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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off | Accept (poster) | Summary: The paper discusses the impact of time discretization on estimating cost functions for continuous-time stochastic optimal control problems. The authors consider a discrete-time Monte Carlo estimator based on the left-rule formula for numerical integration. Under the LQG assumption, the authors derive closed-fo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time reviewing our work and for providing valuable feedback.
We respectfully note that the formulation of MSE under a fixed data budget has not been explored before. While it may be reasonable to anticipate a closed-form MSE under this formulation, we b... | Summary: For the reinforcement learning (RL) setting, many problems are cast into a fixed discrete time sampling of the true underlying system. This paper investigates given some fixed data budget available, what is the optimal sampling rate in terms of temporal resolution to balance the trade-off between approximation... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time reviewing our work and for providing valuable feedback. We will correct the typos pointed out by the reviewer. In the following, we address the main concerns.
> Can you clarify where the $\text{MSE}_T$ between line $168$ and $169$ come from?
`Re:`... | Summary: The authors study the impact of time discretization on RL methods in order to improve data efficiency and show that that data efficiency can be significantly improved by leveraging a precise understanding of the trade-off between approximation error and statistical estimation error in value estimation. They co... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time reviewing our work and for providing valuable feedback.
While it is true that the technical analysis holds only in the case of linear dynamics, we explicitly conducted experiments in nonlinear systems to understand if the theoretical findings could... | Summary: The paper studies the optimal temporal discretization level to achieve the best Mean Square Error (MSE) in continuous value estimation, considering a fixed budget constraint (number of total Monte-Carlo samples). The paper provides theoretical analysis on a 1-dimensional Langevin dynamical system with quadrati... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time reviewing our work and for providing valuable feedback.
One clarification we would like to respectfully add is that tight bounds for the general case of a linear $n$-dimensional system are established in the paper. That is, the theoretical result... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper examines the time discretization for continuous value estimation. By analyzing Monte-Carlo value estimation for LQR systems for both finite-horizon and infinite discounted horizon settings, the authors finds that there is a fundamental trade-off between approximation error and statistical error in v... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time reviewing our work and for providing valuable feedback.
We are motivated by real-world scenarios where one must choose the frequency at which sensors operate to get samples of the system signal. Indeed, in many real-world applications, sensors sam... | null | null | null | null | null | null |
False Discovery Proportion control for aggregated Knockoffs | Accept (poster) | Summary: This paper presents a method KOPI, based on the knockoff framework that controls the false discovery _proportion_ instead of the false discovery _rate_. The key idea of the approach is to note that certain summaries of the knockoff statistics are exactly equal in distribution to certain functions of independe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. We also thank the reviewer for the typos found: they have been fixed in the manuscript. Please find our answers to the points raised below.
> One minor weakness of the paper is that there are a number of potential tweaks to the method... | Summary: I can understand this paper but I don't have sufficient knowledge to make a solid judgment on it, please ignore my review.
Strengths: I can understand this paper but I don't have sufficient knowledge to make a solid judgment on it, please ignore my review.
Weaknesses: I can understand this paper but I don't ... | null | Summary: This paper studies an important topic: variable selection. Concretely, the authors proposed a novel method KOPI which can theoretically control false discovery proportion at a pre-specified level. Besides, the authors also conduct lots of experiments based on simulated data and real data to verify the effectiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
> Simulation experiments and real-data experiments are conducted, However, in order to verify the effectiveness of KOPI, more experiments on other real-data datasets are needed;
To a... | Summary: In this paper, the authors discuss controls for false discoveries in variable selection. While how to perform FDR control is known, authors opt to control FDP, which is a random quantity depending on a specific dataset. The main concept is an upperbound of FDP (proposition 1), namely JER. The monte carlo versi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
> It is not entirely clear to me the delta between this paper and an important related work, i.e, [4]. From Section 4, it seems that using JER to control FDP has already been proposed.... | Rebuttal 1:
Rebuttal: Rebuttal Summary
----
We thank all three reviewers for their time and comments. Here is a summary of the elements we addressed in our answers:
* We have added **two experiments** to address the reviewers' concerns on **real-world datasets** and the choice of the aggregation scheme. We use a gene... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks | Accept (poster) | Summary: The paper provides a thorough investigation of the generalization behavior of t-NNs for the first time, which closes the gap between the practical success of t-NNs and their theoretical analysis. To be specific, the authors derived the upper bounds for the generalization gaps. The authors also propose that com... | Rebuttal 1:
Rebuttal: We are truly grateful for your favorable evaluation of our work. Your recognition of our strengths, including the comprehensive investigation into t-NNs' generalization behavior, rigorous derivation of generalization bounds, and exploration of transformed low-rank structures, reinforces our commit... | Summary:
The paper "Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks" considers neural networks with t-product layers, that encode the weights in tensor format equipped with the t-product as the corresponding tensor operation and ReLU activations.
The paper is an analyti... | Rebuttal 1:
Rebuttal: We wish to express our appreciation for your positive and thorough evaluation of our technical paper. Your acknowledgment of our dedication to presenting the research findings with rigor, the incorporation of an extensive appendix, and the thoughtful comparison with existing works has provided us ... | Summary: The paper analyzes the generalization ability of t-product layers (t-NNs) by deriving upper bounds on generalization error in standard and adversarial settings.
Strengths: The paper advances the theoretical understanding of t-NNs and derives their generalization behavior in two practical settings.
Weaknesses... | Rebuttal 1:
Rebuttal: **Weakness 1** (Writing).
**Response:** Thanks for your comment on the writing.
Firstly, we would like to address the need for references to [7] and [28].
- We appreciate your highlighting the reference to patent [7] for the scientific rigor and integrity of our work. However, patent [7] (gra... | Summary: The paper studies the generalization error in the standard and the adversarial settings of t-NNs, neural networks with layers and features parametrized via t-vectors and t-products.
Strengths: - The provided generalization bounds for t-NNs show that neural networks with low-rank parameters have the potential... | Rebuttal 1:
Rebuttal: **W1** (Novelty).
**Re:** We appreciate the reviewer for acknowledging the novelty of our analysis for t-NNs. We believe that our analysis can bridge the gap from the existing applications to a more rigorous understanding of t-NNs in machine learning. Notably, t-product's distinction from linear ... | Rebuttal 1:
Rebuttal: ## **Contributions and Numerical Evaluations**
We thank all the reviewers for their valuable comments and suggestions. We first clarify our contributions and novelty again, and then report the initial numerical evaluations as suggested by Reviewers (R1, R3, R4).
---
**Contributions & Novelty**... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DreamHuman: Animatable 3D Avatars from Text | Accept (spotlight) | Summary: This is a paper focusing on generating 3D animatable full-body human avatar from text using pretrained 2D diffusion model and Score Distillation Sampling (SDS).
The proposed approach differs from the existing approach in that, instead of directly representing a canonical space using surface template e.g. SMPL,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and will address all raised questions.
**Weaknesses**
- *Concurrent works*: Thanks for the suggestion. We will cite and discuss the suggested references in the final version of the paper. We would like to highlight how our method compares to these 2... | Summary: This paper presents a method to generate animatable 3D human avatars from text. The pipeline is similar to DreamFusion and is built upon the Nerf representation and diffusion model. However, a key difference is that an imGHUM body model is introduced as prior, which allows for the construction of a deformable ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and will address all raised questions.
**Weaknesses**
- *Shape parameters*: The shape parameters are treated as additional parameters of the overall model during optimization and we compute the gradient of the loss with respect to them as if they we... | Summary: This work proposes a method for text-driven human avatar generation. It combines animatable human nerf and diffusion model to implement avatar generation and animation. This work produces photorealistic avatars with high-quality details by incorporating spherical harmonics lighting model and semantic zoom. Ext... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and will address all raised questions.
**Weaknesses**
1. *Hands and facial expressions*: Thanks for the very good suggestion. For the rebuttal we have produced examples of renderings with varying facial expressions (Figure 2) and hand poses (Figure ... | Summary: This paper proposes a method to generate high-quality and animatable 3D human from textual input.
Strengths: - The result is good, showing clear improvement compared to the previous text-to-3D method.
- The method can be learned without 3D GT.
- The ablation study is thoroughly done.
Weaknesses: - What is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and will address all raised questions.
**Weaknesses**
- *Runtime*: The rendering time for a 512x512 image is 2.6 seconds, whereas for 256x256 it is 0.67 seconds. These timings were measured on a TPU v3 chip with 8 cores.
- *Density loss*: The purpos... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable feedback.
- Reviewer KMW9 states that our method shows clear improvements over previous methods without the need for 3D ground truth data, and that we have a comprehensive ablation study.
- Reviewer urhH notes that our method produces animat... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Counterfactual Evaluation of Peer-Review Assignment Policies | Accept (spotlight) | Summary: In this paper, the authors study the counterfactual evaluation of peer-review assignment strategies with randomness. The authors adapt existing off-policy evaluation methods for the specific problem to handle non-positivity, missing reviews and attribution. A framework of off-policy evaluation with different i... | Rebuttal 1:
Rebuttal: Thank you for the comments! We appreciate that you recognize the importance of the problem we consider and the value of our off-policy evaluation framework.
> One major concern is that there is no evaluation on the quality for the counterfactual evaluation. It would be much more convincing if so... | Summary: This paper leverages recently proposed strategies that introduce randomness in peer-review assignment—in order to mitigate fraud—as a valuable opportunity to evaluate counterfactual assignment strategies.
This paper introduces novel methods for partial identification based on monotonicity and Lipschitz continu... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We are pleased to hear that you found the paper well-written and the experiments convincing. Below we provide responses to the questions posed in the review.
> Why the proposed off-policy evaluation is effective?
The key idea of our proposed approach to off-policy ev... | Summary: The authors consider the problem of evaluating alternative reviewer matching algorithms. They motivate this problem as the cost of running A/B tests. They propose to use off-policy evaluation by exploiting the randomness in review assignments introduced by a fraud-mitigating scheme in recent years. They tackle... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments! Thank you also for your recommendations on the exposition of the results and the organization of the manuscript. We greatly appreciate them and look forward to improving the clarity of the manuscript with these recommendations in mind.
>The authors consider se... | Summary: This work uses the randomness of a recently-implemented peer review paper assignment algorithm in order to perform off-policy evaluation of other (nearby) randomized assignment strategies. It uses multiple methods for imputing missing values and estimating the errors in the estimators of alternative policies' ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review—we very much appreciate your feedback and are pleased to hear that you found the paper well-executed, the off-policy evaluation framework technically and theoretically interesting, and the recommendations based on our analyses actionable.
> In your discussion of... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Policy Optimization for Continuous Reinforcement Learning | Accept (poster) | Summary: Reinforcement learning (RL) is a powerful tool for solving sequential decision making problems but has primarily been formulated for discrete-time Markov decision processes. However, many real-world systems are more naturally expressed in continuous time, and the proper choice of discretization time step may b... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments, especially on the experiments.
1. There is no discussion or experimental results which illustrate how the continuous-time formulation is advantageous...
A: We have conducted extra experiments to compare the CPG and CPPO to their discrete counterpa... | Summary: This paper investigates the continuous reinforcement learning problem. The proposed method is based on the notion of occupation time for policy gradient, which is analogous to the visitation frequency in discrete Markov decision processes. Empirical evaluations are conducted on two example scenarios for two ve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments, especially on the experiments.
1. Lack of comparison to the discrete counterpart.
A: We have conducted extra experiments to compare the CPG and CPPO to their discrete counterparts. (The results will be included in the revision). Specifically, we d... | Summary: This paper seeks to answer three research questions: 1) Is there a continuous time analog of the state occupancy measure, 2) Is there a convenient expression for the performance difference between two policies in the continuous time setting, and 3) Can PPO be adapted to fit the continuous time setting? In answ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions and comments.
1. How does the sampling at discretization impact the gradient estimate?
A: We have implemented our proposed algorithms with different (time) step sizes: $\delta t=0.02$, $\delta t=0.05$ and $\delta t=0.1$, and found that the performances a... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies | Accept (poster) | Summary: This paper introduces the parallel spiking neuron (PSN) and several variants. The primary benefit of PSN over the existing spiking neurons is the parallel implementation on digital hardware, which brings dozens of times of acceleration on GPU. The accuracy also demonstrates the effectiveness of this paper.
S... | Rebuttal 1:
Rebuttal: Thanks for your encouraging comments about the acceleration and accuracy of the PSN family. Our responses to the weakness are as follows.
> I was wondering whether the optimization on GPU is really useful cause people can always use ANNs on GPU.
A typical workflow to use SNNs is:
1. Train SNNs ... | Summary: The paper presents an approach to improve the efficiency and accuracy of Spiking Neural Networks by using a dependency method to generate hidden states, resulting in parallelizable neuronal dynamics and a significant increase in simulation speed.
Strengths: The authors analyze the impact of removing the rese... | Rebuttal 1:
Rebuttal: Thanks for your positive comments. Please refer to "To All Reviewers" for our discussions about the trade-off with vanilla spiking neurons. Our responses to your constructive questions are as follows.
## **Question 1**
Thanks for your variable question. We have summarized the number of memory re... | Summary: This paper proposes the Parallel Spiking Neuron (PSN), which generates hidden states that are independent of their predecessors, resulting in parallelizable neuronal dynamics and extremely high simulation speed. The weights of inputs in the PSN are fully connected, which maximizes the utilization of temporal i... | Rebuttal 1:
Rebuttal: Thanks for your comprehensive comments. Please refer to "To All Reviewers" for responses to **Question 3**. Other point-to-point responses are as follows.
## **Weaknesses 1 and Questions 1, 2**
> Please explain the advantage of the parallel spiking neurons as mentioned "high simulation speed " i... | Summary: This paper removes the reset mechanism from the dynamics of conventional LIF/IF neurons, and proposes to reformulate the neuronal dynamics using matrix multiplication instead of the iterative updating for the membrane potential. This matrix multiplication then can be simulated in parallel to accelerate the tra... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments and questions, which are also helpful for other reviewers. Please refer to "To All Reviewers" for responses to **Weaknesses 1: Lack of Reset Mechanism**. Responses to other comments are as follows.
## **Weaknesses 2**
Although the PSN family uses extra train... | Rebuttal 1:
Rebuttal: Thanks for all reviewers' valuable comments. We are encouraged that reviewers find the idea of parallelizing spiking neurons interesting and commend the fast simulation speed of the PSN family. Meanwhile, most reviewers are concerned about the hardware implementation of the PSN family and the trad... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Why think step by step? Reasoning emerges from the locality of experience | Accept (oral) | Summary: This paper aims to investigate in a control and toy setup why it is that zero-shot chain-of-thought reasoning (e.g. prompting a model with "let's think step by step" and letting the model output intermediate reasoning traces before generating the final answer) improves downstream performance of language models... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and supportive review. We are glad that the reviewer found our paper to be a pleasure to read and that they feel they understand zero-shot chain-of-thought better having read it.
We have responded to the main weakness the reviewer identified, about the con... | Summary: The starting point of this paper is the observation that large language model benefit from chain-of-thought reasoning. Namely, when prompt with a reasoning task, LLMs benefit from generating intermediate steps before reaching the final answer. The paper investigates this phenomenon. The authors hypothesize tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising the important issue of architecture choice. We also appreciate that the reviewer finds the topic of chain-of-thought reasoning important and finds our results convincing.
We have responded to the reviewer’s point about the choice of architecture, including new re... | Summary: This paper provides a theoretical analysis of situations in which chain-of-thought reasoning should be helpful. They do this by considering the task of predicting variable values in a Bayes net. Specifically, it is a Bayes net where child nodes are a nearly-deterministic function of their parents. During train... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. We appreciate that the reviewer found the paper’s result new and exciting, and that they thought it provides useful intuition about when chain-of-thought is useful.
We respond to the weakness about variable values being nearly deterministic given p... | Summary: This work investigates why and how chain-of-thought reasoning works in language models in the aspect of
**local structure** in the training data. To this end, this work first proves the hypothesis that there exists a reasoning gap where reasoning through intermediate variables improves inference and then test... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and thoughtful review, and are happy to see that the reviewer found our theoretical analysis and simulation results insightful.
We describe how we will address the second weakness in the general author rebuttal. We also respond to the question about the cho... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments on this paper. These comments have informed additional analyses and clarifications.
# Architecture
Reviewers Jbx9 and tGUI ask about our choice of architecture. Jbx9 asks why we chose a smaller model given that models smaller than GPT-3 often f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive Test-Time Personalization for Federated Learning | Accept (poster) | Summary: This paper considers the federated learning setting of adaptive test-time personalization. Traditional test-time adaptation (TTA) can only handle specific target domain distributions, while federated learning requires flexible handling of multiple target domains. Existing TTA methods pre-define which modules t... | Rebuttal 1:
Rebuttal: Dear Reviewer yJjv,
We sincerely thank you for your comprehensive review and insightful feedback on our paper. We appreciate your positive feedback on the crucial experiment and effective algorithm of our work. We would like to address your concerns as follows.
## W1. Comparison to FedTHE
> 1. T... | Summary: The paper studies test-time personalization in a federated learning setting --- after training on participating clients, the goal is to locally adapt the global model given unlabeled test data. The paper's main idea is by pointing out that label non-IID and domain non-IID require adaptation on different layer... | Rebuttal 1:
Rebuttal: Dear Reviewer 2b9x,
Thank you for your detailed and insightful review of our paper. We appreciate your positive feedback on the novelty and empirical results of our work. We have carefully considered your concerns and suggestions, and will address them point by point as follows.
## W1. Overcla... | Summary: This paper proposes a new setting called test-time personalized federated learning (TTPFL) and proposes an Adaptive Test-time Personalization algorithm. The authors show effectiveness of proposed method over other test-time adaptation methods.
Strengths: The paper proposes an Adaptive Test-time Personalizatio... | Rebuttal 1:
Rebuttal: Dear Reviewer h3BM,
Thank you for your insightful review and valuable feedback on our paper. We sincerely appreciate your recognition of the effectiveness of our algorithm. Regarding the weaknesses, we address them point by point as follows.
## W1. TTPFL setting
> 1. The proposed setting is s... | Summary: This paper introduces a novel setting where personalized FL during the test procedure is considered and multiple distribution shifts are involved. A method termed ATP is proposed to solve the challenges posed in this setting. Adaptive learning rates are learned for the model. Both theoretical and empirical stu... | Rebuttal 1:
Rebuttal: Dear Reviewer A9BH,
We would like to express our sincere gratitude for the thoughtful review and constructive feedback provided. We are grateful for your positive feedback on the paper's organization, empirical support, and theoretical analyses. We have carefully considered your comments and wil... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our sincerest gratitude for your thoughtful and insightful reviews of our paper. We are particularly grateful for the recognitions bestowed upon our work, including
- novel and effective algorithm (from all reviewers),
- satisfying experiments (from revi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Swarm Reinforcement Learning for Adaptive Mesh Refinement | Accept (poster) | Summary: This paper formulates h-adaptive mesh refinement (AMR) as a decentralized partially-observable Markov decision process (Dec-POMDP), and proposes a methods ASMR with parameter-sharing among agents and individual rewards to find refinement strategies. Refinement policies are parameterized by message-passing grap... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback, particularly noting the innovative use of swarm reinforcement learning for AMR and the comprehensive clarity and rigor with which we presented our findings. We now address individual concerns, including the clarification needed in the experimental... | Summary: This paper proposes a novel MDP formulation and policy architecture for adaptive mesh refinement.
The MDP formulation (ASMDP) defines the components of the MDP in order to account for the changing number of agents across timesteps, and the reward is formulated in a manner to make credit assignment easier and t... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their detailed evaluation of our paper, particularly emphasizing our method's significant contributions and robust experimental claims. We will now respond to the individual concerns raised by the reviewer, including the generalization capabilities of the method... | Summary: This paper builds on recent advances in learned adaptive mesh refinement method to scale to complex physical simulations. Instead of formulating AMR as a reinforcement learning problem with a single agent, the authors formulate AMR as a swarm reinforcement learning problem, in which multiple agents collaborate... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive feedback. We are pleased to hear that they found our experiments and ablations to be conclusive and convincing. We also thank them for their remarks on the notations in Section 3 and their inquiries about the challenges of solving AMR for specifi... | Summary: This paper presents a novel framework for adaptive mesh refinement for solving PDEs describing physical systems. The refinement is done as a Markov Decision Process in a Swarm RL setting, with each element of the mesh being an agent in the swarm. The agents' action space is binary and a learned policy decides ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable insight and suggestions, and particularly for highlighting the significance of our work's potential in addressing complex PDEs for efficient simulation. In the following, we want to address the individual points raised by the reviewer.
> While the ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback on our submission. We are delighted to hear that the reviewers found our reward function and agent mapping innovative and useful (Tf6s), the speedup impressive (fwaD), the experiments convincing (ymhS), the improved stability of our method signifi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The article presents an RL-based framework for adaptive mesh refinement in FEM. In this framework, each element in the mesh is perceived as an agent from RL's perspective. The observation for each agent is then constructed using a variety of local and global features relevant to the PDE of interest. The reward... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their insightful feedback, particularly on the significance of our reward formulation and agent mapping. We acknowledge their concerns about the scalability of our method and the necessity for a comparison with existing error estimators. In the following, we add... | null | null | null | null | null | null |
On the impact of activation and normalization in obtaining isometric embeddings at initialization | Accept (poster) | Summary: A study of layernorm and Gramm matrix isometry is presented in both theory and emprical results. The results explain effectiveness of transformer (and similar) architechtures. Application of layernorm after activation seems to be a correct strategy when we are interested to isometry of output Gramm matrix.
S... | Rebuttal 1:
Rebuttal:
We would like to thank the reviewer for their positive review on our work
> My main issue with the paper is not with the theory, but how it is presented in the main paper. ... some rigorous exposition is absent in the main paper. Main paper should be readable without referencing to the appendic... | Summary: The paper investigates normalization and non-linear activation functions from the perspective of isometry.
Strengths: - This paper provides interesting analysis into normalization and non-linear activation from the perspective of isometry.
- The paper has provided interesting discussion on future research. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments helping us to improve the writing.
> The paper may benefit more from slightly larger scale data. For example, using MNIST could be a good way to improve the paper without too much additional work.
To address the reviewer's concern about sca... | Summary: The paper is a theoretical analysis in the mean-filed regime the of the second-to-last gram-matrix of MLP. It proves that the presence of layer normalization in conjunction with non-linear activation functions biases the input-output mapping at initialization towards an isometry.
Strengths: This paper present... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and their constructive comments.
> ## Limitations:
> - Not really. Perhaps the main limitation is that the author limit their analysis on MLP architectures. This makes the results of their paper not immediately applicable to transformers (due to ... | Summary: This paper studies the isometry of Gram matrix under the effect of BN, LN and activation at initialization. For BN and LN, some results are obtained. For activation, most are empirical results.
Strengths: Study multiple factors that affect the isometry of Gram matrix.
Weaknesses: 1) The novelty of the theory... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing our work, providing constructive feedback, and pointing out potential points of confusion.
> This paper studies the isometry of Gram matrix under the effect of BN, LN and activation at initialization. For BN and LN, some results are obtained. For ac... | Rebuttal 1:
Rebuttal: We appreciate constructive reviews that helped us to improve the paper. Let us recount our main contributions by showing excerpts from the reviews.
This excerpt from `B3t5`'s captures the main part of contribution:
> A study of layernorm and Gramm matrix isometry is presented in both theory and... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the isometric properties of a randomly initialized neural network. The authors show layer normalization and proper activation functions can mitigate rank collapse. In addition, they also quantify the normalization bias for different type of normalization layers. They use the Hermite expansio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing our work, providing constructive feedback, and pointing out potential points of confusion.
*Detailed responses:*
> There are also a few confusing statements such as the authors show higher He coefficients have negative impact on isometry properties... | null | null | null | null | null | null |
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies | Accept (poster) | Summary: The paper proposes a new approach to adapt robot motion policies for different task conditions. It suggests leveraging the structure of probabilistic policies, specifically Gaussian mixture models (GMMs), and formulating policy optimization as an optimal transport problem. By using the L2-Wasserstein distance ... | Rebuttal 1:
Rebuttal: We are very grateful for your time reviewing our work and the provided suggestion to compare against PMOE, an algorithm we were not aware of. We also appreciate the positive feedback about both the theoretical and practical aspects of our paper. Below we address the main concern of the review:
1.... | Summary: This paper proposes to formulate policy optimization as a Wasserstein gradient flow over the Gaussian Mixture Model (GMM) space, which enhances the stability of policy optimization processes. In the proposed GMM policy updates, the mean and variance of gaussians are optimized through Riemann gradient descent v... | Rebuttal 1:
Rebuttal: We are very grateful for your time reviewing our work and the provided suggestions. We appreciate the positive feedback about our paper, and are glad to read that our approach " to optimize GMM policies to model more complex policy distributions is a meaningful research direction"! Below we addre... | Summary: The authors investigate Wasserstein gradient flows (WGF) for a Gaussian mixture model policy.
The WGF is a principled natural gradient method for updating the parameters since it follows the metric space defined by the Wasserstein-2 divergence.
This approach is compared to deep RL methods on some planar cont... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We are very pleased to read that the reviewer found our paper "very nicely written paper with also excelled presentation"! Below we address the issues raised in the review.
1. **Natural gradients (NGs) in RL**:
* NGs leverage a metric to locally control th... | Summary: This paper proposes an algorithm for RL in continuous action spaces based on the Wasserstein Gradient Flow formulation of Richemond and Maginnis. By specializing to the case of Gaussian Mixture policies, a simpler formulation is obtained, where the Gaussian part of the parameters (mean and covariance for each ... | Rebuttal 1:
Rebuttal: We would like to thank you for taking the time to review our work. We are delighted to read that "our splitting operation over two Riemannian gradients is nicely executed"! Below, we address some of the key concerns raised as part of the review.
1. **Baselines**:
We would like to point out th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers and the area chair for their work and feedback on our manuscript. Below we summarize the main points addressed in our rebuttal. We also attach a PDF with two figures showing newly added results for additional experiments as requested by the reviewers.
**Rebut... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PRED: Pre-training via Semantic Rendering on LiDAR Point Clouds | Accept (poster) | Summary: This work incorporates images into point cloud pretraining since images contain richer semantic information. Instead of adopting the back propagation strategy which can not handle the misalignment between camera and LiDAR, this paper leverages the neural rendering technique to injecting the semantics into the ... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback regarding the paper's clarity, motivation, and experimental results. We understand your concerns and would like to address your points one by one.
**Q1: Concerns regarding technical contribution.**
**A1:** Our main contribution lies in a novel pre-training fr... | Summary: This paper investigates weakly-supervised representation learning for outdoor LiDAR point clouds. To start, the authors point out that the inherent incompleteness of outdoor LiDAR points would reduce the effectiveness of self-supervised representation learning approaches. To mitigate this, the authors propose ... | Rebuttal 1:
Rebuttal: We are encouraged by your acknowledgment of our work's novelty and thoroughness in experimentation. We aim to address the concerns raised to offer clearer insights into our methodology.
**Q1: Clarity on image signals in Tables 1 and 2.**
**A1:** To offer greater clarity, we will introduce a dedi... | Summary: This paper proposed a novel point cloud pre-training framework, PRED, which leverages the semantic information consistency between the LiDAR point clouds and the camera images to improve the point cloud pre-training performance. The author proposed (1) a novel semantic rendering module for decoding the semanti... | Rebuttal 1:
Rebuttal: The recognition of our approach's potential significance in both academic and industrial circles is particularly encouraging. We acknowledge the concerns you've highlighted and would like to offer clarifications:
**Q1: Is there anything special or novel for handling occlusion compared to [46]? Wh... | Summary: This work proposes a new pretraining algorithm for outdoor 3D perception tasks, where images are utilized to provide comprehensive semantic information. The main algorithm is to leverage the semantics of images for supervision through neural rendering. The authors also apply point-wise masking with a high mask... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback regarding the motivation and results of our research. We understand your concerns about certain aspects of our paper and would like to provide some clarification.
**Q1: On the selection of the pre-trained segmenter.**
**A1:** We will definitely explore the po... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: 1. The paper proposes PRED, a novel pre-training framework for outdoor point clouds that leverages image semantics through neural rendering. The paper addresses the challenges of incompleteness and occlusion in point clouds, which are common in outdoor LiDAR datasets for autonomous driving.
2. The paper uses a... | Rebuttal 1:
Rebuttal: We're gratified by your acknowledgment of our approach's novelty and efficacy. We also value the concerns you've raised and here's our detailed response:
**Q1: Evaluating the Impact of Image Segmentation Model Choices on Pre-training Performance.**
**A1:** We acknowledge the importance of examin... | null | null | null | null | null | null |
Locality Sensitive Hashing in Fourier Frequency Domain For Soft Set Containment Search | Accept (spotlight) | Summary: This paper presents a novel approach, called FourierHashNet, for fast soft set containment search. The key idea is to extend set containment to soft set containment by representing query and document elements as embedded representations, instead of atomic IDs. The authors propose a dominance similarity measure... | Rebuttal 1:
Rebuttal: The authors thank reviewer wHYk for the positive feedback.
In the general response, we delve into a broader application context of FourierHashNet, particularly for similarity search involving shift invariant functions.
Please reach out to us if you have further inquiries or points for clarifica... | Summary: Locality-sensitive hash (LSH) functions are mappings from sets of queries and documents to "buckets," in such a way that similar queries and documents are assigned to the same bucket. This paper proposes an asymmetric LSH where the notion of similarity is related to the hinge distance (called dominance similar... | Rebuttal 1:
Rebuttal: We thank reviewer AbHb for the spectacular feedback.
> Proof that FourierHashNet is an ALSH
Note that $p(\omega^j_k)\propto|Re(S(\omega _k ^{j}))|+|Im(S(\omega _k ^{j}))|$. Let $I$ be the proportionality constant. Assume $\mathrm{sim}(q,x)>s _m>0$ and $\cos^{-1}$ is $L _{\cos}$-Lipschitz.
We ... | Summary: This paper studies a new search problem called vector dominance (or set containment), where the authors provide strong motivation from various real-world applications. They present a new approach named FourierHashNet along with a fresh asymmetric vector dominance distance to address this problem. Through exten... | Rebuttal 1:
Rebuttal: The authors thank reviewer 2JDE for the positive feedback. We will undertake thorough proofreading and polishing for the final version of the manuscript. | null | null | Rebuttal 1:
Rebuttal: # Truncate-transform-sample paradigm
### General Framework
Indeed, our algorithm can be generalized to include a wide variety of scoring functions, including Box embedding based volume scores, facility location scores used by ColBERT, etc.
Our framework extends to any shift-invariant scoring fu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Homotopy-based training of NeuralODEs for accurate dynamics discovery | Accept (poster) | Summary: This paper proposes a novel method for training NeuralODEs, based on synchronization and homotopy optimization. They show that the addition of the synchronization module can smooth the loss landscape, on which homotopy optimization can be applied to enhance training. The new training method achieves competitiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and the constructive feedback. We have added the responses to both of the reviewer's questions in the global response above. We look forward to additional discussions with the reviewer.
We also add a bit more theoretical description about the synchr... | Summary: Training neural ODE models on long sequences of data from a dynamical system is difficult. The authors argue empirically that this is due to a poorly conditioned loss landscape leading to difficulties in optimization. To rectify this difficulty the authors use tools from the literature on synchronization (whic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interest in our paper and the positive comments. We have listed the answers to the reviewer's question below, and have grouped the answers to some of the more commonly occurred questions from multiple reviewers in the global response above.
> The theoretical explanat... | Summary: The authors present a new method for training Neural Ordinary Differential Equations (NeuralODEs), a modeling approach that merges neural networks with the paradigm of differential equations from physical sciences. While NeuralODEs offer significant potential for extracting dynamic laws from time series data, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments as well as the detailed questions. We have listed the responses to the questions below and grouped the responses to some of the more common questions in the global comment above.
> How does the efficacy of homotopy and multiple-shooting methods change with ... | Summary: The paper proposes a homotopy-based training method for NeuralODE models, in particular for the case with cases with long sequence of training data. Comprehensive experimental results demonstrated the effectiveness of the proposed method.
Strengths: 1. The proposed method is based on homotopy method, which ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and the very interesting questions. We list our answers below, and have grouped the common questions in the global comment.
> A few key references related to homotopy methods are not cited. The references by JH He really put homotopy methods in the mainstrea... | Rebuttal 1:
Rebuttal: 1. Can our algorithm be applied to potentially partially observed high dimensional data?
We believe that our algorithm can be applied to higher-dimensional problems, provided that a couple of challenges due to the increased dimensionality are addressed. Depending on the nature of the data, we bel... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a new training method for Neural Ordinary Differential Equations (NeuralODEs) that aims to improve their performance in extracting dynamical laws from time series data. The proposed method is based on synchronization and homotopy optimization, which does not require changes to the model arc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and suggestions. We have written our responses to some of the common questions in the global comment above, and have listed responses to the rest of the reviewer's questions below.
> Considering that there are five main hyperparameters, if there are... | null | null | null | null | null | null |
Efficient Learning of Linear Graph Neural Networks via Node Subsampling | Accept (poster) | Summary: The work tackles the problem of scaling GNNs used for regression tasks to large graphs by subsampling given nodes. The technique consists of first performing node sub-sampling to estimate the leverage scores of $AX$ and then performs leverage score sampling on $AX$. The authors show that this technique is a go... | Rebuttal 1:
Rebuttal: W1 & W2. Lack of run-time comparison:
Thank you for pointing out this issue. Please see the common response and the additional experiments in the attached pdf above.
W3. Extension to nonlinear GCN:
Please see the common response.
W4. Some typos (line 60, line 95):
Thanks for pointing out thes... | Summary: This paper proposes a method to overcome the computational difficulties often encountered when using graph neural networks (GNNs) for large-scale datasets, by subsampling in the adjacency and feature matrices. By assuming a two-layer linear GNN for the regression problem and performing subsampling based on the... | Rebuttal 1:
Rebuttal: W1. Extension to nonlinear GCN:
We thank the reviewer for suggesting interesting directions for future work. We leave comments in the common response section and provide additional experimental results for nonlinear GCN in the attached pdf.
W2. Extension to the more-than-two-layer case:
We than... | Summary: The authors proposed a sampling method to train graph neural networks efficiently. However, the current implementation of graph neural networks is based on the sparse matrix multiplication and thus the complexity is not O(n^2d). The authors only analyze the complexity theoretically without experimental support... | Rebuttal 1:
Rebuttal: W1. The statement about the complexity of the graph neural network is not correct. The current implementation is based on sparse multiplication, so the complexity is not O(n^2d).
We thank the reviewer for pointing this out. The main motivation for our graph subsampling procedure is settings where... | Summary: The paper presents an efficient GNN training solution through node subsampling and leverage score sampling, which is proven to be efficient in learning a regression model with bounded entry access and running time.
Strengths: * The proposed technique leads to a proven efficient approach for GNN training, with... | Rebuttal 1:
Rebuttal: W1. Comparison with other baselines (e.g., GraphSage and GraphSaint) and the pros and cons:
We thank the reviewer for the suggestion. Please see the comments in the common response section and the attached pdf for additional experiments.
W2. MSE was the only evaluation criterion:
Since we focuse... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. For weaknesses/questions raised by multiple reviewers, we provide common responses. For the remaining comments, we provide point-by-point responses below.
We would like to first highlight what we see as the main contribution of our paper. Our pa... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Continuous-time Analysis of Anchor Acceleration | Accept (poster) | Summary:
The paper extends the continuous time analysis anchor ODE in [55] to involved a more general choice of the coefficient $\beta(t)$ and show that the choice in [55] is in some sense optimal. They then go on to show:
- correspondance with the discrete anchoring schemes APPM/FEG/AEG
- Anchor ODE converges to a mi... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive review, we're glad that the reviewer found our paper to be "impressive" and "complete".
We would like to start by sharing the most interesting observation we've gained while addressing your questions.
**Q5.**
The anchor ODE introduced in our paper d... | Summary: This paper conducts a continuous-time analysis of an acceleration method called "anchoring", where the main contributions are four-fold. The authors provided a unified analysis of the convergence rate of anchor acceleration, which includes both the constant and adaptive cases. Then the authors presented an ada... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer found our paper to "provide a valuable contribution to the field of optimization".
Especially, we're sincerely grateful that the reviewer thoroughly engaged with the technical proofs and highlighted the potential applicability of the ideas to more generalized case... | Summary: The paper focuses on the analysis of anchor acceleration, a recently discovered acceleration mechanism for minimax optimization and fixed-point problems. The authors provide tight and unified analyses to characterize the convergence rate of anchor acceleration and present an adaptive method inspired by continu... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback and thoughtful questions.
The answers to your questions are as follow.
**Importance of anchor acceleration, difference between Nesterov's acceleration (W2, Q1) :**
The main difference between Nesterov acceleration and anchor acceleration is that they are opt... | Summary: The paper analyzes the dynamics of anchor acceleration using a differential inclusion. It derives a convergence rate that depends on the anchor coefficient and shows that the rate is tight using a certain instance. By discretizing the differential inclusions, the authors derive an algorithm that generalizes th... | Rebuttal 1:
Rebuttal: Thank you for the valuable review.
We're glad that you've felt our adaptive anchor acceleration method in Section 7 as a "meaningful example of the potential of ODE analysis for optimization algorithm design".
We address the answers to your questions and feedbacks as follows.
**Q1.**
- As m... | Rebuttal 1:
Rebuttal: # Common Response
First and foremost, we thank all reviewers for their time and feedback on our paper. We are pleased to see that most of the reviewers found our contributions to be valuable, especially the adaptive anchor method that we obtained using the insight gained through the continuous-ti... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent | Accept (poster) | Summary: This paper considers the low-rank matrix factorization problem (LRMF). Recent work provided global convergence for gradient descent on LRMF starting from small random initialization and small learning rate, but the convergence rate there depends on the matrix condition number. This paper considers a variant ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for your positive evaluation of our work, and we also thank you for your valuable and constructive suggestions. As for your concerns, we make detailed responses as follows. We will deeply appreciate that you can raise your score if you find our responses resolve ... | Summary: This paper considers the problem of low-rank matrix recovery using preconditioned gradient descent. Traditionally, although GD can be used to solve this problem, it becomes extremely slow when the problem is ill-conditioned. Recently a method called ScaledGD was proposed that makes GD immune to ill-conditionin... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your valuable comments on our work. As for your concerns, we give detailed response below which we hope can help you fully understand our work. Please feel free to let us know if you have any further concerns. We will deeply appreciate that you ca... | Summary: This paper studies low rank matrix factorization, which is an important topic with many applications in machine learning. The main challenge of this problem comes from the non-convexity of the objective function, especially, this objective function can be non-smooth. As a consequence, the global convergence r... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for the valuable comments and the constructive suggestions. Here we response your concerns one by one. We will deeply appreciate that you can raise your score if you find our responses resolve your concerns.
**Q1: How does the algorithms work on real data set.**
**... | Summary: This paper considers the classical problem of low rank approximation. In particular, given a matrix $M\in \Re^{m \times n}$ with rank $d$, we want to find $U\in \Re^{m\times d}, V\in \Re^{n\times d}$ that minimizes $f(U,V) = |UV^T - M|_F$ (i.e $U,V$ are low rank matrices whose product approximate $M$). The pro... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for your careful review, constructive suggestions and positive feedbacks. The followings are our responses to your concerns. We will greatly appreciate that you can raise your score if you find our responses resolve your concerns.
**Q1: Write some of the proof st... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
Thank you so much for your valuable comments and we truly appreciate the time and effort you've taken to review our work. We are also glad that the reviewers found our work valuable and give us positive feedbacks. Your feedback is very important to us, and according to the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections | Accept (poster) | Summary: ARTIC3D is a self-supervised framework that reconstructs 3D articulated shapes and textures of animals from sparse and noisy online images. It uses a skeleton-based surface representation and 2D diffusion priors to enhance the input images and guide the 3D optimization. It also enables realistic animation by f... | Rebuttal 1:
Rebuttal: ---
**”More details of LASSIE and Hi-LASSIE are needed”**
We thank the reviewer for the feedback and will add more details to the preliminaries section (3.1) in the manuscript.
---
**”3D skeleton from Hi-LASSIE may not work for occluded/truncated images”**
Our 3D skeleton initialization is pe... | Summary: This paper proposes a method to reconstruct the shape and texture of articulated objects from noisy web image collections. To achieve this, ARTIC3D proposed a diffusion-based 2D image enhancement module DASS, and then reconstructed the shape and texture maps using Hi-LASSIE. Moreover, to increase the animation... | Rebuttal 1:
Rebuttal: ---
**3D inconsistency and unfaithful texture from Stable Diffusion**
Most diffusion-based 3D generation methods like DreamFusion [21] only rely on the 2D diffusion prior to produce 3D shapes and texture, and thus they are prone to inconsistent outputs from different views (e.g. multiple faces on... | Summary: This paper proposes a new framework, named ARTIC3D, to address the task of 3D reconstruction of articulated shapes and texture from noisy and few images. It is based on pre-trained diffusion models. Specifically, the authors use a novel decoder-based accumulative score sampling (DASS) to replace score distilla... | Rebuttal 1:
Rebuttal: ---
**Contribution beyond LASSIE / Hi-LASSIE**
Please see “Contribution beyond LASSIE / Hi-LASSIE” in the General Response above.
---
**”There are many other types of noises that are not explored”**
We thank the reviewer for the feedback and will revise the wording in the manuscript. Please no... | Summary: This paper introduces an articulated 3D shape reconstruction method from noisy web images with the help of diffusion models. The authors use a diffusion method to enhance the noisy input images to get clean reference 2D images and masks. Then, skeleton-based surface representations are optimized from the refer... | Rebuttal 1:
Rebuttal: ---
**Contribution beyond LASSIE / Hi-LASSIE**
Please see “Contribution beyond LASSIE / Hi-LASSIE” in the General Response above.
---
**Unfaithful texture from Stable Diffusion**
Please see “Unfaithful texture from Stable Diffusion” in the General Response above.
---
**“Figure 2 is not cle... | Rebuttal 1:
Rebuttal: # General Response
We thank the reviewers for the constructive feedback. We address the common concerns in the General Response and specific comments in the individual response to each reviewer.
---
### **Contribution beyond LASSIE / Hi-LASSIE**
While ARTIC3D deals with the same reconstruction ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adversarial Counterfactual Environment Model Learning | Accept (spotlight) | Summary: An accurate environment dynamics model is crucial for various downstream tasks, such as counterfactual prediction, off-policy evaluation, and offline reinforcement learning. Currently, these models were learned through empirical risk minimization by step-wise fitting of historical transition data. However, we ... | Rebuttal 1:
Rebuttal: We express our gratitude for your constructive feedback and the time you dedicated to evaluating our paper. Your insightful remarks help us in refining our work and emphasizing its importance in the reinforcement learning domain.
**Clarify Experiments and Metrics:**
We understand the concerns r... | Summary: The paper presents a novel method for improving the accuracy of environment dynamics models for counterfactual prediction, off-policy evaluation, and offline reinforcement learning. Currently, these models learn via empirical risk minimization (ERM), which the authors show can lead to failures in counterfactua... | Rebuttal 1:
Rebuttal: We'd like to genuinely express our gratitude for your thoughtful feedback and the time you've invested in reviewing our work. We've taken your concerns to heart and have attempted to address them as follows.
**1. Contextualizing the Work:**
We acknowledge the feedback on the need for a more det... | Summary: This paper considers the problem of environment modeling. An adversarial method is proposed that the adversarial counterparts is trained to exploit the model to generate a data distribution that weakens the model’s prediction accuracy, and then the model is trained under the adversarial data distribution with ... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for your thoughtful feedback on our submission. We've taken your concerns to heart and have attempted to address them in the following manner.
**Addressing Stability Concerns of GALILEO:**
1. **Instability of training two discriminators:** The reviewer are... | Summary: The paper proposes a model-learning approach for counterfactual prediction (CP), off-policy evaluation (OPE), and offline reinforcement learning (ORL). The authors introduce the adversarial weighted empirical risk minimization (AWRM) objective to facilitate learning models that accurately evaluate target polic... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you have put into reviewing our paper. We appreciate your feedback and would like to address the concerns you've raised as follows:
1. **Experimental Evaluation**:
1. *Limiting the Time Horizon to {10, 20, 40}*:
As detailed in Line 328, our motivation... | Rebuttal 1:
Rebuttal: We would like to extend our heartfelt appreciation to the esteemed reviewers for their invaluable feedback and insightful comments. Their constructive input has undoubtedly enhanced the quality of our manuscript. The common strengths highlighted across the reviews are encouraging, and we are grate... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces an adversarial training approach to model learning that improves performance for counterfactual data that may differ widely from the data used to train the model. This is particularly relevant when training from offline data and expecting the model to generalize when deployed later. This ... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback on our paper. Your constructive comments are greatly appreciated. We would like to address the concerns and questions you raised as follows:
1. **Concern about Offline Data Bias and other potential solution in Fatemi, et al [1] and Killian, et al [2]**
We ... | null | null | null | null | null | null |
Formalizing locality for normative synaptic plasticity models | Accept (poster) | Summary: Many biologically plausible learning algorithms have been developed in the recent years but much less effort have been deployed to compare them and understand how they could yield experimentally testable hypothesis. The present work introduces a framework that allows comparing existing algorithms. In particula... | Rebuttal 1:
Rebuttal: Thank you for your feedback, here we will do our best to clarify the points that you raised. In some cases we will refer to the General Comments [GXX] if your question was also raised by other reviewers.
Regarding your question about whether or not p-locality is fully aligned with our intuitions ... | Summary: The paper describes a systematic and mathematically formal method for categorizing learning algorithms into different types and degres of locality. It also applies the method to certain such algorithms and determines their type of locality.
Strengths: The authors chose to work on a topic that is indeed very i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very detailed comments! We will respond to your specific comments here, and for questions asked by multiple reviewers, we will refer you to the appropriate response in the General Comments section [GXX]. All of our comments below will be incorporated into our subseq... | Summary: The paper introduces a general definition of locality of updates in neural networks, which is an important requirement of biological plausibility. Variants of locality requirements allow to describe classes of networks and algorithms. Several important algorithms are analyzed through this lens.
Strengths: De... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for their detailed feedback on our manuscript. We will make sure to add relevant material in relation to our responses below in our subsequent draft.
For a discussion of the current state of the field, and an explanation of what our framework adds to discussion surrou... | Summary: The authors propose "Sp-locality" as a formal definition of locality in models of synaptic plasticity, as well as two intermediate definitions, S-locality and p-locality. S-locality explicitly enumerates the set of variables which directly participate in a synaptic update for each synapse. p-locality replaces ... | Rebuttal 1:
Rebuttal: Thank you for your very comprehensive review; we will elaborate here on your criticisms and requests for clarification. All of our comments below will be incorporated into our subsequent draft.
For our response on previous related work, please see general comment [G1].
It is a genuine issue tha... | Rebuttal 1:
Rebuttal: #### General comment
We would like to thank the reviewers for their extremely helpful feedback. Here, in the global reply, we will address critiques that were raised by multiple reviewers. Importantly, we are grateful that most reviewers recognized the importance of a formal framework to study le... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | Accept (poster) | Summary: The paper proposes (Federated Learning using Invariant Dropout) FLuiD to address the straggler problem in FL. Due to the presence of system heterogeneity, straggling nodes (nodes with resource constraints) become a bottleneck in the FL training process. In this paper, the authors propose a FLuID to address thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments, which have enabled us to add scalability analysis and sampling to the paper.
**Question 1: Scalability**
We scale FLuID to **1000 clients** with the FEMNIST dataset for 500 global training rounds. We run with a **client sampling ratio of 10%**,... | Summary: The paper focuses on the issue of mitigating stragglers in a heterogenous FL environment through dynamic load balancing, introducing a technique called Invariant Dropout and an adaptive training framework called FLuID. Invariant Dropout dynamically creates customized sub models that include only the neurons ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We answer the most pressing questions first, followed by the remaining questions.
**Weakness 1: Accuracy Gains of FLuID**
We have implemented FLuID as a lightweight system with minimal overheads. We empirically observe that the FLuID calibrati... | Summary: FLuID authors tackle a straggler problem in Federated Learning, where a central model is trained across a set of heterogeneous devices.
This problem is particularly challenging when performance capabilities at training time are actually variable. This variable heterogeneity at training time requires a mechanis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We provide the comparison against SuperFed below:
Both SuperFed and FLUID propose sending a subset of a global model to edge clients, and both frameworks can send submodels of varying sizes to each client. However, these two frameworks differ significantl... | Summary: The paper presents a framework, FLuID, for cross-device federated learning, where some of the clients are “stragglers”. The training time of these stragglers is significantly higher, hence they dictate the overall training time.
FLuID uses Invariant Dropout to dynamically reduce the stragglers’ training time,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We answer the questions in order.
**Weakness 1: Training time of stragglers**
FluID considers the end-to-end latency, upload/download latency, communication time, and training time of the device to determine if it is a straggler.
However, sim... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning better with Dale’s Law: A Spectral Perspective | Accept (poster) | Summary: The author present a simulation study that investigates gradient-based optimisation of RNNs that obey Dale’s Law, i.e. networks with neurons that are strictly excitatory or inhibitory at initialisation and during training. In particular, the authors disentangle the effect of enforcing Dale’s Law during trainin... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thoughtful review. We are pleased they share our perspective that this paper investigates an important, unanswered question: the origins of the performance gap between EI and standard RNNs. Furthermore, it is our desire that this paper contributes to a broad s... | Summary: The authors apply Dale's law (that neurons provide exclusively excitatory or inhibitory outputs) to RNNs with two different architectures: ColEIs which are the “straightforward” way of applying sign constraints per neuron, and recurrent DANNs based on an architecture with two layers per recurrent step. They co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful review. Please see the general comment for the references list.
> **Strengths**: Dale’s law is an important and ubiquitous property...
Many thanks!
**Weaknesses:**
>While being advertised…
Thank you for highlighting this aspect of DANNs, we will add to ... | Summary: The paper investigated the problem of why columnEI networks have impaired learning, and they experimentally found that instead of sign constraint, the spectral property of weight at initialization contributed the most; they further experimentally showed that E/I ratio and network size change the spectral prope... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful review. Please see the general comment for the references list.
>**Weaknesses** The discussion of initialization spectral property is only done for sequential MNIST. Does the observation on SVD entropy hold across data distributions? I'm a bit concerned on ... | Summary: This paper presents a comparison between the performance of standard RNNs and two classes of models: the ColEI network and the DANN, which incorporate Dale's Law with the constraint that units in a circuit should be excitatory or inhibitory but not both. The DANN achieves similar performance to a non-signed RN... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. Please note that the reference numbers refer to the references list in the general comment.
>**Weaknesses:** However, the practical application for designing EI networks that perform well requires further clarification. It is unclear to me whether DANNs can... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort in providing a set of positive, high-quality helpful reviews.
We would like to take this opportunity to reiterate the main contribution of our work. We investigate the following question: why do recurrent neural networks (RNNs) that have separate e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Invariant Molecular Representation in Latent Discrete Space | Accept (poster) | Summary: This paper presents a new graph neural network architecture and objective function that encourages models to identify features that are invariant to distribution shifts in the data. The proposed method, iMoLD performs invariant feature extraction in the latent embedding space and leads to improved performance ... | Rebuttal 1:
Rebuttal: ### (Q1) Distribution definition
The $P_{train}$, $P_{test}$ and $P_{all}$ given in the manuscript are the form of a collection of distributions, but not a distribution, which needs to be renormalized to avoid ambiguity. We will clarify that in the manuscript.
### (Q2) The claim of discrete space... | Summary: While significant advances have been made in molecular representation approaches, conventional approaches typically assume that data sources are independent and sampled from the same distribution. However, molecules in real-world drug development often show different characteristics, which might be from a diff... | Rebuttal 1:
Rebuttal: ### (Q1) Typos
Thanks for pointing out these typos, we will revise them accordingly in the updated version.
### (Q2) 3D visualization
We provide the 2D and 3D visualization of extracted features together in the uploaded PDF file in the global response. The 3D visualization results show similar ch... | Summary: This paper proposes a molecular self-supervised learning method for out-of-distribution generalization. The authors introduces a "first-encoding-then-separation" framework to learn invariant features. For doing so, the authors design discrete latent space with VQ-VAE. The experimental results show that their m... | Rebuttal 1:
Rebuttal: ### (Q1) Hyperparameters
The results presented for the baseline models within the benchmark are derived from an exploration of their respective hyperparameters. In a congruent manner but limited by computational resources, we have searched a partial set of hyperparameters of our method and empiric... | Summary: The paper presents an invariant and robust representation learning approach for molecules to improve the out-of-distribution generalization performance of the predictive models. Specifically, they first map the molecule to the latent representation and then do a separation step where they separate the latent w... | Rebuttal 1:
Rebuttal: ### (Q1) The difference between Frobenius norm and the regularization in Equation (14).
The $<\mathbf{J},\mathbf{S}>\_F$ in Equation (14) is equal to the 1-order Frobenius norm of $S$.
However, the Frobenius norm merely encapsulates the summation of the elements in the matrix, and the range of i... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for providing us with their valuable and affirmative comments regarding our submission.
First of all, we want to clarify our technical contributions. The proposed method mainly consists of three technical contributions:
1. _**The first-encoding-then-separatio... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a new approach to obtain robust molecular representation through a first-encoding-then-separation method. The proposed method utilizes a graph neural network (GNN) as a molecule encoder and applies a residual vector quantization module to modify the representation. Additionally, a scoring G... | Rebuttal 1:
Rebuttal: ### (Q1) Motivation for "first-encoding-then-separation"
In contrast to previous works, we adopt a novel paradigm that encodes the whole molecule first, then do separation in the latent representation space. The motivation for this approach is detailed in the global response.
### (Q2) Technical c... | null | null | null | null | null | null |
Learning to Group Auxiliary Datasets for Molecule | Accept (poster) | Summary: This paper studies an interesting and meaningful problem, that is, how to make the best use of available molecule data for achieving superior performance on the prediction of molecule properties. This paper first conducts an extensive study on many widely used benchmark datasets and discovers some interesting ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments on our paper. We will explain your concerns point by point.
```
Q1: Computation cost will be high if there are many tasks involved.
```
**Response**: It is indeed true that handling numerous datasets, especially when including large auxiliary set... | Summary: The authors address the problem of, in a transfer learning, meta-learning, or few-shot learning setting involving molecules, which datasets might be most useful in providing positive auxiliary information that does not damage model performance on the task of interest.
The paper proposes a method, MolGroup, t... | Rebuttal 1:
Rebuttal: Thanks for your feedback on our work. We will address your main concern point by point.
```
Q1: It is rather unclear initially whether the authors propose to calculate an affinity score based upon calculated task embedding and fingerprint distribution differences between datasets, or learn an aff... | Summary: The paper addresses the challenge of limited annotations in small molecule datasets and proposes a method called MolGroup to identify auxiliary datasets that can benefit the target dataset when jointly trained. MolGroup utilizes a routing mechanism optimized through a bi-level optimization framework to separat... | Rebuttal 1:
Rebuttal: Thanks for your feedback on our work! We will explain your concerns point by point.
```
Q1: The Pearson coefficients presented in Figure 3 are relatively low, all below 0.5. This raises doubts about the claim that the combination of task and structure leads to better performance. There is a poten... | Summary: The paper proposed MolGroup, a dataset grouping method designed to aid molecule property prediction. Motivated by preliminary empirical analysis, MolGroup separates the dataset affinity into task and structure affinity, and uses a routing mechanism to quantify the affinity between a pair of datasets. The routi... | Rebuttal 1:
Rebuttal: We sincerely thank you for the insightful comments! We will address your concerns point by point.
```
Q1: In Fig. 3a, I don't think we can draw the conclusion that structure and task affinities are compensatory. If we remove the outliers, the points seem randomly distributed, which means that the... | Rebuttal 1:
Rebuttal: We thank the reviewers for noting that we propose a novel method (VvkN,ohh4) to address a meaningful problem (B1V2,VvkN,sjEd) with a clear motivation (VvkN,NGKd,ohh4,sjEd), and the paper is well-written and easy to follow (VvkN,NGKd,ohh4,sjEd). We further summarize our key contributions as follows... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates how different molecule datasets affect each other’s learning, considering both task and structure aspects. It proposes a routing-based molecule grouping method to calculate the affinity scores of each auxiliary dataset based on the graph structure and task information, and select the au... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments on our paper! Here we additionally present the performance of GCN (#layer=2, hidden dim=300, dropout rate=0.5) with different grouping methods in the following table, where a consistent improvement can be observed. Such a phenomenon also aligns with the performanc... | null | null | null | null | null | null |
Grounding Neural Inference with Satisfiability Modulo Theories | Accept (spotlight) | Summary: This paper proposes SMTLayer, a layer that incorporates SMT solvers (Z3 in this case) into neural networks. The layer itself is not differentiable. The forward and backward passes of SMTLayer are derived thoroughly. Experiments show that this innovation results in overall more robust, interpretable, and effici... | Rebuttal 1:
Rebuttal: Thanks for you considerable review and we address the reviewer's questions as follows.
> Some claims can be made more concrete. Line 13: "that are robust to certain types of covariate shift" what types of covariate shift exactly?
**A**: In Section 5, we study MNIST addition and Visual Algebra u... | Summary: This paper studies neuro-symbolic learning tasks on weakly supervised setting (i.e., lacking direct label supervision of neural networks). To incorporate symbolic knowledge into training, this work integrates SMT solvers into the forward and backward passes of a deep network layer. The key idea is to establish... | Rebuttal 1:
Rebuttal: Thanks for your considerable and insightful review. We address the reviewer's concerns & questions as follows.
> The exactly-one assumption made by Theorem 2 is quite vacuous. From my understanding, with this assumption, one can derive the label supervision by using SMT solver (or correct me if n... | Summary: This work proposes to incorporate SMT constraints in the neural network to encode domain knowledge. Specifically, it proposes an unsat core based approach, and an MaxSAT based approach for the differentiable training in the presence of SMT constraints. Empirical evaluations on several benchmark problems are pr... | Rebuttal 1:
Rebuttal: Thanks for your considerable review. We address the reviewer's questions as follows.
> The proposed method seems limited to classification task, without discussions on its generalization. Throughout this work, the loss is always assumed to be binary cross-entropy. I wonder how much this method g... | Summary:
The authors implement SMTLayer, which integrates an SMT solver into a differentiable module, suitable for use with deep learning. SMTLayer takes a vector of floating-point values as input, and produces a vector of floating-point values as output. These input vector is cast to boolean values, and the output... | Rebuttal 1:
Rebuttal: Thanks for your considerable review. We address the reviewer's questions as follows.
> Can you elaborate how gradients are computed in the SMTLayer.
**A:** This is elaborated further in the proof of theorem 2, which is given in the supplementary material; it is far from obvious that this is whe... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration | Accept (poster) | Summary: This paper starts from an interesting viewpoint that the synthetic of the previous ZSQ method usually fails to model the similar text feature like real data. As a result, the authors suggest retaining the texture feature. At first, they synthesize calibration images with a LAWS texture feature energy preserve ... | Rebuttal 1:
Rebuttal: ### **We would like to thank the reviewer for the thorough and thoughtful revision of the paper. In the following, we will address each point raised.**
We not only respond to or clarify the points but have also made changes accordingly to the main text to make them more clear to future readers.
-... | Summary: TexQ is a novel zero-shot quantization (ZSQ) method that addresses the limitations of conventional synthetic samples in retaining texture feature distributions. It achieves state-of-the-art results in ultra-low bit width quantization, with a significant accuracy increase compared to existing methods on ImageNe... | Rebuttal 1:
Rebuttal: ### **Thank you for your positive review and constructive comments. We have performed some of them and will include them in the camera-ready version.**
---
**Q1: Maybe the structures used are insufficient with ResNet-18 and MobileNet-V2. What about the most commonly used ResNet-50?**
**A1: Than... | Summary: This work proposes TexQ, which targets keeping the texture information of the synthetic samples of zero-shot quantization. The texture feature energy distribution calibration method is applied to the synthesized samples, and mixup knowledge distillation is introduced to improve the diversity of the synthetic s... | Rebuttal 1:
Rebuttal: ### **Thank you for providing new ideas for straightforward visualization and interesting transferred language tasks. We would like to address the concerns below:**
---
**Q1: The introduction of the concept "LAWS texture feature energy" needs to be improved. The example given in Eq.5 is not stra... | Summary: The paper points out that there is a strong dependency between the performance of CNN and the texture feature of the dataset. For extending this concept to the quantization field, the paper adopts calibration samples which are trained with manually designed texture filters. In addition to synthetic samples whi... | Rebuttal 1:
Rebuttal: ### **We thank the reviewer for the helpful reviews that will help strengthen our paper. Our replies are as follows:**
---
**Q1: In equation 1, only a rounding operation is applied to obtain quantized integers without a clip operation?**
**A1: Thank you for reminding us that we omitted a clip o... | Rebuttal 1:
Rebuttal: ## **[Global Response] Tables for supplementary experiments**
************************************************
### **Tabel A Comparisons with MixMix and KW (4 bits MobIleNet-V2 on ImageNet)**
| Method | Settings | Acc. of quantized model | Acc. of pre-trained model ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: They suggested a zero-shot quantization method to retain the detailed texture feature distribution and introduced the mixup knowledge distillation module to diversify synthetic samples for finetuning
Strengths: They identified the new feature required when generating synthetic data for quantization.
Weakness... | Rebuttal 1:
Rebuttal: ### We would like to thank the reviewer for the thoughtful reviews that will help strengthen our paper. In the following, we address each individual question in detail.
**Due to characters limit, all tables for supplementary experiments are displayed in the global response area.**
***
**Q1: pl... | null | null | null | null | null | null |
Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction | Accept (poster) | Summary: The authors address the problems of denoising and tomographic reconstruction, specifically in the context of cryoelectron tomography (cryoET), in which 3D structures (e.g. of proteins) are reconstructed using a tilt-series of tomograms. CryoET holds much promise for the elucidation of biological structures wit... | Rebuttal 1:
Rebuttal: Thank you for you recommending. We thank Reviewer 4 for their valuable feedback, and we will make the necessary changes to the font size and color of the graphs. | Summary: This paper proposed a field-based method for 3D image reconstruction in Cryo-EM, which is a challenging task due to strong measurement noise and ill-posedness. The proposed method, **TensorDF**, has the following features: 1) it combines implicit representation and a quadtree, where each node corresponds to a ... | Rebuttal 1:
Rebuttal: a1: All the Vs and Ms are trainable variables. Vs and Ms work like volume factorization. Our target is to optimize the volume, and it is represented as a product of Vs, and Ms. Decoder is also optimized by default.
a2: The reviewer is correct that the absence of the MLP necessitates the additiona... | Summary: In their paper, the authors present a learning-based framework that tackles the challenges faced in reconstructing 3D structures from tilt-series cryo-Electron Microscopy (cryo-EM) data. Cryo-EM is a powerful imaging technique known for its ability to achieve near-atomic resolutions. However, it is not without... | Rebuttal 1:
Rebuttal: a1: Recently, several works [1,2,3,4] have demonstrated the superiority of Coordinate-based representations in solving tomographic problems, especially in missing-wedges scenarios, compared to traditional methods. So there is excellent evidence for the superiority of CBNs in that regard. However, ... | Summary: The work combines quad-tree structure with low-rank tensorial representation to adaptively model cryo-EM volumetric density for reconstruction problem. The quad-tree structure is updated by merging or splitting to encourage uniformity in the area of each node, while the feature tensors for each node are obtain... | Rebuttal 1:
Rebuttal: a1: In our implementation, we start with a fixed number of nodes (for example, 70, as mentioned in the paper), we initialize each tensor representation with random values between -0.5 and 0.5. After each iteration, some nodes may become inactive, because they are subdivided or merged with others. ... | Rebuttal 1:
Rebuttal: We include in the rebuttal.pdf file some additional experiments to answer question 3 of Reviewer 3.
Pdf: /pdf/b8f5892e232898894d34c778b6b177f6e51a643e.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Meta-Learning Adversarial Bandit Algorithms | Accept (poster) | Summary: This paper considers learning several adversarial tasks with different loss functions simultaneously, hoping to attain better task-averaged regret if the tasks are "similar" enough (e.g., the optimal actions of all tasks concentrate on a small subset). A general meta-learning framework is derived by deploying ... | Rebuttal 1:
Rebuttal: Thank you for your positive review; we hope to address your questions below.
1. [*[...] Given the expression of $U_t$ in Eq. (3), it appears unsurprising that the adopted optimizers (FTL, EWOO, and MW) can be applied to their respective objectives. [...] the analyses of meta-learners and base-lea... | Summary: This paper focused on online meta-learning with bandit feedback, and developed and applied a meta-algorithm for learning to initialize and tune bandit algorithms, obtaining task-average regret guarantees for both MAB and linear bandits. Specifically, a meta-algorithm was developed for learning the variants of... | Rebuttal 1:
Rebuttal: Thank you for your positive review; we hope to address your questions and concerns below.
1. [*[C]an you envision what is the major challenges to generalize the solutions to the best-of-both-worlds settings, from both the algorithmic and performance analysis perspectives?*]
- One challenge is tha... | Summary: In this paper, the authors consider an online meta-learning problem with the adversarial online-with-online partial information setting,
where a learner selects parameters across T tasks with m rounds and can utilize similarity between tasks to achieve a low regret.
First, the authors propose a meta algorithm ... | Rebuttal 1:
Rebuttal: Thank you for your positive review; we hope to address your questions and concerns below.
Weaknesses:
1. [*There is no discussion on lower bounds. Therefore, it is not clear whether the proposed method is optimal.*]
- While we agree that lower bounds can be informative, our goal in this paper was... | Summary: The paper proposes an online mirror descent approach for adversarial bandit feedback-based online meta learning, utilizing FTL for initialization, EWOO for step-size, and MW for regularizer-specific parameters. The proposed method is applied to two widely adopted applications: multi-armed bandits (MAB) with Ts... | Rebuttal 1:
Rebuttal: Thank you for your positive review; we are happy to answer additional questions later. With respect to your concern about experiments: our goal was theoretical depth and breadth, with the application of our framework to four different bandit algorithms (Sections 3.1, 3.2, 4, and D.3) across three ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their helpful reviews, which among other things have led to several useful references and an analysis of the robustness of our result to outliers (c.f. [our response to Reviewer fExn](https://openreview.net/forum?id=r6xGZ0XL2g¬eId=hvQFk8uNIu)); we plan t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper discusses online meta-learning with bandit feedback to enhance performance across multiple tasks that share a natural similarity measure. The study focuses on the adversarial online-within-online partial-information setting and proposes meta-algorithms that combine outer learners to optimize the init... | Rebuttal 1:
Rebuttal: Thank you for your positive review; we hope to address your questions and concerns below.
1. [*It will be better if authors can provide some real-world applications that the designed algorithm can adapt to.*]
- As noted in the introduction, single-task bandits are widely used in applications suc... | Summary: This paper introduces a meta-learning algorithmic framework for adversarial bandits, designed to fine-tune the initialization and hyperparameters of the internal learner, thereby enhancing performance across multiple tasks. The efficacy of this approach is validated via a theoretical analysis on multi-armed ba... | Rebuttal 1:
Rebuttal: Thank you for your review; we address your questions and concerns below.
Weaknesses:
1. [*The paper does not have any numerical validations of the proposed algorithm. [...]*]
- Our goal in this work was theoretical depth and breadth, with the application of our framework to four different bandit... | null | null | null | null |
Deterministic Strided and Transposed Convolutions for Point Clouds Operating Directly on the Points | Reject | Summary: This paper focuses on applying strided and transposed convolutions to point cloud data so that the deterministic network can be directly operated on points. To achieve this, a strided convolutional layer with auxiliary loss is proposed, which ensures a consistent selection of points across the whole learning p... | Rebuttal 1:
Rebuttal: Dear Reviewer,
First and foremost, thank you very much for taking the time to review our paper and providing us with helpful comments to improve it. We are looking forward to discussing the contents with you.
We agree that more figures could help the understanding of the paper and will add them... | Summary: This paper introduces a learning-based point sampling strategy to deterministically downsample point clouds, which can be used to build a U-shaped network for point cloud reconstruction and representation. To enforce a stable and meaningful sampling (or selection), an auxiliary selection loss is proposed. The ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
First and foremost, thank you very much for taking the time to review our paper and providing us with helpful comments to improve it. We are looking forward to discussing the contents with you.
Thank you for pointing out SampleNet. We agree that it is highly relevant to our work ... | Summary: This paper presents a learnable and deterministic point selection layer to uniformly downsample points and a point transposed convolution layer to upsample points.
Strengths: 1. The auxiliary loss (Eqn. 1) proposed to supervise the point selection is interesting.
Weaknesses: 1. Deficient theoretical soundn... | Rebuttal 1:
Rebuttal: Dear Reviewer,
First and foremost, thank you very much for taking the time to review our paper and providing us with helpful comments to improve it. We are looking forward to discussing the contents with you.
Regarding the first weakness mentioning the discrepancy between the selection being ba... | Summary: This paper aims to propose a new type of convolutional neural network to point cloud understanding tasks. Besides, the authors present a new loss function to the sampling process of feature extraction for point clouds. And the authors provide theoretical analysis to prove that their method is better for sampli... | Rebuttal 1:
Rebuttal: Dear Reviewer,
First and foremost, thank you very much for taking the time to review our paper and providing us with helpful comments to improve it. We are looking forward to discussing the contents with you.
Among the critical points you raised, you noted deficiencies in the writing of our pap... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Accept (poster) | Summary: This work proposes a novel decentralized learning algorithm based on gradient tracking mechanism, called Global Update Tracking (GUT), which aims to mitigate the impact of heterogeneous data distribution. The proposed GUT algorithm overcomes the bottleneck of communication overhead by allowing agents to store ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback.
The following Table 1 provides the communication cost for the experiments on 16 agents ring topology measured in terms of the total amount of data transferred in GB during training per agent. The communication costs for the remaining experiments a... | Summary: The performance of decentralized learning is limited to the different distribution over devices. To address this issue, this paper proposes a method that is less susceptible to variations in data distribution, named GUT. The proposed GUT tracks the global/average model updates. Experiments show GUT achieves go... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We answer each of the questions raised in the weakness section here.
1. We will update the literature review to only include decentralized learning works on heterogeneous data.
2. The difference in data distribution across agents results in a hug... | Summary: The paper proposes a new decentralized learning algorithm called Global Update Tracking (GUT) that can mitigate the impact of heterogeneous data in decentralized learning without introducing any communication overhead. The proposal was demonstrated 4 computer vision datasets.
Strengths: The paper proposes an ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We answer each of the questions raised in the weakness section here.
1. (a) GUT/QG-GUTm communicates model updates $(x^{t}-x^{t-1})$ whereas DSGD/QG-DSGDm communicates model parameters $x^{t}$. Both these vectors are of the same size i.e., model ... | Summary: The authors propose an algorithm for decentralized learning, where training datasets with heterogenous distributions are collected across different devices. They propose a global update tracking (GUT)-based approach where the IID assumption for the data is removed, and aim to generalize their method to non-IID... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We answer the questions raised in the weakness section here.
1. In the proposed methods, the local model parameters are updated using the tracking variable $y_i$ instead of the local gradient $g_i$. The key idea is that the tracking variable $y_i$... | Rebuttal 1:
Rebuttal: We present the additional results and the quantitative results on the overheads in the attached pdf.
We reiterate the contributions of the proposed methodology.
Decentralized machine learning on heterogeneous data has poor performance due to huge variations in the local gradients across the ag... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information | Accept (poster) | Summary: This paper considers the class of problems known as Smart Predict and Optimize (or Decision-Focused Learning) where the learning task consists in learning the parameters of an optimization model given some of their features. The difficulty comes from trying to include the optimization model into the learning p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing valuable comments. While we partially agree with the summary provided by the reviewer, it is essential to note that LANCER is not only applicable to SPO problems but also to another crucial class of problems: solving mixed-integer nonlinear programming... | Summary: This paper proposes the a novel framework for learning the predicted value of an optimization task under limited information. Specifically, the goal is to learn $\mathcal{M}\_w(y, z) = f (g\_\theta (y), z)$ function where $y$ is the limited information, $z$ is the complete information, $f$ is the objective val... | Rebuttal 1:
Rebuttal: 1. **Weaknesses**
> 1.a."Do you not observe this phenomenon of 'convexity being important for loss functions'?"
Please check the main rebuttal (points 2 and 3).
> 1.b. Regarding follow up to LODLs.
Thanks for the reference. We were unaware of this work as it appeared after the submission dea... | Summary: This paper presents a unified framework for "predict-then-optimize" and surrogate cost learning for Mixed Integer Nonlinear Programming (MINLP). These problems are cast as learning an optimizer g with f as the objective. Current solutions either suffer from scalability issues or the sparse gradient problem. To... | Rebuttal 1:
Rebuttal: 1. **Weaknesses**
> 1.a. On utilizing a neural network to parameterize the landscape and comparison with LODLs
Please check our response to all reviewers above (points 2 and 3)
> 1.b. Why LANCER outperforms SurCo?
**LANCER allows nondifferentiable objective $f$**. SurCo relies on (appr... | Summary: `This paper is concerned with an amortized optimization scheme for challenging variants of canonical decision problems such as MINLPs and nonlinear portfolio selection. The authors propose a method with two components, a target mapping $c_\theta$ that maps partially observed problem descriptions $\mathbf y$ to... | Rebuttal 1:
Rebuttal: 1. **Weaknesses**
> 1.a. Convergence guarantees and stability
We acknowledge that, at present, we lack theoretical guarantees on convergence for LANCER. However, we observe empirically that the objective improves as the total number of alternating optimization iterations (T) increases, as shown ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for providing valuable comments and taking the time to review our paper. Here we address questions raised by several reviewers.
> 1. [1xim,kDSQ,Nect] **Stability of LANCER to hyperparameters and NN architecture**
This is an empirical question and depends on var... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion Schrödinger Bridge Matching | Accept (poster) | Summary: This paper formulates a new framework of a probabilistic model called Diffusion Schrödinger Bridge Matching (DSBM) and an iterative algorithm which is called iterative Markovian fitting (IMF). Firstly, the paper has contribution of pointing out a relationship between famous generative models (score matching an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and their positive evaluation. We appreciate their interest in our work.
We address here the reviewer's concerns raised in the review.
**“Difficult to observe DSBM-IMF excels DSMB-IPF ...”**: We would like to first make a clarification that DSBM-IPF is al... | Summary: Flow matching and alpha blending have achieved tremendous attention in the matching problems. Although the straight trajectories based on $X_t = (1-\alpha)X_0+\alpha X_1$ yield **fast inference**, it doesn't necessarily mean they are efficient in score estimations in training. To make the training theoreticall... | Rebuttal 1:
Rebuttal: We thank the reviewer for your thorough review. We will incorporate your suggestions regarding the clarity of Algorithm 1 and the related work. We now address your comments in more detail.
**“Clearer clarification between IPF and IMF”**: Like you mentioned, we have related IPF with the forward KL... | Summary: The submission suggests a numerically effective approach for solving the Schroedinger Bridge (SB) problem and illustrates its potential for generative modeling. The approach generalizes in some sense some recent flow matching methods. Furthermore, it provides a more efficient (no full trajectory caching) algor... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough evaluation of our work. We appreciate your acknowledgment of the paper’s merits. Here we would like to expand on the other comments raised in the review.
**“Computational cost of the suggested method”**: At training time, our proposed method... | Summary: The paper introduces Iterative Markovian Fitting (IMF) as a new method to compute Schrödinger Bridges (SBs), which are dynamic versions of entropy-regularized optimal transport. IMF alternates between projecting on the space of Markov processes and the reciprocal class, and it preserves the initial and termina... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our submission and for their constructive feedback. We address here the main points from the review.
**“Analysis of scalability”**: Our proposed DSBM method leverages tools from the recent flow/bridge matching literature, which are highly scalab... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and their very helpful feedback.
We will take care to address all suggestions on improving clarity and minor typos in the next version of the paper.
We would like to take the opportunity here to address a few common questions raised by several ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes IMF, an iterative method for solving Schrodinger Bridge problems where the solution at each iteration preserves the correct marginal distributions at times 0 and T. This is in contrast to the existing method IPF which only satisfies this in the limit (and is very difficult to reach in pract... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough evaluation of our work. We appreciate their interest and their thoughtful questions. We would like to address the raised questions here which hopefully would clarify the role of stochasticity further.
**“Algorithm presentation can be simplified.”**:
We h... | null | null | null | null | null | null |
Reinforcement Learning with Simple Sequence Priors | Accept (poster) | Summary: This work proposes to use action-based complexity cost. This work argues that this action-based cost can be formulated either as compression over the experienced so far actions or using a transformer that predicts the next action given the past. This approach is then claimed to provide access to simplified / p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and thoughtful feedback. We are glad that the reviewer found our work interesting, and we are encouraged by the fact that the reviewer thinks it has potential. The reviewer points out four main limitations of the submitted paper. We have addressed all fou... | Summary: This paper proposes the idea of using simplicity as a prior when learning control policies using RL. Simplicity (or complexity) here is defined as the cost of predicting action $a_t$ given past actions $(a_\{t - \tau:t-1})$ and current state ($s_t$). Two different approaches are proposed to learn this complexi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and thorough feedback, and we are glad that the reviewer found our idea of using sequence simplicity as a policy prior in RL interesting. In the following we address the issues raised by the reviewer: We have clarified what we mean by simplicity, as well as t... | Summary: This paper proposes two approaches to regularizing RL policies based on sequential action priors, rather than on single action priors. One approach is SPAC, which trains an autoregressive open-loop policy prior and uses it as a reference regularizer. Another approach uses the LZ4 compression algorithm to estim... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful feedback and the encouraging review. We particularly appreciate that the reviewer found our method and results novel and interesting - both our use of dictionary-based compression and generic compression with sequence models. We have focused on r... | Summary: In this paper, the authors propose a reinforcement learning method that produces simplified action sequences. Simplicity is defined as the predictability of the next action taken by the RL agent and is measured using the number of bits required to encode the action sequence. The authors propose two methods to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and feedback. We are happy that the reviewer found our idea of using action sequence compression interesting for the RL community. Moreover, it is encouraging that the reviewer acknowledges the possible impact our method could have for regularizi... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for the time and effort put into providing thoughtful feedback on our paper.
* Reviewer teDV found the paper “well-written and presented” and our idea “interesting for the community.”
* Reviewer 6AwJ praised our paper for its “novelty of using LZ4 for a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice | Accept (poster) | Summary: The authors propose a neural network for predicting activity of V1 neurons in freely moving mice. The data consists of ~hour long electrophysiological recordings of a neuronal population while the mouse is freely exploring the space. Simultaneously with the neural activity, the experimental setup allows the au... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments.
> It would be really nice to have a calcium dataset of thousands of neurons in a freely moving mouse (similar to the Sensorium one)
We agree with the reviewer that creating a large and standardized neural recording dataset from freely moving ... | Summary: The authors use convolutional neural networks to fit data from neuronal recordings in primary visual cortex (V1) of the mouse while the animal is freely exploring the environment. The model incorporates visual signals but also other behavioral variables (“multimodal” aspect). The proposed model provides better... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and constructive comments. We agree that data fitting is not the final answer to understanding the functioning of mouse visual cortex, and we do not presume to be able to provide such an answer. However, we believe that our modeling efforts may pro... | Summary: The authors introduce a multimodal recurrent neural network to integrates information beyond vision - behavioral and temporal dynamics processed by a separate head to explain V1 activity in mice. This model (vision + behavioral and temporal dynamics) is the state-of-the-art in prediction of V1 activity during ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and suggestions.
> Vision only model seems to have fewer parameters compared to the ones with GRU on top. This makes it hard to say if the improvements come from the extra parameters (unlikely).
It is true that our vision-only models had fewe... | Summary: The authors in this work propose a novel multimodal approach to design encoder models of mouse visual processing. The authors identify the limitations of unnaturalistic (head-fixed) recording, limited behavioral inputs and lack of temporal dynamics in prior recordings and models of mouse visual system and addr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and are pleased that they agree about the originality of the work. We do believe that this work constitutes a significant modeling contribution, as most previous modeling attempts have focused on head-fixed datasets with static stimuli, which ma... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. We are pleased that reviewers agreed this is a clearly written paper (ZQ1m, xdTa) describing novel work (ZQ1m, NhDi) with strong results (NhDi, riS4, xdTa) that may help build theories and models of brain function (riS4).
Reviewers raised ins... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Robustness of Mechanism Design under Total Variation Distance | Accept (poster) | Summary: The paper studies the following question: in what way can approximation guarantees of (approximately) IC mechanisms be preserved when the prior distribution is perturbed by a small amount in the total variation distance? The main technical lemmas state that the guarantees of DSIC and BIC mechanisms are in fac... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and questions. Below we answer your questions.
- "Line 263, "two conditional P_{X, Y}, Q_{X, Y}": do you mean "joint"?"
- "Line 316: "Bei et. al." => "Bei et al." (consider using \citet or \citeauthor?)"
We will fix both typos.
- Lemma 3: could mention this is... | Summary: This paper studies the robust mechanism design. In this problem, there is a set of items and a set of agents whose valuation functions are drawn from a batch of unknown distributions. These distributions are correlated, i.e., they are close to a known distribution under the total variance distance. The agents'... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. | Summary: This paper studies the robust design of mechanisms for a designer with general bounded objective, when the true distribution of the agent types (possibly correlated) is not the actual distribution. More precisely, the main idea is that the optimal incentive compatible mechanism designed for the a priori distri... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and questions. Below we provide answers to your questions.
- "l638 : Does 'single agent' described in this context mean that n=1? In this case what would the product distribution signify?"
Indeed this is the case of a single agent. The product distribution her... | Summary: The paper studies a robust auction design problem for multiple items. In the model, the authors assume that they can access a (predicted) valuation distribution over all agents, and the total variance between the predicted distribution and the actual distribution is bounded. The goal is to design a truthful me... | Rebuttal 1:
Rebuttal:
Thank you for the thoughtful review and question. Indeed, when the total variation is large, our results don’t have any bite, which, of course, does not imply that some sort of robustness is impossible to show. The approach of using learning-augmented algorithms that the reviewer suggests is a gr... | Rebuttal 1:
Rebuttal: We thank all reviewers for the thorough reviews and helpful comments. We will incorporate the valuable suggestions from all reviewers in the final version of this paper.
A common question that reviewers acoM, XhLk, and yCAy ask is whether our various bounds are tight. One can construct trivial ti... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the design and performance guarantees of various mechanisms under prior distributions, and aims to provide a general account of what happens to these mechanisms and their guarantees when these (joint) prior distributions are perturbed. They use the definition of TV distance in terms of the... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and questions. We will update our theorems and lemmas, whenever applicable, to state the relevant conditions/restrictions necessary.
Below we answer your questions.
- Where is the crux of your invocation of duality, and how are you using the connection between... | null | null | null | null | null | null |
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources | Accept (poster) | Summary: In this paper, the authors address the problem of data selection, acknowledging that complete data availability is often not possible, and data can only be obtained from specific providers. They recognize that different data sources may have varying impacts on the performance of the model, emphasizing the impo... | Rebuttal 1:
Rebuttal: > *"impractical since scenarios where different data providers offer data for the same task with identical distribution are uncommon... implicitly assume that data from all providers share the same distribution..."* (*Weaknesses: 1*)
**Re:** We appreciate the valuable feedback on the presentation... | Summary: Developing ML systems typically requires collecting data from multiple sources. A natural question is how much data to collect from each source. This paper proposes a two-stage estimator that (1) estimates the relationship between data set proportions vs validation loss using Optimal Transport and then (2) opt... | Rebuttal 1:
Rebuttal: > *"This paper proposes a two-stage estimator that (1) estimates the relationship between data set proportions vs validation loss using Optimal Transport and then (2) optimizes the proportion..."* (*Summary*)
**Re:** The authors would like to thank the reviewer for providing detailed and in-depth... | Summary: This paper considers the problem of predicting model performance (and subsequent data selection) under a partially revealed setting. The two challenges are estimating the right proportions as well as extrapolating to dataset scales beyond the observed scales. The paper proposes a two-stage approach called proj... | Rebuttal 1:
Rebuttal: > *"...experiments are a bit disappointing in terms of their practicality ...would have liked to see more experiments on more realistic/noisy data sources. "* (*Weaknesses: 1*)
**Re:** We appreciate the review for the crisp understanding of the conceptual narrative of this work, and would like to... | Summary: The paper considers the data acquisition setting where benefit to model performance from acquiring new streams of training data may be supported by the inspection of limited segments of a candidate corpus, such that one may wish to evaluate the benefit to model performance to support selection from a set of ca... | Rebuttal 1:
Rebuttal: > *“...claims of significant improvements to computational costs of the application with use of this approach was suggestive of a material contribution but I had difficulty evaluating…”* (*Strengths*)
***TL;DR: Scalability improvements of the proposed framework are on magnitudes.** Methods propos... | Rebuttal 1:
Rebuttal: ### Summary:
**All reviewers recognize the importance of the problem and the conceptual novelty of the proposed framework and original methods.**
Reviewers WNMz and WiXX confirm the **solid development** of this work and its **multi-faceted technical contributions**. Reviewers WNMz and WiXX appre... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TabMT: Generating tabular data with masked transformers | Accept (poster) | Summary: This paper introduces TabMT, a new Masked Transformer architecture designed for generating synthetic tabular data. While Transformers are predominantly used in natural language processing (NLP), TabMT demonstrates their effectiveness in dealing with heterogeneous data fields, such as images and tables, and eff... | Rebuttal 1:
Rebuttal: Hi Reviewer EYXT, Thank you for the time you took to read our paper and write your review.
**W:** The motivations for designs are not very clearly written. Please consider reorganizing the structure, adding highlighted paragraphs, and moving line 129-138 to the beginning of the method or merging ... | Summary: The paper proposes a masked transformer model that can be used to generate tabular data. The authors propose modifications in the masking strategy in the transformer, in order to make it more effective for generating data. Empirically, the proposed generator improves performance in various datasets compared to... | Rebuttal 1:
Rebuttal: Hi Reviewer aH7d, thank you for your rating and the time you took to carefully read and review our paper.
**W:** ”As noted in the paper, the major weakness would be the resource it takes to train the model. For general users, a pre-trained model with learning across multiple tables might be bene... | Summary: This paper proposes a new generative model of table-type data based on the transformer. And it can address the unique challenges posed by heterologous data fields and natively handle missing data.
Strengths: 1. TabMT is a simple but effective Masked Transformer design for generating tabular data.
2. We hig... | Rebuttal 1:
Rebuttal: Hi Reviewer gJJU, Thank your for taking the time to read and review our paper.
**W:** The TabMT structure has only been modified in terms of the input layer, without improving the multi-layer transformer structure. In other words, this paper only made adaptive improvements to the data format and ... | Summary: This paper explores the effectiveness of masked transformers as generative models for synthetic tabular data generation. The proposed TabMT architecture effectively handles challenges related to heterogeneous data fields and missing data. The model shows promising experimental performance and demonstrates good... | Rebuttal 1:
Rebuttal: Hi Reviewer sX8C, thank you for your careful consideration and review of our paper.
**Q:** Could you please clarify the training procedure? When training the masked transformer, does every feature have its own classification head for predicting the mask?
**A:** When predicting we use a separate ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PHOTOSWAP: Personalized Subject Swapping in Images | Accept (poster) | Summary: This paper presents a combination of DDIM inversion and Dreambooth to customize the appearance of existing images.
The authors also experimented with some attention layer hacking.
Strengths: 1. Paper is easy to read.
2. Framework is reasonable. Dreambooth+DDIM inversion+Attention hacking will really lead to ... | Rebuttal 1:
Rebuttal: Thanks for the suggestions, we will explain the concerns as follows:
> About the paper novelty.
a. Personalized subject swapping is an emerging vision task that has abundant user applications in practice. The task we undertook is inherently challenging, due to the lack of training pairs. In t... | Summary: This paper introduces a new method for inserting a subject into a target image. The approach consists of 1) using dreambooth to extract the appearance information of the subject, and 2) copy the attention from the target image (obtained by regenerate the target image using DDIM inversion) to control the layout... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for your insightful suggestions. Here are our response to your concerns.
> What is technical contribution. What is the main difference between the proposed method and the baseline.
Quote Reviewer z7Bi, “The task of personalized subject swapping in images is fancy and inte... | Summary: The authors present a solution to the problem of personalized subject swapping, where the goal is to replace the subject in an image with another user-defined subject. Authors leverage pre-trained diffusion models to make local edits to an input image based on a collection of images of the subject to be insert... | Rebuttal 1:
Rebuttal: Thanks reviewer for the appreciation on our motivation, paper writing, technical contribution and results. We will explain also the concerns as follows:
> Comparison with PnP+Dreambooth.
We have attached a comprehensive human evaluation both here and in Table 1 from the pdf. We could see that... | Summary: In this paper, they propose Photoswap, which could seamlessly swap personalized subjects into source images. The swap process is training-free and only leverages the manipulation of self-attention and cross-attention. The swapped object could maintain the pose of the source image without hurting the coherence ... | Rebuttal 1:
Rebuttal: Thanks for the reviewer on the acknowledge of our task and appreciation of the results. Besides the great suggestion, we will explain all the concerns as follows:
> larger scale human evaluation comparison with P2P on both synthetic and real images
Thanks so much for the suggestion. During thi... | Rebuttal 1:
Rebuttal: Attached pdf
Pdf: /pdf/9977078dc30c7032f9315db6a39e080c27706bba.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Lower Bounds on Adaptive Sensing for Matrix Recovery | Accept (poster) | Summary: Sparse recovery for vectors has been studied for a long time. It has been shown that allowing adaptive queries will give extra power to reduce the number of queries. We also have upper and lower bounds in this setting. More recently, sparse recovery for low-rank matrices has also been extensively studied. With... | Rebuttal 1:
Rebuttal: - Uniform number of measurements: We note that our lower bound says that even when one is allowed $n^{2 - \beta}$ linear measurements in *each* round, the algorithm must use $o(\log n/ \log \log n)$ rounds to be able to approximate the matrix. So, allowing for the number of measurements to be adap... | Summary: This paper focuses on investigating the lower bound of the adaptive low-rank matrix sensing problem. Specifically, the authors demonstrate that when the noise level significantly exceeds the signal, any adaptive algorithm requiring fewer than $o(\log(n)/\log\log n)$ rounds must utilize at least $\Omega(n^2)$ l... | Rebuttal 1:
Rebuttal: - Noise level: You are correct that the lower bounds we study are in the high-noise regime. A main reason for this is that adaptivity does not reduce the number of linear measurements by a large factor in the low noise regime since the non-adaptive sensing algorithms already recover the low rank m... | Summary:
In this paper, the authors discuss the power of adaptive algorithms in low-rank approximation. This is a setting where one observes general linear measurements of a matrix and wants to produce the best r-rank approximation of it.
It is known that non-adaptive algorithms need order n^2 measurements and that w... | Rebuttal 1:
Rebuttal: - $o(\log n)$ vs $o(\log n / \log\log n)$: Our Bayes risk analysis, which union bounds over the information growth in each of the rounds, is what leads to the $\Omega(\log n/\log \log n)$ rounds lower bounds instead of the more desirable $\Omega(\log n)$ rounds lower bound. However, we do note tha... | Summary: This paper studies the problem of low-rank matrix reconstruction from linear measurements, which is a matrix generalization of the well known sparse reconstruction setting. The provide new lower bounds for this problem under different error metrics, such as the Frobenius norm, essentially showing that non-triv... | Rebuttal 1:
Rebuttal: - Thanks for the comments and question. Since the value of an entry of a matrix can be computed using a linear measurement, it is indeed true that lower bounds on linear measurements must also carry over to the matrix completion setting. As our lower bounds are in the “noisy” linear measurements m... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Using persistent homology to understand dimensionality reduction in resting-state fMRI | Reject | Summary: The paper is an empirical study of different dimensionality reduction (DR) methods for brain activity data.
Authors compare various approaches to brain representation using the MRI data from Human Connectome Project.
In the manuscript, "brain representations" are called "dimensionality reduction" (DR) since ... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. [...] The definition is different from the one used in ML/AI community, where by DR we mean algorithms like t-SNE, UMAP, etc.
* We apologize for our confusing terminology. As the reviewer correctly notes, we focus on domain-specific features sets that typically involve a b... | Summary: This paper investigates shared geometric structure across different (very broadly speaking) dimension reduction algorithms for functional brain connectivity. The authors examine different connectivity representations through persistent homology via topological statistical and bootstrap.
Strengths: - The pape... | Rebuttal 1:
Rebuttal: **Weaknesses:**
This paper is quite ambiguously written and not self-contained although the languages are clear.
* We apologize for our lack of clarity. Please see the ‘Terminology’ section in the general response for further information.
I believe the presentation can be far improved by expla... | Summary: The authors study shared geometric structure across different dimensionality reduction (DR) algorithms applied to neuroimaging data (fMRI data from the Human Connectome Project). In particular, they compare different DR algorithms (which they call "brain representations") by applying them to the same data samp... | Rebuttal 1:
Rebuttal: The writing and definitions could be much more clear throughout. Some are used before they are defined well, some terms with precise meaning seem misused (e.g., induced topology seem misused; "induced on...data"), some terms are highly redundant and misleading (e.g., "brain representations").
* W... | Summary: The paper proposes an approach of comparison of different standard dimensionality reduction technics (DRT) of fMRI, using topological data analysis tools. In this case, these DRT computes lower dimensional representations of the Human Connectome Project dataset.
Then, for this specific dataset, the authors ass... | Rebuttal 1:
Rebuttal: **Weaknesses**
Some techniques used to tackle this problem are not natural or are not motivated enough [...], especially the prevalence-weighted wasserstein distance. In particular, I have the following comments:
* The bijections between persistence diagrams usually add the points of the diagonal... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable input to our work. Please find our general response summary below, in addition to the item-wise responses we provide to individual reviewers. We also include figures showing typical persistence diagrams and a naive approach.
**Novelty and contribution**
... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: A very interesting and rigorous exercise of comparison of embeddings for the purpose of evaluating manifold learning is presented. The chosen framework is root in trendy topological concepts such as persistent homology for the purposes of analyzing the data topology mixed with geometry-based measures of simila... | Rebuttal 1:
Rebuttal: Questions:
Lns 150-2: Is the separated treatment of the vectors and matrix intentional? Would a tensorial treatment lead to a more homogenous framework?
- The treatment is intentional, since we chose dissimilarity metrics based on common methods of comparison in the application (neuroimaging) li... | null | null | null | null | null | null |
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback | Accept (spotlight) | Summary: This paper proposed to use the diffusion model with expert feedback to improve the quality of synthetic medical images. The key motivation is the embedding of RLHF for medical image generation. To achieve this, there are three steps: (1) pre-trained a generation model and collected expert feedback based on sev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please refer to the combined response above for a general overview of the improvements and changes that have been incorporated in the revised manuscript.
**Weaknesses**
1- We have prepared a Github repository with all the codes required to repro... | Summary: This paper develops a framework that generates synthetic medical images aligned with the clinical knowledge of doctors through training a reward model based on pathologists' image plausibility annotations. The intuition for their very simple but powerful approach that depends on incorporating clinical expert k... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please refer to the combined response above for a general overview of the improvements and changes that have been incorporated in the revised manuscript.
**Weaknesses**
This paper serves as an application submission to NeurIPS, aligning with the ... | Summary: The authors proposed to include pathologist in the loop for medical data synthesis. The pathologist can be replaced by training a reward model, which is used to fine-tune the generation model.
Strengths: - I admire that the authors broke down the barriers between disciplines, and I believe their proposed path... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please refer to the combined response above for a general overview of the improvements and changes that have been incorporated in the revised manuscript.
**Weaknesses**
1- Thanks for suggesting a direct verification of the reward model. We provi... | Summary: This paper introduces a pathologist-in-the-loop framework for generating clinically plausible synthetic medical images. The training process is similar to generative adversarial nets, with two major modifications. First, the discriminator was trained by human input instead of real/fake labels. Second, the gene... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please refer to the combined response above for a general overview of the improvements and changes that have been incorporated in the revised manuscript.
**Weaknesses**
1- We appreciate the reviewer's suggestion to include a baseline method that... | Rebuttal 1:
Rebuttal: We thank all reviewers for the comprehensive and constructive feedback on our submission. The valuable input received has significantly contributed to improving our work. We have prepared an extensive, point-by-point response for each reviewer, outlining our plans to address their concerns and sug... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a pathologist-in-the-loop framework for generating clinically plausible synthetic medical images using diffusion models. The process involves pretraining a conditional diffusion model on real medical images, then using synthetic images labeled by expert pathologists to train a "clinical pla... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Please refer to the combined response above for a general overview of the improvements and changes that have been incorporated in the manuscript to address this issue.
**Weaknesses**
*1- Criteria for evaluating clinical plausibility*
In the fin... | null | null | null | null | null | null |
Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability | Accept (poster) | Summary: In this paper, the authors introduce the Bayesian nonparametric non-renewal (NPNR) process to model variability in neural spike trains with covariate dependence. The method generalizes modulated renewal processes using sparse variational Gaussian processes. Tested on synthetic data, as well as mouse head direc... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and helpful feedback. We provide brief comments on the mentioned weaknesses in the relevant question.
*1. The authors mention that the variational inference framework is scalable...*
The datasets in this work each contain around 30 selected neurons that are f... | Summary: This paper proposes the Bayesian nonparametric non-renewal process (NPNR) for inferencing both neural spiking intensity and variability. The tuning curve is based on a sparse variational Gaussian process (GP) prior, considering both spatial and temporal factors. They compare NPNR with other competitors on a sy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful feedback on the work.
**Q&A**
*1. I think Eq. 15 should be = rather than ∝.*
This would indeed be true if the denominator (normalization constant) in equation 20 (appendix A) was 1. For a Poisson process with $t_i = 0$ (i.e. the most recent spike... | Summary: The authors proposed a scalable Bayesian approach which generalizes modulated renewal processes using sparse variational Gaussian processes. They applied the proposed method to simulated and two real neural datasets and showed that the proposed method is effective on these datasets and outperforms other baseli... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and helpful feedback.
*1. Method Section: all the formulas are described for 1 neuron. It might be clearer to write likelihood and loss function in terms of multiple neurons.*
The current work inherently models each neuron separately while fitting the neural... | Summary: The variability of neural data is widely observed in many neuroscience experiments. Using statistical model to capture the variability structure plays an essential role in understanding neural computations. Generally, the variability of neural data is a result of non-stationary activities and dependencies on b... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and helpful feedback.
*1. The proposed method captures ISI statistics using a spatio-temporal GP prior over CIF. Could the Neural Temporal Point Process (NTPP) perform similarly to your method in capturing ISI statistics? The CIF in NTPP is usually modeled by... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a Bayesian nonparametric approach using modulated renewal process to model neural spike train, and capable of modeling the covariability. The method includes a nonparametric priors on conditional interspike interval distribution and automatic relevance determination for lagging interspike i... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and helpful feedback.
**Q&A**
*1. Give a detailed introduction about the parameters that would be optimized under eqn 18. and list other hyper-parameters for reproducibility.*
The NPNR model parameters consist of:
- Gaussian process hyperparameters (kernel h... | null | null | null | null | null | null |
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm | Accept (poster) | Summary: This paper establishes the algorithmic-stability analysis of the decentralized stochastic gradient descent-ascent (D-SGDA) algorithm which is commonly used for min-max problems in distributed settings. This is turn leads to generalization bounds of D-SGDA for various assumptions on convexity-concavity of the o... | Rebuttal 1:
Rebuttal: # Response to Reviewer Z6QM
Thank you for your careful reading and constructive suggestions! We have answered your questions in the following comment.
***Q1: Vacuous results for generalization bounds.***
1.Our results of generalization are not vacuous even for fixed learning rate conditions. I... | Summary: this paper provides an analysis of the stability and generalization of decentralized GDA algorithms in SCSC, CC, and NCNC settings. the main results indicate that a decentralized setting GDA has similar error bounds as centralized settings. numerical experiments on AUC and GAN problems are reported.
Strengths... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We have answered your questions in the following comment.
***Q1: Size of the figures.***
Thank you for your valuable suggestions. We will zoom in on Figure 1 and Figure 2 for better readability.
***Q2: Technical difficulty of D-SGDA.***
Thank you for you... | Summary: The paper investigates the primal-dual generalization gap bound of the decentralized stochastic gradient descent ascent (D-SGDA) algorithm. The authors start with a general decentralized minimax stochastic optimization problem, and its empirical counterpart calculated by the training dataset consisting of loca... | Rebuttal 1:
Rebuttal: # Response to Reviewer ipno
Thank you for your careful reading and constructive suggestions! We have answered your questions in the following comment.
***Q1: More explanations on the mixing matrix $W$ and the crucial constant $\lambda$.***
Thank you for your kind suggestions. We will provide ... | Summary: The paper studied the generalization analysis of the decentralized stochastic gradient descent ascent algorithm through the lens of argument stability for solving minimax problems. Strong/weak primal-dual population risks are established for both convex-concave, strongly convex-strongly concave and nonconvex-n... | Rebuttal 1:
Rebuttal: # Response to Reviewer FcPE
Thank you for your positive feedback, constructive suggestions, and insightful questions! We have answered your questions in the comment below.
***Q1: The results in Theorem 1 might be improved. Specifically, [1] established the connection between on-average argument ... | Rebuttal 1:
Rebuttal: # Common Response
***Technical challenges***
The major difference with vanilla SGDA lies in the communication through different local agents. The vanilla SGDA can be seen as a special case where there is only one agent and it can train on its own dataset and update its parameters all by itself.... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper analyzes the generalization of the Decentralized Stochastic Gradient Descent Ascent (D-SGDA) Algorithm for min-max problem using the algorithm stability framework. Specifically, the paper analyzes the weak and strong primal-dual generalization gap under both convex-concave (C-C) and nonconvex-noncon... | Rebuttal 1:
Rebuttal: # Response to Reviewer 5Agt
Thank you for your careful reading and constructive suggestions! We have answered your questions in the comment below.
***Q1: Limited technical novelty.***
We answer this question on the common response. Please refer to part ***Technical novelty*** in the common re... | null | null | null | null | null | null |
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization | Accept (poster) | Summary: In this work, the authors proved the global convergence of the gradient descent algorithm with a small initialization for the rank-1 matrix completion problem.
Strengths: The results in this work is novel and should be interesting to audiences in optimization and machine learning fields. This work shows that... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**A more detailed comparison with Chen, J., Liu, D., & Li, X. (2020) ... should be included.**
The paper that reviewer mentioned is an extension of the local convergence result [a] to the asymm... | Summary: The paper studies global convergence of GD (with a fixed step-size) for the rank-1 matrix completion problem (with symmetric and i.i.d. Bernoulli(p) observation and Gaussian noise on the entries) started from "small" random initialization. The authors prove that such vanilla GD, without any explicit regulariza... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**Minor Comments: ...**
We will change the manuscript accordingly. Thanks for your careful review.
---
>**eq. (8): Is it intentional to not to cancel out $n^{1/4}$ factor in the upper bound?*... | Summary: The authors study a random initialization scheme for gradient descent applied to the problem of rank-one matrices, assuming a known ground truth. Their results concern global convergence properties of the with respect to a particular random model of partially observed matrices. Specifically, starting from a $n... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**One issue I have with this paper is that rank-1 symmetric matrix completion (as well as rank-1 general matrix completion) is simply a much easier problem than general matrix completion.**
The... | Summary: This paper studies the global convergence of vanilla GD for the rank-1 matrix completion problem. It is shown that with small random initialization and after a logarithmic number of steps, GD enters a region around the global minimizers in which linear convergence happens. The paper provides sufficient conditi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**The sample complexity seems to be quite large (for example, compared to that in [1]). In this light, can the authors elaborate further on this aspect (my question is despite the further commen... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper shows some convergence properties of gradient descent with small random initialization for rank-1 noisy matrix completion.
Strengths: This paper uses a new approach (gradient descent with small random initialization) to solve the nonconvex formulation for rank-1 noisy matrix completion. It is show... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**1. My major concern of this paper is a lack of theoretical novelty. ... If I underestimate the technical novelty of the paper, I hope the authors could clarify and please highlight their tech... | Summary: This work considers the convergence analyses of gradient descent method for rank-one matrix completion problem. In particular, this work assumes small random initialization, which is relatively relaxed condition compared to existing work. With such assumption, the logarithmic convergence of the gradient descen... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback. Please see our response to the reviewer’s question.
>**I have concern for the novelty or the contribution of this work. Rank-one matrix completion problem is the simplest problem for matrix completion problems. Also there have constraints for the ... | null | null | null | null |
High-Fidelity Audio Compression with Improved RVQGAN | Accept (spotlight) | Summary: In the present paper, the authors introduce RVQGAN, a neural audio codec that uses a convolutional encoder / decoder along with Residual Vector Quantization as a bottleneck, with a multi scale mel reconstruction loss and different adversarial losses.
They show state of the art performance from 3 to 8kbps, comp... | Rebuttal 1:
Rebuttal: Thank you very much for you review and constructive criticism of our model. We appreciate the effort in getting to understand the details of our model, and we’ve made our best efforts to answer each of your concerns and questions.
> *incremental improvement over previous work: overall method is c... | Summary: This paper introduces a novel high-fidelity neural audio compression algorithm that achieves impressive compression ratios while maintaining audio quality. The authors combine advancements in high-fidelity audio generation with improved vector quantization techniques from the image domain, along with enhanced ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and feedback. Please find below answers and clarifications to your questions.
> *The novelty of the proposed model structure is a combination of existing models:*
>
> - *factorized codes and L2-normalized codes are from Improved VQGAN image model;*
> - *Snake ac... | Summary: This paper introduces a RVQGAN-based neural audio codec method, demonstrating superior audio reconstruction quality, a high compression rate, and generalization across diverse audio domains. The authors substantiate the significant performance superiority of their model over alternatives through extensive and ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and feedback. Please find below our comments and clarifications.
> *The authors derived the proposed methods from existing studies and experimentally validate them in the neural audio codec domain. This approach seems to compromise the scientific novelty of the r... | Summary: The authors propose a neural audio codec model that demonstrates superior performance compared to previous works, and present experimental results.
Strengths: - The authors appropriately explain the problem they aim to address.
- Their method is adequately described.
- The authors provide a specific implement... | Rebuttal 1:
Rebuttal: Thank you very much for your time and feedback. Please find our answers below to clarify some details about our model.
> …*it is necessary to validate whether the proposed audio codec works well on more diverse and completely different audio data. Additionally, finding failure cases of previous ... | Rebuttal 1:
Rebuttal: The authors of this paper would like to sincerely thank all the reviewers for their time and feedback. Specifically, we thank the reviewers for positively acknowledging:
1) the key challenges in the neural audio codec domain successfully addressed in this work
2) the strong results significantly ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transformers learn through gradual rank increase | Accept (poster) | Summary: This paper presents theoretical justifications and empirical evidence that transformers demonstrate incremental learning dynamics in the low-initialization regime. The authors consider a very restricted diagonal attention model along with a range of restrictive assumptions to theoretically characterize a numbe... | Rebuttal 1:
Rebuttal: Thank you for your positive review and for your helpful suggestions on presentation and paper organization. We are happy that you found our theory and experiments convincing. We answer your questions below.
* Q1: “Why is the rank at initialization bounded (at 128?) in the plots in Figure 5?“
... | Summary: This paper conducts solid analysis and experiments to demonstrate the theory and proofs provided in the paper offer valuable insights into the incremental learning dynamics in transformers and how they can be better understood.
Strengths: 1. The theory and proofs provided in the paper offer valuable insights... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our paper. We were happy to read that you thought our contribution provided valuable insights and that our experiments gave good evidence. We answer your question below:
* Q1: Besides LoRA, are there any other methods and techniques related to the proof a... | Summary: In this paper, the authors study the learning dynamics of transformers and argued that the difference between weights and their initial values increase in rank as the training progresses. Under small initialization, smoothness, non-degeneracy. convergence and robustness assumptions, the authors proof the incre... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We answer all your questions and address all your comments on weaknesses below. We hope that our responses will be sufficient to clear up any concerns and confusion. We would be happy to answer any more questions if you have them.
* Q: On NLP transformers
* Thanks... | Summary: The article "Transformers learn through gradual rank increase" considers the dynamics of training for the neural networks models with attention mechanism. The authors relate the training dynamics to a particular type of gradient flow. They show under 3 important assumptions:
i. diagonal weight matrices
ii. ini... | Rebuttal 1:
Rebuttal: Thank you for the positive review of our paper and your questions about the theory. We are glad that you found the results interesting and found our paper to be well-written. Please find below our response to your questions:
* Q1: "Is the paper more about attention in general than specifically ab... | Rebuttal 1:
Rebuttal: We thank the reviewers for their generally positive evaluations and for their helpful feedback that has helped us improve the paper in the revision. As suggested by the reviewers, we have **added new experiments**:
* on NLP transformers (GPT2 trained on Wikitext)
* and SGD-trained transformers (Vi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion Self-Guidance for Controllable Image Generation | Accept (poster) | Summary: The paper introduces a new method for detailed and versatile image editing and controlled image synthesis with large-scale text-to-image diffusion models. In particular, the authors propose self-guidance: Self-guidance uses the internal representations of the denoiser neural network of diffusion models for gui... | Rebuttal 1:
Rebuttal: Thanks very much for the in-depth reading of our paper and for the helpful comments and suggestions.
**Limitation to entities mentioned in prompt:** Yes, this is indeed a primary limitation of self-guidance as presented. However, other forms of self-guidance could help in situations where a text ... | Summary: Large-scale text-to-image generative methods have demonstrated impressive performance given a detailed text prompt. However, many aspects of an image are hard or impossible to specify with texts. The authors proposed self-guidance to guide the generation of diffusion models. Experimental results demonstrated t... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback on our work.
We respectfully disagree that self-guidance is not a “good solution” to “add more control over image generation beyond text prompts”. The edits that we perform, such as moving or resizing objects, are very challenging (if not impossible) to affect... | Summary: This paper introduces diffusion self-guidance, an inference-time technique for controllable image generation using pre-trained text-to-image diffusion models. The key finding is that internal representations of the denoiser network carry meaningful information about the scene, and one can build custom energy f... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper!
**Paper layout:** Thanks for the feedback on this. We entirely agree and have restructured the text so that the energy functions are in the main paper rather than the supplementary material.
**Sensitivity to hyper-parameters:** We alway... | Summary: This paper proposes a method for image editing using pretrained text-to-image diffusion models. The method guides the sampling process with energy functions that are added similarly to classifier guidance. These energy functions are computed as the difference between some object's property and the target state... | Rebuttal 1:
Rebuttal: Thanks for your thorough review and thoughtful comments.
**Open-source Stable Diffusion implementation:** To facilitate an even better understanding of the limitations of self-guidance, as well as address questions about whether shown examples are representative of the method’s abilities and whe... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and detailed reading of our paper. Reviewers found our approach to controllable image generation “conceptually simple, easy to implement, computationally efficient and highly flexible” with “clear and concise presentation” in an “interesting and important rese... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Convolutional Visual Prompt for Robust Visual Perception | Accept (poster) | Summary: The paper addresses test time adaptation by employing learnable convolution operation named CVP on out-of-distribution (OOD) images. The motivation is to take advantage of the inductive bias imparted by convolution operations with the added advantage of learning fewer parameters compared to existing approaches... | Rebuttal 1:
Rebuttal: **Clarification for contrastive formulation**
The loss indeed considers the negative pair, which is in the denominator. If $x_{i}$ and $x_{j}$ are positive pair, the $y_{i,j}$ is 1; then we maximize the similarity between the pos on the nominator and minimize the denominator, which is similarity ... | Summary: This paper proposes a novel label-free approach CVP for test-time adaptation on out-of-distribution data. The main idea is to use a convolutional kernal as the visual prompt. It captures the structure of data distribution shift, and reduces the trainable parameters. Experiments show that CVP improves model rob... | Rebuttal 1:
Rebuttal: **Thank you for endorsing and recognizing our work as realistic, interesting, and well-studied.**
**CVP would still work well for high-level distribution shift, such as style changes?**
- It's a very good point. We do have experiments on those style changes benchmark such as the ImageNet-Renditi... | Summary: .The paper proposes a new method for test-time adaptation (TTA) to structured distribution shifts. The proposed method learns convolutional visual prompts to prevent the model from overfitting to SSL objectives due to high dimensional prompts. The results show that the proposed approach consistently results in... | Rebuttal 1:
Rebuttal: **We appreciate the reviewer’s comments and suggestions. Thanks to the reviewer for recognizing the novelty of our work. We have answered and addressed the questions.**
**Comparison with other prompts**
- Thank you for your suggestion of comparing with other prompts. We have followed your sugges... | Summary: The paper proposes a variant of Visual Prompt Tuning (VPT) where the prompt is applied to a penultimate layer of the encoder network and is a result of a convolution with a 3x3 or 5x5 kernel (essentially it is an added residual block or residual adapter if you will). The added adapter is trained at test time b... | Rebuttal 1:
Rebuttal: **Novelty of our work**
- Our paper provides deep insights on what is a good visual prompt design for test-time adaptation, which is an important problem. The key novelty of our work is to propose this simple and effective convolutional visual prompt to address the overfitting challenge at test ti... | Rebuttal 1:
Rebuttal: - We thank the reviewers for the constructive feedback and insightful questions. We are delighted that most reviewers like the well-motivated novel method of CVP and think it would interest the visual prompt community, the extensive experiments conducted across various OOD recognition at large sca... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: - The paper introduces Convolutional Visual Prompts (CVP), a novel methodology designed to increase the model robustness when faced with Out-of-Distribution (OOD) data during test time.
- The primary innovation of CVP is the use of convolutional structures as inductive biases for adapting to visual OOD instanc... | Rebuttal 1:
Rebuttal: **Motivation of using convolutional visual prompt for test-time adaptation**
Thank you for your question. We agree with the reviewer that our CVP can be viewed as a de-corruption operator for natural images. Our paper studies how to be robust under major natural distribution shifts, such as style... | null | null | null | null | null | null |
Affinity-Aware Graph Networks | Accept (poster) | Summary: This work proposes to incorporate affinity measures as features into message-passing networks (MPNNs) in order to enhance the expressivity without enlarging the computational cost notably.
The authors introduce three examples of random-walk-based affinity measures, e.g., effective resistance, hitting, and comm... | Rebuttal 1:
Rebuttal: > 1. The paper does not have more throughout comparisons with other positional encoding enhanced graph neural networks...
> If possible, it would be perfect if you could provide more comparisons...
We note that we have already provided comparisons to the GSN model of Bouritsas et. al. for ogbg-m... | Summary: The paper proposes a strategy to strengthen the node and edge features in order to enhance the expressiveness of a message passing neural network. In particular, the paper introduces a set of effective resistance (ER) features, including node-level resistive embedding, which further derives two edge-level affi... | Rebuttal 1:
Rebuttal: > 1. I would like to seek clarification from the authors regarding the connection between...
Indeed, the message passing mechanism of a GNN gives rise to a computation tree with depth given by the number of message passing steps. As an example, for the graph in Figure 1, if we use two rounds of m... | Summary: This paper proposes to use affinity measures as additional features that can incorporate in common standard MPNNs and theoretically and empirically shows the improvement in expressiveness and performance in some datasets without loss of much scalability. The main contributions are listed below.
1. Generality. ... | Rebuttal 1:
Rebuttal: > 1. The motivation needs...
Features in GNNs are typically subject to a tradeoff between (a.) efficiency and (b.) high expressivity. For instance, random features or node index features address (a.) at the cost of (b.), while other features such as substructure counts help with (b.) but fall sho... | Summary: In this paper, the authors present MPNN using affinity measures as node and edge features. As the affinity measures, the authors propose effective resistance, hitting times, and resistive embeddings. The authors demonstrate the effectiveness of using the affinity measures through experiments on various dataset... | Rebuttal 1:
Rebuttal: > Q1. Do the authors think that the experiments with the current hyper-parameter setting are fair?
We note that the hyperparameters for each model/baseline were chosen via a hyperparameter sweep. This includes, for instance, the hidden size. For each model in the table, we provide the best settin... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to look through our submission and provide insightful comments and questions. In light of the comments raised by reviewers, we have provided further results and addressed several concerns. We respond to each reviewer individually.
There is one **experime... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation | Accept (poster) | Summary: The paper approaches the problem of designing wave function ansatz for the ground state estimation of the Heisenberg model. This problem introduces several challenges including the rich parameterization of the wave function, sampling of the states from the parameterized wave function, and incorporating symmetr... | Rebuttal 1:
Rebuttal: > **Summary:**
> The paper approaches the problem of designing wave function ansatz for the ground state estimation of the Heisenberg model. This problem introduces several challenges including the rich parameterization of the wave function, sampling of the states from the parameterized wave funct... | Summary: The paper proposes generic Autoregressive Neural TensorNet (ANTN), for quantum many-body simulation. In order to achieve high expressivity, sign structure preserving, physics inductive bias, accurate sampling, and symmetry preservation, ANTN combines tensor networks and autoregressive neural networks. The auth... | Rebuttal 1:
Rebuttal: >**Summary:**
>The paper proposes generic Autoregressive Neural TensorNet (ANTN), for quantum many-body simulation. In order to achieve high expressivity, sign structure preserving, physics inductive bias, accurate sampling, and symmetry preservation, ANTN combines tensor networks and autoregressi... | Summary: In this work, the authors introduce ANTN (Autoregressive Neural Tensor Net) as a novel architecture that combines features from both ARNN and Tensor Networks for simulating challenging quantum many-body systems. The main objective of this research is to utilize an ARNN-type network to generate a conditional wa... | Rebuttal 1:
Rebuttal:
**Thanks for the appreciation of our work. We apologize that due to the character limit, we have to simply the response. We will provide additional explanations in the follow-up discussion.**
>**Weaknesses:**
>The manuscript frequently mentions the concept of "expressivity," .....
**Thanks for... | Summary: This paper proposes Autoregressive Neural TensorNet (ANTN): a novel blend between Matrix Product States (MPS) and AutoRegressive Neural Network. Theoretically, this paper shows that ANTN parameterizes normalized wavefunctions, allows for exact sampling, generalizes the expressivity of tensor networks and autor... | Rebuttal 1:
Rebuttal: **We appreciate the reviewer's acknowledgment that our work is theoretically novel and experimentally nontrivial. The authors apologize that due to the character limit, we are unable to respond to all the questions and have to omit some contents. The authors would provide additional explanations i... | Rebuttal 1:
Rebuttal: **We would like to thank all the reviewers' comments and suggestions. We apologize that due to the character limit and math rendering issue, some explanations are simplified/omitted. We will provide additional explanations in the follow-up discussion period.**
**Reviewer 2bnN acknowledges our nov... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Cross-Domain Policy Adaptation via Value-Guided Data Filtering | Accept (poster) | Summary: The authors propose a method that does domain adaptation for RL. Unlike other approaches that do this via domain randomization, offline interaction from the target env, or adjusting the sim with system ID from a small real dataset, the aim is to learn from a large source of sim transitions and a small number o... | Rebuttal 1:
Rebuttal: Thanks for your feedback, and we will respond to each one of your questions as follows. (**W and Q denote weakness and question, respectively.**)
(W1) Thank you for reminding us of the related work RL-CycleGAN, which utilizes value equivalence for sim2real visual control. We will add RL-CycleGAN ... | Summary: The paper is concerned with the online dynamics adaptation problem, where an agent is tasked to generalize from a source domain with cheap access to a target domain with a limited online interaction budget. The approach filters what data from the source domain is used in the target domain based on whether the ... | Rebuttal 1:
Rebuttal: Thanks for your feedback, and we will respond to each one of your questions as follows.
**(W1) The paper could potentially benefit from additional experiments in environments that are not in the "short-term control" regime ...**
We appreciate your suggestion regarding additional experiments in ... | Summary: This paper considers a setting of online dynamics adaptation, where the goal is to train a near-optimal policy in the target domain using transition data from the source domain and the target domain with different dynamics. The authors propose to select source domain data to train Q-functions if the value disc... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, and we appreciate the opportunity to clarify and improve our paper based on your suggestions.
**(W1) The proposed idea seems to be simple and obvious. VGDF wants to select source domain data with similar state transition dynamics for additional training data... | Summary: Disclaimer: I found it challenging to fully comprehend the paper. This may be due to either inadequate clarity in the writing or my own limitations in understanding the subject matter.
This work introduces a method called Value-Guided Data Filtering (VGDF), which aims to enable online dynamic adaptation. By u... | Rebuttal 1:
Rebuttal:
Thank you for your valuable feedback, and we appreciate the opportunity to clarify and improve our paper based on your suggestions.
**(W1) The main weakness of this paper lies in its lack of clarity in communication and presentation. ...**
We apologize for any confusion regarding the contribut... | Rebuttal 1:
Rebuttal: We appreciate valuable feedback and suggestions from all reviewers. If you have any further suggestions or questions, please feel free to share them with us. We value your feedback and are committed to addressing all aspects to enhance the quality of our work.
Pdf: /pdf/ab14660129f582d9df02302ec13... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the online dynamics adaptation problem where the agent has access to a large number of samples or an offline dataset from a source domain, and must adapt with a smaller number of samples in a target domain.
To address this problem, this paper introduces a framework for using a value disc... | Rebuttal 1:
Rebuttal: Thanks for your feedback, and we will respond to each one of your questions below.
**(W1) I would appreciate comparisons in Section 6.3 to more offline RL algorithms that were designed for offline pretaining and online finetuning, like IQL.**
We would like to highlight the difference between t... | null | null | null | null | null | null |
Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions | Accept (spotlight) | Summary: The paper proposes Parsel, a framework that decomposes algorithmic reasoning problems into subparts, samples programs for each subpart and verify them. To decompose a problem, Parsel transforms it into an intermediate language that describes the functionality for each subpart and how the subparts depend on eac... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and helpful questions!
> As claimed on L142, the comparison uses an “effective number of complete programs” to represent the sample budget. So the “50 (improved)” setting has a sample budget of 50 while the “8x16” has 128 sample budget. On the own interpolat... | Summary: The paper presents a system called Parsel for program synthesis. First, a LLM predicts a Parsel program given a task specification or natural language plan. The Parsel program contains a hierarchy of functions which themselves might have functions inside. Each function is described with a function signature, a... | Rebuttal 1:
Rebuttal: Thank you so much for the thorough and helpful review. We sincerely appreciate the attention to detail and the encouraging and supportive framing. Note we are constrained to 6,000 characters here (a new rule…), but are happy to elaborate during discussion.
> I’m also interested in the other kind o... | Summary: This paper proposes Parsel, a framework for algorithmic reasoning with LLMs. Parsel can be seen as a kind of “programming language” that is implemented with mostly natural language which describes the functionality of the program -- that is, the algorithmic reasoning plan. Then, the Parsel synthesizer can tran... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments – the use of Parsel by human users was a key motivation, so these questions are really useful!
> Although Parsel can be generated by LLMs, the Parsel synthesizer is not reliable for human users. When coding with Parsel and the synthesized Python code is wron... | Summary: This paper proposes Parsel, a code generation framework that decomposes a problem specification into subproblems specified in a intermediate pseudocode-like language (the Parsel language) and then searches over combinations of subproblem solutions. The experiments show that decomposing and searching over subpr... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and questions! We intend to incorporate our responses into the revised paper.
> When you set a budget of number of evaluations, how do you decide what to evaluate?
We consider a random subsample of the possible combinations.
> The "Functions without constra... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful, positive, and constructive comments! Your suggestions have helped strengthen the work and clarify key points. Based on the reviews, here are some of the main changes:
1. We conducted a new ablation study generating Parsel directly from problems, ablati... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces Parsel—an intermediate programming language for large language model program synthesis. It decomposes a complex programming task into several strong-connected components and uses large language models to generate candidate code pieces for each component, combined with combinatorial search... | Rebuttal 1:
Rebuttal: Thank you for the encouraging review!
> Parsel design: I am thinking of if the Parsel language is expressive enough (for example, it may lack data structure design (which is something that makes APPS hard). Can Parsel generate data-structure specs?
Thank you for this excellent point. This is def... | null | null | null | null | null | null |
Multi-Head Adapter Routing for Cross-Task Generalization | Accept (poster) | Summary: This paper proposed a new parameter-efficient few-shot fine-tuning method for pretrained language models. The method is a follow-up work of Poly. The authors proposed to fine-tuning the routing function and freezing the multi-head adapters. This way, the number of updated parameters reduced significantly while... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address below your concerns point by point.
#### **1. On the novelty of the proposed method**
We refer the reviewer to the global comments.
#### **2. On why finetuning (only) the router is necessary**
We do not mean to claim that fine-tuning only the router is n... | Summary: The authors propose a new parameter-efficient routing method, Multi-head routing (MHR), which combines parameter subsets as opposed to averaging all weights together. They find this yields better performance, and even only finetuning the routing matrix after initial training works well. Furthermore, they explo... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a thorough and constructive review. We will address the questions one by one.
#### **1. AdapterSoup Baseline and Backbone Clarifications**
We apologize for the lack of clarity regarding this. Indeed, our AdapterSoup baseline uses the same LoRA configuration (s... | Summary: This paper introduces a method called Parameter-efficient Fine-tuning (PEFT) to improve how pre-trained language models adapt to new tasks. They use small adapters and a routing function to select specific adapters for each task. The authors found that finer-grained routing provides better results and propose ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address the raised concerns point by point.
#### **1. On the novelty of the proposed method**
We refer the reviewer to the global comments
#### **2. Additional information about model parameters and configuration**
For all experiments in the paper, ... | Summary: In this work, the authors proposed MHR for cross-task generalization. To achieve extreme parameter efficiency, MHR- z and MHR-u are proposed to balance the performance and efficiency. Besides, this work emphasized the importance of the routing function, which is very insightful for the community.
Strengths: 1... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below we address your concerns point by point.
#### **1. On statistical efficiency and pertinence of results**
We refer the reviewer to the global comments.
#### **2. Clarification of the task-module routing matrix $Z$.**
The task-module routing matrix has shape `(n... | Rebuttal 1:
Rebuttal: Dear Reviewers, we thank you for your valuable feedback! We really appreciate the time you spent providing us constructive advice to improve our submission. We list below our general response to all of you, and address individual concerns in separate threads.
#### **1. Novelty w.r.t to prior wor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning | Accept (poster) | Summary: This paper proposes a new model-based offline RL algorithm that learns a policy which is risk-averse (wrt aleatoric uncertainty measured by some dynamic risk measure) and pessimistic (wrt epistemic uncertainty from modeling). First, the algorithm learns a posterior distribution over MDP transitions given the d... | Rebuttal 1:
Rebuttal: Thank you for the valuable time you have spent reviewing our paper. We respond to your comments and questions below.
### Theory and Dynamic vs Static Risk
We have **added theoretical justification** for why our approach avoids both epistemic and aleatoric uncertainty in Proposition 1 in the resp... | Summary: > Our concerns are addressed by the authors. Thanks for your effort. We will upgrade the rating to weak accept.
This paper introduces a model-based risk-averse algorithm that utilizes risk aversion as a mechanism to jointly address the distributional shift problem and risk-related decision-making problems in... | Rebuttal 1:
Rebuttal: Thank you for the valuable time you have spent reviewing our paper. We respond to your comments and questions below.
### Flexibility of the Approach
We agree that our approach is simpler yet less flexible than existing approaches that can separately adjust how they address each source of uncerta... | Summary: This paper considers the problem of offline reinforcement learning for risk-averse decision-making with the distributional shift. The core insight of this paper is to incorporate epistemic uncertainty (from the distributional shift) and aleatoric uncertainty (from usual statistical errors) together and develop... | Rebuttal 1:
Rebuttal: Thank you for the valuable time you have spent reviewing our paper.
We agree that deciding how to represent epistemic uncertainty in deep learning is an important problem, and a key aspect of our work.
We used a small ensemble of 5 neural networks to ensure that our work is a fair comparison to ... | Summary: One Risk to Rule Them All (1R2R) is an offline-RL method that seeks to reduce aleatoric and epistemic uncertainty. Their method is simple; the authors introduce the notion of risk in the bellman update by adding learned adversarial perturbation to the learned transition dynamics models in the model-based setti... | Rebuttal 1:
Rebuttal: Thank you for the time you have spent reviewing our paper. We respond to your main comments below. We are unable to respond to all comments due to space constraints.
### Why does our approach reduce both epistemic and aleatoric uncertainty?
In Proposition 1, and the explanation that follows, we ... | Rebuttal 1:
Rebuttal: Thank you for your reviews.
Please find attached the Global Response. In the attachment, we provide updated results which include an **additional ablation**, as well as comparisons to **additional baseline algorithms**: MOPO, COMBO, and ATAC.
We also include additional figures. The summary figur... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems | Accept (poster) | Summary: In this paper, the authors propose an algorithm for learning the intrinsic reward function in order to solve the problem of poor results due to improper design of the reward function of the dialogue strategy module in current conversation recommendation systems. Specifically, a multi-objective bi-level optimiz... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestions to strengthen the empirical support of our work by adding more advanced baselines and significance testing.
---
**[Q1]** Compare with CRIF
**[A1]** We discussed the CRIF model in our appendix and reported the experiment comparisons with it... | Summary: This paper study the problem of reinforcement learning based conversational recommender systems. The paper claims that it is difficult to design a handcraft reward function for each step of the conversations. Thus the paper proposes a multi-objective bi-level optimization method that the inner level optimizes ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the motivation of our paper and our idea being interesting. We hope the following responses can address the reviewer’s concerns.
---
**[Q1]** Clarification about how the components of CRSIRL work together
**[A1]** We develop a bi-level optimization framewo... | Summary: Mainstream reinforcement learning-based CRS solutions heavily rely on handcrafted reward functions, which may not be aligned with user intent in CRS tasks. Therefore, the design of task-specific rewards is critical to facilitate CRS policy learning, which remains largely under-explored in the literature. This ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the constructive comments on enriching experiment results and more practical CRS paradigm.
---
**[Q1]** What are the comparison results, using the metric SR@K, where K is a small number (e.g., 3 or 5)?
**[A1]** We report the SR@5 of CRSIRL, compared with the stron... | Summary: The paper addresses the problem of designing effective reward functions for conversational recommender systems (CRS), which is critical but largely under-explored in the literature. The paper proposes a novel approach to learn intrinsic rewards from user feedback, which can better capture the user intent and o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work, and the constructive questions to help enrich our work in the future.
---
**[Q1]** How does the user simulator approach compare to other methods of evaluating CRS solutions?
**[A1]** Due to interactive nature of conversational recomme... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful comments and constructive suggestions, which will help us strengthen our paper. We are encouraged to find that the reviewers appreciate the clear presentation (reviewer reFg, vRxN), motivation of our study (reviewer reFg, Kjom, vRxN, 5ZDc),... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Are GATs Out of Balance? | Accept (poster) | Summary: The work derives a conservation for the dynamics of the GAT gradient flow during training. The conservation law is used to explain why it is challenging to train deep GAT models and in particular why a large portion of parameters do not change much throughout training. A new initialization scheme is introduced... | Rebuttal 1:
Rebuttal: We thank reviewer hDWR for their interesting comments on our work and the constructive feedback.
In response to their question, we discuss the adaptability of the balanced initialization scheme to other MPGNNs, which we would be happy to add to the manuscript.
While in principle the argument of... | Summary: This paper focuses on the parameter struggle training problem of the well-known GNN structure GAT. The authors propose to alleviate the issue via parameter norm balancedness. A conservation law is derived for GATs with positive homogeneous activation function as the theoretical support for the parameter norm b... | Rebuttal 1:
Rebuttal:
We thank reviewer d9X8 for their constructive feedback. In line with their suggestions, we will i) include the details of equations used in lines 140-142 arising from the definition of Xavier initialization, and ii) increase figure sizes and improve the table style for better illustration.
The ... | Summary: The authors propose a theoretical analysis of the initialization of GATs and their impact on the performance of such networks, focusing on the performance vs depth aspect.
The authors propose an initialization algorithm, that starts from a random initialization and modifies the initial random weights to adher... | Rebuttal 1:
Rebuttal: We thank reviewer yMby for recognizing the novelty and relevance of our work and appreciate their suggestions, for which we will be taking the following actions:
i) introduce more subsections and break down larger paragraphs to improve the readability and comprehension of the paper;
ii) cite the ... | Summary: This work proves a conservation law for GAT architectures, which is similar to conservation laws shown for fully connected networks. The conservation law shows a simple connection between the norms of weights of two consecutive layers. Using this law, an intuitive explanation is given for the trainability issu... | Rebuttal 1:
Rebuttal:
We thank reviewer cXS5 for their insightful review and constructive questions, which are answered below. We would be happy to include the additional experiments and clarifications in the main paper as well.
**1. Effect of orthogonal initialization:** We include results to compare the LLortho an... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging the importance and relevance of our work and appreciate their constructive feedback, insightful questions, and encouraging comments. We take several actions to improve the paper in line with the reviewers' suggestions, summarized as follows:
i) We provide ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference | Accept (poster) | Summary: This paper presents a framework that simultaneously optimizes the 2PC inference protocol and the neural network architecture to achieve a significant reduction in communication. The framework outperforms state-of-the-art (SOTA) approaches by achieving communication reduction.
Strengths: 1. The paper is well-w... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer P7t8 for your thoughtful feedback!
---
**Q1:** Include more ablation experiments to demonstrate the individual contributions of ReLU pruning and re-parameterization.
**A1:** Thanks for your suggestion! In Section 5.5, we perform an ablation study by adding our propose... | Summary: The paper presents optimizations to secure two-party computation of convolutional network inference. There are optimizations for both linear and non-linear layers, resulting in an overall single-digit factor improvement.
Strengths: The optimizations look interesting and are underlined well with benchmarks. I... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer AJR8 for your thoughtful feedback!
---
**Q1:** Table 1 should contain numerical speed-ups.
**A1**: We thank the reviewer for the valuable feedback. As shown in Figure 1 in the paper, operations like ReLU, truncation, and convolution, are major contributors to the onl... | Summary: The paper introduces CoPriv, a framework that optimizes the 2-party computation (2PC) inference protocol and the deep neural network (DNN) architecture to reduce communication overhead. CoPriv features a new 2PC protocol for convolution based on Winograd transformation and develops DNN-aware optimization to re... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer vLvc for your thoughtful feedback!
---
**Q1:** Figure 1 could benefit from more details.
**A1:** Thanks for your advice! We will improve this figure carefully to include more details.
For ReLU, [22] represents Gazelle which uses garbled circuit (GC), [33] represents... | Summary: This paper presents CoPriv that jointly optimizes 2PC protocols and DNN architectures. It argues that SOTA 2PC protocols mainly focus on minimizing ReLU-based metric, which no longer contributes to the majority of communication. It proposes a new protocol for convolution with Winograd transformation and propos... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer yVeD for your thoughtful feedback!
---
**Q1:** The implication of end-to-end speedup is not well studied.
**A1:** Thanks for the meaningful suggestion on the inference speedup! In our experiments, we compare the inference latency of MobileNetV3 (with different capaci... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the thoughtful feedback and helpful comments!
**Rebuttal One-page PDF:** we attach the one-page PDF here to include the figures and tables mentioned in the following responses. We give a brief introduction of these figures and tables below to provide conve... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas | Accept (poster) | Summary: The paper extends the CrossBeam method with synthesizing intermediate lambda
functions to solve the programming by example task. The authors introduce the
Merge operator to construct new lambda functions by choosing an operator from
the DSL, and its arguments from existing terms (variables or lambda
functions)... | Rebuttal 1:
Rebuttal: Thank you for your review!
> include CrossBeam without lambda functions as a baseline
CrossBeam would not perform well because 85/100 handwritten evaluation tasks and 53/100 synthetic evaluation tasks use a lambda function in the solution. CrossBeam would not be able to solve those problems and ... | Summary: The paper presents LAMBDABEAM, a nn-based search method for program synthesis which is built upon CROSSBEAM and can handle lambda functions and higher-order functions. Specifically, to build lambda terms, LAMBDABEAM enforces that every term constructed during search has no free variables by introducing a novel... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Weakness 1.
We will include more info about CrossBeam (see global response).
> what causes the differing number of I/O examples for list output and integer output in the hand-written?
In general, PBE tasks can be specified with fewer examples if the examples are mo... | Summary: In this work the authors introduce LambdaBeam a method crafted to explicitly handle lambda functions and higher-order functions for neurally guided program synthesis. Towards this goal the authors first introduce a method to represent lambda functions which enables variable order independent canonical represen... | Rebuttal 1:
Rebuttal: Thank you for your review!
> Weakness 1.
We were careful with our wording, but we will revise to make it more clear. When we discuss previous methods that cannot handle lambda functions (line 23), we are referring specifically to the types of prior works listed on lines 22-23, NOT referring to L... | Summary: This paper presents a method for training a neural module to guide a search-based program synthesis procedure that supports lambda functions. This is accomplished by leveraging the existing technique of property signatures, which essentially represent program constructs using a hand-designed vector of features... | Rebuttal 1:
Rebuttal: Thank you for your review!
> there are almost no examples in the paper
This is a great point. Please see the global response for some example tasks and synthesized programs.
> many of the implementation details are not listed out fully, which impedes reproducibility (e.g., the architecture of t... | Rebuttal 1:
Rebuttal: We appreciate all of the insightful reviews! We will revise our paper to incorporate our clarifications and new information wherever appropriate.
This global response includes information helpful for multiple reviewers, and we also respond to each reviewer individually.
**The paper could use mor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Multi-resolution Spectral Coherence for Graph Generation with Score-based Diffusion | Accept (poster) | Summary: This paper proposes Wave-GD, a score-based generative model, to generate graphs with high fidelity. By capturing the dependency between nodes and edges at multiple resolutions in the spectral space, it claims it overcomes the over-smoothing problem and achieves real-like frequency characteristics of nodes and ... | Rebuttal 1:
Rebuttal: W1) Compare with more baselines.
A) We thank the reviewer for introducing great references. We compared Wave-GD with Martinkus et al (ICML 2022) and Chen et al (ICML 2023) and reported the results in the pdf of the global response. As shown in the result, our method outperformed both baselines in... | Summary: In this work the authors tackle the problem of graph generation learning where the goal is to learn the key features of a set of graphs and be able to generate graphs with similar properties. To that extend, the authors extend the GDSS (Jo 2022) method through an additional loss term (Eq. 7). This loss term en... | Rebuttal 1:
Rebuttal: W1) Improve clarity on SDE learning, Fig. 2a, Sec 3.1, Lemma 1, and Tab. 1.
A) We thank the reviewer for the detailed constructive comments.
We presented a revised version of Fig. 2a with its improved description in the pdf of the general response, so please check on it. We will also clarify all... | Summary: The paper introduces a graph generative approach that leverages diffusion models and wavelet theory. The key concept revolves around utilizing the wavelet transform of the adjacency matrix across various scales, and learning a joint backward diffusion process that remains valid at all considered scales simulta... | Rebuttal 1:
Rebuttal:
W1) Ablation study on $J$ and analyses of the impact of graph statistics on the optimal J are needed. Also, how the J’s were set to obtain the main results?
A) We provide our answers in the following, and we will discuss them in the main manuscript to make the paper more clear.
* We agree that... | Summary: This paper claims the node feature and graph topology are not coherent in most previous generative graph models and high-frequency signals in node features and graph topology may neglect during the generation process. Therefore, they propose a Wavelet graph diffusion model (Wave-GD) with score-based diffusion.... | Rebuttal 1:
Rebuttal: W1) Change descriptions of the node and edge in line 26.
A) We will use the terms ‘node features’ and ‘graph structure’ in the intro properly as the reviewer suggested. We appreciate the reviewer's comment.
W2/Q1) Does performance improvement come from coherence or multi-resolution?
A) As we h... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive reviews with anonymously positive evaluations.
In the pdf of the general response, we present $\bf{1) }$ A revised version of Fig. 2a and its description, $\bf{2) }$ an analysis on the computational time of eigendecomposition, $\bf{3) }$ optimal scale... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction | Accept (spotlight) | Summary: This paper presents a new approach to generalized fMRI-to-image reconstruction with a focus in incorporating mage semantics and addressing semantic gaps during the reconstruction. To address inside-space semantic gap, a CLIP based feature space is utilized. To address outside-space semantic gap, a structural i... | Rebuttal 1:
Rebuttal: ## To Reviewer AK4m
### W1. About only considering one baseline model for comparison on NSD.
Given that in Section 4.5, we want to illustrate that following the dataset split method in [a], even a very simple baseline model (k-nearest-neighbor) can achieve a comparatively good decoding result. It ... | Summary: This paper addresses the problems of semantic gap between training and testing fMRI neural responses and generalization of fMRI-to-image reconstruction models. A pre-trained CLIP model is leveraged to map the training data to a latent feature space in which sparse semantics are extended into dense semantics, t... | Rebuttal 1:
Rebuttal: ## To Reviewer qNjJ
### Q1. About the confusing Notations.
We acknowledge the potential confusion caused by the notations employed in the paper and recognize the need for adopting clearer conventions. Accordingly, we will make revisions to define vectors with bold lowercase letters and represent m... | Summary: This paper proposes a GESS model to solve the semantic gap between the training and the testing data in the generalized fMRI-to-image reconstruction task. A CLIP based method is used to alleviate semantic gap for instances with known semantic space, and a structural information guided diffusion model is used t... | Rebuttal 1:
Rebuttal: ## To Reviewer NuyD
### W1. About the limited comparison experiments.
When we submitted the paper, we strived to find limited open-source methods ([a], [b], [c], etc.), among which [c] is the state-of-the-art of CVPR 2023. We will continue searching and include more up-to-date methods for comparis... | Summary: This paper's objective is to enhance the generalization performance of the fMRI-to-image reconstruction task through dense representation learning. To achieve this, a pre-trained CLIP is utilized to establish a semantic space, thereby bridging the gap between the training and test sets. Specifically, the paper... | Rebuttal 1:
Rebuttal: ## To Reviewer ciXJ
### Q1. About the novelty of data augmentation in momentum alignment
Different from previous works [b][d], we explicitly define the semantic gap and reduce it through momentum alignment. The alignment requires accurate descriptions of the data distribution while estimating stat... | Rebuttal 1:
Rebuttal: ## To Reviewers
We sincerely appreciate all reviewers devoting time for our paper and provide valuable comments. We also feel encouraging that all reviewers agree with our contributions in addressing the semantic gaps, introducing the adaptive confidence-weighted approach, and presenting the speci... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks | Accept (poster) | Summary: This paper studies the implicit bias of gradient descent for two-layer ReLU networks for cluster data distribution, and shows the implicit bias towards solutions that generalize well but are vulnerable to adversarial examples.
Strengths: The paper builds upon previous work by Vardi et al, removing the ortho... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the positive feedback.
Regarding the reviewer’s question: Assumption 2.2 (3) essentially requires that $k$ cannot be too large and the correlations between cluster means cannot be too large. We will discuss this inequality in the camera-ready version to make it a bit ea... | Summary: In this paper the authors study the implicit bias of two layers neural networks with ReLU activation, in the setting where the data is composed of independant clusters that "dont' overlap" (i.e the probability of having the point of a cluster falling outo the support of another is small, e.g Gaussian of subgau... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. We respond to their main questions and comments below.
### Assumptions on $Q_{\pm}$, $k$ and $c$:
We apologize for the confusion here. Although our analysis can accommodate non-constant $c$, for simplicity one can take $c$ to be a constant, in... | Summary: The paper studies the implicit regularization brought by the neural network itself. The authors theoretically prove that in two-layer ReLU networks trained with the logistic loss or the exponential loss, the implicit bias would lead to the solutions that generalize well but non-robust, regardless of the size o... | null | Summary: The authors show under a special data cluster model, the implicit bias of gradient flow converge to KKT points that generalize well but are not robust under l2 perturbations, when robust solutions of the problem exists. Their results are built upon earlier works on KKT points by LL20 and JT20, and the more rec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of our work. We respond to two comments/questions below:
### Difference with VYS22:
At a high-level, the reviewer is correct that there are parallels between clusters in our setting and samples in their setting. However, there are a few important ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the generalization and robustness of solutions obtained by gradient flow on two-layer ReLU networks. Under a distributional setting where the data is sampled from a Gaussian mixture distribution, this paper shows that the gradient flow is biased towards solutions that generalize well, but ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond to specific points below.
### Assumption of small training loss:
Indeed, the implicit bias result kicks in when gradient flow reaches a sufficiently small training loss. One of the advantages of relying on the KKT conditions o... | null | null | null | null | null | null |
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability | Accept (poster) | Summary: The paper proposes the VP-learning algorithm to solve offline goal-conditioned RL in the context of general function approximations. The algorithm is based on a previous empirically successful algorithm proposed by [1], and the author proves the finite sample complexity for VP-learning under mild assumptions. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>But it seems that the whole theory does not essentially rely on the setting of goal-conditioned RL
We provide the following reasons (motivations) that our paper uses goal-conditioned settings.
1. The origin... | Summary: This paper provides a rigorous theoretical analysis to a modified version of existing offline Goal-conditioned RL algorithm, and proves that it has an $\tilde O (poly(1/\epsilon))$ sample complexity, where $\epsilon$ is the desired suboptimality of the learned policy. The algorithm requires minimal assumptions... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>Overall, I think the paper would have been a valuable contribution to the field of offline RL. However, the flawed derivation is a serious issue that needs to be addressed.
We really appreciate the reviewer’... | Summary: This paper aims at improving the theoretical understanding of offline goal-conditioned RL (GCRL). In particular, this paper modifies an existing offline GCRL algorithm and shows an O^˜ (poly(1/ϵ)) sample complexity under minimal assumptions of single-policy concentrability and realizability. Their algorithm, c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>How do you expect the modified algorithm, VP-learning to compare to GoFAR in terms of empirical performance? How do you expect the modifications will impact things adversely?
The modified algorithm, VP-learn... | Summary: This paper establishes a rigorous theoretical analysis for offline goal-conditioned reinforcement learning algorithms (GCRL). To achieve that, the authors made a slight modification on top of an existing offline GCRL algorithm (GoFAR), achieve a polynomial sample complexity by regression instead minimax optimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>There is no experiment to support the correctness of the theoretical analysis.
We provide experiments to support the correctness of our theoretical analysis (especially the choice of $\alpha$). Please see th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful and insightful comments. Below we first address common issues. Since several reviewers mentioned that our paper does not provide empirical results of the modified algorithm, we provide experimental results of our VP-learning algorithm with different ch... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints | Accept (poster) | Summary: The paper extends the traditional complex query-answering task into an eventuality-centric complex query-answering task to understand the reasoning at the eventuality level. Specifically, the paper divides the discourse rations into two types of implicit constraints: occurrence constraints and temporal constra... | Rebuttal 1:
Rebuttal: Re W1 & Q1:
The relation projections and intersection operations are adopted from backbone models. For different backbone bone models, we have different parametrization of Intersection/Union.
GQE [1] model uses a feed-forward layer, followed by an average pooling, and then followed by a matrix m... | Summary: The paper proposed a reasoning task "Complex Eventuality Query Answering (CEQA)". CEQA is performed over EVKG and is different from traditional CQA over entity-centric KG. Authors of the paper clearly explain the new task, and further discussed a memory-augmented method to improve models' performance on CEQA.
... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and efforts for reviewing this paper.
W1 System 1 and System 2:
In short, the theory of System 1 and System 2 reasoning, proposed by Daniel Kahneman, suggests that human thinking can be divided into two systems. System 1 operates automatically and intuitively, making... | Summary: This work aims to conduct complex logical query task over eventuality-centric KG (EVKG) and propose the Complex Eventuality Query Answering (CEQA) setting that considers the implicit constraint of the temporal order and occurrence of eventualities. The authors also propose a memory-enhanced query encoding meth... | Rebuttal 1:
Rebuttal: Re W1:
Thank you for pointing out this reference, and we will cite the corresponding papers. Meanwhile, we would like to argue that, we are the first work to use memory modules in the problem of query encoding, and we are the first work to propose using memory modules to encode the logical constr... | Summary: This paper proposes an approach to address the challenge of complex query answering on eventuality knowledge graphs by integrating implicit logical constraints. The authors introduce the task of complex eventuality query answering (CEQA), which requires considering the occurrence and temporal order of eventual... | Rebuttal 1:
Rebuttal: Thank you for your review! I would like to address your concerns one by one.
Re: W1
We clarify that the proposed problem is formally defined in logical form. Meanwhile, relational encoding is part of the query encoding method. It is possible that there will be other methods for this task that do... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new task of complex event knowledge graph completion, curates the datasets from existing event knowledge graph, and designs a solution. However, the tasks motivation is not very clear regarding the formal logic form as each event is still in the form of natural language, especially conside... | Rebuttal 1:
Rebuttal: | | Commonsense KG | | Eventuality KG |
|---|---|---|---|
| KG | Atomic | ConceptNet | ASER |
| Node | Event-Centric (mostly) | Entity-Centric (mostly) | Eventuality |
| Edges | If A then B; | Entity relations | If A then B; |
| | A because B; | … | A because B; |
| | A as a result B; | ... | null | null | null | null | null | null |
Statistical Insights into HSIC in High Dimensions | Accept (poster) | Summary: The paper investigate the performance of HSIC to test the independence of two random vectors. The focus is on high but not ultra high dimensional scenarios where the theory is lacking. More specifically, the paper presents convergence rates for HSIC as the dimensions grow at different rates, and demonstrates ... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for your valuable feedback and constructive suggestions.
Comment 1: How different is the theory between yours and the 2021 paper by Gao et al. in the Annals of Statistics? What are the differences in technical tools and level of difficulties?... | Summary: The paper provides insights into the properties of HSIC in high dimensions, more specifically the rate at which sample size must grow in order to detect non-linear correlations if data is high dimensional. The results are categorized based on scenarios where either one or both variables have a "growing" dimens... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for your valuable feedback and constructive suggestions.
Comment 1: I'm happy to see empirical study (section 5.3) in a theoretical paper. Having said that, I would be surprised if a linear test would not reject the null (pool the returns per... | Summary: A paper providing tighter analysis and tests for HSIC statistics for independence in some regimes of interest
NB I have only a nodding acquaintance with this statistic, but have used it and regard it as of high importance. I have done my best to learn the background in the time available.
Strengths: The mode... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for your valuable feedback and constructive suggestions.
Comment 1: Is the isotropy essential to these bounds or can we use other kernels? Do the bound still hold if we relax isotropy to some "better" distance metric? How about if we use a ke... | Summary: The authors provide the statistical properties of HSIC, which is the measure of independency between two random variables. When the random variables have high-dimensionality and have nontrivial dependency, the authors provide the condition for the number of samples in order to successfully detect the dependenc... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for your valuable feedback and constructive suggestions.
Comment 1: Choice of kernels should be related to the argument that HSIC only measures linear dependences (l336).
Response 1: Thank you for raising this important point about the choi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This article deals with the problems of measuring nonlinear dependence between random vectors living in Euclidean spaces and testing for their independence. The authors provide statistical insights into the performance of one of the two major criteria, the Hilbert-Schmidt independence criterion (HSIC), when th... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for your valuable feedback and constructive suggestions.
Comment 1: The paper will not appear as self-contained to the non specialist (like me). The naive reader will find it contrary to intuition that the computation of a criterion measuring... | null | null | null | null | null | null |
Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context | Accept (poster) | Summary: Authors propose to use static analysis to guide LLM decoding process to improve generation of code that may not be available in the context or training data. Authors show that their approach improves identifier generation compared to baseline and in certain cases compared to larger models.
Strengths: - Author... | Rebuttal 1:
Rebuttal: > this testset is limited and geared towards identifier completion ... Authors do not show a comparison in a general test set where identifier completion may be small part of accuracy.
We would like to clarify that each testcase in DotPrompts is obtained by identifying a dereference location in a... | Summary: The paper proposes a framework that modifies the output logits of a language model using a monitor-guided decoding approach. The monitor consists of type-guided method invocations across the repository obtained via static analysis tools and heuristics to update the contents of the monitor along with when to tr... | Rebuttal 1:
Rebuttal: > Main concern: scope of applicability to other settings. Authors describe framework as general ... have shown results in narrow setting of type constraints
As discussed in the common response, MGD is generalizable across programming languages, coding scenarios as well as different static analyse... | Summary: This paper proposes to use the output of a code static analysis tool, of the sort used in IDE code completion tools, to constrain the output of an LLM to improve code generation. Concretely, the paper focuses on type-consistency in object dereference for Java code. When an LLM is called to generate a use of an... | Rebuttal 1:
Rebuttal: > Q2) ... is it rest of the method?
Yes, the task is to generate the rest of the method, starting from the dereference location. Since the methods in DotPrompts consist of at least 7 lines of source code, a typical completion consists of multiple lines (Appendix line 75). On average, the number o... | Summary: Inspired by the fact that IDE static analysis helps in code writing, this work proposes an MGD approach that uses a monitor to guide the decoding generation of the code language model. The authors started with the motivation that the code language model generation process does not perceive the global informati... | Rebuttal 1:
Rebuttal: > Authors mention ... MGD is targeted at a general-purpose programming languages and focuses on static analysis through repository-level context. However, MGD cannot be theoretically justified to support general purpose programming languages, or even to effectively limit output in only some coding... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' constructive feedback and suggestions. We first answer some common questions and present individual responses later.
# Generalization and Applicability of MGD
We demonstrate MGD’s applicability to more coding scenarios, programming languages, and different static anal... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals | Accept (poster) | Summary: RL has a notorious sample efficiency problem, and the authors propose to tackle this problem by having RL agents read instruction manuals. The authors propose a novel framework called Read and Reward (R&R), which is composed of two modules. The first module is QA which extracts and summarizes relevant informat... | Rebuttal 1:
Rebuttal: Thank you for recognizing that our proposed framework is interesting and promising. Here are our responses to the questions and concerns:
W1 Multi-modality models:
Thank you for recognizing the important future direction to train and incorporate multi-modal LMs. We have been actively experimenti... | Summary: The authors introduce Read and Reward framework, where RL agents accelerate their learning of a new environment by interpreting user manuals. Specifically, the framework consists of a QA Extraction module that extracts and summarizes relevant information and a Reasoning module that evaluates object-agent inter... | Rebuttal 1:
Rebuttal: Thank you for recognizing the potential of our work.
Here are our responses to the questions and concerns:
W1 Document Length: We would like to reiterate that our *main contribution* is to sketch out a framework making use of LLMs for exploiting external textual data that future RL algorithms m... | Summary: This paper proposes Read and Reward (RR), a method to incorporate prior human knowledge about the environment to achieve performance and efficiency gains in RL environments. The paper instantiates RR in several Atari environments by reading the information from the instruction manual. A full end-to-end pipelin... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and significance of our work. Here are our responses to the questions and concerns:
W1 Generalization to noisier data sources:
Current implementation of RR uses small language models (RoBERTa and Macaw), which already demonstrate some degree of robustness t... | Summary: This paper presents a novel method for Single Agent Reinforcement Learning which utilises computer game instruction manuals to enhance learning efficiency and performance. The Atari game manuals are used to accelerate RL algorithms in learning to play four different games. The framework comprises a Question-An... | Rebuttal 1:
Rebuttal: Thank you for recognizing that our method is novel and the results are promising, and thank you for pointing out concerns about the reliability of our evaluation (W1, Q1). We would like to reiterate that our *main contribution* is to sketch out a framework making use of LLMs for exploiting externa... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments. We are encouraged by *all reviewers'* appreciation that our proposed framework achieves promising results (Reviewers 3Ei6, qAA2, LzHL, 7mzB). In addition, we are encouraged by acknowledgements for our contributions on 1) an important connec... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
From Comprehensive Study to Low-Rank Compensation: Exploring Post-Training Quantization in LLMs | Reject | Summary: This paper conducts a comprehensive analysis of various quantization methods for large language models (LLMs). Some interesting takeaways were shared, for example, activation quantization is generally more susceptible to weight quantization; none of the current quantization methods can achieve the original mod... | Rebuttal 1:
Rebuttal: Thank you for valuing our research's importance in LLM deployment and acknowledging the relevance of our insights on quantization. We're pleased our proposed improvements resonated and welcome further feedback.
*Q1:* The proposed method is based on low-rank approximation, which can be viewed as a... | Summary: This paper analyzes post-training quantization (PTQ) techniques in large language models, exploring various schemes, model families, and bit precision. The authors propose an optimized method called Low-Rank Compensation (LoRC) to enhance model quality recovery with minimal size increase.
Strengths: 1. An eva... | Rebuttal 1:
Rebuttal: Thanks for finding our paper easy to read and understand. We appreciate your positive feedback.
----
*Q1:* The points in Figure 1 are too dense and lack recognition.
*A1:* Thanks for the suggestions. We thanks reviewer pointing out that figure 1 is hard to differentiate the difference of di... | Summary: This paper studied the post-training quantization method for 4-bit weight quantization and W4A4 quantization. The authors further proposed a Low-Rank Compensation (LoRC), to enhance model quality with low-rank matrices.
Strengths: The paper is well-written and easy to follow.
Weaknesses: The novelty is not v... | Rebuttal 1:
Rebuttal: We thank the reviewer for your comments and appreciate the opportunity to address your concerns. As we strive to maintain the highest level of integrity in our research, we welcome any feedback that will help us improve our work.
---
*Q1:* The novelty is not very significant. The PTQ with fine... | Summary: This work focuses on systematic examination of various post training quantization techniques in large language models. Experimental analysis include comparison of different model sizes, different numerical precision, and quantization of only weights vs activations. In addition, the Low Rank Compensation method... | Rebuttal 1:
Rebuttal: We are glad that you find our work significant & timely in advancing the deployment of LLMs. We appreciate your positive feedback on the logical flow & structure of our paper, and on our proposed compensation method. Please find our responses below:
*Q1:* Results in tables are not clear
*A1:* Th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion | Accept (poster) | Summary: The paper concerns catastrophic forgetting in neural networks and draws from neural mechanisms contributing to the absence of such phenomena in the brain to propose a method to overcome CF through targeted neuron retraining, task knowledge revision, and enhanced learning for less active neurons. The authors pr... | Rebuttal 1:
Rebuttal: We thank the reviewer taking time to review our work in detail. We appreciate your encouraging words on our manuscript. Our response to weaknesses and questions are as follows:
`While the paper is nice, one can see how TriRE won't work on transformers due to computational expense.`
We agree with... | Summary: The paper proposed a new continual learning (CL) method that updates partial neurons while rewinding other neurons to the previously stored weights. To do this, the authors utilize sparsity constraints to the network weights and select highly activated neurons. Consequently, the proposed method outperforms the... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough review of our paper. Below, we have addressed each of your concerns to the best of our understanding, aiming to enhance the paper's overall contribution.
`What is the overhead cost for rewinding, such as training time, memory cost f... | Summary: The paper proposes a method for avoiding catastrophic forgetting in continual supervised learning that operates in three stages. In the first stage (retain), a subnetwork for the current task is identified by detecting most/least activated neurons of the main network. In the second stage (revise), both the mai... | Rebuttal 1:
Rebuttal: Firstly, we would like to express our gratitude for the time and attention you dedicated to reviewing our paper. Below, we have carefully addressed each of your concerns to the best of our knowledge to enhance the paper's overall contribution.
`no technical details are provided regarding how the ... | Summary: This article introduces a multi-faceted approach to continual learning. combining features of many other approaches and relying on a three-phase training process, such that, as each new task in a sequence of tasks is encountered, subsets of weights in the network are 'retained', 'revised' or 'rewound' to value... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for providing thoughtful feedback and sharing a constructive evaluation of our work. Your valuable input has greatly contributed to the enhancement of our paper.
`complexity of the TriRE scheme made the gaps between it and some of the other approaches relative... | Rebuttal 1:
Rebuttal: In this section, we aim to address the overarching concerns raised by reviewers, ensuring a comprehensive response to the broader themes highlighted in their feedback.
`The key contribution part is rewind while other parts are peripheral or have no novelty / It seems to be a mere combination of e... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
VaRT: Variational Regression Trees | Accept (spotlight) | Summary: The authors propose a non-parametric Bayesian model for Regression trees. Variational inference is used approximate the posterior distribution. Extensive experiments are conducted to show the superiors of the method.
Strengths: The authors proposed a non-parametric method to learn regression trees. The paper... | Rebuttal 1:
Rebuttal: Firstly, we extend our gratitude for your assessment of our paper. Your feedback has been instrumental in shaping our work and we value the insights you've shared. We acknowledge your observation about the apparent simplicity of our approach, which centers around non-parametric Bayesian models for... | Summary: This paper introduces a non-parametric Bayesian model taking the form of a stochastic decision tree and proposes to approximate the posterior distribution with variational inference. The prior involves three parts, a prior on the tree structure, a prior on the probabilistic splitting criteria, and a prior on t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your exceptional review of our manuscript. Your insightful feedback has significantly impacted our research, and we are grateful for your valuable insights. Your evaluation not only validates our efforts but also provides us with invaluable perspectives that will undoubtedl... | Summary: The paper develops a new generating process for soft decision trees. The proposed process is then adopted as a prior model in a Bayesian nonparametric regression setting, and a novel variational inference algorithm is developed using the truncated version of the tree generating process as the variational distr... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for taking the time to review our manuscript. Your insightful comments and suggestions have been instrumental in guiding our work towards refinement, and we appreciate your thorough evaluation.
Regarding your recommendation to evaluate our method against the... | Summary: The authors propose to use variational inference to train regression trees. The innovation lies in using variational inference as the optimization process rather than Markov Chain Monte Carlo. The authors demonstrate this method on 18 ML UCI problems and some causal inference and toy problems.
Strengths: The... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you've dedicated to reviewing our manuscript. Your thorough evaluation offers valuable insights that will undoubtedly enhance the quality and impact of this work.
Regarding the concern on training time, we acknowledge that there are blazingly fast boost... | Rebuttal 1:
Rebuttal: We extend our heartfelt gratitude to the reviewers for their insightful evaluations of our paper. The feedback we received has played a pivotal role in the evolution of our work, and we have approached our revisions with utmost seriousness and dedication.
One area that emerged as a significant op... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | Accept (poster) | Summary: The authors propose a scheme to handle the test-time shift at each FL client. To achieve this, during training, a contrastive learning is adopted to extract invariant information within the same class (class-level invariant information) at each client. The updated models at the clients are also aggregated via ... | Rebuttal 1:
Rebuttal: Thanks for all the valuable comments and questions.
**Comparison with the combination of DG and FL.** We used three DG-originated test-time adaptation (TTA) methods as baselines because they are more aligned with our setting and can be easily implemented in an FL framework. Note that not all exi... | Summary: This paper propose FedICON, which uses inter-client heterogeneity to handle intra-client heterogeneity. During training, FedICON uses contrastive learning locally to extract invariant class-conditional information, and performs global invariance sharing under inter-client heterogeneity. During testing, the fea... | Rebuttal 1:
Rebuttal: **The reason why global invariance sharing can work.** Thanks for the valuable comments. There might be some misunderstanding towards the global invariance sharing part. There is no actual global invariance extracted among participating clients. The shared global invariance in the paper refers to ... | Summary: To deal with the feature-level test-time shift problem in federated learning, this paper proposes to leverage the inherent heterogeneity across clients based on a contrastive learning method, named FedICON. Clients acquire invariance encoding ability on heterogeneous source data and further boost the performan... | Rebuttal 1:
Rebuttal: **Difference between test-time shift and intra-client heterogeneity.** Thanks for the valuable comments. The intra-client heterogeneity we defined in this paper refers to the test-time shift issue, especially in federated learning. The concept of test-time shift is commonly used in general machine... | Summary: This paper focuses on the federated learning (FL) scenario with the test-time shift problem, which is a practical yet challenging research topic. With empirical study, the authors find that the inter-client heterogeneity in personalized FL can be further leveraged to build a robust FL framework against the tes... | Rebuttal 1:
Rebuttal: **Lack of discussion for the motivated experiment.** Thanks for the valuable comments. In the paper, we empirically prove and illustrate the fact that inter-client heterogeneity in FL can help alleviate the test-time shift problem. As for the underlying reason for that, the naturally-existing hete... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We are glad that the reviewers found that the problem we are solving is valuable and practical in federated learning (Reviewers 9n3S, CniS, v7Ae); our idea of leveraging inter-client heterogeneity to handle test-time shift problem is novel and in... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming | Accept (poster) | Summary: The paper studies the sparse dictionary learning and k-means clustering problems, using tools from sketching. Various results are obtained under different settings and assumptions.
The first part of the paper considers lower bounds in the streaming setting. Here, the main technical result, which is a lower bo... | Rebuttal 1:
Rebuttal: >In the statements of several of the Theorems, the running time is not given (either at all, or not with enough precision). This makes me think that the algorithms are probably not very practical.
We will add more precise time complexity results to our upper bounds, so that our work is easier to ... | Summary: This theoretical paper discusses algorithms and lower bounds regarding time and space complexity for two interesting machine learning problems:
1) Euclidean k-means clustering
2) Sparse dictionary learning
I briefly summarize the results based on the order that they are presented in the main paper (which d... | Rebuttal 1:
Rebuttal: > There is no clear-cut summary of the results in the introduction. There are many interesting results and improvements, but it takes significant time to identify them. E.g., the order in which the results are presented in the introduction does not agree with the order that the results are present... | Summary: The paper considers the well studied $k$-means clustering problem and the $r$-sparse dictionary learning problem. The paper has multiple contributions:
(1) It presents a new approach for obtaining a PTAS for $k$-means clustering which matches the time complexity of previous algorithms for the problems. This a... | Rebuttal 1:
Rebuttal: > It seems that some of the algorithms proposed by the paper might be implementable and it would be nice to see some experiments on their performance.
While we agree that experimental inquiry on these streaming/sketching algorithms would be interesting, we believe they would be best situated in a... | Summary: This paper presents results for the k-means and sparse dictionary problems, both of which ask to summarize an $n$ point data set in $d$ dimensions in terms of $k$ points. In the former we map each point to a center, in the latter, we are allowed sparse linear combinations of points. The paper considers two mod... | Rebuttal 1:
Rebuttal: > The paper has too many results, at least some of them rather partial or for rather restricted models. I have a hard time deciding what the main contribution of the paper is. No one result stood out either in terms of the statement, or in terms of new techniques.
> I would suggest that the write... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models | Accept (poster) | Summary: This paper proposes the use of nonmonotone line search methods to speed up the optimization process of modern deep learning models, specifically Stochastic Gradient Descent (SGD) and Adam, in over-parameterized settings. The proposed method relaxes the condition of a monotonic decrease in the objective functio... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the accurate comments and the time spent reading the paper.
Weaknesses:
The performance of PoNoS is actually not sensitive to hyperparameters. In fact, the same values work across experiments and there was no need to fine-tune them. Most of PoNoS's hype... | Summary: This paper proposes a non-monotonic line search method for choosing step sizes in stochastic optimization. Convergence rates are proved for strongly convex, convex, and PL functions, and the rates match those of previous work. Experimental results show that (1) for MLPs and CNNs, the proposed algorithm outperf... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and the time spent reading the paper. As a general answer, it appears that many of his/her statements are influenced by the reviewer's belief regarding our theory not containing any novelty. In the reply to Weakness 2. below, we clarify that our... | Summary: This paper presents a non-monotone line search method for optimizing over-parameterized models. The method is equipped with some theoretical support for strongly convex, convex and the PL condition. Furthermore, experimentally, the method is shown to have favorable performance when optimizing various deep lear... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and the time spent reading the paper.
Weaknesses:
· We understand the reviewer's position on this matter, however we would like to stress a few factors that make our contribution non-incremental. PoNoS is the first stochastic nonmonotone lin... | Summary: This submission proposed a new linear search method to ensure convergence without the monotone decrease condition of the (mini-)batch objective function. The method is quite suitable for the modem DNN training, which prefers the larger training learning rate.
Strengths: - The explanation of motivation is very... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and the time spent reading the paper.
Weaknesses:
· We don't understand the reviewer's comment, in what sense is our discussion on the difference between the proposed and the previous methods inadequate?
· While it is true that nonmonoto... | Rebuttal 1:
Rebuttal: We would like to thank all six reviewers for their feedback and the time spent reading the paper. Below we comment several reviewers' concerns regarding the lack of a theoretical result showing PoNoS's advantages over the existing methods.
We understand the reviewers' opinion, however we would li... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new line search method for determining the step size in SGD within the interpolation regime. In contrast to the previous approach called SLS, which relies on the monotonically decreasing Amijo condition, the proposed method adopts the non-monotone Zhang & Hager line search. The authors es... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the accurate comments and the time spent reading the paper.
Weaknesses:
Additional studies on the Polyak step alone show its connections to the "better model" of Asi and Duchi [2019] in Gower et al. [2021] and to the "passive aggressive" optimization fr... | Summary: The paper presents a proposed Polyak nonmonotone stochastic (PoNoS) method which combines a nonmonotone line search with a Polyak initial step size. It builds on the work of Vaswani et al. [2019] by modifying the monotone line search to incorporate a nonmonotone approach.
Strengths: Originality:
The introduc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and the time spent reading the paper.
Strengths:
We will follow the reviewer's suggestion and highlight our contributions in Section 6.
Weaknesses:
· We are not sure we understood the comment of the reviewer regarding the adverse effect o... | null | null | null | null |
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective | Accept (oral) | Summary: The paper mainly focuses on theoretically proving the effectiveness of the CoT in autoregressive Transformer models for solving fundamental mathematical and decision problems through generating intermediate steps. It demonstrates that any finite-depth Transformer model cannot directly output correct answers to... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 7MZe for the careful reading, thoughtful inquiries, and positive feedback. Below, we are happy to provide further elaboration on each of the points you raised:
> Q1: Is there a specific reason for choosing autoregressive Transformer? Were other models, such as the enco... | Summary: The paper contributes to theoretical and empirical understanding of Chain-of-thought, i.e. intermediate process generation to assist desired output generation. In the theory part, authors show
- log-presicion Transformer with bound depth cannot solve simple math tasks (calculate, solve linear equations) unless... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer P6n5 for the positive feedback, valuable suggestions, and two insightful questions regarding the related work and other architectures. Below, we would like to give detailed responses to each of your comments and questions.
**Regarding related work**. Thanks for the val... | Summary: This paper presents various separation results, showing that a Transformer with CoT can solve certain formally-defined reasoning tasks, but a Transformer *without* CoT cannot (assuming bounded depth). This sheds light on the power of CoT. The formal results are supplemented with empirical results that support ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer QoTB for the careful reading, positive feedback, valuable suggestions regarding presentations, and detailed comments. Below, we would like to give detailed responses to each of your comments and questions.
**Regarding the scope of the paper**. Thanks for the suggestion... | Summary: This paper studies the theoretical power of the Chain-of-Thought (CoT) prompting. In particular, this paper mathematically confirms that two well-chosen tasks (e.g., arithmetic and equation) and the problem of Dynamic Programming are beyond bounded-depth Transformer models without CoT (unless their size grows ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer C8fV for the positive feedback, appreciation for our work, and insightful questions.
> **Question**: Line 245, the success of solving Dynamic Programming problems critically depends on the input sequences being laid out in topological order. In the two DP experiments (... | Rebuttal 1:
Rebuttal: We would like to express our sincere thanks to the reviewers and the area chair for taking the time to review our paper. We have responded to each reviewer's comments separately and will incorporate their suggestions into the next version of our paper. We hope that our response can adequately addr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | Accept (poster) | Summary: The authors propose a new analysis method that unifies the analysis of noisy gradient descent-based algorithms, where the noise is added to the matrix factorization, which provides a tight analysis of both the algorithms simultaneously, and hence would give the analysis for convergences for a general class of ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and positive review! Below we address the raised points.
## Weaknesses
1. While our analysis is restricted to the specific class of algorithms, this class of algorithms capture significant advances in differentially private optimization which were recently show... | Summary: This paper conducted a theoretical study of gradient descent with linearly correlated noise, with strong motivation from the theory and applications of differential privacy (in particular, the popular DP-SGD algorithm).
The main contribution is a new convergence analysis based on the idea of "restart iteratio... | Rebuttal 1:
Rebuttal: ## Questions
1. Thanks for your question. Due to the space limitations, we discussed why the two analyses for PGD and anti-PGD are incompatible in the appendix F, where we showed that
- using the real iterate sequence $x_t$ we cannot prove the tight convergence bound of Anti-PGD (12).
- using the... | Summary: This paper studies (stochastic) gradient descent with linearly correlated noise. The paper builds upon recent results on (MF-)DP-FTRL. It highlights the limitations of this methods, proposes a restarting trick to improve it, and derive the corresponding analysis. Interestingly, the proposed analysis gives a un... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and their positive assessment of our paper.
## Weaknesses:
1. In this work we aimed to find a simple theoretical model for which it would be interesting to study the effect of linearly correlated noise on the optimization dynamics. Since the sto... | Summary: This paper develops many techniques to analyze gradient descent convergence with linearly correlated noise, a setting motivated by many DP algorithms. The work derives tighter bounds, and use the resulting insights to motivate DP-MF and DP-MF+.
Strengths: - The paper is structured well, and is very notational... | Rebuttal 1:
Rebuttal: ## Clarity
1. Due to the page limitations we unfortunately were unable to fit all the background information. Upon your suggestion, we will add more details in the appendix in the next version or in the main text if space permits. In particular we will include details of how to compute sensitivity... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their time spent to review our paper, their useful comments that will allow to improve our paper, and for their positive assessment of our work. Below we address comments from each of the reviewers separately. We will try to clarify most of these poi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies variants of noisy gradient descent, focusing on when the noise added at different time steps may be dependent. The motivation of this is differentially private optimization: while the standard DP-(S)GD adds independent noise at each time step, the recent DP-FTRL mechanism [1] adds carefully ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed review and positive assessment of our paper ! Below we address the raised questions:
## Weaknesses:
1. We write that $‖\Lambda_{\tau} B‖^2_F$ is a better proxy for learning performance because it captures convergence of the algorithm more ... | null | null | null | null | null | null |
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models | Accept (poster) | Summary: The authors present a new adversarial attack framework, VLATTACK, to evaluate the robustness of large vision-language models (VLMs). The paper introduces a two-step adversarial attack approach. The first step involves attacking each modality (image and text) independently, and the second step employs a cross-s... | Rebuttal 1:
Rebuttal: Thank you for the valuable review. \
`>>> W1` ***Elaborate more on the source of transferability***\
`>>> A`: We agree that the pre-trained and fine-tuned models share almost the same architecture in the current setting. Such mutual information should help to improve the attack success rate.
As... | Summary: In this paper, the authors explore the adversarial vulnerability in visual language models. Specifically, a block-wise similarity attack is proposed to generate adversarial image examples, and the BERT-attack method is used for generating the adversarial text examples. The image and text pairs are perturbed by... | Rebuttal 1:
Rebuttal: Thank you for the valuable review. Our responses are addressed below.\
`>>> Q1` ***It is better to provide the analysis of the semantic similarity and the perturbation degree, as mentioned in Weaknesses 1***\
`>>> A`: Thanks for your constructive suggestions. We compare the semantic similarity sco... | Summary: The paper presents VLAttack, which is a method for perturbing multimodal examples such that a multimodal model would get them wrong. VLAttack does not assume access to fine-tuned models but does assume access to foundation models that are used to create these downstream models. The authors argue that this leve... | Rebuttal 1:
Rebuttal: Thank you for the valuable review. Our responses are addressed below.\
`>>>Q1` ***Systematic validation***\
`>>>A`: Adding a human evaluation experiment significantly increases the reliability of the proposed attack method. We conducted the human evaluation experiments using the results output fr... | Summary: This paper focuses on adversarially attacking the multimodal finetuned models without getting access to the finetuned weights. By utilizing the activations and the parameters of the open-accessed pretrained model, this work proposes a method called VLATTACK for creating the adversarial attack samples for downs... | Rebuttal 1:
Rebuttal: Thanks for the valuable review. Our responses are addressed below.\
`>>> W1` ***Experiments or analysis on adversarial multimodal dataset***\
`>>> A`: Some methods [a,b] focus on constructing adversarial datasets, but they significantly diverge from the nature of our research work. Specifically, t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a substitute black-box attack strategy called VLAttack to generate adversarial examples by perturbing both images and texts on pre-trained models and then transferring them to finetuned models. At the image-modal level, they introduce a block-wise similarity attack (BSA) strategy to disrup... | Rebuttal 1:
Rebuttal: Thank you for the valuable review. Our responses are addressed below.\
`>>>Q1` ***Lack of technique contributions***\
`>>>A`: The multimodal attack is a new research topic, and only one work Co-Attack has been proposed recently. However, its performance is even worse than state-of-the-art image at... | null | null | null | null | null | null |
Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent | Accept (poster) | Summary: The authors presents theoretical results on learning real-valued and complex-valued neurons with gradient descent. The key takeaways are that:
* complex-valued neuron learns real-values neurons and complex-valued neurons with convergence rate $O(t^{-3})$ and $O(t^{-1})$ respectively
* two-layer real-valued neu... | Rebuttal 1:
Rebuttal: Thanks for the comments and feedback.
Q1: About empirical experiments to further strengthen the paper.
A1: Thanks for your valuable suggestions. We provide toy experiments in the **global response**, where we verify our findings in more general settings.
Q2: About the typo in Table 2.
A2: Than... | Summary: The goal is to explore the novel approach of complex valued neural networks using gradient descent. The paper investigates when and to what extent CVNNs outperform RVNNs using gradient descent for learning tasks. The researchers prove that a single complex-valued neuron can efficiently learn functions expresse... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough feedback.
Q1: About tables to layout the important notation.
A1: Thanks for your constructive suggestions. The notations in this paper are self-consistent and are equipped with informal explanations when they occur in the context for the first time. Readers... | Summary: The authors contrast the learning and convergence properties of real-valued neural networks and complex-valued neural networks. Specifically, they study the problem of learning the function implemented by a single neuron using a 2-layer finite width network. Notably, they show that a complex valued neural netw... | Rebuttal 1:
Rebuttal: We thank the reviewer for these constructive suggestions.
A1: About the extent to which these assumptions would hold in practice.
Q1: The settings and assumptions used in this paper are common ones [1,2,3,4] to make the analysis tractable. Neuron learning aims to provide meaningful insights for ... | Summary: The paper develops theoretical results for the convergence rates of complex and real valued neural networks on complex-valued problems. It develops analytical models for the training regimes of complex valued NNs.
Strengths: The results would be useful for those attempting to apply complex-valued networks to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback but would like to point out a few misunderstandings in the review.
1. About assumptions on different training regimes.
In the proofs and proof sketches, training regimes come from the idea of divide and conquer and we do not make any assumption abo... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback. We are thankful that our theoretical results are approved by most reviewers, many important questions are raised, and many constructive suggestions are proposed. A general consensus is that additional experimental verifications can help our paper... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The theoretical paper compares the learnability of real-valued neurons and complex-valued neurons via gradient descent. In a nutshell, the paper proves that a complex-valued neuron has a lower convergence rate than its real-valued counterpart, but learn exponentially slower. The theoretical discoveries help ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments.
Q1: About generalizing to practical RVNNs and CVNNs.
A1: The analysis of neural networks faces the tradeoff between simple assumptions and strong conclusions in the neural network literature. It is quite difficult to analyze practical neural n... | null | null | null | null | null | null |
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints | Accept (poster) | Summary: The paper proposed a trust-region method for handling multiple constraints in safe RL for CMDPs. If the TRPO-like gradient calculation with multiple constraints is not feasible, the authors proposed a gradient integration method to calculate feasible gradients. The authors also proposed a TD-$\lambda$ method t... | Rebuttal 1:
Rebuttal: We thank reviewer Cyuw for the feedback and thorough review on our work.
We respond to reviewer Cyuw's comments and questions below.
# Weakness: The performance are not convincing
**Performance improvement**
The most comparable method with SDAC in the safety gym is WCSAC.
However, as mentioned in ... | Summary: This study focuses on multiple constraints setting in safe reinforcement learning, and an interesting method is proposed by leveraging gradient integration methods. Moreover, the feasibility of multi-constraint problems is addressed, TD distribution method is introduced to decrease the estimation bias.
Streng... | Rebuttal 1:
Rebuttal: We thank reviewer HJro for the feedback and thorough review on our work.
We respond to reviewer HJro's comments and questions below.
# Weakness: Some papers are not investigated.
I will add the two papers mentioned by the reviewer to the safe RL part of the related work section.
# Weakness: Ex... | Summary: The paper tries to address the problem of safe RL with multiple constraints with a safe distributional actor-critic (SDAC) approach. The approach includes a gradient integration method to manage the infeasibility issues in multi-constrained problems and a TD$(\lambda)$ target distribution to estimate risk-aver... | Rebuttal 1:
Rebuttal: We thank reviewer dXag for the feedback and thorough review on our work.
We respond to reviewer dXag's suggestions and comments below.
# Weakness: The writing of the paper can be improved.
As the reviewer commented, in constructing the subproblem in equation (6), many parts are enumerated withou... | Summary: The paper presents a safe reinforcement learning (RL) algorithm called SDAC for handling multiple constraints in safety-critical robotic tasks. SDAC incorporates risk-averse constraints and makes two key contributions: a gradient integration method for handling infeasibility issues and a TD($\lambda$) target d... | Rebuttal 1:
Rebuttal: We thank reviewer A8gA for the feedback and thorough review on our work.
We appreciate that the reviewer commented that the proposed method is novel and solid.
We respond to the reviewer's comments and questions below.
# **Weakness: Presentation of the quantile regression can be improved.**
As t... | Rebuttal 1:
Rebuttal: # General response
We thank all reviewers for their valuable comments and suggestions.
In the following, we respond to the comments on comparison with WCSAC and the sample complexity.
### **Q1. Comparison with WCSAC.**
We first examine the experiments in Section 5.1.
For $\alpha=1.0$ (risk-neutral... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Contract Design via Discontinuous Networks | Accept (poster) | Summary: This paper studies the problem of contract design, focusing on a scenario where a single principal aims to design the reward structure for different outcomes, followed by a self-interest agent. The principal's utility function, which relies on the agent's best response strategy, is a piecewise affine funciton ... | Rebuttal 1:
Rebuttal: > (Q1) _`What is the significance of using deep learning in optimal contract design? Why do we care about this?`_
The reason, which is similar to the case of auction design, is that previous theoretical and empirical work has encountered bottlenecks.
A particularly interesting angle is the compu... | Summary: The authors focus on the problem of contract design. In this problem, there is an agent which can take some costly action, each action resulting in a distribution over possible outcomes. A principle gets utility based on outcomes, and incentivizes the agent to take actions which will benefit them by transferri... | Rebuttal 1:
Rebuttal: > (Major concern) _`Does learning the utility function and using it significantly improve downstream performance compared to just using the fixed training dataset as a "pointwise" utility function?`_
Thanks for this question. We present the results from a new set of experiments to demonstrate tha... | Summary: This paper considers an offline learning problem of optimal contract through neural network. The authors propose a novel neural network architecture, called Discontinuous ReLU (DeLU) network, which models a piecewise affine function with discontinuous boundaries --- a representation that captures the principal... | Rebuttal 1:
Rebuttal: >(Major concern) `One major concern... is that it attempts to approximate the optimal contract as the argmax to the approximated principal’s utility. In particular, the argmax contract at some boundary... is not robustified from the inaccuracies at the boundary.`
Thanks for this point, which moti... | Summary: This paper proposes a deep-learning approach for contract design. The rationale is:
- the principal's utility function should be learned from the data
- the problem of approximating accurately the utility function is non-trivial, and the utility function may be discontinuous --- suggesting the development of ... | Rebuttal 1:
Rebuttal: > (Q1: Technicalities) _`Are the techniques for training and inference provided by the authors important advancements of the deep learning techniques?`_
A longstanding challenge in the deep learning community has been approximating discontinuous functions. While the Universal Approximation Theore... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewers for providing exceptionally high-quality and insightful reviews. Your thoughtful insights and valuable suggestions have significantly enriched our work. Thank you for your time, effort, and commitment, and we look forward to addressin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces an automated optimal contract design method from offline data using deep learning. In this setting, a principle deigns a contract establishing an agreement of payments the principle will undertake for the outcomes arising from the actions of an agent. Given a contract, the agent privately... | Rebuttal 1:
Rebuttal: Thanks for the thought-provoking review, which prompts us to think deeper about the applicability and extension of our work.
> (Q1) _`How easy is it to extend your method to contracts with mixed payment-penalty structure?`_
For this, we can consider the case $\mathbf{f}\ge-c$, for a positive con... | null | null | null | null | null | null |
Learning from Active Human Involvement through Proxy Value Propagation | Accept (spotlight) | Summary: The work introduces Proxy Value Propagation (PVP), an approach to reinforcement-learning without ground-truth rewards by instead using human intervention as a signal of quality. The value of a state-action pair is determined by a human intervention, where interventions signal that the agent's behavior was bad ... | Rebuttal 1:
Rebuttal: Thank you for your review!
---
> **W1:** *Prior work in human-labeling and intervention has found that human labelers are slow to react, and this time-delay must be accounted for. Strange that this did not surface as a problem in these studies.*
**Re:**
Indeed, we do not consider the time-dela... | Summary: The paper proposes an approach for learning from human interventions based on offline RL with pseudo-rewards. The paper assumes a shared autonomy setting where a human monitors an agents rollouts and can intervene whenever deemed necessary and provide corrective demonstrations. The algorithm relabels those int... | Rebuttal 1:
Rebuttal: Thank you for your review! Please find our responses as follows.
---
### Weaknesses
> **(A1)** *Why not set rewards instead of Q-targets?*
Please refer to the response to W1 of Reviewer pvGG.
> **(A2)** *The current objective assigns low Q-value to all policy actions during human intervention... | Summary: This paper focuses on human-in-the-loop for reward-free policy learning, wherein a human has the option to override the policy by taking over control when the agent attempts to perform risk behaviors. To solve this problem, this work presents Proxy Value Propagation (PVP), which directly assigns positive value... | Rebuttal 1:
Rebuttal:
Thank you for your review! Please find our responses as follows.
---
### Weaknesses
> **W1:** *One major concern is that the technique of directly manipulating the value seems to be not well-motivated. As claimed in lines 178-180, the optimal policy should approximate the behaviors of human s... | Summary: This paper introduces a reward-free approach termed Proxy Value Propagation (PVP) to facilitate safe and human-aligned reinforcement learning (RL). PVP assigns high Q values to human actions (the state-action pairs in the human demonstration) and low Q values to agent actions that necessitate human interventio... | Rebuttal 1:
Rebuttal: Thank you for your review!
---
### Weaknesses
> **W1:** *I am unsure if the experimental setup for Base RL Methods is fair. Why is the Return so high, but the Success Rate is lower than humans? For instance, GT Sophy has already surpassed human champions. Can the authors provide more detailed de... | Rebuttal 1:
Rebuttal: In this common rebuttal, we provide a one-page PDF containing two sections:
1. Human subject research protocol: including information on human subjects recruitment, onboarding procedure, the information we provide to them, the main experiment and the questionnaire. We will update Appendix B to i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present Proxy Value Propagation for policy optimization. They model a proxy value function so that human intents receive high value (i.e. actions for which the human has inputted as corrective actions over a policy) while on-policy agents have low values for actions that caused an intervention. Whe... | Rebuttal 1:
Rebuttal: Thank you for your review! Please find our responses as follows.
---
### Weaknesses
> **W1:** *nit: "It (the proposed PVP method) is also compatible with different forms of human control devices, including gamepad, driving wheel, and keyboard" - True of baselines (and many other methods) as wel... | null | null | null | null | null | null |
Triple Eagle: Simple, Fast and Practical Budget-Feasible Mechanisms | Accept (poster) | Summary: The paper studies the design of budget-feasible mechanisms: a buyer wants to purchase from a set of potential sellers with different production costs which are private information, and the goal is to maximize the total value subject to a budget constraint. The main result is a new framework that achieves (1) ... | Rebuttal 1:
Rebuttal:
**Comment\:While I like the paper overall, one minor complaint is it doesn't say much about lower bounds. Of course this is largely due to the intrinsic complexity of the BFM problem.**
Response: Thanks a lot for your comments! For BFMs with additive valuation functions, Singer \[35] has propose... | Summary: This paper proposes a novel technique in designing budget-feasible mechanisms (BFMs), which improves the state of the art results on approximation guarantees and query complexity simultaneously. In particular:
* With monotone submodular functions, the newly proposed mechanisms improve the approximation from 4... | Rebuttal 1:
Rebuttal: **Comment: No empirical evaluation for the non-monotone submodular function case.**
Response: Thanks a lot for your comment! Due to the page limits, the empirical evaluation for non-monotone submodular objectives is provided in the supplemental file (Appendix F), and we have mentioned about this ... | Summary: The paper considers the problem of designing budget feasible mechanisms, in which the designer has a budget B, agents have private costs, and to each subset S of agents is associated a reward. The goal is to design a mechanism that incentives agents to truthfully reveal their privare costs and allows the desig... | Rebuttal 1:
Rebuttal: **Question: Can you stress the difference and similarities between your mechanisms and sequential posted-price mechanisms by Chawla et al., 2010?**
Response: Thanks a lot. Nice question! We guess that \[Chawla et al., 2010] refers to the STOC’10 paper “Multi-parameter Mechanism Design and Sequent... | Summary: This paper studies the budget feasible mecchanism design, where a buyer needs to select a subset from strategic sellers with private costs , and pays them under a budget $B$. The goal is to maximize the valuation of the subset $S$ defined as $f(S)$. This paper gives both deterministic and randomized solutions ... | Rebuttal 1:
Rebuttal: **Comment: There is no formal problem setting, so it might be hard for reviewers from outside AGT community to understand this problem at the first sight.**
Response: Thanks a lot for your comment! We have provided the formal problem setting in Section 2, including the formal problem formulations... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting | Accept (poster) | Summary: The paper focuses on generative diffusion modeling when some of the samples are known, which is advocated as the right setup for regression. Indeed, in a time-series context, those known samples may lie in the past (prediction), or provide boundary conditions (interpolation).
In this context, the classical di... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and positive comments on the writing and the merits of our ideas. In the following, we respond to specific questions and concerns raised by the reviewer.
**Comment:** On the Bayesian view of self-guidance and Eq. 5.
**Response:** We introduced self-guida... | Summary: This paper proposes TSDiff, which is an unconditional diffusion model for time series generation. Besides, the authors propose self-guidance and prediction refinement. The empirical results showcase the superiority of TSDiff over existing baselines on forecasting, refinement, and generating synthetic samples.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and positive comments on our method and writeup. In the following, we respond to specific concerns and questions raised.
**Comment:** "...is the conditional generation similar to an inpainting..." and discussion of image inpainting models.
**Response:** ... | Summary: This paper describes a diffusion model for time series problems. Contrary to the popular approach of using a conditional diffusion model, the authors proposed to use the unconditional diffusion model, which is supplemented by a self-guidance mechanism. The authors also proposed a prediction refinement algorith... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their comprehensive review and appreciation of our work. Our response to specific questions raised by the reviewer follows.
**Comment:** On model details (input and output dimensions, how missing values are treated, etc.).
**Response:**
- The denoising netw... | Summary: In this paper, the authors proposed an unconditional diffusion model and a self-guidance mechanism for time series data that can be used for conditioning diffusion model for downstream tasks, e.g. time series forecasting and imputation. The effectiveness of proposed model is evaluated from three aspects: predi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and constructive feedback. In the following, we respond to specific questions raised by the reviewer.
**Comment:** Refinement has no real application scenario / Too much space dedicated to refinement.
**Response:** The primary goal of the refi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful reviews and their constructive feedback to improve the quality of our paper. We are pleased to note that the reviewers appreciate:
- the **technical significance of our self-guidance approach** ("an interesting method of bypassing training a conditional... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models | Accept (poster) | Summary: This paper proposes a new method for performing the task of "Foley", which entails the creation of sounds that match a video, for example, making an audio clip in a studio that matches a character's footsteps in a film, as a post-production task. The authors proposed to do this automatically via deep learning,... | Rebuttal 1:
Rebuttal: We sincerely thank the positive feedback from you. We respond to the weakness and questions below.
### 1. About more baselines.
> *1.1: FoleyGAN (2021) seems to perform the same task, but no comparison (or citation) is present.*
Thanks for pointing out, we are glad to cite the references in our... | Summary: The paper presents an approach to generate and align audio with an existing video track, using a diffusion process. This is useful in video post-processing, where frequently sound effects have to be aligned with existing video footage (and not just match semantically). The authors demonstrate the quality of th... | Rebuttal 1:
Rebuttal: We sincerely thank the constructive feedback from you. We address your concerns below.
### 1. About Temporal Split & Merge Augmentation.
> *1.1: Explain for sentences: "We validate the effectiveness of this augmentation method in Sec 3.3."*
My apologies for the confusion. The correct sentences ... | Summary: This paper present DIFF-FOLEY, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) that generates audio with improved synchronization and audio-visual relevance. The method adopts contrastive audio-visual pretraining (CAVP) to learn more temporally and semantically aligned featur... | Rebuttal 1:
Rebuttal: We sincerely thank the constructive feedback from you. We address your concerns below.
### 1. Discussion on metrics.
> *1.1: The proposed method falls below the baselines in other metrics such as FID and KL divergence.*
Thanks, please refer to **Global Response 2**.
### 2. Human evaluation.
>... | Summary: The focus of the paper is audio synthesis. More specifically, it focuses on video to audio synthesis and in particular on synchronized synthesis of audio. It relies on Latent Diffusion models for the synthesis and proposes an aligned audio-visual representation learning approach to improve synchronization of s... | Rebuttal 1:
Rebuttal: We sincerely thank the positive feedback from you. We respond to the weakness and questions below.
### 1. Formula Clarification.
> *1.1: In Eq 1, aren’t the two terms same ? Why have they been separated ? Same is true for Eq 2.*
In Eq 1, the two terms are distinguished by the normalized value i... | Rebuttal 1:
Rebuttal: ## Global Response 1: Human Evaluation Results
As suggested by reviewer k7sd and tzzm. We've conducted a human evaluation by randomly selecting 60 videos from the VGGSound test set and having different models to generate corresponding audio samples. The output and groundtruth audios were anonymiz... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability | Accept (poster) | Summary: This paper introduces VerT, a method to distill *verifiable* models from black-box models. Concretely, These verifiable models are distilled by fitting a model $f_v$ that reproduces the predictions of the black-box model $f_b$ on a training set. Unlike the black-box model, the model $f_v$ is made verifiable by... | Rebuttal 1:
Rebuttal: Thank you for your constructive review!
*“Restrictive assumption on replacement distribution. In Lines 125-137, the authors discuss the importance of the choice of the replacement distribution Q in order to avoid creating OOD examples by masking. It should be mentioned that some important maskin... | Summary: The present work introduces a theoretical framework of verifiability of feature attributions based on the sparest (binary) feature attribution mask that only barely changes the models' output. The authors theoretically (and empirically) show that for signal-distractor decomposable datasets off-the-shelf black-... | Rebuttal 1:
Rebuttal: Thank you for your constructive review!
*“The major weakness of the present work is that it does not compare to any removal-based feature attribution methods (e.g., SHAP, FastShap, etc.), models also trained on feature attributions (e.g., right for the right reasons), nor inherently interpretable... | Summary: This paper proposes a method called Verifiability Tuning (VerT), which transforms black-box models into models that naturally yield faithful and verifiable feature attributions. Authors further conduct experiments on semi-synthetic and real-world datasets to verify the effectiveness of the proposed VerT method... | Rebuttal 1:
Rebuttal: Thank you for your constructive review!
*“The equation in Definition 1 is problematic. For example, let three positive input variables a=b=c ? 0 have a MAX operation, output= max{a,b,c,0} ...actual importance of a, b, c to the inference is the same...”*
Great example! This shows that with duplic... | Summary: The paper proposes a way to verifiably get feature attributions of the ground truth signal when the input can be decomposed into independent signals and distractor features, assuming there is a counterfactual generator Q that can provide sparse attributions. The process consists of first deriving (\epsilon, Q)... | Rebuttal 1:
Rebuttal: Thank you for the constructive review! Overall, we feel you may have misunderstood some parts of the paper, and we’d like to clarify these below.
*“The signal decomposition assumes that the signal and distractors are generated independently, and that therefore the correct feature attribution is t... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive feedback. We are glad that reviewers found our theory “simple yet sound” (reviewer qGoL) and that our paper had “solid empirical evaluation” (reviewer dxrg). However, we feel that there were also some misunderstandings with respect to... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks | Accept (poster) | Summary: The authors train 1) an architecture encoder using a GNN with a variational graph autoencoder loss to learn a good representation for model architectures, 2) a model approximator taking the GNN representation and a sample x to predict the output of the model given x. The goal is to learn a probability distribu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review of the paper!
> *Have you considered using pre-trained graph models for the architecture representation? It seems like there should exist some, and they may be trained with more than 250 architectures. If there are, it would be a worthwhile comparison... | Summary: Current subset selection methods are architecture specific and requires solving an optimization problem for each architecture individually. The subset selected through solving an optimization for one architecture does not generalize to another model. This paper addresses this problem and introduces an end-to-e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed reading of the paper and their positive feedback.
> *The paper will be stronger if more complex datasets are added. Currently only smaller datasets are used, which can obfuscate the real-world capabilities of this method. While Tiny-ImageNet is used, a f... | Summary: This work proposes a new method to select subsets of valuable training examples, with an emphasis on specializing the selections to new model architectures. The motivation is that selections made for one architecture may not work well when used with a different architecture (a claim that the authors did not sh... | Rebuttal 1:
Rebuttal: Thanks for detailed feedback which would help us improve our paper.
> *evidence of lack of transferability*
We provided a comparison with Selection via proxy in Fig 7 of App E.1 (also Fig 3, global-pdf), where we outperform it. Also, we select 5% subsets of CIFAR10 for 4 architectures with the ... | Summary: The paper presents a model agnostic method of subset selection using a graph neural network a surrogate.
Strengths: - Problem area is an interesting space, and an area of interest to the community at present.
- Novel application of GNN for subset selection
- Seems to be a performance gain in some settings for... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> *The paper presents a model agnostic method of subset selection using a graph neural network a surrogate.*
We believe that there is a misunderstanding. Our method is not model agnostic. Rather, it chooses a different subset for each architecture using ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and insightful comments. We would like to summarize the reviews and the global-pdf (attached with this rebuttal) here.
> (GR.1) *Results after addition of pre-training time*
Currently, the approximator is trained with 250 architectures. However,... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new subset selection method, SUBSELNET, for efficient training of neural network models. SUBSELNET selects optimal subsets for a model based on its architecture with minimal overhead. SUBSELNET outperforms existing subset selection methods in terms of computational efficiency.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough reading and valuable feedback. Please find our answers below:
> *how effective does model approximator generalize to unseen architectures?*
Ablation study (L337) in our paper provides an evaluation on model approximators. Further, the analysis from L349 and ... | Summary: This paper focuses on the Subset Selection problem for selecting the best subsets training samples with which a well-performed model can be trained. This paper focuses on the challenge that previous methods for subsets selection cannot transfer across architectures. Specifically, this paper proposes to use a t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback. We in turn answer the questions below:
> *Despite the promising results presented in the manuscript, one key concern that emerges is the absence of a justification for the methods put forward. Specifically, it is not well-established whether the ... | null | null | null | null |
Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images | Accept (poster) | Summary: The authors train CNNs to predict the responses of individual voxels across several ROIs to the natural scenes dataset (NSD). They then develop and employ a strategy termed “brain dissection” to uncover the properties/features of images for which specific visual regions are tuned. They highlight gradients with... | Rebuttal 1:
Rebuttal: **"More comprehensive methods and related work description" "Tie in more related work supporting or refuting the hypotheses"**
Thank you for your feedback on the need for comprehensive methods and an expanded discussion on related works.
- We have included additional motivation points for the m... | Summary: Understanding the organization of higher level information representation in the brain is a challenging task in neuroscience. Modern deep learning methods together with big data of brain recording have opened up new opportunities for constructing large-scale models in a data-driven way and for gaining valuable... | Rebuttal 1:
Rebuttal: **The performance of the trained network on estimating fMRI data is missing.**
Thank you for highlighting the importance of performance reporting for all ROIs. Based on your recommendation, we've provided the Pearson correlation coefficient plotted on a flatmap for all ROis on a held-out test set... | Summary: This study uses the network dissect method to investigate the feature selectivity of RSC, OFA, and PPA in the human brain. This method is called "brian dissection". In particular, this study focuses on some ecologically important intermediate features, such as depth, surface normals, curvatures, and object rel... | Rebuttal 1:
Rebuttal: **The theoretical contribution is unclear. These types of results may be good for a neuroscience journal. The key point which is missing here is how these representations are formed.**
Thank you for your insightful comments. To enhance the clarity on the theoretical contribution of our paper:
1.... | Summary: This paper utilized network dissection model to understand how human brain is functionally mapped to perception of natural scenes. The proposed method is used to examine a range of ecologically important, intermediate properties, including depth, surface normals, curvature, and object relations and find consis... | Rebuttal 1:
Rebuttal: **Lacking on machine learning technical contribution. Lacking novelty in a way that the NeurIPS community expects.**
We value the feedback provided. To enhance the clarity on the novel and improved findings:
1. Our work has introduced a novel scale of examining pixel-level spatial feature select... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback from the reviewers. We are grateful that reviewers HWnv, 9bSv, and MstC acknowledged the clarity and quality of our paper's writing. The novelty of our approach was positively highlighted by HWnv, and the pioneering application of the network disse... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt | Accept (poster) | Summary: The paper introduces a learning-to-search (L2S) solver called Neural k-Opt (NeuOpt) for routing problems. NeuOpt learns to perform flexible k-opt exchanges using a tailored action factorization method and a customized recurrent dual-stream decoder. The paper also proposes the Guided Infeasible Region Explorati... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our approach is reasonable, the MDP is interesting, and the paper is mostly well-written. We understand that the main concerns are the significance of the performance and the code availability. We hope our response below will clarify any misunderstandin... | Summary: The paper aims to learn the k-opt operation, one of the famous local search methods, via neural networks. In particular, the authors model the k-opt operation as a sequential node selection process and use a recurrent dual-stream (RDS) decoder. Furthermore, a guided infeasible region exploration (GIRE) is sugg... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive and valuable comments. Thank you for acknowledging that NeuOpt improves the efficiency of L2S solvers, exhibits unique benefits for practical use, and achieves promising performance. We hope that the following response, along with additional experimental... | Summary: In this paper, the authors propose Neural k-Opt (NeuOpt) that factorizes a generic k-opt exchange operation as a series of base operations. They also introduce Guided Infeasible Region Exploration (GIRE) scheme for Capacitated Vehicle Routing Problem (CVRP) where one augments a reward function for RL with sign... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive and valuable comments. We are delighted that the reviewer found our approach novel, general, and promising. Thank you for recognizing our efforts to include extensive baselines. We hope that the following response, along with additional experimental resu... | Summary: The paper introduces Neural k-Opt (NeuOpt), a deep learning-based vehicle routing solver, designed to handle k-opt exchanges for any k≥2. Unlike existing Learning-to-Search (L2S) solvers, NeuOpt employs a tailored action factorization method, which allows complex k-opt exchanges to be broken down into simpler ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive and valuable comments. We are delighted that the reviewer acknowledged that our contributions (NeuOpt and GIRE) are novel and significant, and our experiments are comprehensive and extensive. We hope that the following response will clear the remaining c... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments. We are pleased to see that the reviewers have recognized our NeuOpt approach (including the k-opt action factorization, the RDS decoder, the fresh constraint-handling scheme GIRE, and the dynamic data augmentation) as being **novel** (#NeSp a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing | Accept (oral) | Summary: The authors study the problem of learning latent causal models from interventional data. The problem was formulated under linear or polynomial mixing in previous works and this work considers a more general setting of nonlinear mixing. The main contributions are the identifiability results of the latent causal... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and their suggestions. We will address their concerns in order.
**Regarding the identifiability of $d$:**
Yes, the dimension $d$ is identifiable from the observational distribution in our setting for the following reason. The image $f(\mathbb{R}^d)=M\subset \... | Summary: This paper aims to identify latent variables via a nonlinear mixing function interventional data. The authors prove strong identifiability results for unknown single-node interventions, extending previous work that focused on linear maps, polynomial mixing functions or paired counterfactual data. The paper pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and are glad the reviewer likes both our tight identifiability results and the contrastive learning algorithm. We agree with the reviewer's suggestions to improve the exposition and are happy to be revise the wording accordingly, including being more... | Summary: The paper considers the task of causal representation learning (causal disentanglement) from interventions, with (1) a linear latent structural causal model, (2) a nonlinear mixing function, and (3) single-node interventions. They show that, under perfect interventions, the generative model is identifiable up ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and positive feedback.
We are glad the reviewer also expects our ideas to contribute towards generative modeling in the real world, as that is one of the stronger motivations behind our work.
**Regarding the questions:**
1. We can identify $\eta$ exactly be... | Summary: In recent years, the theory of non-linear independent component analysis and causal representation learning has witnessed a lot of interesting developments. In this work, the authors study the problem of causal representation learning in the presence of interventional datasets, where interventions occur on the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and their insightful suggestion of a simpler proof strategy.
However, the model considered in their argument is the very special case when the causal graph's weights do not change at all under all considered interventions. In particular, their review assumes $z... | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews pointing out that the paper 'presents a significant theoretical contribution' (R. 2jDD) that studies 'an important problem in interventional causal representation learning' (R. LWnm), proposes a 'novel and sound algorithm' (R. RqDB), and is 'very clearly wr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning | Accept (poster) | Summary: The paper proposes the SPQR loss to regularize the independence of the Q-network ensemble. The authors apply the random matrix theory and a spiked random model to derive the KL loss between Wigner’s semicircle distribution and the empirical spectral density of eigenvalues. The authors show that the independenc... | Rebuttal 1:
Rebuttal: We appreciate your considerate feedback and emphasis on improving our work. We address your questions and concerns as follows.
Q1. Explanations about the spiked random model and GOE.
A1.
**1. spiked random model**
The goal of the spiked random model is to detect/recover whether the given data ... | Summary: The paper deals with the problem of alleviating overestimation bias in RL, using ensembles Q-functions. The authors argue that previous methods do not provide a theoretical guarantee of the independence of the members of the ensemble. To provide this they propose an approach based on random matrix theory, whic... | Rebuttal 1:
Rebuttal: We appreciate the careful reading and interesting review. We address your questions and concerns as follows.
Q1. Detailed explanation about Figure 3.
A1.
Thank you for providing a detailed and thoughtful comment to improve the presentation of our paper.
During the rebuttal phase, it is not poss... | Summary: This paper proposes a new regularization loss that improves the independence of Q ensemble, thus improving the performance on online and offline DRL settings. The authors first point out that previous works with Q ensemble either rely on assumptions that are inaccurate in practice, or rely on heuristics to imp... | Rebuttal 1:
Rebuttal: Thank you for your interest in our research and for providing us with constructive feedback.
We address your questions and concerns as follows.
Q1. Typos, grammatical errors, and font size mistakes.
A1.
Thank you for kindly pointing out our typos, grammar, and font size mistakes.
We will take ... | Summary: This work proposes a spiked Wishart Q-ensemble independence regularization (SPQR) to improve the independence of ensembling in Q-learning. SPQR encourages the ensemble to be closer to an ideal independent ensemble by penalizing the KL divergence between the eigenvalue distribution of the current ensemble and a... | Rebuttal 1:
Rebuttal: We thank the reviewer for your detailed feedback. We address your questions and concerns as follows.
Q1. Comparison with other RL algorithms.
A1.
We appreciate for informing us about meaningful prior studies. We will try our best to compare and evaluate each previous study based on their concept... | Rebuttal 1:
Rebuttal: For reviewer ppMG, we attach a pdf file for illustration about constructing a symmetric matrix here.
Pdf: /pdf/0c579593a91749696e4d4adc9e019407effefa0c.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Mitigating overestimation bias is crucial for deep reinforcement learning. Existing works about the ensemble techniques for Q-learning have been explored to leverage the diversity of multiple Q-functions. The authors argue that there has been no attempt to ensure ensemble independence from a theoretical standp... | Rebuttal 1:
Rebuttal: We are grateful for your constructive and thoughtful feedback. We address your questions and concerns as follows.
Q1. Verification of the invalidation of the i.i.d assumption in various environments.
A1.
As you mentioned, we evaluate the acceptance ratio in various environments and datasets.
Ac... | null | null | null | null | null | null |
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis | Accept (poster) | Summary: This work studies the consistency of the maximum likelihood estimates of probabilistic PCA. These estimates are unique up to a rotation, so they use the quotient space and claim in their Lemma 6.1, ..., 6.5 stated in Redner[1981] are verified and apply the consistency results available in this book.
Strengths... | Rebuttal 1:
Rebuttal: $\textbf{Respectful Disagreement}$
Thank you for your careful review.
We have clarified the details of our theoretical contributions and we respectfully disagree with the assessment that the contribution is mainly an application of the result of Wald and that there is inadequate reproducibility ... | Summary: In this work, the authors propose a novel topological framework and show that the maximum likelihood (ML) solution of probabilistic principal component analysis (PPCA) is consistent in an appropriate quotient Euclidean space. The consistency results encompass more estimators beyond the ML solution. In addition... | Rebuttal 1:
Rebuttal: $\textbf{We have addressed all the questions raised by the reviewer}.$
Thank you for your careful review and assessment.
$\textit{You are correct}$. If we assume $\sigma^2=\sigma_0^2$ is known then our result does imply that $\widehat{W}$ converges to $W_0R$ for some orthogonal matrix $R$, which... | Summary: The paper discusses consistency of the maximum likelihood (ML) estimation in probabilistic principal component analysis (PPCA). Despite its wide applicability, proving ML estimation consistency in PPCA has been a challenging task because of the non-identifiability of the problem. The author(s) extend the quoti... | Rebuttal 1:
Rebuttal: $\textbf{We addressed all the questions raised by the reviewer.}$
Thank you for your careful review and meticulous reading and assessment. We are very happy that you asked two excellent questions which we would like to address below:
Let us clarify the picture in depth before answering the quest... | Summary: paper addresses "Probabilistic PCA" which stands for a setup where the vector of p observations x can be written as x = Wz + eps where eps stands for an additive (Gaussian, centered, iid) noise, the matrix W \in R^{p x q} is an unknown and z in R^q is the other unknown . Only the value of q is known in advance... | Rebuttal 1:
Rebuttal: $\textbf{We addressed all the questions and weaknesses raised by the reviewer}$.
Thank you for your careful review and valuable assessment. First, we address your questions.
1) You are correct that `There is a broader set of problems where rotation invariance causes trouble' and we agree that PPC... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
StateMask: Explaining Deep Reinforcement Learning through State Mask | Accept (poster) | Summary: The paper presents an interesting model (so called statemask) to identify critical states for an agent's final reward. The goal of statemask is to find the non-important time steps and randomize their actions without changing the expected total reward of the target agent. A PPO based algorithm is leveraged to ... | Rebuttal 1:
Rebuttal: We thank Reviewer AA7o for the constructive and insightful comments. Please see our response to each of your questions below.
**1. The paper seems to claim the method is suitable for all decision-making processes. However, for the type of shortest path finding problems, it is questionable whether... | Summary: This paper focuses on providing an explanation for deep reinforcement learning agents by identifying the important time steps within an episode. The authors propose a module called StateMask, which replaces the original agent's policy with random actions in specific time steps. By preserving the overall episod... | Rebuttal 1:
Rebuttal: We thank Reviewer qciU for the constructive and insightful comments. Please see our response to each of your questions below.
**1. Questions about Eqn. (2)**
**Q(1):** The optimal solution of Eqn. (2) is $\pi=\bar{\pi}$, failing to identify any important time steps.
Please note that our goal is... | Summary: This submission focuses on explaining which states are important to the agent’s final reward.
By utilizing a mask to learn and assess which actions are critical. When learning the mask, it focuses on the random actions without affecting the agent’s performance. They evaluate on 10 different tasks such as Pong,... | Rebuttal 1:
Rebuttal: We thank Reviewer oH2u for the constructive and insightful comments. Please see our response to each of your questions below.
**1. Evaluation: More evaluations among other networks to see how versatile it is. Plus other ablations such as do you vary the amount of time steps for the input, like fr... | Summary: This paper aims to explain deep RL through identifying the critical states at which the action of policy significantly impacts the final reward performance. The main idea is to learn a state-mask, which is modeled as an additional policy, to determine whether to mask original action output by a random one and ... | Rebuttal 1:
Rebuttal: We thank Reviewer ENqW for the constructive and insightful comments. Please see our response to each of your questions below.
**1. The paper is implicitly built upon a restrictive assumption that there are some specific single timesteps/states that contribute to the final reward significantly in ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our sincere gratitude for your thoughtful and constructive feedback on our manuscript. Your insights and suggestions have significantly enriched the quality of our work, and we appreciate the time and effort you have dedicated to reviewing our paper.
In r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy | Accept (poster) | Summary: This paper introduces a framework for analyzing mixture distributions via $f$-DP and its tradeoff function. Additionally, the paper leverages this framework for improving bounds for shuffling mechanisms in DP, and the same framework to prove a statement about privacy of a single-step of gradient descent from r... | Rebuttal 1:
Rebuttal: Thank you very much for your extremely positive feedback and your valuable comments. I have organized the responses to the weaknesses and questions as follows, corresponding to the 8 items in weaknesses. The reference number provided here aligns with the supplementary material, which slightly diff... | Summary: One of the main challenges the differential privacy frame work is facing these days, is the gap between the variety of randomization techniques applied in the machine learning community for various reasons, and our limited capability to prove the privacy amplifications they entail. Among primary examples we ca... | Rebuttal 1:
Rebuttal: We greatly appreciate your extremely positive rating and valuable comments. Thanks to your feedback, we revisited the numerical results of Feldman et al. (2023) and compared them with our own. The comparison showed that our methods outperformed their numerical upper bound. Additionally, we conduct... | Summary: Randomization is an essential too in deriving differentially private algorithms. However, sophisticated randomization techniques such as shuffling induce complicated distributions on outputs, making analysis of privacy loss difficult. The paper uses the framework of f-differential privacy to provide tighter an... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive comments and acknowledgment of the novelty of our results. In response to the identified weaknesses, we provide the following explanations:
1. Response to the first item. In Section 3.2, we applied Theorem 3.5 to analyze privacy amplification in noiseless 1-d... | Summary: This paper studies an important problem in DP: the privacy quantification based on a mixture of randomness. Based on f-DP, the authors point out the joint concavity of the tradeoff function. Two potential examples are proposed, including the shuffling model and the privacy amplification from random initializat... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper and insightful comments. We have carefully considered the raised questions and weaknesses and offer the following responses and modifications. The reference number here is aligned with the supplementary materials.
Responses to weaknesses:
1. The f... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviews from all the reviewers, which have contributed to the thoroughness of our paper. We have attached a 1-page PDF here that provides detailed comparisons and extra numerical results.
Furthermore, we have noticed several frequently asked questions and would like to em... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies improving the analysis of privacy bounds for two randomization processes (privacy amplifications): shuffling models (where each user record is privatized by some local randomizer like randomized response mechanism) and differentially-private gradient descent (DP-GD, where a Gaussian noise is... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and valuable suggestions regarding our paper, particularly in acknowledging our main contributions. In response to the raised questions and weaknesses, we would like to provide the following explanations:
Response to the weakness:
In the revised version, we h... | null | null | null | null | null | null |
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection | Accept (poster) | Summary: This paper presents a framework called FASTEN (Flow-Attention-based Spatio-Temporal Aggregation Network) for 3D mask presentation attack detection. In previous works, as a recent technology rPPG addresses some of the limitations but also sensitivity to noise and high computational overhead.
To overcome these... | Rebuttal 1:
Rebuttal: Reviewer jUZR:
Q1: Limited novelty as a substantial portion of its components is derived from existing work.
A1: Simply using both spatial and temporal features is out of intuition and not new for detection tasks, but our contribution focuses more on the frame-wise attention to aggregate the fe... | Summary: In this paper, To enhance the accuracy of face presentation attack detection by effectively incorporating temporal information，the author proposes a novel 3D mask detection framework. The architecture integrates: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention... | Rebuttal 1:
Rebuttal: Reviewer iaxD:
Q1: Novelty.
A1: Simply using both spatial and temporal features is out of intuition and not new for detection tasks, but our contribution focuses more on the frame-wise attention to aggregate the feature information of multiple frames, rather than simple addition or concatenation... | Summary: This work proposes a 3D mask detection system, successfully deployed on mobile devices. In addition, the number of frames required for making predictions is minimal, improving the latency of responses. Experiments, performed with face mask datasets showed improved performance compared to existing techniques.
... | Rebuttal 1:
Rebuttal: Reviewer U1im:
Q1: More backbones.
A1: Thanks for your advice. We use MobileNet due to its excellent accuracy-efficiency trade-off. Replacing it with other backbones may accelerate the speed but the performance improvement is limited. We will consider this advice in our future work.
Q2: Fonts ... | Summary: This paper presents an approach to 3D mask detection using a small number of face video frames. Experiments were conducted on several databases, and comparisons with other methods are presented.
Strengths: Using small networks for 3D mask detection and a small number of frames;
Both spatial and temporal fea... | Rebuttal 1:
Rebuttal: Reviewer bEHS:
Q1: Only focusing on 3D mask detection, which is quite narrow in terms of applications for face anti-spoofing.
A1: Owing to the rapid development and maturity of 3D printing, detecting 3D masks has become a rising challenging task in the field of face anti-spoofing, which threaten... | Rebuttal 1:
Rebuttal: Thanks for your feedback! We summarize our responses by questions in this rebuttal. Please see more details in the responses to each reviewer.
* Ethics [Ethics Reviewer HeTH, Nmac]
We acknowledge there might be social impacts associated with our work. We will accordingly add a section to the pap... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a 3D mask detection tool called FASTEN, aimed at making face recognition systems more secure. The proposed network focuses on fine-grained details in large movements, which helps eliminate redundant spatio-temporal feature interference. This approach lowers computational overhead and it out... | Rebuttal 1:
Rebuttal: Reviewer VV4D:
Q1: I think the authors should state the explicit insights of this paper, compared to the simple baseline of FlowNet+mobileNetv3.
A1: It is intuitive to consider both spatial and temporal features when detecting 3D masks. However, as shown in the ablation study (Table 4), simply c... | null | null | null | null | null | null |
Most Neural Networks Are Almost Learnable | Accept (poster) | Summary: The paper examines the extent to which neural networks learn deep models. Given the fact that there almost do not exist any 'rich' families of constant depth random neural networks that are efficiently learnable by some algorithm, the authors assert the existence of an algorithm that approximates these familie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review. We will improve the writing according to the outlined suggestions.
We next address the reviewer’s concerns:
- Experiments: Thanks for the suggestion. We will run experiments and consider whether to add them to the final version.
- Finding the pol... | Summary: The paper proves that a sufficiently wide neural network which is randomly sampled according to Xavier initialization can be learned in polynomial time (in the network size) up to an additive error $\epsilon$ if the numbers of layers, the Lipschitz-constant of the activation function, and $\epsilon$ are fixed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review. We will improve the writing according to the outlined suggestions.
First, the reviewer says that our paper “seems to be a major milestone in learning theory for deep networks” and does not raise soundness concerns. We believe that this may qualify th... | Summary: The work shows that there is a PTAS for learning a random xavier feedforward network. They show rigorously that the algorithm runs in time and sample complexity $d^{t}$ where $t = poly(\frac{1}{\varepsilon})$. This is quite an impressive result since it is a distribution-free result in terms of the input $x$. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review. We will improve the writing according to the outlined suggestions.
We next address the reviewer’s questions:
- *“The abstract mentions Xavier networks - shouldn't this only be feedforward networks? Or is it easy to extend to other kinds?”:*
Our tec... | Summary: This work presents a polynomial-time approximation scheme (PTAS) for learning random Xavier networks of depth $i$ up to a fixed additive error of $\epsilon$ with respect to any distribution on the hypersphere. For a fixed $\epsilon$, the time and sample complexity is polynomial w.r.t. $\bar{d}$, the total para... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review.
Regarding the questions:
1. This is a good question. In our result, we need a lower bound $D$ on the width which is of the form $L^{n^i}$, where $L$ is the Lipschitz constant of the activation function. We also conjecture that the theorem is correct ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation | Accept (poster) | Summary: This paper addresses the issue of degree bias in node classification, which refers to the poorer performance of Graph Neural Network (GNN) models on nodes with lower degrees compared to the average level. While previous works have attempted to mitigate this bias, they often trade off the performance on higher-... | Rebuttal 1:
Rebuttal: Dear Reviewer LJuX,
Thank you for your valuable and kind feedback. We sincerely appreciate your acknowledgment of the good writing quality, motivation, and performance of our proposed framework. Our detailed response to your concerns is listed as follows:
**[Relevance of our theoretical analysis... | Summary: This paper addresses degree bias in node classification. The authors show that current methods suffer performance degradation for high-degree nodes. Thus, they freeze the original GNN and train GraphPatcher to enhance low-degree nodes with node patching in the testing stage. In the experiments, they demonstrat... | Rebuttal 1:
Rebuttal: Dear Reviewer Cs3J,
Thank you for your valuable and kind feedback. We sincerely appreciate your acknowledgment of the good motivation and performance of our proposed framework. Our detailed response to your concerns is listed as follows:
**[Node-parallel manner/Training time/Memory usage]** \
P... | Summary: This paper introduces GraphPatcher, a novel test-time augmentation framework designed to mitigate degree bias in graph neural networks (GNNs). Degree bias causes GNNs to perform well with high-degree nodes (rich neighbor information) and poorly with low-degree nodes. Current strategies tend to focus on low-deg... | Rebuttal 1:
Rebuttal: Dear Reviewer ZZgb,
Thank you for your valuable and kind feedback. We sincerely appreciate your acknowledgment of the motivation and reproducibility of our proposed framework. Our detailed response to your concerns is listed as follows:
**[Baseline not strong]** \
The baselines we compare GraphP... | Summary: The paper proposes GRAPHPATCHER, a test-time augmentation framework for graphs, to mitigate the degree biases in Graph Neural Networks. GRAPHPATCHER adopts a corruption function with increasing strength to simulate low-degree ego-graphs from a high-degree one. From the most corrupted graph, it then iteratively... | Rebuttal 1:
Rebuttal: Dear Reviewer xNZm,
Thank you for your valuable feedback. We sincerely appreciate your acknowledgment of the effectiveness of our proposed framework. Our detailed response to your concerns is listed as follows:
**[Limited expressive ability]** \
We agree with you on the fact that the way we patc... | Rebuttal 1:
Rebuttal: Dear ACs and reviewers,
We thank the reviewers for their feedback and constructive suggestions. We are pleased that most reviewers appreciated **the promising effectiveness and performance of our framework**, e.g.: “simple with good performance” (xNZm), “method improves performance without perfor... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Predictive Universal AI | Accept (poster) | Summary: The authors propose Self-AIXI, an extension of the universal Bayes-optimal agent (AIXI), combining it with self-prediction for use in combination with reinforcement learning instead of planning. They provide theoretical evidence that Self-AIXI inherits optimality properties from AIXI.
Strengths: - Overall, th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
* “As Self-AIXI inherits its optimality properties from AIXI, the overall contribution seems limited.”: We argue that one of the key results (and the main effort) of our paper is to show that Self-AIXI can perform equally optimally and achieve AIXI optima... | Summary: This paper presents a new universal Bayesian AI agent framework that uses reinforcement learning to predict its own learning to form incrementally better and ultimately optimal policies. The paper then shows theoretically that this agent, Self-AIXI, converges to the same policies as a planning based universal,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
* “mainly a formal, theoretical endeavor with no direct known (or anticipated) practical applications”: As the reviewer correctly states, the main goal of this paper is to work out the theory for self-prediction and distillation in the limit. We can antic... | Summary: The paper introduces a reinforcement learning version of an agent
(Self-Predictive Universal AI == Self-AIXI) that converges to the
AIXI universal Bayes optimal agent. The advanage of the approach is
it is based on learning/reinforcement learning rather than planning and
thus opens up new avenues for practica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
* “it is unclear whether there are benefits of the approach, eg: faster learner for a given error rate, more accurate practical approximation to AIXI, etc”: From a theoretical perspective the benefits are that we have shown how predictors can be used to a... | Summary: The paper proposes a Bayesian agent for reinforcement learning called Self-AIXI. Self-AIXI learns from its own Q-value maximizing actions and performs action prediction, instead of using extensive search to find optimal action at each step like AIXI. The paper proves that Self-AIXI converges to AIXI and theref... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
* “the main theorem requires that $\pi_S$ be a sensible off-policy that satisfies conditions of Lemma 15, but the authors leave the proof of the satisfiability as a conjecture. This would make it hard to evaluate the significance of the condition and cons... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments. We are pleased to see that all reviewers found our paper easy to read (giving the maximum score of 3 for presentation), and all reviewers consider the theoretical claims and analysis sound and easy to follow. We respond to comments shared by revie... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the issue of inefficiency in planning for AIXI agent, particularly lacking an alternative universal agent that maximally exploits learning and distillation. To address the issue, this work proposed a new method, named self-AIXI, that maximally exploits self-prediction instead of planning ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
* “It would be better if some preliminary results towards a concrete algorithm can be included in this work.”: We are conducting some proof-of-concept experiments with MC-AIXI-CTW, which will be included in the updated manuscript. Please see our main resp... | null | null | null | null | null | null |
Label Poisoning is All You Need | Accept (poster) | Summary: This work introduces a novel backdoor attack, (soft)FLIP, which only requires modifying the labels of the training samples and supports arbitrary choice of triggers. (soft)FLIP is based on a trajectory-matching algorithm, which optimizes the poison labels by simulating how normal backdoor poison samples affect... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer and address their comments point-by-point below:
### Weaknesses:
1. We refer the reader to the **Experiments on Larger Models + Transformers** section of the general rebuttal above for details on our experiments against the VGG and Vision Transformer architectures.
2.... | Summary: The paper studies backdoor attacks, in which a subset of training examples are poisoned with the goal of flipping predicted labels at test time with minimal modifications to the test instances (e.g., by adding a small logo or invisible noise).
Previous backdoor attacks either inject malicious data or modify t... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer and address their comments point-by-point below:
### Weaknesses:
1. *(Poor Presentation.)* Based on the constructive feedback from our reviewers, we are revising the writing significantly.
2. *(Clean Instances.)* We will clarify this in the revisions.
3. *(Knowledge D... | Summary: This paper studies an interesting problem whether backdoor attacks are effective if only manipulating the labels. They propose a method based on searching labels that can match parameters with pre-trained conventional backdoored models. An effective algorithm is shown and experiments are conducted to validate ... | Rebuttal 1:
Rebuttal: We thank the reviewer and address their comments point-by-point below:
### Weaknesses:
1. We are adding several missing details to the revision as per reviewers’ feedback. In conjunction with other edits, we believe this has greatly improved the quality of the presentation (and experimental result... | Summary: The paper first proposes a method that a backdoor attack can be done with only label poisoning. The authors introduce a new algorithm called FLIP that corrupts only the labels in the training set to create a backdoor attack. The first step of FLIP is to collect a set of training trajectories of backdoored ‘exp... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer and address their comments point-by-point below:
### Weaknesses:
1. We refer to the **Defense** section of the general rebuttal above for details on our experiments against backdoor attacks in the literature.
2. We refer to the **Experiments on Larger Models + Transfo... | Rebuttal 1:
Rebuttal: ## Overall Rebuttal: these are additional experiments we ran that were commonly asked by multiple reviewers.
1. **Defenses:** As many of our reviewers judiciously pointed out, FLIP’s resilience to various backdoor defense strategies is of interest. To this end, we evaluate FLIP on CIFAR-10 with al... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This manuscript proposes a new backdoor method for two typical scenarios: crowd source annotation and knowledge distillation. The adversaries are assumed to control the training dataset and inject a backdoor through label poisoning. Specifically, the method train several backdoored models using data poisoning ... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer and address their comments point-by-point below:
We will first clarify our threat model. This will explain the advantage of FLIP and why existing attacks cannot be applied in this setting. In both the crowdsourcing and knowledge distillation scenarios, we assume that... | null | null | null | null | null | null |
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning | Accept (poster) | Summary: The paper introduces a new approach for solving Multi-Objective Reinforcement Learning (MORL) problems. Traditional MORL methods aim at optimizing multiple objectives, but generally focus on the expected values of returns, which can be inadequate in real-world scenarios with diverse preferences over returns. T... | Rebuttal 1:
Rebuttal: To Reviewer 1W6z:
Thanks for your valuable feedback! We provide the response to each of your questions as follows.
**Q1:** “I would appreciate further clarification regarding Table 2. Especially, what are the constraints being referenced in this table? Could you provide illustrative examples to... | Summary: The authors propose extending multi-objective RL (MORL) to policies that are Pareto optimal over distributions of non-negative utility functions. Specifically, they define distributional Pareto optimal (DPO) policies as those whose expected returns are non-dominated for any non-negative utility function. They ... | Rebuttal 1:
Rebuttal: To Reviewer GTy6:
Thanks for your valuable feedback! We provide the response to each of your questions as follows.
**Q1:** “Why is the set of utility functions restricted to non-decreasing ones? Is this a fundamental limitation of the proposed framework?” and “the proposed algorithm for finding... | Summary: This work introduces distributional Pareto-optimality for multi-objective reinforcement learning. Multi-objective RL (MORL) is typically formulated as to find Pareto optimality of all objectives.
To build the ground, the paper defines stochastic dominance for multivariate distribution and then stochastic dom... | Rebuttal 1:
Rebuttal: To Reviewer JBHP:
Thanks for your valuable feedback! We provide the response to each of your questions as follows.
**Q1:** “The authors list safe RL as a special case to MORL. That would great if we can apply the proposed method and other baselines to safe RL benchmark such as Safety Gym to see... | Summary: In Multi-Objective Reinforcement Learning (MORL), the goal is to learn Pareto optimal policies that achieve a balance among multiple conflicting objectives while considering the user's preferences. Existing MORL methods optimize the Pareto frontier by using a single value obtained from the linear combination o... | Rebuttal 1:
Rebuttal: To Reviewer y77J:
Thanks for your valuable feedback! We provide the response to each of your questions as follows.
**Q1:** “The main experiment shows superior performance of the proposed algorithm compared to existing MORL methods but seems to do not fully explain the motivation”
**A1:** The m... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all reviewers for their constructive feedback and insightful suggestions. In this global response, we include a supplementary PDF comprising additional experimental outcomes. We summarize the experiment result as follows:
### **Figure 1: Enhanced Case Studies ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation | Accept (spotlight) | Summary: The paper presents a nice distillation pipeline that can perform high-quality text-to-3D generation using 2D diffusion priors. By introducing the score on NeRF / mesh rendered images, the authors make score distillation work with a classifier-free guidance weight as low as 7.5. This approach largely improves ... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions. Below we provide a point-to-point response to all comments. If our response has addressed the concerns and brings new insights to the reviewer, we will highly appreciate it if the reviewer considers raising the score.
***Q1: Is using a single pa... | Summary: This paper proposes an interesting and novel technique for the task of text-to-3D generation. It defines a distribution of the target 3D scene, which is implemented as particles. Given the textual description, the distribution is updated using the Wasserstein gradient flow. Moreover, the paper proposes to fine... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions. Below we provide a point-to-point response to all comments. If our response has addressed the concerns and brings new insights to the reviewer, we will highly appreciate it if the reviewer considers raising the score.
***Q1: I am curious about h... | Summary: In this paper, a novel method for generating 3D representations from text prompts is proposed. More specifically, a novel variational score distillation (VSD) based optimisation strategy is proposed that allows for optimising NeRF- and mesh-based 3D representations by utilising pre-trained 2D diffusion models.... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions. Below we provide a point-to-point response to all comments. If our response has addressed the concerns and brings new insights to the reviewer, we will highly appreciate it if the reviewer considers raising the score.
***Q1: Is n=1~4 is really e... | Summary: This paper proposes Variational Score Distillation (VSD), which is a new generalization of Score Distillation Sampling (SDS) that is based on
Strengths: The biggest strength of this paper is likely in its practical applicability; notably, I find it impressive that the paper is able to produce good results wi... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and suggestions. Below we provide a point-to-point response to all comments. If our response has addressed the concerns and brings new insights to the reviewer, we will highly appreciate it if the reviewer considers raising the score.
***Q1: What exactly is the ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers's efforts and their appreciation of our novel contributions as well as very detailed and insightful suggestions to further improve our paper. We find there are common concerns to our paper, and we'd like to clarify here. We also add a pdf file to add more experimen... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors introduce the VSD algorithm that generalizes SDS to optimizing a distribution of shapes in terms of KL divergence to the image diffusion model.
Lacking analytic score of the implicit rendered image distribution, they train a surrogate by using LORA finetuning to the base image diffusion model.
The ... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions. Below we provide a point-to-point response to all comments. We hope you will find our response satisfactory and raise your score accordingly.
***Q1: Can multiple particle results be demonstrated for the prompts used in a/b？***
**A**: Yes. We... | null | null | null | null | null | null |
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation | Accept (spotlight) | Summary: The paper provides a framework to derive a new loss for Variational Monte Carlo methods. They introduce Wasserstein Quantum Monte Carlo, which uses a gradient flow based on the Wasserstein metric. The method is empirically tested for fermionic systems, e.g. a Hydrogen chain and compared against another Quantum... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and the time spent. We are glad that you appreciate our developments in the design of new optimization procedures for variational Monte Carlo methods. In what follows we address the concerns raised and answer the questions.
> The lack of immediate relevance to... | Summary: The authors show that the optimization objective typically used in variational Monte Carlo (VMC) solutions of the Schrödinger equation can be interpreted as the Fisher-Rao gradient flow in the space of Born distributions. Based on this insight, they suggest to substitute the Fisher-Rao metric with Wasserstein ... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive feedback. We are glad that you appreciated both the simplicity and non-trivial nature of our idea. We also believe that our underlying theory has many potential implications for developing even better VMC solutions. In what follows we address the raised c... | Summary: The authors propose a novel approach named Wasserstein Quantum Monte Carlo, which uses the gradient flow induced by the Wasserstein and Wasserstein Fisher-Rao metrics and improves the convergence rate of Quantum Variational Monte Carlo. The numerical results show that following the gradient flow under Wasserst... | Rebuttal 1:
Rebuttal: Thank you for your time spent and valuable feedback.
> I'm not familiar with this subject but I strongly believe that this paper belongs to a Physics conference or journal instead of NeurIPS
We respectfully disagree that this paper does not belong to NeurIPS. The NeurIPS community has a rich tra... | Summary: The paper proposes a new way to compute gradients for parameters in quantum Monte Carlo. The authors propose to update the network parameters by following the Wasserstein Fishier-Rao gradient flow, which is composed of a continuity equation and a growth term. The authors show that when only the latter term is... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and time spent. We are glad you found our paper well-written. In what follows we address the concerns raised and answer the questions.
> It's not explicitly mentioned in the paper. But I think the extra gradient computation may cause more computation.
Indeed,... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time spent and valuable feedback on the paper. In what follows we would like to address the common concerns raised by most of the reviewers.
**Runtime.** As requested by most of the reviewers, we include the runtime of all the algorithms in the plots (see the new ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper shows that quantum variational Monte Carlo can be written as energy minimizing gradient flow under Fisher-Rao metric, and proposes an alternative to traditional quantum Monte Carlo by following Wasserstein gradient flow. Experiments with Be, B, Li2, and H10 chain show lower energies achieved by inclu... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and time spent. We are glad that you find the presentation of our ideas to be clear and rigorous. In what follows we answer the questions asked.
> Why does W(FR)QMC seem to be better than WQMC alone for most of the experiments? What was the hyper-parameter con... | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.