the sellers an... | Rebuttal 1:
Rebuttal: **Social welfare definition:** We thank the reviewer for this pointer and will add the distinction from the classical definition of social welfare and what we consider. Further while defining social welfare, we will write a sentence crediting [10] for establishing the equivalence of the classical ... | Summary: This paper studies double auction markets where both buyers and sellers receive bandit feedback. There are $N$ buyers and $M$ sellers trading a single type of item in the market during a time horizon $T$. They don't know their own valuations so they need to learn through repeated interactions. The auctioneer i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments, and highlighting insightful future directions. Please find our response below.
**General Applicability of Proof Techniques:** In bandit literature, handling stochastic reward and time-variant reward requires different algorithmic and technical i... | Summary: The paper studies an online learning problem related to double auctions. In particular, the paper assumes that the auctioneer uses a average price mechanism. The goal is to study the online learning problem faced by sellers and buyers that do not know they own valuations. The authors design a algorithm based ... | Rebuttal 1:
Rebuttal: **1. Regarding instance dependent versus instance independent bounds**
***All our upper bounds in Theorems 1 and 2 are instance dependent***, including the $\sqrt{T}$ for participating agents. Theorem 1 bounds the social-welfare regret as a function of $\Delta$ and is thus instance dependent. In ... | Summary: This work considers learning in a two-sided double auction setting in which both sides must confront uncertainty over their valuations (which are realized upon winning in the auction) and choose to adhere to the same protocol in order to perform that learning. The work shows that buyers and sellers bidding les... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Which many-to-many double auction bandit settings is the motivation?**
Online learning in economic markets is an active area of research evident from the abundance of research works in the past few years. This is also acknowledged by *Reviewer BEVv*. Ou... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer | Accept (poster) | Summary: This paper propose DOSTransformer, a transformer architecture for the task of DOS prediction, that takes energy levels as input instead of predicting a list of energies for different energy levels. The performances of this proposed DOSTransformer is better than MLP, GNN, and E3NN instances they implemented.
... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work and for recognizing that our work is the first work that integrates energy level as input! We are more than willing to address each of the specific weaknesses and questions in a detailed manner.
**W1 (Limited novelty & ablation studies).**
**[Rega... | Summary: This paper proposes a prompt-based Transformer network for predicting the density of states of crystalline materials. The prompts are used to represent and control the energy and the additional structure information of the materials. Experiments show the proposed method can perform very well on two datasets un... | Rebuttal 1:
Rebuttal: **W1.**
As the reviewer pointed out, using only 7 structural systems may make the dataset seem limited. However, the reason for having 7 structural systems is not a limitation of the dataset but rather a reflection of the knowledge used in crystallography when classifying the structures of materia... | Summary: The paper proposes a new transformer-based method DOSTransformer for predicting density of states of crystalline materials. Different from previous methods, the energy level is additionally modeled as an input modality, i.e. the model takes in material configuration and energy level as input to predict DOS(mat... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work and for acknowledging the novelty of our work in DOS prediction! We are more than willing to address each of the specific weaknesses and questions in a detailed manner.
**W1 (Simple prompts).**
As pointed out by the reviewer, our crystal system pr... | Summary: This work proposes a transformer architecture for predicting density of states of crystalline materials for different energy levels.
The distribution of states is spectral property that is approximated as a function of both the material and energy levels.
The architectures proposed consists of a multi-modal tr... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work and for acknowledging the efforts in experiments, including out-of-distribution scenarios! We are more than willing to address each of the specific weaknesses and questions in a detailed manner.
**W1, Q1 (Appropriate level of MSE).**
To the best o... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for your valuable comments on our work. We are more than willing to address each of the weaknesses and questions in detail. We also attach a PDF file that contains Figures and Tables for rebuttal.
Pdf: /pdf/d0b58d6c0e474caed75bff6d8bb744b86b572d54.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a transformer model to predict the density of states of given materials. The model takes in all energy levels that are desired for the Density of States calculation as a 'prompt' and information about the materials structure, the output is a prediction of the DOS at the given energy levels.
... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work. We are more than willing to address each of the specific weaknesses and questions in a detailed manner.
**W1 (Downstream tasks of DOS prediction).**
- How are the DOS outputs for predicting bandgap energy / electrical conductivity?
DOS serves as ... | null | null | null | null | null | null |
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization | Accept (poster) | Summary: This paper presents a method PEQA that combines quantization and parameter-efficient fine-tuning. It takes both advantages, including updating only a tiny fraction of model weights and saving the memory by quantization. The model weights will be decomposed into a matrix of low-bit integers and a scalar vector.... | Rebuttal 1:
Rebuttal: [**Weakness1**] As the reviewer pointed out, when comparing PEQA with LoRA in terms of the number of trainable parameters (e.g., LLaMA-13B+LoRA vs. LLaMA-13B+PEQA), the degradation of the performance of PEQA relative to LoRA seems to be non-negligible. However, when comparing PEQA with LoRA in ter... | Summary: This paper introduces a new framework, PEQA, for efficiently tuning Large Language Models (LLMs). PEQA uniquely fine-tunes the scale parameters during the instructing tuning phase while keeping the backbone parameters frozen. This approach allows PEQA models to maintain advantages during both the training and ... | Rebuttal 1:
Rebuttal: [**Weakness1**] We appreciate the reviewer's insightful suggestion. In response, we have updated Table 1 to include both PEFT+PTQ and PTQ+PEFT as per your recommendation. We understand the importance of showcasing that other parameter-efficient tuning methods can also apply post-training quantizat... | Summary: The paper presents a novel approach named Parameter-Efficient and Quantization-aware Adaptation (PEQA) to address the challenges of efficiently fine-tuning and deploying large language models (LLMs). The paper demonstrates PEQA's effectiveness and scalability through extensive experiments, comparing it with co... | Rebuttal 1:
Rebuttal: [**Weakness1**] As s0 and z0 are quantization parameters for pre-trained weights W0, our intention was to imply that both s0 and z0 are not related to any downstream task at all. The term “integer quantization index” is also used in [1], which means the rounding of W0/s0, so we refer to $\overlin... | Summary: This paper introduces a model compression method called Parameter-Efficient Quantization-Aware Adaptation (PEQA) to tackle the size challenge of Large Language Models (LLMs) while enhancing their task-specific fine-tuning. The PEQA technique quantizes the fully-connected layers into quantization scales and int... | Rebuttal 1:
Rebuttal: [**Weakness1**] When diving deeper into the quantization scale specifics which are learnable parameters of both methods, it's worth noting that PEQA's adherence to uniform quantization means there's only one shared quantization scale for integer weight. Conversely, AlphaTuning's non-uniform approa... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We wish to express our sincere gratitude for your diligent review and insightful feedback on our manuscript.
Your comments have greatly enriched our understanding and enabled us to identify areas where further clarification and improvement were needed.
We have uploaded this su... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Combinatorial Algorithm for Approximating the Optimal Transport in the Parallel and MPC Settings | Accept (poster) | Summary: This paper gives the first parallel, combinatorial algorithm to approximate the optimal transport distance. Moreover, the algorithm nearly matches the run-time of the best known parallel algorithms for the problem, with expected parallel runtime of O(log(n)/eps^2) (for eps*n the additive error of the OT cost).... | Rebuttal 1:
Rebuttal:
*Is the Sinkhorn runtime tight around $O(\log n/\varepsilon^{O(1)})$? ... Also can you explicitly state whatever constant the $O(1)$ term is hiding at least once?* Over time, the analysis of the Sinkhorn algorithm has seen multiple improved bounds to its $\varepsilon$ dependency. To our knowledge... | Summary: This paper presents a novel combinatorial algorithm that finds an $\epsilon$-optimal transport plan for the optimal transport problem. Additionally, it introduces a variant of this algorithm in the MPC model, exhibiting an expected $O(\log\log n)$ communication rounds, with each machine having a memory of $O(n... | Rebuttal 1:
Rebuttal:
*Using similar combinatoric techniques, is it possible to obtain an algorithm that has better performance than previous works in parallel time?*
We believe the improving our parallel OT epsilon dependency from $O(1/\varepsilon^2)$ to $O(1/\varepsilon)$ is plausible, but difficult – we have spent... | Summary: The paper presents a parallel combinatorial algorithm for the optimal transport (OT) and minimum weight perfect matching. The algorithm computes an additive epsilon approximation.
The algorithm runs in O(n^2 / epsilon^O(1)) time and (in a straightforward way) gives an algorithm running in O(log log n / epsilo... | Rebuttal 1:
Rebuttal:
*The paper presents a parallel combinatorial algorithm for the optimal transport (OT) and maximum weight matching.*
We believe that the reviewer has misunderstood the problem being solved in this paper.
We present an algorithm for the *minimum* weight matching (i.e., the assignment problem) whic... | Summary: The paper considers the Optimal Transport problem, which is a metric for the distance between distributions, used by some ML/DL methods. It presents a combinatorial algorithm for that problem, which can be parallelized and implemented efficiently both in map-reduce systems (MPC) and on a GPU. It also addresses... | Rebuttal 1:
Rebuttal:
*The code used for the experiments was not provided...* Actually, our code was provided as part of the supplement. You can find it in the provided zip file, alongside the appendix PDF. The code also includes a detailed README that explains how to run the experiments, specifically meant to facilit... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their helpful suggestions. The reviewers pointed out some minor grammatical and presentation changes; we appreciate these suggestions and will address them in the final paper version. For questions and comments that warrant a more significant discussion... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neuro-symbolic Learning Yielding Logical Constraints | Accept (poster) | Summary: This paper takes inspiration from convex and bilevel optimization to derive a learning algorithm that simultaneously learns neural network perception and rules on the perceptions. The authors derive an efficient training algorithm involving multiple minimization steps.
Post rebuttal: I thank the authors for a... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer MqQr**
Thanks for the comments.
**The role of SMT solvers in our framework:** Yes, SMT solvers are only used during inference.
**Replace $\ell_1$ loss by cross entropy:** Yes, we can directly change equation (6) to the gradient descent of cross entropy loss, and it ... | Summary: This work provides a neural framework that combines network training, symbol grounding, and logical constraint synthesis.
It utilizes the cardinality constraints to express the logical constraint learning and a DC penalty for constraint relaxation.
The evaluation demonstrates that this method outperforms stat... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer MGh8**
Thanks for the comments. Following are our responeses.
**Limitation of cardinality constraint:** Essentially, cardinality constraints can represent any propositional logic formula, i.e., they have the same expressiveness with CNF (Conjunctive Normal Form) or DNF... | Summary: The authors propose a fusion of neural network and symbolic domain via logical constraints to learn specific vision tasks in a weakly supervised way. They break it down to two optimization problems for neural and symbolic domains. The logical constraints are solved using a deterministic solver and grounded so... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer w2hx**
Thanks for the comments.
**The motivation of tasks:** The visual SudoKu solving task is a standard and commonly-evaluated task from existing neuro-symbolic learning methods. The self-driving planning task is proposed by ourselves. Specifically, we aim to intro... | Summary: The paper proposes a new neurosymbolic approach for learning symbolic representations and logical constraints on top of these simultaneously. The authors propose a new penalty term based on Difference of Convex (DC) programming in order to relax the optimzation problem. The performance of the proposed approach... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer X5tc**
Thanks for the comments.
**The challenge of two tasks:** The visual SudoKu solving task is a standard and commonly-evaluated task in existing neuro-symbolic learning methods. Particularly, only 17 out of 81 cells are initially filled in some of the SudoKu board... | Rebuttal 1:
Rebuttal: ### **General response to reviewers**
We would like to thank all the reviewers for their kind and helpful feedback.
We start by clarifying that we have indeed discussed the limitations of our approach which is included in Appendix A. We will summarize and include them in the main paper.
We th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Estimating Propensity for Causality-based Recommendation without Exposure Data | Accept (poster) | Summary: This paper presents a review of a novel method proposed for estimating causal effects in situations where no observation of the treatment variable is available. The authors introduce an innovative approach that utilizes interactions to approximate missing exposure or propensity data. The method relies on sever... | Rebuttal 1:
Rebuttal: Thanks for acknowledging our novelty and literature review. we will give answers to each question asked by this reviewer below.
**Q1:** PropCare is a solid method when all the assumptions are met: The method assumes popularity as a strong confounder of treatment and of Y. I wonder how general the... | Summary: This paper proposes a propensity estimation model for causality-based recommendation without accessing the ground-truth propensity score or exposure data. Prior knowledge about item popularity is utilized to estimate the propensity score. A theoretical analysis is provided to understand the proposed model.
St... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments from this reviewer. we will give answers to each question asked by this reviewer below.
**Q1:** Suggestion to compare with SOTA causal recommendation approaches.
**A1:** Thanks for the suggestion. However, this review contains **factual errors** which we'd like ... | Summary: This paper proposes a framework for causality-based recommendation system. Different from traditional correlation-based recsys (e.g. collaborative filtering), causality-based recsys makes recommendations based on the causal "uplift". While there are several causal recsys models in the literature, they rely on ... | Rebuttal 1:
Rebuttal: Thanks a lot for acknowledging the contribution of our work and paper presentation. we will give answers to each question asked by this reviewer below.
**Q1:** Below the theoretical property in 4.5, the authors mentioned that the proposition guides certain design choices, such as regularization o... | Summary: The authors propose a propensity estimation/learning method based for unbiased recommendations. The method assumes no external data and only uses the user interaction data for learning. The main idea is well explained in Assumption 1 in the paper, which states that for two items with similar click/interaction ... | Rebuttal 1:
Rebuttal: Thanks for acknowledging the strengths of our paper. We will give answers to each question asked by this reviewer below.
**Q1:** What is the effect of the regularization term in propensity training? An ablation experiment could help, additionally, an experiment with varying $\mu$ values could als... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all the reviewers for their valuable feedback and insightful comments on our paper. We are humbled by the positive reception and are encouraged by the recognition of the efforts we put into this research. We acknowledge the time and expertise each reviewer has i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on causality-based recommendation system, by proposing a PROPCARE method that estimates the propensity score by using its correlation with popularity. The motivation is well stated and related work is well discussed. Through experiments, the proposed method outperforms the baselines.
Streng... | Rebuttal 1:
Rebuttal: Thanks for acknowledging our motivation, writing and experiment. we will give answers to each question asked by this reviewer below.
**Q1:** Distinguish popularity and propensity as correlation or causality.
**A1:** Thanks for the question. To model propensity (the probability of exposure), we f... | null | null | null | null | null | null |
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models | Accept (poster) | Summary: The paper studies the minimizers of the loss surface of deep matrix factorization that have a minimal trace of the Hessian. The trace of the Hessian is a measure of flatness of the minimum, that is favored by SGD.
The authors show that for matrix sensing with observations that satisfy RIP (which is in particu... | Rebuttal 1:
Rebuttal: **implicit bias**: We would like to point out that in the paper [28] authors mathematically show that in the limit of step size going to zero, label noise SGD evolves according to a gradient flow according to the trace of hessian of the loss. The same fact can be seen for 1-SAM, but the reviewer ... | Summary: In the context of matrix sensing with deep matrix factorizations, the paper analyzes the inductive bias of interpolators with minimal Hessian trace, which is a well-known measure of sharpness. Specifically, under a Restricted Isometry Property (RIP) assumption on the linear measurements, it establishes that th... | Rebuttal 1:
Rebuttal: 1. **If possible, it is worth (even if in an appendix) extending the generalization results to interpolators whose Hessian trace is approximately minimal.**
**Reply**: We will include this generalization in the appendix in the final version of the work.
2. **Since using explicit regularizatio... | Summary: The manuscript seeks to understand Hessian trace regularization in the case of deep linear network training with the mean squared error of linear measurements. It obtains a description of the effective regulariser which can be approximated and made more explicit in some cases. The manuscript obtains results on... | Rebuttal 1:
Rebuttal: 1. **The presentation of the results does not make the assumptions sufficiently clear in a timely manner.**
**Reply**: We would like to point out that the RIP and width assumptions are mentioned in pages 2 and 3, but we are happy to follow the reviewers’ suggestions on stating them earlier.
... | Summary: This work considers the implicit regularization of deep neural networks with linear activations and linear data. While previous works have shown that stochasticity in optimizers has the effect of smoothing the loss function, this work derives how this less sharp loss function can result in better generalizatio... | Rebuttal 1:
Rebuttal: **"While this conference is a suitable venue for a theory-style paper such as this, I would argue that the choice of whether to accept such a paper should be heavily weighted by its potential practical impact (otherwise we can always invent interesting, but purely fictitious problems)"**
**Reply*... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their effort to provide helpful comments regarding our work. In the following, we have replied to their comments separately.
Pdf: /pdf/0182e22dd091fd4f28069b199850bc1e9b2f4583.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mode Connectivity in Auction Design | Accept (poster) | Summary: The paper seeks to provide a theoretical foundation for empirical results demonstrating that optimal auctions (both known and novel) can be discovered by differentiable auction theory. It proves that two such auction formats satisfy a condition called ‘mode connectivity’: epsilon-mode connectivity is satisfie... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading and assessing our paper and for the valuable feedback. We provide a detailed response to the raised issues / questions below:
> p.1: DSIC printed as DISC
A: Will be fixed, thanks!
> l.79: “straight-jacket” or “strait-jacket”?
A: This should be “strai... | Summary: This paper studies the mode connectivity for specific neural network architecture, i.e., RochetNet and Affine Maximizer Auctions(AMA), where the local optimal solutions produce close local optimal solutions. To be specific,
- for linear utilities, $\epsilon$-reducible solutions implies $\epsilon$- mode connec... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading and assessing our paper and for the valuable feedback.
We regret that the reviewer perceives the presentation of our paper as poor. We are thankful for the constructive feedback and will do our best to address the issues raised by the reviewer in the fi... | Summary: The starting point for the paper is recent work in the area of “differentiable economics”, in which high-revenue strategyproof auction mechanisms are found by optimizing parameters using machine-learning-inspired gradient descent techniques.
The authors consider the problem of selling multiple goods to a sing... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading and assessing our paper and for the valuable feedback.
> I recommend further discussion of Shen et al. “Automated mechanism design via neural networks”, which presents an architecture that for certain cases is equivalent to RochetNet. It’s cited at one ... | Summary: This paper studies the mode connectivity in neural networks for design auction mechanisms. Specifically, the authors prove that locally optimal solutions are connected by a simple, piecewise linear path such that every solution on the path is almost as good as one of the two local optima.
Strengths: The paper... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading and assessing our paper and for the valuable feedback. As we state in the paper already, we do not believe that mode connectivity in auction settings follows as a special case from general mode connectivity results. We will make this clearer in the final... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on justifying the empirical success of this differentiable economics, particularly in the context of menu-based methods like RochetNet and Differentiable AMA auctions.
The authors introduce the concept of the $\epsilon$-mode connectivity property, which establishes that two locally optimal ... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading and assessing our paper and for the valuable feedback. In the following we respond to the three questions/weaknesses raised by the reviewer.
>This paper would benefit a lot from a discussion on how exactly mode connectivity can be used to justify empiri... | null | null | null | null | null | null |
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models | Accept (poster) | Summary: The paper observes that the existing metrics for evaluating fidelity and diversity of generative models can be unreliable when outliers or non-IID perturbations are present. Thus, the authors propose a new metric based on Kernel Density Estimation under topological conditions that is more robust in the earlier... | Rebuttal 1:
Rebuttal: ## Reviewer 5
**R5-1. Dataset used for Table 1:** (G1-2) CIFAR-10. We apologize for not mentioning the datasets. We revised the caption of Table 1 in the revised paper.
**Reason why TopP&R shows similar performance to P&R in Table 1:** (G1-3, G1-4). TopP&R shows better performance than the ot... | Summary: The paper presents a novel evaluation metric called Topological Precision and Recall (TopP&R) for generative models. Existing metrics often suffer from unreliable support estimation and yield inconsistent results. In contrast, TopP&R systematically estimates supports by retaining topologically and statisticall... | Rebuttal 1:
Rebuttal: ## Reviewer 4
**R4-W1, W2. Explanation of line 107 and 139 (Algorithm 1):** Added. We apologize for missing the appropriate reference link to “Appendix H.2” for the algorithm. Unfortunately, this happened while we were breaking the manuscript into two (main and supplementary) documents for the s... | Summary: The paper introduces TopP&R, a comprehensive evaluation metric for generative models that enhances the accuracy of sample quality assessment in comparison to existing metrics. TopP&R incorporates topological data analysis and statistical inference, effectively estimating supports through the application of Ker... | Rebuttal 1:
Rebuttal: ## Reviewer 3
**R3-1. Trade-off between precision and recall:** To quickly check whether TopP&R exhibits trade-off between fidelity and diversity as in improved P&R, we used $\mathcal{X}\sim{\mathcal{N}(0,1)}$ and $\mathcal{Y}\sim{\mathcal{N}(0.6, \sigma^2)}$ with a sample size of 10k and 32 dim... | Summary: The paper discusses robust estimation of precision and recall in high dimensional and potentially noisy data. The approach relies on using kernel density estimation to approximate the underlying distributions and using a bootstrap estimation of confidence interval to ignore support with low probability mass. T... | Rebuttal 1:
Rebuttal: ## Reviewer 2
**R2-1. Shaded area in Figure 1b:** For the sake of clarity in our explanation, it would be beneficial to provide a concise description of our Persistence Diagram (PD). At first glance, our PD might appear to be a swapping of the x-label and y-label, a confusion commonly associated... | Rebuttal 1:
Rebuttal: We deeply appreciate your feedback. Your insights greatly helped us to refine our presentation and better illuminate the key advantages of our metric. Your input has also greatly enhanced our discussion about the metric's limitations, which not only clarifies our current work but also highlights p... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the need for more reliable evaluation metrics for deep generative models. The existence of noise in real data, such as mislabeled data and adversarial examples, affects the reliability of existing metrics and may lead to false impressions of improvement when developing generative models. To... | Rebuttal 1:
Rebuttal: ## Reviewer 1
**R1-1. Adaptive kernels.** (G4)
**R1-2, W2.**
**The dataset utilized in Table 1:** (G1-2) CIFAR-10. In the revised version, we have mentioned this in the caption.
**Effectiveness on a large-scale dataset:** (G1) Yes, it scales well. For all the experiments, we used ... | null | null | null | null | null | null |
Effective Targeted Attacks for Adversarial Self-Supervised Learning | Accept (poster) | Summary: This paper highlights that untargeted attacks for adversarial self-supervised learning result in poor downstream robustness. Instead, they suggest utilizing targeted attacks and propose a scoring method for selecting the target samples. They show that this approach results in significant robustness improvement... | Rebuttal 1:
Rebuttal: **Weakness 1:** Individual contributions of two components (i. changing it to a targeted attack and ii. selecting the target using a score function.) are not studied. Lower improvements in Table 4 compared to Table 3 suggest that [i] might be driving the improvement, rather than [ii].
**Response:... | Summary: This paper proposes a new adversarial attack on self-supervised learning methods which is called TARO. The method performs an attack "on the positive-pair that perturb the given instance toward the most confusing yet similar latent space, based on entropy and similarity of the latent vectors." Authors show tha... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our work, that our work **highlights the improvement of robustness** in the positive-pair-only self-supervised learning approach, **acknowledges the well-written and comprehensive** paper, and **has merits**.
-----
In the following response, we have done ou... | Summary: The paper proposed the targeted attack for adversarial self-supervised learning to solve the suboptimal learning issue in positive pair-only self-supervised learning. To improve the robustness of adversarial self-supervised learning (SSL), the author leverages the targeted selection mechanism based on the scor... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our work, which highlight good motivation clear idea with well-organized writing.
---
In the following response, we have done our best to resolve all the concerns that you raised regarding our work. Please find our detailed response below, and if there are... | Summary: This paper investigates the problem of unsupervised adversarial training. It claims that previous non-contrastive, positive-only SSL frameworks suffer from ineffective learning with untargeted adversarial samples. Based on this, this paper proposes a new TARO paradigm, which conducts targeted attacks to select... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our contributions, which highlight clear motivation and address the limitations of existing positive-pair-only SSL frameworks in robustness.
----
In the following response, we have done our best to resolve all the concerns that you raised regarding our wo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank you for the time and effort you've invested in reviewing our paper, and for the constructive feedback you have provided. During the initial response period, we did our best to address all the concerns you raised and to improve our paper according to your ins... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Foundation Model is Efficient Multimodal Multitask Model Selector | Accept (poster) | Summary: This paper proposed an efficient multi-task model selector (EMMS) to address the inapplicability in a multi-modal multi-task scenario. Specifically, the proposed method achieves a new state-of-the-art in performance and speedup through the incorporation of design elements such as the F-label, Weighted Linear S... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the useful and helpful comments on our manuscript. We address the reviewer's concern below.
**Q1:** "It is recommended to include a complete demo in the code."
**A1:** Thanks for your suggestion. We have open-sourced complete code at https://github.com/an... | Summary: This paper focus on an under-explored problem of estimating neural network transferring capability without actually fine-tuning the multi-modal multi-task model on individual downstream tasks. The problem is well-motivated and is of great practical importance. The solution proposed in this paper is straightfor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and valuable suggestions. We address the reviewer's concern as follows.
**Q1:** "The Figure 1 bottom is not very informative ..."
**A1:** Good advice. We have redrawn a table of checkboxes to illustrate the ability of EMMS. Please see Fig.A and... | Summary:
This paper proposes to utilize large-scale foundation models for efficient multi-task model selector (EMMS). Concretely, the authors utilize foundation model to transform different label format (category label, text, bounding boxes) into unified noisy label embeddings. EMMS then could measure the compatibili... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and helpful suggestions. We have provided a detailed general response to the concerns of all the reviewers. We address the reviewer's concern as follows.
**Q1:** "As the authors mentioned in the limitation section, the proposed method is bottlen... | Summary: This paper introduces EMMS, an efficient multi-task model selector for predicting the performance of pre-trained neural networks on multi-modal tasks without fine-tuning. EMMS employs large-scale foundation models to transform diverse label formats into a unified noisy label embedding. Through weighted linear ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions. We list responses to the reviewer's concerns below.
**Q1:** "The proposed method is difficult to follow..."
**A1:** We are sorry for the vague presentation of our method. In principle, our EMMS is well established by maximizing the log-likeliho... | Rebuttal 1:
Rebuttal: # General Response
We thank the reviewers for their detailed reviews and thoughtful suggestions on our work.
In general, two main concerns are raised, including (1) the effect of using a single foundation model and (2) the computational complexity of EMMS. To address the reviewers’ concerns, we ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces the Efficient Multi-task Model Selector, which utilizes foundation models to convert diverse labels into unified label embeddings. These embeddings are then used to calculate a transferability metric within a weighted linear square regression (WLSR) framework. The proposed method achieves... | Rebuttal 1:
Rebuttal: We thank the reviewer for the recognition of our work. We are happy to run more experiments if the reviewer has any pieces of interest.
---
Rebuttal Comment 1.1:
Comment: Dear authors,
Thanks for your response. I have thoroughly reviewed both your rebuttal and the feedback provided by the other... | null | null | null | null | null | null |
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head | Accept (poster) | Summary: This paper proposes a novel algorithm for learning invariant representations using a fixed head that is the sum of similarities of the query features with a set of support features, weighted by one-hot-encoded class label (in other words, the head predicts the class whose support features most align with the q... | Rebuttal 1:
Rebuttal: > Practically speaking, the proposed method is computationally expensive because… [...] It would be helpful to compare the computational costs of the proposed approach with [50], or run further experiments demonstrating a more clear advantage.
We agree with the reviewer that computational cost is... | Summary: The authors address an important problem of reliability in deep learning, given data collected from different sources (environments).
For this, the authors propose a method that allows the separation of style and content of input objects, which ensures stable behavior in different environments. The authors dev... | Rebuttal 1:
Rebuttal: > The overall novelty and the extent of the contribution made by this paper are unclear…
We have enumerated our specific contributions in the general response. To reiterate here, to the best of our knowledge, we believe that the manipulation of the support set for learning invariant features is a... | Summary: The authors apply a recently proposed method for similarity-based prediction to the problem of invariant learning. This paper builds off of the Nadaraya-Watson architecture where predictions for a test input are derived via nearest proximity according to a learned kernel. The proposed method uses a NW head for... | Rebuttal 1:
Rebuttal: > [...]. A discussion on how the proposed method differs from these papers would be useful.
We agree with the reviewer and reiterate that our causal setup and DAG in Fig. 2b is not novel, and has been proposed in many works within this literature (see also [1-2]). As mentioned in the general resp... | Summary: This works proposes a causally motivated new setting for domain generalization building on the existing nonparametric Nadaraya-Watson head on top of a learned neural encoder. More precisely, it assumes that data inputs are causally generated from style latent features, which are environment-dependent, and cont... | Rebuttal 1:
Rebuttal: > The only remark I would have is that the difference between NWb and NWBe-implicit could be better explained and highlighted in the paper.
We plan to add additional clarifying statements in our revised manuscript. To be clear, the difference between them is that in $NW^B_e$-implicit, we not only... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and constructive feedback. Here, we provide a general response to all reviewers, and provide a point-by-point response to each reviewer’s comment/questions below their corresponding review.
### Contributions
As raised by reviewers 48ug and xs7Q, we enu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Probabilistic Exponential Integrators | Accept (poster) | Summary: This paper proposes a probabilistic integrator for solving stiff semi-linear ODEs that decouples linear and nonlinear components for improved accuracy. The approach uses an integrated Ornstein-Uhlenbeck process (IOUP) prior and posterior inference extends classical exponential integrator to exactly solve the ... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their insightful comments, feedback, and
questions. In the following we will address each point separately.
> The paper fails to highlight any benefit of treating the initial value problem
(IVP) as a Bayesian inference task. The posterior distribution shou... | Summary: This paper presents probabilistic exponential integrators as a solver for stiff semi-linear ODEs.Their solver can also be extended to solve general non-linear ODEs with iterative re-linearization. The proposed method is shown to be L-stable in theory and empirically more stable than existing methods.
Strength... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their insightful comments and feedback.
> the experiments mainly focus on final errors across various step
sizes. A detailed investigation of stability (boundedness of solutions?)
behaviors of solvers under large step size may help readers better understand... | Summary: This paper proposed probabilistic exponential integrators, which is a new class of probabilistic solvers for stiff semi-linear ODEs. More specifically, the integrated OU process is introduced for a functional prior that directly incorporate the linear part of the dynamics. As a result, the proposed methods can... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their comments, feedback, and questions.
In the following we will address each open point separately.
> Weakness 1. The proposed methods are only for semi-linear ODEs, while other baselines can be applied to more general cases.
The main "probabilistic expo... | Summary: The paper studies classical exponential integrators using the framework of probabilistic numerics, where Bayesian formalism is used to study numerical approximations of deterministic dynamical systems. The main result seems to be Proposition 1, which establishes exponential stability (called L-Stability for so... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their comments, feedback, and questions.
In the following we will address each open point separately.
> Calling the Ornstein-Uhlenbeck process a Gauss-Markov in eqn 3 is overkill.
Probabilistic numerical ODE solvers have in general been established with an... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm | Accept (poster) | Summary: Past works have limitations in terms of scalability, formulation approximation, or performance. This paper offers an efficient coreset selection problem with provable theoretical guarantees. That is, the authors solve a bilevel optimization on a probability distribution over the dataset with loss minimization ... | Rebuttal 1:
Rebuttal: Dear Reviewer b44u,
Thanks for your reviews. We have addressed your concerns below.
**Q1: Further analysis into the top-K loss and its causal effects would aid in understanding the mechanics of probability distribution being regularized into a low-dimensional manifold.**
**A1**: We guess you me... | Summary: The authors present a new approach to coreset selection in rehearsal-based continual learning. The authors claim that traditional methods optimise over discrete decision variables, resulting in computationally expensive processes. To address this, they propose a new bilevel formulation where the inner problem ... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We have addressed your concerns below.
**Q1: Could you please clarify the differences and advantages between your method and [2]? In lines 135-139, the author simply states that "so this formulation [2] oversimplifies the coreset selection problem." However, this statemen... | Summary: This work addresses the coreset selection problem in rehearsal-based continual learning, focusing specifically on the application of bilevel optimization. The authors identify limitations in existing bilevel optimization-based coreset selection methods for continual learning, including high computational costs... | Rebuttal 1:
Rebuttal: Dear Reviewer AXy9,
Thanks for your reviews. We have addressed your concerns below.
**Q1: Is it possible to improve the efficiency of the proposed method by utilizing Hessian-free bilevel algorithms based on recent advancements?**
**A1**: Yes, two types of Hessian-free methods may be considere... | Summary: The paper presents a better approach for grasping the coreset-based bi-level optimization procedure used in the field of continual learning. The proposed method relies on the use of the proxy model to obtain a coreset, which in turn will affect the training of the original model. With that being said, the ide... | Rebuttal 1:
Rebuttal: Dear Reviewer FPqc:
Thanks for your reviews. We have addressed your concerns below.
**Q1: The proxy model $M_{cs}$ might be a limitation in the presence of large models, i.e., models containing >100M parameters. Is it possible to circumvent the need for a proxy model in the case of working with... | Rebuttal 1:
Rebuttal: **General Response:**
We would like to thank all reviewers for their constructive comments. We have answered the corresponding questions from reviewers and provided new experiment results requested by reviewers. The main summary of our responses includes:
1. Per Reviewer FPqc suggestion:
- We h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Online Clustering with Moving Costs | Accept (spotlight) | Summary: The paper considers the $k$-median clustering problem in the online learning framework. More specifically, at the beginning of each round, the algorithm needs to maintain a set of $k$ centers (or facilities). After this, the actual set of clients is revealed. The algorithm incurs a cost equal to the total assi... | Rebuttal 1:
Rebuttal: We thank the reviewer of their work and the insightful feedback. In the revised version of our work we will incorporate the following discussion addressing your comments.
*Weaknesses*
1. *As mentioned earlier, most of the ideas follow along standard lines.*\
The cornerstone of our approach was t... | Summary: This paper studies online clustering with moving costs problem. In the problem, the client sets change over time. In each round t, the algorithm has to place k facilities F_t before seeing the set R_t of clients. Two costs will be involved in each round t: the connection costs and the moving costs. The connec... | Rebuttal 1:
Rebuttal: We thank the reviewer of its work and the insightful feedback. In the revised version of our work we will incorporate the following discussion addressing your comments.
*The parameter beta is too big:* (Similar to Reviewer wiCY) The additive regret term $\beta = O(k * n^{3/2} * D * \gamma * \sq... | Summary: This paper considers a regret framework for the following online learning problem. As input we are given a weighted graph and a number k of facilities. At each time step, we must select k vertices of the graph to serve as facilities. Afterwards, we learn the clients (also vertices of the graph) that the facili... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer of all its work and the insightful feedback on our paper. In the revised version of our paper we will incorporate the following discussion, addressing the reviewer's comments.
1. *Without lower bounds...the regret factor:* Our work indeed does not provide lower... | Summary: The authors study the k-median problem in an online learning setting with incorporated moving costs. In this setting the instance is revealed in batches, and before each batch the algorithm is required to place k centers so that to minimize the assignment costs of the (unseen) points that are then revealed in ... | Rebuttal 1:
Rebuttal: Thank you very much for your work and valuable comments. We commit on addressing them in the revised version of our work.
1. *The additive...model:* As mentioned correctly by the reviewer, the additive term typically vanishes for large values of $T$, and this is indeed a standard convention that ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for all their work and insightful comments.
In the attached pdf we present a set of supplementary experiments, addressing some of the reviewers' comments.
Pdf: /pdf/7a07db3c29092ae2324732852a9f2e77af7f1205.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing | Accept (poster) | Summary: The authors focus on the problem to find approximate second-order stationary points (SOSPs) in the strong contamination model, where they propose an efficient algorithm with an approximate SOSP as an output. The algorithm is proved to have dimension-independent accuracy guarantees. In particular, the proposed ... | Rebuttal 1:
Rebuttal: About Weakness 1: We addressed simulations and experiments in the global response. To the best of our knowledge, the only first-order method that can robustly find approximate second-order stationary points is [Yin+19]; note that projected gradient descent can only find approximate first-order sta... | Summary: This paper considers the problem of finding a second-order stationary point in corrupted settings. In particular, it considers the adversarial settings, where a fraction of the observations are arbitrarily corrupted after they are observed. In this setting, under certain assumptions on the cost functions, the ... | Rebuttal 1:
Rebuttal: For Weaknesses, we responded to the restrictiveness of the bounded region assumption in the global response. We provided details of our novel theoretical techniques in Section 1.2, starting Line 152. Our main conceptual contribution is to propose a unified framework for designing provably robust l... | Summary: In this work, the authors proposed a new algorithm to find approximate second-order stationary points for stochastic optimization problems under the strong contamination model. The general algorithm is applied to the robust matrix sensing problem and the convergence results are proved for the robust matrix sen... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the typos in Questions (1)(6)(12)(14) and suggestions for our presentation Questions (3)(13). Question (15) was addressed in the global response. As for the weakness about our presentation, we provided a sketch of the proof in the main body and deferred algor... | Summary: This paper studies the problem of finding approximate second-order stationary point when a constant fraction of datapoints are corrupted by outliers. It proposes an algorithm with provable guarantees which matches the statistical query lower bound established in the paper. The general result is applied to stud... | Rebuttal 1:
Rebuttal: Weaknesses 1 & 2 and Question 2 were addressed in the global response.
For Question 1, we intended to present our robust low-rank symmetric matrix sensing algorithm as a simple application of our general robust nonconvex optimization framework. The techniques are generalizable to asymmetric groun... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful consideration of our work and the positive feedback. Below we address some common concerns raised by the reviewers. We hope that the provided clarifications will help clear possible misunderstandings and elevate the reviewer’s assessment of our contribution... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots | Accept (poster) | Summary: This paper proposes a new active defense framework for PLMs against backdoors attacks. The proposed defense inserts a honeypot module into the original PLM such that the backdoor information is absorbed only by the honeypot module and does not impact the main task module. The authors leverage the observation t... | Rebuttal 1:
Rebuttal: **Q1**: Section 5.2 mentions that the proposed defense has better performance on large models, according to Table 1. It would be great to provide an analysis/hypothesis about this observation. What is the possible reason that the proposed defense has better performance on large models (Table 1)?
... | Summary: The paper presents a new defense against backdoor attacks on pre-trained language models (PLMs). By leveraging the observation that the loss of poisoned samples drops faster in early layers of PLMs compared to clean samples, it dynamically reduces the weight of suspicious samples in fine-tuning. Empirical resu... | Rebuttal 1:
Rebuttal: **Q1**: The threat model needs better motivation. It assumes a clean PLM, which is fine-tuned using potentially poisoned data. Typically, the PLM is provided by external parties (e.g., downloaded from the Web) while the fine-tuning dataset is managed by the user. It seems a more practical setting ... | Summary: This paper first makes an observation that in a backdoor poisoning attack against a pretrained language model, the lower layers learn the backdoor feature quickly and easily. Based on this observation, the authors then design a honeypot-based defense that catches the training samples that could be learned with... | Rebuttal 1:
Rebuttal: **Q1**: Anti-Backdoor Learning (ABL) by Li et al. proposed a similar defense based on isolating easy-to-learn samples. What is the difference between ABL and your proposed work?
**R1**: Thank you for highlighting the ABL method. In general, ABL employs a two-stage gradient ascent mechanism in st... | Summary: This paper proposes a method to defend against NLP backdoors during training.
The proposed method works by using an honeypot module to absorb backdoor
information, and prevent the backdoor behaviors to be learned by the stem
network. Experiments on SST-2, IMDB, and OLID demonstrate the effectiveness of
the pro... | Rebuttal 1:
Rebuttal: **Q1**: The proposed method is based on the observation that learning the backdoor task is generally easier than learning the main task. However, in the case of the all-to-all attack, where samples with different original labels have different target labels, the backdoor task becomes even more com... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
We sincerely thank the reviewers for dedicating their time and providing invaluable feedback. We present a general reply below in response to the concerns raised regarding baseline comparisons.
**Q1**: Comparison with more baselines.
**R1**: Thank reviewers f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Accelerated Model Training via Bayesian Data Selection | Accept (poster) | Summary: This paper followed up reducible hold-out loss selection (RHO-LOSS) method for data selection and improved it by proposing a reasonable approximation for the non-trivial objective function and eliminating the need for extra hold-out data.
Strengths: 1. Authors present both theoretical results (the lower bound... | Rebuttal 1:
Rebuttal: We thank you for providing valuable comments. Below, we address each concern in detail, and we sincerely hope that our response proves satisfactory and leads to a higher score.
**Q1: More intuitive comparisons between the proposed method and RHO-LOSS**
**A1:** We first clarify that theoretically... | Summary: The paper builds on recently proposed work that accelerates training through online batch selection using generalisation loss as selection criterion, by using LaPlace approximation for a stronger Bayesian approximation and using off-the-shelf pre-trained models. The paper presents theory deriving their selecti... | Rebuttal 1:
Rebuttal: We appreciate the acknowledgment of the novelty of our method in comparison to RHO-Loss, as well as its good performance. Below, we provide detailed responses to the specific comments, hoping that you find them satisfactory and raise your score accordingly.
**Q1: RHO-Loss does not require access ... | Summary: This paper studies data selection methods because training examples may be of different importance/quality. By selecting a subset of high-quality/high-usefulness examples, the model’s performance can be improved when training on this subset. In Particular, the paper proposed a method by leveraging a light-weig... | Rebuttal 1:
Rebuttal: We thank you for your supportive reviews and for finding our work clearly motivated and well-explained. We address the detailed concerns below.
**Q1: Some discussion and comparison with recent works**
**A1:** Thanks for the constructive suggestion. The problem of data selection is indeed import... | Summary: This works is situated in the field of **robust generalization**; in particular it studies the problem of how models that were trained on noisy or imbalanced data perform on clean data. They achieve this via **online batch selection**, and in particular they contribute to the development of a **Bayesian framew... | Rebuttal 1:
Rebuttal: We appreciate your positive review and recognition of the presentation, novelty, and effectiveness of this work. Below, we provide answers to the specific questions raised.
**Q1: To what extent does the effectiveness of this work stem from the effectiveness of the CLIP-based zero-shot predictor t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning | Accept (poster) | Summary: This paper is about the exploration of using pseudolabels for VLMs on different downstream tasks. Specifically, the authors experimented CoOp, VPT and UPT with pseudolabels generated from CLIP on semi-supervised learning, transductive zero-shot learning and unsupervised learning. They proposed three training s... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below we address all your questions.
> Novelty is limited. The use of CLIP's pseudolabels are not new and the proposed training strategies are also widely used in self-training methods.
We respectfully disagree that novelty is limited. Our study centers on... | Summary: The authors provide compelling evidence that underlines the power of a repetitive prompt-training approach, which leverages CLIP-based pseudo labels. Regardless of the learning model (SSL, TZSL, UL) or the type of prompt (text, visual), this strategy significantly enhances the image classification capabilities... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below we address all your questions.
About the layout and typos we will address all the points in the final version of the paper.
> Setting the trade-off parameters in unified objective function (L157-L158), e.g., gamma, and lambda, is too heuristic, and t... | Summary: In this paper authors extend empirical study of self-training for the case when pseudo-labels are generated by models (CLIP) in zero-shot regime (models are trained on unlabeled data with respect to a downstream task, but can be used for zero-shot prediction for the downstream task). Authors investigate self-t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below we address all your questions that space allows. We do not seem to be able to reply to our own rebuttal to answer more. Please reply if you would like us to answer the remaining questions in our own reply.
> Absence of any ablations on balancing betwe... | Summary: The paper explores the use of CLIP for pseudo-labeling for various tasks such as SSL and on various datasets. Overall - while not being very surprising - the results can clearly outperform prior work (that is not using CLIP it has to be said) - and thus showing the potential of CLIP for such tasks.
Strengths:... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below we address all your questions.
> The results can clearly outperform prior work (that is not using CLIP it has to be said) - and thus showing the potential of CLIP for such tasks.
We would like to clarify that the baselines and comparisons described ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable feedback. Addressing your concerns during the discussion phase will significantly enhance the paper.
We clarify common questions here and address your reviews individually below.
### Novelty
Review qyTk expressed concern about the novelty ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores the concept of prompt tuning in the context of limited labeled data. The authors propose a unified objective function that encompasses three different learning paradigms, and investigate three distinct training strategies for leveraging pseudolabels. The experimental results on six datasets... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Below we address all your questions.
> Does line 217 means reinitializing the prompts at the beginning of each iteration? Or does it mean only reinitializing the set of pseudolabels while the prompts are kept?”
That’s correct. In line 217, we say that af... | null | null | null | null | null | null |
Multi-Fidelity Active Learning with GFlowNets | Reject | Summary: In this manuscript, the authors propose a multi-fidelity active learning scheme based on GFlowNets.
The work mainly aims to tackle scientific discovery problems, where one often faces exploring a huge high-dimensional space to identify novel, diverse, high-quality solutions.
In many scientific applications, ac... | Rebuttal 1:
Rebuttal: Dear Reviewer 3rWi,
Thank you for the insightful review. We particularly appreciate the accurate summary, highlighting the specific challenges of scientific discovery that our work tackles, as well as the fact that you identified the most relevant strengths of our submission.
Regarding the limit... | Summary: This paper introduce an algorithm for multi-fidelity active learning with GFlowNets and demonstrate that the proposed algorithm outperforms the baseline methods.
Strengths: The paper is well written and includes two synthetic benchmark tasks and four practically relevant tasks for extensive experiment analysi... | Rebuttal 1:
Rebuttal: Dear Reviewer TG5X,
Thank you for the reviews, for highlighting some of the strengths of work and for suggesting avenues for improvement. Below, we attempt to address your questions and the weaknesses one by one.
### Limited novelty
The novelty of our contribution seems to be biggest concern in... | Summary: The authors adapt the standard GFlowNet framework to include a fidelity measurement for the oracle, and demonstrate on synthetic, biological and chemical datasets that, in almost all cases, MF-GFN outperforms relevant baselines in terms of achieving sampling performance within a fixed budget.
Strengths: ### O... | Rebuttal 1:
Rebuttal: Dear Reviewer qmsr,
Thank you for your insightful reviews. We appreciate your comments about the strengths and weaknesses of our work. Below, we address each of the weaknesses you pointed out as well as your questions.
### Non-uniform per-sample oracle costs
This is an interesting and important... | Summary: In this submission, the authors proposed to apply GFlowNets as a sampler to sample for active learning based on the selected acquisition functions, instead of directly optimizing them in the procedure of Multi-fidelity Bayesian Optimization (MFBO). Even though focusing on active learning applications, the auth... | Rebuttal 1:
Rebuttal: Dear Reviewer KJZ2,
Thank you for your insightful feedback. Regarding the missing GFlowNet code in the submission, this is indeed a mistake which we will fix, as the GFlowNet code is actually based on open-sourced implementations. In what follows, we address each of the weaknesses you mention in ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive feedback about our paper. We are sure that the changes motivated by this feedback have improved the present manuscript and will also positively impact our future work.
We have responded to each reviewer individually, trying to addres... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper offers a new framework for multi-fidelity active learning using Generative Flow Networks. Given the recent success of GFlowNets as models for sampling diverse candidates among terminating states in a DAG, the authors attempt to leverage this property to put a new spin on active learning, where inste... | Rebuttal 1:
Rebuttal: Dear Reviewer tpVK,
Thank you for your review. We appreciate the positive comments about our work as well as your helpful feedback to improve our paper. We have made sure to fix the typos and improve the clarity in the updated version. In the remainder of our rebuttal, we address each of your con... | null | null | null | null | null | null |
Implicit Convolutional Kernels for Steerable CNNs | Accept (poster) | Summary: This paper tackles one problem of Steerable CNN: one needs to analytically solve a group G-specific equivariant constraint (eq 2 in the paper) in order to obtain the basics for the kernel.
The authors propose to avoid this analytical solution by using a G-equivariant MLP to parameterize a G-steerable kernel b... | Rebuttal 1:
Rebuttal: We appreciate the reviewer found our contribution to be a positive improvement for the community. We will address the reviewer's questions and suggestions one by one.
### Weaknesses
- We would like to emphasize that the learned kernels are already equivariant to a pre-defined group $G$ by constru... | Summary: This work considers the setting of steerable networks, in which a particular kind of group-equivariant (alternatively, G-steerable) kernel is translationally convolved with an input vector (or matrix) field. The group-specific design of such a network focuses on the derivation of the group-equivariant kernel. ... | Rebuttal 1:
Rebuttal: We are happy the reviewer found our idea elegant and well-founded and appreciated the clarity of the presentation. We now would like to address the concerns that were raised.
### Weaknesses
- We would like to emphasize that 2 out of 3 experiments deal with symmetry groups other than $E(3), O(3), ... | Summary: The paper proposed implicit neural representations via MLPs to parameterize G-steerable kernels. Additionally, the performance of the approach is extensively empirically tested against alternative methods on multiple tasks.
Strengths: - Conceptually
In many cases, steerable group equivariant neural networks... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the scientific and conceptual strengths of the manuscript. We have carefully considered your comments and suggestions and provide a detailed rebuttal below.
### Weaknesses
- We want to emphasize that implicit kernels do allow us to alleviate solving Eq.2 by ... | Summary: This work proposes a novel method for achieving a G-equivariant neural network by implicitly parameterizing the steerable filters by G-equivariant MLP instead of learning them with a steerable basis function. The proposed frame is flexible, and the implicit kernel can also consider the problem context by expan... | Rebuttal 1:
Rebuttal: We are delighted that the reviewers acknowledge the novelty and flexibility of our proposed method as well as the clarity of our manuscript. Below, we address the reviewers' concerns point-by-point:
### Weaknesses
- We would like to emphasize that each of the approaches mentioned by the reviewer ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ thoughtful feedback on our submission and have carefully addressed each of the questions raised in separate responses. We are glad to hear that reviewers found the proposed approach of using implicit neural representation to parameterize steerable kernels novel and fle... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments | Accept (spotlight) | Summary: This paper studies adaptive Neymann allocation and proposed an algorithm that achieves expected Neymann regret $\tilde{O}(\sqrt{T})$. I am not an expert in sequential experiment design so my ability is limited to assess the impact/relevance of this paper. Yet, I do consider myself well-versed in the potential-... | Rebuttal 1:
Rebuttal: # Response to Reviewer 5 (wYBx)
We thank you for your thoughtful reading of our paper.
We are happy to hear that you found the writing and analysis to be rigorous and precise.
Moreover, your comments have helped us improve the paper in places where there were ambiguities or confusion.
We respond... | Summary: The authors study adaptive allocation of samples into control and treatment groups in an experiment. They (i) define new performance measures for such allocations, namely Neyman ratio and Neyman regret, (ii) introduce a new algorithm called Clip-OGD that achieves optimal Neyman regret, and (iii) provide asympt... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4 (etbf)
We thank you for your careful reading of our paper and we are happy to hear that you find the paper well-written and its results well motivated.
Your comments in this review have been very helpful, as they have identified a few weak points in the paper which we hav... | Summary: The work considers adaptive experimental design for sequential experiments. To do so, a new regret-like measure, called Neyman regret is defined that compares the ratio of the variance under the chosen experiment design with respect to the variance under the optimal experiment design. Drawing connections to on... | Rebuttal 1:
Rebuttal: # Response to Reviewer 3 (2JiC)
We thank you for your careful review of our paper and we are happy to hear that you believe the core idea to be well-presented and find the comparison to alternative designs valuable.
We are grateful for the questions and concerns you raised in your review, which h... | Summary: This paper studies the problem of “Adaptive Neyman Allocation”, which involves designing an efficient, adaptive experimental design. Neyman allocation is an infeasible experimental design which would be optimal (minimum variance) if the planner knew all the exact potential outcomes under different treatments. ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 2 (NsFr)
We thank you for your thoughtful review of our paper and we are glad to hear that you found our exposition crisp and the results well-grounded in theory.
We are grateful for the comments, questions, and concerns raised in your review: they have highlighted weak poi... | Rebuttal 1:
Rebuttal: # Global Response
We thank the five reviewers for their careful reading of our paper.
We are happy to see that that the reviews were overall positive.
Moreover, we are very grateful for the critiques and questions from reviewers that revealed certain weaknesses in our submission.
We believe that ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new adaptative Neyman allocation for experimental design. The proposed adaptative design gets close to the optimal non-adaptative strategy without suffering from the same infeasibilities.
Strengths: 1) The writing and structure of the paper are OK.
2) The topic is very relevant, and the ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 1 (xwuB)
Thank you for your careful reading of our submission.
We are glad to hear that you find the topic relevant and the formal guarantees interesting.
Below, we respond to the specific critiques raised in your review.
## Broader Movtivations
> It seems that the motiva... | null | null | null | null | null | null |
Structured Federated Learning through Clustered Additive Modeling | Accept (poster) | Summary: In this paper, the authors study heterogeneous federated learning, an important problem in federated learning, where the goal is to leverage the collective intelligence of multiple clients with diverse data distributions, features, or models to train a global model that can generalize well across all clients' ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback regarding our 'clear writing' and 'promising empirical performance.' Below, we provide our responses to the weaknesses and questions you mentioned in relation to this paper.
***Impact of warmup rounds***
Please refer to the general response.
***Extra cost of ... | Summary: This paper studied the problem of clustered federated learning. It analyzed the limitations of existing clustered FL algorithms, including clustering collapse and missing shared knowledge among clusters. Then this paper introduced a novel Clustered Additive Modeling (CAM) framework for learning both a globally... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and positive comments regarding our 'novel framework,' 'flexible framework,' and 'better empirical performance.' Below, we provide responses to the weaknesses and questions in your comments. Should you have additional questions, we are readily available for further disc... | Summary: This paper proposes a new federated learning with a specified structure for the prediction produced for each client, called "clustered additive modelling", which adds the prediction of a global model to the prediction of the model trained on a local cluster of clients. It is a modification to existing clustere... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback regarding our 'clear motivation tackling a fundamental problem in clustered FL,' 'intuitive and convincing algorithms,' 'nice analysis for clustering collapse,' and 'high reproducibility.' Below, we provide our responses to the weaknesses and questions you ment... | Summary: This paper proposes a novel clustered federated learning method using additive modeling to tackle the clustering collapse problem. The paper contributes to advance the research domain of clustered federated learning which is an important and practical solution to solve non-iid problem in federated settings.
... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback regarding our 'simple-yet-effective method,' 'well-suited theoretical analysis,' and 'well-supported claims.' Below, we provide our responses to the weaknesses and questions you mentioned in relation to this paper.
### Weakness Clarifications
***How does CAM ... | Rebuttal 1:
Rebuttal: ## General response
Our proposed method mentioned warmup stage can improve the performance of clustered FL methods, and the framework is flexible to be integrated with existing FL methods. Reviewers show significant interest in these two points by asking for more details and support to these two p... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Scaling laws for language encoding models in fMRI | Accept (poster) | Summary: The authors evaluate the effect of LM scale (in terms of N parameters) and fMRI dataset size on the performance of downstream encoding models, trained to predict the activity of individual voxels across the brain as a function of the input those participants received during a scanning session. They explore bot... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and comments. We have included responses to your questions below.
>Weaknesses:
>The sole evaluation of LM scale as a function of N parameters is reductive and should be addressed. There is a lack or under-specification of uncertainty quantification. See below ... | Summary: Researchers compared the effectiveness of larger open-source language models, such as those from the OPT and LLaMA families, in predicting brain responses recorded using fMRI. They found that brain prediction performance improves logarithmically with model size, with a 15% increase in encoding performance as m... | Rebuttal 1:
Rebuttal: Thank you for your review and thoughtful comments. We have included responses to your questions below.
>Weaknesses:
>Most of my concerns are addressed in the questions section. One concern I have is that the code is not publicly available. If the authors published the code it would be greatly app... | Summary: The authors study how the scaling of large language models produce accurate features to predict human brain fMRI activity. They report scaling-like laws for fMRI encoding models – models with more parameters tend to more accurately predict fMRI activity (as measured with the Pearson correlation of the encoding... | Rebuttal 1:
Rebuttal: Thank you for your review and thoughtful comments. We have included responses to your questions below.
>Weaknesses:
>It is hard to determine whether this result should be expected, and what to make of its scientific significance ... So what does one make of this discrepancy? Do the authors believ... | Summary: Previous works have demonstrated that activations from Language Models fit, to some extent, brain activations in participants listening to audio texts. Here, the authors examine how the fit is influenced by model size (number of parameters) and the training dataset size.
The main observation is that performa... | Rebuttal 1:
Rebuttal: Thank you for your review and thoughtful comments. We have included responses to your questions below.
> Weaknesses:
>
>As the authors mention (at line 300), when models grow in size, the regression problem may become ill conditioned. Would there be any strategy to remedy this problem?
Naturally... | Rebuttal 1:
Rebuttal: Thank you to all our reviewers for their detailed and thoughtful feedback. For our general response, we are attaching a series of figures that were requested or that we believe resolves the outstanding concerns that each of you have voiced. The figures will each be included somewhere appropriate i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors of this paper delve into the investigation of the scaling law between the performance of predicting brain activity (measured using BOLD) and the number of parameters in large language models, coupled with the amount of training data for the linear readout for retrained LLM used in AI. This pursuit ... | Rebuttal 1:
Rebuttal: >More work is needed to demonstrate that the current LM are not saturating the performance of the neural prediction task. This is very critical since the main contribution of this work is that even the largest LM do not saturate the performance.
Thank you for your review and thoughtful comments. ... | null | null | null | null | null | null |
Revisiting Implicit Differentiation for Learning Problems in Optimal Control | Accept (poster) | Summary: This paper considers the optimization problem in the context of discrete-time control of dynamic systems. The paper proposes a method for efficiently and effectively evaluating the Jacobian of saddle points of constrained optimal control problems w.r.t. COC problem parameters. The main technical result is that... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and kind words around the technical writing.
> **From what I understand, no new tools were developed in proving these results.**
We agree that no new mathematical tools were developed. However, identifying and exploiting the link between the IFT identities of G... | Summary: This paper analyzes and develops an efficient method to differentiate through a constrained discrete-time optimal control system, i.e., computing the derivative of an optimal trajectory of a constrained discrete-time optimal control system with respect to the parameters in the system’s cost function, dynamics,... | Rebuttal 1:
Rebuttal: Thank you for your review. We will address your questions as follows:
> **Line 159: since the algorithm requires the identification of a set of active constraints, \tilde{g}_t, from all inequality constraints, will the use of a threshold cause the numerical issues, eventually leading to bad quali... | Summary: There has been recent interest in differentiating through trajectories to obtain first-order derivatives for optimization problems including policy learning, inverse optimal control and model learning. Previous work usually uses a backward recursion which computation scales quadratically with the length of the... | Rebuttal 1:
Rebuttal: We appreciate you taking the time to review our paper and drawing our attention to specific claims within it that appeared unclear.
In particular, we apologise for the confusion regarding our comments around quadratic complexity -- we were not intending to make a misclaim here.
> **The paper is ... | Summary: Prior work shows that computing trajectory derivatives scales quadratically w.r.t to timesteps. This work proposes that trajectory derivatives scales linearly w.r.t. to timesteps, which can be parallelized, resulting in decreased computation time and increased numerical stability.
Strengths: - Good paper stru... | Rebuttal 1:
Rebuttal: Thank you for your review. We would like to emphasize not only the decreased computation time, but the significant performance gains on differentiating through inequality constrained problems without resorting to a log-barrier approximation. Additional compute and scalability experiments are prese... | Rebuttal 1:
Rebuttal: # Global Response Overview
We thank the reviewers for providing detailed comments and feedback on our work. We are pleased the reviewers appreciated the computational and numerical performance improvements compared to existing state-of-the-art offered by our use of (structured) implicit different... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Differentiating through optimal control problems to learn various components, such as the dynamics model or cost function, is a promising method for inverse reinforcement learning (IRL) or incorporating more structure in learned control policies. The central component of these approaches is to differentiate th... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the contributions proposed in our work. We would like to address your concerns.
> **It would strengthen the paper to see a breakdown of how the improvements over PDP scale with the horizon length of the problem, and if these trends carry over to even longer hori... | Summary: The paper introduces a method for calculating analytic trajectory gradients in constrained optimal control problems using implicit differentiation, with following contributions:
* Shows that computation of these derivatives can be linear in trajectory time-steps, utilizing the structure in the matrices in the ... | Rebuttal 1:
Rebuttal: Thank you for your review. We will gladly address editorial comments around legends and writing. In addition, we will address the concerns you have raised under both **Strengths** and **Weaknesses**.
> **Quality - The quality of contribution is moderate. Since the paper is mainly is analytical, b... | null | null | null | null |
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness | Accept (spotlight) | Summary: The paper introduces an empirical study for visual pre-training. By focusing on the pre-training, the authors conduct wide-range experiments through (i), data quantity, (ii) label granularity, (iii) label semantics, (iv) image diversity, (v) data sources. The empirical study considers how to use pre-training d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback.
**“How about implementing vision transformer (ViT) architecture?”** We understand that ViTs have gained popularity in recent years. We include results with ViTs in Figure 2 of our rebuttal PDF, specifically ViT-B/32, pre-trained in both supervise... | Summary: This paper delves into the impact of pre-training data construction on fine-tuning robustness, encompassing aspects such as dataset size, class granularity, in- and out-class diversity, class similarity, and the use of synthetic data for pre-training. The evaluation metric employed is the in- and out-of-distri... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for providing valuable suggestions on how we can improve our work.
**“The reliance on a single metric to measure effective robustness, which might not provide a fully comprehensive evaluation of the connection between pre-training datasets and fine-tuning robustness.”** Eff... | Summary: In this paper, the authors investigate the influence of pre-training data on the robustness of fine-tuning. The authors design several experiments by following a common pre-training, fine-tuning and evaluation pipeline. They found that the quantity of the pre-training
data and the granularity of the label se... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback!
**“It is not clear if different fine-tuning methods (only fine-tune the last layer, etc) would change the conclusions of the paper.”** This would be a useful direction for future investigation. Our paper includes experiments with linear probes ... | Summary: This paper investigates the role of pre-training data diversity on fine tuning robustness. They vary various factors like label space, label semantics, image diversity, data domains, and data quantity of the pre-training distribution to investigate how these factors impact the robustness of the models. Some in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the effort they have put into reviewing our paper.
**“It would have been interesting to look at self-supervised learning methods in much more depth.”** This is a good direction for future work. The focus of our current work is on supervised pre-training. Per the reviewer... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough, insightful comments and have made revisions based on their feedback. We are glad they found the work well motivated, novel, and the contributions to be of value to the community. Here we include new results and plots to respond to some of the concerns by ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method | Accept (poster) | Summary: This paper is concerned with parameter free - adaptive first order optimisation method, that is a method that achieves optimal rates of convergence in the class of function considered without having access to a priori quantities such as smoothness of the function or the minimum value of the function.
The autho... | Rebuttal 1:
Rebuttal: Thank you so much for your evaluation of our work.
1. "The resulting algorithm is probably not going to have any impact on the optimization method in deep learning." On the contrary, the method has been independently implemented in at least one GitHub repository with 500+ stars for fine-tuning st... | Summary: The paper presents a modification to the recently proposed DoG algorithm to obtain a adaptive algorithm for the deterministic, convex and [Lipschitz or Smooth] setting that does not need any hyperparameter to achieve a convergence rate competitive with algorithms that know the problem-specific constants.
The ... | Rebuttal 1:
Rebuttal: Thank you for your comments and constructive criticism.
We address your concerns below:
1. On normalized gradient descent: We thank you so much for pointing out that the algorithm of Levy (2017) reduces to normalized gradient descent. We were not aware of this work and will include it. We'd like... | Summary: This paper considers the problem of optimizing a convex function over a convex, closed, and possibly compact set $\mathcal{X}$. In particular, they are interested in finding a first-order method which is (i) universal (i.e., the same algorithm can be used when the objective is Lipschitz or smooth), (ii) parame... | Rebuttal 1:
Rebuttal: Thank you so much for your positive evaluation of our work.
1. On the stochastic setting: We agree that the stochastic case is important and merits exploration on its own, but we note that the results on the convergence of DoG in the stochastic case are not parameter-free, as they require knowle... | Summary: This paper introduces a new algorithm for optimization problems. The paper introduces DoWG (Distance over Weighted Gradient) which is a simple extension of a previous work DOG (Distance over gradient). It is a parameter-free gradient optimization method where the step size is automatically adjusted to the func... | Rebuttal 1:
Rebuttal: We thank you very much for your review.
Please find our rebuttal below:
1. On motivation for DoWG: "The paper mentions that DoWG gives higher weight to later gradients, but why is this important?" The practice of assigning higher weights to later gradients is crucial to adaptivity to the loss cur... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new algorithm, DoWG, which modifies DoG (Ivgi et al., 2023) by utilizing the weighted sum instead of the usual sum for the squared gradients.
Convergence is established for convex and convex & smooth deterministic settings, under the assumption of a (possibly unknown) bounded domain.
The m... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation and constructive criticism of our work. We agree that DoWG's main strength is that it allows for much larger stepsizes than DoG both in theory and practice.
We now address the weaknesses and questions:
1. On the stochastic case: We agree that the s... | Summary: The paper proposes a new optimization algorithm, DOWG, that does not rely on additional hyperparameter tuning or a line search subroutine. Interestingly, the paper also contains a proof and analysis regarding the behavior of NGD and shows that (1) NGD adapts to the smoothness of continuous loss surfaces and (2... | Rebuttal 1:
Rebuttal: Thank you so much for your very positive evaluation of our work. We agree that Adam with cosine annealing is an incredibly strong baseline, and we hope that future work can reveal a principled way of improving over that.
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Rebuttal Comment 1.1:
Comment: Thanks. I've read the response and have ... | null | null | null | null |
Group Robust Classification Without Any Group Information | Accept (poster) | Summary: The paper identifies that current methods tackling spurious correlations requires group annotation in either the training or validation stage. To address this limitation, the authors propose uLA, a bias-unsupervised method that achieves superior empirical performance without any group annotation.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed assessment of our work and for highlighting the merits of our approach, as well as the importance of the problem. We address all concerns below:
### Weakness 1/Question 3.b.: Even though SSL is demonstrated to provide benefits over pure supervised learning... | Summary: This work aims to evaluate and introduce a method to make classifiers perform well across subgroups of the data, focusing in particular on “spurious correlations” where one attribute correlates with the output label in the training set, but need not at test time. While most past work doing this requires “bias ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment of our paper. We address the raised concerns in what follows:
### Weakness 1: There are places where the paper could use more exposition: how they extract bias variable observations, or time to how they construct the dataset (the two core contrib... | Summary: The paper works on mitigating the impacts of spurious correlations during the risk minimization. The authors firstly introduce a systematic generalization task and illustrates existing methods implicitly made the assumption that all group combinations are represented within the training procedure. And then rev... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and their questions.
### Weakness 1.a: Presentation of MPI3D
We update line 73 with: “In particular, we use the ‘real’ split of MPI3D which consists of photographs of a robotic arm that has a colored rigid object attached to its end effector. Th... | Summary: This paper introduces a new bias-unsupervised method for addressing spurious correlations, encompassing a debiasing training algorithm and a model selection paradigm. Specifically, the method employs a Self-Supervised Learning (SSL) pre-trained model as a bias proxy. The SSL model's fixed predictions are used ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, recognition of the novelty of our approach and positive remarks on the presentation of our method.
### Weakness 1/Question 1: It is not clearly introduced how the proposed method could achieve improvement in systematic generalization, intuitively or theo... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful assessment of our work and for providing useful feedback and actionable suggestions. We are glad they found the method we proposed to be simple yet effective (reviewer Tfdv) and efficient since no bias labels are required during training or validation (r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning | Accept (poster) | Summary: The authors propose a novel ways to train audio-text model by adding a token interaction module followed by the same frozen text encoder from the text modality after audio encoder to learn audio representation. And they show that proposed approach yield state-of-the-art results in couple audio tagging and clas... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and feedback. Please refer to the attached PDF in the global response for supplementary results.
__1. About the selected template__ Following the zero-shot performance of the strongest baselines in our experiments [1], we employ the template in Section 4.1 rat... | Summary: This paper presents a novel approach to train audio embeddings grounded in text embeddings for audio classification. They propose a trainable audio embedding layer while keeping the pre-trained text embedding module frozen. Apart from the contrastive loss term that maximises similarity between corresponding au... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and feedback. Please refer to the attached PDF in the global response for supplementary results.
__1. Initialization of the input to the token interaction module__ All elements in the input token embedding matrix were randomly initialized, following a normal d... | Summary: This paper introduces a method to train an audio encoder to produce audio embeddings that match text embeddings. Contrary to other methods, the authors suggest to keep the text encoder fixed so as to enable its use in other contexts. More specifically, a Transformer audio encoder produces audio embeddings. A s... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and feedback. Please refer to the attached PDF in the global response for supplementary results.
__1. Unclarity about the fine-tuned CLIP models in Fig. 1b__ In Fig. 1b, CLAP, AudioCLIP, and Cons-CLAP models fine-tune the pre-trained CLIP text encoder. In cont... | Summary: The paper introduces CoLLAT, an audio-language framework that makes use of a pre-trained language model. The framework is trained with a contrastive objective which encourages learning of fine-grained audio-text grounding. The paper presents very strong results for diverse downstream tasks, such as zero-shot a... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and feedback. Please refer to the attached PDF in the global response for supplementary results.
__1. Results for several additional tasks.__ Please find the results for text-to-audio retrieval, audio-to-text retrieval, and audio captioning using the AudioCap... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments and feedback. Kindly find the attached PDF in this global response for additional results that support our rebuttal.
Pdf: /pdf/c1ece049e6cb2e9c913c84397c2fddc84ef3c305.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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