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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
Making Scalable Meta Learning Practical	Accept (poster)	Summary: The paper "Making Scalable Meta Learning Practical" introduces a novel approach called SAMA (Scalable Meta Learning with Arbitrary Optimizers) to address the scalability issues in meta learning. The authors combine advancements in implicit differentiation algorithms and systems to develop SAMA, which supports ...	Rebuttal 1: Rebuttal: We thank you for the positive review as well as the helpful feedback. Here, we address each of the questions and comments that you raise. ### Clarification on the application scenarios > Q1. This paper claims that the proposed SAMA makes scalable meta-learning practical while the experim...	Summary: This paper proposes a novel framework that could achieve scalable meta-learning algorithms from the perspectives of algorithms and systems. For algorithms, some approximations are proposed for base Jacobian inverse and adaptive optimizers; for systems, it implements the distributed algorithms to ensure differe...	Rebuttal 1: Rebuttal: We thank you for the valuable review. We try our best to address your concerns and questions here and in the global response. ### Ablation study > Q. Three issues (section 3) are solved to ensure the scalability of meta-learning. Which issue is the major one to slow down the process? ...	Summary: The authors explore the issues impacting the scalability of Gradient-based Meta-Learning (GBML), including high memory/compute costs, algorithmic instability, and poor support for distributed training. The causes identified are: the base Jacobian inversion, the absence of algorithmic adaptation for adaptive op...	Rebuttal 1: Rebuttal: We thank you for the valuable feedback that will improve the quality of our work. We attempt to clarify and address your concerns regarding our work here and in the global response. ### Comparison with DARTS While we recognize the similarity, SAMA is different from DARTS in two major aspe...	Summary: This paper tries to scale current meta learning algorithm and make scalable meta learning practical. Specially, the authors propose a novel algorithm SAMA from the perspective of algorithm and system, which can support arbitrary optimizers in the base level of meta learning and reduce the computation cost. The...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our work, and for the useful feedback. We address the comments and questions raised in your review below and in the global response. ### Additional scalability analysis > Q. This paper mainly focus on scaling and maybe you should provide more ex...	Rebuttal 1: Rebuttal: We first want to express our gratitude to all reviewers for their reviewing efforts. In our global response, we address two issues raised by reviewers: 1) Ablation study and 2) (empirical) justification of the identity base Jacobian approximation. ### Ablation Study & SOTA comparison While th...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper addresses the challenges of scalability in meta learning by introducing SAMA, a novel approach that combines advancements in implicit differentiation algorithms and systems. SAMA demonstrates improvements in computational efficiency and memory consumption compared to other baseline algorithms, and it...	Rebuttal 1: Rebuttal: We appreciate your positive review and valuable comments. We strive to address concerns and questions that you raised below and in the global response. ### Ablation Study & SOTA comparison Though we didn’t explicitly frame it as an “ablation study”, the effectiveness of each component in ...	null	null	null	null	null	null
Provable Training for Graph Contrastive Learning	Accept (spotlight)	Summary: The goal of this paper is to investigate the properties of different nodes in GCL with different graph augmentations. The paper discovers the imbalanced training issue of GCL methods, and proposes the concept “node compactness”, measuring how each node follows the GCL principle. Finally, the paper proposes the...	Rebuttal 1: Rebuttal: We sincerely appreciate the positive comments and valuable feedback from the reviewer. Below, we address the reviewer's concerns one by one, hoping that a better understanding of every point can be delivered. 1. > One of the motivations is that “how to distinguish these nodes”, while it seems that...	Summary: The paper considers an important problem in graph contrastive learning, i.e., the relationship between the node property with the graph augmentations. The main takeaway is that the training of GCL on different nodes is imbalanced, and the concept “node compactness” is introduced to guarantee the training of GC...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the precious time spent reading through the paper and giving constructive suggestions. To address the concerns, we clarify the experiment section and some notations as follows. 1. > The experiments section could be strengthened. I’m not sure whether the followin...	Summary: Graph augmentation is a fundamental component for graph contrastive learning. When augmenting graph structures, how the change of structures affects the GCL is an interesting problem. In this work, the paper proposes the “node compactness” to describe the behavior of different nodes, i.e., whether there are so...	Rebuttal 1: Rebuttal: We greatly thank the reviewer for your interest in our paper and constructive suggestions. To make further clarification on the techniques and experimental details, we respond to the reviewer's questions one by one. We look forward to assisting to have a better understanding of our paper. 1. > The...	Summary: The paper aims at studying the node properties given different graph augmentations in graph contrastive learning. It has the following contributions. 1) It discovers the training of GCL methods is severely imbalanced. 2) It proposes a novel concept of “node compactness”, and the provable training for GCL with ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the positive comments and valuable feedback on our paper. To further address your concerns, we provide additional experiments as well as a more detailed discussion of the limitations. 1. > In Section 3, the authors observe the training imbalance in GCL and prop...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for the acknowledgment of our paper and for many constructive comments. Since some reviewers mentioned that the discussion of limitations may be relatively brief, we expand that part as follows. Due to the limited time, we have tried our best to explore different a...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Binary Radiance Fields	Accept (poster)	Summary: This paper introduces a new voxel grid radiance field representation in which the feature vectors are restricted to contain binary values. The motivation for this is to greatly reduce the storage requirements of voxel grid radiance fields. They use the straight-through estimator to allow backpropagation thro...	Rebuttal 1: Rebuttal: We appreciate your thoughtful comments. Following your comments, we will cite the missing related work in the manuscript (Q1, Q2 in global response) and supplement per-scene videos for the results in the supplementary material (Q2). Also, we have elaborately explained several questionable parts of...	Summary: This paper proposes a new representation, binary radiance fields (BiRF), for memory-efficient novel view synthesis tasks. The representation is inspired by the binary neural network. BiRF is built upon Instant-NGP. The critical component of this representation is the binarization of real-valued feature grids, ...	Rebuttal 1: Rebuttal: We appreciate your thoughtful comments. Following your comments, we have additionally performed more experiments on the binary feature encoding for training time (Q1), memory requirement (Q2), and reconstruction quality (Q3). The detailed responses to your comments are as follows. We have compared...	Summary: The paper proposes a novel approach called binary radiance fields (BiRF) for efficient storage and representation of radiance fields. BiRF utilizes binary feature encoding, where local features are encoded using binary parameters of +1 or -1. This compact encoding significantly reduces storage size and computa...	Rebuttal 1: Rebuttal: We sincerely appreciate your thoughtful comments. Following your comments, we have performed more experiments on inference speed (Q1 in global response, Q1) and convergence speed (Q1, Q2). Also, we have clearly explained several questionable parts (Q3, Q4) and the strategy for hash collision (Q5)....	Summary: This paper proposes a novel binary radiance fields (BiRF) which binarized the feature encoding to save memory usage of NeRF. In the experiments, the binary radiance field representation demonstrates superior reconstruction performance compared to state-of-the-art efficient radiance field models, all while requ...	Rebuttal 1: Rebuttal: We appreciate your comments. We have found that your concerns are mainly from two sources: the limited contribution of binary feature encoding (Q1) and the analysis of the bottleneck of NeRF quantization (Q2). Thus, we focus on addressing these concerns in this rebuttal. The detailed responses to ...	Rebuttal 1: Rebuttal: Global response Dear reviewers, We thank all reviewers for their insightful feedback. As highlighted by reviewers, our paper proposes concise (EsYg, fKiM, VKML) and innovative (ptCH, VKML) ideas and is well-written (Bcw2, ptCH, fKiM). Also, we are delighted that the reviewers thoroughly agre...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors are proposing BiRF (BInary Radiance Fields), a storage-efficient representation for neural radiance fields. The technique relies on a hybrid representation that leverages explicit feature grids (both one 3D and three 2D, each at multiple resolutions) combined with density and color M...	Rebuttal 1: Rebuttal: We sincerely appreciate your thoughtful comments. We have additionally performed more experiments following your comments: inference speed (Q1 in global response), more baselines (Q1), and extended ablation study (Q3). Also, we positively consider adopting stochastic binarization for further impro...	null	null	null	null	null	null
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification	Accept (poster)	Summary: The paper presents a novel language-driven ordering alignment method called L2RCLIP for ordinal classification. The authors leverage pre-trained vision-language models to incorporate rich ordinal priors from human language. They propose RankFormer, a prompt tuning technique that enhances the ordering relation ...	Rebuttal 1: Rebuttal: # Response to the Reviewer v94e Comment: Thank you for your positive review and constructive feedback.	Summary: The paper presents L2RCLIP, a novel language-driven ordering alignment method for ordinal classification. The authors propose to leverage the rich ordinal priors in human language by converting the original task into a vision-language alignment task. The method introduces a complementary prompt tuning techniqu...	Rebuttal 1: Rebuttal: # Response to Reviewer 6B9T We sincerely appreciate your positive review and valuable comments. Please find our responses below. * Q1: Compred with previous method with the same architecture [Reply]** Our method doesn't show promising result using VGG16, which may be reasonable since our ...	Summary: In this paper, a novel ordinal regression framework based on CLIP is proposed. The proposed algorithm, which is called L2RCLIP, exploits language priors together with image features. It encourages that image features at each class locate around the text feature of that class in the embedding space. To this en...	Rebuttal 1: Rebuttal: # Response to Reviewer XrDm We sincerely appreciate your positive review and valuable comments. Please find our responses below. * Q1: To prove the effectiveness of proposed losses [Reply] Thank you for your suggestion. We provide a detailed analysis of Eq(2) to Eq(6) as follows: - ...	Summary: The paper proposed to leverage vision-and-language models to improve ordinal classification. This is a follow-up work on the previous OrdinalCLIP paper. The major contribution is RankFormer, which is designed to enhance the ordering of the original rank prompts. Also a cross-modal ordinal pairwise loss is prop...	Rebuttal 1: Rebuttal: # Response to Reviewer GKDm We would like to thank the reviewer for the valuable comments. However, we feel there is some misunderstanding. We clarify the issues and address the questions accordingly as described below. * Q1: Choice of using powerful language model [Reply]** That may be a...	Rebuttal 1: Rebuttal: # Response to All Reviewers Thank you for your valueble review and insightful suggestions. The .pdf file includes extra figures. Please download it if needed. We have made every attempt to address your comments in the revised manuscript and hope that you find this revision satisfactory. If you...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes L2RCLIP, which features two modules for ordinal classification with vision-language models, i.e., CLIP. The first is a token-wise attention module called RankFormer to tune the rank prompts. And the second is a pairwise ordinal loss to inject rank information into the supervision. Synergica...	Rebuttal 1: Rebuttal: # Response to Reviewer GKDm We thank the reviewer for the valuable feedback and a positive assessment of our work. We are happy the reviewer finds the paper well-organised and our method interesting, valuable, and innovative with good performance. Below we detail our response to the review concern...	Summary: This paper proposes a language-driven ordering alignment method for ordinal classification. For the language prompt, this paper introduces the RankFormer, which uses Transformer to learn token-wise attention over a set of rank templates. For the loss function, this paper presents a cross-modal ordinal pairwise...	Rebuttal 1: Rebuttal: # Response to Reviewer VQC9 We appreciate the reviewer's insightful comments. However, there seems to be some misunderstanding. We would like to clarify the issues and address the questions as follows. Q1: The design of RankFormer. [Reply] First, we want to clarify the misunderstanding...	null	null	null	null
Have it your way: Individualized Privacy Assignment for DP-SGD	Accept (poster)	Summary: This paper designs variants of differentially private SGD to satisfy different privacy expectations, e.g., users can choose one from high, medium, or low levels of privacy. There are two variants of DP-SGD, one changes the sampling probability of different groups, and the other changes the clipping threshold o...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments and provide our responses below. > W1: Influence on utility for underprivileged groups We already present such a result in Table 8 in our submission. We selected the CIFAR10 dataset and assigned lower or higher privacy budget to one of the classes. Cla...	Summary: This paper proposes extensions of the DP-SGD algorithm to support individualized differential privacy (called the IDP-SGD approach). Unlike traditional differential privacy, which imposes a single privacy budget epsilon to all data points, the data points may now have different privacy budgets. Two extensions ...	Rebuttal 1: Rebuttal: > W1 & Q6: Application of SGM theorems to IDP The proof in G.1 showing that the entire mechanism satisfies $(\{\varepsilon_1, \varepsilon_2, \dots, \varepsilon_P\}, \delta)$-IDP is built on the observation that our methods can be considered as $P$ simultaneously executed SGMs that update the ...	Summary: This paper proposed two variants of DP-SGD by manipulating the sampling rate and the gradient clipping bound for different groups to achieve the goal of having different privacy budgets for those groups and improving the overall performance of DP-SGD. The authors proved theoretical privacy guarantees for both ...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and suggestions and provide our answer below: >W1: Composition theorems We would like to point out that our algorithms have the exact same composition (within every privacy group individually) as the standard DP-SGD and do not require additional theor...	Summary: This paper proposes two variants of Differentially Private Stochastic Gradient Descent (DP-SGD) to train machine learning models that satisfy approximate personalized differential privacy (PDP), following the definition of Jorgensen et al. [15]. In contrast to vanilla DP-SGD where all points in the training da...	Rebuttal 1: Rebuttal: First of all, we would like to express our gratitude to the reviewer for their thorough review and detailed feedback that goes beyond any expectation. > Limited empirical evaluation We thank the reviewer for their suggestion to extend our experimental evaluation on other architectures, tasks...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their feedback which has greatly helped us improve the paper. We are glad that the reviewers recognize our work to address an important problem by proposing the first personalization mechanisms for DP-SGD (3QXJ) which are novel, easy to implement, and provi...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On the Size and Approximation Error of Distilled Datasets	Accept (poster)	Summary: This paper provides theoretical analysis towards recent errors of recent dataset distillation methods based on kernel ridge regression (KRR). It mainly utilizes some previous results on KRR and applies them in the context of dataset distillation. Some simple simulation results verify the derived bounds. Stre...	Rebuttal 1: Rebuttal: We greatly appreciate the insights shared by the reviewer and the expert evaluation they provided. Integrating their feedback, have already made significant improvements to the paper. We are looking forward to further engagement with the reviewer as we enter the upcoming open discussion phase. We...	Summary: This manuscirpt first give theoretical understanding on synthetic dataset generated in dataset distillation task. In concrete, the authors prove (1) the existance of distilled datasets and (2) the generalization error is related to the "number of effective degrees of freedom" in the random Fourier features (RF...	Rebuttal 1: Rebuttal: We extend our appreciation to the reviewer for their expert evaluation, insightful remarks, positive feedback, and valuable suggestions that have contributed to the enhancement of our manuscript. We now delve into a comprehensive discussion of the concerns raised by the reviewer. We trust that ou...	Summary: The paper attempts to provide the first theoretical guarantees on the existence of dataset distillation, under the setup of kernel ridge regression. The proof techniques are mainly based on theory of random fourier features. They also provide experiments which are indicated to support their theoretical results...	Rebuttal 1: Rebuttal: We wish to extend our heartfelt appreciation to the esteemed reviewer for their dedicated commitment to meticulously evaluating our paper. The thoughtful points and careful reading hold a pivotal role in the refinement of our work. We have diligently addressed each of these valuable concerns, and ...	Summary: This paper presents a theoretical analysis of dataset distillation, specifically focusing on the size and approximation error of distilled datasets. The authors provide bounds on the sufficient size and relative error of distilled datasets for kernel ridge regression (KRR) based methods using shift-invariant k...	Rebuttal 1: Rebuttal: The reviewer's comments and expert evaluation are highly valued by us. Indeed, incorporating your feedback has already led to enhancements to the paper. We eagerly anticipate continued interaction with the reviewer during the forthcoming open discussion phase. We have meticulously addressed each o...	Rebuttal 1: Rebuttal: We deeply thank the reviewers for providing us with both positive feedback and valuable constructive criticism. Your professional review and careful reading have already helped us improve our work. We have thoroughly addressed all the comments raised during the initial review. If further clarity i...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Implicit Contrastive Representation Learning with Guided Stop-gradient	Accept (poster)	Summary: The paper proposes the implicit contrastive learning algorithm, which uses the guided stop-gradient to push away negative samples without the uniformity term in the contrastive loss. By applying the method to the non-contrastive methods including SimSiam and BYOL, the paradigm combines the advantages of contra...	Rebuttal 1: Rebuttal: [W1] asymmetric architecture + contrastive loss MoCo is an algorithm that combines asymmetric architecture (stop-gradient, momentum encoder) and contrastive loss (InfoNCE). We refer the reviewer to Figure 2c in [1]. As shown in Table 4, even in the case of MoCo, the performance is not good when t...	Summary: This article presents a way to improve the learning of non-contrastive self-supervised learning methods such as BYOL and SimSiam. It uses incorporates implicitly contrastive notions, by removing elements of the loss that may lead to close representations collapsing together. This modification is proven to impr...	Rebuttal 1: Rebuttal: [W1-1] using $N$ examples To use $N$ examples at once when constructing the loss, we need to create decision criteria like Equation (6) that considers $4{N \choose 2}$ distances together. We think it is hard to be done by a straightforward extension of our idea since when deciding which side to a...	Summary: This paper proposes a novel SSL technique that can be applied on top of SimSiam or BYOL to select where to apply asymmetric predictor. This method first computes embeddings (before the predictor) and compute relevant distances. Then based on this distance, it chooses where to apply the predictor. Experiment re...	Rebuttal 1: Rebuttal: [W1] generalizability Asymmetry is an important topic in recent self-supervised representation learning [1]. Algorithms that utilize asymmetry such as SimSiam, BYOL, SwAV, and DINO are continuously emerging. In this paper, we showed that asymmetry, which was previously introduced to prevent colla...	Summary: The paper introduces the Guided Stop-Gradient (GSG) method that can be applied to SSL algorithms that adopt asymmetric dual encoders such as BYOL and SimSiam in order to boost their performance and stabilize their training. The idea of the GSG is to augment the loss function to attract different views of two d...	Rebuttal 1: Rebuttal: [W1] analysis on the learned representation space Please see [G1] in the global response above. --- [Q1] choices of $k$ for $k$-NN We tried to make the experimental settings identical to previous studies for easy and fair comparison. So for the value $k$, we used the default value in [1] for t...	Rebuttal 1: Rebuttal: Dear reviewers, We thank you for your careful reading and constructive feedback. Your comments will help us improve the quality of the paper. We have detailed responses to each reviewer individually below. We also write responses to some common questions here. We write [Gx], [Wx], [Qx], and [Lx] ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Multitask Learning for Face Forgery Detection: A Joint Embedding Approach	Reject	Summary: The proposed method introduces a novel approach to deepfake detection by integrating natural language and image information. Moreover, it attains state-of-the-art performance on several contemporary deepfake datasets and can generate explanatory sentences that justify the authenticity or falsity of the input i...	Rebuttal 1: Rebuttal: Q1. It would be preferable for the authors to compare the performance of their method with the proposed dataSup scheme and previous methods such as Face X-Ray, SBI and SLADD using a unified backbone and then analyze the impact of different dataSup schemes. A1: Thanks for the excellent com...	Summary: This paper proposes a multitask learning framework for video deepfake detection. The idea is to rely on a joint embedding architecture and define a set of coarse-to-fine face forgery detection tasks with corresponding textual descriptions for fake face images (binary level, global-attribute level and local-att...	Rebuttal 1: Rebuttal: **Q1. The technical description of the method based on multitask learning (Section 3.3) is very generic and not related at all with the problem of deepfakes. In addition, the technical contribution seems to come from already published work: the joint embedding formulation is inspired by minimizing...	Summary: The paper appears to be about a method for detecting manipulated facial images, specifically deepfakes. The authors have used a model that employs a joint embedding architecture, using ViT-B/32 as the visual encoder and GPT-2 as the text encoder. The model is trained using AdamW with a decoupled weight decay ...	Rebuttal 1: Rebuttal: Q1. The majority of the contributions in this study are essentially modifications of existing work. A1: We respectfully disagree with the comment and kindly refer the reviewer to the general response. In short, the most significant contribution is defining a set of coarse-to-fine face for...	Summary: This work proposes an automated multitask learning framework for face forgery detection from a joint embedding perspective. The central idea is to utilize the multi-modality of visual and textural features to enhance blending-based face forgery detection with the global and local semantic face attributes. Expe...	Rebuttal 1: Rebuttal: Q1. The technical contributions that inspire the following research are quite limited: The majority of technical components of this work are borrowed from existing works, e.g., multitask learning, embedding space representation (latent space), textural space and etc. A1: We respectfully d...	Rebuttal 1: Rebuttal: ### A general response regarding the contributions of our work We thank all reviewers for the detailed and constructive comments. We are glad to find that most reviewers generally acknowledge the following contributions of our work. This paper explores multitask learning of face forgery dete...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a joint embedding approach for multitask learning in face forgery detection. The method defines a set of coarse-to-fine face forgery detection tasks based on face attributes at different semantic levels, and describes the ground truth for each task via a textual template. CLIP is used to ...	Rebuttal 1: Rebuttal: Q1. Regarding the limited technical contribution. The authors apply the existing technologies, including CLIP and fidelity loss for joint-embedding-based multitask learning. A1: Please refer to the general response for technical contributions. In short, the most significant contribution ...	null	null	null	null	null	null
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning	Accept (poster)	Summary: The paper studies the challenges in measuring the performance of training data attribution (TDA) methods arising from stochasticity in training large deep neural networks. Specifically, the authors use various existing approaches to obtain many samples from the posteriors of the model weights instead of a poin...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and questions. We hope to clarify concerns in the following. ### It is not clear if the phrase Bayesian perspective on TDA is useful, authors could say more on the connection between using Bayesian DL methods, Student t-test for measuring the noise in TDA...	Summary: This paper aims to examine training data attribution (TDA) methods from a Bayesian perspective, assuming the learned model parameters are samples from a posterior distribution. The paper illustrates how this perspective might affect TDA methods. It conducts experiments comparing and contrasting different TDA m...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review and constructive suggestions. ### The paper [...] would benefit [...] from some mathematical analysis [...] of how a Bayesian [...] perspective might affect the validity of TDA. Our probabilistic conversion of TDA methods does not affect theoretical soundness. W...	Summary: This paper presents a Bayesian perspective on Training Data Attribution (TDA), a technique that identifies influential training data for model predictions. The authors propose treating the learned model as a Bayesian posterior and TDA estimates as random variables. This approach reveals that the influence of i...	Rebuttal 1: Rebuttal: We appreciate the encouraging review. In the following, we answer the questions posed by the reviewer one by one: ### [The paper] does not discuss a solution or path towards a solution of the TDA problem. Our focus is to identify issues with prior problem definition of the TDA task and to propo...	Summary: In this paper the authors investigate training data attribution (TDA) through a bayesian lens by explicitly considering the randomness in estimating the model parameters with and without a given training example. To generate approximate bayesian posteriors on model parameters the authors use deep ensembles an...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and recommendation to accept our work. We wish to address the remarks and questions raised by the reviewer one by one: ### One of the weaknesses is the small dataset sizes considered. We understand that considering greater dataset sizes would be d...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive comments and suggestions. Reviewers agree that we “study an important practical problem” (fmRD, CbTs) and provide “thorough experiments” (wqEs) and a “strong analysis” (fmRD). We have addressed individual reviewers’ comments and questions in the dedi...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions	Accept (spotlight)	Summary: The manuscript introduces FairFront i.e., an estimation for the upper bound on the Pareto Frontier for Fairness and Accuracy. The authors empirically show the tightness of this bound by showing how SOTA approaches perform close to the FairFront, while the gap may be attributed to the distributional variations....	Rebuttal 1: Rebuttal: We appreciate the reviewer’s their careful reading of our paper and thoughtful comments! --- Q1. Epistemic and aleatoric discrimination and their link to uncertainty literature. A1. We thank the reviewer for highlighting this crucial point. Below, we discuss their link to uncertainty litera...	Summary: This paper splits discrimination in machine learning into aleatoric (which is that inherent to the data distribution), and epistemic (which is that due to choices in the model). They use Blackwell’s results to characterize the fairness Pareto frontier curve. Then, on 4 datasets with 5 fairness interventions, t...	Rebuttal 1: Rebuttal: We thank the reviewer for the kind comments and the encouragement! --- Q1. Algorithm (L236) is always the upper bound for FairFront. More upfront with this throughout. A1. Thank you for raising this concern. We will clarify that we provide an upper bound estimate of $FairFront$ in the intro...	Summary: This paper proposes a decomposition of discrimination (in ML classifiers) into aleatoric (irreducible) and epistemic (reducible) components. The paper surveys related work in fairness. It then introduces and discusses the Fairness Pareto Frontier, which is essentially an upper bound on the accuracy of the best...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful review and for appreciating the merits of the work! --- Q1. An illustrative example, helping to build intuition about the terms and concepts introduced. A1. We thank the reviewer for this valuable suggestion. One concrete example is the COMPAS dataset...	Summary: The paper casts the issue of fairness in machine learning from the perspective of two classes of discrimination: those due to aleatoric uncertainty, or where the inherent limitations of the data distribution, and epistemic discrimination, which is due to modeling choices. It then uses this framework to analyze...	Rebuttal 1: Rebuttal: We thank the reviewer for the kind comments and the encouragement! --- Q1. Contribution feels limited both in terms of experiments and its relevance in modern machine learning. A1. Thank you for raising this important point. First, please note that we provide numerical results for four b...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this manuscript, the authors make two main contributions to the technical study of algorithmic fairness. Firstly, on the conceptual level, they propose to distinguish between aleatoric and epistemic discrimination. By the former term, they refer to the notion that the optimal achievable performance level m...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments and for appreciating the novelty of the work! --- Q1. Approximation of $g$ and the impact on the estimated upper bound. A1. Yes, your understanding is correct! The approximation error of $g$ can influence the estimation of $FairFront$. If $g$...	null	null	null	null	null	null
Block-State Transformers	Accept (poster)	Summary: The authors present a new long-range transformer architecture by incorporating SSMs. This novel model outperforms several established baselines, such as Transformer XL, Block Recurrent Transformer, and Sliding Window Transformer, in terms of cost-effectiveness trade-off for tasks involving long-document or cod...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We have taken your comments and concerns into careful consideration and conducted additional experiments to address them. These experiments have been included in the 1-page PDF, focusing on scaling aspects and performance in areas beyond language tasks, na...	Summary: This paper focuses on combining two efficient techniques for long-range modeling: state-space models (global contextualization) and block-recurrent transformers (local contextualization). In particular, they propose two different approaches, the first uses SSMs to output contexts for multiple heads (multi-head...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. In response to your comments and concerns, we have conducted additional experiments, which we have included in the 1-page PDF. These experiments address the points you have raised and also explore areas that you alluded to in your review, namely scaling ...	Summary: State space models (SSMs) perform well on modeling long-range dependencies with good efficiency scaling, but on language modeling, transformers still outperforms SSMs. This paper tries to combine the best of both worlds and proposes a hybrid model, Block-State Transformer, which combine SSMs’ capacity on long ...	Rebuttal 1: Rebuttal: Thank you for your generally positive review. We have taken your valuable feedback into account to improve our current version of the paper. A more comprehensive review of related works, including H3, will be provided. Additionally, we have included a number of experiments in the 1-page PDF, t...	Summary: This paper proposes block-state transformers, a method to combine state space models with transformers for language modeling. The paper evaluates block-state transformers on PG19 and arxiv math and finds promising results. Strengths: Combining state space models and Transformers is an interesting idea worth e...	Rebuttal 1: Rebuttal: Thank you for your review. We hope that we have addressed most of your concerns with the additional experiments and comparisons in the 1-page PDF. Our new experiments show: 1. Large improvements on PG-19 compared to Hyena and Hybrid-H3. Specifically, integrating Hyena and attention in `BST:...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their insightful comments. We believe that we addressed the vast majority of reviewer’s concerns by conducting additional experiments, found in the attached 1-page PDF attached to this message, and responding to reviewer’s individual questions. Our ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors present a novel architectural framework called the Block-State Transformer (BST), which integrates State Space models and Block-Recurrent Transformers to create a competitive autoregressive language model capable of effectively processing lengthy sequences. The input sequence is passed through a St...	Rebuttal 1: Rebuttal: We appreciate your valuable and constructive comments. We agree that Block-State Transformer is a novel architecture that shows strong results and computational efficiency. We think that we have only scratched the surface of possibilities with this interesting combination of ideas. We have conduct...	null	null	null	null	null	null
Mirror Diffusion Models for Constrained and Watermarked Generation	Accept (poster)	Summary: This paper proposes a new class of diffusion models called the Mirror Diffusion Model (MDM), which confines the generation to a constrained convex set. The MDM transforms the generation from a constrained original space to an unconstrained dual space. With this transformation, MDM can be trained and sampled li...	Rebuttal 1: Rebuttal: 1. Conditions when $\nabla\phi(\mathcal{M})= \mathbb{R}^d$ - We first note that the gradient map of a strictly convex function $\phi$ needs not span $\mathbb{R}^d$, unless additional conditions are satisfied. For mirror maps, we follow the literature (e.g., [1,2]) and require $\phi$ to be a...	Summary: When the data distribution is constrained in some boundary, This paper introduced Mirror Diffusion Models (MDM), where the diffusion process runs not in the distribution of the (constrained) primal space, but in the distribution of the (unconstrained) dual space. For constrained datasets such as simplex, poly...	Rebuttal 1: Rebuttal: 1. Performance of dual-space diffusion models - We first thank the reviewer for raising the comment. While we do notice a gap between `MDM-proj` and `MDM-dual` (first 2 rows in Table 6), we stress that these FID values are evaluated w.r.t. the original, constraint-violated, training set dis...	Summary: This paper studies how to learning diffusion model when the data is in a constrained domain. The idea is to map the data into an unconstrained domain using the mirror map and conduct the diffusion on that mirror space. Once the generation finishes, map the data back to the original space. Strengths: Using the...	Rebuttal 1: Rebuttal: 1. Discussion on [1,2] - We first thank the reviewer for bringing up these missing references, which are indeed relevant to our Mirror Diffusion Model (MDM). Both [1,2] and our MDM generate samples in constrained domains. While [1,2] can be applied to, e.g., discrete domains [1,2], equality c...	Summary: The submission "Mirror Diffusion Models for Constrained and Watermarked Generation" describes a new approach to generate constrained data with diffusion models. Using mirror maps, the diffusion process proceeds as usual in an unconstrained space, but the generated data can be converted into the constraint spac...	Rebuttal 1: Rebuttal: 1. Clarification on watermark generation in Sec 5.2 - The reviewer’s understanding of MDM-proj and MDM-dual is correct: MDM-proj projects samples generated by pretrained diffusion models to a constraint set whose parameters (i.e., tokens) are visible only to the private user. In contrast, MDM...	Rebuttal 1: Rebuttal: ### Author response to all reviewers We thank the reviewers for their valuable comments. We are excited that the reviewers identified the novelty of using mirror maps in learning constrained diffusion models ( Reviewers **i5Cc, kFs5, Bc59, VdTP**), acknowledged our sup...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models	Accept (poster)	Summary: The authors propose to use paired synthetic data to train a semantic segmentation model. Specifically, a filtering strategy and a resampling strategy are proposed to control the quality of synthetic data. In this way, the paired synthetic data could further promote the standard segmentation model. Strengths: ...	Rebuttal 1: Rebuttal: We are sincerely grateful for your efforts and constructive feedback. We hope the concerns are well addressed. Q5-1: Conflict between two strategies: the generated additional hard samples are filtered out Our filtering strategy does not discard all hard samples. It mainly aims to detect nois...	Summary: The paper introduces an automatic dataset generation with mask-to-image translator. The proposed dataset generator enables to generate controllable and consistent semantic labels in generated images. The labels can be treated as fully supervised teachers from a generator and create pre-trained segmentation mod...	Rebuttal 1: Rebuttal: We are sincerely grateful for your appreciation of our work. Many thanks for your efforts and constructive feedback. We hope the concerns are well addressed. Q4-1: How can the proposed method be more "controllable"? Thank you. We claim our framework of learning from synthetic images is more ...	Summary: The Paper talks about using synthetic images generated using generative models as the training set to achieve stronger semantic segmentation models. The efficacy of the model is evaluated on ADE2K and COCO dataset, using SegFormer model. Authors propose pretraining with synthetic and joint images to evaluate w...	Rebuttal 1: Rebuttal: We are sincerely grateful for your efforts and constructive feedback. We hope the concerns are well addressed. Q3-1: Mining hard examples are not novel We are only related to existing works (e.g., OHEM) from the aspect of motivation. It is indeed a widely shared motivation. However, we u...	Summary: This paper proposes to generate densely annotated synthetic images with generative models to help supervise the learning of fully supervised semantic segmentation frameworks. To improve the effectiveness of synthetic images, the authors further design a robust filtering criterion to suppress noisy synthetic sa...	Rebuttal 1: Rebuttal: We are sincerely grateful for your efforts and constructive feedback. We hope the concerns are well addressed. Q2-1: Our re-sampling strategy is similar to OHEM In L236-L241, we have compared our re-sampling method with OHEM. In semantic segmentation, OHEM ignores high-confidence pixels and ...	Rebuttal 1: Rebuttal: [Contributions] Our technical contributions mainly lie in three folds: - [New target \& new roadmap] We present a new roadmap to enhance fully-supervised semantic segmentation via generating densely annotated synthetic images with generative models. Our data-centric perspective is orth...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a method of synthesizing training images and corresponding semantic masks for training a semantic segmentation network. The off-the-shelf semantic image synthesis model, FreestyleNet, is used to generate images from existing semantic masks. Following the proposed re-sampling technique based...	Rebuttal 1: Rebuttal: We are sincerely grateful for your efforts and constructive feedback. We hope the concerns are well addressed. Q1-1: Novelty of our filtering and re-sampling strategies. Please refer to our global response for clarification on the filtering strategy. Please refer to our response Q2-1 to Rev...	null	null	null	null	null	null
TD Convergence: An Optimization Perspective	Accept (poster)	Summary: This work studies the TD learning algorithm from an optimization point of view which differs from the more classical fixed point Bellman operator point of view. The goal of the paper is to argue that this other viewpoint permits a better understanding of TD learning and a generalization of its convergence resu...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing that we have tackled an important research goal, for stating that our paper is insightful, clearly-written and well-organized, and also for your diligence in checking our proofs. In terms of weakness 1, we will emphasize better that one of the major contribut...	Summary: This paper studies convergence of the TD algorithm from the perspective of solving a shifting optimization problem. Through a classic failure case, the authors uncover two forces, whose interplay reveals TD's convergence properties. These two forces both depend on the state visitation distribution, the state f...	Rebuttal 1: Rebuttal: We appreciate the reviewer's carefully reading our paper as well as the overall quite positive assessment. Thanks for pointing out that the paper is clearly written and also thanks for recognizing the novelty of our work in terms of extending TD convergence proof to general $K$, as well as extendi...	Summary: The paper studies TD-learning with target network update. The authors recast the TD-learning algorithm into a time-varying optimization problem. The authors proves convergence for a function class with strong convexity and smoothness. Strengths: The paper is easy to follow and the motivation of the work is w...	Rebuttal 1: Rebuttal: We thank the reviewer for the review. In what follows, we address the particular weaknesses and questions raised. - A discussion seems to be missing whether it is a common condition to be met or not. Please see our detailed discussion in the general comment part. - The analysis on iterative opt...	Summary: The paper studies the convergence conditions for Temporal Difference (TD) learning utilizing a target network, and further extends its findings to scenarios where TD minimizes alternative losses beyond mean square errors. The study is conducted by formulating TD updates as iterative optimizations, under the he...	Rebuttal 1: Rebuttal: We thank the reviewer for the time spent carefully reviewing the paper and for appreciating our work. Please find below some clarification regarding your questions. - The paper focuses on Markov Reward Process, which is not a common setting for TD convergence proof. Why authors did not focus on e...	Rebuttal 1: Rebuttal: We appreciate the thoughtful feedback provided to us by our reviewers. All reviewers agreed that our results are clearly articulated. Notably, Reviewer 16Sb believes that the paper is of high quality and is accompanied by well-presented proofs. Also, Reviewer fasm confirms that our results are nov...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
CosNet: A Generalized Spectral Kernel Network	Accept (poster)	Summary: The authors propose a complex valued neural architecture, composed of two modules: a Spectral kernel mapping generalization module and a Complex-valued spectral kernel embedding module. They provide a generalization error bound for their model. In addition they propose a novel initialization scheme and provid...	Rebuttal 1: Rebuttal: Q: In my view, the main weakness of the manuscript is the presentation. While the approach itself seems sound, I find the presentation too poor for a conference like Neurips. Therefore I encourage the authors to re-write the manuscript and make sure it reads much better. Response: We sinc...	Summary: The paper proposes a new framework called Complex-valued spectral kernel network (CosNet) that generalizes the spectral kernel to include complex-valued representation. The proposed framework improves the representational capability of the spectral kernel and outperforms existing kernel methods and complex-val...	Rebuttal 1: Rebuttal: Q1: The initialization of CosNet's parameters varies across different datasets, and a unified initialization method is needed for consistency and reproducibility. Response: To ensure the reproducibility of our experimental findings, we unify the hyper-parameters, and the partial updated r...	Summary: The paper aims to extend the reach of kenel-based inference for time series by using a hilbert space over a complex field rather than working over the reals, by not discarding the phase component of the implied spectral representation of the kernels, which apparently is a common strategy. This method makes it ...	Rebuttal 1: Rebuttal: Thanks for your comments! We will provide a point-to point response in the rebuttal. 1 Notations In this paper, the matrices, vectors and scalars are denoted by bold capital letters (e.g. $\pmb{X}$), bold lower-case letters (e.g. $\pmb{x}$) and lower-case letters (e.g. $x$), respectivel...	Summary: This paper mainly focuses on the issue that spectral kernel-based methods often eliminate the imaginary part when analyzing the characteristics of time-sequential data. This limits the representation capability of the spectral kernel. To address this issue, the authors propose a complex-valued spectral kernel ...	Rebuttal 1: Rebuttal: Q1: In Section 3.3, the authors define the complex-valued weight matrix by Equation 11. But is unclear why this design ensures that the sub-network containing the first layer to arbitrary l-th layer is seen as a spectral kernel. Response: Thanks for this valuable comment. For our CosNet, ...	Rebuttal 1: Rebuttal: We include additional experimental result in the pdf. Pdf: /pdf/4fccb1c5cc6c34bac6a39d22f62d86beedd9e9a8.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DiffUTE: Universal Text Editing Diffusion Model	Accept (poster)	Summary: This paper describes an application of diffusion models (Sohl- Dickstein et al., 2015; Ho et al., 2020) to text editing. Methodologically, this work differs from previous text diffusion (Li et al., 2022) by leveraging insights on glyph encoder and OCR detector. Empirically, this work advances the state of the ...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and detailed review. We would like to response as below to adress your remaining concerns. > [W1] Novelty Concern. The core idea of this paper, i.e. latent diffusion, has been demonstrated to be successful in many generation tasks. Thus it is not su...	Summary: In this paper, the authors present DiffUTE, a universal self-supervised text editing diffusion model for language-guided image editing. They address the limitations of existing diffusion models by focusing on rendering accurate text and text style during image generation. DiffUTE incorporates modifications to ...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and detailed review. We would like to response as below to adress your remaining concerns. > [W1] The paper claims significantly better results than other baselines in Table 1. However, it would be helpful to clarify if there are other baselines tha...	Summary: The authors propose a method of fine-tuning Stable Diffusion to modify words in images, while maintaining the original font style and the background region. Specifically, they first fine-tune the VAE with text images from several datasets. Then, utilizing an off-the-shelf OCR detector, they randomly mask out o...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and detailed review. We are encouraged that the reviewer find that our DiffUTE 'is simple and easy to reproduce' and 'demonstrates improved performance on various evaluation metrics'. We would like to response as below to adress your remaining concer...	Summary: The paper proposes DiffUTE for general text editing. DiffUTE utilizes Stable Diffusion model with several specific model designs, progressive training strategy, positional and glyph guidance, and a self-supervised training framework. Equipped with these designs, DiffUTE achieves remarkable results compared...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and detailed review. We are encouraged that the reviewer find that our DiffUTE 'shades an interesting perspective to edit text using pre-trained Stable Diffusion model' and 'interaction module is interesting and easy to use'. We would like to respons...	Rebuttal 1: Rebuttal: We thank the reviewers for the positive reviews and constructive feedback. We thank the AC, SAC and PC for facilitating the review process. It is very encouraging to hear from the reviewers that: - Performance of DiffUTE: “exhibits impressive editing performance; has strong ability to accurately...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces DiffUTE, an innovative diffusion-based text editing framework designed to seamlessly fill in missing words in an image with user-specified text. By employing a self-supervised training framework, the model effectively learns from an extensive collection of synthetic data pairs, enabling i...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and detailed review. We are encouraged that the reviewer find that our DiffUTE 'exhibits impressive editing performance, as demonstrated through extensive experiments' and 'leveraging LLM, the model offers broad applicability across many possible app...	null	null	null	null	null	null
Incentives in Private Collaborative Machine Learning	Accept (poster)	Summary: This paper studies the problem of learning a machine learning model collaboratively under differential privacy and the incentives for parties to participate in the effort. More specifically, in the papers setting, multiple parties are sharing private sufficient statistics to a central aggregator. DP induces no...	Rebuttal 1: Rebuttal: Thank you for your detailed summary of our paper and comments. We will address some of the weaknesses and questions below and include them in the revision of our paper. > Weaknesses. Thank you for referring to Appendix I for our discussion on the truthfulness assumption. We will find the space t...	Summary: This research paper investigates the intersection of data sharing incentives and privacy concerns within the realm of collaborative machine learning (ML). Collaborative ML aims to improve model quality by leveraging diversified data from multiple parties, yet the potential benefits of this practice are often ...	Rebuttal 1: Rebuttal: Thank you for your encouraging feedback! We appreciate your detailed and accurate summary of the contribution of our work and the strengths. We also appreciate that the reviewer has recognized the novelty of our work as the process of designing incentives that take into account privacy considerati...	Summary: This paper proposes a mechanism to do single-round private collaborative model learning by several agents, each with access to a dataset. The agents do not want to share their data and instead exchange perturbed sufficient statistics of their data, which the central server must aggregate and learn from. Since ...	Rebuttal 1: Rebuttal: We thank you for your helpful comments and suggestions! We have responded to them below and hope it will improve your opinion of our work. >Weakness 1 Our paper is written in a bottom-up approach and defers information to when it is needed or the appendix: App.A.3 contains the pseudocode for sam...	Summary: From a mostly empirical angle, the paper studies a new valuation metric for incentivizing agents to share their data for collaborative ML while ensuring the data they share is Renyi-DP. Particularly, the KL-divergence between agents' prior and posterior, or the Bayesian surprise, is used as the value of the (p...	Rebuttal 1: Rebuttal: Thanks for your detailed review & questions! We will address your concerns below and include them in the revised paper. We hope our clarifications will improve your opinion of our work. > W1: Math justifications To clarify, our work includes both mathematical and empirical justifications of the ...	Rebuttal 1: Rebuttal: We thank all reviewers for the encouraging feedback that recognizes the novelty of our work. We appreciate the high-quality reviews and valuable feedback which we will consider carefully in revising our paper. In our rebuttal, we have - Clarified the (mathematical) justifications of P1-6 (Reviewe...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models	Accept (poster)	Summary: This paper proposes a method to deal with linear inverse problems using pre-trained latent diffusion models (LDM) as a prior. The main idea is to extend the original diffusion posterior sampling (DPS) to the case of LDM by approximating the gradient term of the intractable likelihood. Two approximations (GLM-...	Rebuttal 1: Rebuttal: ## Response to Reviewer xKok Dear Reviewer xKok, Thank you for the review and for pointing out that our study achieves state-of-the-art performance in addressing inverse problems with latent diffusion models, and unleashes the capacity of large-scale pre-trained LDMs for sample recovery....	Summary: This paper focuses on solving inverse problems using diffusion based probabilistic models without retraining. The authors build on "Diffusion posterior sampling" which basically builds a diffusion model for the posterior using only the score of the prior distribution. Indeed, the score of the posterior $\n...	Rebuttal 1: Rebuttal: ## Response to Reviewer S3JW Dear Reviewer S3JW, Thank you for the review and for pointing out the importance of our work in leveraging the power of latent-based diffusion models in solving inverse problems with state-of-the-art performance. Below, we provide answers to your remaining com...	Summary: While the preivous methods have focused on solving linear inverse problems based on diffusion models, the paper presents a first extension to latent diffusion models (LDM). The core idea is developed upon the existing DPS method, which forms an approximation to p(y\|xt) by using the denoising score estimate. Th...	Rebuttal 1: Rebuttal: ## Response to Reviewer yVXi Dear Reviewer yVXi, Thank you for the review and for pointing out the fact that our study proposes the first method for inverse problems with latent diffusion models, and unlocks the potential of large pre-trained LDMs for sample recovery. Below, we respo...	Summary: This paper investigates the use of diffusion models for solving inverse problems. While many recent papers have explored diffusion models for inverse problems, to the best of my knowledge, this study is the first to propose a method for inverse problems with latent diffusion. One advantage of latent diffusion ...	Rebuttal 1: Rebuttal: ## Response to Reviewer vbWT Dear Reviewer vbWT, Thank you for highlighting the fact that our study proposes the first method for inverse problems with latent diffusion models. We also thank you for your comment that the theoretical results are valuable and noteworthy and the **empirica...	Rebuttal 1: Rebuttal: ### Response to all reviewers Dear Reviewers, We thank you for carefully reading our paper and providing us with valuable feedback. Below, we summarize the reviews and newly added experiments to substantiate our contributions. (1) We are encouraged by the unanimous comment by all reviewers t...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise	Accept (poster)	Summary: The paper presents a general method for learning to de-corrupt datapoints in order to perform generative modelling. The paper proposes a sampling method that applies iterative updates to the current state that significantly outperforms a naive sampling algorithm which reconstructs and denoises alternatively. T...	Rebuttal 1: Rebuttal: > the performance of the method is just not that good in terms of sample quality. We acknowledge that the quality of images generated in Section 5 is not comparable to that of Gaussian noise as a degradation. This work is not meant to be a SOTA method paper, instead we challenge both theory frame...	Summary: This paper extends the Gaussian diffusion model toward arbitrary image-to-image translations, named Cold Diffusion. Specifically, the authors define a generalized forward diffusion process and its training process, then propose a novel Transformation Agnostic Cold Sampling (TACoS) process for generations. Exp...	Rebuttal 1: Rebuttal: We deeply appreciate your careful review and positive assessment of our work. Your recognition of the novel approach and potential impact in the field of diffusion models is highly encouraging. Below, we address the points you raised: > Is it possible to apply the diffusion process to any domain ...	Summary: This work introduces a novel approach called cold diffusion, in which both the forward and backward processes are deterministic. The authors propose a scheme called Tacos, which predicts x_{s-1} from x_s by leveraging the estimated increment D(\hat{x}_0, s) - D(\hat{x}_0, s-1). Strengths: The autors propsed n...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and for recognizing the nontrivial generalization of our work. We address each of your points below: > Higher-order terms may have a significant impact that is not adequately addressed. In lines 156-160, we mention that the analysis in Section 3.3 is not a co...	Summary: This paper introduces a method for image generation based on generic degradation and reconstruction operators. The approach generalizes diffusion models, which correspond to degradation by additive Gaussian noise, and reconstruction by denoising. In TACoS, the sampling scheme is agnostic to the choice of image...	Rebuttal 1: Rebuttal: Thank you for your detailed feedback. We address each of your points below. > Unclear practical value and unclear applications We agree with the reviewer that cold diffusion does not outcompete the much more highly engineered and compute intensive state-of-the-art Gaussian diffusion models. We a...	Rebuttal 1: Rebuttal: We thank all of our reviewers for their thoughtful comments. Based on all the suggestions, we have updated our draft and we would like to highlight a few central contributions of our work. 1. In this paper, we aim to challenge the common belief that Gaussian noise in any form, either during train...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars	Accept (poster)	Summary: The paper presents an approach for creating animatable avatars from text prompts. It builds on DreamFusion and makes it articulated by incorporating articulated NeRF and SMPL body model. It also replaces the vanilla text-to-image model (StableDiffusion) with ControlNet to introduce 3D consistent SDS loss. The ...	Rebuttal 1: Rebuttal: Thank you for the detailed feedback and questions! Below we address the questions and concerns separately. ### Q: The paper is a straightforward combination of existing methods and lacks novelty. A: Our work addresses two important problems in text-dirven avatar generation: i) SDS-b...	Summary: This paper proposes a new method for text-to-3D avatar generation. The proposed pipeline has two stages. The first stage generates a static avatar while the second stage learns the deformation properties of the avatar for animation. The authors propose 3D-consistent occlusion-aware score distillation sampling ...	Rebuttal 1: Rebuttal: Thank you for the thoughtful feedback and valuable questions! Below we address the questions and concerns separately. ### Q: Why is DreamAvatar regarded as non-animatable? A: Although DreamAvatar can repose the avatar via retraining, it is impractical for animation due to the ineffic...	Summary: The work proposes a method for generating 3D skeleton animatable characters by distilling a latent diffusion model. It uses Control Net to add additional key point map conditioning to the diffusion process, improving the granularity of pose control. It uses DreamFusion to distill a NeRF model given a text prom...	Rebuttal 1: Rebuttal: The authors are grateful for the reviewer's valuable feedback and insightful questions. We are encouraged by your support for this work! Below we address the concerns separately. ### **Q: Further evidence of animatability is needed. No animation results of complex characters (with long skirts or ...	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. Extensive experiments demonstrate that its performance outperforms existing works. Also, this work supports avatar-avatar, avatar-object, and ...	Rebuttal 1: Rebuttal: The authors are grateful for the detailed and in-depth feedback from the Reviewer. We have substantially revised the manuscript as suggested by the reviewer. Below we address the mentioned concerns separately. ### **Q: Overclaiming in Line 49-51: “for the first time capable of generating avatars ...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers and ACs for their time and efforts. Below we provide the responses to some frequently asked questions and main concerns, as well as the discussions of limitations and societal impacts. ### Q1: Contribution of our work w.r.t previous methods. A: Previous me...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels	Accept (poster)	Summary: The article presents a continuous-time optimization framework for convex objectives which streamlines the process of giving rate guarantees. Beginning from (Exact PEP), a difficult looking infinite-dimensional optimization problem. This is relaxed using convexity, and then recast in a dual form, wherein it s...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and thoughtful comments. > While the paper removes the need to produce a Lyapunov function, it introduces the need to produce the Lagrange multiplier function $\Lambda$. This does not appear to be systemized, and so it raises the obvious question to...	Summary: This paper proposes a novel methodology that analyzes ODE models for first-order optimization methods by converting the task of proving convergence rates into verifying the positive semidefiniteness of specific Hilbert-Schmidt integral operators. Based on the performance estimation problems (PEP) and functiona...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and thoughtful comments. > The results of this paper lack experimental validation. In the initial submission of our paper, we did not include experimental validations, as our primary focus is to provide a new theoretical framework for convergence ...	Summary: This paper presents a framework for analyzing convergence rates of a class of ODE models via the continuous-time performance estimation problem (PEP). The task of solving the PEP problem is relaxed into verifying the positive semidefiniteness of specific integral operators. The convergence rates of several acc...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments. > The intuition to consider the PEP kernel ... We want to clarify that selecting the multiplier functions is not such a challenging task. Although not explicitly mentioned in the paper, there is a rule of thumb for choosing $\Lambda$ (or $\lamb...	Summary: This paper provides a new technique (that is fundamentally different from the Lyapunov approach) for systemically analyzing the convergence rates of ODE models for first-order optimization methods, which reduces to verifying the positive semidefiniteness of specific Hilbert-Schmidt integral operators. This is ...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and thoughtful comments. > One of the important features of the discrete PEP is that one can numerically find the values of Lagrange multipliers by numerically optimizing them, which can be used to reveal an analytical form of Lagrange multipliers....	Rebuttal 1: Rebuttal: Dear all reviewers, # Figures The attached PDF file contains the figures mentioned in the rebuttals: Visualization of PEP kernels, and numerical experiment for the convergence rate of AGM-SC ODE obtained in Section 4. # Relaxing assumptions in Theorem 2 We have relaxed the assumption (16) in T...	NeurIPS_2023_submissions_huggingface	2,023	Summary: Continuous counterpart of the work by Drori and Teboulle [2] was presented. Specifically, through the dual objective of the relaxed PEP in continuous time, the convergence rates of various ODEs were obtained. The analysis on the Lagrangian dual will lead to a dual solution based on a symmetric kernel of the Hi...	Rebuttal 1: Rebuttal: Q2. In Summary: Our continuous PEP is intrinsically associated with certain Lyapunov functions. However, if you are asking about the conventional Lyapunov function argument, where the Lyapunov function takes specific forms like $\mathcal{E}(t)=a(t)(f(X(t))-f(x^)+b(t)\Vert Z(t)-x^{}\Vert^{2},...	null	null	null	null	null	null
Learning Regularized Monotone Graphon Mean-Field Games	Accept (poster)	Summary: The paper focuses on two fundamental problems in regularized Graphon Mean-Field Games (GMFGs). The first problem is to establish the existence of a Nash Equilibrium (NE) of any $\lambda$-regularized GMFG (for $\lambda \geq 0$). The second problem is to propose provably efficient algorithms to learn the NE in w...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We address the major concerns in the following. Relationship between NEs of $\lambda$-regularized GMFGs and MFGs To build an NE of $\lambda$-regularized GMFGs from an NE of the constructed $\lambda$-regularized MFG, we take the position of the...	Summary: The paper analyzes policy mirror descent for solving regularized GMFG. The results provide new guarantees for learning GMFG without stringent oracle assumptions, and unlike some past works, it does not restrict the results to continuous time analysis. Furthermore, the paper provides an analysis of the case of ...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We address the major concerns in the following. Empirical Action-Value Function Estimation and Simulation Benchmark Our work focuses on the optimization complexity and the sample complexity of the algorithms. The efficacy of our proposed algor...	Summary: Intuitively, a "graphon mean field game" (GMFG) describes the large-$N$ limit of a game with $N$ players, where the payoff of a player $i$ depends on a weighted average of the states of other players $j\in [N]$. The graphon aspect comes from the fact that players have "identities" given by numbers $U_i\in [0,1...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We address the major concerns in the following. Lipschitz Continuity of $W$ We thank the reviewer for this suggestion. For the proof of Theorem 1, the Liphschitz assumption on $W$ is only used to establish (D.5), and this can be proved with co...	Summary: This paper studies regularized Graphon Mean-Field Games (GMFGs). They make two theoretical improvements over previous works on this topic: * They prove existence of Nash equilibrium under weaker assumptions (e.g., weaker requirement on the continuity of the game) than previous works. * For the special case of ...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We address the major concerns in the following. Detailed Comparison of NE Existence Conditions Theorems 1 and 2 in our work, Proposition 3 in [1], and Proposition 3 in [2] all derive the existence of Nash Equilibrium (NE) in the regularized M...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
An Inductive Bias for Tabular Deep Learning	Accept (poster)	Summary: The authors address the problem of fitting deep nets to tabular datasets. This is a challenging task due to the heterogeneity of tabular datasets. Following a recent work, the authors first demonstrate that tabular data require learning prediction functions with nonnegligible high-frequency components. Since d...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments. Please see our responses below. **Comment 1: “My biggest concern is the empirical evaluations conducted in the paper. It is focused on relatively low dimensional datasets, and with a large sample size, this regime is typically less challenging fo...	Summary: the paper “An Inductive Bias for Tabular Deep Learning” presents an interesting exploration of inductive biases for deep learning applied to tabular data. The paper introduces a novel inductive bias, named frequency reduction, which is specifically designed for tabular data. The authors propose a novel approac...	Rebuttal 1: Rebuttal: **Comment 1: “Lack of theoretical analysis: The paper lacks a deeper theoretical analysis of the proposed inductive bias. While the empirical results are convincing, a more thorough theoretical explanation of why and how the approach works would enhance the paper’s contribution. Including a theore...	Summary: # Summary The paper introduces a hypothesis that tabular datasets are best described by functions with high frequency. They connect this finding to existing empirical knowledge in the tabular space, and introduce formal tools to measure spectral properties of target functions in tabular data. The authors prop...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments. Please see our responses below. **Comment 1: “A major limitation of the empirical results in the experiments section is that the authors only show results on an assortment of tabular datasets that are themselves heterogeneous and have a number of...	Summary: This paper proposes an inductive bias for tabular deep learning to bridge the performance gap between deep learning and tree-based methods on tabular data by reducing the frequency of irregular target functions through scaling and ranking transformations. Deep learning methods underperform tree-based methods o...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments. Please see our responses below. Q1: “How does the proposed method compare with other existing methods for improving the performance of neural networks on tabular data, such as feature engineering or model ensembling?” We believe that the do...	Rebuttal 1: Rebuttal: Dear reviewers, we thank you for all of your constructive feedback. In this general response, we would like to address some questions/concerns that arose from multiple reviewers. We address specific questions further on their corresponding threads, **Focus of our work and its impact on the exper...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection	Accept (poster)	Summary: The work looks at leveraging gradient-level attribution information in order to detect semantically shifted OOD samples. In particular, the paper proposes two post-hoc OOD detection methods that leverage the extracted gradient attribution called GAIA-A and GAIA-Z. Both the proposed GAIA-A and GAIA-Z methodolog...	Rebuttal 1: Rebuttal: # Response to Reviewer CiZ8 We appreciate your thoughtful review of our work. And we address your questions below: > Q1: Additional runs of the method under models trained with differing seeds. Thank you for providing valuable suggestions. For the CIFAR benchmarks (CIFAR10 and CIFAR100), **we ...	Summary: The proposed gradient-based attribution method in this paper is a promising approach that can help distinguish between ID and OOD patterns. By analyzing the uncertainty that arises when models attempt to explain their predictive decisions, the method can provide a more robust and reliable approach to detecting...	Rebuttal 1: Rebuttal: # Response to Reviewer qAeX Thank you for your constructive feedback. Before addressing your concerns, we believe there might be some misconceptions about our method that need clarification. We will begin by clarifying your certain viewpoints. > Clarification point 1: **Our methods (both GAI...	Summary: In this paper, the authors propose a novel perspective for quantifying the disparities between in-distribution (ID) and out-of-distribution (OOD) data by analyzing the uncertainty that arises when models attempt to explain their predictive decisions. They investigate the abnormality in gradient-based attributi...	Rebuttal 1: Rebuttal: # Response to Reviewer kfJE We sincerely appreciate your valuable feedback on our paper and thank you for taking the time to review it. In response to your review, we have addressed the issues raised and made improvements to enhance the clarity, organization, and overall quality of the paper. Bel...	Summary: This paper presents an approach to OOD detection in deep neural networks. The authors propose a method based on analyzing the uncertainty that emerges when models attempt to rationalize their predictive decisions. The abnormalities are found by using two strategies: the zero-deflation abnormality that takes ad...	Rebuttal 1: Rebuttal: # Response to Exp8 Thank you for your positive evaluation of our paper. Below, we will address each of your questions: >Q1: Could the authors provide a comparison with other gradient-based methods? Regarding the comparative table, please refer to table 1 in the PDF of the global rebuttal. A...	Rebuttal 1: Rebuttal: We appreciate all the reviewers' time and valuable feedback. We are delighted that the reviewers found our article to be clear, easy to read (R1, R2), and regarded our method as both simple and effective (R1, R3, R4, R5). It is also great to hear that our findings are **interes...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors present an innovative perspective on quantifying the disparities between in-distribution (ID) and out-of-distribution (OOD) data. The authors observed that gradient-based attribution methods face challenges when assigning feature importance to OOD data, leading to significantly diver...	Rebuttal 1: Rebuttal: # Response to Reviewer TjRi Thank you for your constructive feedback. We are pleased that you find our presentation clear and easy to follow. And we address your questions below: **For the open questions, we have included the replies in the global rebuttal due to the limit constraints of the re...	null	null	null	null	null	null
Binarized Neural Machine Translation	Accept (poster)	Summary: They propose a novel binarization technique for Trans3 formers applied to machine translation (BMT), the first of its kind. They identify and address the problem of inflated dot-product variance when using one-bit weights and activations. Specifically, BMT leverages additional LayerNorms and residual connectio...	Rebuttal 1: Rebuttal: Q1: How the training and inference efficiency change compared to the float model is not discussed. Thanks for commenting on the efficiency side. It is not accessible because we are not aware of an ecosystem (accelerator combined with software stack) that supports such measurement for 1-bit mo...	Summary: The paper proposes a novel quantization scheme to binarize transformer machine translation models. The method consists of inserting additional layer normalization for activations and also additional residual connections. The authors demonstrate good results on the WMT test set especially for weight-only bina...	Rebuttal 1: Rebuttal: Q1: Binarized activation results are still poor. Yes, as highlighted in line 237 in Section 4.1, one challenge we identified in this work is, more precisely, that the attention layer activations are the bottleneck to a high-quality binarized Transformer for a sequence generation task. We hope...	Summary: The authors introduce a new technique for binarization in Transformers that can be applied to machine translation known as Binarized Neural Machine Translation (BMT). They have adapted the binarization functions and training methods from PokeBNN to help address the "inflated dot-product variance" issues that a...	Rebuttal 1: Rebuttal: Q1: What’s the difference between the proposed layernorm in Section 3.4 compared to the existing pre-layernorm Transformer? Note that pre- or post-layernorm for the Transformer architecture indicates the layernorm position outside of the entire FFN module. Whereas in our proposal, each li...	Summary: This paper presents a binarized neural translation model based on an encoder-decoder structure. The proposed method initially analyzes the challenges associated with binarized encoder-decoder models. The primary challenges arise from the significant impact of binarizing both weights and activations on result v...	Rebuttal 1: Rebuttal: Q1: Are 8-bit or 4-bit subject to the scaling law? Thanks for pointing out this comparison and we will add it to the scaling law section. 8-bit and 4-bit models are studied more often and they do exhibit a scaling law where larger models yield better performance [1]. However, in the previous ...	Rebuttal 1: Rebuttal: We thank all reviewers for their positive feedback, considering our problem analysis and empirical experiments as a good contribution to the community. We also appreciate all comments and suggestions. We will address questions below in separate threads.	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work proposes to binarize matrix multiplication for significantly saving memory and, thus, reducing latencies at inference time that is crucial for serving encoder-decoder model. Basic idea is to employ a binary variant of weights and inputs with scaling parameters in feed-forward and multi-head attention...	Rebuttal 1: Rebuttal: Q1: I understand the condition might be varied, it is better to measure the speedup of the proposed method. Thank you for commenting on the speedup measurement. It is not accessible because we are not aware of an ecosystem (accelerator combined with software stack) that supports such measurem...	null	null	null	null	null	null
Non-Convex Bilevel Optimization with Time-Varying Objective Functions	Accept (poster)	Summary: This paper studies bilevel optimization in an online setting where the objective in both the levels are allowed to vary with time, and the goal is to develop an algorithm with sublinear regret. This paper proposes a practical single-loop algorithm that updates the lower-level variable only once for each upper-...	Rebuttal 1: Rebuttal: Thank you for your thorough reviews and constructive comments. We provide our response to your comments below. If our response resolves your concern, we would greatly appreciate it if you could consider increasing your score. Q1: Hyperparameters in the definition of regret. Is it standard in dyn...	Summary: The authors consider bilevel optimization in the online setting. In this setting, we have access at iteration $t$ to the outer function $f_t$ which is assumed to be differentiable and possibly nonconvex. We also have access to the inner function $g_t$ which is assumed to be twice differentiable and strongly-co...	Rebuttal 1: Rebuttal: Thank you for your thorough reviews and constructive comments. We provide our response to your comments below. Q1: The idea of single-loop updates was already exploited in offline context [1, 2, 3]. The authors should mention it. A1: Thank you for bringing up these studies. We will add them in ...	Summary: This paper proposed a new method for solving online bilevel problem that only required one-step $y$ update and leveraged the historical information to smooth the update. Extensive experiments are provided to validate their theories. Strengths: 1. This work is the second one considering the online bilevel opti...	Rebuttal 1: Rebuttal: Thank you for your thorough reviews and constructive comments. We provide our response to your comments below. If our response resolves your concern, we would greatly appreciate it if you could consider increasing your score. Q1: Is it possible to characterize these two terms explicitly by the v...	Summary: This work studies the online bilevel optimization problem with nonstationary and time-varying objective functions. This line of research can cover applications with online nature like online meta-learning, online hyperparameter tuning, wireless networks. Compared to widely studied offline bilevel problem, the ...	Rebuttal 1: Rebuttal: Thank you for your thorough reviews and constructive comments. We provide our responses to your comments below. Q1: Is it possible to design fully first-order methods without matrix-vector computations? A1: Many thanks for the insightful comments and suggestions. Our current algorithm involves ...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration	Accept (poster)	Summary: This paper discusses the limitations of contrastive learning (CL) in multi-view scenarios and proposes a novel framework called SElf-weighted Multi-view contrastive learning (SEM) to address these limitations. The contributions of SEM framework are as follows: - Alleviating representation degeneration: In mult...	Rebuttal 1: Rebuttal: Response To Reviewer tV6r: >Q1: I have some concerns regarding the rationale behind introducing the reconstruction module. If the aim of incorporating reconstruction is to enhance the discriminative information of the representation, why does the process involve an additional encoder after obtain...	Summary: This paper researches the representation degeneration of multi-view contrastive learning. To address it, this paper proposes a simple but effective framework of self-weighted multi-view contrastive learning. Strengths: ++The manuscript is well-written and self-consistent. For example, the visualization analys...	Rebuttal 1: Rebuttal: Response To Reviewer dnwM: >Q1: Figure 5(a) shows that weights are updated dynamically in different iterations. Here, are the weights incrementally and linearly increased or are they only updated 4 times? We are sorry that the illustration of Figure 5(a) is not clear enough. The weights are only...	Summary: In this paper, the authors show that the representation degradation could limit the application of contrastive learning in multi-view scenarios. To mitigate this issue, they propose the self-weighted multi-view contrastive learning, a general framework that has different options in the contrastive loss, weight...	Rebuttal 1: Rebuttal: Response To Reviewer USXC: >Q1: Are the representations learned from useful pairwise views artificially selected to evaluate the performance of downstream clustering tasks? No. To comprehensively evaluate the performance, our experiments use the concatenation of all learned representations from ...	Summary: This work proposed the SEM: SElf-weighted Multi-view contrastive learning framework, which first performs discrepancy measures on representations, and then obtains weights to assist adaptive contrastive learning. Meanwhile, the decoders are leveraged to avoid losing discriminative information. Extensive experi...	Rebuttal 1: Rebuttal: Response To Reviewer tvpR: >Q1: To reduce losing information, the proposed method treats the last layer of encoders as hidden features. It's not clear exactly which layer is as H, and which layer is as Z. Thanks for this valuable comment. We present the network setting in Appendix B (Page 7). Sp...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: In summary, this paper investigates an important question about how to mitigate representation degradation in multi-view contrastive learning. Considering the quality difference of views and losing useful information during nets, the proposed method uses adaptive weighted contrastive learning and adds informat...	Rebuttal 1: Rebuttal: Response To Reviewer gird: >Q1: Due to the high complexity of MMD computation, it seems difficult to obtain MMD results on YoutubeVideo dataset (over 100,000 samples). Yes, it is indeed difficult to obtain weights of the MMD weighting strategy on YoutubeVideo. Therefore, for reducing the computa...	null	null	null	null	null	null
Stochastic Approximation Algorithms for Systems of Interacting Particles	Accept (poster)	Summary: This paper analyses discretisations of mean-field type SDEs arising in several areas of machine learning. The main contribution is a convergence result (Theorem 1) stating that under appropriate conditions on the drift and diffusion coefficients, the discretised dynamics convergence in 2-Wasserstein distance, ...	Rebuttal 1: Rebuttal: Thank you for your valuable and insightful review, especially for pointing out the missing references. We will incorporate Reviewer's recommendation in the revision accordingly. > There is a rich body of literature on SDE discretisation scheme for McKean-Vlasov SDEs and interacting particle syste...	Summary: This paper develops a theoretical mathematical framework to characterize the convergence properties of discrete particle systems to their mean-field limit. Strengths: The mathematical theory in this paper is beyond my scope, but it appears to be mathematically sound. The paper is well-written. Weaknesses: I ...	Rebuttal 1: Rebuttal: We thank the Reviewer for raising issue of practical relevance. We have taken this concern seriously and made the necessary adjustments to address it in the rebuttal below, which will be incorporated into the revision. Having addressed the concerns and made the appropriate changes, we sincerely ...	Summary: This work considers the convergence of discrete time interacting particle systems to their respective continuous time limits (i.e, McKean-Vlasov type equations) under general assumptions which are applicable to varied contexts like neural networks, kinetic theory, game theory and sampling algorithms. The fini...	Rebuttal 1: Rebuttal: Thank you for your valuable input and remarks. We are dedicated to addressing your concerns through revisions in our upcoming review. Having taken all your feedback into account, we kindly ask for your consideration in potentially revising the score. > The notation in the algorithmic template is ...	Summary: This paper fills a theoretical gap between the application of ideas interacting particle systems to algorithms in machine learning--algorithms, like SGVD, that are almost always realized as discrete-time routines with a finite number of particles--and the substantial existing body of theoretical work on finite...	Rebuttal 1: Rebuttal: Thank you for your input and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the upcoming revision. > In terms of the overall presentation of results, I was surprised to see interacting particle systems (IPS) as the frame for this theory rather than sim...	Rebuttal 1: Rebuttal: Dear AC, dear reviewers, We deeply appreciate your time, input, and thoughtful critiques, as well as your positive evaluation. Your contributions have our sincere gratitude, and all your questions are addressed in a separate point-by-point thread below. A focal concern that has emerged pertains...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On the Planning Abilities of Large Language Models - A Critical Investigation	Accept (spotlight)	Summary: This paper conducts a systematic study by generating a suite of instances on domains similar to the ones employed in the International Planning Competition and evaluate LLMs in two distinct modes: autonomous and heuristic. The experiments show that LLMs' ability to generate executable plans autonomously is rat...	Rebuttal 1: Rebuttal: We would like to thank reviewer Y3FL for their thoughtful feedback. We are glad that the reviewer found our work to be well-written, detailed and interesting. We will incorporate all the reviewer's suggestions such as citing other relevant papers. Below, we provide responses to some of the concer...	Summary: This paper provides a systematic evaluation of the Planning abilities of a class of LLM (GPT series until the latest GPT-4), using standardized planning problems such as those provided in symbolic planning competitions. It analyzes LLM as autonomous planners, but also as heuristic planners providing suggestion...	Rebuttal 1: Rebuttal: We would like to thank reviewer GZ9T for their detailed feedback. We are glad that the reviewer found our work to be systematic, comprehensive and likely to be a standard. We will incorporate all the reviewer's suggestions such as referencing other relevant papers and additional justifications. B...	Summary: This work evaluates the planning abilities of LLMs in two distinct settings: (1) As generators of final plans, with or without feedback from a validator, and (2) as generators of seed plans which are then corrected by a standard planner. The evaluations are performed on two commonsense domains for which human...	Rebuttal 1: Rebuttal: We thank reviewer JKYr for their valuable comments. We are glad that the reviewer found our work to be detailed and well-presented. Below, we provide responses to the questions raised by the reviewer. > Line 97 says that “our approach of specifying the domain as part of the prompt ensures that the...	Summary: The paper investigates the (lack of) capabilities of pretrained LLM for solving classical well-known planning benchmarks. They study both the case of fully autonomous LLM without any external feedback and the case of using external tools, for validation feedback or as a seed for improving an external planner. ...	Rebuttal 1: Rebuttal: We would like to thank reviewer NTC1 for their valuable comments. We are glad that the reviewer found our work to be important and comprehensive. Below, we provide responses to the concerns raised by the reviewer. > 1. Are there any indications of how LLM would perform in problems with shallow pl...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful feedback. We have provided our responses separately for each reviewer. We have attached a PDF containing the images and tables which we refer to in the individual responses. Pdf: /pdf/7840395a675841768dfab06a742c8009695aa510.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Explain Any Concept: Segment Anything Meets Concept-Based Explanation	Accept (poster)	Summary: This paper proposed EAC, which lines up SAM with XAI. The technique includes there phases: (1) generates concepts with SAM; (2) trains a surrogate model to represent the target model, using the same FC layer; (3) regards results of SAM in the first phases as players and calculates Shapley values with the surro...	Rebuttal 1: Rebuttal: Q1: explain the trade-off in sec 3.1 A1: Sorry for the confusion. Our responses follow: First, the “trade-off” primarily refers to previous super-pixel based XAI methods (the mainstream), because it’s generally hard to decide an “one-size-fit-all’ superpixel size for previous works. We pres...	Summary: This work proposes EAC to study the interpretability of models. Instead of making element-wise explanations, EAC segments an input into sub-parts, then uses Shapley value to characterize important features for a model decision. User studies were conducted to show the explainability ability of this method. Str...	Rebuttal 1: Rebuttal: Q1: EAC built upon LIME, compromise technical novelty A1: Thanks for the comment. Indeed, we found that the binary feature expression by LIME is very inspiring, such that we adopted those expressions in our pipeline. Nevertheless, there are two main major differences between our work EAC and...	Summary: This article proposes EAC, which aims to use the Segment Anything Model to generate some prior concepts. By constructing a surrogate model, the concept combination area most relevant to the decision category is calculated by Shapley Value. The author evaluates the proposed model from three perspectives: faithf...	Rebuttal 1: Rebuttal: Q1: compare our results with GradShap+SAM or FastShap+SAM. A1: Thank you for this insightful question. We indeed explored this direction before, and our preliminary observation shows that this is unpromising; please see our response to Q2 below on the conceptual-level clarification of “...	Summary: The paper introduces Explain Any Concept (EAC), a concept-based explanation approach that enhances the interpretability of deep neural networks in computer vision. EAC utilizes the Segment Any Model (SAM) as a concept discovery technique, and the authors propose a lightweight Per-Input Equivalence (PIE) scheme...	Rebuttal 1: Rebuttal: Q1: why not include some knowledge-specific domain dataset to eval? A1: Thank you for your comments, and we agree with you. Yes, our observation and exploration also show that the Meta SAM model was only trained on the general image domain, and it may struggle to segment images in knowledge-s...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Latent SDEs on Homogeneous Spaces	Accept (poster)	Summary: In this paper the authors develop machinery for performing variational inference on latent functions in models where observations are generated by a latent stochastic process. That is, the generative model is that a path in some latent space is generated according to a prior, and then we observe (noisy) value...	Rebuttal 1: Rebuttal: Below, we address all points and outline how we will revise our manuscript. If there are further questions, we are more than happy to answer. > ... (2) is listed as the objective function, but then it's not clear what the objective function would be for the regression and classification tasks ... ...	Summary: This paper deals with the problem of variational inference for sequential data, i.e. time series, using latent SDEs. The idea of this class of methods is to assume the stochastic process that is observed is related to a generative SDE in a latent space, whose parameters need to be inferred from data in a Bayes...	Rebuttal 1: Rebuttal: First, we thank the reviewer for the positive feedback! The suggestion to extend our method to hyperbolic spaces $\mathbb H^n$ is quite interesting and opens up a new direction of future applications that we did not think of so far, e.g., relativistic dynamics in Minkowski space-time. The hyperbol...	Summary: The authors are interested in learning neural SDE models. Instead of parameterising arbitrary latent SDEs, the authors restrict their attention to homogeneous spaces, and in particular the unit sphere, in order that they can leverage the transitive group (the Lie group) to construct an SDE in the space in term...	Rebuttal 1: Rebuttal: >(major) The method is claimed to be efficient for learning, but no evidence is provided to support this claim -- the experiments as they are demonstrate that the method can produce a test performance which is competitive with apparently more flexible approaches, and that the relative performances...	Summary: The paper provides an affirmative answer to a very natural and intriguing question: Can we simplify the underlying latent model describing the dynamics of a temporal process so that it can overcome the computational and technical challenges with neural ODEs/SDEs while accurately modeling the real-world phenome...	Rebuttal 1: Rebuttal: Reviewer GYzQ identified two weaknesses in our submission that we will address below. > The paper claims in the introduction that their approach significantly reduced computing efforts ... To address the remark on significantly reduced computing effort, we reran our experiments on the rotated ...	Rebuttal 1: Rebuttal: We like to thank all reviewers for their overall positive feedback, valuable comments and suggestions! While we address all issues point by point per reviewer, we first like to clarify issues that are common to (almost) all reviews: (♠) to substantiate the computational claims from the ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly	Accept (poster)	Summary: This paper introduces a benchmark, called TA-Bench, for transfer-based attacks. The authors implement 30+ transfer-based attack methods that are mostly proposed in the last 3 years. This paper takes several aspects of transfer-based attacks into consideration, including augmentation, optimizer, substitute mode...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Except for the comment about "submitting to the benchmark track" which is addressed in our general response, all the comments are replied to as follows.   > The recent paper "Reliable Evaluation of Adversarial Transferability" by Yu et al. also provides ...	Summary: This paper establishes a transfer-based attack benchmark (TA-Bench) so that researchers could take advantage of this to comare different methods systematically, fairly and practically. TA-Bench implements 30+ methods and evaluate on 10 popular victim models (architecture) on ImageNet. Strengths: 1.The paper...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Except for the the questions about the the D&B track which is answered in our general response, all your comments are replied as follows.   > The benchmark focuses on ImageNet. Is it possible to extend to other datasets, smaller (MNIST) or larger (JFT300...	Summary: This paper explores the problem of adversarial transferability evaluation on image classification tasks. The authors feel that there are a large number of migration attacks, but this community lacks a standard benchmark. Therefore, this paper establishes a transfer-based attack benchmark (TA-Bench) which imple...	Rebuttal 1: Rebuttal: Thanks for the feedback. Except for the questions about defense methods, targeted attacks, and the taxonomy which are answered in our general response, all your comments are replied to as follows.   > We think it is best for the authors to start with the challenge of transfer attacks to ...	Summary: The paper proposes a new benchmark of techniques designed to increase the transferability of adversarial examples. The paper implements more than 30 of these techniques to compare the success rate of the corresponding attacks. The paper identifies several flaws of current evaluation protocols, for example, not...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Except for common questions which are answered in our general response, all your comments are relied to as follows.   > Unfortunately, the review of techniques is not exhaustive. A: Thanks for pointing out these methods. The implementation of "UN" i...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for the valuable feedback. Our responses to some common questions are given as follows.   > The codebase. A: As promised in the paper, the codebase will be made publicly available. With the codebase, APIs are directly provided for evaluating atta...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper is a benchmark paper on transfer-based attack in the area of adversarial machine learning. The paper benchmarks 30+ methods on ImageNet, grouped into four principal categories: augmentation and optimizer; gradient computation; substitute model training, and generative models. The extensive evaluatio...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Our responses to the comments are given as follows.   > One concern is the absence of significant novel insights. A: The novelty of our paper shows in several different aspects. First of all, our work develops a new combination of input augmentatio...	Summary: In this paper, the authors present a new transfer-based attack benchmark (TA-Bench) to evaluate the transferability of adversarial attacks. TA-Bench implements 30+ adversarial attacks with 10 substitute models and introduces more advanced optimization back-ends that incorporate augmentation and different choic...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Except for the question about codebase which is answered in our general response, all comments are replied to as follows.   > It is worth discussing the standards and metrics for the evaluation. However, I don't find an explicit explanation of such stand...	Summary: In this paper, the authors have presented benchmark for transfer-based attacks, in which they have implemented 30+ advanced transfer-based attack methods, including those focus on augmentation and optimizer innovation, those “gradient computation” methods, those “substitute model training” methods, and those a...	Rebuttal 1: Rebuttal: Thanks for the positive feedback. Our responses to the comments are given as follows.   > There is only one type of dataset used in the experiment, which makes results lack credibility. A: We focus on ImageNet first for several reasons. First of all, almost all papers studying trans...	null	null
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex	Accept (poster)	Summary: The paper discusses the observation that improved imagenet classification performance is no long correlated with neuron prediction performance in macaque IT. To validate this claim, the authors perform experiments where an image was moved across the visual field while the monkey maintained fixation. This pro...	Rebuttal 1: Rebuttal: > The paper provides few details on how exactly the images were presented to DNNs. For the monkey presentation, fixation was maintained and the images were shifted. Line 141 onwards provides an explanation, however it is very unclear how exactly you perform this step. We apologize for the lack of...	Summary: This work address an important question in the construction of computational models of object recognition: the increase in performance of recent DNN models is not anymore accompanied (like in the past) by an increase in their ability to predict neural responses. This is a very relevant problem for the advancem...	Rebuttal 1: Rebuttal: > Is it possible that, as a speculative question, the mismatch between the different features learned by DNNs and IT can be partially accounted for by the usage of backpropagation? Fantastic question. As we wrote in our discussion, we believe that a wholesale revision of DNN training routines may...	Summary: This work summarizes the trend in DNN models of biological vision that networks that perform better on imagenet no longer necessarily provide better fits to neural data. It also shows neural-harmonized models do provide better fits to a data of mostly face-selective neurons. Strengths: Neural harmonizing mak...	Rebuttal 1: Rebuttal: > The framing of the paper (particularly the title) suggests that this paper is making a novel claim about DNN performance, when in fact this claim is based on a re-plot of BrainScore data and has been made before (in ref 11 and here: https://www.biorxiv.org/content/10.1101/688390v1). While it is ...	Summary: This paper investigates the general finding that modern deep neural network architectures have worse predictions of primate IT cortex, even though they perform better at object recognition. The authors investigate a set of IT recordings that incorporate spatially resolved population maps, and show that the bes...	Rebuttal 1: Rebuttal: > The authors present the lack of a correlation between neural predictivity and brain responses for modern models as a new finding, however this is a generally known phenomena mentioned in various papers as motivation for developing better metrics of similarity. Thank you for this comment. We add...	Rebuttal 1: Rebuttal: # Response to all reviewers We thank the reviewers for their extensive feedback. We are confident that we have addressed their main critiques, which we summarize below along with our responses (the relevant reviewers are in parentheses): (nyjr) How was the best layer for each model selected?...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Causal Imitability Under Context-Specific Independence Relations	Accept (poster)	Summary: The paper studies causal imitation learning. In particular, it extends traditional causal graphs with context specific independence. Although the original causal imitation learning problem can be reduced to d-seperation test, causal imitation learning with context specific independence is NP hard. But under ot...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback, and we are delighted to learn that they found our contribution to be solid. Below, the main points and questions raised by the reviewer are addressed. --- >Experiments are relatively weak as they are only tested on synthetic datasets. Indeed, ou...	Summary: This paper studies the problem of causal imitation learning, where the goal is to construct a policy that replicates the outcomes of an expert. The challenge is that the expert may e.g., make decisions based on unobserved variables. Prior work (e.g., [36] as cited in this paper) has established graphical con...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough feedback. We acknowledge their positive feedback regarding the novelty and clarity of our work. We will now turn our attention to the points raised by the reviewer. --- >The main weakness of this paper, in my view, is the plausibility of finding context-s...	Summary: This paper explores the potential benefits of incorporating context-specific independence (CSI) information into causal imitation learning, where CSI relations are known. The authors prove that the decision problem for the feasibility of imitation in this setting is NP-hard, provide a necessary graphical crite...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable input and appreciate the positive feedback regarding the significance of our work. We address the main points and questions raised by the reviewer below. --- Regarding the assumption of Proposition 3.9 >I believe that the assumption used in Proposition...	Summary: This paper extends studies on causal imitation learning to settings in which additional information can be provided in the form of context-specific independences (CSIs). Causal imitation learning seeks to maximize some unobserved reward $Y$ by finding a policy $\pi^*$ from the space of policies $\Pi$ such that...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable input. Regarding NP-hardness and polynomial-time solutions We agree that providing settings where a polynomial-time solution exists is insightful, and we thank the reviewer for this suggestion. We have come up with certain restrictions that allow for ...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Nearly Optimal Bounds for Cyclic Forgetting	Accept (poster)	Summary: Authors provide theoretical bounds on the forgetting quantity in the continual learning setting for linear tasks, where each round of learning corresponds to projecting onto a linear subspace. For a cyclic task ordering on $T$ tasks and an arbitrary iteration $m$, they prove the upper bound of $O(T^3/m) $ on t...	Rebuttal 1: Rebuttal: Notation: We will clarify the notation in the introduction as suggested. Gap of $T$: To clarify: The upper bound given in our paper has worse $T$-dependence than the lower bound given in [Evr+22], but it has the same $T$-dependence as their upper bound for any fixed dimension. Our bound of $O...	Summary: The paper studies the setting of continual learning for linear tasks, and in particular the phenomena of catastrophic forgetting. They prove the best known upper bound on forgetting for the setting of cyclic tasks. Strengths: The paper demonstrates an upper bound on Forgetting that is independent of the dim...	Rebuttal 1: Rebuttal: Significance: We would be happy to add a remark about other settings and compare our approach to that of [Evron et al.22]. The challenge with experimental results is that our bounds are worst-case bounds. Empirically, nearly-worst-case examples seem to be quite infrequent. However we could include...	Summary: This paper describes bounds for cyclic forgetting when an overparametrized linear model is fit successively to a series of tasks. The exact same setting has been studied fairly recently before in the , and the main contribution here is to improve dimension dependence of the bounds. Strengths: The theory of Th...	Rebuttal 1: Rebuttal: Precedent for Problem: There are indeed motivations for our work discussed in referenced paper [Evr+22], that we list here, and will elaborate on in our introduction. Many data sets in machine learning are cyclic or periodic in nature, for example, due to the "day of the week effect" in financi...	Summary: Consider an overparametrized system solving a periodic sequence of tasks in linear (least squares) regression in the following way: starting from a weight vector $w_t$ for task $t$, perform gradient descent to solve task $t+1$, which, due to overparametrization, will eventually return an exact solution $w_{t+...	Rebuttal 1: Rebuttal: Technical Proofs: As Reviewer eHkq suggested, we will move part of the proofs to the appendix and include a high level proof in the main body. We summarize the proof of Theorem 5 as follows: To compute the range of $P$, we show that the outer boundary of $P$ is the claimed sinusoidal spiral and sh...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback. The following are our responses to the questions and concerns raised by reviewers. We will move the proofs of the main lemmas and theorems (through Theorem 4) to an appendix. This will leave room for additional discussion of the connection to previous ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning	Accept (poster)	Summary: The authors consider a distribution over transition models and tackle the safe RL problem by applying a risk-averse perspective towards model uncertainty through coherent distortion risk measures. The proposed formulation can ease the burden of solving a min-max problem, which is often encountered in many wors...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. Please see below for detailed responses to your questions. In particular, we highlight the main benefits of our RAMU framework and the key takeaways of our experimental results. We hope that these responses address your main concerns, and we ask t...	Summary: This paper proposes a methodology for distributionally robust RL via the use of risk measures and leveraging risk (Fenchel) duality, dealing with what they call model uncertainty. The paper introduces the RAMU Q function and Bellman operators respectively, which are based on modifying the standard risk-neutral...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We are glad that you enjoyed reading it, and found our RAMU framework to be interesting and effective. We also appreciate your thoughtful suggestions, which will help to improve our paper. Please see below for responses to all of your questions an...	Summary: This paper presents a Temporal Difference (TD) learning method for addressing the ``Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning'' problem. Specifically, the authors consider a Constrained Markov Decision Process (CMDP) combined with Bayesian uncertainty sets. They are ...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We are glad you agree this is an important problem, and we appreciate your comments on our paper’s clear presentation and strong experimental results. Please see below for responses to your questions. We hope that these clarifications address your...	Summary: The paper introduces a deep reinforcement learning framework for safe decision-making in uncertain environments. The authors propose a risk-averse approach towards model uncertainty using coherent distortion risk measures. They provide robustness guarantees for the framework by showing its equivalence to a dis...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We are glad you found the paper to be clear and well-written, and appreciated the theoretical support for our proposed framework. Please see below for responses to your questions, which clarify the importance of both safety and robustness. If thes...	Rebuttal 1: Rebuttal: Thank you to all of the reviewers for their thoughtful feedback. We are excited to see the reviewers agree that the paper is clear and well-written (ZX7b, mRCw, 22QQ), proposes a novel framework with a practical and efficient implementation (F3rP, 22QQ, JpRL), and provides strong theoretical (ZX7b...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces the Risk-Averse Model Uncertainty (RAMU) framework for safe reinforcement learning in uncertain environments. RAMU incorporates a distribution of transition models and applies a risk-averse perspective using coherent distortion risk measures. The framework offers an efficient, model-free i...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. Please see below for clarifications and responses to your questions. In particular, we clarify the definition of “distributionally robust RL” [a, b] considered in our theoretical results, and how this differs from other definitions that have appea...	null	null	null	null	null	null
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning	Accept (poster)	Summary: The paper introduces the Stochastic Transformer-based wORld Model (STORM), an efficient world model architecture. STORM proposes to encode image inputs using a stochastic variational autoencoder, and predicts latent state using a GPT-like sequential model. It then trains dynamics and policy based on the output...	Rebuttal 1: Rebuttal: Thanks for your effort spent reviewing our paper and providing many valuable suggestions. We will include the suggestions and the pointed concurrent work in the revised paper. Below, we want to address the main concerns raised in the review. - Response to (1): We appreciate your observation r...	Summary: This paper proposes a world model architecture (STORM) to train RL agents in imagination. The world model is composed of an autoencoder with categorical latents and a Transformer. These modules are trained jointly with a reconstruction loss, a next latent state prediction loss, as well as reward, episode termi...	Rebuttal 1: Rebuttal: Thanks for your effort spent reviewing our paper and providing many valuable suggestions. We will include the suggestions in the revised version. Below, we want to address the main concerns raised in the review. - Response to Weakness 1 and Question 2 about `comparison with TWM`: For furt...	Summary: The authors propose several modifications to the recently proposed Transformer based world model for Model-based reinforcement learning. Specifically, they come up with a single latent stochastic state and treat action as an explicit input to the state as opposed to a token (as in previous works) and show sign...	Rebuttal 1: Rebuttal: Thanks for your effort spent reviewing our paper and providing many valuable suggestions. We will include the suggestions in the revised version. Below, we want to address the main concerns raised in the review. - Response to (1a, 1b): We sincerely appreciate your suggestion, and we will incl...	Summary: The paper presented a Transformer-based model-based RL framework. As with earlier approaches, online data is gathered into the replay buffer with the learned reactive policy, the Transformer-based world model is trained with segments sampled from the replay buffer, then the policy is optimized with imaged data...	Rebuttal 1: Rebuttal: Thanks for your effort spent reviewing our paper and providing many valuable suggestions. We will include the suggestions in the revised version. Below, we want to address the main concerns raised in the review. - Response to Weakness 1`criteria for task selection in the ablation studies`: ...	Rebuttal 1: Rebuttal: We express our gratitude for the valuable feedback and for the recognition of STORM's efficiency as emphasized by reviewers (4iYJ, rfCq, E5z3), along with the commendation for the paper's coherent structure and presentation, as indicated by reviewers (Cg1v, 4iYJ, rfCq). In the subsequent discussio...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Probabilistic Invariant Learning with Randomized Linear Classifiers	Accept (poster)	Summary: The authors propose probabilistic linear classifiers (RLCs) that are able to solve relatively general binary classification problems. They derive a trade-off between the probability that sampling of RLCs leads to an accurate prediction and the number of samples to obtain the majority vote of an RLC. Furthermor...	Rebuttal 1: Rebuttal: Thank you for the valuable comments. We are glad the reviewer appreciated our contributions. In the feedback summary we address your questions about i) settings where the deterministic models succeed and how it can be compared to RLCs in terms of resources and ii) how the number of samples $m$ can...	Summary: The paper introduces a novel approach for achieving universality and invariance in binary classification tasks, while minimizing computational requirements. Instead of relying on deterministic neural networks such as DeepSet, which have parameterization complexity proportional to the set size, the paper propos...	Rebuttal 1: Rebuttal: Thank you for the review! In the feedback summary we address your questions about $m$ and the choice of randomness source. Please, let us know if you have any extra input, we'd be interested in discussing.	Summary: This work presents a very interesting method for training efficient invariant classifiers by leveraging randomness. More precisely, it proposes to train a neural network to sample linear classifier weights, by pushing forward some data-independent distribution, and using the majority vote over sampled classifi...	Rebuttal 1: Rebuttal: Thank you very much for your feedback. We refer the reviewer to our feedback summary for an additional experiment. We will also take in your suggested references in the final version. If you have any other input we'd be happy to discuss. > "As far as I understood, the model invariance relies on un...	Summary: The authors introduce Randomized Linear Classifiers, which are a way to randomly represent the weights of a linear classifier. Because there is a random reconstruction of the network, a sample majority of which is used to determine the actual inference of the network. The authors show a universal representatio...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper. Next, we address your comments. Please, let us know of any additional feedback you might have. We would be very happy to discuss. > "From what I can tell, the paper only considers the expressivity of RLCs, and the number of parameters needed. Is there a result a...	Rebuttal 1: Rebuttal: ### _Feedback summary_ We thank all the reviewers for the valuable feedback on our manuscript. In general, reviewers found the paper to be well written and appreciated the novel direction given by our theoretical contributions. Here we address three common points raised across reviews. Then, w...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work proposes a framework called Randomized Linear Classifiers (RLCs) that leverages external randomness to build models that are expressive and can encode invariance in the input space. The authors establish probabilistic versions of universal approximation theorem and invariance for several types of RLC...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. Please, refer to our feedback summary where we address your questions about the randomness distribution and the deep sets comparison. If you have any extra input, we would be very happy to discuss. >I am not so sure about the benefits of "Online computati...	null	null	null	null	null	null
Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning	Accept (poster)	Summary: This paper proposed more robust learning using combining the WAUC and cost learning. The authors claim that it can be vital to the shifts of cost function and covariate distribution. The algorithm is constructed using bi-level optimization with inner and outer parts. Experiments show high performance, especial...	Rebuttal 1: Rebuttal: ## Author response to Reviewer Trb9 Thank you for your detailed and constructive feedback on the paper. We value your insights and have taken your suggestions into consideration. Here are our responses to your specific comments. Q(1) What the meaning of $\mathcal{K}(S_w^-,\tau)$ A(1) We...	Summary: This paper proposes a weighted AUC (WAUC) loss that is robust to both class distribution shift and cost distribution without class and cost priors. A bilevel optimization paradigm is proposed to bridge WAUC and cost. The authors propose a stochastic optimization algorithm for WAUC, and prove its convergence ra...	Rebuttal 1: Rebuttal: ## Author response to Reviewer N1ih Thank you for your detailed and constructive feedback on the paper. We value your insights and have taken your suggestions into consideration. Here are our responses to your specific comments. **Q(1) Complexity analysis of the proposed optimization approach i...	Summary: This paper considers usage of the AUC, in a cost sensitive setting, i.e. where miss classification cost is not uniform. Extensions of the AUC have been considered on parametrised cost distributions, such as the WAUC. In this paper the authors aim to develop a cost sensitive extension to the AUC that does not d...	Rebuttal 1: Rebuttal: ## Author response to Reviewer uAig Q(1-1) Proposition 5.1 is not a proposition A(1-1) We present it as a proposition because by Lemma 5.2, we can derive a convergence result for WAUC, showing that $\\|\hat{WAUC}-WAUC\\|$ converges at a rate of $O(\sqrt{\frac{\log n_-}{n_- m}})$. Please re...	Summary: In this paper, a bi-level optimization method is proposed for binary classification with unknown cost distributions. The motivation is to propose an adaptive method to deal with different class and cost distributions, getting rid of the assumption of traditional AUC which assumes the uniform cost distribution....	Rebuttal 1: Rebuttal: ## Author response to Reviewer mY4P Thank you for your detailed and constructive feedback on the paper. We value your insights and have taken your suggestions into consideration. Here are our responses to your specific comments. **Q(1) Only binary classification is studied. For binary classifica...	Rebuttal 1: Rebuttal: ### Dear the ACs, and the Reviewers, Thank you so much for your valuable comments! They really helped us improve our manuscript! In order to facilitate reviewers' comprehension of our paper, we want to summarize our contributions again: - **We propose a setting that focuses on the robustness of...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a method that combines WAUC (weighted Area under ROC curve) learning with cost-sensistive learning. They propose a bilevel optimization algorithm to solve the formulated problem and provide theoretical analysis for convergence. According to their experiments on three datasets, the practical...	Rebuttal 1: Rebuttal: ## Author response to Reviewer uhz4 Thank you for your detailed and constructive feedback on the paper. We value your insights and have taken your suggestions into consideration. Here are our responses to your specific comments. **Q(1-1) WAUC method doesn't demonstrate better AUC performance in ...	null	null	null	null	null	null
Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training	Accept (poster)	Summary: - The authors proposed a novel Distributionally Robust Optimization (DRO) framework to achieve an ensemble of lottery tickets toward calibrated network sparsification. - The proposed DRO ensemble aimed to learn multiple diverse and complementary sparse sub-networks with the guidance of uncertainty sets, which ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable comments/suggestions. We summarize our responses as follows. Q1: Ablation study regarding V or V’ feature representations. Thank you for this great suggestion! Following the reviewer's idea, we conduct additional experiments on the Waterbi...	Summary: In this paper, the author proposes a Distributionally Robust Optimization (DRO) framework, which utilizes the ensemble of multiple sparse sub-networks to improve the network calibration. The author argues that the previous ensemble method, i.e., AdaBoost, will make the sub-network severely underfit the trainin...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable comments/suggestions. We summarize our responses as follows. Q1: Authors completely ignore very popular (static/dynamic) sparse training methods. Please refer to the answer to Q3 of the general response. To more clearly demonstrate this c...	Summary: The paper utilizes Distributionally Robust Optimization (DRO) framework to achieve an ensemble of lottery subnetworks for better calibration performance. Recently developed sparse network training methods, such as Lottery Ticket Hypothesis (LTH) and its variants, largely focus on sparsifying deep networks and ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable comments/suggestions. We summarize our responses as follows. Q1: Comparison with OOD Works [1, 2]. Thank you for pointing out these important references, which we would like to include and discuss in the related works section of the revis...	Summary: The authors propose a method of sparse training of deep neural networks with the objective of confidence calibration. The method is based on learning an ensemble of sparse models where each model begins with the same training dataset, and is increasingly diversified such that each model in the ensemble is trai...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable comments/suggestions. We summarize our responses as follows. Q1: Motivation of using sparse ensemble. Please refer to our answer to Q1 in the general response. In addition to a better calibration performance, sparse ensembles also achiev...	Rebuttal 1: Rebuttal: First of all, we would like to thank all the reviewers for spending time to review our paper and providing many constructive suggestions and comments. Here, we summarize our responses to some major questions raised by reviewers: Q1: Motivation of using sparse ensemble (Reviewer TYXu) Besides...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
MotionGPT: Human Motion as a Foreign Language	Accept (poster)	Summary: This paper proposes MotionGPT, an approach that unifies language modeling with human motion modeling by treating each new motion token the same as a language token. To achieve this, a motion tokenizer based on VQ-VAE is first learned. Then, a pretrained language model is fine-tuned to learn from a unified voca...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea, human motion as a foreign language, as well as what it could enable on applications. We will fix the mixed citations, add more failure cases, and analyze the zero-shot ability of MotionGPT in the paper. 📝 **Q: Failure analysis. Zero-shot ability on...	Summary: In view of the idea that motion could be perceived as a form of body language, the authors propose to fuse motion and language to perform a unified motion-language pre-training. In detail, motion is quantized into discrete tokens in the same form as natural languages. Then, language modelling is performed on b...	Rebuttal 1: Rebuttal: 📝 Q: Motion Down-sample, if only given a start frame and an end frame as the in-between input, would the model perform well? 💡 A: VQ-based methods, such as MotionGPT and T2M-GPT, employ downsampling tricky to enhance the density of the codebook or tokens and reduce comput...	Summary: This paper presents a motion-language model via a shared vocabulary, where the texts are represented by original tokens, and the motions are encoded by a trained discrete tokenizer. Based on a pre-trained encoder-decoder framework, i.e., T5, the authors fine-tune the T5 with masked modeling on motion-language ...	Rebuttal 1: Rebuttal: Thanks for your approval and insightful comments. We will address your concerns in the following comments, re-organize this redundant Tab. 2, and update our paper accordingly. 📝 Q: Instruction tuning, Reasoning, and Zero-shot learning 💡 A: We propose instruction tuning to t...	Summary: This paper introduces a motion generation pipeline called MotionGPT, which is based on GPT. MotionGPT utilizes VQ-VAE to discretize human poses into tokens and combines them with language tokens to create a unified codebook. The model is initially pre-trained on motion language data and subsequently fine-tuned...	Rebuttal 1: Rebuttal: 📝 Q: Motion Quality and Performance Gain \| Method \| FID $\downarrow$ \| \|:--\|:--\| \| MDM \| $0.544^{\pm.044}$ \| \| MotionGPT \| $0.160^{\pm.008}$ \| \| T2M-GPT \| $\boldsymbol{0.116}^{\pm.004}$ \| Comparison of FID in text-to-motion task on HumanML3D dataset. \| Method \| FID $\downar...	Rebuttal 1: Rebuttal: We thank all the reviewers for approvals: The idea of unifying motion and language into tokens for uniform pre-training is novel and sound (R3, R4), and this motivation is clear and interesting (R2). This paper provides inspiration for future research (R1) and impressive demo (...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Regularizing Neural Networks with Meta-Learning Generative Models	Accept (poster)	Summary: This paper proposed a regularization method 'Meta generative regularization' based on the bi-level optimization frame addressed for the generative data augmentation. The MGR is consisited of two terms: pseudo consistency regularization (PCR) and meta pseudo sampling (MPS). The training using MGR is formalized ...	Rebuttal 1: Rebuttal: Thank you for your comments on the various points of view. ### W1: The data-driven data augmentation is not novel. What is the advantage of the proposed method over existing data augmentation methods? First of all, **the novelty of our work is mainly in solving the performance degradation of ...	Summary: The paper proposed meta-generative regularization (MGR) for improving generative data augmentation. MGR is optimized by alternative training between the main and finder network. To train the main network, contrastive learning is used. To train the finder network, the authors propose the bilevel optimization an...	Rebuttal 1: Rebuttal: We appreciate your constructive comments and suggestions. ### W1: Comparing MGR with CutMix and MixUp Thank you for this suggestion. We provide the comparison in Table R-3. MGR outperformed CutMix and Mixup. As well as the cases of other DA methods, **the combination of MGR and CutMix/Mix...	Summary: The authors propose a method for using synthetic images from GANs to augment training image classifiers. The naive approach to this problem is to generate samples for each class and treat these as supervised examples, but this can degrade performance due to image artifacts. Instead, the authors propose to use ...	Rebuttal 1: Rebuttal: We sincerely appreciate your carefully reading and thoughtful feedback. ### W1: How does the proposed method compare to the method using consistency regularization on real samples? Thank you for your insightful suggestion. Although the consistency regularization with real samples brings some ...	Summary: This paper leverages synthetic images from generative models to train classifier models, effectively using them as an augmentation tool. However, instead of incorporating these images in a simplistic fashion, the synthetic images are utilized as a regularizer. The paper posits that synthetic samples may not al...	Rebuttal 1: Rebuttal: We appreciate your careful reading and many insightful comments. ### W1: Is the proposed method effective on more complex, large-scale datasets such as ImageNet? Yes. By following your suggestion, we evaluated our methods on ImageNet. We confirmed the same trend as Table 1 even on ImageNe...	Rebuttal 1: Rebuttal: # General Response We greatly appreciate the reviewers for providing many constructive and insightful comments. We are happy to find all reviewers give scores of 3 (good) or better for the soundness and presentation of our paper. We are also pleased that most reviewers recognize the effectiveness ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Joint Training of Deep Ensembles Fails Due to Learner Collusion	Accept (poster)	Summary: The authors aim to answer why joint training of ensembles does not work as well as separately training the members of the ensembles, before ensembling them. This is a well-known empirical fact, but the authors attempt to give a theoretical understanding of it, which is novel to my knowledge. To reach their ob...	Rebuttal 1: Rebuttal: We thank this reviewer for their constructive feedback. We have addressed the points raised below which have helped us to further improve our submissions clarity and contribution. $~$ * _Further discussion on the effects of varying $\beta$_ - The non-linear relationship between $\beta$ an...	Summary: Ensembles are a simple but powerful way to improve model performance. Typically, ensembles are used to improve performance by training each model independently and then using them jointly. However, unlike previous ML methods that require ensemble members to be trained individually, Deep Ensemble, an ensemble o...	Rebuttal 1: Rebuttal: We thank this reviewer for their constructive feedback. We have addressed the points raised below which have helped us to further improve our submissions clarity and contribution. $~$ * _Practical contribution_ - While we appreciate that the focus of this work was not on proposing new met...	Summary: This paper explores the joint training of deep ensembles, wherein the ensemble error is directly optimized during training. The authors find that joint training leads to poor performance, which they posit it due to phenomenon they call "learner collusion", where base learners artificially inflate their diversi...	Rebuttal 1: Rebuttal: We thank this reviewer for their constructive feedback. We have addressed the points raised below which have helped us to further improve our submissions clarity and contribution. $~$ * _Facebook dataset & Formatting_ - Please see our response in the general comment section. $~$ * _On...	Summary: This paper mainly studies the reason behind the failure of jointly training deep ensembles. It discovers that joint optimization results in a phenomenon in which base learners collude to artificially inflate their apparent diversity. Both theoretical and empirical evidence are provided further to verify the hy...	Rebuttal 1: Rebuttal: We thank this reviewer for their constructive feedback. We have addressed the points raised below which have helped us to further improve our submissions clarity and contribution. $~$ * _What joint training approaches are used_ - Joint training refers to the case when the aggregated predi...	Rebuttal 1: Rebuttal: We thank all four reviewers for their constructive feedback. We have found their feedback to be instructive with their suggestions and questions helping us to further improve our submissions clarity and contribution. $~$ * _Formatting error_ - We wish to sincerely apologize to all four rev...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Approximating Nash Equilibria in Normal-Form Games via Unbiased Stochastic Optimization	Reject	Summary: This paper formulates a Lipschitz loss function that makes computing approximate interior Nash equilibria in normal-form games amenable to unbiased Monte Carlo estimation, opening the door to using a number of scalable stochastic optimization techniques. They also provide a loss function with similar propertie...	Rebuttal 1: Rebuttal: Thank you for your review and your encouraging statements. We believe we can address your concerns by clearing up a few misunderstandings and reporting the results of some additional experiments in accordance with your feedback. We hope you will consider increasing your score in light of these upd...	Summary: This paper presents a novel approach for determining the Nash equilibrium of normal form games, utilizing a solution to a non-convex stochastic optimization problem. It defines the Nash equilibria in normal form games as the global minima of a specifically cunstructed loss function. Moreover, a randomized algo...	Rebuttal 1: Rebuttal: Thank you for your review and your encouraging statements. We have answered both your questions below. We hope you will consider increasing your score in light of these updates. Why Stochastic Non-Convex Opt? Isn’t that hard?: You are correct. Solving a stochastic non-convex optimization prob...	Summary: This work studies the computation of Nash equilibria (NE) of normal-form games and proposes a new loss function: the (weighted) sum of the squared norms of the projections of each player gradient onto the tangent space of the unit simplex. The authors show that this loss function is a meaningful surrogate of e...	Rebuttal 1: Rebuttal: Thank you for your review and your encouraging statements. Your summary was spot on and your intuition regarding your first question is exactly correct. We hope you will consider increasing your score in light of these updates. BLiN Steps and Oracles: We pass an oracle that is able to produce...	Summary: This work studies solving Nash Equilibria (NE) by stochastic unbiased optimization. The main contribution is providing a new loss function based on the gradient norm of the utility function, and finding the NE by using standard stochastic optimization methods (like Lipschitz bandit algorithms and stochastic gr...	Rebuttal 1: Rebuttal: Thank you for your review and your support! Indeed, we see this as a completely novel and scalable approach to solve games and we hope others can build and improve on this work. We believe we can easily answer each of your questions as follows: - Thank you for pointing out our omission of any desc...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback! We really appreciate the interesting and constructive questions that were raised, and believe they will help us improve the exposition of the paper. In each of our responses, we answer your questions and propose edits to address them in the paper. Please l...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a loss function (optimization problem) for normal-form games to estimate Nash equilibria which can be solved via unbiased stochastic optimization. They do this by relating their proposed loss function with exploitability. They also provide theoretical guarantees (under some technical condit...	Rebuttal 1: Rebuttal: Thank you for your review and your intriguing question! We have answered below. We hope you will consider increasing your score in light of these updates. SGD Lacks Global Guarantees: We agree that it remains unknown whether SGD and/or other gradient-based methods can cope with the potentiall...	null	null	null	null	null	null
Accelerating Value Iteration with Anchoring	Accept (poster)	Summary: New variants of value iterations based on anchoring Complexity lower-bound that shows that their algorithms is optimal in a worst-case sense Nice extensions for various important cases ($\gamma =1$, approximate VI, gauss-seidel, infinite state-action spaces). Strengths: * Strong theorems, able to overcome the...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful feedback. Weakness (i) (+Questions (iv) (vii)) Thank you for this point. By "rate", we meant the $\mathcal{O}$-dependency of the error. We will define our use of the term "rate" and we will revise the statement "$O(1)$ rate for VI when $\gamma\approx 1$" ...	Summary: The authors focus their study on the theoretical analysis of a (simple) variation of the Value Iteration (VI) algorithm, a classical and grounding tool behind many modern (deep)-RL algorithms. The variation considered by the authors incorporates anchor acceleration mechanisms leading to what the authors call A...	Rebuttal 1: Rebuttal: We are happy to hear that the reviewer found our work interesting. Questions (i) Referring to prior works [1,2,3], we conjectured that the anchoring mechanism, which pulls the present iterates toward the anchoring point, provides stability and prevents a certain type of cycling behavior. Howev...	Summary: This paper considers an anchored version of Value Iteration and derives accelerated rates in terms of the Bellman error for both the Bellman consistency and optimality operators. Then, the work addresses the particular case of $\gamma =1$ with a $O(1/k)$ rate that VI fails to guarantee via the standard contrac...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the highly detailed and constructive feedback. Weakness (i) As an analogy in the optimization literature, the recent discovery of OGM [1] and OGM-G [2] demonstrate that considering a different performance measure leads to a different optimal algorithm. In this set...	Summary: The paper introduces an accelerated version of the Value Iteration (VI) algorithm, called Anc-VI, based on the anchoring mechanism. The proposed method achieves faster reduction in the Bellman error compared to standard VI even when the discounting factor is close to 1. Meanwhile, a complexity lower bound is ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments. Weakness (i) (+Question (iii)) Although in some practical setups, discount factor $\gamma$ could be chosen freely, we assumed that environment and MDP and $\gamma$ are given constants. If $1/2 \le \gamma<1$, Anc-VI exhibits a provably faster c...	Rebuttal 1: Rebuttal: # Common Response First of all, we thank the reviewers for their constructive and detailed feedback. We were excited to see that all the reviewers found our work valuable. Indeed, as reviewer e9mJ and tQc7 mentioned, acceleration of Anc-VI is guaranteed by ''strong theorem'' and ''leads to some s...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Contextual Bandits and Imitation Learning with Preference-Based Active Queries	Accept (poster)	Summary: This paper considers the learning problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed actions's reward (feedback), instead, the learner is only able to request the expert at each round to compare two actions. [Interaction Protocol] The interaction be...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We would like to address your concerns as follows. 1. Comparison to regret bound for dueling bandits As established by prior works [1,2], for dueling bandits, the minimax regret rate is $\tilde\Theta(\sqrt{AT})$ and the instance-dependent regret rate is ...	Summary: This paper studies the contextual bandit and imitation learning problem with preference-based feedback. The authors propose an oracle-based contextual bandit algorithm, which attains both worst-case and instance-dependent regret bounds. Besides, the algorithm has an instance-dependent guarantee on the querying...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We would like to address your concerns as follows. 1. Practical implementation of the online regression solver / concrete examples of the online regression oracle As a concrete example, when the reward function $r:\mathcal{X}\times\mathcal{A}\rightarro...	Summary: The paper gives “best-of-both-worlds” results for an imitation-learning problem in contextual bandits and MDP settings. With small orthogonal changes to assumptions, the algorithms primarily improve over prior work by considering instance-optimal bounds both in regret and queries, and require only ordinal pref...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We would like to address your concerns as follows. 1. Highlight of the methodological contributions used. We highlight some of the novelty and methodological contributions of the proposed algorithm below: - Active learning via candidate arm set: while t...	Summary: This paper develops the provably efficient algorithms AURORA and AURORAE, which are able to achieve the optimal regret bound under contextual dueling bandit setting, and imitation learning respectively, at the same time minimizing query complexity. The key idea behind is that the algorithm only makes a query w...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We would like to address your concerns as follows. The computational complexity for the candidate arm set As mentioned by the reviewer, if $\mathcal{F}$ is a $d$-dimensional linear class, the computational complexity will be $\tilde{O}(d T A)$. We believ...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Large Language Models as Commonsense Knowledge for Large-Scale Task Planning	Accept (poster)	Summary: The paper proposes to use large language models (LLMs) as a world model instead of a policy for task planning. Specifically, the LLM is used to approximate the state of the world and acts as a heuristic policy in Monte-Carlo Tree Search (MCTS). Experimental results demonstrate the effectiveness of the method, ...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. We will improve and revise the paper according to your suggestions. Our reply to your question is enclosed below. Q1: > It is better to include more baselines that employ special design for task planning, e.g., SayCan [1], Zero-Shot Planner [2], et...	Summary: This paper proposed to leverage LLMs both as a (commonsense) world model and heuristic policy within the MCTS search algorithm to tackle household planning tasks namely object rearrangements. The main idea is that for each simulation phase in MCTS, the algorithm sample from LLM to obtain the initial belief of ...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. We will carefully consider and incorporate your comments and suggestions into our manuscript. The reply to the questions is enclosed below. Q1: > The paper only tested on object rearrangement tasks with limited object relationships (on, inside). M...	Summary: The paper introduces a new methodology _Monte Carlo planning with common sense knowledge_. The idea is to rely on LLMs to integrate common background knowledge into Monte Carlo Tree Search algorithm with application to language-instructed object rearrangement tasks. Assuming access to a dataset of expert act...	Rebuttal 1: Rebuttal: We sincerely appreciate your effort in reviewing our paper and providing feedback. Please see our responses below. Q1: > The paper mostly relies on experiments and ideas from S.Li 2022. and would gain clarity if clearly stated. For instance, using LLMs to initialize the belief state was already...	Summary: This work demonstrates that LLMs can be used as the commonsense models of the world and serve as the heuristic policy in search algorithms. Specifically this paper uses Monte Carlo Tree search to explore word states sampled from the output of LLMs and commonsense policy from LLMs can effectively guide the sear...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. We will carefully revise our paper to incorporate your suggestions. The following are our responses to your questions. Q1: > This work argues that planning policy may suffer from hallucination issues of LLMs in the related work. However this work ...	Rebuttal 1: Rebuttal: # Global response We thank all the reviewers' efforts invested in reviewing our work and providing valuable feedback. We summarize the main concerns raised by reviewers and our corresponding responses. One review (RKS5) states that > The paper mostly relies on experiments and ideas from S.Li 2...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a technique to incorporate large language models' commonsense knowledge into Monte Carlo Tree Search to guide planning. It uses LLMs to obtain probabilities over the initial belief of the state, and as a heuristic policy to guide simulation. Evaluation on household object rearrangement ta...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. We are grateful for the many suggestions for improvement, which we will incorporate in the revised manuscript. We would like to further clarify the questions and concerns you raised. Q1: > The paper evaluated this technique in only occurred in one...	null	null	null	null	null	null
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models	Accept (poster)	Summary: This paper considers the setting where we have access to a dataset containing states and actions for pre-training and must then learn to solve a task given a new observation-only dataset from a downstream task. The key assumption here is that we have access to this action labelled dataset, and once we have it,...	Rebuttal 1: Rebuttal: > The experiments are fairly mundane. It would be fantastic to see an example of this method at larger scale. For example, this could be done using the dataset from VPT. The Minecraft images could be resized to make them smaller, and then it would be possible to use the DreamerV3 codebase (which w...	Summary: This paper presents an imitation learning approach by first training a world model to predict next observations conditioned on (given) actions, and in a second phase training a policy that amortizes action inference by maximizing the likelihood of observations under a dataset of expert demonstrations. The auth...	Rebuttal 1: Rebuttal: > The experimental results are hard to parse and have some anomalies (see my questions). Also the strongest claims "AIME outperforms the baselines by a large margin, which indicates the strong generalisability of a forward model over an inverse model. We also find that AIME makes substantially bet...	Summary: This paper presents an algorithm named AIME to learn the world model and apply it to downstream tasks. In the first stage, AIME learns a world model from a dataset with actions to maximize the likelihood via EBLO. While in the second stage, given observation-only demonstrations, AIME optimizes the action seque...	Rebuttal 1: Rebuttal: > The major concern is the problem setting of AIME. In my view, more general setting the agent can only get state trajectory in the world-model learning stage while can obtain action-labeled data in the second stage. Then the agent has much more data in training (e.g., human data without actions) ...	Summary: The paper proposes action inference by maximising evidence as a way for an MBRL to replicate most likely actions using appropriate world models. The algorithm has two phases: 1) Learn the world model based on a replay buffer, and 2) imitate the expert's behaviour by inferring the policy that maximizes the evid...	Rebuttal 1: Rebuttal: > Does the world model training in Phase 1 have to converge because imitation learning can happen? Is this primarily for changes in the task? E.g., going from walking to hopping but with the same agent in the same environment? No, it is not necessary to train the model until converge to enable im...	Rebuttal 1: Rebuttal: We thank all reviewers for their time and insightful feedbacks. We conduct three new experiments suggested by the reviewers, the results are provided in the pdf: **We evaluate multiple checkpoints during the course of the world model pretraining to address the convergence concerns from reviewer...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective	Accept (poster)	Summary: This paper studies the cause of neural text degeneration, i.e. language models tend to generate repetitive loops. They design an experiment showing that text degeneration is correlated with the amount of repetitive text in the training data. Motivated by this finding, they propose repetitive dropout, which app...	Rebuttal 1: Rebuttal: Q: The authors only compare their method with one baseline in Table 1. Table 1: Added experiments of ScaleGrad on FreeLaw \| \| Rep-2 \| Rep-3 \| Rep-4 \| Rep-w \| Rep-r \| \|-------------\|-------\|-------\|-------\|-------\|-------\| \| MLE \| 51.74 \| 46.19 \| 42.22 \| 39.22 \| 73.06 \| \| ScaleGrad \| 15....	Summary: In this paper, the authors demonstrate that repetition in the training data is a major cause of the neural text repetition problem. They first show a strong correlation between the repetition ratio of training data and generated text. Based on the observation, they propose repetition dropout to prohibit the mo...	Rebuttal 1: Rebuttal: Q: metrics such as MAUVE Table 1: MAUVE scores on Wikitext-103 \| \| Rep-4 ⬆️\| MAUVE ⬆️\| PPL ⬇️ \| \|-------------\|-------\|-------\|-------\| \| MLE \| 32.64 \| 49.70 \| 21.98 \| \| HI-Re \| 28.35 \| 35.83 \| -- \| \| ScaleGrad \| 5.01 \| 52.80 \| 39.11 \| \| UL \| 22.88 ...	Summary: The paper explores the issue of degeneration in text generation, which refers to the generation of repetitive words and dull loops by neural language models. The authors focus on the impact of repetition in the training data and propose a method to address this issue. Specifically, they suggest dropping out re...	Rebuttal 1: Rebuttal: Q: While it is reasonable to assume that data quality has a direct impact on degeneration, this finding is not surprising We'd like to emphasize that the goal of our work is to investigate the relationship between repetition in training data and degeneration. It is important to note that data...	Summary: This paper explains the repetition in model generated text from a data standpoint, pointing out that there is a strong correlation between the degeneration issue and the presence of repetitions in training data. The authors find out that penalizing repetitions in data can alleviate degeneration, and propose a ...	Rebuttal 1: Rebuttal: Q: Currently it seems that the repetition dropout mask is applied on each instance, but what about repeated text in different instance? Thanks for the question. To ensure that we understand your query correctly, we would like to confirm that by "repeated text" you are referring to repetitive ...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching	Accept (spotlight)	Summary: This paper introduces a framework for registering 2D images and 3D point clouds. The framework consists of three branches, for processing 2D images, 3D point clouds and 3D voxel constructed from the original 3D point clouds. The fused features of 3D point clouds and 3D voxel are used to establish correspondenc...	Rebuttal 1: Rebuttal: Q1: Practical meaning of the proposed work. There are two types of calibration between vehicle-mounted camera and LiDAR, which is pre-calibration and online-calibration, both of them play important roles in the autonomous driving systems. We focus on the online-calibration which is much harde...	Summary: This work aims to address the image-to-point cloud registration task. The authors propose a VoxelPoint-to-Pixel matching framework, which consists of three network branches dedicated to extracting features from voxel, point, and pixel representations, respectively, for 2D-3D matching. The network is trained wi...	Rebuttal 1: Rebuttal: Q1: Technical innovations. We did get inspiration from previous methods on some loss designs. However, to best of our knowledge, almost no work explored the cross-modality contrastive learning between image features and voxel-point features, where we design a triplet network to learn VoxelPoi...	Summary: The paper proposes a 2D to 3D registration pipeline using a differentiable PnP method (Epro-PnP) and integrates 3D information using both voxel and point-based features. These designs target on previous problems such as the domain differences when fusing MLP-based point features and CNN-based pixel features, a...	Rebuttal 1: Rebuttal: Q1: Why differentiable PnP can improve the accuracy? The main insight that we introduce the end-to-end PnP is to impose supervisions directly on the predicted transformations. Previous works learn 2D-3D feature matching with non-differentiable PnP as a post-processing procedure to estimate th...	Summary: This paper proposes a method to register an image with its nearby Lidar scan, and it comes with three modules: 1) A sparse 3D conv-net and point-net to extract 3D features; A 2D conv-net to extract 2D features; 2) An intersection detection module to discard non-matchable 2D and 3D points; 3) Modified Circl...	Rebuttal 1: Rebuttal: Q1: Cross dataset validation. We refer the reviewer to ”Global-Q1: Cross dataset validation.“ of the global response for justifying cross dataset validations. Q2: Conducting 6-DoF registration, rather than 3-DoF registration. We follow previous methods (e.g. CorrI2P and DeepI2P) to cond...	Rebuttal 1: Rebuttal: We upload a rebuttal PDF with some experimental results requested by the reviewers. For the following rebuttals, we use “rebuttal PDF” to point to the provided PDF like “in Table A of the rebuttal PDF”. We respond to some common questions in reviews as follows. **Global-Q1: Cross dataset validat...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes to learn a structured cross-modality latent space to represent pixel features and 3D features via a differentiable probabilistic PnP solver, which designs a triplet network to learn VoxelPoint-to-Pixel matching. The proposed method is trained in end-to-end manner by imposing supervisions dir...	Rebuttal 1: Rebuttal: Q1: Motivation of introducing voxel branch. Our motivation is that irregular points are merely suitable to be processed by MLP to learn representations, while pixels are regular and processed by CNNs. The large differences between points and pixels and the one between calculations in MLPs and...	null	null	null	null	null	null
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems	Accept (poster)	Summary: Authors found novel discoveries in policy optimization problems: 1.the superlevel set of the objective function related to the policy parameter is always a connected set and the optimization objective as a function of the policy parameter and reward satisfies a stronger “equiconnectedness” property. Based on t...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing the paper. We agree that it would be good to complement the theory with simulations in some way, but so far the results mostly focus on the fundamental structure of the optimization problem, and it is unclear what simulations would be mean...	Summary: This work shows the superlevel set of the objective function in reinforcement learning is always equiconnected for both tabular policy and neural policy. An application of the connected property is the minimax theorem. As a consequence, reward attack robust RL can be shown to have Nash equilibrium. Strength...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the feedback, which we will incorporate when making the next revision. We confirm that our results can be used to drive the design of algorithms (please see our response to Reviewer ceyJ above). The technical reason for considering Assumption 2 is to make the a...	Summary: This paper aims to enhance the comprehension of the optimization landscape in reinforcement learning (RL) for policy optimization problems. The primary contribution of this work is to demonstrate the connectedness of the superlevel set of the policy optimization problem in RL under a tabular policy representat...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to read the paper and for providing important feedback, which we will carefully consider and incorporate in the next revision. First of all, we confirm that studying SLSC can inform algorithm design. As we discuss in the last paragraph of page 5, given an...	Summary: This work studies the connectedness in (deep) reinforcement learning. First, the authors show that the superlevel set of average reward objective in reinforcement learning is connected under both tabular and over-parameterized policies. The objective is shown to satisfy a stronger equiconnectedness property. S...	Rebuttal 1: Rebuttal: We thank the reviewer for bringing to our attention this highly relevant pseudo-linearity structure. From the way we construct the path map in the proof of Theorem 1, it is not difficult to see that our result implies the pseudo-linearity, but pseudo-linearity does not imply connected superlevel s...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization	Accept (poster)	Summary: This work considers linear regression on data $(x_i, y_i)_{i=1}^n$ where each $x_i \in \mathbb{R}^d$ is sampled from one of $k$ Gaussian clusters, each with probability $\pi_k$, and $y_i = x_i ^T \theta_0 + \varepsilon_i$ with $\varepsilon_i \sim N(0, \sigma^2)$ representing noise. The goal of this paper is to...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper! ## General comments on weaknesses: 1) Limitations of the model: We expect that the virtue of look-alike modeling on generalization to apply in a broad range of settings. The high-level intuition is that the look-alike modeling acts ...	Summary: In this paper the authors propose a look-alike clustering technique that replaces sensitive feature of individuals with the cluster’s average value – the cluster in which that individual belongs to. The authors provide precise analysis of how replacing sensitive features with cluster center value affect the ge...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper! ## General comments on weaknesses: 1) We respectfully disagree with your comment. Our theory indeed captures the effect of cluster size and the number of clusters as well as other factors precisely in the studied asymptotic regime. Howeve...	Summary: This work presents a generalization analysis for look-alike clustering. In this type of clustering, the features in a model are divided into two groups: sensitive and non-sensitive, and the values of the sensitive features are replaced by the mean of the cluster to ensure K-anonymity (if the size of the cluste...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper! ## General comments on weaknesses: 1) The effect of the number of sensitive features on the generalization is indeed more complicated. For example, in the underparamterized regime as shown in Theorem 3.1, if we fix $n,d$ and increase $p$ ...	Summary: This paper extends look-alike clustering to anonymize sensitive features of data points by replacing them with cluster means. It provides a theoretical analysis of the generalization error of models (linear regression estimator) trained using the anonymized sensitive features in the asymptotic regime. The pape...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper! ## General comments on weaknesses: 1) We will add related work on other data anonymization methods, including differential privacy, k-anonymity and aggregate learning. 2) Since the focus has been developing on “precise characterization”...	Rebuttal 1: Rebuttal: We would like to sincerely thank all the reviewers for taking time to review our paper and for the valuable feedbacks. A common comment raised by some of the reviewers was on the limitation of the work and whether the message of our paper goes beyond linear regression. We expect the virtue of lo...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper studies linear regression where some coordinates of the covariate x are not revealed directly to the learner, and only a cluster-wise average is revealed for those coordinates. This change in the covariates x is called look-alike clustering and it has been used for protecting sensitive attributes in ...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper! ## General comments on weaknesses: 1) We expect that the virtue of look-alike modeling on generalization to apply in a broad range of settings. The high-level intuition is that the look-alike modeling acts as a regularizer and can improv...	null	null	null	null	null	null
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning	Accept (poster)	Summary: This paper addresses automating diagnosis coding via tree-based contrastive learning. It uses an established benchmarking dataset for this task, and achieves insightful results from its comparative performance evaluations and ablation studies. Strengths: This paper addresses automating diagnosis coding via tr...	Rebuttal 1: Rebuttal: ### Summary We truly appreciate your suggestions. We understand your concerns are from multiple perspectives, and we try our best to answer them in this discussion. We sincerely hope our answers can address your concerns. --- ### Weakness 1: The paper is somewhat lacking in its qualitative anal...	Summary: This paper describes a novel method of ICD coding that explicitly model clinical note sections. Instead of treating a clinical note as a long sequence of tokens, the authors propose to segment a clinical note into sections and then use contrasive learning to pre-train the model. Experiment results on MIMIC-III...	Rebuttal 1: Rebuttal: ### Summary We are glad to know you think our work is effective. We truly appreciate your suggestions. We believe your concerns are mainly due to the baseline selection, especially for Transformer-based models. We carefully read your suggested paper and add the result of a stronger Transformer-ba...	Summary: The paper proposed a semi-structured automatic ICD coding algorithm with a contrastive pre-training and masked section training and evaluate the algorithm using MIMIC-III dataset. Strengths: The paper is well structured with clear explanation in research motivation, related work, experiment configuration and...	Rebuttal 1: Rebuttal: ### Summary We truly appreciate your suggestions and understand your concerns mainly come from the result presentation. We have added the confidence intervals for Table 1 and Table 2. We have also added one representative table with confidence intervals of Figure 4. We sincerely hope these update...	Summary: The paper tackles automatic ICD coding. It lists challenges and proposes solutions to them: 1. For ignoring structural information, it proposes a content-based algorithm that automatically segments clinical notes into sections. 2. For limited availability of data and variability of clinical notes, it proposes ...	Rebuttal 1: Rebuttal: ### Summary We are delighted to know that you think our work has multiple strengths. We understand that your primary concern is the role of medical experts in the title selection. To address this, we have added the comparison between the titles extracted by our algorithm and those selected by med...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Low Tensor Rank Learning of Neural Dynamics	Accept (poster)	Summary: Proposes a low tensor rank recurrent neural network (ltrRNN) architecture, in which the tensor constructed by stacking the RNN weight matrices of different trials is constrained to have low tensor rank. Empirically shows that ltrRNNs can fit neural recordings during a motor learning task, achieving lower unexp...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback regarding the novelty and utility of our work. Q1. Relationship between continuous-time and discrete-time RNNs. A. We used a formulation of a continuous-time RNN that is commonly used in neuroscience applications of machine learning resear...	Summary: The presented work investegated the 3-tensor formed by the weight matrices of RNNs across trials and found it is low-rank. The authors also conducted a mathematical proof that the weights learned by gradient-descent on low-dimensioanl tasks are low-rank. Strengths: First I should ackonwledge that I am not an ...	Rebuttal 1: Rebuttal: We thank the reviewer for noting that our framework is valuable for understanding gradient based learning as well as neural dynamics. Q1. Generality of low-rank learning dynamics. A. We have now included three additional task-trained RNN simulations and an additional neural dataset to va...	Summary: The work "Low Tensor Rank Learning of Neural Dynamics" investigate the low-rankness of RNNs with application to neural data, i.e. neural signals of a test subject performing a motor task. The authors describe that RNNs are of low rank in the trial mode when parametrized as a 3-tensor where one dimension repre...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback regarding our paper and its potential reach as interdisciplinary work. Q1. Clarification of ltrRNN model and training. A. We agree with the reviewer that having a good grasp of the ltrRNN training procedure is important for the reader to un...	Summary: In this paper, the authors explore the tensor rank of learning in artificial and biological neural networks. They showed that learning leads to low-tensor-rank weight updates, and derived upper bounds on the singular values of gradient dynamics of nonlinear RNNs, as well as on the matrix and tensor ranks in th...	Rebuttal 1: Rebuttal: We thank the reviewer for noting that our paper has a strong motivation, and that our claims are supported both by our empirical and our mathematical results. We agree that providing a clear presentation of these results to researchers unfamiliar with neuroscience is important for the broade...	Rebuttal 1: Rebuttal: We thank the reviewers for their helpful and supportive comments. We are pleased to have received positive feedback regarding the novelty and interest of our submission from the reviewers, several of whom are self-described non-experts in neuroscience. We believe this highlights the potential for ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense	Accept (poster)	Summary: The paper can be split into two parts. In the first part, the authors propose a paraphrasing-based attack that circumvents various AI-generated text detectors. The authors introduce DIPPER, an 11B parameters Transformer model obtained by fine-tuning T5-XXL. By using DIPPER to create paraphrases from texts gene...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful feedback! We are grateful that the reviewer appreciated our writing, paraphraser, the paraphrasing attacks on detectors, and discussion on the limitations of retrieval. The reviewer voiced concerns about the practicality of retrieval as a detection algor...	Summary: The authors developed a powerful paraphrase generation model called DIPPER to test the robustness of AI text detection algorithms. DIPPER successfully evaded several detectors by paraphrasing text generated by large language models. To improve detection, they proposed a defense mechanism based on retrieving si...	Rebuttal 1: Rebuttal: We thank the reviewer for their useful feedback and support for our paper! In particular, we appreciate the reviewer for highlighting that 1) we study important research questions on robustness of AI-generated text detection; 2) our defense mechanisms for our attacks; 3) our paper shows solid expe...	Summary: This paper investigates the robustness of AI-generated text detection algorithms to paraphrasing. The authors train a language model to paraphrase text in an attempt to evade detection algorithms. The proposed model leverages longer contexts than existing sentence-level paraphrasers and offers users control ov...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful feedback and support for our paper! In particular, we are grateful for reviewer’s appreciation for our 1) novel paraphraser training algorithm DIPPER; 2) attacks on AI-generated text detectors using DIPPER; 3) novel defense mechanism and a discussion of i...	Summary: The submission "Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense" investigates detection methods for text output generated by modern large language models. The contribution of this submission consist of two parts. First, the submission describes, trains and provides a...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and thoughtful feedback, and for strongly supporting our paper! In particular, we are grateful to the reviewer for supporting our 1) novel paraphraser and its open-sourcing; 2) our experimental analysis testing the robustness of AI-generated text detectors;...	Rebuttal 1: Rebuttal: We are very grateful to the reviewers for their detailed feedback. While we address each reviewer’s questions in the individual rebuttals, we use this “global rebuttal” to address concerns shared by multiple reviewers. We thank the reviewers for supporting the three contributions in our paper: * ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper first demonstrates the vulnerability of existing AI-generated text detectors to paraphrases, and then proposes a retrieval-based method to alleviate the issue. For the first experiment, the authors trained a paragraph-level paraphrase generation system, called DIPPER, by fine-tuning an existing tex...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and thoughtful feedback! We thank the reviewer for supporting our contributions on paragraph-level paraphrasing, attacking AI-generated text detectors, and introducing a retrieval-based defense mechanism. The reviewer voiced concerns about the practicality...	null	null	null	null	null	null
DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation	Accept (spotlight)	Summary: This paper proposes an EEG to text model that feeds into the raw EEG signals and predicts the corresponding words or long sentences. The model is optimized in two stages: (i) matching EEG to Text by contrastive learning with codex quantization; (ii) fintune the BART model for EEG embedding decoding to text. Th...	Rebuttal 1: Rebuttal: We appreciate the reviewer's thoughtful feedback and recognize the significance of the highlighted concerns. Below, we address the identified weaknesses point by point: 1. Data Volume Concerns for the CLIP Model: The limited data scale problem has been a key challenge in the BCI area. The tra...	Summary: This paper introduces a new method, “DeWave”, for decoding text strings from EEG data recorded from subjects while reading. This method seems to differ from earlier ones in two important and useful ways: (1) it uses a discrete “codebook” to represent the EEG data, which helps to control noise in the extremely ...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your thorough review and constructive comments on our manuscript. We genuinely appreciate your insights and will address your concerns point by point below. 1. *In section 3.2 under “Inference”, it would be very useful to state how temporal information is encoded....	Summary: The authors propose a new framework called DeWave, which integrates discrete encoding sequences with EEG-to-text translation tasks, using a quantized variational encoder and pre-trained language models. This approach overcomes the mismatch between eye fixations and spoken words and reduces interference from in...	Rebuttal 1: Rebuttal: Dear Reviewer, We appreciate your thorough review and insightful feedback. We will address each of your comments and concerns below and also in our revised manuscript. 1. Improve Figure 5’s title: Thank you for your suggestion on slight confusion on the perception field graph. We will revis...	Summary: The authors present an approach to decode language from EEG data. The proposed approach can work on both time-locked and raw data (i.e. without markers indicating when a word was read). The model is trained to (1) reconstruct its EEG input using a vector-quantized representation (learnable "codex") in a pretra...	Rebuttal 1: Rebuttal: Thank you very much for your comprehensive review, appreciation, and insightful inquiries. We will address your concern step by step below and improve in the final version. 1. Regarding Section 3.3, lines 136-141 and beyond: This paragraph is to introduce how we construct the word-level EEG f...	Rebuttal 1: Rebuttal: Dear chairs and reviewers, We express our profound gratitude for the comprehensive feedback and comments on our manuscript. This paper receives Accept, Weakly Accept, Borderline Accept, and Weakly Accept during the review period. We are excited about the consensus among the reviewers regardi...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Online learning of long-range dependencies	Accept (poster)	Summary: The paper looks at the problem of online learning in RNNs from a perspective of exact gradient computation: it looks for network architectures for which exact gradients can be computed. It uses the recently proposed LRU (Orvieto et. al., 2023) to show that with linear and diagonal structure in recurrent depend...	Rebuttal 1: Rebuttal: Thank you for your useful comments and thoughts. We reply below to each point raised individually. > Along with other claims that are well supported, the authors claim that they have pushed the standard for what is possible through online recurrent learning. However, I believe that the current ex...	Summary: The authors provide an online learning algorithm for linear recurrent units [23]. They take advantage of the fact that each unit of the LRU is an ‘independent recurrent module’ and thus RTRL for each LRU layer simplifies substantially in this case and becomes tractable. They test on some long-range dependency ...	Rebuttal 1: Rebuttal: Thank you for your useful review. We reply to your specific questions and concerns individually below. > The learning algorithm proposed seems to me just e-prop [13] applied to the LRU. Indeed, e-prop also takes into account self-recurrence of each unit and 1-step lateral recurrence. With LRU, si...	Summary: The authors show that applying an online learning algorithm to independent recurrent modules of linear recurrent units, drastically reduces the algorithm’s computational and memory requirements. They then show numerically that the algorithm’s gradient approximation for multi-layer networks is close to the “rea...	Rebuttal 1: Rebuttal: Thank you for your useful review. We reply to your specific questions and concerns individually below. > While I want to strongly emphasise that I think it is awesome that the authors flag that the SnAp-1 algorithm is reducing to the proposed algorithm when being applied to the proposed network a...	Summary: The authors introduce a forward-only approach to gradient computation in linear recurrent layers. The approach yields exact gradients for a single layer and approximate gradients for multilayer networks. They show that their approach yields more accurate gradients and results in more successful learning than a...	Rebuttal 1: Rebuttal: Thank you for the constructive and useful criticism. We reply point by point below. > The authors spend half of the discussion arguing for the potential importance of their work for neuroscience, which seems somewhat implausible and at odds with a relatively tight architectural and algorithmic fo...	Rebuttal 1: Rebuttal: We thank the reviewers for the useful comments and questions. The points raised in the reviews led us to run several new experiments and to clarify certain aspects of our manuscript. We believe that these changes have significantly improved our paper. We summarize below the major changes and reply...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces an online learning algorithm for recurrent neural networks, particularly targeting the learning of long-range dependencies. It builds upon the linear recurrent units and independent recurrent modules in multi-layer networks with complex-valued neural activities. The online update approach...	Rebuttal 1: Rebuttal: Thank you for the useful questions and comments. We reply below to each of them. > The novelty of the algorithm is also somewhat constrained, as it builds upon existing concepts of Linear Recurrent Units (LRUs) and online Recurrent Neural Network (RNN) training methodologies, adapting the mechani...	null	null	null	null	null	null
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming	Accept (poster)	Summary: The authors present a dynamic programming (DP) method for constructing optimal decision trees. The method is more general than previous DP methods. The authors report extensive experiments to compare with previous methods which including MIPS and DP methods. Strengths: The paper is well-written and there is e...	Rebuttal 1: Rebuttal: Thank you for your positive words about our work! In response to your questions and comments: Markovian 1. "In Appendix A (lines 46-47) you give the impression that a Markovian cost function can depend on the 'history' of branching decisions, which seems to go against standard definitions (i...	Summary: The paper introduces STreeD, a novel dynamic programming (DP) framework designed for learning optimal decision trees. By expanding the range of solvable objectives and constraints, STreeD offers significant advancements in decision tree optimization. The authors also offer theoretical insights to aid in determ...	Rebuttal 1: Rebuttal: Thank you for your positive words about our work! In response to your questions: 1. "Regarding section 4.4, would it be possible for you to provide an example that illustrates a situation where the optimization task is non-separable?" An example of a problem for which we expect no efficient sepa...	Summary: The paper a proposes a novel framework for constructing Dynamic Programming (DP) algorithms for learning decision trees on Separable objectives. Historically DP methods are among the fastest methods that build optimal decision trees, and generatlization to a wide class of objectives is a useful contribution. ...	Rebuttal 1: Rebuttal: Thank you for your positive words about our work! An example of a problem for which we expect no efficient separable optimization task can be formulated is the optimization decision tree policies for Markov decision processes, for which Vos and Verwer present a MIP formulation (Vos & Verwer, arXi...	Summary: The paper investigates the conditions under which an optimal binary decision tree problem can be formulated as a dynamic programming (DP) problem, proposing the so-called STreeD framework. More specifically, the text establishes a general concept of separability for the objectives and constraints of DP-repre...	Rebuttal 1: Rebuttal: Thank you for your positive words about our work! In response to your questions: Novelty 1. "My major concern is that I struggle to understand the novelty of the work and its relationship to more fundamental DP theory." We provide a tailored DP theory for decision trees. This has several be...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper discusses the necessary and sufficient conditions for training optimal classification trees using dynamic programming (DP). In particular, the authors replace the commonly used -- and suficient -- notion of additivity by order preservation, which is shown to be necessary. The authors also present a ...	Rebuttal 1: Rebuttal: Thanks for the review and for the positive words about our work! We here respond to each of your questions: Novelty 1. "What is it that we gain from the more generalized setting described in this paper?" Recent publications at premier venues on using DP for optimal DTs for varying optimizati...	null	null	null	null	null	null
Max-Margin Token Selection in Attention Mechanism	Accept (spotlight)	Summary: This paper aims to provide an optimization-theoretic characterization of the softmax attention model $f(X)=v^{\top}X^{\top}{\rm softmax}(XW^{\top}p)$ by linking it to max-margin problems. The authors established the convergence of gradient decent on $p$ for a fixed $v$ choice, and further explored the joint co...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and helpful comments. > W1: Some assumptions are relatively strong. In Assumption B, they assume all non-optimal tokens have equal scores, which may not be true in practice. R: Thanks for raising this. We agree that this assumption is fairly strong, h...	Summary: The paper is clear and well-written. Understanding the e optimization dynamics and implicit bias is a significant theoretical issue, especially for morden neural network models. This paper provides a preliminary theoretical analysis of the margin maximization bias of attention-like models. Theoretically, the...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and suggestions. Below, we respond to their concerns point by point. We would be happy to respond to future concerns they may have during the discussion period. > *W1: This study is overly simplistic... With such a simplification.. it becomes almost a on...	Summary: The paper focuses on the attention mechanism which is commonly used in transformer architectures. In particular, the authors introduce a certain attention model and investigate its optimization dynamics and inductive biases under various assumptions on token's scores. In particular, the setting is a single-hea...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough feedback and helpful suggestions. >W1: Is the problem in theorem 1 convex, any other reason? R: Thank you for the great question. First, let us clarify that the problem is not convex even under Assumption B. One reason is that Assumption A actu...	Summary: This work studies the mechanism for relevant token selection in the attention model by drawing connections with the implicit bias literature and max-margin SVM formulation. The authors consider the prompt attention model $f(X)=v^TX^T\text{softmax}(XW^Tp)$, with tokenized input $X$, value weights $v$, key-query...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough review and helpful suggestions, they will definitely help improve the paper. > Q1: The numerical experiments such as those considered in [1], [2]. R: Following your suggestion, we conducted additional experiments using real-world datasets to furth...	Rebuttal 1: Rebuttal: We sincerely appreciate the time and efforts of the reviewers. We highlight the main contributions (C1-C3) of the paper and present new experiments (E1-E4) along with explanations for the corresponding attached figures (Figs 1-4). We would be grateful to respond to any reviewer inquiri...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes to give a mathematical explanation and analysis for widely used attention mechanism. They formulate normal attention, self-attention and prompt tuning into one single formulation. And they connected attention to max-margin problems. Strengths: 1. This paper proposes a mathematical analysis...	Rebuttal 1: Rebuttal: Thank you for your time and helpful suggestions. > W1: To be honest, ... improving network structure or losses? R: Thank you for your questions. In response to reviewer’s concern, under W2, we provide and discuss real data experiments which demonstrate that our theory successfully p...	Summary: The paper focusses on the optimization dynamics of attention mechanism. The authors analyze a softmax-attention model and demonstrate that running gradient descent on its parameters leads to a max margin solution, separating optimal tokens from non-optimal ones. The authors also present a regularization path a...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and helpful suggestions. > Q1: Can you provide more concrete examples/applications on real-world tasks? R: Thank you for this suggestion. We have conducted new experiments using real data, demonstrating how our theory successfully predicts two impor...	null	null	null	null
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation	Accept (poster)	Summary: This paper motivates the mixture weight estimation problem using Multi-source Multi-target Domain Adaptation problem. More specifically, this paper considers how to estimate the optimal mixture of sources, given a target domain; also, when there are multiple target domains, how to solve empirical risk minimiza...	Rebuttal 1: Rebuttal: This paper is more like optimization not M2DA paper We are afraid that we have to respectfully disagree with you on this point. As we mentioned in global rebuttal, the mixing domain type of multi-source learning algorithm typically contains two parts: finding good mixing weights and solving ER...	Summary: Authors formulate the problem of optimizing mixture weights given the target domain as a compositional convex-concave minimax optimization problem. Then, they propose a stochastic descent ascent algorithm for solving the problem, which improves upon previous method of [31] by allowing stochastic updates. Then,...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! We will try to address your concerns as follows. The connection between two parts of the paper We agree with you that the two parts of the paper already have their own independent interests. The reason we put them into one paper is that the two parts put ...	Summary: This paper is about the multi-source multi-target domain adaptation problem. The authors formulate a minimax algorithm to find the mixture weights of source domains. Furthermore, the authors extend it to the scenario of multi-target domains and introduce the co-component ERM problem. For this problem, this pap...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! We will try to address your concerns as follows. No experiments As we mentioned in global answer, we provide experiments with two layer MLP on MNIST dataset in rebuttal pdf. Comparison with deterministic convex-nonconcave optimization In Xu et al 20...	Summary: Authors propose a new way to compute mixture coefficients for combining multiple empirical risk minimization objectives (w.r.t. different sources in domain adaptation) in a way that takes into account the relation to a new target domain. As an application, the authors consider the multi-source multi-target dom...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! We will try to address your concerns as follows. No empirical intuition if the algorithm can be implemented with reasonable effort We implement our Algorithm 1 and provide results on MNIST dataset. It turns out the mixing parameter output by our algorithm...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their time and constructive comments. We will gladly incorporate the suggestions. We observe that reviewers have two primary concerns: the consistency of the story and the lack of empirical evaluation, which we will try to address as follows. **The cons...	NeurIPS_2023_submissions_huggingface	2,023	Summary: Summary: The paper addresses the problem of multi-source multi-target domain adaptation, where the goal is to learn a model from multiple sources in such a way that it performs well on a new target distribution. The context for this problem includes scenarios like learning from data collected from various sou...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! We will try to address your concerns as follows. Complexity to implement algorithm Our mixture weight estimation algorithm is a single loop primal-dual algorithm, which is widely used in minimax optimization and easy to implement. The only additional effo...	null	null	null	null	null	null
Rank-DETR for High Quality Object Detection	Accept (poster)	Summary: This paper focuses on the ranking problem in object detection. Inspired by the misalignment between class and location scores in DTER models, the paper proposes to redesign the model architecture and the loss to modulate the rank information. Experiments show that , the proposed method can build upon the curre...	Rebuttal 1: Rebuttal: ## To Reviewer kDha Thanks for your detailed comments. The mentioned questions are addressed as follows. --- > The whole process seems a little engineering. E.g., the addition of learnable bias. A: We appreciate your feedback and are grateful for the opportunity to address your concern reg...	Summary: In this paper, the authors study the problem of object detection. To be specific, they introduce rank-awareness into transformer-based detectors both at the architecture-level and the loss/cost-level. After the rebuttal: The authors have addressed my concerns about the comparison with other ranking-based so...	Rebuttal 1: Rebuttal: ## To Reviewer vAjC --- > Missing citations and comparison to significant ranking-based object detectors. A: Thanks for sharing so many valuable ranking-based object detectors which surely will help us to improve our work! We would like include the missing citations and the following compari...	Summary: This paper proposes a DETR training method named Rank DETR that integrates multiple (four) rank-oriented designs, i.e., rank-adaptive classification head (RCH), query ranking layer (QRL), GIoU-aware classification loss (GCL), and high-order matching cost (HMC). Among these four components, the former two (RCH ...	Rebuttal 1: Rebuttal: ## To Reviewer JDo7 We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows. > "Two (out of four) major components ..." A: 👉 First, we summarize their mathematical formulations as follows: \| method \| classification loss modificati...	Summary: This paper proposes Rank-DETR for image object detection. The key contributions of Rank-DETR include (1) a rank-oriented architecture design, which comprises a rank-adaptive classification head and query rank layer to ensure lower FP and FN in predictions; (2) a rank-oriented loss and matching design, which in...	Rebuttal 1: Rebuttal: ## To Reviewer m522 We thank the reviewer for the careful reviews and constructive suggestions. We answer the questions as follows. --- > **"Some module designs are not very novel. (1) Rank-adaptive Classification Head learns a class-aware and input-independent logits vector to model the class ...	Rebuttal 1: Rebuttal: ## To AC and All Reviewers We thank all the reviewers for their careful reviews and constructive suggestions. These constructive feedbacks has significantly contributed to the improvement of our paper. We are glad to find the initial ratings of three reviewers (Reviewer m522, Reviewer kDha, and R...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Orthogonal Non-negative Tensor Factorization based Multi-view Clustering	Accept (poster)	Summary: Existing NMF-based multi-view clustering methods perform NMF on each view respectively and ignore the impact of between-view. To solve these problems, this paper proposes an orthogonal non-negative tensor factorization method with one-side orthogonal constraint. This method can process the multi-view data dire...	Rebuttal 1: Rebuttal: __Q1__: The Introduction and Related work mainly introduce the NMF and its advantages and disadvantages. However, the authors did not adequately explain the motivation for designing the tensor factorization and orthogonal constraint. __A1__: Thank you for highlighting that. Non-negative Matrix Fa...	Summary: This paper presented an orthogonal semi-nonnegative tensor factorization and proposed a novel tensorized anchor graph factorization model for Multiview clustering. Compared with existing NMF-based multi-view clustering methods, the proposed model has the following advantages: First, the proposed model directly...	Rebuttal 1: Rebuttal: __Q1__: The paper does not provide the storage complexity and computational complexity. __A1__: Thank you for the clarification. For Orth-NTF, the storage requirements for $\mathcal{G}$, $\mathcal{Q}$, $\mathcal{H}$, $\mathcal{J}$, $\mathcal{Y}_1$, and $\mathcal{Y}_2$ have complexities of $\mathc...	Summary: This article proposed a novel orthogonal non-negative tensor factorization strategy for multi-view clustering, which well takes into account within-view spatial structure and between-view complementary information. Meanwhile, the optimization step has good convergency. Strengths: [a] The paper is well-written...	Rebuttal 1: Rebuttal: __Q1__: Some formulas are not strictly written, which variable to solve should be clearly written. __A1__: Thanks very much. We double checked our manuscript and corrected formulas to explicitly indicator which variables need to be solved. __Q2__: What does each letter of the matrix size represe...	Summary: In this paper, the authors focus on the problem of multi-view clustering using semi-non-negative tensor factorization (Orth-NTF) with a one-side orthogonal constraint. The proposed model extends Non-negative Matrix Factorization (NMF) to Orth-NTF, allowing for the utilization of spatial structure information f...	Rebuttal 1: Rebuttal: __Q1__: The paper seems to lack a detailed explanation of the advantages of extending NMF to 3rd-order tensor NMF. __A1__: Thank you for your attention. Non-negative Matrix Factorization (NMF) is designed primarily for second-order matrices. When handling third-order tensors, one must first trans...	Rebuttal 1: Rebuttal: Supplementary PDF is uploaded here as required. Pdf: /pdf/ab56da7caf5a4b78b87cf91a04e67d04885ddca2.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting	Accept (poster)	Summary: This paper proposes DYffusion, an approach that mimics the diffusion models for spatiotemporal forecasting. It treats the noising process as interpolation (parameterized by $I_\phi$) and the denoising process as forecasting (parameterized by $F_\theta$), i.e., it reimages the noising step $T$ in the original ...	Rebuttal 1: Rebuttal: Thank you for your positive comments regarding the novelty, connection to existing diffusion models, and efficiency of our approach, as well as your valuable feedback to which we respond below. Q1: > Eq. (6) is incorrect. There should be a differential instead of a derivative in the integr...	Summary: In this paper, the authors propose to build on diffusion model for modelling spatio-temporal data. In particular, they first train a time-dependent interpolation network which learn to interpolate the temporal dynamics given a frame at the horizon time $x_{t+h}$, a frame $x_t$, and an index $i$ interpolating b...	Rebuttal 1: Rebuttal: Thank you for the positive comments regarding the memory efficiency, continuous-time forecasts, and SST experiments of our work, as well as your valuable feedback to which we respond below. Q1: _Confusing section 3_ A1: > I found the writing to be confusing at times, I think that Sectio...	Summary: This paper proposes a new forecasting model for spatiotemporal data. The idea is based on separately training an interpolator and a forecaster network and applying them in an alternating fashion at inference time to iteratively refine the forward prediction. The inference procedure loosely resembles the denois...	Rebuttal 1: Rebuttal: We would like to thank you for the positive comments regarding the novelty, clarity, and experiments of our work, as well as your valuable feedback to which we respond below. _Potential misunderstanding:_ We would like to point out that there seems to be a key misunderstanding regarding our ...	Summary: In this paper, authors tackle the long-term forecasting problem applied to dynamics system. To solve this problem, they propose to use diffusion principle along with interpolating and forecaster mechanisms. The former interpolates timestep data in between lookback and target windows (therefore, at a lower reso...	Rebuttal 1: Rebuttal: We would first like to thank you for the positive comments and valuable feedback. We respond to your comments and questions below. Q1: _Reproducibility_ A1: 1. Code: We have shared with the AC an anonymous link to our code for reproducibility. We will open-source our code and data wh...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful comments. We are particularly encouraged by the reviewers finding our work _”interesting”_ (WiMo and 7msc), _”quite novel”_(7msc) and _”quite promising”_ (XpdM). We are glad to hear that reviewers found our paper _“well written and easy to follow”_ (7...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Full-Atom Protein Pocket Design via Iterative Refinement	Accept (spotlight)	Summary: In this paper, the authors proposed a Full-Atom Iterative Refinement framework (FAIR) for protein pocket sequence and 3D structure co-design. Generally, FAIR has two refinement steps (backbone refinement and full-atom refinement) and follows a coarse-to-fine pipeline. The influence of side-chain atoms, the fle...	Rebuttal 1: Rebuttal: Thanks for your appreciation and suggestions! Following your suggestions, we added new experiments, clarifications of formulations, and analyses. This revision has considerably improved our initial submission thanks to your constructive comments. We would love to know what you think about our resp...	Summary: The paper introduces FAIR, the pipeline for protein pocket sequences and 3D structures co-design. It's important in drug design applications, since most of the small molecule drugs (ligands) bind their targets (proteins) inside the pockets. Curranty existing methods have disadvantages (inefficient generation, ...	Rebuttal 1: Rebuttal: Thanks for your appreciation and suggestions! We are really grateful for your feedback and acknowledgment of FAIR’s novel contributions and experiments. Comment 1: Can you please provide the information about FAIR speed (how much time does FAIR need to finish one protein co-design)? Is it po...	Summary: The authors study the 3D protein-ligand interaction problem. They introduce a novel method for designing protein biding pockets conditioned on the ligand structure, termed FAIR. Unlike existing methods, FAIR co-designs sequence and structure of the pocket by iteratively modeling both backbone atoms and side ch...	Rebuttal 1: Rebuttal: We thank the reviewer for their appreciation and detailed suggestions! If you have any additional questions. Let us know about any other comments we can address. We would be very grateful if you considered increasing the score. Comment 1: The way section 3.2.1 is written, it seems that the e...	Summary: This paper is the first to introduce a deep learning pipeline for the protein binding pocket re-design task. The architecture consists of a rotation invariant, hierarchical encoder at the residue and all atom level, followed by a hierarchical iterative refinement generative process at the residue and all atom ...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive comments! We hope our detailed response and added experiments better highlight FAIR’s novel contributions. If any remaining questions/concerns make you hesitate to raise the score, we would be grateful if you let us know so we could further improve ou...	Rebuttal 1: Rebuttal: Global response to all reviewers: We thank the reviewers for their appreciation and valuable comments! Generally, the reviewers find our paper a novel approach for designing protein pockets that bind to ligand molecules. In the rebuttal, we have done additional experiments and added more disc...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The Full-Atom Iterative Refinement framework (FAIR) is a novel approach for designing functional proteins that bind with specific ligand molecules. FAIR consists of two steps: full-atom generation and 3D structure co-design. It uses a coarse-to-fine pipeline, updating residue types and structures together in e...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable questions! We have provided detailed responses to your comments. Please let us know whether we have addressed your concerns. We will be very grateful if you consider increasing scores to support our work. Comment 1: Figure 2 of the paper shows that the p...	null	null	null	null	null	null
A Robust Exact Algorithm for the Euclidean Bipartite Matching Problem	Accept (poster)	Summary: The paper presents a randomized algorithm for computing a minimum-cost matching of a bipartite graph induced by two point-sets, A and B, in the Euclidean plane. The best running time for the problem under consideration is n^2polylog(n). When A and B are drawn independently and identically from a fixed probabil...	Rebuttal 1: Rebuttal: We would like to thank you for reviewing our submission. > Could you please explain the relevance of your results to NeurIPS, beyond the importance of the matching problem as a generic optimization problem? Minimum-cost bipartite matching is extensively used in many applications in Machine Learn...	Summary: This paper studies the Euclidean bipartite matching problem. In the problem, there is a complete bipartite graph on parts A and B, and the cost of edge (alb) is \|\|a-b\|\|^p. The goal is to compute the minimum cost perfect matching as quickly as possible. This setting is most motivated in the paper by computing t...	Rebuttal 1: Rebuttal: We appreciate your thorough review and feedback. > It was not totally clear what techniques from the authors are totally new and what is building off of prior work. I would recommend adding a brief discussion of the work by Sharathkumar, at least. My understanding is that the big technical idea o...	Summary: This paper proposes a new, exact algorithm for solving the Euclidean weighted bipartite matching problem. Here, we have data sets $A,B \subset \mathbb{R}^d,$ each of cardinality $n,$ and the weight of an edge $ab$ is defined to be $\lVert a - b \rVert^p$ for any integer $p\ge 1.$ This formulation is motivated ...	Rebuttal 1: Rebuttal: We appreciate your thoughtful review and constructive feedback. We address your concerns below. > My main criticism, with a view towards the proposed application of $p$-Wasserstein distance computation, is that the case of unequal distributions is treated mostly as an afterthought. Arguably, thi...	Summary: The paper considers matching two sets $A$ and $B$ of $n$ points in the Euclidean space so as to minimize the sum of distances of matched points, when both pointsets are drawn independently and identically from the same (unknown to the algorithm) distribution. The authors extend the well-known Hungarian method...	Rebuttal 1: Rebuttal: We appreciate your thoughtful review and feedback. > The result of the paper is novel but I am not sure if I would call a better performance for only a special class of inputs an improvement over (slightly worse) results but that hold for general inputs. Response. We do not claim a better...	Rebuttal 1: Rebuttal: Thank you for the very positive feedback on our work. We want to emphasize a few important points that were also presented in our manuscript and hope that these points may help address some of the reviewers' criticisms. Novelty. The novelty of our algorithm is in placing the classical Hungari...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems	Accept (poster)	Summary: This paper studies the constrained nonconvex-concave minimax problem. This problem has been studied in several papers in the literature, but this paper proposes a projection free (single loop) algorithm to solve this problem. Strengths: The proposed algorithms are interesting and extend Frank-Wolf type method...	Rebuttal 1: Rebuttal: **Q1 The authors have made some effort into explaining why the FMO oracle might be much more computationally efficient as compared to a projection onto a set. The motivating example seems to be that of the nuclear norm constraint. Can the authors describe in a little more detail as to why this is ...	Summary: This paper investigates algorithms for constrained saddle point (SP) problems where the objective function is nonconvex-concave and smooth. Existing methods are usually projection-based and this paper focuses on developing single-loop projection-free algorithms which only use linear minimization oracles. In ...	Rebuttal 1: Rebuttal: **Q1 When analyzing the convergence guarantees for the R-PDCG method, the author also assumes that Y is S-Convex set. Is this condition inevitable? Also, the rate for R-PDCG is slightly worse than projection-based methods, is this rate improvable, or it is already optimal for projection-free algor...	Summary: This paper proposes projection-free optimization algorithms for constrained nonconvex-(strongly) concave saddle point problem. Solution concept: $\epsilon$-stationarity To this end, they propose R-PDCG and CG-RPGA algorithms. 1. Without projection, iteration complexity for R-PDCG is $O(\epsilon^{-6})$ for n...	Rebuttal 1: Rebuttal: Q1 Could you comment on the optimality of the bounds, i.e., how tight are the bounds? A1 Thank you for raising this question. We would like to remark that the lower bound complexity for finding an $\epsilon$-stationary of the problem (1) in nonconvex-concave setting is not known yet. Howe...	Summary: This paper proposed two projection-free algorithms for solving smooth nonconvex- (strongly) concave saddle point problems. The authors showed that the convergence rates of the proposed algorithms matches the state-of-the-art convergence rate of projection-based methods. Experimental results on dictionary learn...	Rebuttal 1: Rebuttal: Q1 It is recommended that the authors discuss the works on projection-free methods for solving bilevel optimization. A1 Thanks for the great suggestion. There is indeed a connection between bilevel optimization and saddle point (SP) problems and we will add the related work on bilevel o...	Rebuttal 1: Rebuttal: In response to the questions from reviewers, we have implemented our proposed algorithms to address the Robust Multiclass Classification example and have compared the results with those of competitive schemes. Moreover, in response to the reviewer WRhg, for the Dictionary Learning problem, the plo...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes two Frank-Wolfe (FW) based algorithms for solving a class of nonconvex-(strongly) concave saddle point problems. The proposed algorithms are among the first projection-free methods with convergence guarantees for such problems as authors claimed. The paper uses regularization and nested app...	Rebuttal 1: Rebuttal: Q1 Add an example where projecting on constraints are difficult A1 Thank you for your suggestions. We will add more experiments to the revised manuscript. Specifically, we have implemented our proposed algorithms to address the Robust Multiclass Classification example and have compared th...	null	null	null	null	null	null
Unsupervised Anomaly Detection with Rejection	Accept (poster)	Summary: The authors address the topic of rejection of samples in an unsupervised anomaly detection setup. Their approach focuses on determining a constant rejection threshold, which allows the detector to reject examples with high uncertainty. The new proposed method introduces this rejection threshold based on a conf...	Rebuttal 1: Rebuttal: Dear Reviewer imnX, We appreciate your positive feedback, and that you eventually were able to follow our theoretical contribution. In the revised version, we will address your points about the notation and try to give some more intuitions prior to the theoretical sections. We will f...	Summary: This paper presents a rejection scheme for the task of unsupervised anomaly detection. Learning to reject enables a predictor to withhold from making a prediction; this paradigm is more common in unsupervised learning. Here, the authors extend the rejection idea to the unsupervised anomaly detection task. Th...	Rebuttal 1: Rebuttal: Dear Reviewer Xnvu, Thanks for your positive and constructive feedback. Here are our responses: 1. [Experimental setup] Section 5.1 clarifies our experimental setup, including how we set hyperparameters. If you can let us know specific things that are unclear, we will add them. Also...	Summary: This paper suggests applying the stability metric computed by EXCEED for anomaly detection. The authors present theoretical findings regarding this metric, including the test rejection rate, as well as upper bounds for both the rejection rate and the expected prediction cost. Furthermore, comprehensive experim...	Rebuttal 1: Rebuttal: Dear Reviewer R9xH, We appreciate your positive review. Our method is anomaly detector-agnostic, which means that it can be applied on top of any anomaly detector. We ran the experiments using $12$ anomaly detectors included in the most recent and largest experimental comparison [23], whi...	Summary: - The authors proposed a selective predictor (learning to reject) for fully unsupervised setting in anomaly detection problems given an unsupervised anomaly detector. - The proposed method is based on the theoretical supports and the threshold can be selected without any labeled data. - The experimental result...	Rebuttal 1: Rebuttal: Dear Reviewer 3a9y, Thanks for the very specific and helpful feedback. We will address all your comments in the final version of the paper. Here is our response to your questions: 1. ExCeeD uses a Bayesian formulation that simulates bootstrapping the training set as a form of perturbati...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes an approach to perform learning to reject for anomaly detection in a completely unsupervised manner. The authors make three major contributions: (1) a novel theoretical analysis of a stability metric for anomaly detection, (2) a mechanism for designing an ambiguity rejection mechanism witho...	Rebuttal 1: Rebuttal: Dear Reviewer 6n88, Thanks for the constructive feedback. We will include a more thorough overview of how ExCeeD works in the final version of the paper to improve the readability of the paper. Lines 268 - 273 state that all the hyperparameters are set to their default value becau...	null	null	null	null	null	null
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning	Accept (poster)	Summary: The paper studies modular multi-task reinforcement learning to address negative transfer problem. The proposed method has two components: 1. contrastive learning on module outputs, to encourage model expressiveness and generalization. 2. use temporal information to combine module outputs, to address negative t...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback and would like to thank you for your time and effort in reviewing our manuscript. Any further discussion will be appreciated. > W1: The experiments don't reflect the claim that the method improves generalization. We believe that the following changes in sett...	Summary: This work proposes to enhance the expressiveness and generalization capability of the modular methods in multi-task reinforcement learning by applying contrastive loss over different task modules and encode the task related information with a temporal attention module. This work shows by applying both techniqu...	Rebuttal 1: Rebuttal: We extend our sincere appreciation to the reviewer for their valuable insights and constructive feedback. Any further discussion will be appreciated. > W1.1.1: According to the Sec 5, the Mixed version of MetaWorld benchmark is supposed to be more difficult than the fixed version. Why the Single-...	Summary: The paper introduces an approach for multi-task RL. Their approach is similar to CARE, which learns separate encoder modules, but they add a contrastive task loss on top of the encoders. They show this approach outperforms all reported baselines on Meta-World (MT-10 and MT-50) and a variant of Meta-World where...	Rebuttal 1: Rebuttal: We wish to thank the reviewer for their thorough review and valuable recommendations that have strengthened our paper. Any further discussion will be appreciated. > W1: A lot of the methods section should be moved to a preliminary section because it is difficult to understand what is novel and n...	Summary: This paper proposes an approach to multi-task RL called Contrastive Modules with Temporal Attention that aims to address the issue of negative transfer between tasks in multi-task RL. The proposed method consists of two main components: contrastive learning and temporal attention. The contrastive learning com...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their thoughtful suggestions and comments that have greatly improved our manuscript. Any further discussion will be appreciated. > W1: Limited insight into hyperparameter sensitivity The ablation of experts number can be seen in the pdf of global response. Th...	Rebuttal 1: Rebuttal: The PDF here includes our ablation experiments on the number of experts and the t-SNE visualization of CMTA attention weights of different tasks. Pdf: /pdf/f9aa8e1e49041a0fb8a66596d97ced199ac74490.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper focuses on multi-task reinforcement learning. Motivated by the negative task transfer within each task, the paper proposes the Contrastive Modules with Temporal Attention (CMTA) method, which utilizes temporal attention to modulate the weights of experts. Concretely, temporal attention takes recent h...	Rebuttal 1: Rebuttal: Our gratitude goes to the reviewer for their insightful comments, which have significantly enhanced the quality of our work. Any further discussion will be appreciated. > W1: The paper poses the multiple skills that may be utilized in each task as negative transfer problems within a task. I bel...	null	null	null	null	null	null
Rank-N-Contrast: Learning Continuous Representations for Regression	Accept (spotlight)	Summary: The paper proposes the framework Rank-N-Contrast in order to learn regression-aware feature representations. Authors claim that this representation learning mechanism captures the continuous nature of sample orders and helps achieve better performance in downstream regression task. Strengths: 1. The paper pro...	Rebuttal 1: Rebuttal: Dear Reviewer 2Kp1, Thank you for your valuable questions and thoughtful feedback. Your comments have helped us to further improve the quality of our paper. However, we believe that there are several important misunderstandings which we would like to clarify and address point-by-point. We hop...	Summary: The paper introduces a deep learning method, Rank-N-Contrast, for regression tasks. This method aims to capture continuity in data, something existing methods struggle with. The authors define a concept of $\delta$-ordered feature embedding and show theoretically that if Rank-N-Contrast loss is minimized, feat...	Rebuttal 1: Rebuttal: Dear Reviewer dBkT, Thank you very much for acknowledging the novelty and the contributions of our work. We sincerely appreciate the time and effort you have dedicated to evaluating our work. In the following, we address your concerns in detail. > *The precise relationship between the continual...	Summary: The authors present a new loss for representation learning in regression, RNC (Rank-n-contrast). RNC can be seen as the SupCon loss adapted to the regression setting, where the labels given are not hard class labels but rather continuous regression labels. In SupCon the negatives for each example are members...	Rebuttal 1: Rebuttal: Dear Reviewer wKSy, Thanks for the constructive comments and insightful feedback. We are glad that you found the method novel and simple to implement, the theory simple and well-motivated and the evaluation thorough. Here we address your concerns one by one. > *Currently the datasets that are e...	Summary: The authors discuss the benefits of contrastive learning for learning structured representations in a regression setting. While contrastive losses are typically formulated in terms of “similar” and “dissimilar” examples, the authors make use of the extra information conveyed by the continuous target label. The...	Rebuttal 1: Rebuttal: Dear Reviewer D7S9, Thank you very much for your valuable feedback. We are delighted to see that you found the method intuitive, the results compelling and the paper well-written, and we wish to express our gratitude for bringing the ethics considerations to our attention. Here we address your co...	Rebuttal 1: Rebuttal: We are grateful to all the reviewers for the time and effort they invested in reviewing our paper. It is heartening to note that the reviewers found: - The paper addresses an important (D7S9), ubiquitous (dBkT), and interesting (6JYz) problem. - The proposed method is novel (wKSy, ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors proposed a novel framework that learns continuous representations for regression problems, by contrasting samples against each other based on the rankings induced by the target values. The proposed method is evaluated on several regression tasks and the results show that the proposed...	Rebuttal 1: Rebuttal: Dear Reviewer 6JYz, Thanks for your constructive comments and insightful questions. We are delighted to see that you appreciate the contributions of our work. Below, we address your concerns in detail. > *It would be nice if the authors can further theoretically connect the delta-ordered featur...	null	null	null	null	null	null
Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification	Accept (poster)	Summary: This paper focuses on why the ensemble method works. Authors prove that the ensemble has a lower selective risk than the member model for any coverage within a range, based on some assumptions. Authors further conduct experiments on both computer vision and natural language processing tasks to verify proofs an...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. This paper is the first to provide the theoretical foundation of Deep Ensemble in selective classification. All the other three reviewers agree that our analysis is sound and insightful. Maybe some points in the paper are not well explained, but we can clarify th...	Summary: This paper aims to investigate why ensemble models perform superiorly compared to member models. The authors conduct an empirical study to demonstrate their assumptions. They separate the data into two categories: high-ambiguity samples and low-ambiguity samples. Their findings reveal that while ensembling hig...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. Maybe some points in the paper are not well explained. We will clarify them now. In addition, we encourage you to read the global response where there are some common questions you are interested in. In the following, we will answer your concerns point by point. ...	Summary: This paper provides a rigorous analysis of the reason for the success of the ensemble method, which includes both empirical evidence and theoretical proof. They found that the power of the ensemble method comes mostly from top-ambiguity samples where the member model diverges, and they provide theoretical evid...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. We encourage you to read the global response to find if there are some common questions you are interested in. In the following, We will answer your concern. > The discussion around Figure 2 is in fact a bit hard to digest, the author might want to think about a...	Summary: The authors present an analysis of deep ensembles in the context of selective classification, where a classifier has an option to abstain from providing a response in situations where it lacks confidence in its predictions. They prove that under reasonable assumptions, the performance of a deep ensemble in sel...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. We encourage you to read the global response to find if there are some common questions you are interested in. In the following, we will answer your concerns point by point. > I found section 4 of the paper to be weaker than some of the other sections. One insta...	Rebuttal 1: Rebuttal: Thank all reviewers for their valuable comments. This paper is the first to provide a theoretical foundation of Deep Ensemble in selective classification. All reviewers, except Reviewer qEtZ, agree that our paper has the following strengths: 1. The analysis in this paper provides some insight into...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
NCDL: A Framework for Deep Learning on non-Cartesian Lattices	Accept (poster)	Summary: This paper generalizes common machine learning operations from Cartesian lattices to other regular lattices, such as the hexagonal lattice. The authors argue that the Cartesian lattice is a sub-optimal representation for important natural signals, and that operating on their non-Cartesian structure natively le...	Rebuttal 1: Rebuttal: Thank you for your review : ). Please, see the global rebuttal, it should address most your comments. I believe the only issue left unaddressed is the question about numerical stability. This is a good catch, and something we didn't touch on in the paper. Since we leverage PyTorch for (almost al...	Summary: This works introduces a framework as well as software for computing convolutions on non-Cartesian lattices. The method is compared to existing software for hexagonal lattices as well as on image data. Strengths: The method is put in a strong theoretical framework that also explores the very important up and d...	Rebuttal 1: Rebuttal: Thank you for your review : ). With respect to the missing references, see the main rebuttal. This work does indeed require you to bend the notion that images are composed of square pixels. Given an isotropically band-limited image/function (which is really most natural images) it is possible re...	Summary: This paper introduces a high-quality software extension for PyTorch that enables seamless computations with non-Cartesian lattices for 1D, 2D and 3D images. The key observation made by the authors of this work is that non-Cartesian lattices can be decomposed as "sums" of Cartesian lattices: up to some clev...	Rebuttal 1: Rebuttal: Thank you, I believe no comments are necessary from me, here. --- Rebuttal Comment 1.1: Title: Acknowledgement of the rebuttal Comment: You are welcome. I am very satisfied with the paper: having read all reviews and rebuttals, I am convinced that this submission is a very clear accept. Good luc...	Summary: he authors claim that the concept of tensors, a fundamental cornerstone in machine learning, assumes data are organized on Cartesian grids. They further suggest that alternative non-Cartesian representations may be more beneficial in certain situations. One case is when the data is inherently non-Cartesian — f...	Rebuttal 1: Rebuttal: ### "It will be very helpful if the authors can construct a table which outlines several typical use cases, with the following information: (1) domain (vision/language/graph/etc), (2) dataset name and description of data format, (3) machine learning task (classification/segmentation/reconstructio...	Rebuttal 1: Rebuttal: First of all, I’d like to thank the reviewers for their time and detailed reviews. We will first address comments common to multiple reviewers. ### Derivatives Multiple reviewers pointed out some variation of concern towards the derivative computation. To clarify this, all of the computation f...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Nonparametric Boundary Geometry in Physics Informed Deep Learning	Accept (poster)	Summary: The Authors introduce a cross-attention (without self-attention) based decoder architecture for neural operators for solving PDEs. The architecture contains an encoder component that has the boundary defined by a triangular mesh as an input. The mesh encoder uses graph convolutions on the edges with uniform 4...	Rebuttal 1: Rebuttal: Please see our other response and the attached diagram for an explanation of our architecture. We hope this provides a clearer picture of our model. It is true that non-smooth boundaries (eg. sharp points or corners) can lead to PDE solutions which are non-differentiable, discontinuous or even ha...	Summary: This paper proposes a neural operator methods that can take different boundary geometry, in the representation of a mesh, as input to solve different PDEs. To the best of my understanding, the proposed method can take geometry represented in different triangular meshes as input and predict PDE solutions (i.e. ...	Rebuttal 1: Rebuttal: Please see our other response and the attached diagram for an explanation of our architecture. We hope this provides a clearer picture of our model. We have added in the attached pdf two examples of vanilla PINNs trained on test geometries for comparison with our neural operator. For the architec...	Summary: Physics Informed Neural Nets (PINN) have gained considerable attention lately. However, they are significantly expensive as compared to FEM or other classical PDE solvers. Moreover, any trained PINN is specific to the object geometry it has been trained upon. This paper proposes a solution to reuse the trained...	Rebuttal 1: Rebuttal: Please see our other response and the attached diagram for an explanation of our architecture. We hope this provides a clearer picture of our model. We have attempted to explain above why it is undesirable for information to be shared between different points. For a bit of further clarification, ...	Summary: The article describes a method to obtain the solution of a PDE given just the boundary mesh as an input. A variety of edge features are first transformed using MeshCNN, which are then used with a Transformer decoder to obtain the solution. The method is demonstrated to work for a few different PDEs on relative...	Rebuttal 1: Rebuttal: Please see our other response and the attached diagram for an explanation of our architecture. We hope this provides a clearer picture of our model. Regarding the complexity of the geometry, it is important to state that the quality of predictions will be highly dependent on the size of the model...	Rebuttal 1: Rebuttal: # Response to all Reviewers We kindly thank all reviewers for their time and helpful feedback on our paper. All reviewers agree that our description of our model architecture was confusing and unclear. We aim to give a more concrete and precise description below, which we hope will improve the cla...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking	Accept (poster)	Summary: This paper proposes a poison backdoor attack for visual object tracking, which only needs to use a preset backdoor trigger to poison a small number of training samples, so that the model makes wrong predictions on the backdoor samples. The authors evaluate multiple types of trackers on multiple tracking benchm...	Rebuttal 1: Rebuttal: Q1: In addition to [16], there are other similar methods, such as: [#1] TAT: Targeted backdoor attacks against visual object tracking A1: We appreciate this comment. TAT is a concurrent work with our paper. To achieve the attack purpose, TAT adds triggers to both the template and the sear...	Summary: This paper addresses an interesting topic. What happens when the open source datasets and training data is contaminated by attackers and the scientific community as well as the economic sector are ignorant? The work focuses on backdoor attacks where only the training data is tampered and builds on BadNets [Gu ...	Rebuttal 1: Rebuttal: Weakness: why DiMP is invulnerable; guidelines and best practices how to design a robust architecture of a neural network for learning tracking from contaminated data. A1: We appreciate the valuable comment. We can add more discussion in the revised paper or in supplementary material, giv...	Summary: This paper presents BadTrack, a poison-only backdoor attack on visual object tracking (VOT) models. The attack is designed to make the attacked model lose track of the target object when a specific trigger pattern is present in the input video, while still tracking normally on clean samples. The authors evalua...	Rebuttal 1: Rebuttal: Q1: More experiments on different trackers are needed, such as SiamCAR, SiamAPN. Temporal information-based tracker such as TCTrack. A1: We greatly appreciate this review. We understand the concerns of the reviewer, and we address each of them as follows: 1. Generalization to different ...	Summary: This paper studies backdoor attacks on video object tracking task, which is one of the most fundamental tasks in video surveillance. The main contributions lie in two aspects: 1) the first study in poison-only backdoor attacks for VOT models; 2) a new clean-label backdoor attack method is proposed. To verify ...	Rebuttal 1: Rebuttal: Q1: The technical contribution in this paper is somewhat incremental, which heavily borrows the idea from BadNets. A1: We appreciate the kind concerns. BadNets is one of the most classic backdoor attack methods in image classification problems that many excellent follow-up works exis...	Rebuttal 1: Rebuttal: We appreciate all the valuable and insightful comments. Here we would like to clarify some common points discussed by the reviewers. 1. Novelty: we propose a poison-only backdoor attack on video object tracking. To the best of our knowledge, this is the first feasible poison-only backdoor attack ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Black-box Backdoor Defense via Zero-shot Image Purification	Accept (poster)	Summary: In this paper, authors proposed a two-stage framework. Based on Range-Null Space Decomposition theory, they first utilize image transformation to destruct the trigger pattern, and then leverage a pre-trained diffusion model to restore the semantic information. Strengths: (1) To the best of my knowledge, autho...	Rebuttal 1: Rebuttal: ***W1&Q2:The chosen baselines of experiments in Section 4.3 (the results in Table 1) are not convincing and sufficient. As shown in this paper(line 261), “Defense methods available for black-box purification in zero-shot are rare”, why not setting the black-box backdoor defense Salient Conditional...	Summary: The paper proposes a backdoor defense for real-world black-box models through zero-shot image purification (ZIP). The proposed defense framework includes two stages: (1) applying linear transformation on a poisoned sample (2) using a pre-trained diffusion model to recover semantic information removed by stage ...	Rebuttal 1: Rebuttal: *W1: The authors only use three backdoor attacks including BadNet, PhysicalBA and Blended. Please use more attacks such as WaNet, label-clean attacks to verify the effectiveness of proposed method.* Thanks for the suggestion. We conduct further experiments with the **WaNet[1], Blind[2], Label...	Summary: This work proposes backdoor defense for scenarios in which a defender does not need to access the internal model, a.k.a., the black-box backdoor defense. Firstly, a linear image transformation is used to destroy potential trigger patterns. Then, a pre-trained diffusion model is used to reconstruct the missing ...	Rebuttal 1: Rebuttal: W1: The concept of black-box defense does not seem very practical and hence not quite attractive to me. We appreciate your concerns and wish to highlight that the black-box defense setting has been widely explored[1, 2, 3, 4]. In applications like fraud detection, organizations (e.g., fin...	Summary: This paper proposes a novel backdoor defense framework called "zero-shot image purification" (ZIP) designed to protect against backdoor attacks on real-world black-box models. The proposed ZIP framework consists of two steps: a linear transformation applied to the poisoned image to remove the backdoor pattern ...	Rebuttal 1: Rebuttal: *W1: The idea of destroying backdoor trigger and reconstructing the images is very similar to pre-processing defenses. Both defenses aim to remove the backdoor trigger by reconstructing the images.* The contribution of our work is different from existing pre-processing defenses [1,2,3,4,5] in...	Rebuttal 1: Rebuttal: # Global Response to All Reviewers Thank you for your time and efforts in reviewing our work. We greatly appreciate reviewers’ recognition of the quality and novelty of our research. Here is a summary of our response. Related Work: Our paper addresses the challenge of achieving practical *...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The work deals with the challenge of defending against backdoor attacks for black-box models in zero-shot settings using pre-trained diffusion models. Based on some theoretical insights, the proposed method modifies the reverse process of the diffusion model so the recovered images can be more high-fidelity an...	Rebuttal 1: Rebuttal: *W1: Empirical evidence supporting the proposed enhancements to [1,2,3] and validating theories is currently lacking.* Thanks for the thoughtful question. We would like to clarify the novelty and contribution of our work as follows. 1. **An effective black-box defense against backdoor attac...	null	null	null	null	null	null
Toward Understanding Generative Data Augmentation	Accept (poster)	Summary: The paper presents a theoretical study of the generalization properties of training when the training set is augmented with artificially generated data. The main result of the paper is a theorem which bounds the generalization error by two terms – one representing the divergence between the original training d...	Rebuttal 1: Rebuttal: # Response to Reviewer sAdG We thank Reviewer sAdG for the positive score and valuable comments. ## Weakness 1 & Q1: Assumption "distribution learned by the generative model is dependent on the sampled train set” Thanks for the suggestion. **This is not an assumption but a main challenge we ha...	Summary: The paper provides a theoretical analysis of the stability bound for generative data augmentation. The authors provide empirical evidence to validate the proposed theory on bGMM and GNAs. Strengths: Data augmentation plays an important role in deep learning. The paper provides a theoretical analysis of using ...	Rebuttal 1: Rebuttal: # Response to Reviewer hFUC We thank Reviewer hFUC for the valuable comments. ## Weakness 1: Standard augmentation Thanks for the helpful advice. **There is no comparison between the CIFAR-10 and the augmented CIFAR-10, and the standard augmentation is used to approximately verify our theory wi...	Summary: This paper studies generative data augmentation (GDA), in which the samples from trained generative models are added to the training dataset for training discriminative models. There have been several empirical research reports on GDA, and it is known that GDA is unlikely to be effective when real learning dat...	Rebuttal 1: Rebuttal: # Response to Reviewer jB6r We thank Reviewer jB6r for the acknowledgment to our contributions and insightful and constructive comments. ## Weakness 1: Constant-level improvement Thanks for the nice advice. In general, given a fixed $m_G$, it is challenging to obtain an explicit form of "consta...	Summary: Generative data augmentation (GDA) aims to improve model performance by generating artificial labeled samples to enlarge the limited training dataset, but is also highly influenced by the size of training dataset, choices of augmentation methods and the number of augmented data. The paper seeks to develop a th...	Rebuttal 1: Rebuttal: # Response to Reviewer Dxq5 We thank Reviewer Dxq5 for the valuable comments. ## Weakness 1: Organization Thanks for the advice. We will re-organize the separated remarks in a coherent and integrated manner. ## Weakness 2: Figures We will make the figures more recognizable by removing some unimpo...	Rebuttal 1: Rebuttal: # Summary of the revision We sincerely thank the reviewers for their valuable comments, which help to further improve the quality of our work. We have thoroughly addressed the detailed comments. and summarize the revision in the next version as follows: ## New results * **We add the results of ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this work the authors present new theoretical results for generative data augmentation. In particular, the authors introduce a new result that gives a bound on the generalization error of a model trained with data augmentation provided by a generative model. The authors use this bound to illustrate when GDA...	Rebuttal 1: Rebuttal: # Response to Reviewer SG4V We thank Reviewer SG4V for the positive score and valuable comments. ## Weakness 1: Scope of Theorem 3.3 Thanks for the helpful suggestion. Though we choose specified GANs and supervised learning in Theorem 3.3, **our general framework (Theorem 3.1) is a foundation ...	null	null	null	null	null	null
Fair Canonical Correlation Analysis	Accept (poster)	Summary: This paper proposes a fair CCA algorithm that aims to find fair CCA projection matrices. The authors claim that it is necessary to develop an appropriate algorithm for CCA with fairness guarantee. In the presence of sensitive attribute and unfairness in observed data, the proposed algorithms successfully reduc...	Rebuttal 1: Rebuttal: > W1: There is no theoretical guarantee for fairness provided. If the authors could theoretically demonstrate that the solutions to equations 7 and 9 have low $\epsilon^k(\mathbf{U},\mathbf{V})$ as has already been empirically shown, the contribution would be more novel. Response: Thank...	Summary: This paper addresses fairness and bias in Canonical Correlation Analysis (CCA). The authors propose a framework that minimizes correlation disparities associated with protected attributes, reducing unfairness without compromising accuracy. Experimental evaluation validates the effectiveness of the approach. Th...	Rebuttal 1: Rebuttal: > Q1: In the experimental setup, what considerations … real-world datasets? Were there any specific characteristics of these datasets that influenced the results or generalizability of the findings? Response: We carefully selected synthetic and real-world datasets to evaluate the fair ...	Summary: This paper investigates the concept of Fair CCA, focusing on addressing the potential bias that arises when analyzing the relationship between two sets of variables using CCA, a widely utilized statistical technique. The conventional application of CCA fails to account for the impact of sensitive attributes li...	Rebuttal 1: Rebuttal: > W1: Limited discussion on multiple modalities: While CCA is not restricted to two modalities, the paper primarily focuses on this scenario. It would be beneficial to discuss a more general setting involving multiple modalities and computing correlations under the fairness setting. **Respons...	Summary: This paper addresses a fairness issue that arises in CCA, proposing a fair CCA that well trade-offs correlation disparity errors w.r.t. sensitive attributes against correlation w.r.t. global projection subspaces. It introduces two optimization frameworks (multi-objective and single-objective), then developing ...	Rebuttal 1: Rebuttal: > W1: The translations for efficient algorithms, (8) and (9), can further be detailed for those who are not familiar with the manifold literature. Response: Thank you for your valuable comment. We have addressed the efficient solutions to subproblems (8) and (10) in Appendices A2 and A3,...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments and suggestions. We summarize all the most-concerned questions raised by the reviewers below and present new experiments, accompanied by detailed explanations for the corresponding attached Figures 1-3. * Reviewer kQSh has highl...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models	Accept (poster)	Summary: The paper presents a diffusion-based approach for reconstructing polygonal shapes from floorplan images and auto-drive sensor images. The idea is to refine an initial rough reconstruction through iterative denoising steps which revert a forward process that diffuses regular reconstructions into random noises t...	Rebuttal 1: Rebuttal: We thank you for the valuable questions and thoughtful feedback. We answer your questions and comments as follows. (A clarification: "elements" are the polygons/polylines in our tasks) --- W1&Q1. Computational costs of the iterative denoisers. Due to the space limit, we refer to *Q3 of Revi...	Summary: This paper proposes a Guided Set Diffusion Model for reconstruction, addressing the challenges of ambiguous denoising and selecting appropriate initial noise. By learning guidance networks, the model ensures distinct representations for samples with multiple permutations in structured geometry. During testing,...	Rebuttal 1: Rebuttal: We thank you for the valuable comments and appreciate the overall positive feedback. The question of extending PolyDiffuse to 3D reconstruction is quite open-ended and worth a detailed discussion. We provide our thoughts in the following, and the answer is organized into two parts: 1) describe t...	Summary: This paper proposes a novel method for reconstructing multiple polygon shapes using a conditioned diffusion model. The method first learns a score-matching-based prior diffusion model from data. This model is then used to denoise the sensor data, resulting in the reconstruction of the polygon shapes. To handle...	Rebuttal 1: Rebuttal: We thank you for the constructive questions and appreciate the overall positive comments on our presentation, idea, and experiments. We answer your questions/concerns as follows. --- **Q1. The maximum number of polygons and their vertices used in the training of the diffusion model should be cla...	Summary: - The paper introduces PolyDiffuse, a novel structured reconstruction algorithm that incorporates Guided Set Diffusion Models (GS-DM). - The core concept involves splitting the reconstruction pipeline into two distinct stages. - The forward diffusion process focuses on learning guidance networks to address ...	Rebuttal 1: Rebuttal: We thank you for the time and effort in providing insightful feedback and appreciate the overall positive comments. We answer the questions and concerns as follows. --- **Q1. It is worth exploring whether incorporating the condition as an input to the guidance network leads to performance improve...	Rebuttal 1: Rebuttal: We thank all reviewers for your time and efforts in providing valuable comments and constructive feedback. We are glad that all the initial reviews are on the positive side (two accept, two weak accept, and one borderline accept). --- We use this global response as **a complement to the individu...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The manuscript introduces a adaptation of the DDPM paradigm to enable denoising sets (instead of single data points like images). The method called GS-DM does this by introducing by adding noise per set element via learned guidance networks. This approach requires the addition of a proposal generator that init...	Rebuttal 1: Rebuttal: We thank you for the valuable comments and appreciate the overall positive feedback on the writing, the experiments, and the potential extension of our approach to broader domains. We will address your questions/comments as follows. --- **Weaknesses: Clarifications of the guidance network learni...	null	null	null	null	null	null
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?	Accept (poster)	Summary: This work investigates the learning efficiency of vision transformers by comparing their invariant object recognition performance to that of newborn chicks, when being exposed to similar number of images. The authors find that ViTs learn view-invariant representations like chicks and therefore claim that they ...	Rebuttal 1: Rebuttal: Thank you for your feedback and time. We address your 3 critiques below. >#1: The biggest weakness is that the starting point, that ViTs are thought to be more “data-hungry” than brains, is already proven false in earlier works... We thank the reviewer for pointing out this citation. This concer...	Summary: This study challenges the notion that Vision Transformers (ViTs), which excel in many computer vision benchmarks, require more training data than biological brains. The study involved controlled experiments on both ViTs and newborn chicks in impoverished visual environments, using a video game engine to simula...	Rebuttal 1: Rebuttal: Thank you for the positive feedback. We address your questions below: > In line 233, it is stated that the “embodied visual data streams acquired by newborn animals are rich in their own right.” How do you ensure that the agent visual stream is matched to that of the animal in terms of number of ...	Summary: This article examines the issues of data-hungry between chicks and ViT. For the sake of experimental accuracy, this paper makes a dark room and controls some variables in terms of organisms. In terms of ViT, ViT-CoT is proposed, and data enhancement, embedding and simple modification of the model are carried o...	Rebuttal 1: Rebuttal: Thank you for your feedback and time. We address your critiques below. Reviewer raises two concerns: (1) Why use chicks as a model system for studying newborn vision? (2) Our design can only address some features of animals. For (1), our revision will clarify why chicks are optimal for studyin...	Summary: The authors present a study showcasing, in one specific scenario, a vision transformer can match the visual learning performance of newborn chicks. The setup is as follows. Newborn chicks are raised in a dark enclosure for a week, and given only one visual stimulus from a variety of angles. Then, the chicks ar...	Rebuttal 1: Rebuttal: Thank you for your valuable questions and feedback, which we address below: > Not sure I really buy the 3-frame learning window idea. Does the algorithm actually not work if you only associate two frames with each other instead of three? We agree that the learning window duration is an interesti...	Rebuttal 1: Rebuttal: Our paper tackles a question at the heart of biological and artificial intelligence: Are vision transformers (ViTs) more data hungry than newborn visual systems? The answer to this question will have significant implications on (1) how transformers are viewed by AI researchers (e.g., brain-like or...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Exposing Attention Glitches with Flip-Flop Language Modeling	Accept (spotlight)	Summary: The paper introduces Flip-Flop Language Modeling (FFLM) to examine closed-domain hallucinations of Large Language Models (LLMs). Flip-Flop languages are synthetic benchmarks which model a single bit of memory and its operations: read (r), ignore (i), and write (w). For example, the string "w 1 i 0 r 1" is vali...	Rebuttal 1: Rebuttal: Thank you very much for the encouraging feedback and for the helpful suggestions! We are glad that you find our results interesting and significant, and would like to discuss the remaining concerns below. [W1] Generalizing insights to natural languages This is a great question; we provided ...	Summary: The paper identifies a simple task for which the Transformer architecture fails. The authors introduce flip-flop language modeling, towards quantifying the extrapolation capabilities of different architectures. Transformer models are determined to suffer from long tail errors, in a phenomenon termed "attention...	Rebuttal 1: Rebuttal: Thank you very much for the careful read and for the insightful comments! We are glad that you find the FFLM task to be well-motivated and helpful for steering the direction of the community towards fundamental concerns about the models. We hope to address your concerns below. **[W1] Connection t...	Summary: This work studies the phenomenon of "attention glitches" in LLMs. Attention glitches are instances where an LLM's attention mechanism fails to capture long-range dependencies, resulting in factual inaccuracies or erroneous reasoning. The authors introduce a new synthetic benchmark called flip-flop language mod...	Rebuttal 1: Rebuttal: Thank you very much for your careful review! We are glad that you find the attention glitches phenomenon to be a critical problem that is worth studying, and hope to address your concerns below. For the first three concerns in Weakness, please refer to the global response: - **[W1] Hallucination ...	Summary: This paper introduces Flip-Flop Language Modeling (FFLM), a synthetic benchmark designed to evaluate language model's (LMs) ability to perform operations on a single-bit of memory. LMs are evaluated on their ability to generalize to out-of-distribution (OOD) sequences. The training setups are varied along seve...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful comments! We are glad that you found findings from our minimally sufficient FFLM task to be original and significant. There were some great points raised in the review, which we’d like to address below. Providing a list of actionable future directions: ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their thoughtful comments. In this global response, we address questions posed by multiple reviewers, and outline additional experiments we ran during the author response period. [G1] Gap between FFLM and natural languages: We certainly agree that there are nume...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents a paradigm for diagnosing one cause of closed-domain (intrinsic) hallucinations in large language models (LLMs) and presents its analysis results. To understand why LLMs can be factually inaccurate or prone to erroneous reasoning, the authors proposed a flip-flop language model (FFLM) task...	Rebuttal 1: Rebuttal: Thank you very much for the thoughtful comments and great questions! We are glad that the reviewer finds it interesting that FFLM exposes the failure of multi-layer Transformers when smaller architectures can perform better, and hope to address the concerns below. **Why LSTM / smaller Transformer...	null	null	null	null	null	null
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage	Accept (poster)	Summary: This paper proposed a unified framework of Robust Markov Decision Process, which includes many newly proposed models as special cases. Under their generic framework, they proposed Doubly Pessimistic Model-based Policy Optimization (P^2MPO) that adopts a double pessimism principle for policy optimization. They ...	Rebuttal 1: Rebuttal: Response to Reviewer Ty5v Thanks very much for your appreciation of our work! In the following, we will try our best to address all your concerns and questions. Q1: The algorithm is computationally inefficient, i.e., not sure how to solve (4.1), (4.2) and (3.2) efficiently. A1: We ...	Summary: This study focuses on distributionally robust offline reinforcement learning to discover an optimal robust policy using an offline dataset for effective performance in perturbed environments. A novel algorithm framework called Doubly Pessimistic Model-based Policy Optimization (P2MPO) is proposed. P2MPO combin...	Rebuttal 1: Rebuttal: Response to Reviewer F7Eb Thanks so much for your appreciation of our work! We will keep improving our paper following your suggestions. In the following, we address all your concerns and questions. **Q1: The robust Bellman equation for $d$-rectangular sets are not formally proven. The proo...	Summary: This paper studies distributionally robust offline reinforcement learning. They propose a general learning principle, double pessimism, as well as a generic algorithm framework P$^2$MPO for robust offline RL, and show that it is provably efficient in the context of general function approximation. Strengths: ...	Rebuttal 1: Rebuttal: Response to Reviewer otjP Thanks you so much for your appreciation of our work! We will keep improving our paper following your feedbacks. In the following, we address all your concerns and questions. **Q1: Some minor typos: i) On line 100, is the policy a function from $\mathcal{S}$ to $\De...	Summary: This paper proposed a generic framework to study distributionally robust offline reinforcement learning problems, which included a model estimation step and a robust policy optimization step. Previous works in the literature usually assume finite state action spaces; this framework can incorporate function app...	Rebuttal 1: Rebuttal: Thanks for your detailed review and the meaningful suggestions! In the following, we will try our best to address all your concerns and questions. **Q1: It is better to present one example in details in the main paper, move the others to the appendix and put relevant literature review in the mai...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies the offline robust RL problem. A double pessimism approach is proposed and studied. Strengths: 1. The approach is novel and new compared to previous offline robust RL ones. 2. The approach can be used for large-scale problems. 3. The theoretical analysis is comprehensive. Weaknesses: 1...	Rebuttal 1: Rebuttal: Response to Reviewer NCKb Thanks for your review and the feedback. We will try our best to address all your concerns and questions in the following. Q1: The model the authors proposed, seems hard to solve. Do you have some efficient approach to solve the model you proposed? A1: We c...	null	null	null	null	null	null
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks	Accept (poster)	Summary: The paper presents a new vision-language model where tasks are specified via a language interface instead of being “hard-coded” into the architecture. The model relies on a pretrained decoder model to simply output the answer as text. E.g., for the object detection task the model would output a set of coordina...	Rebuttal 1: Rebuttal: Q1: There are many components and exactly how they interact is not clear from the paper. A1: There are only two main components in the VisionLLM: language-guided image tokenizer and LLM-based decoder, each of which has specific designs for open-ended tasks. We kindly invite the reviewer t...	Summary: This work presents a LLM-based framework VisionLLM for vision-centric tasks. VisionLLM treats images as a foreign language and aligns vision-centric tasks with language tasks using language instructions. Extensive experiments show that VisionLLM deliver comparable performance with task-specific models over di...	Rebuttal 1: Rebuttal: Q1: This work follows the idea of pix2seq to build a generlist (generalist) model and the authors believe that it is natural to do so, without providing a sufficient explanation of the motivation behind it. A1: We argue that _VisionLLM is not the scaling up of Pix2Seq_. Although both ...	Summary: This paper introduces VisionLLM, an instruction-following agent that can perform various vision-only (classification/detection/segmentation) and vision-language (captioning/VQA) tasks. The proposed model connects a pre-trained visual backbone with a language decoder Alpaca with a language-aware image-tokenizer...	Rebuttal 1: Rebuttal: Q1: Is pre-trained instruction-following LLM (Alpaca) crucial in your system design? A1: The instruction-following LLM is important for the convergence of VisionLLM. We have observed that Alpaca converges more easily than LLaMa. But the LLM is not limited to Alpaca, Flan-T5 (an instructio...	Summary: This work proposes VisionLLM, a unified framework for vision tasks and vision-language tasks, using natural language task prompts. It demonstrates capabilities in a good variety of tasks. Strengths: - This is the first attempt to use natural language prompts for vision-centric tasks such as object detection a...	Rebuttal 1: Rebuttal: Q1: Pipeline seems a bit complicated. A1: We would like to recap the components of VisionLLM. It has two key components: the language-guided image tokenizer and the LLM-based decoder. They work together in the following way: (1) Firstly, the visual encoder extracts features from the imag...	Rebuttal 1: Rebuttal: Dear all reviewers: We sincerely appreciate the reviewers for their time and effort in the review. This submission received 5 review comments, and 4 reviewers gave positive scores. We first address some common questions, followed by detailed responses to each reviewer separately. We hope our resp...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper construction a vision/language model by passing visual features into a LLM. They trained the model on several standard tasks as well object detection and referring expression by adding special location tokens to the LLM's vocabulary. The model features a text-guided image tokenizer and an efficient ...	Rebuttal 1: Rebuttal: Q1: Details of the `output-format-as-query` decoding. A1: We thank the reviewer's appreciation of our output-format-as-query design. In Common Questions Q1, we provide more details about output-format-as-query regarding the data construction, training, and inference process. Here, we answ...	null	null	null	null	null	null
RevColV2: Exploring Disentangled Representations in Masked Image Modeling	Accept (poster)	Summary: In this paper, the authors propose a new backbone RevColv2, which is suitable to the MIM pretraining and could learn disentangled representations during the pretraining. The strong experiment results show its effectiveness. Strengths: Please refer to Questions Weaknesses: Please refer to Questions Technical...	Rebuttal 1: Rebuttal: Dear Reviewer W68W, Thank you for your feedbacks. We will address your concerns below. Q1: Some details are unclear, line 103 says 'the un-masked patches are input into each bottom-up column'. While line 134 and Fig.2 say 'masked image patches are fed into the bottom-up columns and reconstru...	Summary: This paper proposed novel architecture to explore disentangled representations with masked image modeling. Different from previous MAE-like methods, this paper design a unified network and do not drop the decoder in downstream task. This paper showed that the disentangled representation is learned in different...	Rebuttal 1: Rebuttal: Let us answer your questions point by point. Q1: Figure 2 is misleading, the pre-training target on ImageNet-1k is single MIM or combined MIM with image labels? A1: Thanks for pointing out this issue. We do not depict the training task as a single task. Figure 2 illustrates the training ...	Summary: This paper proposes to keep the entire auto-encoder architecture during both pre-training and fine-tuning based on RevCoI. It contains bottom-up columns and top-down columns, and the information is reversibly propagated and gradually disentangled between them. Better results are achieved on ImageNet-1k and dow...	Rebuttal 1: Rebuttal: Dear Reviewer DHnY, Thank you for your feedbacks. We will address your concerns below. Q1: This paper is a little bit hard to follow, and I do not think the figures help a lot for understanding this paper. Maybe better visualizations/figures are needed. A1: Figure 1 shows the motivation...	Summary: The paper proposes a revised version of RevCol, referred to as RevColV2, which is applicable for MAE training. RevColV2 consists of an encoder-decoder framework. The encoder is the same as RevCol, while the decoder uses reversed column connections. The paper also uses a unified fine-tuning framework utilizing ...	Rebuttal 1: Rebuttal: Dear Reviewer xDT5, Thank you for your valuable feedback. We will address the concerns and answer them below. Q1: Although it is interesting, the contribution of V2 paper is limited. A1: RevColV2 is a new macro design that handles the inconsistent representations between pre-training an...	Rebuttal 1: Rebuttal: Deal all, We thank all reviewers' efforts in the comments of our submission. The original review comments recognised our novelty (xDT5, yT3P, W68W) and motivation behind RevColV2 (6koM, DHnY), and acknowledged the performance of RevColV2 (xDT5, yT3P, W68W). While the main concerns of reviewers a...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces "RevColv2," an advancement over the RevCol model, enabling compatibility with MIM training. The authors propose the new architecture comprising a bottom-up reversible column encoder and a top-down decoder, facilitating MIM compatibility while preserving disentangled low-level and semantic ...	Rebuttal 1: Rebuttal: Dear Reviewer 6koM, Thank you for your valuable feedback. We will address the concerns and answer them below. Q1: The results on ImageNet-1K are strong; however, there is a lack of speed comparison with other methods. It would be valuable to assess the runtime speed for both pre-training and...	null	null	null	null	null	null
Multiplication-Free Transformer Training via Piecewise Affine Operations	Accept (poster)	Summary: This paper argues that multiplications are the main bottleneck in modern neural network training and inference, and proposes to reduce the cost by replacing them with a cheap piecewise affine approximation. This can eliminate all multiplications in the training and inference process as claimed. Strengths: * I...	Rebuttal 1: Rebuttal: We appreciate the time spent reviewing our submission as well as your feedback and suggestions! Below we try to address your concerns and questions. Other multiplication-free works We apologize if this is an overclaim, that was definitely not our intention. We are aware of other works e.g. Ad...	Summary: This paper proposes to replace all multiplications involved in a Transformer training process with bit additions of input floating-point representations. This is shown to be an approximation to the piecewise affine function that is again the approximation of common functions in Transformer training. Results sh...	Rebuttal 1: Rebuttal: Thank you for your time and effort in reviewing our paper! Below we try to address your concerns and feedback. (1) PAM as bit addition We apologize that this can be misunderstood and propose rephrasing this paragraph as: We observe that Aˆ· B is roughly achieved by adding the signs (Equation ...	Summary: The paper introduces a novel approach that replaces all multiplications in Transformer training with a cost-effective piecewise affine approximation achieved by adding the bit representation of floating-point numbers. This method allows for a full multiplication-free training of Transformer models, covering li...	Rebuttal 1: Rebuttal: Many thanks for your time and the feedback you've given. Below we try to address the main concerns you list one by one. Fully multiplication free (1) You are correct that from a hardware perspective focusing on the matrix multiplications (on both the forward and backwards passes) may be suffi...	Summary: This paper presents a method for training deep networks completely without multiplication, via approximating multiplication using piecewise affine operations. The authors show that their method can be used to train modern deep networks, including Transformers. Strengths: The paper is extremely interesting...	Rebuttal 1: Rebuttal: Thank you for the time spent reviewing our submission and as well as your feedback and suggestions! We agree that it would likely have been better to de-emphasize the hardware efficiency and focus more on the multiplication-free aspect. We are unfortunately unable to change the title here, but wi...	Rebuttal 1: Rebuttal: # Global Response We are grateful to the reviewers for their efforts, insightful comments and constructive suggestions. We respond to all reviews individually. In this response we discuss the compatibility of PAM with lower precision formats, a question raised by several reviewers. In the manuscri...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval	Accept (poster)	Summary: This paper describes a test battery to test for the emergence of cognitive maps and planning abilities in LLM. The tests are based on existing cogsci experiments converted into text prompts. For example, to test for a planning ability, the prompt first describes an apartment layout, and then asks the model to ...	Rebuttal 1: Rebuttal: Weaknesses 1. Thank you for this thoughtful suggestion. We have edited longer sentences and improved grammar, punctuation, as well as the figures. 2. Yes, all graphs are evaluated in experiment 1, each with 3 instantiations with different spatial and non-spatial domains (18 total environments), ...	Summary: This paper evaluates LLMs on a set of tasks that could be solved by cognitive maps, such as goal-oriented planning, or incorporating shortcuts. The work finds that LLMs generally perform poorly at these tasks, and their performance is affected by features such as graph sparsity. Strengths: * The paper is adm...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough engagement with our work and thoughtful notes on the strengths and constructive suggestions. Weaknesses: 1- Framing. We agree that nuanced language is more productive. Our goal was to test zero-shot planning behavior in LLMs. If accepted the camera-ready i...	Summary: The paper presents CogEval, a set of best practices ported from cognitive science on how to do behavioral evaluations. The authors also transcribe new tasks from human reinforcement learning and planning into text, such that LLMs can be tested on them. On these tasks, the authors do not observe evidence for an...	Rebuttal 1: Rebuttal: We are deeply grateful that the reviewer finds CogEval clear and thorough and are delighted to read they may potentially try it or use it in their work. We hope that we have addressed their helpful and constructive suggestions below and are more than happy to address any further feedback. Weaknes...	Summary: This paper proposes an evaluation of large language models with respect to their ability to solve problems that require use of latent cognitive maps. Evaluation focuses on different underlying graph structures, and the influence of chain-of-thought inference on performance. Strengths: * I really liked the ver...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their positive evaluation of our work as well as their careful and constructive questions and suggestions. Please see our responses below. We hope that we have addressed any concerns and are happy to address further questions in the discussion period as well. W...	Rebuttal 1: Rebuttal: Dear reviewers, We are deeply grateful for your careful and detailed review of our work as well as your constructive questions and suggestions. Given the space constraints we have tried to address your questions as best as we could and have provided further material in the attached PDF. We hop...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking	Accept (poster)	Summary: The paper tackles user-oriented tasks, e.g. personalized recommendation, and proposes a self-supervised method named AdaptSSR to replace the contrastive learning pre-training target task. It adopts a ranking loss that selects samples of smallest similarity differences and assigns dynamic weight coefficients to...	Rebuttal 1: Rebuttal: We are sincerely grateful for the time and effort you have invested in reviewing our paper. In response to your insightful comments, we have provided detailed explanations and clarifications, which are enumerated below. Weakness and Question 1: The Multiple Pairwise Ranking loss, which is the...	Summary: Recent studies have explored pre-training user models with contrastive learning tasks to address data sparsity issues in user-oriented tasks. However, existing augmentation methods may introduce noisy or irrelevant interests, leading to negative transfer. To overcome this, a new approach called Augmentation-Ad...	Rebuttal 1: Rebuttal: We appreciate your time in reviewing our paper. However, we suspect that there may be certain misconceptions. In order to address your concerns, we provide detailed, point-by-point responses as follows. **Due to the character limit of each rebuttal, more responses are provided in the global respon...	Summary: This paper proposes Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), a new user model pre-training paradigm, which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Conventional methods assume that different views of the same...	Rebuttal 1: Rebuttal: We very much appreciate your positive opinions on the contribution and presentation of this paper. We also thank you for the valuable comments and our detailed responses are as follows. Weakness 1: An important hyperparameter sensitivity analysis is missing: how does the value of $\lambda$ af...	Summary: The authors tackle the problem of doing self-supervised learning for user modeling. Inspired by the successes of contrastive learning approaches in the image setting, they adapt contrastive learning to the user modeling setting. However, in user modeling the augmentations typically used are not very suitable...	Rebuttal 1: Rebuttal: We really appreciate your careful reading and constructive comments. We also very much appreciate your acknowledgment that our proposed method is novel and well-designed. Following are our detailed responses to your comments. Weakness 1: Going back to the example where the user behavior is re...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their appreciation and constructive comments. We have provided detailed responses to each reviewer's concerns and questions in the following rebuttals. We hope our responses will address your concerns and strengthen our paper. We are happy to respond to any...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings	Accept (poster)	Summary: This paper introduces QACE, an algorithm for automatically learning the capabilities of sequential decision making agents through distinguishing queries. The problem is formulated in terms of the predicates and capabilities of an agent, which are assumed to be known a priori. QACE then aims to compute a transi...	Rebuttal 1: Rebuttal: We thank the reviewer for detailed review and suggestions. We plan to use the additional page to incorporate them including suggestions for the description of FOND and PPDDL models. Quality: GLIB uses such a method that generates random traces and hence is used in our comparison as a baseline...	Summary: The paper proposes an algorithm for learning a probabilistic model of a black box agent's capabilities. The method assumes the existence of a vocabulary to describe the environment's state and the set of capabilities. The proposed method generates all possible hypotheses using three ways to add a predicate (as...	Rebuttal 1: Rebuttal: We thank the reviewer for the review and suggestions. We address your concerns below: Weakness) As mentioned in lines 177-181, there are just three hypotheses corresponding to any $\langle l,p \rangle$ pair. Hence the number of hypotheses to be considered are a small constant (=3) at any ste...	Summary: The paper tackles the problem of modeling the capabilities of a block-box sequential decision-making agent (SDMA) by querying the SDMA agent along the way. The presented method (QACE) uses an active learning approach to interact with the block-box SDMA and learn an interpretable probabilistic model of its capa...	Rebuttal 1: Rebuttal: Thank you for the detailed review. We address your questions and concerns below: Q1) No, QACE doesn’t assume that there is a single model that defines the given SDMA’s functionality. QACE can return a functionally equivalent model when there are multiple correct representations. E.g., if p(x)...	Summary: This paper addresses the problem of creating a user-interpretable probabilistic model of the capabilities of a sequential decision-making (SDM) system (through only interacting with the system as a black-box (rather than inspecting its internal structure, e.g., reasoning dynamics). In particular, PPDDL is pro...	Rebuttal 1: Rebuttal: Thank you for the detailed feedback and questions. We address your questions and other concerns below: Q1) The reviewer correctly points out that the search space of possible preconditions and effects is exponential. We also mentioned this in lines 40-42. Verma et al. [1] showed that precondi...	Rebuttal 1: Rebuttal: We thank the reviewers for their detailed reviews and comments. We answer the questions posed by the reviewers separately. Please find them in the response below the reviews. We are also adding a supplementary page with two plots. One showing the zoomed in version of the plot for variational dista...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a method for modeling the capabilities of black-box artificial intelligence systems, which can plan, act, and execute in a stochastic setting. Specifically, the proposed method introduces an active learning approach to interact with the black-box SDM system and learn a probabilistic model t...	Rebuttal 1: Rebuttal: We thank the reviewer for questions and support. We address your comments on the weakness of the approach below: Weakness 1) The test set is not the same as the training set. As mentioned in lines 332-334, we used a single problem as input. Additionally, QACE (our approach) generates queries ...	null	null	null	null	null	null
Does Graph Distillation See Like Vision Dataset Counterpart?	Accept (poster)	Summary: This paper mainly focuses on the Laplacian Energy Distribution (LED) shift problem of graph dataset condensation. Strengths: The pipeline figure is clear and straightforward. The studied problem, how to condense a graph dataset, is relatively important. Weaknesses: Compared to existing studies that widely ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the instructive questions. We make responses to the reviewer’s comments as follows. ## Q1: More analyses and explanations of motivation? A1: Thanks for the question, we show the storyline of our paper as follows. - The core question in our paper is: “Does gr...	Summary: This paper proposes a novel method called SGDD for condensing large-scale graph datasets while preserving the original structure information. The proposed method uses a graphon approximation method to broadcast the original structure as supervision for generating the condensed graph structure and optimizes it ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the detailed comments and insightful questions. We make responses to the reviewer’s comments as follows. ## Q1: The motivation for using JS divergence in the calculation of $SC$ is unclear? A1: Thanks for the comment. We list three commonly used distances in t...	Summary: The paper proposes a novel approach for graph dataset distillation called Structure-broadcasting Graph Dataset Distillation (SGDD). The authors explicitly consider the impact of the original structure information on graph condensation and demonstrate that their approach achieves state-of-the-art results on 9 d...	Rebuttal 1: Rebuttal: We appreciate reviewer 9hTZ’s constructive feedback and are glad that the reviewer finds our work novel. We answer the questions one by one as follows. Hope it can address the reviewer’s concern. ### Q1: [Actual Computation Savings] A1: Thanks for the suggestion. We conduct the experiments on 5 ...	Summary: The paper investigates the effects of structural information in graph condensation methods. The authors claim that by maintaining the original structure during condensation using a newly formulated method called Structure-broadcasting Graph Dataset Distillation (SGDD), they are able to achieve more refined res...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful comment. We make responses as follows. Q1: Need experiment for large datasets. A1: Thanks for the reviewer’s suggestion. Dataset Statics: - As illustrated in the table below, there are four datasets that are significantly larger than ...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a new graph condensation framework called SGDD. The authors argue that existing methods overlook the structure information of the original graph during the condensation. And thus they propose to 1) use the Laplacian Energy Distribution (LED) shift to indicate the generalization performance ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the detailed comments and insightful questions. We make responses as follows. ## Q1: The conflict of assumptions from the references [65] and [78]. A1: Thanks for the comment. There are primarily two types of graph optimal transport distances: Gromov-W...	null	null	null	null	null	null
Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution	Accept (poster)	Summary: This paper proposed an efficien training technique for Vision Transformers, called Patch n’ Pack. Specifically, it packs multiple images of various input resolutions into a single sequence as a batch exmaple. Furthermore, based on the modified architecture, the authors proposed NaViT. By combining Patch n’ Pac...	Rebuttal 1: Rebuttal: Many thanks for time spent reading the paper and your critique and comments; we're glad you appreciated the significance of the research problem, clarity of writing, and the strength of results. We address here the mentioned weaknesses. 1. `Packing examples into a single sequence during training...	Summary: This paper focuses on adapting the computer vision model to flexible usage. The authors stand from the ViT architecture and exploit its flexible sequence-based modeling to enable arbitrary resolutions and aspect ratios. The proposed NaViT could benefit the downstream tasks of object detection, image, and video...	Rebuttal 1: Rebuttal: Many thanks for your review and critiques; we have made a few updates to the manuscript based on your feedback, and hope it helps address some of the mentioned weaknesses. We will discuss them in more detail now: 1. `The architecture design of NaViT and its essential components to extract visual...	Summary: This passage discusses the common practice of resizing images to a fixed resolution before processing them with computer vision models, which is not optimal. The author introduces a new model called NaViT (Native Resolution ViT) that takes advantage of flexible sequence-based modeling and allows for processing...	Rebuttal 1: Rebuttal: Thank you for the detailed review and comments. We first address the weaknesses section, and in particular concerns around lack of novelty. Weaknesses Sequence packing, variable aspect ratio, and token dropping are indeed not new concepts, and have independently been studied. This is not true ...	Summary: The authors propose to use example packing to train ViTs, where training examples of various lengths are packed into a single sequence. This requires a few straightforward architecture changes, including modified attention masking, pooling, and positional embeddings. This scheme allows for some interesting ide...	Rebuttal 1: Rebuttal: Firstly, many thanks for your time and detailed feedback. It was very useful and constructive, and we made multiple improvements to the manuscript based on your comments. Please see below for some detailed responses to the weaknesses you pointed out. 1. `Compute matching and asymptotic performanc...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and valuable comments. We were happy to hear that they found the paper “very comprehensive”, leading to “impressive performance” (WGZP), making a “compelling argument” that we need to go beyond fixed-size resolutions (CXkP), based on an “*interesting ide...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception	Accept (poster)	Summary: This paper proposes a learned approximate execution framework named Chanakya. Chanakya considers intrinsic context like images and extrinsic context like latency and predicts runtime decisions. The reward function helps to learn a better trade-off online. Extensive experiments on the Argoverse-HD dataset show ...	Rebuttal 1: Rebuttal: Thank you for your thought provoking review, we shall incorporate the suggested point in the final manuscript! 1. Thank you for the upper bound suggestion, please see main comment. 2. Details of controller training method are provided in the supplementary along with the anonymous code link, please...	Summary: This paper introduces Chanakya, a framework for computation planning in real-time (streaming) perception. Chanakya uses a novel reward to make run-time decisions based on content- and system-based characteristics, simultaneously optimizing for both accuracy and latency. The proposed controller is learned on in...	Rebuttal 1: Rebuttal: Thank you for your insightful review, we shall incorporate these points in the final version of the manuscript. 1. Increasing Search Space: We agree that this is an important direction. However, adding even more decision dimensions – such as edge-cloud processing (should an image be sent to a ...	Summary: The paper looks at the problem of unpredictable compute requirements for real-time perception. It addresses this in term of a multi-objective optimisation problem (quality of results and latency). The authors propose using RL in order to optimise the selection of various characteristics in order to achieve an ...	Rebuttal 1: Rebuttal: Thank you for your suggestions in the review, they will definitely improve the quality of the manuscript. 1. Novelty: Please see main comment. 2. Thank you for this suggestion. We have added some key numerical highlights in main comment, which we will add in the introduction. 3. Thank you for thi...	Summary: This work provides a novel learning-based approximate execution framework to learn runtime decisions for real-time perception. The learned controller proves to be efficient and performant, which appears to be useful for many real-time perception applications in the cloud and edge. Strengths: 1. The focus of ...	Rebuttal 1: Rebuttal: Thank you for your insightful review, we are glad to see that you agree that Chanakya has been proved to learn performant execution policies in a variety of scenarios. 1. Single Task Experiment: We performed a thorough study using one task – detection, but across datasets, scenarios and edge ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments. We are glad to see that reviewers noted that our paper is “well written” (R4, R5, R6) and “well-organized” (R6) as we tackled an “relevant and impactful problem” (R5) with a “clear motivation” (R3). It is heartening to note that our “learne...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The author designs Chanakya, a learned execution framework for streaming perception that jointly optimizes accuracy and latency. To achieve so, the framework captures both intrinsic and extrinsic contexts and utilizes a novel reward function to train a learned controller. Strengths: 1. The motivation is clear...	Rebuttal 1: Rebuttal: Thank you for your insightful review, it has helped us clarify aspects of our work. We believe the raised concerns are addressable in the manuscript. 1. Please see the main comment for novelty concerns. 2. We shall improve the presentation of the work and move the tables and add a high level overv...	Summary: The paper presents Chanakya, a learned framework for real-time perception that automatically balances accuracy and latency. Unlike previous fixed rule-based methods, Chanakya considers both intrinsic and extrinsic factors and is trained to make flexible decisions. evaluations show that it outperforms existing ...	Rebuttal 1: Rebuttal: Thank you for your insightful review! 1. We train the controller from scratch depending on the conditions simulated. We leave robustness as future work, as large number of inferences from existing RL works can be drawn to improve this aspect (such as domain randomization). Cost of training the po...	null	null	null	null
Fine-Tuning Language Models with Just Forward Passes	Accept (oral)	Summary: Fine-tuning with backpropogation becomes infeasible for very large language models because it uses too much memory. While zeroth-order optimization uses far less memory and could in principle fine-tune the model with just forward passes, past theory suggested that the learning rate must scale down with the num...	Rebuttal 1: Rebuttal: Can we study the effective rank assumption in some language models? It is difficult to translate results on very small models, on which we would be able to measure the effective rank, to the large ones that we would find MeZO useful for. We would also likely need to pre-train these very small...	Summary: This paper proposes a new zeroth order optimizer, MeZO, for LM training. This is proposed as an improvement to ZO-SGD. The advantage of this approach is a 12x reduction in the amount of memory required for training compared to backpropagation. This enables the training of much larger models. The effectiven...	Rebuttal 1: Rebuttal: Can the theoretical convergence analysis of MeZO be compared to backpropagation? Corollary 1 directly compares the SGD convergence rate to the convergence rate of MeZO, since the term in brackets in equation 5 is the per-step loss decrease of SGD (see Lemma 1). Two factors make MeZO converge ...	Summary: The paper present a new optimiser MeZo based on stochastic approximation using gradient perturbation. This optimiser is very memory efficient as it only requires to perform 2 forward passes with different deltas/epsilons on the parameters and multiple gaussian samplings. These algorithms allows "finetunning...	Rebuttal 1: Rebuttal: Why does MeZO require using a prompt? What tasks can MeZO work on? Why do you need the Hessian hypothesis (Assumption 1)? Please refer to our general response. In short, we hypothesize that using a prompt makes the fine-tuning objective similar to the pre-training one, which likely has a Hess...	Summary: The paper proposes an enhanced memory efficient zero-order optimization method named MeZO. MeZO only requires the same memory as inference time and thus can enable model tuning for large LMs with limited memory budget. The authors demonstrate the efficacy of MeZO on multiple NLP benchmarks compared with linear...	Rebuttal 1: Rebuttal: Thank you for your suggestion, and we will report average numbers in the experiment results in the next revision. What is the practical training time compared to standard fine-tuning? Please refer to our general response for a wall clock time analysis. In short, MeZO reduces the number of GP...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable feedback. We address some shared questions here. **When can MeZO succeed in fine-tuning? What losses satisfy Assumption 1 (i.e., the Hessian has a low effective rank)? Why is a prompt necessary for MeZO to be able to fine-tune the model? Can you verify th...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work introduced a memory-efficient zeroth-order optimizer that can fine-tune large language models with the same memory footprint as inference, using only forward passes and gradient estimates. Comprehensive experiments across model types, scales, and tasks, showing that MeZO outperforms zero-shot, in-con...	Rebuttal 1: Rebuttal: Does MeZO work for generation tasks? Table 1 and Figure 1 show the performance of MeZO on DROP and SQuAD, which are two question-answering tasks that are formatted as generation tasks in our experiments. For each task, given the question, we train the model to directly generate the answer tex...	null	null	null	null	null	null
Bounded rationality in structured density estimation	Accept (poster)	Summary: The author built a new model to explain the human mental model of density estimation given sequentially observed data points. The model consists of a “Rational component” that generalizes the Chinese restaurant process. And an “Aleatoric component” that adds some error term. The model is fitted on real experim...	Rebuttal 1: Rebuttal: We thank the reviewer for his/her insightful feedback and questions. Reply to *How much of the overestimation of cluster sizes is due to the small size of samples?* Unlikely. If the overestimation of # clusters were due to a small sample size, then we would've observed a lower # clusters in...	Summary: The authors describe a density estimation task with humans wherein participants were asked to identify parameters of an unknown distribution from presented data. While participants did a better job of estimating the overall density with more samples, the authors observed a large error in the reported number of...	Rebuttal 1: Rebuttal: We thank the reviewer for helpful comments and clarifying questions. There are many important choices in the aleatoric component, so we provide an elaborated explanation here. We will revise the manuscript and the supplemental material to add these descriptions. ***Reply to "Intuition and motiv...	Summary: This paper presents a visual density estimation task for human subjects based on sequential data from gaussian mixture models, complete with experimental data analyzed through a bounded-rational model of behavior. The paper reports data on the quality of the density reported by subjects, claiming that it rough...	Rebuttal 1: Rebuttal: We appreciate the reviewer's suggestions on providing more details about human data and model fits, and more quantitative support for qualitative statements. These suggestions have inspired us to dig deeper into our data and model fits, which has yielded richer results (see response PDF) that we b...	Summary: The authors consider the question: how do humans estimate probability distributions? They study this by performing three experiments where human subjects, where they ask participants to recover Gaussian mixture models after seeing IID samples. They find that while subjects do seem to get closer to the true mix...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive and helpful feedback. *Reply to "It seems hard to rule out the interpretations where human subjects just estimate 2--3 clusters across the board"* Behaviorally, we see that the distribution of reported $K$ varies across the number of samples and distri...	Rebuttal 1: Rebuttal: We thank all reviewers for their careful read of the paper and helpful feedback. We are glad that all five reviewers gave fair and comprehensive summaries, indicating that the paper is mostly well written, as also stated by four Rs (Ds9y, Xmkb, ti72 & 8bgA). All Rs acknowledged that this work...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This study investigates how humans infer probability distributions from samples by combining experiments and modeling. The main contributions include a careful characterization of the behavioral tendency to overestimate the number of clusters as well as a modeling framework to identify how this behavior can ar...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and constructive suggestions. *Reply to "Implication of the results to more naturalistic scenarios"* Our experimental design is an abstraction of many cognitive tasks that require density estimation, or finding statistical patterns from sample...	null	null	null	null	null	null
Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence	Accept (poster)	Summary: The authors propose a method to extract per-pixel feature descriptors from multi-scale and multi-timestep feature maps generated by diffusion models. These descriptors can be utilized for various downstream tasks. The framework is evaluated on the task of semantic keypoint correspondence, specifically on the ...	Rebuttal 1: Rebuttal: 1. Clarification on Consolidation Process. We would be more than happy to provide additional details regarding the aggregation network, what specific points were unclear? We discuss details regarding the feature aggregation network as well as how we handle variations in scale and time of the d...	Summary: This paper proposes diffusion hyperfeatures, a framework for integrating different scale and timestep features to form a representative feature descriptors in dense level. Within the U-Net architecture, unlike other works that uses hand-crafted methods to select a particular subset of layers for further proces...	Rebuttal 1: Rebuttal: 1. This paper is one of the first attempt to tackle semantic correspondence with diffusion concept. Our method is novel. We are not claiming to make a contribution for a feature extraction or matching algorithm hand-crafted for the task of semantic correspondence; rather, we propose a simple a...	Summary: This paper proposes improving feature distillation from diffusion models for representation learning by aggregating information from the feature maps of the U-Net at varying timesteps, weighting them with a tunable aggregation network. The authors show that even at timesteps from which features are usually dis...	Rebuttal 1: Rebuttal: 1. For figures 2, 3, would it be possible to show also the network input at these timesteps for reference? In Figure 2 of the main paper, the input to the diffusion model is the text prompt “Cat sitting in a living room” and random noise for $x_{T}$. In Figure 3 of the main paper, the input to...	Summary: This paper proposes an approach for extracting useful features for pre-trained diffusion models for application to dense visual correspondence tasks. How to do this is not clear due to presence of features both through the network, and over diffusion steps. The proposed approach is to learn which features to u...	Rebuttal 1: Rebuttal: 1. How does it quantitatively compare against more recent supervised approaches? Please see the global response for a comparison to CATS++ and DINOv2, where we outperform both methods on SPair-71k by 2\% and 4\% PCK\@0.1_img respectively. 2. Why share bottleneck layers over time steps? ...	Rebuttal 1: Rebuttal: ### Summary We thank the reviewers for their helpful feedback and suggestions, which we will integrate into the final manuscript. In this work we present a “simple and [...] very effective” (Reviewer sN43) framework for consolidating the internal representations of a diffusion model for tasks such...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper explores semantic correspondence tasks with stable diffusion model. Specifically, the authors proposed to first extract feature maps varying across timesteps and layers from the diffusion process and trains a lightweight neural network to aggregate them together for semantic correspondence. Experime...	Rebuttal 1: Rebuttal: 1. Have the authors explored other tasks [...]? Please see the global response PDF for new applications in semantic appearance transfer and video mask propagation. 2. *Concurrent works: there are multiple papers presenting correspondence ability of diffusion models: [...] Could the authors ex...	null	null	null	null	null	null
Efficient Exploration in Continuous-time Model-based Reinforcement Learning	Accept (poster)	Summary: The submission considers online RL, while the true dynamics is continuous-time. To deal with this issue, the authors provide a continuous-time model-based method. The proposed method is special by 1. iteratively fitting ode-based models and deriving/learning the control from the fitted models 2. having the o...	Rebuttal 1: Rebuttal: Thank you for your comments and valuable feedback! ## Weaknesses 1. Why continuous-time modeling: Continuous time learning has several benefits over discrete-time modeling. For example, all systems in natural sciences are continuous in nature, therefore introducing priors into the learning probl...	Summary: This paper proposes a continuous time framework for model based reinforcement learning. Their algorithm OCoRL solves the optimal control problem eq (1) by: 1, selecting optimistic policy. 2, rollout to collect data. 3, update model estimation and statistics. Specifically, they study the measurement selection s...	Rebuttal 1: Rebuttal: Thank you for your positive and valuable feedback! # Weaknesses and Questions 1. A comparison in terms of performance: time/computation complexity and over all cost with existing approach (PPO, etc) would better help readers evaluate this approach: As you correctly notice the first step of OC...	Summary: This paper proposes a continuous-time model-based reinforcement learning method for controlling fully observed dynamical system environments where there is a cost to take a sample of the state. A Gaussian process dynamics model is used, and a novel adaptive measurement selection strategy is proposed to determi...	Rebuttal 1: Rebuttal: Thanks a lot for the positive and valuable feedback! ## Weaknesses 1. Case when state derivatives $\\dot{x}(t)$ are not observed: We thank the reviewer for this very interesting question. When the state derivatives are not observed one can apply several techniques to obtain them (e.g., using fin...	Summary: This paper introduces a novel algorithm for efficient exploration in continuous-time model-based reinforcement learning. The algorithm represents continuous-time dynamics using nonlinear ODEs and captures epistemic uncertainty using probabilistic models. The analysis shows that the approach achieves sublinear ...	Rebuttal 1: Rebuttal: First, thank you a lot for your positive and valuable feedback! We will indeed incorporate the proposed ideas in the updated version of the paper. ## Weaknesses 1. Enhance Clarity: We thank the reviewer for the feedback. We have added the following summary of our method at the end of section ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable and useful feedback. We believe there is been some confusion around the discrete-time control setting we consider in our work. Accordingly, we have clarified this further in the appendix of the updated paper. We include a summary below: 1. When we lea...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Nonparametric Teaching for Multiple Learners	Accept (poster)	Summary: This paper extends nonparametric teaching from the setting of teaching each learner independently to teaching multiple ones simultaneously. The method is about teaching a vector-valued model, which improves over existing methods when multiple learners can communicate with each other. There are both theoretical...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments! We respond in detail to your specific concerns in the following. Q1: Due to the page limitation, we have provided two experiment results in the main paper. We also show additional performance evaluations of MINT under various settings and prese...	Summary: The paper extends non-parametric machine teaching to the case of multiple learners. In particular, each learner learns one component of a vector-valued function. The authors consider the case where learners have no communication with each other and the case where there is "communication" via a matrix transform...	Rebuttal 1: Rebuttal: Thanks for the useful comments. We are deeply appreciative of the reviewer’s efforts to help us improve our paper. We take all comments seriously and try our best to address every raised concern. We sincerely hope that our response can resolve your concerns. Any follow-up questions are welcome. ...	Summary: The paper studied nonparametric teaching in the presence of multiple learners. Following prior works on nonparametric teaching, the paper extended to a scenario where multiple learners simultaneously learn a separate component of the joint model. The paper first analyzed the performance of the Random Functiona...	Rebuttal 1: Rebuttal: Thanks for the encouraging comments. We sincerely thank the reviewer's efforts for helping us improve the paper. We hope that our response resolves your concerns. Q1: Thanks for pointing it out. We introduce the convex loss assumption above Eq.(7), and in the revision, we will make sure to hi...	Summary: This paper investigates the iterative machine teaching problem under the non-parametric learner setting with vector-valued target models, also known as multi-learner nonparametric teaching (MINT). The authors consider two teaching strategies: Random Functional Teaching (RFT) and Greedy FT (GFT). The authors fi...	Rebuttal 1: Rebuttal: Thanks for the useful comments. We are deeply appreciative of the reviewer’s efforts to improve our paper. We take all comments seriously and try our best to address every raised concern. We sincerely hope that our response resolves your concerns. Q1: An important problem towards realistic ap...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Efficient Activation Function Optimization through Surrogate Modeling	Accept (poster)	Summary: The paper presents a new method for improving the performance of neural networks through the design of optimal activation functions. The authors created benchmark datasets by training convolutional, residual, and vision transformer architectures with systematically generated activation functions. They then dev...	Rebuttal 1: Rebuttal: Response to Reviewer sps3 Thank you for the review. Please let us know if you have any questions that we can address in the upcoming author-reviewer discussion period.	Summary: This paper introduces three benchmark datasets created by training CNN, ResNet, and ViT architectures using a set of activation functions generated from a three-node computation graph that combines unary and binary operations. The benchmarks serve to showcase the efficacy of utilizing the 2D UMAP of the Fishe...	Rebuttal 1: Rebuttal: Response to Reviewer x7yQ --- > It would be beneficial to apply the method (KNR on UMAP embeddings) to vision tasks involving new network architectures as well. This is a great idea. We included CNN, ResNet, and ViT models in the paper to cover a wide range of possible architectures and wo...	Summary: This paper addresses the optimization of activation functions in neural networks for improved performance in machine learning tasks. The authors create benchmark datasets and propose a surrogate-based optimization method based on a characterization of the benchmark space. They apply this method to discover bet...	Rebuttal 1: Rebuttal: Response to Reviewer UeiT --- > The motivation and definition of using "Activation Function Outputs" as feature in Section 3 is not clearly explained. The intuition behind using activation function outputs as a feature is that we expect activation functions with similar shapes to have simil...	Summary: This paper introduces a set of benchmark datasets for activation function search, and an efficient search method based on the analysis of the benchmarks. Strengths: 1. The proposed benchmark datasets are beneficial for further research. 2. The method that searches activation functions through the function o...	Rebuttal 1: Rebuttal: Response to Reviewer N57C --- > I suggest the authors refine the structure and make the technical details of the proposed method more clearly. Thanks for the suggestion. Many of these details are currently in the appendix. We will use the extra page in the camera-ready version to include ...	Rebuttal 1: Rebuttal: Additional Response to Reviewer x7yQ --- > How does AQuaSurF compare with PANGAEA in terms of performance? given the partial similarity of the search spaces, is it possible to make a direct comparison between the two methods (e.g. by limiting the space to non-parametric functions)? In princ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions	Accept (poster)	Summary: The paper introduces a novel self-supervised pretext task for Vision Transformers (ViTs), called DropPos. It aims to enhance the spatial reasoning or location awareness of ViTs, based on the observation that ViTs are often insensitive to the order of input tokens. DropPos works by dropping a large random subse...	Rebuttal 1: Rebuttal: We thank reviewer S2Rp for the valuable time and constructive feedback. Point-to-point responses are provided below. Q1: Additional experiments and deeper analysis are required to verify the motivation. A1: We observed that the improved position sensitivity results in better feature repr...	Summary: This paper introduces a novel approach to self-supervised representation learning for vision transformers, focusing on enhancing their positional awareness. The authors proposed a new pretext task called DropPos, which involves reconstructing the positions of dropped tokens in partial observations. By leveragi...	Rebuttal 1: Rebuttal: We thank reviewer 6DGy for the valuable time and constructive feedback. Point-to-point responses are provided below. Q1: DropPos with Swin. A1: We provide experiments when DropPos is equipped with Swin. We follow the implementation of UM-MAE [a] and pre-train a Swin-Tiny from scratch. Pl...	Summary: This paper presents a simple yet effective approach for generative self-supervised representation learning on images, namely DropPos. The proposed approach drops a large random subset of positional embeddings for visible tokens and classifies the actual position for these tokens via visual appearance. Experim...	Rebuttal 1: Rebuttal: We thank reviewer HkQR for the valuable time and constructive feedback. Point-to-point responses are provided below. Q1: About the objective. A1: DropPos uses only the cross-entropy loss mentioned in the manuscript, and the MSE loss used in MAE is not adopted. We will clarify this in our...	Summary: This paper introduces DropPos, a self-supervised pretext task designed to enhance the location awareness of Vision Transformers (ViTs). By dropping positional embeddings and reconstructing the positions of visible patches with some auxiliary strategies, DropPos improves spatial reasoning abilities in ViTs. Exp...	Rebuttal 1: Rebuttal: We thank reviewer e3Vr for the valuable time and constructive feedback. Point-to-point responses are provided below. Q1: The initialization of the positional encoding. A1: DropPos uses fixed 2D sin-cos position embeddings by default. We ablate the initialization of position embeddings in...	Rebuttal 1: Rebuttal: To all reviewers: Thank you so much for your careful review and suggestive comments. Following your suggestions, we present some extra figures and tables in the PDF. We also provide the pseudo-code for computing the objective of DropPos. Specifically, - @Reviewer e3Vr, to clarify the flowchar...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper a method for self-supervised representation learning. Given a ViT architecture, the authors propose to predict the absolute position of masked positional embedings at random. Although the general direction is not new, the authors pose it in a simple an interesting way, that achieves good performan...	Rebuttal 1: Rebuttal: We thank reviewer Pgbn for the valuable time and constructive feedback. Point-to-point responses are provided below. Q1: The advantage of the proposed DropPos should be discussed explicitly and lack of comparison with [a]. A1: Appreciate! We would like to discuss the advantage of our Dro...	null	null	null	null	null	null
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies	Accept (poster)	Summary: The study considers numerically estimating gradients for online (reinforcement) learning. The parameters are perturbed and from the performance of the perturbed models the gradient is estimated. In an online setting, the proposed method decouples the number of steps between gradient estimates (which are then u...	Rebuttal 1: Rebuttal: Originality of contribution. It is correct that NRES, while being simpler to implement than PES, is not a large algorithmic deviation from PES. However, the insight that reusing noise in truncation windows can both theoretically and empirically achieve significant variance reduction for unbias...	Summary: This paper generalizes PES based on noise-reuse, generating a more general class of unbiased online ES gradient estimators. The authors analytically characterize the variance of the estimators and identify the lowest-variance estimator named Noise-Reuse Evolution Strategies (NRES). Experiments on learning dyna...	Rebuttal 1: Rebuttal: Novelty of the contribution. We agree with the reviewer that our proposed class of unbiased online ES gradient estimators GPES is a simple, intuitive generalization of PES. (In fact, we view the simplicity of the method as an important practical benefit.) However, _we respectfully disagree tha...	Summary: This work proposes a method for optimizing unrolled computation graphs (e.g. recurrent networks, etc.). When using ES to optimize a computation graph, the graph must be fully rolled out. Recent methods (PES) have examined using a truncated window to optimize, so that optimization can occur without a full unrol...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing the quality and clarity of our writing and the originality of our idea. Experiment details. We have provided detailed descriptions of the experimental set ups and how we have tuned the hyperparameters for each experiment in Appendix E. We will make this m...	Summary: In this paper, the author(s) extended the well-known Persistent Evolution Strategies via noise-reuse and proposed an improved version with reduced variance. The main contribution of this paper is to provide detailed mathematical proof to validate their claim. Strengths: Considering this significant contributi...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating the significance of our contribution. Non-linear RL policy. We initially experimented with linear policies on the Mujoco tasks because 1. it has been observed by Rajeswaran et al that using linear policies can yield performances comparable to state-of-th...	Rebuttal 1: Rebuttal: We want to thank all the reviewers for your reviews and comments. We address each reviewer’s questions and feedback in an individual response. For new plots created for the rebuttal, we have included them in the uploaded pdf in this common response. Below is a summary of the new plots in the pdf: ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies online evaluation strategies for unrolled computation graphs. Especially, the authors 1). propose a general class of unbiased online evolution strategies that generalizes Persistent Evolution Strategies (PES), named Generalized Persistent Evolution Strategies (GPES). The key idea is to share...	Rebuttal 1: Rebuttal: Comparison between NRES and first order methods. We have provided discussions of automatic differentiation (first order) methods in Section B Additional Related Work in the Appendix. Besides, we have provided empirical comparisons between NRES and 4 different first order AD methods on the Lore...	null	null	null	null	null	null
Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance	Accept (poster)	Summary: The paper presents a novel mechanism for learning a Riemannian metric from distance observations. This is important for applications where relative observations are available (e.g. "objects x1 and x2 are different, while x2 and x3 are similar"), such as perception studies. The approach is based on local regres...	Rebuttal 1: Rebuttal: For concerns regarding scalability to high-dim data, we kindly refer the reviewer to our general point 1. We also thank the reviewer for pointing out many related works, as discussed in our general point 3, we will add citations accordingly. We also discuss the positivity question in the general p...	Summary: The paper aims to extend metric and manifold learning by learning Riemannian metrics from functions of the observed data that are not related to an embedding space metric as is the usual case. Assuming there is an underlying Riemannian metric structure and that the observed dissimilarity is a known function of...	Rebuttal 1: Rebuttal: We acknowledge the existence of related literature learning Riemannian metrics in lines 22-26 and will include more as suggested by reviewers. We hope the proposed framework can shad new light on this topic. See also our general response point 3. See our general point 4 for limitations. ## usefu...	Summary: This paper develops a theory for estimating the Riemannian metric tensor for a given set of observations and some addition information. This additional information includes a (noisy) measure of similarity between the given points in a pairwise fashion. Examples of this information includes the geodesic distanc...	Rebuttal 1: Rebuttal: For the practical problems with pairwise distance but no Riemannian metric, we kindly refer to our general response point 2. See also our general point 4 for limitations. ## The simulation experiments seem to only involve manifolds with constant curvature. Our theoretical foundation allows for ge...	Summary: This paper proposes a method to estimate the Riemannian metric of data space when coordinate representations of each data point and some noisy similarity measurements among data points are provided. The similarity measurements of different types, such as noise-contaminated distances, similarity/dissimilarity l...	Rebuttal 1: Rebuttal: For the weakness pointed out regarding restrictive setup, application, comparison with other methods, and dimensionality, we kindly ask the reviewer to refer to our general response points 1--3. See our general response point 5 for compatibility of the metric. The reviewer is correct that the pr...	Rebuttal 1: Rebuttal: We truly appreciate reviewers' careful read of our manuscript and helpful comments. We summarized common weakness/questions and provide below our general response. See our replies to individual reviews for specific responses. ## 1. Restrictive problem/model setting *The reviewers question that o...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models	Accept (poster)	Summary: This paper presents Uni-ControlNet, a model that aims to enhance Text-to-Image diffusion techniques by allowing the concurrent use of multiple local and global controls. It only requires two additional adapters, regardless of the number of controls used, and circumvents the necessity of training from scratch. ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments! Q1. Novelty and Contribution. Answer: Thanks for your comments! Please refer to the common Q1. Q2: Training strategy and inference process. Answer: As mentioned in line 149 of our main paper, we concatenate all the local conditions along...	Summary: This paper proposes Uni-ControlNet that leverages lightweight local and global adapters to enable precise controls over pre-trained T2I diffusion models. Strengths: 1. This paper is well written and organized. 2. The idea of local/global adapter to achieve all-in-one control is reasonable and interesting. 3....	Rebuttal 1: Rebuttal: Q1: Comparing the training cost of Uni-ControlNet with other methods. Answer: Great suggestions! Since the scale of the training set and training epochs varies across different methods, we present the time cost of a single training step as a measure. The reported result represents the ave...	Summary: This paper proposed a method to do controlleble t2i generation from a pretrained diffusion model. The main contribution is that they only have two adapters one local (e.g., edge map, keypoint etc) and one global (e.g., image). For local, they use the controlnet, but concatenate conditions as input. For global,...	Rebuttal 1: Rebuttal: Thanks for your valuable comments Q1. The technique novelty. Answer: Thanks for your suggestions! Please refer to the common Q1. Q2: The comparison with the GLIGEN. Answer: Thanks for your suggestions! GLIGEN is an excellent paper that introduces a model conditioned on bo...	Summary: This paper proposes Uni-ControlNet for the simultaneous utilization of various local controls and global controls within a single model in a flexible and composable manner. This is achieved by fine-tuning of two additional adapters on top of pre-trained text-to-image diffusion models, eliminating the significa...	Rebuttal 1: Rebuttal: Thanks for your valuable comments! Q1: Fairness in comparisons. Answer: It's worth noting that the training set used for the compared models is not publicly available (we already made the request by email, but no response or data not sharable). Additionally, the specific training set...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for the valuable feedback! Here we first address some common questions. Q1. Re-clarification of Contribution and Novelty. Answer: We would like to emphasize that our primary contribution is proposing a new unified controllable diffusion model...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors proposed Uni-ControlNet, a novel approach that allows for the simultaneous utilization of different local controls and global controls. It uses two additional adapters (local and global) and injects their outputs into the frozen pretrained diffusion models, and only the parameters in adapters need ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments! Q1: How to get the training data of sketches? Answer: Great question! Indeed, annotating a sketch dataset can be challenging. In our experiment, we initially obtain the HED boundary detection of an image and subsequently utilize a sketch simplificat...	null	null	null	null	null	null
Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition	Accept (poster)	Summary: The authors show that, in Gaussian MTP2 distributions, bridges in the graph structure have a closed form solution. They use this observation to suggest practical solutions that can be applied whenever such models are being fit. Strengths: A nice, more or less self-contained theoretical work that unifies and ...	Rebuttal 1: Rebuttal: ## Answer to Questions Part 1 >"calculating the thresholded graph, bridges, and clusters, is negligible" Sure, but I feel that a precise statement and citation is needed here. In particular, you still have to compute the sample covariance matrix for this. __Reply:__ We are grateful for your val...	Summary: This paper studies a graphical lasso problem where the precision matrix is restricted to be symmetric M-matrix and the associated GMRF graph has a special structure. Specifically, the authors consider the situation when the graph allows bridge-block decomposition so that vertices can be partitioned into k part...	Rebuttal 1: Rebuttal: ## Answer to Questions Part 1 >Does the main result holds without MTP2 constraint? __Reply:__ We thank the reviewer for raising this point. The MTP2 constraints are essential prerequisites for our main results. ## Answer to Questions Part 2 > If not, please clarify the the role of the MTP2 c...	Summary: The paper studies the problem of estimating the precision matrix, which is the inverse of the correlation matrix, of a given Gaussian random vector $y$. The precision matrix $\Theta$ is assumed to satisfy a technical condition called MTP2 which states that $\Theta$ is symmetric and $\Theta_{i,j} \le 0$. This s...	Rebuttal 1: Rebuttal: ## Reply to Comments Part 1 > I am not familiar with the literature but it seems like a big assumption to know the threshold graph explicitly. What happens if this graph is unknown? It seems to be more natural that the graph is unknown and one must estimate it. __Reply:__ We appreciate your fee...	Summary: This paper studies the problem of learning Gaussian Graphical Models (GGMs) satisfying a certain positive associativity condition among the variables, namely that the precision matrix has nonnegative off-diagonal elements. This condition is known as being "multivariate totally positive of order two", or MTP$_2...	Rebuttal 1: Rebuttal: ## Answer to Questions Part 1 > One of the key parts of the eventual proof of Thm 3.3 is Lemma A.2, which is simply described as following from the KKT conditions. Presumably the authors mean the KKT conditions associated with Problem (5)? __Reply:__ Thank you for your insightful comment. Here,...	Rebuttal 1: Rebuttal: ## Part 1: Answers to Questions Regarding Roles of MTP2 > We gather this question from Reviewers ghhX and pk8s, who sought the role of the MTP2 constraint in our main results and why these only apply to MTP2 distributions. __Reply__: We thank the reviewers for bringing up this interesting point...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper focuses on the problem of learning large-scale Gaussian graphical models (GGMs) that are multivariate totally positive of order two (MTP2). The high-dimensional, sparse MTP2 GGMs are not easily manageable due to their size and complexity. The authors propose a novel approach, introducing the concept ...	Rebuttal 1: Rebuttal: ## Answer to Questions > Is there any way to characterize the effect of the proposed approach in terms of some global graphical properties, for example, the edge expansion? Asking this because bridge is related. __Reply:__ We appreciate the reviewer's interesting and insightful question. Indeed...	null	null	null	null	null	null

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