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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
A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence	Accept (poster)	Summary: This paper exploits Stable Diffusion (SD) features for semantic and dense correspondence tasks. The authors first conduct evaluations of SD features and found that SD features provide high-quality spatial information but sometimes inaccurate semantic matches. This paper further shows that such SD features are ...	Rebuttal 1: Rebuttal: W1: Handle outliers in the correspondences. A: Our method primarily focuses on analyzing properties of each feature and their fusion, and thus we adopt a very simple matching method (i.e., NN search) without using any matching priors or templates. Despite this, our method achieves outper...	Summary: This paper, for the first time, proposes a novel method to fuse Stable Diffusion features and DINOv2 features to obtain robust feature representation that readily surpasses the SOTA semantic correspondence work without further training, rather simply adopting Winner Takes All yields SOTA correspondence perform...	Rebuttal 1: Rebuttal: W1: Clarity on the visualization of Fig. 1. A: We provide the challenging cross-category semantic correspondence that matches semantically related or geometrically similar parts across different object categories or even domains. The Neural Best-Buddies [1] and DINOv2 paper also visualize...	Summary: The paper explores the use of Stable Diffusion (SD) features for dense correspondence. The authors investigate the potential of SD and DINOv2 features and show some complementarity. SD features provide high-quality spatial information but sometimes inaccurate semantic matches while DINOv2 features offer sparse...	Rebuttal 1: Rebuttal: W1: Quantitative assessments on the non-redundancy of SD and DINOv2 features. A: We include several quantitative evaluations that underscore the non-redundancy and distinctiveness of SD and DINOv2 features. Specifically, we provide: *1) quantitative error analysis on fused and individual...	Summary: This paper proposes to study the effectiveness of features extracted from Stable Diffusion for dense correspondences. The extracted features are compared to that of DINOv2 and shown to be complementary. A very simple fusion scheme is then proposed and evaluation on datasets for sparse and dense correspondences...	Rebuttal 1: Rebuttal: W1: Effect of fine-tuning the features with the projection layer. A: We briefly explored a supervised adaptation by training a projection layer [1] on top of the extracted features, guided by the CLIP-style symmetric cross entropy loss with respect to corresponding keypoints. As in Table ...	Rebuttal 1: Rebuttal: ## Acknowledgements We thank the reviewers for the comments, extensive feedback, and recognition of the strengths of our work: - Comprehensive Analysis: Our "extensive evaluation and analysis" of Stable Diffusion (SD) features and DINOv2 features provide "valuable insights" into their distin...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes using features extracted from a stable diffusion model for dense image correspondence tasks. The paper further proposes using DINO features along with stable diffusion features for the task and empirically shows that the combination has a complementary effect--specifically SD features have g...	Rebuttal 1: Rebuttal: W1: Leveraging SD / DINO features for correspondence learning. A: While [1,3] demonstrate that SD features are useful for depth and semantic segmentation and [2] that DINO features are useful for image retrieval and semantic segmentation, less work has focused on the tasks of semantic and...	null	null	null	null	null	null
A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation	Accept (poster)	Summary: This paper proposes a context-aware method that combines the 2D image feature and the detected 2D key points for 3D human pose estimation. The joint-centric spatial context represented by the intermediate image feature is able to reduce the amguity in 3D lifting. Under this motivation, a novel framework that c...	Rebuttal 1: Rebuttal: # Author Response to Reviewer dcLQ ## 1. Clarifications on Novelty We thank the reviewer for sharing related works, and we will discuss them in the `Related Work` section of the final version to make our work more complete. To address the reviewer's concern regarding novelty, we refer the reviewer...	Summary: This paper proposes Context-Aware PoseFormer, the key idea is to extract multi-scale spatial features for 3D pose lifting from 2D pose. The authors claim that the single-frame beats hundreds of frames and demonstrate that their single-frame methods achieve comparable or better results than multi-frame methods,...	Rebuttal 1: Rebuttal: # Author Response to Reviewer jUmb ## 1. Clarifications on Our Claim In this paper, we enable a single-frame approach to outperform multi-frame ones (e.g., that uses 351 frames) for the first time. We believe that strong experimental results (`Tab. 1` and `Tab. 2 in the main paper`) well verify th...	Summary: The paper targets the challenging 3D pose estimation problem based on a new context-aware lifting algorithm. The proposed approach is simple to implement and reproduce. Attractive experimental results have been reported on the challenging benchmarks. Also, the detailed ablations well validate the design of the...	Rebuttal 1: Rebuttal: # Author Response to Reviewer 4XvM ## 1. Details about Deformable Context Extraction $H_1$-$H_3$ refer to the raw feature maps produced by 2D pose detectors, while $F_1$-$F_3$ are extracted feature vectors named "Context Features" from the corresponding feature maps using *Deformable Context E...	Summary: This paper leverages the readily available intermediate visual representations for 3d human pose estimation. The method discards temporal information to solve the time-intensive issue of existing lifting-based methods. The authors design a simple pipline, named Context-Aware PoseFormer, to extract informative ...	Rebuttal 1: Rebuttal: # Author Response to Reviewer ez57 ## 1. More Comparisons with SOTA in 2023 We thank the reviewer for the advice on performance comparison! We agree that comparing with more papers would make our work more complete. We include recently released CVPR'23 and ICCV'23 papers in the table below and wil...	Rebuttal 1: Rebuttal: # General Response We thank the reviewers for their careful reading and considerate feedback, and we are thrilled to receive the rating of 4 5 5 7 7! We are glad that reviewers unanimously agree that Context-Aware PoseFormer is a simple but effective approach demonstrated by **strong experi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces Context-Aware PoseFormer, a new approach that leverages intermediate visual representations from pre-trained 2D pose detectors to implicitly encode spatial context for 3D human pose estimation. Despite the simple network structure, the proposed method outperforms existing state-of-the-art...	Rebuttal 1: Rebuttal: # Author Response to Reviewer THhA ## 1. Clarifications on Novelty We disagree that the novelty of our idea is incremental. We hope the reviewer could check our `General Response` for more comprehensive clarifications on the novelty of our work. We thank the reviewer for sharing related works tha...	null	null	null	null	null	null
Lift Yourself Up: Retrieval-augmented Text Generation with Self-Memory	Accept (poster)	Summary: The paper proposes a novel framework called Selfmem to address the limitations of retrieval-augmented text generation. Compared with memory retrieval from a fixed corpus, Selfmem iteratively uses a retrieval-augmented generator to create an unbounded memory pool and select the candidate output as memory for th...	Rebuttal 1: Rebuttal: Firstly, we would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our paper. We will address each comment in a point-by-point manner. - Comment 1: discussion about using LLM to generate knowledge instead of retrieval - Response 1: Thank you for mentio...	Summary: The paper presents a framework, Selfmem, aimed at enhancing text generation tasks via memory retrieval. The core uniqueness of the framework resides in its capacity to create an unbounded memory pool by utilizing its retrieval-augmented generator and memory selector components iteratively. This allows the mode...	Rebuttal 1: Rebuttal: Firstly, we would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our paper. We will address each comment in a point-by-point manner. - Comment 1: More generation tasks needed. - Response 1: Thank you for recognizing the versatility of our method. In...	Summary: The traditional approach for memory retrieval is constrained by the quality of the fixed corpus from which memory is retrieved. Based on the exploration that "better generation also promotes better memory", this work proposes the Selfmem framework, which iteratively employs a retrieval-augmented generator to c...	Rebuttal 1: Rebuttal: Firstly, we would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our paper. We will address each comment in a point-by-point manner. - Comment 1: Limited generality and universal metrics for different text generation tasks. Response 1: - We are gr...	Summary: This paper aims to address the retrieval-augmented text generation problem, where the principle of memory retrieval is to select examples similar to the input. The authors are motivated by an observation in their preliminary experiments that the memory examples that better resemble the data distribution during...	Rebuttal 1: Rebuttal: Firstly, we would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our paper. We will address each comment in a point-by-point manner. - Comment 1: Limited novelty - Response 1: We acknowledge that our study's two primary components, namely the retriev...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to express our deepest appreciation for the time and effort you have devoted to reviewing our conference paper. In response to the questions raised by each reviewer, we have provided detailed answers in the corresponding sections, and we hope that these clarification...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes an iterative text generation procedure. First, the authors augment the model with retrieval to generate the initial beam of predictions. Then they apply rescoring model (trained separately to maximize BLEU/ROUGE scores) to select the higher-quality predictions. Finally, the authors augment t...	Rebuttal 1: Rebuttal: Firstly, we would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our paper. We will address each comment in a point-by-point manner. - Comment 1: Regarding the explanation and positioning of memory - Response 1: We maintain that memory plays a crucial...	null	null	null	null	null	null
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining	Accept (spotlight)	Summary: This paper proposes to pretrain language models (LMs) by first automatically learning domain weights using a small proxy model and then pretraining a large model under the learned weights. The proposed method, Domain Reweighting with Minimax Optimization (DoReMi), improves pretraining perplexity across all dom...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. ajJF notes that “the angle and the proposed method are novel” and “generally well-designed”, with “plenty of experiment results”. We address specific questions below: > “domains are quite coarse partitions of the data, and I'm not very convinced that as...	Summary: This paper introduces a significant advancement by exploring the topic of data mixture proportions during pre-training, which holds great importance. Determining how to sample pre-trained data from diverse sources to achieve balanced results is a fundamental question. Previous approaches have often relied on i...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. Overall, uiyj feels that the “the direction is really important”, “the general framework is valuable”, and “the improvements it offers have been convincingly substantiated through extensive experiments”. We address specific questions below: > “the selection...	Summary: The authors proposed DoReMi for optimizing the mixture proportions of pretraining data domains when training language models (LMs). The authors demonstrate that DoReMi, which utilizes a small proxy model trained via group distributionally robust optimization (Group DRO), can be used to determine optimal domain...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. 5e3R notes that “DoReMi offers a unique and efficient way to determine domain weights” with “wide applicability”. We address specific questions below: > “the baselines are the LMs trained on the original data distribution of Pile, but there should be some ...	Summary: This paper introduces DoReMi, a method for automatically deriving optimal/improved domain weights for aggregated pretraining datasets for LLMs. DoReMi works in three 3: (1) train a small reference model to use for the excess loss in DRO; (2) train a small proxy model with DRO to obtain optimised domain weights...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. VPSZ felt that the method “is an incredibly valuable contribution”, “reproduces results obtained from manual tuning” from the community, and “could see wide adoption”. We address specific questions below: > “The choice of a generative exact-match setting i...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work buils upon prior studies’ empirical findings, emphasizing how the composition of pretraining data affects the performance of Language Models (LMs). To avoid reliance on heuristic or iterative performance measurements on downstream tasks, this work introduces a method that employs a trainable model, c...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. MeFt felt that the paper presented a “novel approach to learning the optimal data distribution”, provides “solid experimental evidence, exploring a variety of settings”. We address specific questions below: > “While the experiments in this paper present sol...	null	null	null	null	null	null
Entropy-based Training Methods for Scalable Neural Implicit Samplers	Accept (poster)	Summary: This article studies the problem of learning implicit samplers, for which the score function is not available, and therefore the KL (or the Fisher) divergence cannot be explicitly computed. They introduce an alternating procedure, where, for a fixed sampler parameter $\theta$ they first learn the surrogate sco...	Rebuttal 1: Rebuttal: Thank you for your valuable suggestions. We will address your concerns one by one in the following paragraphs. Q1-2. Stronger evaluation. Compared to other baselines. A1 and A2. (1) It is truly a limitation that neural samplers require additional training, so we think it necessary to c...	Summary: Efficient sampling from unnormalized target distribution is of crucial interest in Bayesian inference. Classic methods such as Markov Chains Monte Carlo sampler provide unbiased samples but could be computationally expensive. This paper proposes a novel approach called neural implicit sampler to employ a neura...	Rebuttal 1: Rebuttal: Thank you for your useful feedback. We will address your concerns one by one in the following paragraphs. A1. In the experiment of Section 4.1, we optimize the HMC to get the step size and LeapFrog iterations. Besides, in the rebuttal period, we conduct a new experiment on 6 more 2D targets. ...	Summary: This submission proposes two training algorithms for implicit samplers, which are based on KL divergence and Fisher divergence, respectively. Tractable objective estimator are derived and practical training algorithms are demonstrated. Numerical experiments are conduct in several different settings. Strengths...	Rebuttal 1: Rebuttal: Thank you for your useful feedback. We will address your concerns one by one in the following paragraphs. A1. Thank you for your reminder. The notation $sg$ does mean the stop-gradient. We feel sorry for the confusion and will refine the notations in the revision. A2. We agree that ther...	Summary: The paper is the area of approximate inference. The goal is to use approximating distributions where the density is unknown but samples can be drawn. The advantage of this approximating class is that it is potentially more expressive and easier to work with ones where the density is known/tractable. The paper...	Rebuttal 1: Rebuttal: Thank you for your helpful feedback, we will address your concerns one by one. Q1. missing quite a few relevant references. A1. Thank you for the reminder. We are sorry for the loss of discussion of VI methods with implicit distributions. We agree that these VI methods have strong relati...	Rebuttal 1: Rebuttal: Thank all reviewers for your valuable feedback. In the rebuttal period, we run a new comparison experiment on 2D targets with a reference of [1], and report the results in Table 1. In this new experiment, we compare our neural samplers with 3 MCM baselines: SVGD[2], LD, and HMC; 1 explicit b...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning	Accept (poster)	Summary: This paper proposes an active learning approach for open-set learning. The proposed approach does not try to avoid sampling OOD data as some of the prior work did. Instead, it samples some of the OOD data intentionally to enhance the OOD detector. Strengths: 1.The proposed idea is easy to implement and unders...	Rebuttal 1: Rebuttal: Q1: "...framework is trivial to me..." A1: Open-set active learning aims to strategically select pure ID data and filter out OOD data, and thus a powerful OOD detector is important. The common practice of traditional open-set active learning methods, as shown in Figure 1 (b) in the manuscript, al...	Summary: This manuscript studies open-set active learning and points out that concentrating solely on selecting pseudo-ID instances may cause the training imbalance of the ID classifier and OOD detector. To address this issue, this manuscript proposes a simple yet effective sampling scheme, dubbed Progressive Active Le...	Rebuttal 1: Rebuttal: Due to the space limit, we put all tables in the attached one-page PDF. Q1: "...lacks corresponding theoretical analysis..." A1: We have provided a theoretical analysis in the Global Rebuttal to support the proposed PAL method. We theoretically show that PAL has a better generalization e...	Summary: This paper considers the open-set active learning problem, a sub-topic of active learning that focuses on non-iid settings. The authors constructed both an ID classifier and an OOD detector to implement open-set active learning. Specifically, they proposed a sampling scheme to ensure a balance of ID and OOD sa...	Rebuttal 1: Rebuttal: Q1: "CCAL is also a well-known open-set active learning method, which should..." A1: We have compared the proposed PAL with CCAL on CIFAR-10 and CIFAR-100 with the ID proportion of 20%. The results in Table 1 reveal that PAL outperforms CCAL, for the reason that CCAL does not actively use OOD dat...	Summary: In this paper, the authors aims to improve open-set active learning, where unlabelled set may contains some open-set instances. To do this, they propose a new sampling scheme, called progressive active learning. Specifically, they use a progressive sampling method to select valuable OOD data for the tradeoff b...	Rebuttal 1: Rebuttal: Q1: " ...The technical novelty of the progressive sampling is not sufficient..." A1: Open-set active learning aims to strategically select pure ID data and filter out OOD data, necessitating the presence of a robust OOD detector. The common practice of traditional open-set active learning methods...	Rebuttal 1: Rebuttal: We sincerely thank the PC, SAC, ACs, and reviewers for handling and reviewing our paper. All constructive and valuable comments are helpful in further improving our paper. Since most reviewers have mentioned the lack of theoretical analysis, we provided a generalization analysis. Our theoretical...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Unconstrained Dynamic Regret via Sparse Coding	Accept (poster)	Summary: This paper studies adaptive online convex optimization, with focus on adapting to unbounded domain and arbitrary time-varying comparator sequence. Different from previous studies which mostly consider the path-length as the prior which appears in the dynamic regret bound, this paper aims to enlarge the range o...	Rebuttal 1: Rebuttal: Thanks for your comments! We hope the following clarifications can answer your questions. - Interpretation of the generic framework Our generic framework aggregates a collection of single-feature learners. Roughly speaking, each of these single-feature learner is in charge of a fixed direction ...	Summary: The problem that this paper tries to tackle is the unconstrained online convex optimization with dynamic regret. Previous works usually assume that the comparator sequence is arbitrary and maybe time-varying with some fixed form of comparator measurement in the final dynamic regret bound. For this paper, it pr...	Rebuttal 1: Rebuttal: Thanks for your feedback! - Improvement over [JC22]. We would like to respectfully clarify that compared to [JC22], our bound depends on a tighter complexity measure of the comparator sequence $u_{1:T}$. Such an improvement is considerably more substantial than improving the multiplicative cons...	Summary: In this paper, the authors examine the dynamic regret of Online Convex Optimization (OCO) within the context of unbounded comparator sequences. To address this issue, they introduce a novel framework of sparse dictionary coding for online optimization. Following this, the authors provide theoretical proof for ...	Rebuttal 1: Rebuttal: Thanks for your feedback and your support of our paper! - Related work [1] Thanks for bringing it to our attention. Both [1] and our paper study how to achieve more adaptivity in dynamic online learning, but they take different directions. For example, [1] has an additional smoothness assumptio...	Summary: This paper studies the universal dynamic regret minimization problem with the unconstraint decision domain. The authors proposed the sparsing coding framework, which converts the dynamic regret minimization problem in the time domain into a static regret minimization problem in the transfer domain. The compara...	Rebuttal 1: Rebuttal: Thanks for your comments and your support of our paper! - Clarification on Eq.(5). Thanks for the suggestion, we will add the remark that the bound holds for $u_{1:T}$ in the span of the feature vectors. - Example of the loss functions. The question is on whether there exist loss functions...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards	Accept (poster)	Summary: The paper proposes rewarded soup (RS), a simple technique for combining policies trained for different rewards into a single policy performing well on a particular convex combination of those rewards. The technique consists in linearly interpolating weights of individual policies, using the fact that they shar...	Rebuttal 1: Rebuttal: We would like to thank R.TSwH for this positive review and the great understanding of the empirical and theoretical components of our work. --- ### Q1. Reward misspecification In the revised version of the paper, we will clarify the discussion l.75 and l.161 on reward misspecification being mos...	Summary: This paper presents reward soups (RS) which is the idea of starting with a pre-trained network, which is finetuned to multiple proxy rewards (say, multiple different criteria), and at test time, infers a reward as a linear combination of these proxy rewards and uses this to linearly combine the corresponding w...	Rebuttal 1: Rebuttal: We thank R.bXWy for reviewing our work. Yet, with all due respect, there is an inaccuracy in the summary by R.bXWy: "at test time, [we do not] infer a reward as a linear combination of these proxy rewards". More precisely, we show that interpolated weights can approximate the optimal policy fo...	Summary: In this paper, the authors propose a multi-policy strategy called "rewarded soups" to fine-tune any foundation model, embracing the heterogeneity of diverse rewards. The method combines multiple networks through linear interpolation in the weight space, despite the non-linearities in the network, which efficie...	Rebuttal 1: Rebuttal: We thank R.QPfR for reviewing our work, and try to address the expressed concerns below. --- ### Q1. Empirical validation of Hypothesis 2 Our introduction in Section 3 and the Remark 2 explain why "the front passing through the point obtained by MORL fine-tuning on the average of the two reward...	Summary: This paper explores a model-soup strategy to efficiently adapt to diverse reward functions from various real-world users. By fine-tuning a pre-trained LLM multiple times each with a specialized reward function and interpolating their weights linearly, the proposed method is able to adapt to various reward func...	Rebuttal 1: Rebuttal: We thank R.bJvT for reviewing our work, and try to address the expressed concerns below. --- ### Q1. Novelty and difference with model soups (extended in [R.DNE5.Q1](https://openreview.net/forum?id=lSbbC2VyCu&noteId=6LMJAJD6vx)) The first conceptual novelty is arguing for a **multi-objective pa...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their time and their insightful feedbacks. We're encouraged by the positive comments, which highlight the main features of our submission. - (topic) We "address the reward misspecification problem [...] in current RLHF frameworks" (R.QPfR), a problem "that f...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors present a new strategy to address the heterogeneity of diverse rewards in reinforcement learning. Specifically, they propose 'rewarded soup,' which involves individually training multiple networks, each assigned to a different proxy reward, and then linearly combining these networks. Compared to th...	Rebuttal 1: Rebuttal: We thank R.ntSF for the deep understanding of the paper, for highlighting its strengths and for asking two intriguing questions - that we try to answer below. --- ### Q1. How does the difference/gap of rewards affect the effectiveness of the MORL baseline and the rewarded soup? Our experiments ...	Summary: This manuscript studies a way to interpolate trained networks' parameters for diverse rewards in a reinforcement learning manner. To be specific, the proposed method introduces a way to achieve Pareto-optimal solutions through linearly weighted parameters after training. Extensive experiments showed the effect...	Rebuttal 1: Rebuttal: We thank R.DNE5 for highlighting the organization of the paper and the diversity of our experiments. --- ### Q1. Similarity and differences with model soups R.DNE5's main concern relates to the similarity between rewarded soups (RS) and model soups (MS). First, we totally acknowledge similarity...	Summary: This paper proposes a method of using linear interpolated weight finetuned on different rewards instead of using linear combination of rewards to finetune weight, which is a solution to applying model under different and multiple preference scenarios. The idea is intuitive but works well, it (the RS) can achie...	Rebuttal 1: Rebuttal: We thank R.stHc for the positive feedback on the clarity of our idea and the experiments. We would like to respond to R.stHc's review as follows. --- ### Q1. Novelty Our approach is novel from two perspectives. The first conceptual novelty is arguing for Pareto-optimality and a **multi-obj...	null	null
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback	Reject	Summary: The paper studies the ability of LLMs to improve in a negotiation game. The find that only a subset of language models can self improve from AI feedback, a model's ability to learn from feedback depends on its role in the game, and stronger agents can go through more rounds of negotiation. Strengths: - The pa...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. Below we address the following suggestions: - “Utilizing the fine-tuning APIs for the given models”: we would very much love to do so, yet we do not have access to finetune GPT-3.5 / GPT-4 / Claude. Note that currently the general research and open-source c...	Summary: This paper investigates the intriguing possibility of autonomous improvement among multiple large language models through a negotiation game. By assigning various LLMs to distinct roles and allowing them to engage in iterative improvement, the paper aims to enhance their negotiation strategies without human in...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. Below are our responses: ## Significance of AI feedback and our contribution Our systematic investigation aims to answer the research question of whether multiple Large Language Models (LLMs) can improve each other in a negotiation game with minimal human ...	Summary: This paper studies whether multiple large language models (LLMs) can improve each other in a negotiation game by playing, reflecting, and criticizing, with minimal human intervention. Two LLMs play the roles of a seller and a buyer, and a third LLM plays the role of a critic who provides feedback to one of the...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. The reviewer is mostly concerned the design of our game setting and asks if our setting can be extended add more factors like “context” “motivation”, “personal perference” and so on. We would like to note that, although we very much love to study how these...	Summary: This paper studies the strategic multi-agent problem setting of two LLM Players interacting in a negotiation (or bargaining) game and proposes to use feedback from an LLM Critic to improve each Player’s expected behavior and performance in the game. Importantly, the paper aims to study how AI Feedback can ena...	Rebuttal 1: Rebuttal: Overall, the design decisions that we make are closely based on our understanding of what the current LLMs can do, and how to push it further. We aim to clarify the following points: - Policy updates by In-context learning from AI feedback: the reviewer discussed that our approach “simply add...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction	Accept (spotlight)	Summary: This paper proposes the masked space-time hash encoding to efficiently reconstruct dynamic scenes. The insights behind the paper is that: most part of the scene is static, simply modelling such static parts can dramatically increase the probability of hash collisions; while increasing the hash table entries re...	Rebuttal 1: Rebuttal: > Q1: Is Figure 3 $c$ revealing that the uncertainty does not accurately model the static part and dynamic part? The dynamic region inferred by the model may include some noise induced by algorithm-irrelevant reasons, such as inaccurate estimated camera poses and parameters, lack of key points...	Summary: The paper presents MSTH, a method that efficiently reconstructs dynamic 3D scenes from multi-view or monocular videos. The proposed solution uses a space-time hash encoding, a prediction of masks, and uncertainty values that help a method to identify 3D points that belong to moving objects. The intuition is th...	Rebuttal 1: Rebuttal: > W1: I think the paper is lacking a more in-depth discussion about the newly created dataset to test MSTH. While I understand that the algorithm is important, I also think the data part is as important as the algorithm. At the end of the day, the data is required and crucial to solve many problem...	Summary: This paper present a method for efficient dynamic scene reconstruction. They represent the dynamic scene with a weighted combination of a 3D hash encoding (for static part) and a 4D hash encoding (for dynamic region). The weight is learnable and can be represented by a multi-resolution hash table or a 3D voxel...	Rebuttal 1: Rebuttal: > Q1: Since the 3D scene is dynamic, the mask(static/dynamic) for each 3D position should also depend on time. Why you choose to use a time-independent representation. We design the time-independent mask mainly due to the inference efficiency. Specifically, we find the efficiency bottleneck of th...	Summary: This paper tries to address the challenge of efficiently representing 3D dynamic scenes. It proposes a decoupled representation that uses separate neural implicit representations for dynamic and static 3D points. This approach can reduce hash collision and save the storage of the multi-level hash feature grids...	Rebuttal 1: Rebuttal: Weaknesses: 1. >1.1.The concept of representing scenes with separate static and dynamic parts has been investigated in NeRFPlayer or NeRF in the wild. Although the abstract concept of separating static and dynamic is not novel, many existing methods like NeRFPlayer, NeRFW, and MixVoxels adopt...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Extensible Prompts for Language Models on Zero-shot Language Style Customization	Accept (poster)	Summary: This paper introduces a solution called eXtensible Prompt (X-Prompt), which enables instructing a Language Model (LM) using imaginary words. These words serve the purpose of providing instructions to the LM that are difficult to articulate using natural language. In order to prevent overfitting of the LM and f...	Rebuttal 1: Rebuttal: We would like to express our appreciation for your recognition of our work, as well as your constructive feedback and thought-provoking questions. We hope that our following responses will help you better interpret the merits and contributions of our paper, and further improve your impression and ...	Summary: This paper proposes X-prompt: a technique that learns an imaginary token to represent a concept that is hard to describe in natural language. Compared to soft prompt tuning, X-prompt is designed to be OOD robust with template and content augmentation, in which the X-token is trained with various prompt templat...	Rebuttal 1: Rebuttal: Thank you for your recognition of our work, your constructive feedback and thought-provoking questions. We hope our following responses will help you better interpret our contributions, and further improve your impression and evaluation of our paper: > Missing baseline: for table 6, the baseline ...	Summary: This paper proposes eXtensible Prompt (X-Prompt), a new way to prompt large language models beyond natural language. With an extensible vocabulary of imaginary words, X-Prompt allows for more descriptive prompts and is designed to be out-of-distribution robust. The paper also proposes context-augmented learnin...	Rebuttal 1: Rebuttal: We would like to express our appreciation for your constructive comments. We hope that our following responses will help you better interpret the merits and contributions of our paper, and further improve your impression and evaluation of our paper: > Weakness 1. The idea is interesting but the ...	Summary: This paper presents several data augmentation methods for training prompts that include both frozen text and learnable soft tokens so that they are still effective on out-of-domain examples. Strengths: The keyword extraction method to create text-prompts that are informative to the current example as a method...	Rebuttal 1: Rebuttal: We would like to express our appreciation for your recognition of our work, as well as your constructive feedback and thought-provoking questions. We hope that our following responses will help you better interpret the merits and contributions of our paper, and further improve your impression and ...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposed X-Prompt which instructs an LLM with not only NL but also an extensible vocabulary of imaginary words. Besides, context-augmented learning (CAL) is introduced to learn imaginary words for general usability, enabling them to work properly in OOD (unseen) prompts. Strengths: 1. A concise ide...	Rebuttal 1: Rebuttal: We would like to express our appreciation for your recognition of our work, as well as your constructive feedback and thought-provoking questions. We hope that our following responses will help you better interpret the merits and contributions of our paper, and further improve your impression and ...	null	null	null	null	null	null
Social Motion Prediction with Cognitive Hierarchies	Accept (poster)	Summary: This paper proposes a novel approach to address the social motion prediction problem by introducing a new large-scale multi-person 3D motion dataset featuring intense and strategic interactions among participants. The authors formulate the problem using a multi-agent reinforcement learning perspective and inco...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer's constructive and positive feedback. We are grateful that you are interested in our 'method of technological innovation' with 'good motivation'. In the following, we seek to address your concerns: Q1: Compare with more datasets and joints. A1: We primari...	Summary: The paper addresses the problem of social motion forecasting utilizing a multi-agent reinforcement learning and combines behavioral cloning and generative adversarial imitation learning. Social and strategic interactions are modeled in a “cognitive hierarchy framework”. The paper further introduces a new large...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s constructive and detailed feedback. We are grateful that the you find our paper 'well-written', our proposed 'well-motivated', and our dataset 'highly relevant in the under-explored area'. In the following, we aim to address your concerns: **Q1: 1s is too sh...	Summary: The paper has two major contributions to the field of multi-person motion detection: 1) A new open source dataset that is sufficiently large scale as compared to those already available. But more importantly is dense in terms of strategic interactions and more diversity of action distributions for reinforceme...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s constructive and positive feedback. We are happy that the you find our paper 'well-written', our proposed dataset 'a welcome addition', and our experiments 'very detailed'. In the following, we aim to address your questions and concerns: **Q1: Lack of survey...	Summary: This paper aims to predict multiperson human motion. The main contributions of this paper are: 1. The paper presents a large-scale multi-human 3D motion dataset with intense, strategic interactions. 2. The paper formulates the multiperson human prediction problem as MARL and presents a hierarchy framework to m...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer's constructive and positive feedback. We are grateful that you recognize our main contributions and find it 'well-written with a clear and well-motivated introduction'. In the following, we aim to address your questions and concerns: **Q1: Provide more insight...	Rebuttal 1: Rebuttal: # General Response We sincerely thank all reviewers for their meticulous reviews and constructive remarks. It is heartening to note that all reviewers have acknowledged the motivation behind our work, as well as its main contributions — especially the proposed method. We will incorporate discussi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: -- Strengths: -- Weaknesses: -- Technical Quality: 3 good Clarity: 3 good Questions for Authors: -- Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with...	Rebuttal 1: Rebuttal: Thank you for taking the time to provide your feedback. However, we noticed that the specific details of your review were not included. To better understand your viewpoint and address any potential issues, it would be immensely helpful if you could provide more detailed feedback.	null	null	null	null	null	null
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration	Accept (poster)	Summary: In this paper, a functional-group-based diffusion model called D3FG is proposed to generate molecules in 3D for target protein binding. Two generation schemes including joint and two-stage generation schemes are formulated. Strengths: 1. The paper is well-written and easy to follow. 2. It is the first functi...	Rebuttal 1: Rebuttal: Thanks for your advice, and we add more experimental details in the Appendix E according to your advice as shown in CQ2 in General Response. Here is the response to your doubts. Response to W1: On one hand, the improvements in docking scores are not significant, according to Table.4, but the oth...	Summary: Pocket-specific molecule generation has received considerable attention in recent years, and the authors propose a functional-group-based diffusion model to address this task. The model considers the generation of complete molecules as the assembly of functional groups (fragments) and atoms that connect these ...	Rebuttal 1: Rebuttal: We sincerely thank you a lot for your appreciation of the work, and the advice with deep insights. Here is the response to your concerns. Response to Q1. The amino acid index set is preserved because, in the diffusion process, only the linkers and functional groups are added with noise, so the i...	Summary: This paper proposes a so-called functional-group-based diffusion generative model, namely D3FG, to generate molecules with realistic substructures conditioned on the protein binding sites. D3FG represents the protein-ligand docking system as a fragment-based system. The molecule fragments are molecule substruc...	Rebuttal 1: Rebuttal: We thank you a lot for your constructive advice and answer your questions one by one, as below. Response to Q1: We add the three methods to the related work as discussed in CQ3 in General Response. In detail, the novelty of D3FG and its differences are listed below: - We did not compare FLAG[2]...	Summary: This paper proposes to generate 3D molecules using functional groups and linker atoms in a diffusion model. Using functional groups as building blocks help the model to generate realistic local structures. In the proposed diffusion model, the atom/functional group type, coordinates and orientation are predicte...	Rebuttal 1: Rebuttal: We sincerely thank you a lot for your appreciation of the work, and the advice with deep insights. Here is the response to your concerns. Response to comparison to baselines for molecule elaboration: As the model for molecule elaboration task in 3D is scarce, we here use STRIFE [1] (auto-regres...	Rebuttal 1: Rebuttal: General Response: Here we conclude several common concerns of the reviewers, and respond to them as below: CQ1: Sensitive Analysis (How the size of functional group repository affects the generative performance?) We conduct experiments about the effects of the size of the repository on perf...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presents a novel method for generating 3D molecules that bind to specific protein pockets based on a functional-group-based diffusion model (D3FG). The model decomposes molecules into functional groups and linkers and generates their types, positions, and orientations gradually through a denoising pr...	Rebuttal 1: Rebuttal: We thank you a lot for your constructive advice and answer your questions one by one, as below. Response to Q1: We use EFGS[1] to decompose the molecules, and analysis the stability of the substructures, so that manually established a repository of functional groups that most molecules can be d...	null	null	null	null	null	null
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models	Accept (poster)	Summary: This paper proposed a training-free algorithm to modife cross-attention during inference time that could implicitly minimize the energy in the latent space. This paper formulate this idea from a energy perspective. The authors conduct three experiments - multi-concept generation, image inpainting, compositiona...	Rebuttal 1: Rebuttal: W1: Lack of quantitative results. We would like to kindly remind the reviewer that the quantitative comparison results have been reported in appendix D. We will move the results to the main paper in our revised version to emphasize the effectiveness of the proposed method. Also, per requests f...	Summary: This paper proposes a novel energy-based framework that can automatically update the context used in cross-attention without additional training. They claim the proposed updating process well solves the semantic misalignment issue in text-to-image diffusion models. Strengths: 1. The paper e...	Rebuttal 1: Rebuttal: In contrast to your concerns, it appears that there are several misunderstandings by the reviewers. To clarify the misunderstanding, we would like to give detailed point-by-point answers below. W1 and Q1: The definition of the energy function does not appear reasonable. Thanks for the importa...	Summary: The paper proposes to formulate cross-attention layers using energy-based models such that by minimizing the cross attention energy with respect to the context latent representation, the method can further alleviate semantic misalignments between generated or edited samples and the input descriptions, and allo...	Rebuttal 1: Rebuttal: W1: Lack of limitations. Thanks for the comment. We only modified the forward path of the cross-attention layer and this is equivalent to a one-step gradient descent of defined energy. Although we did not minimize the energy multiple steps to achieve further convergence, we have shown that the...	Summary: This work tackles the semantic misalignment problem of stable diffusion model using the energy-based model framework.The authors first show that each cross-attention in the diffusion model can be seen as one step optimization of a pre-defined energy function. They then formulate a Bayesian update for the conte...	Rebuttal 1: Rebuttal: W1: Lack of quantitative results. We would like to kindly remind the reviewer that we have already measured CLIP accuracy and DINO-ViT structure distance motivated by [1,2] for the image editing task and compared it with state-of-the-art methods in the appendix. The result shows that the propo...	Rebuttal 1: Rebuttal: We sincerely thank all the Reviewers for their valuable comments. We are encouraged that the reviewers say that “the motivation of the paper is clear” (nFGp), “love the novelty and theoretical framework” (2y1g), “the idea from energy perspective is innovative” (tGPJ) and “Theoretical analysis and ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes an energy-based model (EBM) framework addresses semantic misalignment in text-to-image diffusion models by incorporating EBMs in each cross-attention layer, minimizing a nested hierarchy of energy functions, and achieving highly effective results in diverse image generation tasks. From the ...	Rebuttal 1: Rebuttal: W1: Some quantitative numbers should also be shown in the paper. In contrast to your comment, we would like to remind the reviewer that the quantitative comparison results have already been reported in appendix D. We will move the results to the main paper in our revised version to emphasize t...	Summary: This paper proposes Bayesian Context Update (BCU) and Compositional Averaging of Cross-Attention Output (CACAO). The main idea is to view the cross-attention between text and image as optimizing an energy-based model, and modify the intermediate outputs according to the energy. A series of examples are shown...	Rebuttal 1: Rebuttal: W1: How severe is the shift? How do the authors control the shift?. Thanks for pointing out the important question that is deeply related to the motivation of our work. We would like to emphasize that the change of context vector is adaptive, not an invalid shift, because the proposed BCU allo...	Summary: This paper aims to optimize the context representation through energy based formulation of the cross-attention within the U-Net at test-time to achieve semantic alignment between the textual representation and the image features in the U-Net. Experiments are performed for the multi-concept generation, text-gu...	Rebuttal 1: Rebuttal: W1: The formulation of the gradient posterior in Eq. 9. We would like to clarify that the notation E in eq. (9) denotes the energy function defined in eq. (7) and (8), not the expectation. We realize a typo in eq. (1) that should be corrected as \mathbb{E}. We will fix eq. (1) in our revised p...	null	null
Hypothesis Selection with Memory Constraints	Accept (poster)	Summary: The paper studies hypothesis selection in a streaming setting, with samples arriving online. The main result gives a near-optimal memory-sample trade-off for hypothesis selection. Along the way, the paper invents several new techniques to avoid expensive memory usage of prior work. Strengths: This is one of ...	Rebuttal 1: Rebuttal: > the main result from the random ladder tournament, ... can be derived simply ... from ...(https://dl.acm.org/doi/10.1145/2701427). Thank you for the reference. We will be sure to add it to our discussion of previous work. That said, we did not see how the main result can be derived in a blackb...	Summary: The paper studies the problem of hypothesis selection under a memory constraint. Here one is given $n$ distributions $H_1, \dots, H_n$ with access to an oracle that can output 1) $H_i(H_j > H_k)$ for any $i,j,k$ and 2) $1(H_i(x) > H_j(x))$ for any $i,j$ and any point $x$ in the underlying space. Given a stream...	Rebuttal 1: Rebuttal: > due to the technical nature of the paper the short format of neurips is simply not enough for a meaningful presentation of the results We will endeavor to present at least some of the main ideas concisely in any future short versions, at NeurIPS or elsewhere. Like many NeurIPS submissions with ...	Summary: The authors study the problem of hypothesis testing for pdfs in a streaming model. The problem is to find a hypothesis H* (corresponding to a pdf) from a family {H1,...,Hn} that is closest to an unknown pdf P. The input is a stream of points drawn i.i.d. from P. At any time step the algorithm can ask for a new...	Rebuttal 1: Rebuttal: > Why the restriction to scheffe sets? Scheffé set queries provide a generic computational model for expressing algorithms that apply to many families of distributions; this avoids assumptions on particular data formats or functional forms for the distributions. Scheffé queries are also sufficie...	Summary: This paper studies the problem of agnostic distribution learning where i.i.d. samples are generated from an unknown distribution $X$. The goal is to find the best distribution from a given set of finite distributions $\\{H_1,\cdots,H_n\\}$ that is closest to $X$ under total variation distance. The authors spec...	Rebuttal 1: Rebuttal: > overall significance to the machine learning community Hypothesis selection is a fundamental problem in statistical learning theory. The formulation we study here abstracts and generalizes many specific distribution selection/estimation tasks (some of which are discussed in the book of Devroye ...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and thoughtful comments. We will incorporate all the editorial comments. Please find the individual responses to the reviewers below.	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On the Interplay between Social Welfare and Tractability of Equilibria	Accept (poster)	Summary: This paper considers a specific type of smooth game first introduced in [Roughgarden 2015]. The main result of this paper is that when the game has robust PoA lower bounded by 1-$\epsilon$ (defined by the concept of smoothness of the game) and all the players apply optimistic gradient descent algorithm (OGD), ...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. Below we address the concerns. *“Specifically, it is less clear why the class of game with $rPoA≥1−\epsilon$ is an interesting class of games. [...] Is there a certain class of previously studied games or real applications that satisfy this con...	Summary: This paper studies the convergence properties of no-regret dynamics in $(\lambda,\mu)$-smooth games. More precisely the authors show that for any $(\lambda,\mu)$-smooth game at which the bound of $\frac{\lambda}{1+\mu}$ converges to $1$ as the number of agents grows, the resulting Online Gradient Descent dynam...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “Theorem 4.2 guarantees social welfare strictly more than $\frac{\lambda}{1 + \mu}$ fraction the optimal one only at a specific iteration.” While we stated Item 2 of Theorem 4.2 for a single iteration, it is direct to extend our proof so tha...	Summary: This paper discusses the convergence and welfare of learning algorithms in smooth games. The authors show that when approximate full efficiency can be guaranteed via a smoothness argument, Nash equilibria are approachable under a family of no-regret learning algorithms, thereby guaranteeing fast and decentrali...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “Is there any counterexample that if $rPoA$ does not converge to 1, then the decentralized algorithm (OGD) provided in this paper does not converge to Nash Equilibrium?” Yes. As we point out in Lines 272-274, there is a bimatrix game (see Pro...	Summary: This work studies the connections between the efficiency of Nash equilibria, as measured for example by social welfare, and tractability of computing these equilibria through efficient no regret learning algorithms. The authors provide the key insight that the smoothness framework introduced by Roughgarden, th...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “The paragraph from 179 to 190 could benefit from expansion. In the common setting where we analyze a fixed game, one can always normalize the utilities to get a Lipschitz constant of 1” It is indeed the case that we can appropriately nor...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work studies the connection between efficiency and computation tractability of equilibrium in smooth games. Its major finding is that optimistic mirror descent reaches a weak Nash equilibrium for large games satisfying the property that asymptotically smoothness can guarantee efficiency of equilibria. Thi...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “What are some natural examples where the conditions in Thm 3.1 are satisfied and non-trivial rates are obtained?” The most simple example is when $rPoA = 1$, in which case Thm 3.1 (in particular see the more general version of Theorem A.2) y...	null	null	null	null	null	null
PDP: Parameter-free Differentiable Pruning is All You Need	Accept (poster)	Summary: This paper proposes a parameter-free differentiable pruning, which uses a dynamic function of weights during training to generate soft pruning masks. It can generalize well on random/structured/channel pruning on both vision and NLP tasks. It achieves superior pruning results. Strengths: 1. The proposed PDP a...	Rebuttal 1: Rebuttal: **Q0: This approach may seem counterintuitive, as most weights are obtained and converged through expensive training processes. This approach may seem counterintuitive, as most weights are obtained and converged through expensive training processes. It may be more effective to adjust and learn ma...	Summary: This paper deals with the pruning algorithm PDP on DNN, the main innovation is to generate a differentiable mask based on weights using a designed threshold t, and the softmax function. This mask can ensure the gradient propagation during forward and backward process while training, reducing the accuracy loss....	Rebuttal 1: Rebuttal: Q0: The employment of different masks can impact training process performance, yet a higher SAD[1] may result in a significant decline in accuracy. Given that the masks are still changeable in the later stages of PDP training, how can it provide high accuracy? Thank you for the question. Spar...	Summary: The paper focuses on DNN pruning and proposes a novel approach using a soft mask during training. The soft mask is designed to encourage weights around the pruning threshold to actively switch their states, aiding in the recovery of pruned weights. This method is simple, efficient, and introduces no additional...	Rebuttal 1: Rebuttal: Q0: Insufficient explanation of the design of "t" and calculation of "m(w)": Why is “t” in PDP training flow designed in this way? The paper lacks clear explanations about the design of “t” and calculation of “m(w)”. Thank you for the chance to clarify the important notation in our work. $t$...	Summary: This paper describes a novel pruning algorithm named parameter-free differentiable pruning (PDP). The core idea is to generate soft pruning masks (i.e., mask values are not finalized until the final iteration of fine-tuning) using a parameter-free, differentiable dynamic function of the weights of the network....	Rebuttal 1: Rebuttal: **Q0: Measuring inference efficiency in MACs can often be misleading. For instance, unstructured pruning can reduce MACs by getting rid of individual ineffectual computations, but this is difficult to realize in modern parallel hardware such as GPUs and TPUs. Reporting at least some data points wi...	Rebuttal 1: Rebuttal: We like to thank the reviewers and ACs for the help and feedback. The highlight of our rebuttal includes the following. New experimental results: - Peak memory consumption is measured with PDP and OptG on a small GPT2 model. - PDP result with MobileNet-v3 and ImageNet1k is added. - Latency benefi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces a new DNN pruning scheme called Parameter-free Differentiable Pruning (PDP), which is an efficient and effective train-time pruning method that offers state-of-the-art qualities in model size, accuracy, and training cost. Unlike existing pruning approaches, PDP generates soft pruning masks...	Rebuttal 1: Rebuttal: Q0: The PDP differentiable pruning does not introduce extra parameter, but it still need to generate (soft) mask from weight, which would induce extra activation maps, how is the memory consumption during differentiable pruning compare to other SoTAs? Thank you for raising an important questi...	null	null	null	null	null	null
Unlimiformer: Long-Range Transformers with Unlimited Length Input	Accept (poster)	Summary: This paper proposes to use k-nearest neighbor to extract nearest neighbor encoded tokens in pretrained encoder-decoder transformers. This helps in removing the bottleneck of limited input tokens thereby letting the dataset decide the input length. They show empirically that their proposed unlimiformer can exte...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper! We were happy to read that you appreciated our main points: that Unlimiformer is simple, achieves significant improvements, and allows scaling of the input with less than linear increase in wall-clock time. We think that your concerns are addres...	Summary: The paper proposes Unlimiformer, a new method for increasing the context length of Transformers without any modification by using retrieval. The idea is simple, and immediately improves performance on benchmark tasks. Strengths: The idea is simple, and the experiments show that augmenting Transformers with a ...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper! We were happy to read that you appreciated our main points: that Unlimiformer is simple, immediately improves performance, and does not require any modification to the architecture. We think that all your questions are addressable within this dis...	Summary: The paper proposes a method to increase context lengths of encoder-decoder transformers to very long input sequences. The idea is to essentially encode all of the tokens of the entire input (on overlapping context-length chunks) and create an index of the input tokens. Just prior to decoding, k-nearest neighbo...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper! We were happy to read that you appreciated our main points: that Unlimiformer can be applied to existing pre-trained architectures without the need for additional weights or tuning, our re-order of the computation of cross-attention can use a sing...	Summary: This paper proposes to use kNN based search to replace the notoriously memory consuming quadratic attention in modern Transformers to allow extremely long sequence input. Proposed method is simple, and can be applied to any pre-trained Transformer. The proposed model, Unlimiformer, is evaluated on long text su...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper! We were happy to read that you appreciated our main points: that Unlimiformer is simple, achieves significant improvements, is effective even without any finetuning, and that you think that the strengths outweigh the weaknesses. We think that al...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and feedback! We are encouraged that all reviewers have noted the benefits of Unlimiformer, including that it can be applied to pretrained models with no additional training, has sublinear inference time w.r.t. the length of the input, and leads to significant...	NeurIPS_2023_submissions_huggingface	2,023	Summary: Unlimiformer: Transformers with Unlimited Input Length:- proposes a novel method to overcome the context window limitation in encoder-decoder Transformer models. The key innovation introduced in this paper is a retrieval-based method that integrates a k-Nearest Neighbors (kNN) search into each decoder layer of...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper! We were happy to read that you appreciated our main points: Unlimiformer can be integrated into existing pretrained encoder-decoders, makes it possible to summarize unbounded inputs, and does not require retraining. We think that all your questio...	null	null	null	null	null	null
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism	Accept (poster)	Summary: In this paper, the authors proposed a Gather-and-Distribute mechanism (GD) for efficient information exchange in YOLOs by globally fusing multi-level features and injecting the global information into higher levels. The proposed GD-YOLO architectures show good results compared with the existing YOLO series. Th...	Rebuttal 1: Rebuttal: ### W-1: The motivation is a little bit unclear. The advantage of this GD mechanism compared with traditional FPN are not very clear. Thank you for your suggestions. In traditional concepts, features at different levels contain positional information of different-sized objects. Larger features en...	Summary: In this research, the authors propose a novel Gather-and-Distribute (GD) mechanism implemented through convolution and self-attention operations. This mechanism, incorporated into the GD-YOLO model, significantly enhances multi-scale feature fusion capabilities and achieves a remarkable balance between latency...	Rebuttal 1: Rebuttal: ### W-1: While GD-YOLO-N cannot use the proposed backbone for limited capacity, what will be the approaches for mobile deployment as your main objective is YOLO for mobile deployment? Thank you for your question. The main contribution of our work is the Gather-and-Distribute mechanism. Regarding ...	Summary: This paper studies the problem of efficient object detector and proposes the Gather-and-Distribute mechanism (GD) mechanism to alleviate the information fusion problem. The experiments on the COCO dataset demonstrate the effectiveness of the proposed method. Strengths: + This paper studies an important topic,...	Rebuttal 1: Rebuttal: ### W-1: The structural design of this paper is quite confusing in some aspects, such as the choice of where to inject information. For example, in Low-GD, semantic information is only injected into P3 and P4, while one would expect that global semantic information could also benefit the P5 branch...	Summary: In this paper, the authors proposed an efficient object detection network to make a new trade-off between efficiency and effectivity. In the framework, a lightweight adjacent-layer fusion module termed as gather-and-distribute (GD) mechanism is proposed to take place the conventional neck module in general det...	Rebuttal 1: Rebuttal: ### W1: The novelty of the proposed module can be seen as a compromise similar to the stack of the existing technologies, and lack of testing and verification on other datasets. Thank you for your suggestions. Our core contribution lies in the proposal of the Gather-and-Distribute Mechanism (GD m...	Rebuttal 1: Rebuttal: ### Experiment-1: - Instance Segmentation Task Replace different Necks in Mask R-CNN and train/test on the COCO instance dataset. \| model\| Neck\|FPS\|Bbox mAP \| Bbox mAP:50 \| Segm mAP \| Segm mAP:50 \| \|-\|-\|-\|-\|-\|-\|-\| \| MaskRCNN-ResNet50\| FPN\| 21.6\|38.2\| 58.8\| 34.7\| 55.7\| \| MaskRCNN-ResNe...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presents a real-time object detection method for the YOLO series, introducing a 'Gather-and-Distribute' (GD) mechanism. Despite achieving good results on the COCO dataset, the paper lacks significant novelty and doesn't significantly advance multi-scale feature fusion or FPN-based methods. A deeper c...	Rebuttal 1: Rebuttal: ## Weaknesses ### W-1: The paper lacks substantial scientific novelty, and doesn't significantly advance the field. Thank you for your suggestions. Our core contribution lies in the proposal of the Gather-and-Distribute Mechanism (GD mechanism), which is fast and effective. Our method gathers th...	null	null	null	null	null	null
The CLIP Model is Secretly an Image-to-Prompt Converter	Accept (poster)	Summary: This paper demonstrates that the CLIP model in Stable Diffusion inherently possesses the ability to convert images into text prompts, which can be achieved by utilizing a linear projection matrix that is calculated in a closed form. Strengths: 1. The motivation of image-to-prompt conversion is clear and the p...	Rebuttal 1: Rebuttal: Thank you for the constructive comments. The common questions are first answered in the General Responses, then we clarify specific questions. ___ ### Q1: The quantitative comparison between SD-IPC and SD-IPC-FT can be given. And the quantitative and qualitative results when separately fine-tu...	Summary: This paper focuses on the problem of generating images by a reference image w/ or w/o further text guidance. The core of this problem partly lies in how to convert the images to embeddings that can be directly feed into Stable Diffusion model. To this end, the authors propose to leverage CLIP model, the text e...	Rebuttal 1: Rebuttal: Thank you for your suggestion. The common questions are first answered in General Responses. Below please find the responses to specific comments. ___ ### Q1: The abstract part can be improved. We appreciate your suggestion. We will rewrite the abstract to highlight the relationship of the pro...	Summary: This paper titled presents a method called Stable Diffusion Image-to-Prompt Conversion (SD-IPC) that leverages the inherent capabilities of the Contrastive Language-Image Pre-Training (CLIP) model to convert images into text prompts for image generation tasks. The authors start from the analysis that the contr...	Rebuttal 1: Rebuttal: Thank you for the positive comments. Some common questions are first addressed in the General Responses, followed by answers to individual reviews. ___ ### Q1: Additional comparisons with existing methods could have further strengthened the paper in particular for the evaluation of the editing...	Summary: This paper demonstrates that the CLIP model, used in Stable Diffusion, inherently possesses the ability to convert images into text prompts. They achieve this by utilizing a linear projection matrix calculated in a closed form. Furthermore, the paper shows that this capability can be enhanced by incorporating ...	Rebuttal 1: Rebuttal: Thank you for the constructive comments. The common questions are first answered in General Responses, then we clarify questions from individual review. ___ ### Q1: The visual results of SD-IPC and SD-IPC-FT do not have sufficient preponderance over than previous methods. SD-IPC-FT surpasses p...	Rebuttal 1: Rebuttal: # General Responses We thank all reviewers for your thoughtful and detailed feedback, which is of great importance to our work. Here we address some common issues and share some findings that all reviewers might have interests in. ___ ### Q1: Clarification of certain facets of the proposed method....	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
A New Linear Scaling Rule for Differentially Private Hyperparameter Optimization	Reject	Summary: This paper presents a novel hyperparameter tuning method in the presence of a privacy budget: linearly extrapolating from observations with very low privacy loss. Strengths: The core technique presented here is certainly interesting and deserving of future study. The paper tackles an issue which is often unad...	Rebuttal 1: Rebuttal: > The core technique presented here is certainly interesting and deserving of future study. We appreciate the reviewer's recognition that we have chosen to study an important problem. The majority of our rebuttal will engage with the main stated weakness, that we do not compare with the right nu...	Summary: The paper proposes a linear scaling rule for finding the optimal value of the learning rate and number of training steps for differentially private SGD (DP-SGD). The idea is simple, small amount of privacy budgets are allocated for two initial DP learning rate optimization procedures, and then the values are e...	Rebuttal 1: Rebuttal: We thank the reviewer. We address concerns about scope and clarity of contributions, and explain how we improve over the prior SOTA. >The technique There are a number of hyperparameters for DP; clipping norm, batch size, momentum, optimizer, what parameters to update and how to initialize them. ...	Summary: This study proposes a new algorithm for privately selecting hyperparameters subject to maximizing the model utility. The new algorithm draws inspiration from the linear scaling rule that suggests increasing learning rate as batch size increases. Given the number of hyperparameters in DP-SGD the proposed algori...	Rebuttal 1: Rebuttal: > The main improvement for the limitations is to address the comparison to other tuning algorithms or optimization algorithms that don’t require as much tuning. We appreciate the reviewer's feedback and care in bringing these papers to our attention. In this rebuttal we provide a detailed compari...	Summary: This paper proposes a new method to conduct hyper parameter tuning for DP stochastic gradient descent. The method is based on a linear scaling rule, with two pilot runs using small PLBs and a third run chosen based on a linear extrapolation from the first two. The pilot runs are used to establish an estimate o...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed review; in this rebuttal we will clear up major misunderstandings and provide clarifications. The reviewer has commented that they believe our experimental analysis in Section 3.2 is a 'thought experiment' and that we did not run all the experiments. We wi...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
AutoGO: Automated Computation Graph Optimization for Neural Network Evolution	Accept (poster)	Summary: This paper proposed a new NAS algorithm where a performance predictor is built for acceleration. In addition, the experiments are conducted for verification. ============================ Thanks for the authors' rebuttal. Unfortunately, my concerns are still not addressed. For example, 1) the used data are not ...	Rebuttal 1: Rebuttal: ### W1 "Some claims about neural predictors are not correct." Earlier works like [1] didn't use the term “NAS Benchmarks” as they were published prior to NAS-Bench-101 (which popularized the term). However, [1]'s Introduction says “The training data of the random forest are a set of data pairs, a...	Summary: This paper presents the AutoGO framework, which operates directly on the Computation Graph (CG) of a given DNN architecture. It splits the CG into segments and conducts a search process. Through extensive experiments, the paper shows that AutoGO effectively improves the performance of the top architectures in ...	Rebuttal 1: Rebuttal: ### W1 Verification on ImageNet We indeed provide evaluation for ImageNet tasks by training ResNet-50/101 and VGG16-BN on ImageNet and then further fine-tune these architectures on Cityscapes (Semantic Segmentation) and MPII (Human Pose Estimation). We report the results in Table 3 and list our t...	Summary: This paper introduces AutoGO, an innovative method for evolving neural networks that addresses the challenges of efficiency, low power consumption, and hardware compatibility. AutoGO represents deep neural networks (DNNs) as computational graphs (CGs) comprised of low-level primitives and employs an evolutiona...	Rebuttal 1: Rebuttal: First, we will add some intuitive explanations to the manuscript and more limitation discussions. The current framework effectively solves the AutoGO problem to mutate CNNs for faster inference and hardware-friendly deployment for a range of CV tasks/networks. Note that we do provide more informa...	Summary: The paper proposes to optimise neural networks by exploiting common subgraphs mined from existing NAS benchmarks - this is achieved by building a vocabulary from networks encoded into topologically sorted sequences and using byte-pair encoding (BPE) to obtain common sequences of operations. After that, a given...	Rebuttal 1: Rebuttal: ### W1 Comparison to LEMONADE/uNAS/Blockwise NAS AutoGO differs from these works. LEMONADE uses network morphism (3.1) rules “Inserting a Conv-BatchNorm-ReLU block”. AutoGO uses diverse, data-driven subgraphs of up to 15 nodes for mutation. uNAS predefines a traditional macro NAS structure in Tabl...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their constructive comments, for the suggested references, and for pointing out several typos, which we will fix. In addition to the individual responses, we are providing a PDF containing some new figures/tables. We would like to clarify that the motivati...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
An information-theoretic quantification of the content of communication between brain regions	Accept (poster)	Summary: In the present work, building on recent advances in Partial Information Decomposition or PID, the authors develop a novel information theoretic measure which they term Feature-specific Information Transfer (FIT) and that the authors claim can capture the feature-specific information transfer between brain regi...	Rebuttal 1: Rebuttal: We agree that the nomenclature of the new metric is important and should be chosen with care. We are thus grateful to the Reviewer for raising the issue of how best to name it. In the present study, we consider as “feature” any variable of interest that is external to the considered neural netwo...	Summary: Exploring the content and direction of communication between brain regions is key to understanding the brain. This paper proposes a method called Feature-specific Information Transfer (FIT) to investigate the feature-specific content and direction of information flow in multi-region brain recordings. To isolat...	Rebuttal 1: Rebuttal: Compute DFI on real data. We computed DFI on the 3 real-data sets (Fig. R3). In the MEG dataset (Fig. R3B), DFI was negative values and thus not interpretable as measure of information transfer. Unlike FIT, DFI could not detect that (as predicted by previous studies) stimulus information is stro...	Summary: This paper proposes a novel non-parametric method aimed to quantify the amount of brain communication between (time series representing the activity of) brain regions using information theoretic measures. Concretely, the authors’ method builds on the framework of partial information decomposition of the transf...	Rebuttal 1: Rebuttal: Asymmetric source mixing. We simulated source mixing in different proportions in X and Y due to field spread/ common reference. Such source mixing in real cases is (and is assumed to be in our simulations) instantaneous (i.e. zero lag) and with a stable (across time) proportion of source sharing i...	Summary: The submission proposes a measure called Feature-specific Information Transfer (FIT) which can be used to partial out information transmitted from a sender to a receiver (in this case, two brain regions) about a specific feature (another variable) in a casual way (i.e. it is not present in the history of the r...	Rebuttal 1: Rebuttal: The paper shunts too much important content to the appendix. We agree that it would be better to bring to the main text more theoretical details about the PID used to compute FIT. In case of acceptance, we will use the extra page allowed by NeurIPS to include more PID theoretical details about F...	Rebuttal 1: Rebuttal: We are grateful to the Reviewers for their suggestions and insights. We feel that the new results we obtained in addressing their suggestions (Figs R1-4) significantly elevate the level of conceptual advance provided by our paper. These new results will be included in the revised paper in case of ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning	Accept (poster)	Summary: This work addresses the problem of solving large-scale zero-sum two-team games. To solve this they explore extensions to the Double Oracle and Policy-Space Response Oracle algorithms that solve for a team-based equilibrium concept called TMECor. This is a straightforward extension of both of these algorithms t...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the detailed review and valuable feedback on our work. These insights have greatly helped us in identifying areas of improvement. We would like to address the concerns as follows: Strengths We're pleased that the reviewer appreciated our clear backgrou...	Summary: This paper proposes two algorithms, “Team PSRO” and “Team PSRO Mix-and-Match” for zero-sum two-team games. Team-PSRO is guaranteed to converge to a TMECor. The algorithms extend PSRO to zero-sum two-team games. Team-PSRO Mix-and-Match is an improved version of Team-PSRO with better population policies. The exp...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful examination of our paper and their valuable comments. In response to their concerns, we provide the following explanations and planned corrections: Weaknesses Unclear Description about Team DO-MM and Team PSRO-MM: P is defined in line 262. We will mov...	Summary: The paper presents “game-theoretic” reinforcement learning methods for playing zero-sum games (between teams of players). A theoretical claim (proof is in the supplementary material) is made about convergence of the base tabular methods to the TMEcor solution concept, and empirical results compare the methods...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's time and detailed feedback. We aim to address all the concerns mentioned: Results Explanation and Analysis (Figure 2) Error Bars and Statistical Significance: We acknowledge that the information about statistical tests was not included in the main text. I...	Summary: This work aims to find TMECor in two-team zero-sum games. They extend PSRO from two-player games to two-team games and proposed Team-PSRO which is guaranteed to converge to a TMECor. They further proposed Team-PSRO Mix-and Match which generates more joint policies by mixing individual policy from different PSR...	Rebuttal 1: Rebuttal: We would like to express our gratitude to the reviewer for taking the time to assess our work and providing valuable insights. We would like to clarify certain aspects of our research that may not have been fully grasped, along with a plan to address the constructive suggestions. **Lack of Novelt...	Rebuttal 1: Rebuttal: Details about these experiments have been included in individual responses. Full details will also be included in the camera-ready version. Pdf: /pdf/f95db58d5071c8221164d5685710a1cf2c60d7bc.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Adversarial Learning for Feature Shift Detection and Correction	Accept (poster)	Summary: This paper introduces an invariance-based approach to out-of-distribution generalization where, for a given pair of distributions or domains $p$ a $q$, a subset of the input dimensions is filtered away so as to minimize some divergence between the filtered data, and then classifiers trained on samples from $p$...	Rebuttal 1: Rebuttal: Dear reviewer, **Thank you for your constructive suggestions. We believe there has been misunderstanding regarding the goals and scope of the paper. We introduce two systems: the first localizes faulty features, which can be used to detect incorrectly standardized or processed values or to lo...	Summary: The authors tackle the problems of feature shift localization and correction, i.e., identifying columns leading to divergence between two distributions, and imputing new values in their place, leading to lower divergence. For the first task they employ a random forest classifier trained to predict between samp...	Rebuttal 1: Rebuttal: We want to thank the reviewer for the insightful comments. We want to address each of the questions and concerns: *“Most importantly, how were train/test splits performed? The text implies performance is measured directly on the train tests. Analyzing the code for the feature correction task ...	Summary: This paper proposes a method called Datafix that tries to identify and correct feature shifts in datasets. This method is composed of two distinct algorithms DataFix-locate which first identifies what features have shifted between datasets and Datafix-Correct which tries to correct for these features shifts. ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for the highly constructive suggestions. We want to address each of the questions and concerns: *“the presentation of the work can be improved… lot of discussion in the main paper is not essential … moving parts of appendix D and E to the main paper and maybe m...	Summary: This paper studies the problem when a subset of coordinates in the features have shifts in distributions. It specifically proposes algorithms to detect the shifts, and methods to “repair” the shifted coordinates. Imo, the most interesting part of the paper is that it relates certain inequalities in divergenc...	Rebuttal 1: Rebuttal: Dear Reviewer, **Thank you for the thoughtful review! We agree that some of the introduced heuristics can feel a “bit hackish”, however, they have proved to be more accurate than previous works and than our attempts with more conventional supervised learning methods. One way to get a better i...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their constructive comments, **First, we are restructuring the text to improve the readability of the paper. As suggested by the reviewers, we are including more details about the proposed methods in the main text, and moving some theoretical results t...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Normalization Layers Are All That Sharpness-Aware Minimization Needs	Accept (poster)	Summary: The paper proposes an adversarial perturbation method linked to SAM for the affine normalization parameters, in contrast to perturbing the full set of parameters. The results show this approach improves upon standard SAM and prior sparse SAM approaches. Strengths: The SAM-ON proposed method yields several ben...	Rebuttal 1: Rebuttal: Thank you for your review. Following your suggestion, we are happy to extend the limitations section of the paper. In case there is specific work you think would need to be included, we would much appreciate a pointer. Below we address your other comments. 1. __“gains in Table 1 are marginal”__: ...	Summary: As Sharpness-Aware Minimization (SAM) aims to regularize the flatness of the loss landscape for better generalization, this paper shows that only perturbing the normalization layers is sufficient to achieve this. To prove this, the authors first propose a method called SAM-ON (SAM-OnlyNorm). Then, they conduct...	Rebuttal 1: Rebuttal: Thank you for your review and helpful suggestions. We address your concerns below. 1. __SAM-ON may not always be effective__ We considered a wide range of different models (see Table 1 in rebuttal pdf for even more models), data augmentations, and datasets. For almost all of these a SAM-ON ...	Summary: This paper relates the normalization layer with the Sharpness-Awareness Minimization (SAM). Surprisingly, this paper finds that in the perturbation stage, only perturbing the affine parameters of normalization layers in the networks leads to a better generalization performance. Later, the authors investigate t...	Rebuttal 1: Rebuttal: Thank you for your review and helpful suggestions. We address your concerns below. - __“more network architectures, including VGG-Net”__: Following your suggestion, we have provided results for more neural network architectures: DenseNet and various VGG-Nets in Table 1 in the rebuttal pdf. Ac...	Summary: This paper tries to analyze the effects of different layers for sharpness-aware minimization (SAM). The authors find that normalization layer plays an important role in the improvements of SAM and only perturbing the normalization layer can obtain a comparable result. Therefore, this paper proposes SAM-ON and ...	Rebuttal 1: Rebuttal: Thank you for your review and helpful suggestions. We address your concerns below. - __ImageNet results__: We agree with your comments on the ImageNet results. As you suggested, we have run a ViT from scratch on ImageNet (with additional evaluations on ImageNet-R and ImageNet-sketch) with both...	Rebuttal 1: Rebuttal: We would like to thank you all for your time and useful comments. We are encouraged that you found that our paper addresses an interesting and important problem by demonstrating _"a new understanding of the underlying mechanism of SAM, which is both novel and informative”_ with _"extensive experim...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening	Accept (poster)	Summary: This paper proposes a new virtual screening framework DrugCLIP, which aims to identify molecules most likely to bind to a target. Inspired by the recent multi-modal learning approach CLIP, the authors propose to utilize contrastive learning for learning representations of molecules and proteins. Authors reform...	Rebuttal 1: Rebuttal: ## Response to Reviewer GQNT We sincerely thank the reviewer for the positive feedback. Your support and encouragement are greatly appreciated. We have addressed all your concerns in the following responses. ### Q1: Detailed explanation of the experimental setting We apologize for any inconveni...	Summary: The authors recast virtual screening as an information retrieval problem: by learning appropriate representations of both proteins and molecules, and taking contrastive loss based on binding affinity between protein-molecule pairs, the aim is to learn a model where protein queries passed through one encoder ca...	Rebuttal 1: Rebuttal: ## Response to Reviewer cZpn We extend our heartfelt gratitude to the esteemed reviewer for their generous appraisal of our work and for awarding high scores. Your positive feedback and acknowledgment of our efforts are deeply appreciated. We are thrilled that our research has met your expectatio...	Summary: The authors proposed a contrastive learning framework, DrugCLIP, for the drug virtual screening task which identifies potential drugs from vast compound databases to bind with a particular protein pocket. It reformulates virtual screening as a dense retrieval task and employs contrastive learning to align repr...	Rebuttal 1: Rebuttal: ## Response to Reviewer 8ynk We greatly appreciate your valuable feedback. We are committed to refining our work to enhance its quality and impact. ### Q1: Regarding our paper's contribution Our paper's main contribution lies not merely in applying contrastive learning, specifically CLIP, to th...	Summary: ## DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening The authors present DrugCLIP, a contrastive learning method for virtual screening. DrugCLIP is designed to align representations of small molecules and protein binding pockets. By using a contrastive learning framework, th...	Rebuttal 1: Rebuttal: ## Response to Reviewer Bb5s We sincerely thank the reviewer for your valuable advice. We will address the typos and word usage issues as you have pointed out. Regarding the questions about implementation details, we have included them in the global response. ### Q: L20-22: library composition a...	Rebuttal 1: Rebuttal: We truly appreciate the reviewers' time in reviewing our project, and we will incorporate the suggestions to revise our paper thoroughly. In the global responses, we want to provide explanations on some implementation details for a better understanding of our methodology. ### Q: Explanation (atom...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Unsupervised Learning for Solving the Travelling Salesman Problem	Accept (poster)	Summary: This paper proposes UTSP, an unsupervised learning framework, to solve the Travelling Salesman Problem (TSP). It consists of two phases, including heat map construction and local searching. Specifically, a surrogate loss function is proposed to train the GNNs, encouraging the model to find the shortest path an...	Rebuttal 1: Rebuttal: Q: The review of related work is too limited. ... It is suggested to add the related work section in Appendix. A: We add a new section, refer to the "replies to all reviewers", where we include the recent work of [1][2][3][4]. Q: The presentation of local search (in Section 3) is not c...	Summary: The authors propose a continuous relaxation of the 2D Euclidian Travelling Salesman Problem. Specifically, they replace the combinatorial optimization problem with a continuous optimization problem of the form Minimize f(A) over all column-stochastic matrices $A \in \mathbb R^{n \times n}$ where $f$ is a s...	Rebuttal 1: Rebuttal: We update the experiment section in the one page rebuttal pdf. Q: A more extensive discussion of the existing literature is needed. In particular, the authors should explain the method presented in [1], as it is the only one that achieves results comparable to the proposed approach. I would...	Summary: This paper proposed a novel unsupervised learning method for solving the Travelling Salesman Problem (TSP). It employs a Scattering Attention GNN (SAG) to encode the node information. Then, the learned representation is transformed into a heatmap, which corresponds to the probability of an edge being included ...	Rebuttal 1: Rebuttal: Please check the “replies to all reviewer” and the one page rebuttal pdf. Q Since the proposed method is heatmap based, I suggest to give a detailed review and discussion on heatmap based methods, such as [Fu2021], [Qiu2022], [Joshi2022]. Currently the introduction is mainly from the SL/RL pe...	Summary: The paper proposes an unsupervised learning-based heuristic to solve the Travelling Salesman Problem. The approach consists of two steps: first a GNN is trained using a surrogate loss to output a heatmap of the edges then the heatmap is used to guide a local search heuristic. The proposed model has significant...	Rebuttal 1: Rebuttal: Q: The main weakness is the limited scope: the paper is very specialized to the TSP A: TSP stands as one of the 21 NP-complete problems outlined by Karp [Karp]. TSP holds a foundational position in the field of combinatorial optimization owing to its essence and practical utility. TSP is al...	Rebuttal 1: Rebuttal: Dear Reviewers, thank you for your comments. We updated our model's performance in the tables included in the one-page rebuttal PDF. We have incorporated additional baselines: POMO by Kwon et al. [2020] and more recent approaches such as DIMES by Qiu et al. [2022] and DIFUSCO by Sun and Yang [...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces	Accept (poster)	Summary: This paper consider the Gromov-Wasserstien (GW) problem with quadratic cost, a (non-convex) quadratic optimization problem over the space of probability measures (in this work, restricted to uniform discrete measures supported on $n$ points) which takes the form (in discrete setting): $$\min_{\Gamma} \sum_{1 \...	Rebuttal 1: Rebuttal: Thank you sincerely for your comments and questions. See below for our answers. Weaknesses: On the convergence of the algorithm: We are working on quantifying the convergence rate. The number of iterations are bounded by ($O((1/\epsilon)^{\ell_x\ell_y+1})$), to cover the compact set, but the lo...	Summary: The Gromov-Wasserstein optimal transport problem is a non-convex problem known to be hard, closely related to QAP. The authors consder its special case when the two involved metric spaces are Euclidean with small numbers of dimensions. We want to permute one set of points such that the sum of squares of differ...	Rebuttal 1: Rebuttal: Thank you sincerely for your comments and questions. See below, for our answers. Weaknesses: Limited experimental testing: The main focus of this paper is on difficult problems that appear in, e.g., computational biology and where there are symmetries in the data and thus there is a large set ...	Summary: - The paper introduces a novel algorithm for efficiently computing the Gromov-Wasserstein (GW) distance for low-dimensional point clouds. The proposed method offers a transformative approach by transforming the GW distance problem into a sequence of concave optimization problems over convex sets, thereby impro...	Rebuttal 1: Rebuttal: Thank you sincerely for your comments, feedback, and questions. See below for our answers. Weaknesses: We agree that this is a weakness and we are currently working on quantifying the convergence rate. For a given dimensionality and number of points, it should be straightforward to show that our...	Summary: This paper solves the Gromov Wasserstein (GW) distance problem for squared Euclidean norm by considering a low-dimensional space on which the computation is performed, using a cutting-plane method. To this end, they write the GW problem as a low-rank optimization problem. Their algorithm is supported by theore...	Rebuttal 1: Rebuttal: Thank you sincerely for your comments and questions. See below for our answers. First we would like to clarify that the paper is not just an extension of the work [16] and [17]. Even though we also consider the GW-problem, our method is guaranteed to reach the global optimum for the problems we ...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you sincerely for the work you have put into reviewing the manuscript paper. Your reviews have been thorough, pointing at strengths and weaknesses in the paper and suggested clarifications that benefit the presentation and clarity of the paper. We will do our very best to in...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a new algorithm to solve the Gromov-Wasserstein problem between two sets of points in Euclidean spaces when the ground cost is the squared Euclidean norm. This is done by first reformulating the Gromov-Wasserstein problem as a low-rank QAP problem, then relaxing the set of admissible coupl...	Rebuttal 1: Rebuttal: Thank you sincerely for the comments, feedback, and questions. See below for our answers. Weaknesess: 1. We will go over the mathematical derivations again and try to improve the readability for the final version of the paper. 2. We are planning the expand the numerical results in the final vers...	null	null	null	null	null	null
Visual Instruction Inversion: Image Editing via Image Prompting	Accept (poster)	Summary: The authors propose a method for finding a text-based editing direction extracted from a pair of “before” and “after” images depicting the desired edit. Using a fixed, pretrained diffusion model (in this case Stable Diffusion), the authors optimize the text-conditioning embedding to align with the CLIP-space d...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback! We address your questions/concerns below. It appears that the proposed method is more effective on style-based edits, as I could not find any examples to require a large change in the structure of the object. [...] Edits that “require a large change in...	Summary: This paper proposes a method for image editing via visual prompting. Given pairs of example that represent the “before”and “after” images of an edit, this framework can learn a text-based editing direction that can perform the same edit on the new images. Experimental results show the effectiveness of the prop...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback! We address your questions/concerns below. The results in the first row of Fig. 3 have some artifacts in the hair and face; the results in the third row of Fig. 3 have the undesired changes in the background. We admit that the background can sometimes...	Summary: - This paper proposes a novel image editing method via visual prompts - This paper introduces a new method to bind text-based transferring to a specific conversion between image pairs. Strengths: - The paper is well written and easy to follow. - The motivation is clear and reasonable. - The proposed method of...	Rebuttal 1: Rebuttal: Thank you for your positive feedback! We address your questions/concerns below. **According to section 4.1, a key issue is that this work relies on existing pre-trained models to obtain high-quality paired images, I wonder will the quality heavily relied on the quality of these pre-trained models...	Summary: This paper investigates image editing via visual prompting useful when textual descriptions cannot describe desired edits. The proposed framework inverts visual prompts into editing instructions and learns directions in the text space of the pretrained instruct pix-to-pix model. This edit direction is learned ...	Rebuttal 1: Rebuttal: Thank you for finding our paper "fascinating" and "practical". We answer your questions/concerns as below: **The paper has a significant weakness in that it fails to acknowledge seminal work on image analogies, specifically, the research conducted by Hertzmann et al. (2001) and their deep learnin...	Rebuttal 1: Rebuttal: We propose a framework for inverting visual prompts into editing instructions for text-to-image diffusion models. Furthermore, our method can combine instructions between learned and natural language, yielding a hybrid editing instruction that is more precise. We are grateful that **all revi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors propose a new method that can perform visual prompting via a pair of exemplar images through a pretrained text-based instruction image editing model. The method introduced in this paper only requires optimizing over the text conditioning vector in order to perform visual instruction ...	Rebuttal 1: Rebuttal: Thanks for your positive feedback! We address your questions/concerns below. Only sampled 1k imgs to perform quantitative analysis We believe that ~1000 images are sufficient to validate our approach. This is in line with other related work; e.g., Imagic (CVPR 2023): ~100 image pairs; Null-t...	null	null	null	null	null	null
Generative Category-level Object Pose Estimation via Diffusion Models	Accept (poster)	Summary: This paper proposes a novel approach for generative object pose estimation based on diffusion models. Strengths: 1. Formulating pose estimation as a diffusion process is novel. It shows excellent ability to solve the multi-hypothesis issues in pose estimation caused by symmetry and partial observation. More...	Rebuttal 1: Rebuttal: > **(6D -> 9D)Q1: In Table-1, this paper conducts comparison with category-level object pose estimation methods that predict 9 DoF object pose, consisting of 3D rotation, 3D translation and 3D size. However, the 3D size is not considered by the proposed method. Though the metrics are focused on ro...	Summary: This paper proposes a novel conditional generative approach for category-level pose estimation to solve the multi-hypothesis problem. The proposed method utilize the energy-based diffusion model to aggregate the candidates generated by the score-based diffusion model. Extensive experiments have been conducted ...	Rebuttal 1: Rebuttal: > Q1: I wonder if scoreNet and energyNet are needed for each class. Does a single model infer all classes? A1: Apologies for the confusion. To clarify, we only require one set of both the score and energy models for all classes. Notably, neither model is conditioned on the class label or ...	Summary: This paper mines and formulates ambiguity in the task of object pose estimation, proposing to use a diffuse generative model to generate multiple hypotheses, which are then aggregated through additional scoring and ranking by another scorer net. The model achieves significant improvements with less supervision...	Rebuttal 1: Rebuttal: > Q1: why is mean pooling needed since GT is accessible? What did I miss? A1: To clarify, the ground truth (GT) is not accessible during test time. That's why we employ another energy-based diffusion model to aggregate these candidates into a final output in the absence of the GT. In our...	Summary: To settle the multi-hypothesis issue in category-level 6D pose estimation, this paper formulates the focused task as conditional generative modeling and proposes a novel method based on diffusion models, which utilizes a score-based diffusion model to sample pose candidates with an energy-based one followed to...	Rebuttal 1: Rebuttal: > Q1: The proposed method is not competitive in terms of inference time. A1: Thanks for pointing it out! We acknowledge that the sampling process of the diffusion model does introduce a notable computational overhead. Consequently, our current approach for estimating 6D object pose from i...	Rebuttal 1: Rebuttal: We are grateful to all reviewers for recognizing the merit of our idea, experiments, and presentation: - "I appreciate the observation of symmetric ambiguity and the generative multi-hypothesis formulation." (W8Ay) - "The improvement has seen a significant boost." (W8Ay) - "This surpasses the p...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a diffusion-based model for category-level object pose estimation. Different from the existing deterministic approaches which treat the object pose estimation as a regression problem, the proposed diffusion model alternatively formulated it as a generation problem. In this way, it could tac...	Rebuttal 1: Rebuttal: > Q1: inquiry regarding the reasons about exclusion of scale parameters and the means by which it can be obtained during inference. A1: Thanks for pointing this out! We would like to clarify that our work primarily focus on the estimation of a 6D object pose. Nonetheless, our method posse...	null	null	null	null	null	null
Practical Contextual Bandits with Feedback Graphs	Accept (poster)	Summary: This paper studies online learning in a contextual setting when a feedback graph determines the feedback received by the learner. Namely, the learner observes all the losses experienced by the actions in the graph-neighborhood of the action it played. Feedback graphs are a well-known feedback model for onlin...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the issues you mentioned as follows. 1. Once equation 3 is designed, the rest of the paper seems incremental to Foster et al. We argue that achieving the tight regret bound with respect to the correct graph-theoretic dependence for ...	Summary: The authors consider the adversarial contextual bandit problem with feedback graphs, in the finite function class setting with a realizability assumption, with an access to an online regression oracle. They extend previous approaches for the vanilla contextual MAB setting in order to obtain regret bounds of $\...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the issues you mentioned as follows. **1. This is not a very major issue, but the algorithm suggested by the authors requires knowledge of the feedback graphs' independence number (or a good bound on it), which is a hard quantity to comp...	Summary: This work is concerned with the problem of contextual bandits with graph feedback. The authors consider a setting where the contexts and the graphs are generated in an arbitrary manner and revealed to the learner at the beginning of each round. For a given context, the mean loss of each arm is assumed to be fi...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the issues you mentioned as follows. **1. The regret bounds in Corollaries 3.3 and 3.5 are stated in terms of a uniform upper bound on the independence number or the weak domination number of the observed graphs. This is unsatisfactory s...	Summary: This work studies the contextual bandits problem in the presence of a feedback graph G_t. An edge (i -> j) in G_t means that taking action a_i allows us to observe the loss for action a_j. The work extends the SquareCB algorithm to this setting, the primary difference being the way the action sampling probabil...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the issues you mentioned as follows. 1. By and large, the paper relies heavily on the SquareCB paper's analysis. Although the setting of feedback graphs is new, the analysis seems derivative. The analysis of SquareCB relies on the c...	Rebuttal 1: Rebuttal: We thanks all the reviewers for their valuable comments, especially for pointing out the adaptive tuning issue of the parameter of $\gamma$ without requiring knowledge of the graph-theoretic quantities and beyond the worst-case graph. We address your issues in separate sections as follows.	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work studied contextual bandits with feedback graphs. The authors provided an algorithm based on the recent Decision-Estimation Coefficient (DEC) framework that finds the next-step action distribution by solving a minimax optimization problem. To address the issue that the minimax problem is hard to solve...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the issues you mentioned as follows. **1. The importance of this work remains unclear. The proposed algorithm is very similar to the original E2D algorithm proposed by Foster et al. 2021 with an additional expectation over the feedback g...	null	null	null	null	null	null
Fine-Grained Visual Prompting	Accept (poster)	Summary: This paper proposes Fine-Grained Visual Prompting (FGVP) that incorporates Blur Reverse Mask to improve the semantic localization capability of VLMs, like CLIP. It provides a comparison to other possible methods for highlighting the different parts/objects in the image, based on SAM and other techniques. The r...	Rebuttal 1: Rebuttal: Thank you for the comments and suggestions! Q1: Missing related works in the visual prompting domain.\ A1: Thanks, we will cite these works in the revised version. Q2: The runtime should be discussed since the method requires running the model with a relatively dense grid of keypoi...	Summary: This paper proposes a new “visual prompting” method. Visual prompting refers to the idea of altering images to guide the “attention” of a vision-language model when the model is used to embed the image. For example, to obtain an embedding for an object in an image that contains many objects, the user could dra...	Rebuttal 1: Rebuttal: Thank you for the comments and suggestions! Q1: The inference cost of the proposed method.\ A1: We present the inference cost comparison experiments in the Global Response A1 and A2. Thank you for your advice; we will acknowledge it in our limitation sections in the revised version. ...	Summary: This paper works on visual prompting. They proposed FGVP, together with Blur Reverse Mask, to improve the semantic localization ability of the vision-language model. Strengths: Solid experiments showing the effectiveness of their method. Weaknesses: In general, this is a good work. However, there’re several...	Rebuttal 1: Rebuttal: Thank you for the comments and suggestions! Q1: The novelty of this work is not well established. Seems like an engineering combination of previous works. A1: Firstly, thank you for your concern. With the development of large vision-language models and segmentors, they potentially e...	Summary: This paper proposes a visual prompting method that exploits the segmentation masks of interested objects in images to generate more fine-grained visual prompts. Experiments show that the proposed methods achieve competitive results on zero-shot referring expressions comprehension and part detection. Strengths...	Rebuttal 1: Rebuttal: Thank you for the comments and suggestions! Q1: Ablation studies on different VLMs. \ A1: The results across various VLMs demonstrate the consistent improvement of FGVP. Importantly, we observe that RedCircle experiences a significant performance decline when transitioning from CLIP to ot...	Rebuttal 1: Rebuttal: Thank you to all the reviewers for their valuable comments and suggestions! Here are the responses to some common concerns. Q1: Experiments of inference cost with detector proposals (Figure 2).\ A1: We conducted efficiency experiments, comparing inference costs in terms of computation...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes Fine-Grained Visual Prompting (FGVP), which uses precise semantic masks from SAM as visual prompts to improve spatial localization of vision-language models (VLMs) like CLIP for instance-level tasks. The key contributions are: - Systematically study different visual prompting techniques lik...	Rebuttal 1: Rebuttal: Thank you for the comments and suggestions! Q1: More insights from the study.\ A1: \ 1) Compare the gap in mask quality caused by using ground truth and predicted boxes.\ Firstly, Table 2 in the main paper contrasts results with masks derived from ground truth (left side) and prop...	null	null	null	null	null	null
Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification	Accept (poster)	Summary: The paper proposes an inverse Relative Entropic Optimal Transport (RE-OT) point of view for classification problems. The paper then proposes to use inverse RE-OT with a time-varying prior for solving long-tailed classification problems. Evaluations show improvements compared to existing baselines such as vanil...	Rebuttal 1: Rebuttal: We truly appreciate the time and effort you have spent reviewing this paper. Below are our responses: >*Q1: the paper does not make clear what methodological or practical benefit the proposed framework has.* We think that our (RE-)OT framework for classification is highly motivated and valua...	Summary: The authors propose Relative Entropic Optimal Transport, which allows to incorporate prior information matrix to the learning of optimal transport plan. After studying its theoritical properties, they adapt REOT to the long-tailed classification problem and establish the connection between optimal transport an...	Rebuttal 1: Rebuttal: Thank you for spending your valuable time on this paper. We hope that our responses can address the concerns you may have. >***Q1: "In Prop 1, the authors state that when Qtilda = a otimes b, then the solution of REOT and EOT coincides (which is not true in general), while in the proof, they cons...	Summary: This paper addresses the problem of unbalanced classification using a variant of optimal transport called Relative Entropic Optimal Transport (RE-OT), which alter the Sinkhorn distance with the prior information into the coupling solution. Experimental results across different domains validate the efficacy of ...	Rebuttal 1: Rebuttal: Thank you very much for taking the time to review this paper. Below are our responses. >*Q1: Difference to [1]* A1: [1] views neural networks as mappings in optimal transport and proposes that these mappings need to adapt due to the difference between the training and testing label distribut...	Summary: This works proposed a new view to the classification problem through the lens of Optimal Transpot. They propose a new variant of OT, says Relative-OT, by changing the KL regularizer constraints in a given distribution. By some special properties of KL divergence, the works show a relationship betwen the entrop...	Rebuttal 1: Rebuttal: Thank you for your valuable time in reviewing this paper. Here are our responses. >*Q1: "I do not understand the claim in equation (10). Could the author elaborate it?""* A1: Certainly. Here is our explanation. Assume that $P^\epsilon$ is a coupling from image $a$ to image $b$, and we can ob...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers for investing their valuable time and providing insightful comments on our paper. Overall, the reviewers found our work to be novel (Cbtk, RCtv), with promising experimental results (Cbtk, RCtv, HboX), and easy to follow (pKsm). Additionally, they acknowledged...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces the relative entropic (RE) regularization for optimal transport (OT) problems. Instead of the traditional entropy regularizer (Cuturi'13), the RE-OT is defined through a given prior matrix $\boldsymbol{Q}$, which guides the matching between source and target distributions. Using the approa...	Rebuttal 1: Rebuttal: We appreciate your valuable time and comments and we hope the following answers can address your concerns. >*Q1: Does the prior guide Q belong, in general, to the polytope U(a,b)* A1: No. For example, $Q$ can be set as $1_{n×m}\notin U(a,b)$, and then the RE-OT problem degenerates into Entro...	null	null	null	null	null	null
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space	Accept (poster)	Summary: This paper proposes a self-supervised contrastive learning framework for extracting comprehensive features from multimodal time-series sensing signals. The framework refines contrastive learning by modeling shared and private features and exploiting orthogonality constraints. Moreover, a Temporal Structural Co...	Rebuttal 1: Rebuttal: # **Response to Reviewer 8mBK ****************Q1:**************** In the experiments, what is the intuition of the hyperparameter setting of Eq.5? Sensitivity analysis appears to be lacking. ************Response**************: We have added the sensitivity test and plotted th...	Summary: This paper proposes a multimodal contrastive learning framework (FOCAL) for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. In which, FOCAL first decouples each modality into two subspaces, i.e., shared and private spaces, and uses simple soft ort...	Rebuttal 1: Rebuttal: # Response to Reviewer Q2hY Q1: The biggest concern is the effectiveness of multimodal feature factorization. From line 161, this work uses the simple soft orthogonal constraint to decouple or factorize each modality feature. However, the single orthogonal constraint is too simple and may...	Summary: The paper proposes FOCAL, a contrastive learning method for multimodal time-series signals. The main idea is to first encode each modality into a factorized orthogonal space, and design four pretraining objectives that enforce modality consistency, transformation consistency, orthogonality constraint and tempo...	Rebuttal 1: Rebuttal: # **Response to Reviewer oGNZ ************Q1**************: I wonder if some of the training objectives may compete during training, and how do the authors set the three hyper-parameters $\lambda_p$, $\lambda_o$, and $\lambda_t$? Does it require some manual hyperparameter tuning? Join...	Summary: The paper introduces FOCAL, a new contrastive learning framework for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. Unlike previous frameworks that focused solely on shared information between sensory modalities, FOCAL also factors in exclusive m...	Rebuttal 1: Rebuttal: # **Response to Reviewer WTA9 ************Q1************: Is there any hyperparameter search performed for $\lambda_p$, $\lambda_o$, and $\lambda_t$ which control the weights of each loss component? Additionally, was there any hyperparameter search carried out for other methods? ...	Rebuttal 1: Rebuttal: # General Responses We would like to sincerely thank all the reviewers for their valuable feedback and constructive suggestions for this submission. As a summary of our responses, we have finished the following tasks during the rebuttal period: 1. We added a sensitivity test of loss hyperparamet...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The proposed FOCAL is a constrasitive learning method or time-series signals. The major contributions are: 1. For multi-modality samples, FOCAL learns shared feature that are similar between modalities, and a private features that are similar intra-modality but different across modalities. 2. The shared and pr...	Rebuttal 1: Rebuttal: # **Response to Reviewer zwVA Q 1: It would be very strong to show that the proposed loss serves as a plugin that enhances other contrastive learning methods. Response**: Thanks for the suggestion. We have applied the proposed temporal constraint to multiple contrastive learning base...	null	null	null	null	null	null
BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning	Accept (poster)	Summary: The paper addresses the challenge of detecting backdoored reinforcement learning policies. The injection of backdoors in RL policies was first studied in [19] and while there has been some work on detection of backdoored policies in [2] and [14] - these methods are limited to settings where the trigger is in t...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive review. Please see below for our response and clarifications. The reviewer first questioned the assumption of accessing the target agent’s value function. We thank the reviewer for the valuable feedback. We would like to kindly point out that thi...	Summary: This paper addresses the threat of backdoor attacks against deep reinforcement learning (DRL) policies. To tackle this problem, the authors propose BIRD, a novel generalizable backdoor detection and removal method for pretrained DRL policies in a clean environment without any knowledge of the attack specificat...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive and constructive review. Please see below for our response and clarifications. The reviewer first questioned the presentation of Table 2. We thank the reviewer for the suggestion. We agree with the reviewer that the results of Qbert, COMA, and YSNP ...	Summary: This paper studies the backdoor defense problem in deep reinforcement learning (DRL) policies. Specifically, the authors proposed the BIRD method to address the limited generalizability and scalability of current practices. By analyzing the unique properties and behaviors of backdoor attacks, the authors formu...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive and constructive review. Please see below for our response and clarifications. First, the reviewer asked for an additional ROC curve for the results in Fig. 2. Thank the reviewer for the valuable comment. We follow this suggestion and draw the ROC cu...	Summary: This paper introduces BIRD (Backdoor Identification and Removal for DRL), a method for detecting and removing triggers in reinforcement learning models. In backdoor attacks, an attacker injects a trigger into the agent's environment during training, leading the agent to take backdoored actions that decrease it...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive and constructive review. Please see below for our response and clarifications. **First, the reviewer raised concerns regarding the effectiveness of using reward as the detection metric, particularly for challenging games where even a clean agent may strug...	Rebuttal 1: Rebuttal: We thank the reviewers for the constructive feedback. We addressed all the comments. Below, we summarize our responses: We have added all experiments mentioned by reviewers (All the results are in the submitted PDF): 1. We demonstrated the effectiveness of BIRD in detecting poisoned/backdoored a...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer	Accept (poster)	Summary: The key contribution of this paper is that the training dynamics of a 1-layer Transformer model is demystified theoretically and empirically. The authors have found that the self attention operator performs a discriminative scanning algorithm on the input tokens, attending more on distinct tokens and focusing ...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful and encouraging comments! We appreciate the suggestions! Here are the answers to the questions. > Attention patterns in multi-layer model We haven’t systematically analyzed the attention patterns in multi-layer models yet. Initial experiments show that t...	Summary: This paper analyzes a simple architecture of 1-layer transformer’s SGD training dynamics for the task of next token prediction. The authors prove that self-attention acts as a discriminative scanning algorithm, with an inductive bias to favor unique key tokens that frequently co-occur with the query tokens. ...	Rebuttal 1: Rebuttal: We thanks the reviewer to give insightful comments! > The assumptions used in the analysis need further justifications regarding the learning rate of Y and Z, and the weak correlation assumption in Assumption 2. We explain the intuition of the Assumption 2 (Weak correlation) as below: **Regard...	Summary: This paper theoretically studies the SGD training dynamics of a 1-layer Transformer. Based on some assumptions, this paper introduces the frequency bias and discriminative bias of Transformers. To be more specific, this work shows that Transformers gradually attend more to distinct key tokens and less to commo...	Rebuttal 1: Rebuttal: Thanks the reviewer for the insightful comments. Here are the answers. > I feel the decoder layer is not formally defined. In around line 92, only the self-attention layer is introduced. We define the decoder layer $Y$ and self-attention layer $Z$ after reparameterization $Y = UW_V^TU^T$ and $...	Summary: This paper presents a rigorous mathematical analysis of the training dynamics of a 1-layer Transformer architecture without positional encoding for the task of next token prediction. The authors demonstrate that the self-attention mechanism in the Transformer exhibits a discriminative scanning algorithm that g...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments! > The experimental part of the paper is limited in scope, focusing on 1-layer Transformer models without positional encoding and not addressing more complex architectures. We emphasize that most of the experiments are focused on verification of...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful feedback. We are glad to hear that reviewers agree that a rigorous framework/analysis of the training dynamics of Transformer is valuable, interesting, novel and timely[sxfK, TXYd, nH6i], with clear high-level intuitions [TXYd, nH6i]...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper studies the training dynamics of single layer Transformers. They identify a certain scan and snap procedure of the Transformer that learns a winner-take-all solution given certain data statistics / training dynamics or the self-attention learns to combine tokens. They accompany their theoretical re...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments! Here are the answers: > Lemma 1: gradient dynamics of batchsize 1 -> Transformer trained with single example only. First of all, in machine learning, training with batchsize = 1 means that in each gradient step, only one sample is used to update the gradi...	null	null	null	null	null	null
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples	Accept (poster)	Summary: The paper suggests a sample-based method (termed FLS) for evaluating generative models. Similarly to FID and IS, the method uses a pre-trained network (InceptionV3 or CLIP) for image representation. Unlike FID, the proposed metric is based on a variant of KDE - fitting isotropic Gaussians around each generated...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful comments and feedback and we value the fact that the reviewer felt “the ability to quantitively assess the quality of image generation models is highly important” and that works in this area have a “high potential impact”. We also are thrilled to hear tha...	Summary: The work proposes a new metric (FLS) for image generation tasks that is motivated by the observation that current metrics consider the quality and the diversity of the samples, but not their novelty, and therefore are not penalized by memorization of the training set. The proposed method instead encompasses al...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and detailed review of our work. We are glad that the reviewer finds our paper “well-motivated”, “well-grounded”, and that the holistic evaluation of generative models as we do by introducing FLS is a “useful tool going forward”. We also appreciate that the rev...	Summary: The paper proposes an approach for evaluating generate images by fitting a mixture of Gaussians (MoG) to feature embeddings extracted from the generated and real images. For this, images are first mapped to a feature space (e.g. via an Inception or CLIP image encoder) and then map a Gaussian distribution to ea...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and feedback on our manuscript. We are happy to hear that the reviewer views FLS as “easy to calculate” and that it can scale to a “large number of images”. We also are pleased by the reviewer stating that FLS aims to evaluate the overfitting/memorization behav...	Summary: This paper addresses the problem that there are currently no sample-based evaluation metrics accounting for the trichotomy between sample fidelity, diversity, and novelty. Likelihood based metrics are not particularly interpretable and sample quality based metrics do not take into account novelty -- they are e...	Rebuttal 1: Rebuttal: We thank the reviewer for their time, feedback, and positive appraisal of our work. We are heartened that the reviewer feels that FLS tackles an “important problem and proposes an innovative solution”. We also appreciate that the reviewer finds our evaluation extensive in the number of datasets an...	Rebuttal 1: Rebuttal: We thank all reviewers for their thorough reviews and valuable feedback. We are encouraged that they found FLS well-motivated and that the holistic evaluation of generative models has “potential for high impact” (HK8L, V3Uo). We also thank the reviewers for viewing our paper as “well written a...	NeurIPS_2023_submissions_huggingface	2,023	Summary: Limitation of existing evaluation metrics * Likelihood-based metrics rarely correlated with perceptual fidelity. * Sample-based metrics are insensitive to overfitting. E.g., FID * Copycat (=a model randomly outputs training set) outperforms SOTA generators in $\text{FID}_\text{test}$ Proposed evaluation metri...	Rebuttal 1: Rebuttal: We want to thank Reviewer euCm for their feedback. We are glad that Reviewer euCm highlighted that a strength of FLS is that it “is a one-value metric that reflects three aspects [Fidelity, Diversity, Novelty],” making it “easy to rank different models.” We now address the specific comments points...	null	null	null	null	null	null
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation	Reject	Summary: This paper aims to improve previous Universal Domain Adptation (UniDA) methods by further exploting the intra-class discrimination. For that, they propose a Memory-Assisted Sub-Prototype Mining (MemSPM) method. MemSPM learns to retrieve new task-oriented features given the input embedding features, and apply e...	Rebuttal 1: Rebuttal: ##### Re_Q1: Although the MemSPM has some additional learning challenges and costs, it is worth the cost because 4 commonly used datasets have significant concept shift in 90% of categories (Samples shown in Figure 1(a) ), and other larger dataset such as ImageNet even has more intra-class di...	Summary: This work proposes to exploit the intrinsic structures for each class, where sub-prototypes are devised to associate domain-common knowledge for universal domain adaptation. Specifically, MemSPM employs a memory module to mine sub-class information, and a corresponding reconstruction module to derive task-orie...	Rebuttal 1: Rebuttal: ##### Q1: Why can sub-prototypes benefit the universal domain adaptation scenario? I understand that, even within a domain, samples from the same class can be grouped into sub-classes. But, a critical part is missing why this helps the cross-domain association of common classes. which is the core...	Summary: This paper focuses on Universal Domain Adaptation (UniDA), a practical DA setting that does not make any assumptions on the relation between source and target label sets. The goal is to adapt a classifier from source to target domain such that both source and target domains may have their own private classes a...	Rebuttal 1: Rebuttal: ##### Re_Q1: Although the concept of the prototype is mentioned in [W1] and [W2], there are clear differences between theirs and our MemSPM. First, the meaning of prototype is different between [W1] and ours. In the [W1], the subsidiary prototype is extracted from randomly cropped images, which ...	Summary: This paper proposes a Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them. Strengths: The writing of the article is very good. Graphical expressions such...	Rebuttal 1: Rebuttal: ##### Q1: Some training details need to be explained, such as the selection of hyperparameters. How to adjust the N, S, and lambda, and what criteria are based on? If it is based on the final experimental effect, it also indirectly depends on the label information of the target domain. ##### Re_...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful feedback and list some responses to common questions in this section. *** ##### Q1: Impact of CLIP: CLIP-based embedding does have some cross domains knowledge but it was still influenced by the larger domain gap. The simple baseline of CLIP has bee...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work addresses the problem of universal domain adaptation by focusing on the intra-class structure within categories, which is often overlooked by existing methods. The main contribution is the proposed Memory-Assisted Sub-Prototype Mining (MemSPM) method, which learns the differences between samples bel...	Rebuttal 1: Rebuttal: Thanks for supporting our work. ##### Re_W1: Although CLIP-based embedding does have some cross-domain knowledge, it still cannot address the large domain gap that existed in the benchmarks. The baseline that simply adopts the CLIP encoder has been tested on the Officehome dataset only achieving...	null	null	null	null	null	null
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL	Accept (poster)	Summary: This paper proposes a physics-informed dynamics model TDM and a new offline RL algorithm TSRL, which exploit the fundamental symmetries in the system dynamics for sample-efficient offline policy learning, embedding and enforcing T-symmetry between a pair of latent forward, and reversing ODE dynamics to learn f...	Rebuttal 1: Rebuttal: > W1 & Q1. The performance of TSRL is not comparable with other baselines in most Adroit human and cloned tasks. - The full results for Adroit tasks were listed in Appendix C Table 4 due to the space limit of the main article, please check our supplementary material for details. we observe th...	Summary: The current offline RL algorithm requires a large amount of offline dataset training and has poor performance on small datasets. This article proposes a framework to address this issue. By learning a T-symmetry enhanced dynamic model, capture more fundamental dynamic relationships. Afterward, the article appli...	Rebuttal 1: Rebuttal: > W1. Difference with the origianl definition of time-reversal symmetry. Comparison with the treatment on irreversible actions in https://arxiv.org/abs/2111.12600. We thank the reviewer for providing this reference and will add it to our final paper. Regarding the differences: - As we have d...	Summary: This work introduced a Time-reversal symmetry enforced dynamics model, which leverages the consistency between a pair of forward and reverse latent dynamics for improving the sample efficiency of offline RL algorithms. Conducted experiments demonstrate the effectiveness of the proposed method. Strengths: - Th...	Rebuttal 1: Rebuttal: > W1 & Q1. Rational of the sample efficiency improvement of TSRL The sample efficiency of TSRL is a joint result of a series of elegant and closely related design choices: - Firstly, learning fundamental/parsimonious dynamics is essential to improve model performance under small datasets. Le...	Summary: The paper investigates the time-reversal symmetry of forward and reverse dynamics in reinforcement learning (RL). The authors propose a Time-reversal symmetry enforced Dynamics Model (TDM) that models the consistency between forward and reverse dynamics. Using the TDM, they further propose an offline RL algori...	Rebuttal 1: Rebuttal: We really appreciate the reviewer for the positive feedback and valuable comments. > W1. Comparison mostly to model-free methods rather than model-based methods. In particular, the comparison with Dreamer. - First, we'd like to highlight that our approach is very different from the typical m...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Minimum-Risk Recalibration of Classifiers	Accept (spotlight)	Summary: Background: Generating reliable probability estimates alongside accurate class labels is crucial in classification tasks. Calibration, which refers to the alignment between predicted probabilities and empirical frequencies of labels, is highly desirable in various applications. However, many machine learning a...	Rebuttal 1: Rebuttal: Thank you for the valuable feedback on our work. With gratitude for the positive evaluation, we are committed to further clarifying our contributions by addressing the concerns and questions raised. To streamline this endeavor, our response is organized to initially address the highlighted weaknes...	Summary: The paper studies a very relevant problem of post-hoc calibration in probabilistic classifiers. There is a great body of work on calibrating probabilistic classifiers so that the predicted probabilities match the empirical label frequencies in the popular machine learning literature. However, calibrating proba...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude for the comprehensive understanding and the positive evaluation from the reviewer, especially in acknowledging our theoretical contribution to an important field while providing actionable insights, and recognizing the impact of this work on the commu...	Summary: This paper introduces the concept of minimum risk recalibration, utilizing Mean Squared Error (MSE) decomposition. The authors provide justification for their approach by demonstrating that minimizing the proposed risk yields simultaneous minimization of the calibration risk while preserving the sharpness of t...	Rebuttal 1: Rebuttal: We are overwhelmingly grateful for the reviewer's in-depth understanding and unreserved recognition of our work, encompassing nontrivial concepts, rigorous analysis, practical significance of the tradeoff, applications to downstream tasks, assessment of assumptions, and experiment design for theor...	Summary: This work proposes a calibration method for probabilistic classifiers. It is known that most machine learning models produce predictions with high confidence that yield a distribution different than the underlying true label distribution. The proposed method focuses on calibration without a loss on the predict...	Rebuttal 1: Rebuttal: Thank you for the valuable feedback on our work. With gratitude for the positive evaluation, we are dedicated to clarifying and enhancing our contributions by addressing the raised concerns. ### Weaknesses #### 1\. Insufficient discussion on distinction from existing work: The reviewer's obse...	Rebuttal 1: Rebuttal: We appreciate the reviewers' valuable feedback. Our Author Rebuttal addresses recurring themes, offering clarification on methodological contributions and presenting supplementary numerical evidence. Comprehensive point-by-point responses are available in individual rebuttals. ### **Summary of co...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work looks at methods for calibrating probabilistic classifiers for Mean Squared Error by decomposing it into calibration error and sharpness errors. It gives finite sample error guarantees on both of these for the Uniform Mass Binning method. By balancing sharpness and calibration error, they also propos...	Rebuttal 1: Rebuttal: We appreciate the valuable comments and feedback. Our response is structured to address the highlighted weaknesses first, followed by detailed point-by-point responses to specific questions. ### Weaknesses 1\. Not a significant contribution: We appreciate the reviewer's recognition of our ...	null	null	null	null	null	null
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.	Accept (poster)	Summary: This paper targets injecting personalized prior knowledge into the global model, which attempts to mitigate the introduced incomplete information problem in PFL. The idea is to decouple the personalized prior from the local objective function regularized by Bregman divergence. The mirror descent (RMD) is used...	Rebuttal 1: Rebuttal: *Comments: - We sincerely thank reviewer JezB for appreciating our idea. The main concern of the reviewer is about main idea and our methodology, and we answer the questions one by one and make the structure and design of our paper clear. Responses*: 1. [W1, W2, and W3] Motivation and r...	Summary: The authors of the paper consider the problem of personalized federated learning. The main problem the authors attempt to tackle is the information on sampling of clients being overlooked. Specifically, they attempt to introduce two major steps in the training of personalized models: the first one is the injec...	Rebuttal 1: Rebuttal: *Comment: - We sincerely thank the reviewer qY3t for the appreciation and the constructive suggestion to further improve clarity and readability. Responses*: 1. [W1, Q1] Suggestions for improving readability. - Thanks for the constructive suggestion for readability, we will do the follo...	Summary: This paper proposes pFedBreD, which decouples the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized FL. Extensive experiments validate the effectiveness of the proposed method on 5 datasets. Strengths: S1. The problem of overlooking...	Rebuttal 1: Rebuttal: *Comment: - We sincerely thank the reviewer jGgs for appreciation recognizing the importance of the problem we introduced and suggestion for improving readability. We address the concern raised by the reviewer about motivations and challenges, and we answer these questions as follows. **Res...	Summary: In this paper, the authors propose pFedBreD to decouple prior knowledge from each client. pFedBreD extracts the personalized prior with Bregman Divergence for better performing personalized tasks. The authors provide convergence analysis and experiments evaluated on 5 datasets. Strengths: 1. The authors gi...	Rebuttal 1: Rebuttal: *Comment: - We sincerely thank the reviewer HD18 for the appreciation and constructive comments. The reviewer raises concern about the motivation of using Bregman-Divergence (B-Div) and relaxing Mirror Descent (RMD), and we answer the questions one by one. Responses*: 1. [Q1, W1] The Mo...	Rebuttal 1: Rebuttal: # General Responses: We begin by making the following responses about the structure of our paper and results of additional experiments, and later to allow more space for responses to each author. - More intuitive and high-level line of logic, which may help understanding and reading, are as follo...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces a novel scheme, pFedBreD, which addresses the incomplete information challenge in personalized federated learning by incorporating personalized prior knowledge into the global model of each client. This is achieved by decoupling the personalized prior from the local objective function and ...	Rebuttal 1: Rebuttal: *Comment*: - We sincerely thank the reviewer Q28a for the appreciation and constructive comments to further improve clarity and readability, which are detailed in [general responses](https://openreview.net/forum?id=kuxu4lCRr5&noteId=WzbbMONDyW)[W1]. The concern raised by the reviewer, i.e., th...	null	null	null	null	null	null
Computing Optimal Nash Equilibria in Multiplayer Games	Accept (poster)	Summary: This paper addresses the problem of computing optimal Nash Equilibria in multi-player games. The authors present an optimization algorithm that uses a correlation plan-based formulation with a suitable convex relaxation in order to compute an optimal NE. At its core, the algorithm relies on a binary-tree struc...	Rebuttal 1: Rebuttal: Thank you very much for your comments. Q: About the contribution, significance, and small benchmarks Answer: 1. To our knowledge, as we discussed in the related work section, the approach of breaking the strategy space into bilinear correlation plans based on a binary-tree structure and...	Summary: This papers studies computing Nash equilibrium in multiplayer games that optimizes a given objective function. The designed solving framework first transform the corresponding multilinear program into a bilinear program by introducing auxiliary variables representing probability distribution over players' join...	Rebuttal 1: Rebuttal: Thank you very much for your helpful comments. Q: About “Although the time complexity of solving the optimal Nash is reduced, it is stiil exponential-time, which is not surprised.”\ Answer: Based on our general transformation framework, the straightforward approach is using the vanilla bi...	Summary: This paper tackles the challenge of computing a NE that optimizes some objective (e.g, social welfare). They present an algorithm that avoids the naive exponential blowup that occurs when trying to extend the two-player MILP for optimal NE to multiplayer optimal NE, by using relations between correlation plans...	Rebuttal 1: Rebuttal: Thank you very much for your valuable comments. Q: About experiments combining this algorithm with other techniques to see how it can be used to solve larger scale games.\ Answer: We conducted experiments on real-world network security games (Jain et al., 2011). In these games, the attack...	Summary: The paper attempts an improvement to existing approaches to tackling the problem of optimal Nash equilibrium (NE) computation. In general, the problem is NP-hard. Usually, a common approach in computing the optimal NE is to formulate it as the solution of a constrained mathematical program whose objective fun...	Rebuttal 1: Rebuttal: Thank you very much for your comments. Q: Is the improvement on the term $2^n$ that crucial?\ Answer: Yes. Based on our general transformation framework, the straightforward approach is using the vanilla binary collection, but our CRM can generate a minimum binary collection to reduce tim...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models	Accept (poster)	Summary: This paper explore utilizing the rationales to achieve the multimodal reasoning based on LMs. The author analyzes the challenges on using the LM to perform the multimodal reasoning such as Hallucination problem. Based on the preliminary observations, the authors propose jointly exploit the reasoning ability i...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. Please find our responses below. > Response to the overall weaknesses: > We thank the reviewer for the rigorous consideration. But we have to claim that our contributions and technical novelties are far beyond the “simple combination of LLMs and VQA m...	Summary: This paper proposes a novel DDCoT prompting that maintains a critical attitude through negative-space prompting and incorporates multimodality into reasoning by first dividing the reasoning responsibility of LLMs into reasoning and recognition and then integrating the visual recognition capability of visual mo...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. Please find our responses below. > General insights: Are the analysis results of the single case in section 3.1.1 and 3.1.2 applicable to most samples in the data set? > Our findings in 3.1.1 and 3.1.2 are applicable to most samples. The appl...	Summary: The work focuses on solving multimodal reasoning task. In the zero-shot setting, the work prompts LLM to conduct step-by-step reasoning. To avoid hallucination due to the lack of image features, LLM is asked to leave the answer to sub-questions as “uncertain” if they involve images. Then the corresponding sub-...	Rebuttal 1: Rebuttal: Thank you for your insightful and helpful comments. Our detailed responses are available below for your consideration. > Somewhat scattered key ideas (part 1). The prompt engineering part and the visual component can be individually stand-alone as independent work. We appreciate the review’s...	Summary: The paper proposes Duty-Distinct Chain-of-Thought (DDCoT) Prompting for multimodal reasoning problems (e.g. VQA). Despite CoT's success in language-only reasoning problems, authors argue that multimodal reasoning challenges CoT as the rationale part is sensitive to the input information. Since image caption is...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and the concerns about the presentation. We carefully address your questions and comments below and will improve the writing accordingly. > The presentation is a bit poor (part 1): The concrete problem and main insight. The motivation and organi...	Rebuttal 1: Rebuttal: We really appreciate all reviewers for their valuable feedback. Our code will be made public upon acceptance. We are encouraged by the reviewers’ recognition of our novel/interesting contribution (ka68, oVaJ, aDZq), solid and robust technical design (oVaJ, kQGJ, aDZq), compelling performance impr...	NeurIPS_2023_submissions_huggingface	2,023	Summary: 1. The paper studied the challenges and limitations in rationale generation for multimodal problems. Then the authors propose a Duty-Distinct Chain-of-Thought Prompting (DDCoT) to collect language-related or visual-related information and select valid information to generate the rationale. 2. The rationale can...	Rebuttal 1: Rebuttal: Thank you for the helpful review. We carefully address your questions and comments below. > Some experiment settings are not clearly explained or confused. See questions. Thank you for the valuable feedback, and we will clearly explain experimental settings in the revised paper and address t...	null	null	null	null	null	null
Multi-task learning with summary statistics	Accept (poster)	Summary: The work considers multi-task learning in settings where for each task only summary statistics X.T@Y and ẍ.T@ẍ are made available. The setting is motivated by healthcare and biomedical related research, where sharing individual level microdata is restricted by regulations due to privacy concerns. The authors a...	Rebuttal 1: Rebuttal: Regarding the real-world application: To address this concern, we have included a real data application to polygenic risk prediction. Details of our analysis and the results are provided in the global response. Q1 In statistical genetics applications, many studies that investigate the margina...	Summary: The paper proposed a multi-task method to learn individual models without having access to the raw data but using summary statistics data for each task. The method is only applicable to linear models. Strengths: 1. Theoretical guarantee for the optimal estimator 2. Good experiments on simulated data Weak...	Rebuttal 1: Rebuttal: Regarding experiments on real data: We have included additional results demonstrating our method on a prediction task with real genetic data. We have provided a description of the analysis and the results in our global response. Q1 In statistical genetics applications, it is often the case th...	Summary: This paper presents an approach to learning predictive models from summary statistics in the setting of multi-task learning. Linear model is assumed and least square solution was derived with either the \ell_{2,1}-norm or kernel norm of the parameter matrix to capture relativeness among tasks. Theoretical res...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback, and for recognizing the strengths of our paper and the importance of the problem that we address. Regarding the technical innovation of our paper: The proofs of the theoretical results present unique challenges in the proxy data setting. The deriv...	Summary: The paper addresses the problem of multi-task learning in settings where only summary statistics (instead of individual-level data) is available, which is a common scenario e.g. in medicine. The paper presents a framework for linear relationships between the covariates and the outcomes which uses summary-level...	Rebuttal 1: Rebuttal: We thank the reviewer for excellent feedback about the presentation of our paper, and for raising several important questions. Here we address each of the comments on the paper’s presentation: * (Regarding the related work) Thank you for your feedback on formatting and for pointing out the recent ...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their valuable and insightful feedback. In this global response we would like to describe the major changes we made to address the reviewers’ comments, which greatly improved the quality of our work. **First, we add a new simulation study evaluating our m...	NeurIPS_2023_submissions_huggingface	2,023	Summary: Multi-task learning is a powerful machine learning paradigm for integrating data from multiple sources to improve overall model performance. However, data-sharing constraints in healthcare settings hinder its application. To address this challenge, a flexible multi-task learning framework utilizing summary sta...	Rebuttal 1: Rebuttal: Q1 Our methods build upon classical multi-task learning techniques, and enable fitting models only using basic summary statistics which are often made publicly available. The sparse $\ell_{2,1}$ regularized estimator extends the group-sparse estimators studied in [4,6], while the nuclear norm ...	null	null	null	null	null	null
Towards Last-layer Retraining for Group Robustness with Fewer Annotations	Accept (poster)	Summary: The paper tackles the aspect of last-layer retraining for better group robustness. The authors show that last-layer retraining can greatly improve worst-group accuracy with little worst-group data. Motivated by this, selective last-layer finetuning (SELF) is proposed to improve group robustness. Strengths: 1....	Rebuttal 1: Rebuttal: We warmly thank Reviewer HP8n for their detailed comments, suggestions, and references. Below, we provide responses to each of the reviewer’s points. (Novelty 1) Thank you for the comments and the references [1, 2]; we have added citations and discussion to Section 2. We remark that the reviewer’...	Summary: This paper proposes to collect a dataset from misclassifications or disagreements to fine-tune a classifier for improving sub-group accuracy in presence of spurious correlations. Strengths: The topic that this paper attempts to address is important. The paper is well-written as well. There are some interesti...	Rebuttal 1: Rebuttal: We warmly thank Reviewer x5jy for their comments and suggestions. Below, we provide responses to each of the reviewer’s points. (Setting) We thank the reviewer for the reference [1], and we have added a citation and discussion of [1] in Section 2. With that said, we explicitly focus on the settin...	Summary: The paper provides a detailed analysis of the DFR procedure [1]. The authors specifically consider the case when the group annotations are limited or not available during training, and show that it is still possible to largely remover the DFR performance. Specifically, the authors retrain the last layer on dat...	Rebuttal 1: Rebuttal: We graciously thank Reviewer Ep6f for their in-depth analysis, insightful comments, and attention to detail. Below, we provide responses to each of the reviewer’s points. (W1) Thank you for this great suggestion. We have included the results of the requested ablation as Rebuttal Figure 1; the res...	Summary: This work tackles an important problem of preventing the reliance of neural networks on spurious correlations. It builds on top of the work [1] primarily by using last layer re-training on class balanced held out dataset without the need for group annotations. They also additionally propose a simple but effect...	Rebuttal 1: Rebuttal: We graciously thank Reviewer KCny for their insightful comments and thoughtful suggestions. Below, we provide responses to each of the reviewer’s points. (Weakness 1) We would like to clarify that we do not claim algorithmic novelty for the results in Section 4, only for our disagreement SELF alg...	Rebuttal 1: Rebuttal: Please see the attached PDF file for additional figures and tables. Pdf: /pdf/d0dce44ffda8c2d1993882f3bf41e28661461cb5.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data	Accept (poster)	Summary: This paper casts event forecasting as location ranking problem. They propose a spatial event ranking approach called SpatialRank. SpatialRank optimizes the NDCG metric while taking spatiotemporal autocorrelation into account. Spatialrank uses a graph convolutional network to encode autocorrelated input and a...	Rebuttal 1: Rebuttal: Dear reviewer VBY2, Thank you very much for your comments! Below we answer your questions and address your comments. Q: The reader is greeted with an actual Related Work section on the same page where most of the information is repeated. A: Thank you for your suggestions! We will rephrase the r...	Summary: The paper proposes a method which applies learning to rank losses to spatiotemporal event prediction. The observed data is considered over a discrete spatial partitioning and considers a time series of feature information. Features are grouped into purely temporal, purely spatial (not-time dependent) and spati...	Rebuttal 1: Rebuttal: Dear reviewer JmsG, Thank you very much for your comments and appreciation of our paper! Below we address your questions and concerns. Q: Though I could follow the reasoning of computing a top-k query, the motivation why this information is enough in several applications could be motivated bette...	Summary: The paper proposes a deep learning model called SpatialRank that predicts the top-k riskiest locations of future events such as traffic accidents and crimes by optimizing a spatial version of the NDCG measure. The model features adaptive graph convolution layers that learn the spatiotemporal dependencies from ...	Rebuttal 1: Rebuttal: Dear reviewer yV4P Thank you very much for your comments! Please find our responses below: Q: The motivation for urban event ranking is weak. The paper should provide more evidence or examples to motivate the need and value of ranking locations for future events. A: We believe being able to cor...	Summary: This paper investigates the problem of future urban event prediction on spatiotemporal data. This is an important problem for a broad range of urban application. Different from prior work, this paper for the first time predicts most likely future events by directly optimizing location ranking in the prediction...	Rebuttal 1: Rebuttal: Dear reviewer RZQE, Thank you very much for your comments and appreciation of our paper! Below we address your questions and concerns. Q: In addition to the complexity analysis, the authors should provide additional evidence (e.g., experiments) to justify the impact to training time. A: Thank y...	Rebuttal 1: Rebuttal: Q: The experimental results should be presented with error bars (RZQE) A: Thank you for pointing it out. We have revised the three experiment results and now report the average performance including NDCG, L-NDCG, and Precision with standard deviation over 3 runs in Table 1 and Table 2 of the upd...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Easy Learning from Label Proportions	Accept (poster)	Summary: This paper focuses on the problem of learning from label proportions. It addresses the performance degradation issue of the EPRM method when the hypothesis class lacks sufficient expressiveness. To overcome this problem, the paper introduces EasyLLP as a solution. In practice, EasyLLP differs from EPRM (or Pro...	Rebuttal 1: Rebuttal: > On originality: Comparison to [16], and other papers in this literature. * A similar point was raised by Reviewer c8Fj. Thanks for stimulating a more thorough discussion of the related literature. The paper Reviewer YhoX is mentioning is related to ours, but it is by no means subsuming our resu...	Summary: The paper aims to advance the theoretical understanding behind the LLP problem, and provide the conditions under which the algorithm is expected to work. They propose a theoretically founded algorithm for learning from label proportions called EasyLLP. In particular, they have shown how to estimate the expecte...	Rebuttal 1: Rebuttal: > On reproducibility We find EasyLLP quite simple to reproduce, even without pseudocode. In any event, we will happily make the code for our experiments available. > Limited experimental evaluation We have strived to do as thorough an evaluation as possible by considering different datasets wit...	Summary: The paper presents a debiasing approach called EASYLLP for Learning from Label Proportions (LLP), where only class label frequencies in bags are available. The authors provide theoretical analyses of a label proportion matching algorithm and propose a general debiasing technique for estimating instance loss. E...	Rebuttal 1: Rebuttal: > On originality: Comparison to [16], and other papers like “On the Minimal Supervision…” * We thank the reviewer for stimulating a more thorough discussion of the related literature. The papers the reviewer is mentioning are related to ours, but they are by no means subsuming our results. For in...	Summary: The authors start by providing a theoretical analysis of the proportion matching algorithm, a standard algorithm from the literature that simply minimizes the loss over the average instance-level predictions. Strengths: I find the way the authors approach to the problem of learning from label proportions, an...	Rebuttal 1: Rebuttal: We thank the reviewer for pointing out a number of places where further discussion and intuition would improve the presentation, and we will add additional discussion to the paper. > “Empirical evaluation seems to suggest improvement only with large bag sizes…” Yes, we agree that EasyLLP only se...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks	Accept (spotlight)	Summary: This paper conducts a comprehensive theoretical analysis of the training dynamics of two-layer ReLU neural networks on a linearly separable data. The authors isolate four discrete phases of the training procedure and describe specific nonlinear behaviors that occur during each phase. Moreover, they derive expl...	Rebuttal 1: Rebuttal: We appreciate the reviewer's recognition of our work and helpful comments. Below, we offer detailed responses to the reviewer's questions: **Q1. First, a weakness of the paper is that it focuses on the specific setting of two-layer ReLU networks trained on linearly separable data. While this sett...	Summary: The training of ReLU neural networks involves complex nonlinear phenomena, challenging theoretical analysis due to the nonlinearity of models and non-convexity of loss. This study provides a comprehensive theoretical characterization of the training process of a two-layer ReLU network using Gradient Flow on li...	Rebuttal 1: Rebuttal: We thank the reviewer for the appreciation of our work and helpful suggestions to improve this paper. In the following, we answer the reviewer’s questions in detail. Q1. A missing related work on network's training dynamics and phases [1]. Response. We thank the reviewer for pointing out...	Summary: This paper aims to provide a theoretical understanding of the dynamics involved in training neural networks beyond the linear regime. The authors focus on a specific scenario where a two-layer ReLU network is trained using Gradient Flow (GF) on linearly separable data. The analysis encompasses the entire optim...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work and for pointing out the relevant papers. We answer the reviewer’s questions in the following. Weakness on experimental results and details. Response. We thank the reviewer's suggestions on experiments to improve our paper. - **More expe...	Summary: In this work, the authors attempt an exact analysis of the training dynamics of 2-layer ReLU networks trained via gradient flow on linearly separable data. Specifically, the authors aim to build on related work (e.g., Boursier et al. [2022] on square loss) to the case of: - Exponential loss (a more appropriate...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work, as well as the valuable comments. We address the questions in the following. Response to Weakness 1. - Main advantage over [1]. - We acknowledge the similarity in our network and initialization strategy as [1]. However, our analysis tak...	Rebuttal 1: Rebuttal: ``Global'' Response to All Reviewers. 1. First, we sincerely thank all the reviewers for appreciating our result, i.e., a theoretical analysis of multi-phase optimization dynamics and the rich nonlinear behaviors of ReLU networks. We also thank all the reviewers for their comments and suggest...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples	Accept (poster)	Summary: This paper poses the problem of learning a controller from a batched dataset containing time-series observations of the world, actions, and a value estimate (e.g., for determining how to treat a patient given the results of medical tests based upon patient outcomes). This problem is considered with a POMDP for...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper. We will respond to each question in turn: --- ### Q1: Why is this problem different than offline reinforcement learning? A1: In this paper, we study the accountability of offline decision-making in high-stake systems, and appl...	Summary: This paper investigates imitation learning in scenarios with limited data. The proposed approach (ABC) involves utilizing a linear combination of the belief space to generate accountable decisions. The authors evaluate the performance of ABC in simulated and real-world healthcare scenarios, highlighting its ab...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper. We will respond to each point in turn: --- ### 1. Property 3.2 is not an Assumption Property 3.2 is NOT an assumption, and is INDEPENDENT TO SPECIFIC TASKS. It is about the architecture of the neural network being used ...	Summary: This work proposes accountable batched control with five desirable properties. The design is motivated by the fact that the reward or feedback of trajectories are hard to obtain in high-stake responsibility-sensitive applications. The minimal hull subset of the decision corpus is constructed for the decomposit...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper. We will respond to each point in turn: --- ### 1. Definition of the Decision Corpus The definition of _Decision Corpus_ is explained in line 6 in the abstract and line 61 in Sec. 3. In our context, we use _Decision Corpus_ to r...	Summary: The paper presents the Accountable Batched Controller (ABC) based on the example-based explanation framework as a solution for offline control in responsibility-sensitive applications. Through experiments on simulated and real-world tasks, the method shows accountability, conservation, and adaptability. Stren...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper. We will respond to each point in turn: --- ### 1. Additional Analysis on Table 2 We would start by providing more empirical studies extending the previous Table 2: _The cumulative reward of each method is reported. Additi...	Rebuttal 1: Rebuttal: We extend our sincere gratitude to all reviewers for their insightful comments, valuable suggestions, time, and efforts in evaluating and improving our paper. We thank all reviewers for their affirmation of our work’s novelty (reviewers: 9Kjz, izcS), presentation (reviewers: fhj3, 9Kjz, ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: I found the paper somewhat hard to read and understand, so here I’ll present a summary that’s quite different from the author’s presentation. In offline RL, or other settings where there is a performance metric to optimize, we can consider two simple baselines: 1. Nearest neighbors: For each action, find the ...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper. We will respond to each point in turn: --- ### 1. Properties - Conservatism. We demonstrate the property of Conservation through our experiments provided in Appendix F2. We explicitly visualize the behaviors of ABC under ...	null	null	null	null	null	null
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage	Accept (poster)	Summary: This paper presents algorithms based on soft Q-learning for offline RL with finite-sample guarantees. The guarantees are given for general function approximation hold under weaker requirements such as a (partially) weaker variant of single-policy concentrability and avoids the Bellman-completeness assumption. ...	Rebuttal 1: Rebuttal: **Would you please provide a comparison between the two objectives and comment on the connections between the variable in your paper vs. in their paper (Zhu et al)? Does the function $\Omega$ contribute to avoiding the Bellman completeness assumption? What is the key factor in your proposed objec...	Summary: This paper proposes a q-learning based algorithm for solving offline minimax problem in reinforcement learning. Under certain partial coverage and realizability assumption for the regularized problem, they prove $O(1/\epsilon^4)$ sample complexity. Strengths: This is the first paper for minimax problem in off...	Rebuttal 1: Rebuttal: Comparison with ``Revisiting the linear-programming framework for offline rl with general function approximation'' by Ozdaglar et al. Thank you for bringing this paper to our attention. We will certainly cite and discuss it. Here is the comparison, which we will incorporate into the main text...	Summary: This paper proposed value-based algorithms for offline RL without Bellman completeness and full coverage of data support. By proposing the optimization objective of selecting the soft-Q from the set, where element in set satisfies the empirical soft-Bellman operator and the selected Q should minimize the squar...	Rebuttal 1: Rebuttal: Could the author explain more about the intuitions of the proposed learning objective in Equation 2, though the convergence and sample complexity guarantee is provided? This is an excellent point. We will certainly provide a more detailed explanation in the revised draft. Let us offer an intu...	Summary: This submission studies offline RL with function approximation. Under the relatively mild assumption of partial coverage and realizability of the function approximations, the paper proposes the algorithm M(S)QP that approximates the (soft-)Q-function by solving a minimax optimization problem, and establishes g...	Rebuttal 1: Rebuttal: **(1) In Remark 1, the authors remark that the minimax optimization is computationally feasible when $L$ is chosen to be RKHS or linear function classes. I would like to know if there is a concrete class of offline RL problems where such a choice of $L$ satisfies realizability, or otherwise this r...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies offline reinforcement learning and proposes a soft Q-learning algorithm based on reformulating the solution to the Bellman equation as a saddle point of a certain minimax optimization problem. The authors then provide PAC guarantees for estimating the entropy-regularized Q-function, which in...	Rebuttal 1: Rebuttal: Thank you for reviewing our draft! Q. I am confused about the reformulation of solving the Bellman equation as a minimax optimization problem... A. As you rightly pointed out, the solution to the Bellman equation (3) is unique, giving the soft Q-functions $q^*_\alpha$, and therefore th...	null	null	null	null	null	null
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation	Accept (poster)	Summary: This paper provides both empirical evidence and theoretical arguments for the usefulness of the inverse dynamics criteria as a pre-training objective for multitask imitation learning setups. Furthermore, the authors were able to go beyond the final conclusion and provide insightful analysis where they were abl...	Rebuttal 1: Rebuttal: First, we would like to thank the reviewer for their positive comments about the compelling evidence, novel analysis, and clear writing in the paper. Here we will address each of the weaknesses and the questions raised by the reviewer. Hopefully these comments can provide some additional clarity...	Summary: This paper analysis the downstream fine-tuned performance on various multi-task settings after pretaining with different objectives including - 1) Inverse dynamics 2) Contrastive 3) Forward Dynamics - Explicit 4) Forward Dynamics - Implicit 5) Behaviour Cloning They compare the performance to a model traine...	Rebuttal 1: Rebuttal: First, we would like to thank the reviewer for their comments about the strengths of the paper, namely the "comprehensive evaluation" and resulting "valuable insights". Here we will address each of the weaknesses and questions raised by the reviewer. Hopefully these comments can provide some add...	Summary: The paper studies how to effectively pretraining representations in imitation learning where we have acess to a pretraining dataset with multi-task demonstrations with an unobserved latent context variable for each task and limited amount of finetuning demonstrations for transferring to novel context. The goal...	Rebuttal 1: Rebuttal: First, we would like to thank the reviewer for their thoughtful and detailed comments about the strengths of the paper on both the experimental and analytical/theoretical fronts. Here we will address each of the weaknesses raised by the reviewer in turn. Hopefully these comments can provide some ...	Summary: This paper investigates the effectiveness of several popular representation learning techniques in the context of imitation learning. While there is no novel components in this paper, the main strength of this paper is that it does well in designing the experimental setups to investigate the effect of several ...	Rebuttal 1: Rebuttal: First, we'd like to thank the reviewer for their positive comments about the clarity of the paper and comprehensiveness of our controlled experiments. Here we will address each of the weaknesses and one additional question raised by the reviewer in turn. Hopefully these comments can provide some ...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On permutation symmetries in Bayesian neural network posteriors: a variational perspective	Accept (poster)	Summary: This paper considers mode-connectivity in the context of Bayesian neural networks. It shows roughly what we'd expect, based on the results of Entezari et al. (2022). Strengths: It shows roughly what we'd expect, based on the results of Entezari et al. (2022). Weaknesses: That's my main issue with the paper....	Rebuttal 1: Rebuttal: We appreciate the reviewer's feedback, but we respectfully disagree with their assessment of our paper. Below is our best attempt at addressing the reviewers' concerns: * The paper shows expected results: The reviewer expresses their main issue with the paper, stating that Bayesian neural net...	Summary: The authors conjecture that after accounting for permutation symmetries in overparametrized neural networks that lead to same functional behavior, the low-loss solutions are linearly connected. In the context of Bayesian neural networks with variational inference, the authors use this conjecture to propose a s...	Rebuttal 1: Rebuttal: We appreciate the detailed review and valuable feedback provided by the reviewer. We have carefully considered each point raised and addressed them below. * Scope of the claim: The reviewer suggests that the title should qualify "variational BNNs" instead of "BNNs" to better contextualize the...	Summary: The authors extend the recent idea of linear mode connectivity up to permutation symmetry to the setting of Bayesian neural networks. They demonstrate that two different variational approximations to the Bayes posterior enjoy mode connectivity along the Wasserstein geodesic of one distribution, and a suitably ...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive feedback and insightful comments. * Tractability of Approximation Methods: The reviewer raises a valid concern regarding the reliance of our proposed algorithm on specific approximation methods, such as variational inference with a Gaussian distrib...	Summary: This work permutes together the distributions of bayesian neural network (BNN) parameters in the context of variational inference (VI) so that they are linearly connected. This is done by adapting recent work on permuting together SGD solutions to be linearly connected, by optimizing for similarity of means an...	Rebuttal 1: Rebuttal: We thank the reviewer for insightful comments and discussion points. * Temperature and prior for ResNet20/CIFAR: Thanks for the suggestion, we will take this into consideration. During the limited time span of this rebuttal, we were able to run the ResNet comparison with different prior varia...	Rebuttal 1: Rebuttal: # General comments First, we would like to thank the Reviewers for their comments and helpful feedback. With this paper we analyze the geometry of the Bayesian posterior in deep neural networks by considering the permutation symmetries raising from the neural network parameterization. We do it by...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors study the geometry of SGD-trained Gaussian mean-field variational approximations to the posteriors of Bayesian neural networks (BNN). In large part, the authors propose extensions of the method and analysis of Ainsworth et al. [1] from MAP-estimated neural networks to BNNs. Notably, the authors inf...	Rebuttal 1: Rebuttal: We thank the reviewer for his/her interesting comments. Below we reply inline to the reviewer's questions: * Strength of conjecture 1: We agree with you on this. We decided to have a broad conjecture to leave room for possible extensions to Laplace approximation (easier) and SG-MCMC methods (...	Summary: This paper paper extends linear mode connectivity modulo permutation to Bayesian neural networks posteriors. Authors do this by imposing a Wasserstein metric on the space of distributions and look at how log likelihood changes along the geodesic between two distributions obtained via approximate Bayesian infer...	Rebuttal 1: Rebuttal: We thank the reviewer for his/her interesting comments: * Model architecture: we agree that the choice of model architecture can impact the ability to find good solutions with low/zero barrier. One of this choice is the width of the neural network, for which the wider the model, the lower the...	null	null	null	null
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes	Accept (poster)	Summary: The authors propose Neural Acquisition Process, a novel method for Bayesian optimization by meta-learning a function that jointly performs the surrogate and acquisition steps. The function, i.e. a transformer, is able to work as a policy that predicts the action a given a state s, where the action is t...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and their remarks as well as underlining the soundness of our experimental protocol. We try to reply to their concerns below. >The method might be overkill for small search spaces and nonexpensive functions. However, many black-box functions nowadays, such as ...	Summary: This paper proposed an end-to-end transformer-based framework for meta-Bayesian optimization (meta-BO). It formulated the meta-BO as a RL problem, defining a MDP in which a policy can be trained to solve the meta-BO problem. To help the training of RL algorithm, this paper also proposed an inductive bias via a...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments on the applicability of our work and we try to answer their concerns below. >The experiments is insufficient. In the current experiments, the author only provided the evaluation results of the proposed method NAP and other baselines on several ben...	Summary: The authors present a hyperparameter optimization method that is based on a transformer. In contrast to other recent work on HPO with transformers, this method does not rely on an acquisition function but instead trains an end-to-end model that outputs the acquisition scores directly. Strengths: **Diversity o...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and for stressing the importance of end-to-end training. We try to answer the questions regarding complexity, novelty and hyperparameter tuning below. >The authors combine transformers, reinforcement learning and neural processes. There are most likely a lot o...	Summary: This work proposes an approach to meta-Bayesian optimization (BO) that features a single transformer neural process architecture for both the meta-surrogate function and meta-acquisition function, which are respectively trained via standard neural process maximum likelihood and model-free meta-reinforcement le...	Rebuttal 1: Rebuttal: We thank the reviewer for their comment, for the remark on extending baselines to search spaces they were not designed for originally, and for raising an interesting point regarding meta-RL, We will answer point by point below. >I'm not sure that Lemma 3.1 adds much substance in favor of the pape...	Rebuttal 1: Rebuttal: We thank all reviewers for their time reading our paper and writing reviews. We are very appreciative of all positive comments made about our work, on clarity, applicability of the method, quality of experiments and baselines implementation. We further thank all reviewers for raising interesting p...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this work, they developed the first end-end transformer based architecture for meta Bayesian Optimization and demonstrated empirically state-of-the-art regret minimization on hyperparameter optimization, antibody and chip design problems. They propose using a novel transformer architecture based Neural Proc...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments and for underlining the clarity of the writing. We thank them for raising interesting points that we will try to explain below. > Somewhat limited novelty due to prior separately have utilized transformers for Bayesian optimization and other prior...	Summary: By combining a Transformer-based architecture and reinforcement learning objective, this paper proposes the first end-to-end framework for meta-Bayesian optimization. The proposed framework is claimed to resolve the inefficiency in the existing two-stage approaches. Strengths: - The meta-Bayesian optimization...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and for acknowledging the strengths of our work and the relevance of our experiments. We hope that we can further motivate the use of RL and auxiliary loss in our response below. >Is there any factor that necessitates the use of RL? To answer your first poi...	null	null	null	null
TextDiffuser: Diffusion Models as Text Painters	Accept (poster)	Summary: This paper proposed TextDiffuser to achieve accurate text rendering for diffusion models. TextDiffuser generates character-level text layouts to guide the text rendering process (image generation). The model is evaluated on 3 tasks including text-to-image, text-to-image with template, and text inpainting to de...	Rebuttal 1: Rebuttal: Thank you for your review and feedback. We aim to address and clarify each point raised. Character-Level Layout and Real Character Style: The primary role of our character-level layout is to inform and guide the position and content of visual text, without restricting it to specific styles or...	Summary: The paper proposes TextDiffusers, a 2-stage model to generate images with text per the input text prompt. TextDiffusers also supports text inpainting. The two stages consist of (1) estimation of layout of keywords (inspired by Layout Transformer) to get character-level segmentation masks and (2) image generat...	Rebuttal 1: Rebuttal: Thank you for the comprehensive review and feedback on our work. Comparison with Strong Baselines Fine-tuning with MARIO-10M: As mentioned in Appendix K, DeepFloyd is better on Fidelity due to its use of two super-resolution modules to generate higher resolution images (1024×1024) versus our ...	Summary: This work presents an approach to enhance the text rendering ability of a text-to-image diffusion model. The authors introduce a new diffusion model called text-diffuser that leverages image captions and text segmentation masks to generate text images. They also collect a large-scale image dataset MARIO-10M, w...	Rebuttal 1: Rebuttal: Thank you for your high recognition of our work, particularly the novelty of our TextDiffuser and the significant contribution of the MARIO-10M dataset. We've responded to your comments as follows: Layout generation for scene text with perspective changes: Our MARIO-10M dataset reveals that a...	Summary: This paper introduces a model for generating visually appealing and coherent text within diffusion models. It also presents the MARIO-10M dataset and the MARIO-Eval benchmark for evaluating text rendering quality. Experimental results demonstrate their method's flexibility and controllability in creating high-...	Rebuttal 1: Rebuttal: Thank you for your review and feedback. We've taken care to address each of your concerns below. Handling of rich-text images with limited queries & efficiency with a large number of queries: Our first stage of Layout Generation leverages an auto-regressive Transformer whose prediction time...	Rebuttal 1: Rebuttal: Thank you for taking the time to review. Enclosed in the attached PDF, we have provided some figures for reference. * Figure(a): Pre-trained on high-resolution Stable Diffusion 2.1 significantly enhances the legibility of small text. * Figure(b): Demonstration of using language descriptions to co...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition	Accept (poster)	Summary: This paper proposed a vision-language pre-training method called POMP. It aims to solve the GPU memory problem of the existing vision-language pre-training method like CoOp when the class number is extremely huge. POMP is composed of local contrast and local correction strategies. Local contrast is to sample a...	Rebuttal 1: Rebuttal: Thanks for your positive assessment and insightful comments. >Q1: The claim that the local correction strategy solves the sampling bias problem is questionable. A1: Thanks for your feedback. We agree that sampling bias still exists with our approach. However, our intention is to convey that...	Summary: * This paper present a prompt pre-training method for vision-language models named as POMP, which can be transfer to visual recognition tasks including image classification, semantic segmentation, and object detection. * POMP follow the prompt tuning setting of CoOp. The authors claims that pre-training promp...	Rebuttal 1: Rebuttal: Thanks for your insightful comments. We humbly think that some concerns are caused by misunderstandings, which we will explain in detail below. We hope that our response can clarify the misunderstandings so that you can consider our work more favorably. >**Q1: The performance gain on the object d...	Summary: This paper proposes POMP, a prompt pre-training method for pre-trained vision-language models like CLIP. While existing prompt-tuning approaches usually fine-tune the soft prompts on a specific downstream dataset with a limited number of classes, the proposed POMP conducts prompt "pre-training" on a large data...	Rebuttal 1: Rebuttal: Thanks for your positive assessment and constructive feedback. >Q1: The non-uniform scale in Fig.1 has a potentially misleading effect. A1: Thanks. Considering the tasks covered in the radar chart (Fig.1) have different difficulties and use different metrics (e.g., acc for classification, h...	Summary: This paper introduce POMP, a prompt pre-training method for vision-language models. POMP learns a universal soft prompt that can express a large number of visual concepts and transfer to various visual recognition tasks in a zero-shot manner. POMP uses local contrast and local correction strategies to reduce t...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We hope that our response can address your concerns and you can consider our work more favorably. >**Q1: POMP’s pre-training process for stage 1 is similar to that of CLIP, why can't CLIP directly excel at downstream tasks compared to POMP, and why does tun...	Rebuttal 1: Rebuttal: We want to thank all the reviewers for the positive assessment and insightful comments, which helped improve the quality of our work. We have revised our paper accordingly and provided individual responses to each reviewer. Please find attached a PDF outlining the main changes: 1. Updated Fig.1 w...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries	Accept (poster)	Summary: The authors study the general case of multi-vector retrieval, i.e., ColBERT and beyond. They propose and analyze a new algorithm for this "vector set" search task, with theoretical guarantees. When integrated into ColBERT, the proposed DESSERT method is 2-5x faster at some, relatively small loss in quality. S...	Rebuttal 1: Rebuttal: *>DESSERT is primarily tested on the MS MARCO query set (besides a small synthetic experiment). The authors justify much of this by citing PLAID [35], but that other paper like many IR papers in the last 1.5-2 years tests on several datasets, including especially out-of-domain and larger datasets....	Summary: In this paper, the authors studied a new problem of vector set search with vector set queries. They have formalized this problem and proposed a novel, provable hashing scheme, DESSERT, to efficiently deal with this problem. Moreover, they have provided theoretical analysis for DESSERT and conducted experiments...	Rebuttal 1: Rebuttal: *>I appreciate the authors have provided the theoretical bound and query time complexity for their proposed hashing scheme. One of my concerns is that the improvement of the query time complexity is only marginal compared to the brute force method, which is still O(N) in general. Moreover, the imp...	Summary: The paper studies the vector set search problem, which is an extension of the canonical near-neighbor search problem and finds an application in the semantic search task. The paper claims that existing methods for vector set search are unacceptably slow, and thus, proposes an approximate search algorithm calle...	Rebuttal 1: Rebuttal: W1: Yes, we believe that we are the first to formalize the vector set search problem. While semantic search is the clear “killer application” today, we provide references to other potential applications such as database lineage tracking (line 96), image instance retrieval, market basket analysis, ...	Summary: The paper addresses the problem of set-to-set similarity calculation and retrieval, which is a problem with any downstream applications. While previous approaches inevitably perform a brute force similarity calculation over \|Q\| query vectors and \|S\| target vectors for each set comparison F(Q, S), this paper le...	Rebuttal 1: Rebuttal: Regarding novelty: While LSH methods have been widely used for the last 20 years, our set search departs from the standard approach in some subtle but important ways. The vast majority of LSH search algorithms group points into hash tables, then explicitly compute similarities between points that ...	Rebuttal 1: Rebuttal: We thank all of the reviewers for their thoughtful feedback! It seems the most common theme expressed in the reviews was a desire for evaluation on more datasets. To that end, we have run 10 additional evaluations on the LoTTE dataset for out-of-domain retrieval. Interestingly, we found that DES...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presents a study focused on the problem of vector set search with vector set queries, which is a crucial subroutine for various web applications. The authors highlight the insufficiency of existing solutions in terms of speed. To address this, they propose a new approximate search algorithm called DE...	Rebuttal 1: Rebuttal: Thanks for your review and suggestions. We agree that the theory is a little hard to read, so we’ve added a table to disambiguate. We’ve reproduced (part of) the table below and have added this to the appendix. We have also updated the notation in the algorithm listings, since there were some mist...	Summary: This paper considers a nearest neighbor search problem where each point is a set of vectors, and the distance function is drawn from a general class of aggregation functions over the vectors. The approach presented here is based on the LSH algorithms, but since we're dealing with multiple vectors, the bounds...	Rebuttal 1: Rebuttal: Thank you for pointing out the presentation issues. Regarding high-level overview: Good suggestion. We’ve added the following description to Section 3: “At a high level, DESSERT compresses the collection of target sets into a form that makes similarity operations efficient to calculate. This is ...	null	null	null	null
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model	Accept (poster)	Summary: This paper proposes the universal model for image segmentation using multi-dataset multi-task training. By using universal segmentation representations at the entity (thing or stuff) level, the paper use merge operation for different segmentation task. Experiments show the effectiveness of the proposed method....	Rebuttal 1: Rebuttal: We thank the reviewer for the helpful feedback. >W1: Novelties and difference to OneFormer: There are multiple significant differences between DaTaSeg and OneFormer. Though we discussed the most significant difference in L27 and L90, We detail more differences below: 1. Multi-dataset train...	Summary: This paper proposes DaTaSeg, a universal multi-dataset multi-task segmentation model. DaTaSeg uses a shared representation and different merge operations and post-processing for different tasks. Weak-supervision is employed for cheaper bounding box annotations and knowledge is sharing across different datasets...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our contributions and the helpful feedback. We carefully address the comments below. >Weaknesses and Questions: Hyperparameters and ablation study: - Our settings of $\lambda$ and $\mu$ closely follow Maskformer [A] and Mask2former [B], and we add the we...	Summary: [Tasks] This paper introduces DaTaSeg, a universal multi-dataset multi-task segmentation model that addresses the interconnections between panoptic, semantic, and instance segmentation tasks. [Methods] DaTaSeg utilizes a shared representation, consisting of mask proposals with class predictions, across all t...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and helpful comments, and for recognizing multiple strengths of our submission. We carefully address the comments and questions below. >W1: Technical contributions on multi-task multi-task model: - We train a single universal segmentation model on multi...	Summary: This paper proposes DaTaSeg, a general multi-dataset multi-task segmentation model. It utilizes shared representations and different pooling operations to perform panoramic, semantic and instance segmentation tasks. DaTaSeg benefits from weak supervision and knowledge transfer across datasets. It outperforms s...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our contributions and the helpful feedback. We carefully address the comments below. >Weaknesses: Comparison with SAM: We thank the reviewer for the suggestion. We are happy to include a comparison with the Segment Anything paper in the revision. We note...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces DaTaSeg, a universal multi-dataset multi-task segmentation model. It uses a shared representation for panoptic, semantic, and instance segmentation tasks, with different techniques to address task differences. Weak supervision and knowledge sharing are employed. DaTaSeg improves performan...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our contributions and the helpful feedback. We carefully address the comments below. >W1: Mask proposal: - We agree with the reviewer that the mask proposal is commonly used in instance segmentation. However, mask proposals are used differently in differ...	null	null	null	null	null	null
Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation	Accept (poster)	Summary: This paper introduces a new technique for compression the PLM. Within the context of knowledge distillation, the authors introduce a new type of relation distillation, which focuses on modeling relations between token features at both the token-level and sentence-level. These relations are then utilized as an ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments and efforts in reviewing our paper. We address your comments and questions in the following content. > Weakness 1: The concept of sample-level relation loss seems unreasonable. Comparing the j-th token in each sentence to determine sentence-level relationships is...	Summary: This paper proposes Feature Correlation Distillation (FCD), an approach for distilling pre-trained transformers. FCD involves a two-part distillation loss: (1) token-level and (2) sample-level, which helps to eliminate dependence on matching dimensionality/architectural details between the student and teacher ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. We address your comments and questions in the following content. > Weakness 1: GLUE tasks can have a large amount of variance due to randomness and due to small test sets, single-run scores may not be reliable (see e.g. https://arxiv.org/pdf/1904.09286.pdf: Sect...	Summary: This paper proposes a method for compressing pre-trained language models (PLMs) based on transformer architectures using feature correlation distillation (FCD). FCD models both token-level and sample-level relations between the teacher and student models, and uses a correlation-based loss function to relax the...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. We address your comments and questions in the following content. > Weakness 1: The token-level method is very similar to the attention-based method of Minilm. What is the essential difference? Also, this paper selects the last hidden before the prediction head a...	Summary: This paper proposes a new knowledge distillation method named Feature Correlation Distillation (FCD) for compressing large pre-trained language models. The proposed method novelly uses token-level and sample-level relationship between teacher and student models and a Pearson linear correlation-based loss funct...	Rebuttal 1: Rebuttal: Thank you for your positive assessment and valuable suggestion. > Question 1: Would it be possible to also demonstrate the efficacy of FCD theoretically or intuitively (e.g., through visualization)? The paper is already very good with empirical results, providing more evidence on why such a loss ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable feedback. The detailed responses to the reviewers’ comments will be replied directly to each reviewer.	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper explores the task-specific knowledge distillation from a large teacher model which are pre-trained language models (PLMs) such as BERT-large or BERT-base into a student which is always smaller than the teacher model. For example, the teacher can be a 12-layer of BERT-base model while the student mod...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and questions. We are revising our rebuttal revision to address your concerns. > Weakness 1: It is hard to understand why sample-level matching helps. An intuitive explanation may be required. Response to W1: Thanks for your comments. We assume that tokens at ...	null	null	null	null	null	null
Bayesian Active Causal Discovery with Multi-Fidelity Experiments	Accept (poster)	Summary: The authors introduce the task of active causal discovery with multi-fidelity oracles which they go onto show is superior to many state-of-the-art methods for active causal discovery. They demonstrate their method in multiple settings and compare it alongside the aforementioned methods, to show superior perfor...	Rebuttal 1: Rebuttal: For reviewer ug4Y: Thanks so much for your detailed comments. We will try our best to alleviate your concerns in the following. > Reviews: Abstract > > A very good abstract which I enjoyed reading but it is too long, much too long. The abstract is merely meant to summarise what you are doing...	Summary: This paper presents a method for active causal discovery with "multi-fidelity" oracles. Here multi-fidelity refers to the option to request outcome labels for a given experiment from a set of oracles with different quality levels. The method extends causal experimental design methods where experiments are de...	Rebuttal 1: Rebuttal: For reviewer yEiY: Thanks for your detailed comments. > Reviews: As an example, "which is fundamental for many real-world applications, ranging from health caring, education, to drug discovery, and protein synthesis," is a little awkward and could be improved as, "which is fundamental for many ...	Summary: In this manuscript, the authors propose a novel method for active causal discovery (ACD) in a multi-fidelity setting, where experiments with different cost and accuracy can be designed and performed for network intervention for the purpose of accurately learning the causal structure. For this multi-fidelity AC...	Rebuttal 1: Rebuttal: For reviewer am1k: > Concerns on the multiple SCMs. 1. For the real-world scenarios, multi-fidelity oracles are very common, for example (as mentioned in the paper), to investigate the drug-disease causal relations, one can either conduct clinical tests (high cost but more accurate) or build si...	Summary: This paper proposes an approach for Bayesian active causal discovery with multi-fidelity observations. This approach has two main components: (1) a cascade probabilistic model handling the correlation between fidelity levels, and (2) a cost-aware information-theoretic acquisition function, which quantifies the...	Rebuttal 1: Rebuttal: For reviewer HBUA: Thanks for your comments. In the following, we try to alleviate your concern one by one: > Question 1: How is episilon chosen? More generally, how were all the algorithm hyper parameters chosen? As can be seen in Appendix D, when training our model, the constraint involving...	Rebuttal 1: Rebuttal: Dear reviewers: Thanks for your detailed reviews. Additional tables and figures that mentioned in rebuttals are shown in the submitted one-page pdf. Pdf: /pdf/04c4e8921629fcaeb8abd7ebe20bc78469150d2c.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper addresses the problem of active causal discovery with multi-fidelity oracles, where experiments can be done based on different costs, precisions, and reliabilities. The paper formally defines the task of multi-fidelity active causal discovery and proposes a Bayesian framework consisting of a mutual ...	Rebuttal 1: Rebuttal: For reviewer Xcc7: Thanks for your overall positive comments on our paper. We try to alleviate your concerns as follows: > Concerns: More comprehensive experiments could be conducted to strengthen the empirical findings. Have you considered other evaluation metrics besides SHD, AUPRC, and MSE? H...	null	null	null	null	null	null
A Fractional Graph Laplacian Approach to Oversmoothing	Accept (poster)	Summary: The authors propose two novel Fractional Graph Laplacian (FGL)-based neural Ordinary Differential Equations (ODEs): the fractional heat equation and the fractional Schrödinger equation. These solutions provide enhanced flexibility in the convergence of the Dirichlet energy and make the exponent of the fraction...	Rebuttal 1: Rebuttal: We are very grateful to the reviewer for the time taken to carefully assess our work and for the valuable feedback. We address each point individually. “W/Q” numbers the weakness or question, followed by our response. --- W1: Using FGLs does not lead to a loss of sparsity across all graphs: ...	Summary: The paper extends the notion of Dirichlet energy to define oversmoothing in directed graphs. It introduces a fractional Laplacian operator that is used to create graph neural ODE architectures. The resulting designs are non-local and can alleviate oversmoothing. The paper provides empirical results (accuracy) ...	Rebuttal 1: Rebuttal: We are very grateful to the reviewer for the time taken to carefully assess our work and for the valuable feedback. We address each point individually. “W/Q” numbers the weakness or question, followed by our response. --- W1: Please note that our work is the first in-depth theoretical analysi...	Summary: The authors consider the problem of classification on attributed graphs with a geometrical approach. They introduce a fractional graph Laplacian for undirected and directed graphs. They generalize the notion of Dirichlet energy to directed graphs. They study the ODEs based on this Laplacian and prove that thei...	Rebuttal 1: Rebuttal: We are very grateful to the reviewer for the time taken to carefully assess our work and for the valuable feedback. We address each point individually. “W/Q” numbers the weakness or question, followed by our response. --- W1: We agree that Thm. 5.5 seemed to be restricted, and we have since m...	Summary: The authors propose a neural ODE (FLODE) that uses a fractional graph Laplacian as an alternative to GNNs, which famously suffers from oversmoothing after a few layers. The heat equation $x'(t) = -L^{\alpha}x(t)W$ is shown to possess nice qualities wrt Dirichlet energy such that it does not always end up with ...	Rebuttal 1: Rebuttal: We are very grateful to the reviewer for the time taken to carefully assess our work and for the valuable feedback. We address each point individually. “W/Q” numbers the weakness or question, followed by our response. --- > Q1: “If Film is a heterophilic graph, I expected the learned exponent...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough and insightful remarks. We fully implemented all remarks in the revised version of the paper, which we believe improved the paper significantly. 1. More extensive ablation studies To augment our ablation studies on Chameleon and Citeseer (see Appendi...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Inferring Hybrid Neural Fluid Fields from Videos	Accept (poster)	Summary: The paper proposes a novel approach for recovering the density and velocity fields of inviscid fluids from sparse multiview videos. The model has two main contributions: - First, it incorporates physics-based losses to enforce the inference of a physically plausible velocity field that is divergence-free and d...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and constructive suggestions! Please see our response below. For methodology: 1. Technical novelty: [Chu et.al.] is indeed most relevant. Our approach incorporates novel losses including projection loss and laminar loss as well as a hybrid representation...	Summary: The paper presents a neural reconstruction method for individual fluid flows which is re-trained for each new scene. It combines the established iNPGs for representing densities and velocities with vorticity transporting partices in a hybrid flow representation called HyFluid. A set of appearance- and physics-...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and constructive suggestions! Please see our response below. - Comparison to ScalarFlow and GlobTrans: Thank you for your suggestion! Since both ScalarFlow and GlobTrans are optimization-based methods, we compare to GlobTrans which is a later work and it...	Summary: This paper presents an innovative neural dynamic reconstruction method that achieves good results in recovering fluid density and velocity fields through the introduction of physical constraints and a hybrid neural velocity representation. Despite some weaknesses and issues, this method has significant implica...	Rebuttal 1: Rebuttal: Thank you for your time and comments. Please see our response below. - Detailed presentation of experiment results: We clarify that we aim at reconstructing plausible fluid velocity field from real videos to allow re-simulation and future prediction. For real videos, it is very challenging to...	Summary: This paper works on reconstructing fluid density and velocity from multi-view videos. The main idea is to inject visual clues with NeRF. They propose some physics-based regularization terms to deal with visual ambiguity that video can not reflect the inner fluid states. Strengths: Good motivation: Visual amb...	Rebuttal 1: Rebuttal: Thank you for your time and comments! Please see our response below. - Relation to NeuroFluid: We clarify that our setting is different from NeuroFluid. NeuroFluid focuses on learning fluid dynamics from a large amount of data. Therefore, they train and evaluate the model on synthetic data, ...	Rebuttal 1: Rebuttal: We thank all reviewers for their time and feedback. We clarify that since our goal is to reconstruct plausible fluid fields from real videos, all experiments in our main paper are on real captured data, as specified in L201. Please find our summary of major changes and response to some common ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors propose a method (HyFluid) to infer the fluid density and velocities from multiview videos. To deal with the visual ambiguities of fluid, physics-losses are introduced to try to enforce physics plausible velocities. A neural velocity field and a vortex particle-based velocity are int...	Rebuttal 1: Rebuttal: Thank you for your time and comments! Please see our response below. - Physical intuition on laminar loss: Laminar flow does not manifest local density change, and thus inferring it from pure visual observations is challenging. We introduce this regularization term to account for the fact th...	null	null	null	null	null	null
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA	Accept (poster)	Summary: This paper proposes a tree search-based GNN model that contains three consecutive steps: expansion, backup, and node ranking. It also proposes a relation frequency-inverse entity frequency node embedding method. The proposed model achieves new SOTA on WebQSP and CWQ datasets. Strengths: 1. It proposes a tree-...	Rebuttal 1: Rebuttal: ### Q1. Performance gains are marginal on ComplexWebQuestions and incomplete KG experiments. NuTrea’s 0.7 H@1 gain aligns with the standard improvements observed in recently published works. For instance, TERP (COLING 2022) reported a 0.6 H@1 gain compared to the previous best, Rigel (EMNLP 2021) ...	Summary: In this paper, a retrieval-based solution to the Multi-hop KGQA problem is proposed. The authors introduce a novel approach that incorporates two innovations: a GNN network with bidirectional message passing (Expansion and Backup) and a novel method for embedding knowledge graph vertices, inspired by TF-IDF. ...	Rebuttal 1: Rebuttal: ### Q1. Why is NuTrea not compared with other methods (e.g. DECAF) that have better results? Among the various KGQA methods, the ones that outperform NuTrea belong to the “Semantic Parsing” category, where they leverage ground-truth logical queries (forms) during training (refer to section 2, Rela...	Summary: The paper presented two improvements to graph-based (KG) QA tasks: (1) Backup step, and (2) RF-IEF. Both proposed methods, according to Table 3, lead to significant improvement on multiple multi-hop QA tasks. Strengths: The proposed idea of including a BackUp step, which resembles MTCS, is very interesting. E...	Rebuttal 1: Rebuttal: ### Q1. Can you run your model on a subset of questions with "pronouns" and/or encrypted entity names"? All the questions in WebQuestionsSP and ComplexWebQuestions entail encrypted KG entities. Thus, the results in our paper already represent the experimental setting in question.   ### Q2. ...	Summary: The proposed model adopts a message-passing scheme that probes the unreached subtree regions to boost node embeddings.The work also introduces the Relation Frequency–Inverse Entity Frequency (RF-IEF) node embedding that considers the global KG context to better characterize ambiguous KG nodes. The method shows...	Rebuttal 1: Rebuttal: ### Q1. Model size comparison is needed. Below is the table containing the number of model parameters of NuTrea and ReaRev. Although NuTrea contains more parameters, it requires far less training hours than ReaRev. Also, increasing the model size of ReaRev does not necessarily enhance performance ...	Rebuttal 1: Rebuttal: We thank all five reviewers for their strong support and constructive comments on our work. We are glad that the reviewers found our work promising and interesting. Our responses for all the reviewers' questions are provided below. Please go over our responses and let us know if there are issues t...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposed NuTrea, a graph neural network (GNN) model for Multi-hop KGQA. NuTrea considers broader KG contexts using a tree search scheme to find paths to answer nodes. It uses message-passing layers to explicitly consider question constraints involving bi-directional information (or future context). ...	Rebuttal 1: Rebuttal: ### Q1. What makes NuTrea different from previous embedding-based and GNN-based methods? While previous methods (e.g., EmbedKGQA, GraftNet, SQALER) _simultaneously_ update embeddings of all the KG nodes, our NuTrea gradually expands the subgraph and _sequentially_ updates nodes from the seed node ...	null	null	null	null	null	null
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping	Accept (poster)	Summary: This paper proposes a new type of noisy labels called subclass-dominant noisy labels and introduces an algorithm called NoiseCluster based on this noisy label modeling. In the experimental section, the authors demonstrate the superiority of NoiseCluster over previous approaches in the subclass-dominant noisy l...	Rebuttal 1: Rebuttal: > W1: The justification for the proposed subclass-dominant noisy label model is lacking ... investigate if the Clothing1M dataset exhibits similar phenomena. A1: Thank you for your insightful feedback. We highly agree with the justifying the presence of SDN and the noise clustering phenomena...	Summary: This paper investigates the impact of early stopping on models trained with noisy labels and introduces a new type of label noise called subclass-dominant label noise (SDN). The experiments reveal that later stopping during training can better capture the high-level semantics of noisy examples. Building upon t...	Rebuttal 1: Rebuttal: > Q1: The paper would benefit from a comparison with instance-dependent label noise robust methods, which could provide a comprehensive evaluation of the proposed approach. A1: Thank you for your insightful suggestion. In this work, we have included 12 baselines, many of which have been demo...	Summary: In this work, the authors present a new type of label noise: subclass-dominant label noise. The authors show that the model trained over time can better capture such label noise in the feature space, and based on this idea, a clustering then correcting pseudo labels algorithm, NoiseCluster, is designed to iden...	Rebuttal 1: Rebuttal: > Q1: My main concern is the existence of proposed subclass-dominant label noise (SDN) in the real world scenarios.} A1: Your concern is important. The existence of SDN in real-world scenarios is fundamental to the study of SDN. Since this question is so important, we reponse it in General Q...	Summary: In the paper, the authors uncover the phenomenon that mislabeled examples are quickly learned during the initial stages of training when Subclass-Dominant Label Noise (SDN) is present. This behavior hinders the effectiveness of early stopping-based robust methods. To address this issue, the authors propose an ...	Rebuttal 1: Rebuttal: > Q1: It would be beneficial to delve deeper ... dominance leads to wrong labels being learned quickly. A1: Thanks for the insightful question. To explain why SDN is learned quickly, we must recall why a machine learning model can learn a generalized function and why the early stopping pheno...	Rebuttal 1: Rebuttal: > General Questions > Q1: What is NoiseCluster's performance across different noisy label models, like symmetric, instance, and asymmetric? A1: NoiseCluster has been specifically designed to tackle SDN, utilizing our proposed late stopping strategy. As evidenced in Table 1, representat...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Online Ad Allocation with Predictions	Accept (poster)	Summary: This paper is for the online matching problem in the display advertising domain. Essentially there are shopper/supply side requesting coming in in a sequential order, and static advertiser side demand with budget constraint. The goal is to do some sort of global optimization (as opposed to greedy strategy to m...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and now address the mentioned weaknesses. 1. This is an interesting point and we have now conducted further experiments with synthetic data using #impression to #advertiser ratios from 20 to 2000. We did obtain similar results for these ratios w...	Summary: This paper studied Display Ads and the generalised assignment problem (GAP) where ad impressions arrive online and are allocated immediately to advertisers on the offline side. The difference between the two problems is that in Display Ads each impression takes uniform size of advertisers’ budgets, while in GA...	Rebuttal 1: Rebuttal: We thank the reviewer for their helpful comments and address the weaknesses in the following. 1. We thank the reviewer for this suggestion and we will add such a discussion to the main body. Our reason for discussing only Display Ads in the main body is that it contains some of the most important...	Summary: The paper discusses the problem of ad allocation and its generalization, the generalized assignment problem (GAP), which are two well-studied online packing problems with important applications in ad allocation and other areas. The paper presents an algorithm for both problems that incorporate machine-learned ...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and address the mentioned weaknesses in the following. 1. Our algorithm is specifically designed to perform well even if the predictions are inaccurate (from any source of uncertainty). In the field of learning-augmented algorithms, the measure of robustne...	Summary: This paper considers the online display ads and generalized assignment problems under free disposal in the "algorithms with predictions setting". In the display ads problem there are offline advertisers $a$ with budgets $B_a$. A sequence of impressions arrive online with differing values to each advertiser (...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and address the weaknesses in the following. 1. We thank the reviewer for the suggestions for improvements. We will incorporate them in the paper. 2. Indeed, the improvement over the worst-case algorithm when using the Dual Base prediction is s...	Rebuttal 1: Rebuttal: Following reviewer eG65's suggestions, we ran additional experiments with two additional baselines and varied the number of impressions in our synthetic instances. The two additional baselines are two greedy schemes: One considers only the impression values (Greedy) and the other the gain after di...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Abide by the law and follow the flow: conservation laws for gradient flows	Accept (oral)	Summary: The authors study conservation laws in the gradient flow dynamics of neural networks. They introduce a notion of local factorisation of the loss, stating that the loss in the neighbourhood of a given wieght vector can be decomposed in a composition of functions, a data-independent term $\phi$ followed by a dat...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive comments and constructive suggestions. Weaknesses and Questions addressed > L1e It is not clear to what extent the factorisation property the authors introduce is necessary to their analysis. Said differently, it is not clear whether t...	Summary: This paper studies the conservation law of gradient flow dynamics for training neural networks. The authors propose a method to determine the number of conservation laws in given gradient flow dynamics using Lie algebra generated by the Jacobian vector fields. It is shown, either theoretically or empirically, ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive comments and constructive suggestions. Question addressed > Q1d Do the results stated for ReLU networks hold for any network with homogeneous activation function? Yes, our results also apply to networks using any positively $p$-homoge...	Summary: The paper discusses the geometric properties of gradient descent dynamics in ML models. The authors aim to understand the properties of the optimization initialization that are preserved during the dynamics, which is often referred to as being an "implicit bias" of the training algorithm. They also focus on th...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive comments and constructive suggestions. Weaknesses and Questions addressed > W1c Mostly restricted to deep shallow NNs, continuous-time gradient descent, and simple NN architectures. This limits the applications of the theory to practic...	Summary: This paper studies the conservation laws, which are quantities that remain constant, in over-parametrized gradient flows. The authors provide a formal definition for independent conserved functions, which are required to have linearly independent gradients. By applying Frobenius theorem, the authors show that ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive comments and constructive suggestions. > W1b The paper’s contribution is overall limited in the aspect of applications [...] For ReLU/linear networks of any depth, explicit conservation laws of $\phi$ are known (Prop 4.1) and our algorith...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper studies conservation laws for deep neural networks when trained under gradient flow. Here, conservation laws refer to functions of network parameters that are invariant under gradient flow and such laws can potentially help us understand the training dynamics but constraining the manifold of paramete...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive comments and constructive suggestions. Weaknesses and Questions addressed > W1a The paper is highly technical and only provides generic mathematical tools without bringing additional insights over previous findings. Space permitting, i...	null	null	null	null	null	null
Point Cloud Completion with Pretrained Text-to-Image Diffusion Models	Accept (poster)	Summary: This paper proposes a optimization method for point cloud completion by leveraging a 2D diffusion model. By constructing a neural SDF field, the proposed method make sure that the represented object matches the input partial point cloud, while the rendering images fit a pre-trained Stable Diffusion by the guid...	Rebuttal 1: Rebuttal: We appreciate the comments and the feedback from the reviewer. W1: Technical Contribution and Novelty: To the best of our knowledge, and as stated by reviewer 1, our method is the first to use a pre-trained vision-language model for completing point clouds. That allows us to complete partial poi...	Summary: This paper tackles point cloud completion with SDS-Complete, which aims at improving Out-Of-Distribution results with a pre-trained Stable Diffusion model for score distillation sampling (SDS). Specifically, the author propose to learn the MLPs for SDF and color with rendering-based losses with respect to the ...	Rebuttal 1: Rebuttal: We appreciate the comments and the feedback from the reviewer. W1: Technical Contribution and Novelty: To the best of our knowledge, and as stated by reviewer 1, our method is the first to use a pre-trained vision-language model for completing point clouds. That allows us to complete partial poi...	Summary: This paper proposes a point cloud completion method with the help of text-to-image diffusion model and formulate the point cloud completion as a test-time optimization problem. It exploits the SDS loss proposed in Dreamfusion to generate 3D given text prompt, with a text-to-image diffusion model. Additionally,...	Rebuttal 1: Rebuttal: We appreciate the comments and the feedback from the reviewer. W1: Missing Ablation study for camera handling: Figure 6 in the main paper shows a table with a quantitative ablation study on the camera handling procedure (“Random camera: running without our camera handling that is described in ...	Summary: This paper proposed a novel method for completing a 3D object from its incomplete input shape. It specifically focuses on the out-of-distribution objects and proposed a diffusion model based framework to generatively learns a SDF for the complete shapes. The idea is simple and easy to follow. Strengths: The i...	Rebuttal 1: Rebuttal: We appreciate the comments and the feedback from the reviewer. W1: The method requires 5 input types Our setup follows an existing protocol that is presented in ShapeFormer which uses real partial point clouds that are extracted from real-world sensors. In contrast to synthetic setups where eac...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful comments. Below we answer separately for each reviewer. Pdf: /pdf/7fb0b5ec50fc175a3a92302ef25328254636b7cb.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work proposes to use text-to-image diffusion model for OOD point-cloud completion. Similar to DreamFusion3D that trains NeRF accompanied by an SDS loss for 3D generation, this work applies the idea to point-cloud completion. Experiments show that the performance is good on both Redwood dataset and KITTI d...	Rebuttal 1: Rebuttal: We appreciate the comments and the feedback from the reviewer. W1: Generated completions are not smooth in Figure 3 and Figure 5 Overall our method produces better outputs qualitatively (see more examples in the SM, Figure 4,5 ), and quantitatively where our method’s Chamfer distance is lower...	null	null	null	null	null	null
On the choice of Perception Loss Function for Learned Video Compression	Accept (poster)	Summary: This paper studies the choice of perception loss for learned video compression and summarizes some valuable conclusions. The first one is the pros and cons of PLF-JD and PLF-FMD. The second one is the universality of MMSE reconstructions. Strengths: This paper conduct experiments and give some theoretical ana...	Rebuttal 1: Rebuttal: We thank Reviewer 1Qbf for considering our work “valuable when designing and training a video compression pipeline”. Please find your concerns addressed below. 1) The datasets used in the paper are too simple. It is questionable that the conclusions still work for the datasets that are closer to...	Summary: The paper consider theoretical considerations of generative video compression. While rate-distortion-perception theory (Blau & Michaeli, 2019) explains the trade-off between realism and distortion for generaive compression, the causal processing typically introduced for video models adds constraints that need...	Rebuttal 1: Rebuttal: We thank Reviewer tXFk for considering our work a “relatively readable paper” and “valuable insights into the generative video compression field”. Please find your concerns addressed below. 1) intuition on the use of PLF-FMD in the low-rate regime Reply: Please note that in all our experiments t...	Summary: This paper examines the choice of perceptual loss for causal, low-latency video compression model with distortion-perception optimization. By using information theoretic analysis and deep-learning based experiments, the authors demonstrate that the choice of perceptual loss can have a significant effect on the...	Rebuttal 1: Rebuttal: We thank reviewer QNA6 for considering our work as “a good guideline basis for subjective optimization of learned video compression”. Please find your concern addressed below 1) Since this paper is oriented to the discussion of learned video compression, the experimental setup may be too simple t...	Summary: This paper systematically studies the rated-distortion-perception tradeoff in neural video compression. Since videos are made of consecutive frames, this paper considers two different perceptual metrics, including the joint distribution of all video frames (PLF-JD), and per-frame perceptual loss (framewise mar...	Rebuttal 1: Rebuttal: We thank Reviewer pcBB for considering our work “the first paper that studies the RDP issue in video compression” and praising its “originality and quality”. Please find your concerns addressed below. 1) (Perhaps not as a weakness) I am wondering whether these conclusions of RDP in video compress...	Rebuttal 1: Rebuttal: We thank the reviewers for acknowledging the significance of the work. Reviewer pcBB notes that our work is the first paper that studies the RDP issue in video compression and finds the results and conclusions to be insightful. Similarly reviewers QNA6, txFk and 1Qbf have noted the significance of...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding	Accept (poster)	Summary: This paper proposed a network for point cloud understanding. The basic idea is to process point clouds in 3 orthogonal 2D planes (triplanes). This can largely reduce the computation cost compared to processing point clouds in 3D space. Experiments on several tasks (segmentations and detection) are conducted to...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and constructive comments. We are encouraged by your positive comments on our method (great idea in designing 3D networks, potential usage of the proposed network). In the following, we address your concerns carefully. **Q1: Missing experiments on object-level...	Summary: This paper proposes a new window partitioning method, which can save a lot of computation costs by sacrificing a small amount of precision. At the same time, it proposes a kind of depth wise sparse convolution, which can better capture local structure by using before and after self attention. The experimental ...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and constructive comments. We are encouraged by your positive comments on our method (innovative, effective) and experiments (abundant). In the following, we address your concerns carefully. Q1: The interaction between different planes. A: Thank you for ...	Summary: This paper studies point cloud understanding. They propose ConDaFormer, a novel Transformer architecture for 3D point cloud. Specifically, ConDaFormer disassembles the cubic window into three orthogonal 2D planes, leading to fewer points when modeling the attention in a similar range. Together with local spars...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and constructive comments. We are encouraged by your positive comments on our method (novel, effective, substantial improvements over the existing point cloud Transformers) and the writing. In the following, we address your concerns carefully. **Q1: The simila...	Summary: In this paper, CondaFormer, an innovative 3D transformer architecture, is presented. It ingeniously dissects the cubic window into three orthogonal 2D planes and incorporates a local structure enhancement strategy that uses depth-wise convolutions to capture local geometric information. Through rigorous experi...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and constructive comments. We are encouraged by your positive comments on the rationale behind our method, the improvement in segmentation, ablations (comprehensive), and presentation. In the following, we address all your concerns carefully. **Q1: The overall...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: Recent advancements in 3D point cloud understanding have explored the use of Transformers, resulting in notable progress. However, the computational demands of applying global self-attention to large point cloud datasets, which contain over 0.1 million points, present a significant challenge. To mitigate this ...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and constructive comments. We are encouraged by your positive comments on our method and experiments (novelty and effectiveness). In the following, we address your concerns carefully. Q1: Why is there no result on test set for ConDaFormer in Table 1 A: In...	null	null	null	null	null	null
On Learning Necessary and Sufficient Causal Graphs	Accept (spotlight)	Summary: The paper introduces a novel method to identify causally relevant variables with respect to a specific target node. Leveraging the concept of probabilities of causation, the authors propose an approach that efficiently and systematically identifies a subgraph containing the relevant ancestors of the target nod...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and suggestions! We are honored by the reviewers’ recognition of our well-motivated and nicely introduced setting, comprehensive mathematical definitions/explanations, and utilities of our method. We have diligently addressed all your questions and comments. Be...	Summary: The paper deals with proposing a necessary and sufficient causal graph that explicitly only model the causal variables required rather than the complete causal graphs that can be inefficient and also introduce spurious correlations between variables. Strengths: 1. Important problem, well-defined and well wri...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and suggestions! We greatly appreciate the reviewers’ acknowledgment that our work is an “important problem, well-defined and well written”, our proposed NSCG “is intuitive and makes perfect sense”, and that “using or rather extending the POC concept to assess ...	Summary: This paper is concerned with learning causal graphs from observational data. In particular, the authors propose to learn a subgraph of the full causal graph, which they refer to as necessary and sufficient causal graphs (NSCGs). They propose an algorithm which measures conditional probabilities of causation be...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and suggestions! We're gratified by the reviewers’ recognition of our paper as well-written and interesting, with illustrative examples and an open discussion on limitations. We have carefully addressed all your questions and comments. In the following, your qu...	Summary: This paper studies the problem of feature selection when performing causal discovery and contributes to the limited literature in this field. Given a set of features, the main goal is to learn a causal graph from a subset of these features such that the learned graph only contains features that are “necessary ...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and suggestions. We greatly appreciate your acknowledgment that our work "of variable selection when learning the causal graph is less explored in the literature", "Theorem 4.6 connects the two possible quantities is novel", our method "shows consistent impro...	Rebuttal 1: Rebuttal: We extend our heartfelt thanks to all reviewers for their insightful comments and suggestions. We are encouraged by their highlight of various acknowledgments, which affirm the quality and novelty of our work, as summarized below: - Reviewer vBqs appreciated the novel NSCGL method and its the...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper addresses the challenge of discovering causal relationships among variables within a complex graph by proposing the learning of necessary and sufficient causal graphs (NSCGs). Unlike existing methods that consider all variables in the graph, NSCGs exclusively consist of causally relevant variables f...	Rebuttal 1: Rebuttal: Thanks for your valuable comments and suggestions! We are encouraged by your acknowledgment of the interesting task of learning a class of NSCG, our novel NSCGL method, and the theoretical support, coupled with experiments on both synthetic and real data. Below, we summarize your questions and com...	null	null	null	null	null	null
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability	Accept (poster)	Summary: This paper addresses the question: when do graph-aware models outperform graph-agnostic ones on node classification tasks? First, theoretical analysis is conducted. Most of the analysis assumes the two-class CSBM-H model – a generalization of the standard stochastic block model, where node features are sampled...	Rebuttal 1: Rebuttal: ### Q1: The paper is limited to heterophilous datasets known to have certain drawbacks [1,2]. ### R1: Although our paper studies the effect of heterophily, our analysis and experiments are not limited to heterophilous datasets. In fact, the analysis in Section 3 investigate the impact of graph ...	Summary: This paper studies when Graph Neural Networks (GNNs) can help with node classification tasks. The authors first focus on a variant of the Contextual Stochastic Block Model (CSBM) and propose new metrics for node distinguishability based on this model. The authors carry out comprehensive experiments and empiric...	Rebuttal 1: Rebuttal: ### Q1: I think the overall writing can be improved. I did not feel excited when reading the paper up to and including page 5. This is just my feeling and it does not mean the paper is not good. Moreover, I find myself sometimes lost focus when reading the first 5 pages. Maybe adding 1-2 sentence...	Summary: The authors analyze the node distinguishability in attributed graphs under the prism of homophily. They consider the binary CSBM model. They compute the classification error rate on the original features and of low- and high-frequency filters; they study the influence of inter- and intra-class variance on thei...	Rebuttal 1: Rebuttal: ### Q1: For me it seems the article mainly studies the separability of a binary Gaussian mixture. The principle it derives (l. 347, whether intra-class node embedding "distance" is smaller than inter-class node embedding "distance") just means the two Gaussians do not overlap. ### R1: Thanks fo...	Summary: Recent research indicates that Graph Neural Networks (GNNs) maintain their advantage even in the absence of homophily, as long as nodes from the same class exhibit similar neighborhood patterns. This argument, however, primarily considers intra-class Node Distinguishability (ND) while overlooking inter-class N...	Rebuttal 1: Rebuttal: Thanks so much for your nice review and strong recognition to our contributions.	Rebuttal 1: Rebuttal: In this part, we will provide experimental results of Label Informativeness (LI) and adjusted homophily ($H_\text{adj}$) on small- and large-scale datasets and compare them with the proposed metrics. Before discussing the results, we would like to introduce 2 threshold values for classifier-based ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper delves deeper into the topic of GNNs' superiority in node classification tasks. The authors conduct experiments to show that GNNs do better due to inter and intra class node distinguishability regardless of homophily levels as suggest by current research. They propose a new metric that sheds more lig...	Rebuttal 1: Rebuttal: ### Q1: Since the crux of the paper is about node distinguishability (ND), it would be nice to see inter and intra ND visualizations of the datasets used similar to Figure 1 (toy example). ### R1: Thanks for your suggestion. Actually, we do include the option to visualize CSBM-H at different ho...	null	null	null	null	null	null
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity	Accept (poster)	Summary: Aiming to address rotational invariance of Gaussian sources, this work first proposed an ICA identifiability theory based on Structural Variability. It then proposed two methods based on sparsity regularization and continuous constrained optimization to estimate the mixing matrix. It also made connections betw...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the time dedicated to reviewing our paper and the constructive suggestions. Our responses to these comments are given below. Q1: "no sufficient empirical evaluation" and "how does the proposed theory work on real-world datasets?" A1: See our response to Q2...	Summary: This paper provides theorems under which the mixing matrix of linear ICA with Gaussian sources can be identified. While Gaussian sources are known to be unidentifiable in the classical ica theory, identifiability is possible if certain sparse structure is assumed for the mixing matrix as was initailly shown in...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's constructive comments, many of which will help improve the clarity of our paper. We have tried to address all the concerns in the following. Q1: Justification to start with $AA'=\tilde{A}\tilde{A}'$ is missing. A1: We sincerely appreciate this insightful ...	Summary: This paper considers the problem of estimating a mixing matrix $A^$, given measuremnts $x = A^ s$. The authors show that under Gaussian $s$, some assumptions on $A$, and using infinite $x$, the sparsest $A$ that satisfies $AA^{T} = A^* A^{* T}$ recovers $A^*$ upto permutation and sign of the columns. The au...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and time devoted to our work. Below we give a point-by-point response to the comments. Q1: "Using $H$ for the set of hard constraints and $h$ for the trace of the defined matrix is slightly confusing, please change it". A1: Thanks for this sugg...	Summary: In this paper, the authors develop new identifiability conditions for ICA, based on sparsity constraints. These assumptions, in particular, do not require the sources to be non-Gaussian, as is usually the case in ICA. Mostly, the authors take inspiration in previous work by Zheng et al, and weaken their main c...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the time devoted and the thoughtful comments. Please find the response to your comments and questions below. Q1: Lack of discussion about Assumption 2 and how realistic it is. A1: We sincerely appreciate this very helpful point. Since Assumption 2 allows i...	Rebuttal 1: Rebuttal: We thank all of the reviewers for the valuable feedback and time devoted to reviewing our work. We are encouraged that they found our work to be interesting (zk7j, vUDk), developed (zk7j), and useful (6AWD). We are grateful that the reviewers appreciate our theoretical (zk7j, vUDk, ouDH, 6AWD) and...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage	Reject	Summary: This paper proposed a new NTKCPL method to reduce the approximation error and it has a wider effective budget range in the setting of active learning on top of self-supervised model. The experimental results on several Computer Vision datasets (e.g., CIFAR-10, CIFAR-10, SVHN) validate the effectiveness of the ...	Rebuttal 1: Rebuttal: Thanks so much for your constructive reviews. For weakness 1: Thank you for your suggestion. We incorporated your feedback and added a new table to showcase the class-wise accuracy of our active learning strategy on the CIFAR-10 dataset. With the exception of classes 3 and 5 (true labels: ca...	Summary: Active Learning is a crucial problem that focuses on selecting a subset of examples from an unlabeled dataset to be labeled. The primary objective is to ensure that when the model is trained using these selected examples, it achieves a lower empirical risk upon evaluation, assuming all the unlabeled data point...	Rebuttal 1: Rebuttal: Thank you for pointing out the confusing notation, we will ensure that the revised version incorporates these corrections. 1. It outputs class logits. In paper, the $f$ denote a neural network model, $f: \mathbb{R}^{d}\rightarrow \mathbb{R}^{k}$, which maps a input sample $x \in \mathbb{R}^{d...	Summary: The paper presents a look-ahead strategy for more efficient active learning when used with self-supervised learning features. The approach uses Neural tangent kernels and pseudo-labels generated by clustering self-supervised or active learning features to estimate an approximation of the empirical risk of each...	Rebuttal 1: Rebuttal: Thank you for your constructive reviews, which will help us to improve the quality of the paper. Weakness 1 We appreciate the reviewer's feedback regarding Section 3.3. We will carefully revise it to provide a more comprehensive and intuitive explanation of the arguments that lead to our pro...	Summary: The paper proposes a active learning strategy that combines self-supervised learning with NTK approximation to estimate empirical risk more accurately. The proposed method outperforms state-of-the-art methods and has a wider effective budget range. Strengths: Well-written: The paper is well-written, informat...	Rebuttal 1: Rebuttal: Thanks so much for your constructive reviews. 1. For weakness (1): table 2 is missing: Table 2 was included in line 273 of the paper. 2. For weakness (2): More baseline: Thank you for your comment. We have reported the TypiClust that is tailored for this specific setting in the paper....	Rebuttal 1: Rebuttal: Thanks to all the reviewers for their valuable feedback. The computational complexity of our algorithm is a common issue for several reviewers, so we put the computational complexity analysis in the global response. The computational complexity of our algorithm can be broken down into two main co...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a novel approach that combines active learning with self-supervised learning, known as neural tangent kernel clustering-pseudo-labels (NTKCPL). The method leverages the power of the neural tangent kernel (NTK) in conjunction with self-supervised learning features to enhance the estimation...	Rebuttal 1: Rebuttal: Thank you for your valuable reviews. 1. Weakness (1) “lacks analysis regarding why the proposed method can increase the effective budget range”: In Section 2, we visualize our insight, where we identify a key issue in existing methods rooted in feature distance-based coverage estimation. Thi...	Summary: This paper aim to develop an active learning method effective across various budgets and compatible with self-supervised learning. The proposed approach, NTKCPL, a look-ahead active learning strategy, selects a subset that are expected to train network to minimize error of unlabeled data pool. For efficiently ...	Rebuttal 1: Rebuttal: Thank you for your valuable reviews, which helps us to improve the paper. 1. Weaknesses. technical contribution Yes, our approach is built on the LookAhead framework. However, the key innovation is that we use the CPL to estimate the empirical risk and design a CPL construction method based ...	null	null	null	null
Limits, approximation and size transferability for GNNs on sparse graphs via graphops	Accept (poster)	Summary: This paper analyzes the transferability and approximation qualities of Graph Neural Networks (GNNs) when applied to graphs or Graph Signal Operators (GSOs) sampled from graph limit objects, known as graphops. The authors demonstrate that when a sequence of graphs is sampled from a graphop, the sequence converg...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We name the 5 points raised in the Weakness section W1-W5, points raised in Question Q1-Q7, and points raised in Minor Issues M1-M24. We write B-S to abbreviate Backhaus and Szegedy’s original paper. 1. For comments regarding the assumptions (W3), future i...	Summary: This paper analyzes the transferability of Graph Neural Networks (GNNs) in terms of size. It proposes Graphop Neural Networks that operate on graphop signals and discretization of the network. It proves that the discretization of graphop and the graph are close, and consequently, two discretizations of differe...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and address individual questions and concerns: 1. Scarcity of related work: Past works on size generalizability of GNNs rely either on graphons or random graph models induced by a kernel, while we focus on deterministic sparse graphs. In the latter regime, ...	Summary: This paper explores the theoretical perspective of whether graph neural networks (GNNs) can generalize to graphs that are different from the ones they were trained on. The authors study the transferability and approximation results via graph limits, including sparse graphs such as bounded-degree or power law g...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and would like to address the individual points. - W1. Real-world applications of GNNs on very sparse graphs (bounded degree, linear order of edges, etc.) have been demonstrated in various disciplines elsewhere. Our paper aims to explain the transferabilit...	Summary: This work is essentially extending the existing theoretical study of GNNs through graphons by replacing them with graphops, an operator view on graphs that allows for results of sparse graphs. Then, standard results on size generalization for instance, are presented. Strengths: The paper is technically sound ...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Regarding the questions and concerns: 1. On the view of graphs as P-operators: We first want to note that P-operators are very general operators satisfying a bounded (operator) norm condition. Viewing graphs as operators is not unfamiliar to finite graph a...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback and would like to emphasize: 1. Our work establishes quantitative, non-asymptotic transferability bounds for a wide range of sparse graphs, which are notoriously hard to analyze. Please refer to Table 1 in the additional rebuttal pdf file for a concise co...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?	Accept (poster)	Summary: The objective of this paper is to harness the capabilities of pre-trained text-to-image models and image editing techniques in order to facilitate robot learning. This is achieved by leveraging these tools to modify the current scene towards the intended image objective. Strengths: The paper presents a novel ...	Rebuttal 1: Rebuttal: Dear Reviewer WQFw, we thank you for your detailed and thorough review. In the following sections, we seek to address each of your concerns. --- Q: Figure 1 can be enhanced to effectively communicate the problem the author intends to address, while also succinctly highlighting their cont...	Summary: Post rebuttal updating score to 5 This work proposes to use generated visual goals for RL. A diffusion model based approach is used to edit visual observations based on text prompts. The proposed image editing approach is shown to be better than prior text-based image editing approaches based on a human evalu...	Rebuttal 1: Rebuttal: Dear Reviewer NX6j, we thank you for your detailed and thorough review. In the following sections, we seek to address each of your concerns. --- Q: Experimental results do not demonstrate the need for visual goals: no baseline in the RL experiments that are not based on imagined goals. ...	Summary: This paper demonstrates a novel way to utilize existing large-scale text-to-image models for robot learning. Specifically, they modify existing pre-trained text-to-image models to produce visual goals for example-based reinforcement learning, before learning policies from the generated images. Experimentation ...	Rebuttal 1: Rebuttal: Dear Reviewer pQmz, we thank you for your detailed and thorough review. In the following sections, we seek to address each of your concerns. --- Q: Would it be possible to provide more qualitative examples? There are about three more examples per task in the appendix, but these all seem ...	Summary: In this paper, authors propose a new method called LfVoid to tackle RL tasks. LfVoid can generate consistent visual goal frames through using pretrained LDM and subsequently train a discriminator to output rewards for downstream RL task. In order to improve editing consistency, DreamBooth, null-text inversion,...	Rebuttal 1: Rebuttal: Dear Reviewer jYPU, we thank you for your detailed and thorough review. In the following section, we seek to address each of your concerns. --- Q: "LDM is kind of overkilling in this benchmark." and the advantage of LDM over VAE to generate the goal frame. A: In LfVoid, the LDM bea...	Rebuttal 1: Rebuttal: Dear reviewers, we appreciate all your helpful feedback. In this response, we address the common questions and comments. We welcome further discussion with each reviewer to address any remaining concerns. We’d like to thank Reviewer jYPU for acknowledging that our method is “An organic integratio...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Coherent Soft Imitation Learning	Accept (spotlight)	Summary: This paper studies the imitation learning (IL) problem. There are two main classes of IL methods: behavioral cloning (BC) and inverse reinforcement learning (IRL), each with its own advantages. This paper proposes an IL method Coherent Soft Imitation Learning (CSIL) which combines BC and IRL. CSIL first learns...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and critical feedback. Weakness 1. The reviewer highlights an important aspect of CSIL: If the demonstration data covers the entire state-action space and the BC fit is exact, then indeed CSIL will leverage and match this expert BC poli...	Summary: This study seeks to leverage the advantages of Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL) to develop a sample-efficient imitation learning algorithm. However, the integration of these two approaches is not straightforward, as optimizing the policy using a dynamic reward diminishes the ben...	Rebuttal 1: Rebuttal: We thank the reviewer for their comprehensive review and feedback. We were able to address all the issues and address the specific questions below. Policy prior $p(a\|s)$. Maximum entropy RL is a specific case of KL-regularized RL when $p(a\|s)$ is a uniform distribution. We mention this in ...	Summary: The authors propose a hybrid BC and IRL method, which uses a maximum entropy KL-regularized BC policy to define a shaped reward which can be optimized with IRL. The authors derive a “coherent” reward which is defined as a reward for which the BC policy is optimal, by inverting the soft policy iteration update...	Rebuttal 1: Rebuttal: We wish to thank the reviewer for their comments and kind words about the submission. We have revised Section 3 to make the derivation easier to follow and emphasize the key technical points and motivations of the KL-regularized BC. We discuss more details of this text revision in our main rebutta...	Summary: The paper proposes an approach to inverse RL to fine-tune a behavior cloning policy using RL on online of offline data sources. Adopting the KL-regularized view to RL/IRL, CSIL expresses the reward in terms of the behavior cloned policy and connects the result with the well-known reward shaping results. This "...	Rebuttal 1: Rebuttal: Thank you for your review. We have revised Section 3 to improve clarity and minimize jargon, and revised the end of Section 4 to add more technical details regarding the reference policies and critic pretraining. To answer the questions here: In the coherent reward, the prior policy is always unif...	Rebuttal 1: Rebuttal: We wish to thank all the reviewers for their comments and feedback. We believe we have been able to address all issues. To summarize the four reviews: General Positives * CSIL is novel and interesting [tsdg, qely, nmwn, ba8w] * The proposed method is sound theoretically [qely] * Extensive co...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations	Accept (poster)	Summary: A GNN-based Shapley value for GNN explanation is proposed, which is a novel Structure-Aware Shapley-based Multi-Explanation (SAME) technique for fairly considering the multi-level structure-aware feature interactions over an input graph It is computed by an expansion-based Monte Carlo tree search (MCTS) and it...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. **Q1. The algorithm and the contribution of its parts are not clear. Neither the game theoretic approach is explained. It would also be important to discuss the detailed differences be...	Summary: The paper introduces a novel method for explaining GNNs called SAME. SAME is theoretically grounded and has some good properties over exisiting methods. SAME uses an expansion-based Monte Carlo tree search algorithm to approximate the optimal Shapley-based explanation, which is proven to be better than pruning...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. Q1. The level of detail for different parts of the paper can be adjusted. Thank you for your excellent suggestions. We agree that we should adjust the detail for Section 2. We hav...	Summary: The paper introduces SAME, a novel method for post-hoc explanation of Graph Neural Networks (GNNs). SAME leverages an expansion-based Monte Carlo tree search to explore structure-aware connected substructures and provides explanations that are as explainable as the theoretically optimal Shapley value. Experime...	Rebuttal 1: Rebuttal: Thank you for your careful review and comments. Below please find our point-to-point responses to your comments. Q1. The theoretical foundation of SAME. How does the MCTS contribute to the Structure-awareness, and how does SAME ensure the theoretically Optimal? 1. To our best knowledge, no e...	Summary: The paper proposes a novel method for explaining GNNs called SAME. SAME addresses the challenges of structure-aware feature interactions in GNN explanation by using an expansion-based Monte Carlo tree search. The authors evaluate SAME on a variety of benchmarks and show that it outperforms previous state-of-th...	Rebuttal 1: Rebuttal: Thank you for your time and thorough comments! Below please find our point-to-point responses to your comments. Q1. More insight into the computational complexity of SAME and how it scales to larger graphs and datasets. For providing insight into the computational complexity and scalability ...	Rebuttal 1: Rebuttal: Dear Area Chairs and Reviewers, We appreciate the valuable feedback and suggestions from the reviewers. Overall, the reviewers deem our paper well written, our method "novel" (qWxr,dDYx) and "effective" (8xQs), our theoretical analysis "well-developed" (qWxr), our evaluation results "standard" (x...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design	Accept (poster)	Summary: This paper focuses on the problem of learning update rules in reinforcement learning. By combining the advantages of the Learned Policy Gradient (LPG) algorithm and the Unsupervised Environment Design (UED) technique, the authors propose a meta RL algorithm, namely, GROOVE (General RL Optimizers Obtained Via E...	Rebuttal 1: Rebuttal: Thank you for your positive feedback about our writing and results. We have introduced new results and edits in our summary response, which we encourage you to read. Please find our response to each of your comments below: 1. Thank you for this suggestion, we agree that this is an interesting...	Summary: The paper "Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design" proposes an automatic curiculum approach for meta-learning of RL optimizers. It is based on a notion of regret of the optimizer, to choose environment parameters that are at a good level for the optimizer to l...	Rebuttal 1: Rebuttal: Thank you for your kind words about our problem, method and results. We appreciate the time you have taken to give such extensive feedback regarding our formalization. We address each of your concerns below and refer you to the edits described in our summary response, which we believe will res...	Summary: The paper presents an approach, i.e. GROOVE, to learning an update rule for generalization on unseen tasks automatically, based on the idea of Unsupervised Environment Design, where a student agent is trained on an adaptive distribution of environments proposed by a teacher and the teacher seeks to propose ta...	Rebuttal 1: Rebuttal: Thank you for your feedback and overall positive review of our work. We have provided edits and new results in our summary response, which we encourage you to read. Please find our response to the weaknesses below: 1. We thank you for this suggestion and agree that listing contributions would ...	Summary: The authors present a meta-learning method to enhance an RL algorithm's performance across diverse RL tasks. They utilize the concept of unsupervised environment design (UED) methods for training RL agents and adapt this approach for the task of meta-learning a policy optimizer. The authors propose a novel alg...	Rebuttal 1: Rebuttal: Thank you for your supportive review of our method, evaluation, and clarity. In response to your comments, we have provided new results in the author rebuttal. Please find our response to each of your comments below: 1. We have now added a comparison of A2C, PPO, expert, and random antagonist ...	Rebuttal 1: Rebuttal: Thank you to all of the reviewers for their detailed and insightful feedback. We appreciate the positive comments describing our problem setting as important (8Dkv, 4F7T) and well-motivated (TbjH), in addition to our proposed method containing innovative components (4F7T), with Algori...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposed a new framework, GROOVE, to solve a new Meta-UPOMDP problem. It also introduces algorithmic regret (AR) to approximate the regret to update the curator and generator (sampler?). The results show that the meta-optimizer learned by GROOVE can be used to improve the games in Atari. Strengths...	Rebuttal 1: Rebuttal: We thank you for your kind words regarding the problem’s importance, the novelty of algorithmic regret (AR), and the demonstrations of its performance. We encourage the reviewer to read the edits and new results outlined in the author rebuttal. We respond to each of your comments below: Weakn...	null	null	null	null	null	null
Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation	Accept (poster)	Summary: This paper strives to capture various category-level object style information and then compensate the style information of the object instances from the target to the source domain in the open compound domain adaptation for semantic segmentation. And they evaluate the proposed method on various source and targ...	Rebuttal 1: Rebuttal: >### 1. You raise a valid point regarding the terminology "Instance-Key" which may not be appropriate in the context of semantic segmentation tasks where instance-level annotations are not available. Please see our discussion in the second part of “Response to Common Questions”. The term “instanc...	Summary: This paper deals with the open compound domain adaptive semantic segmentation problem. The motivation is to compensate the object style gap across domains to obtain more accurate pseudo labels for self-training. The main framework consists of two parts: discrepancy memory and style compensation. For discrepan...	Rebuttal 1: Rebuttal: >### 1. Some details are not stated, like the intermediate segmentation head and the definition of OCDA for semantic segmentation. Figure 2 of the rebuttal file illustrates the intermediate head as the convolutional layers. This head takes input as the target feature for regressing the category s...	Summary: The paper introduces Object Style Compensation which involves constructing an Object-Level Discrepancy Memory consisting of multiple sets of discrepancy features to minimize the style differences between the source and target domains and ensure the styles of different object categories or instances within the ...	Rebuttal 1: Rebuttal: >### 1. What's the value of the parameter $\gamma$ set in Eqn (2) and (4), and how to determine its value? I would like to suggest the authors to run a group of experiments with different values of $\gamma$ to see how the value impacts the final performance. Thanks. We have provided this analysis...	Summary: The authors propose a novel target-to-source feature style transfer approach for open compound domain adaptation. Inspired by the observation of the existence of object style discrepancy when converting a target image to a source style, they design Object-Level Discrepancy Memory (OLDM). This module saves the ...	Rebuttal 1: Rebuttal: >### 1. In the related work section, there is a simple listing of various papers, and the flow of each research is not well organized. Furthermore, there is little mention of papers published after 2021, which seems to indicate a lack of investigation into recent papers. Thanks. We have added 14 ...	Rebuttal 1: Rebuttal: ## Response to Common Questions >### 1. The advantage of style compensation based on the discrepancy features >- Reviewer LZYW-Q2 “Are there any advantages to storing these discrepancies rather than just performing style regression?” >- Reviewer uGsB-Q2 “The proposed method is not specificall...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a new method for OCDA called Object Style Compensation, which focuses on adapting the style changes of different categories or instances of objects rather than just the overall scene style. This stored information is used to select the appropriate discrepancy features for compensating the...	Rebuttal 1: Rebuttal: >### 1. It's unclear how different instances within the same class are differentiated. Thank you for pointing out this confusion between the instance-key feature and the object instance. In this paper, we do not need to differentiate the instances as those in the instance segmentation task. Pleas...	null	null	null	null	null	null
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation	Accept (poster)	Summary: This paper proposed a point cloud-based representation for neural rendering of large-scale scenes. Point cloud diffusion model is designed to upsample the point cloud for better performance. The proposed method achieves state-of-the-art performance in the tested scenes. However, I still have some concerns abo...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea and the detailed and insightful comments. Your concerns will be addressed in the following comments and the final version of our paper will be updated accordingly. 📝 Q: Size of the train dataset for diffusion. 💡 A: Our method requires a d...	Summary: The paper presents a method for reconstructing large-scale scenes from multi-view images. Given the sparse point cloud computed from an MVS pipeline, the algorithm first employs a point cloud diffusion model to upsample the point cloud. The utilization of the diffusion model could effectively create a dense cl...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea and the detailed and insightful comments. Your concerns will be addressed in the following comments and the final version of our paper will be updated accordingly. 📝 **Q: The combination of novel diffusion-based point cloud upsampling models and Point-NeR...	Summary: This paper proposes an implicit neural representation for large-scale scenes with two major components, the first one is the point diffusion implicit function (PDF) which adopts diffusion process to generate dense point cloud from point cloud produced by Colmap, the second one is the background rendering which...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea and the detailed and insightful comments. Your concerns will be addressed in the following comments and the final version of our paper will be updated accordingly. 📝 **Q: The combination of novel diffusion-based point cloud upsampling models and Point-NeR...	Summary: In this paper, the authors propose a new approach to tackle the reconstruction of Neural Radiance Fields from large, unbounded scenes. A major limitation of current neural fields is the lack of scalability due to the number of sampling points in empty space, which is limiting the achievable scale or quality wi...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea and the detailed and insightful comments. Your concerns will be addressed in the following comments and the final version of our paper will be updated accordingly. 📝 Q: long training times caused by per-scene optimization. 💡 A: Due to the ...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive feedback and recognizing our method as achieving better qualitative and quantitative performance (yNkC, DZNn, rzgR, H9co, v7wN); introducing a novel and effective diffusion-based up-sampling module for large-scale point clouds (yNkC, DZNn, H9co...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposed a novel method for the novel view synthesis of large-scale outdoor scenes. The method combines a Point-NeRF-baed rendering stage for the foreground, as well as a background stage based on Mip-NeRF 360. To cope with the issue of overly sparse point cloud from COLMAP MVS, a point could diffusi...	Rebuttal 1: Rebuttal: We appreciate your approval of our idea and the detailed and insightful comments. Your concerns will be addressed in the following comments and the final version of our paper will be updated accordingly. 📝 Q: The significant computation caused by per-scene optimization. 💡 A: As...	null	null	null	null	null	null
L2T-DLN: Learning to Teach with Dynamic Loss Network	Accept (poster)	Summary: The paper introduces the concept of teaching in machine learning and proposes a framework called L2T-DLN (Learning to Teach with Dynamic Loss Network). The framework aims to address the limitations of existing approaches by incorporating the temporal nature of loss function adjustment and utilizing the states ...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. Weakness: 1. Previous studies involve directly supplying certain states of the student model to a teacher for dynamic loss adjustment (kindly refer to Lines 28-30, main paper). This direct provision of states without integration hinders L2T convergence, as t...	Summary: This paper introduces an improvement towards the learning-to-teach framework. Compared with the previous works, the authors made an innovation that a dynamic loss network is added with LSTM acting as teacher model to enhance the temporal memorization. A three step optimization procedure is also proposed with a...	Rebuttal 1: Rebuttal: 1. Our target for the submission is to re-visit L2T and formulate the loss adjustment as a temporal task. We hope our discussions inspire other further work. Thank you for your valuable and attractive suggestions for using transformer architecture as a teacher. Using a transformer as the teacher m...	Summary: In this paper, the authors state that existing methods only employ a simple feedforward network as the teacher model, which limits the potential of L2T. This issue motivates authors to propose a network with a memory unit to enhance the temporal analyzing ability of the teacher in the learning to teach (L2T) t...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. Weaknesses: 1. The novelty of our approach is introduced in Lines 8-13 and Lines 50-55, main paper, which are: (1) design a teaching strategy based on the gradient concerning DLN; (2) use LSTM as the teacher model to update the DLN with the tempor...	Summary: This paper presents a three-stage framework that dynamically adjusts the learning process of student model (i.e., target model). The loss value is calculated via the proposed Dynamic Loss Network (DLN), parameterized as neural network. And the DLN is updated by the teacher network implemented as LSTM. The auth...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. Weaknesses: 1. Objective detection involves two sub-tasks, i.e., regression and classification. It is challenging to handle two tasks simultaneously with one dynamic loss. An alternative way commonly used in existing dynamic loss-based works is to dynamical...	Rebuttal 1: Rebuttal: We thank all reviewers' valuable comments and efforts. Our proposed L2T-DLN is technically very sound with enough insights and depth [79Na], provides a thorough convergence analysis [wSe9, HMfR], and outperforms state-of-the-art methods in various downstream tasks [9Vkw,wSe9]. We provide point-to-...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Streaming Algorithms and Lower Bounds for Estimating Correlation Clustering Cost	Accept (poster)	Summary: The paper studies correlation clustering in the streaming model where the edges of the underlying graph are updated one at a time. Where previous work has focused on the semi streaming model with $\Omega(n)$ space, they consider more classic streaming with a polylogarithmic number of bits. Here it is not poss...	Rebuttal 1: Rebuttal: Thank you for your detailed review and your positive feedback and also for catching all our tiny errors. Below are our responses: Point1: Which ideas are new and which are existing. Getting approximate clustering in $\tilde{O}(n)$ space via the algorithms (SDD/Pivot) was already known. The no...	Summary: The paper gives polylog space streaming algorithm for approximately computing the value of the cost of correlation clustering. An algorithm for finding the (approximate) correlation clustering in polylog space is known to be not possible. It is also known that approximately computing the cost within a multipli...	Rebuttal 1: Rebuttal: Thank you for your detailed review and your positive feedback. Our responses are as follows. Point 1: Practical motivation. Thanks for pointing this out, and we will add the discussion of the motivations in our later version. The motivation can be described roughly as follows. In large-scale ...	Summary: The authors initiate a study of the correlation clustering problem in streams, in the setting when only the cost of the clustering needs to be output. Prior work on correlation clustering in streams focused on the "semi-streaming" model, in which the clustering must be output but a space of $\tilde\Theta(n)$...	Rebuttal 1: Rebuttal: Thank you so much for your detailed review and your positive feedback. Here are our responses to the questions: Point 1: Problems with the axes in the Figures. Thanks for spotting out the issue, and we will fix the figures by making the text on the axes larger. **Point 2: Implementation of s...	Summary: This paper studies the correlation clustering problem in the streaming setting. Unlike previous work they consider algorithms with space much smaller than the number of vertices and only find the cost of the optimal clustering, not the clustering itself. They define an (alpha, beta) approximation to be an addi...	Rebuttal 1: Rebuttal: Thanks for your detailed review and the critical new idea. Please find our responses below. Point 1: The suggested $(1, \delta n^2)$-approximation algorithm. Thank you for raising this observation. If we understand it correctly, the key idea of your suggestion is to reduce the min-disagreeme...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference	Accept (poster)	Summary: This paper defines a new diagnostic to check posteriors learned from simulation-based inference. The main issue in this setting is that one does not have access to likelihoods, and so one needs diagnostics that do not rely on being able to calculate likelihoods. Here the authors present L-C2ST, which essenti...	Rebuttal 1: Rebuttal: We thank the reviewer for saying that our paper is well written and clearly explained. We also appreciate that he found our method "simple and elegant" and recognized that we aim at an important practical question for simulation-based inference. Here below we address some of the remarks and questi...	Summary: The authors present a new diagnostic tool for simulation-based inference. The method learns the C2ST without knowledge of the true posterior distribution. If the density estimator is a normalizing flow, the authors propose to perform the classification in latent space. Strengths: Originality: The method t...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing the potential and usefulness of the method that we propose, as well as appreciating its extension to the case of normalizing flows. We also thank the reviewer for the very deep and interesting questions related to our method. We have followed the reviewer's su...	Summary: An algorithm for evaluating amortized posterior estimators $q(\theta\|x)$ in simulation-based inference (SBI) is proposed. In the setting considered, we have the ability to sample $p(\theta)$ and to sample $p(x\|\theta)$ but not to evaluate its density. The algorithm modifies the common classifier two-sample tes...	Rebuttal 1: Rebuttal: We thank the reviewer for finding our paper "clear and well-written", as well for having checked our code. We now answer the two main questions raised by the reviewer. Please note that we have added results on more SBI benchmark examples that you will find in Figures 1 and 2 of the PDF attached to...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough reading of our manuscript and their very interesting questions. We have had very positive remarks concerning the clarity of our text and the potential of our method to the SBI community. We are very grateful for all this. Some interesting questions that we...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Lookaround Optimizer: $k$ steps around, 1 step average	Accept (poster)	Summary: The paper presents Lookaround, a novel optimizer for weight average ensembling (WA). Unlike existing approaches that perform weight averaging post-training, Lookaround adopts a two-step process throughout the training period. In each iteration, the "around" step trains multiple networks simultaneously on trans...	Rebuttal 1: Rebuttal: Thanks for your encouraging words and constructive comments. We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below. Q1: How are the epoch and iteration defined in Lookaround's setting? Under the standard training process, an...	Summary: This work provides a new optimizer, "Lookaround optimizer," which is built upon a previous proposed "Lookahead optimizer" [40]. By incorporating data augmentation, this work shows an improved convergence rate under low condition numbers. It also empirically shows some improvement in classification accuracy und...	Rebuttal 1: Rebuttal: Thanks for your encouraging words and constructive comments. We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below. Q1：What is the d (number of data augmentation) used in the experiment? In the experiments corresponding to T...	Summary: This paper introduces a new optimization algorithm named Lookaround, which draws inspiration from the recent achievements of weight averaging (WA) techniques in deep learning. The proposed Lookaround optimizer looks around nearby points by performing multiple gradient computations for a given training input us...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. In the following, your comments are first stated and then followed by our point-by-point responses. Q1: There are no error bars. The error bar (i.e., standard deviations) is depicted in Figure 6 in our paper. The detailed results are provided in Tabl...	Summary: This paper proposes Lookaround, a new optimization method that incorporates weight averaging into the optimization process. The algorithm consists of two steps: 1) the around step launches several parallel runs of gradient descent led by different data augmentations, 2) the average step does weight averaging o...	Rebuttal 1: Rebuttal: Thank you for your detailed comments. We hope the following response will address your concerns. C1: Two contemporaneous works propose approaches very similar to Lookaround. Thanks for sharing the two great works! After carefully reading the two papers, we found these three works share moder...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This article introduces the Lookaround Optimizer, a novel optimization algorithm for deep neural networks. The Lookaround Optimizer is based on the idea of lookaround, which involves maintaining two sets of weights. The first set of weights is updated using the standard gradient descent algorithm, while the se...	Rebuttal 1: Rebuttal: Thank you for your constructive comments and suggestions. In the following, your comments are first stated and then followed by our point-by-point responses. Q1: Similar ideas that utilize multiple models have already in proposed in varioius area, especially in meta learning. Thanks for the...	Summary: Flatness-aware optimizers have gained significant attention in the field of research for training deep neural networks that are robust. Weight Averaging (WA) is a popular approach to finding solutions within the flat regions of the loss surface. However, previous WA methods have two limitations. First, when WA...	Rebuttal 1: Rebuttal: We sincerely appreciate your comments on our work. We hope the following response will address your concerns. Q1: Lookaround uses $d$ times more computation. How does Lookaround fairly compare to other methods? We agree that it is imperative to establish equitable comparisons in computationa...	null	null	null	null
Metropolis Sampling for Constrained Diffusion Models	Accept (poster)	Summary: The paper presents a rejection-sampling technique applied to diffusion models when sampling defined on manifolds. The paper contains some theoretical results on the convergence of the proposed algorithm, in particular the assymptotic convergence to the reflected Brownian motion. The also paper contains a fe...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review emphasising the implementational simplicity of our approach, as well as the resuting ease with which it can be integrated into existing sampling algorithms. ...	Summary: The paper proposes a new discretization of the forward and backward SDEs used for learning and sampling in the diffusion generative model framework in manifolds with inequality constraints. The method relies on "metropolising" a given discretisation of the "unconstrained" manifold (either Euler-Maruyama if the...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review emphasising the improved computational efficiency and empirical performance of our approach and acknowledging its usefulness to a range of practitioners. Th...	Summary: This paper proposes a Metropolis sampling for constraint diffusion in the context of generative modeling. The authors show that the proposed algorithm is a discretization of reflected Brownian motion on manifold (with rejection). The authors also apply the proposed algorithm to several synthetic and real data ...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript and for the helpful and constructive feedback. We were happy to see you emphasize that we extend methods for generative modeling on constrained Riemannian manifolds [1,2] and are glad you enjoyed the paper. The concerns you raised relate to c...	Summary: This paper introduces a new approach for generative modelling on constrained Riemannian manifolds, building upon diffusion models. The proposed method implements a Metropolis sampling scheme and offers computational efficiency and improved empirical performance over previous models, particularly with increased...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review emphasising the improved computational efficiency and empirical performance of our approach, as well as acknowledging the quality of the paper and its releva...	Rebuttal 1: Rebuttal: # General Response We would like to thank all reviewers for the time and effort they have put into reviewing our manuscript and for the valuable and constructive feedback they have provided. --- In our manuscript, we present a new method for generative modelling on constrained Riemannian manifo...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Generalized test utilities for long-tail performance in extreme multi-label classification	Accept (poster)	Summary: This paper focuses on the long-tail problem in extreme multi-label classification. To address this problem, the authors propose a new method to optimize performance metrics for extreme multi-label tasks via the expected test utility (ETU) framework. Experimental and theoretical results are also provided. Str...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the review and questions. We appreciate the hard work. Below we address the main question. In contrast to many works in extreme classification, which are mainly algorithmic and aimed to obtain "better" performance on tail-labels with existing metrics, our work ...	Summary: This paper analyzes generalized metrics budgeted “at k” by formulating it in the expected test utility (ETU) framework. They derive optimal prediction rules and construct their computationally efficient approximations with provable regret guarantees and being robust against model misspecification. Strengths:...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the review and questions. We appreciate the hard work. Below we address the main concerns. Regarding the extensibility of the proposed approach We first want to stress that the class of linearly decomposable into labels functions studied in this paper is a...	Summary: In this paper, the authors studies the evaluation metrics for the long tailed extreme multilabel classification problems. Compared with existing heuristics, such as PSP, they formulate the metrics in the expected test utility framework. Inference rules are derived to obtain optimal metrics. Approximations are ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the review and questions. We appreciate the hard work. Below we address the main questions. > *Inference with a global budget k is easy to implement but not always optimal in terms of utility-budget trade-off. If there's no top-k constraint, how can we design t...	Summary: This paper critiques that the existing extreme classification evaluation metrics don't give the complete picture with respect to all labels (more specifically performance on head labels overpowers performance on tail labels), hence it recommends using macro-averaged metrics which are more favorable to tail lab...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the review and questions. We appreciate the hard work. Below we address the main questions: **Regarding the importance of macro measures for evaluating performance on XMC datasets as the data imbalance and a trade-off between performance on standard metrics and...	Rebuttal 1: Rebuttal: We thank the reviewers for their thorough comments and questions. We answer them in specific responses to the reviewers. We use the global comment to attach a PDF with additional plots containing results for “meta-measures” that combine instance and macro-averaged measures. These plots have been p...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper discusses evaluation metrics to measure the long-tail labels' performance of the extreme multi-label classification (XMLC) problems. The author proposed that macro-average based metrics (e.g., macro-avg Precision/Recall/F1 at k) are more suitable to measure the performance of long-tail labels compar...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the review and questions. We appreciate the hard work. Below we address the main questions: > *Selecting thresholds to optimize Marco-average metrics are not new in multi-label community. The author seems not aware of some classical/heuristic approaches. For ex...	null	null	null	null	null	null
Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations	Accept (poster)	Summary: The paper presents a semi-supervised approach to InfoGAIL for learning from demonstrations that enables disentangled learned behaviors. The proposed approach outperforms all considered baselines in MuJoCo simulation in imbalanced settings, and is robust to very little labeled data as well as varying levels of ...	Rebuttal 1: Rebuttal: # Response to Reviewer dvkr > Q1. From Fig. 1, it is unclear what about the expert trajectories indicates imbalance. What are the considered labels in the experiments? How many label classes are there? Fig. 1 illustrates a simple 2D-Trajectory scenario, facilitating the visual comparison of algo...	Summary: This paper focuses on semisupervised imitation learning with imbalanced data. Mainly, the approach extends InfoGAIL with a semisupervised learning architecture, inspired by ss-InfoGAN, where the latent variable is decomposed into a semisupervised part and an unsupervised part. The semisupervised part is define...	Rebuttal 1: Rebuttal: # Response to Reviewer 18Ku > Q1. Confused about Fig. 1. Do we want the agent to imitate the expert's different behaviour styles? Why is learning different modes of behaviour important in this context? How is this achieved in the Mujoco environments? Why is classification relevant in this context...	Summary: # Problem Statement The paper addresses the problem of imitation learning in the context of real-world demonstrations that often present challenges such as multimodality, data imbalance, and expensive labeling processes. # Main Contributions The authors propose a novel semi-supervised imitation learning arch...	Rebuttal 1: Rebuttal: # Response to Reviewer fzCM > Q1. Minor typo: The minimization should have value function as a optimization variable in addition to $\pi$. A1. Thank you for your valuable suggestion. We have incorporated the optimization of the value function into Section 3.3 of the main paper. > Q2. The tasks ...	Summary: The paper introduces a semi-supervised imitation learning architecture that addresses challenges associated with real-world demonstrations, such as multimodality, data imbalance, and expensive labeling processes. The proposed method utilizes three key components: adapting semi-supervised generative adversarial...	Rebuttal 1: Rebuttal: # Response to Reviewer iRnn > Q1. The novel seems to be limited. More discussions with ss-InfoGAN needs to be added. A1. Indeed, we draw inspiration from ss-InfoGAN and extended it to the imitation learning framework, addressing key issues that still persist in the field of imitation learning, s...	Rebuttal 1: Rebuttal: # Common Response We are thankful to the reviewers for their valuable feedback. We first address the comments that are common to multiple reviewers and then response to the reviewers individually. > Q1. The significance of this work. A1. Our method draws inspiration from semi-supervised GANs an...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Auxiliary Losses for Learning Generalizable Concept-based Models	Accept (poster)	Summary: The paper proposes multiple improvements to current paradigm about learning, application and evaluation of concept bottleneck models (CBMs) for by-design interpretable classification. They propose to improve supervised learning of concepts in CBMs through a concept-orthogonality loss (COL) that encourages samp...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their feedback. > Loss weights: experiments around $\lambda$ We experimented with different loss weights for $\lambda$ in our experiments and the model+COL seemed to be fairly robust with different values of $\lambda$. We have put those results in Table 5 ...	Summary: This paper proposes Coop-CBM, a concept bottleneck model (CBM) trained using a novel multi-task loss and orthogonality regularizer. The proposed losses discourage undesired leakage in a CBM's concept representations while encouraging a better balance of their accuracy and interpretability. By incorporating the...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for very detailed feedback. We have updated the readme. > 1. 1. Clarification on sparse concept experiment. To provide clarity, when we mention "10%", it signifies that merely 10% of randomly chosen concepts were employed across all the images. Hence the same...	Summary: The authors introduce an auxiliary task in CBM training to incorporate more information regarding the downstream task in the input representation for predicting concepts. They also introduce Concept Orthogonal Loss (COL) to enforce orthogonalization between input representations of different concepts, and vice...	Rebuttal 1: Rebuttal: > My main concern is that coop-CBM improves downstream task performance because downstream task information is leaked through the concepts. This makes intuitive sense as the explanation of performance gain is the representation used to predict concepts contain more downstream task information[.......	Summary: The author proposes coop-CBMs + concept orthogonal loss (COL) to solve previous limitations in Concept Based Models with a focus to learn relevant concept representations that consecutively boost model performance. Strengths: In general, the idea of coop-CBMs+COL is simple and effective. The method increases...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their feedback. > The overall structure of the paper is reasonable, but part of substructure in some sections are not well organized. Thank you for your feedback. We shall rewrite that section by moving the two paragraphs about distributional shifts and li...	Rebuttal 1: Rebuttal: Global rebuttal. Pdf: /pdf/a021ddf7098ef265989c2426033a4a29f8a84247.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes auxiliary losses to learn concept-bottleneck models: a coop task loss, and a COL (orthogonal concept loss). The experiments show that the proposed coop-cbm can perform 1-3% better than standard black-box models and outperform other CBM models on three datasets: CUB, AwA2 and TIL. =======...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their feedback. >The authors mentioned it's an unfair comparison to [33] and why [18] is not compared either. [18] and [33] used a pre-trained model - CLIP which was trained on a massive corpus of data to obtain concepts. This can potentially introduce inh...	null	null	null	null	null	null
Improving Language Plasticity via Pretraining with Active Forgetting	Accept (poster)	Summary: This paper proposes active forgetting, a rather straightforward method that resets the embedding layer every K updates during pretraining, to quickly adapt PLMs to new languages. Through experiments on different language pairs with RoBERTa, the authors claim that the proposed method can induce faster convergen...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. ### We appreciate that you recognise the soundness and contribution of our work. We would like to address your comments as follows. Figures 4 and 6: Thank you for pointing out the issues. We will polish the figures in the camera-ready version. Q1:...	Summary: The paper proposed an active forgetting mechanism for PLMs pre-training for cross-lingual transfers and adaptations. The authors propose a multi-stage adaptation framework for better cross-lingual transfer/adaption: 1) First, by resetting embedding layers every K update during pre-training for monolingual RoBE...	Rebuttal 1: Rebuttal: Thank you for your review. We appreciate that you recognise the effectiveness of our method and find our finding interesting. We would like to address your concerns as follows. ### First, we want to elaborate on our experimental setup. > this work lacks proper ablation experiments on showing the g...	Summary: This work introduces a training technique that leverages actively resetting token embedding to improve zero-shot language transfer. Experiments on RoBERTa show consistent improvement in multiple languages, distant languages in particular. Strengths: 1. Simple and innovative approach. 2. Consistent improvement...	Rebuttal 1: Rebuttal: Thank you for your review. ### We appreciate that you recognize the simplicity and effectiveness of our method. We address your comments as follows. > Only experimented on one pretrained model. We would like to extend our experiments to more pretrained models. However, we are limited by the co...	Summary: This paper follows the language adaptation procedure of MonoTrans (Artetxe et al., 2020), and proposes a new pre-training method with active forgetting. By resetting the embedding layer every K updates during training, the language model learns to learn the new embedding fast, similar to a meta-learning effect...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We would like to address your concerns as follows. ### First, we would like to clarify our goal, motivation and contributions. > The goal of this paper seems to improve language adaptation results, rather than the goal behind language adaptation. The key i...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments. We would like to address some common questions in this general response. Figures are attached in the rebuttal pdf. ### Active Forgetting Creates An Episodic Learning Pattern Reviewers are curious about the loss curves comparison for stan...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents an embedding forgetting mechanism for pre-training, aimed at enhancing robustness in downstream shift embedding fine-tuning. Focusing on the low-resource regime, the study conducts experiments on 10 simulated low-resource languages across three tasks: XNLI, XQUAD, and MLQA. Strengths: (1) ...	Rebuttal 1: Rebuttal: Thanks for the review. We want to clarify a few misunderstandings and address your concerns as follows. ### First, we want to revisit and emphasise the scope of this work. > (1) The paper is somewhat limited in scope, as it only applies to low-resource multilingual tasks within the framework of A...	null	null	null	null	null	null
Deep Reinforcement Learning with Plasticity Injection	Accept (spotlight)	Summary: This paper studies the loss of plasticity in deep RL and proposes a method for mitigating it at the cost of more overhead memory and computation, but doing so in such a way that does not necessitate adding more trainable parameters or affecting predictions. The latter two are key when using their approach for ...	Rebuttal 1: Rebuttal: Thank you for the review and for the positive evaluation of our submission. We address your questions and concerns as follows: - We acknowledge that plasticity injection is likely not the end solution to the phenomenon of plasticity loss. Currently, the phenomenon is not clearly understood and eve...	Summary: The author propose a solution to the problem of loss of plasticity (decrease in learning effectiveness over time). Their solution is to freeze the existing network and add two new heads, one free to learn, the other frozen to the negative of the free-learning new head. The result is that the intervention has z...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We address your concerns below (AR = author response). *“The method actually imposes a considerable computational cost and further complexity, while apparently providing a small benefit vs. the much simpler alternative of training a larger networ...	Summary: The work attempts to study the role of plasticity loss in deep reinforcement learning. The paper proposes a new tool to diagnose plasticity loss. The new tool is designed to minimize the effect of other confounders like exploration. The results on Atari show that plasticity loss affects the performance of doub...	Rebuttal 1: Rebuttal: Thank you for taking the time to write the review. We are grateful for the positive evaluation of our submission. We provide responses to your questions and concerns below. We acknowledge that due to the scale of the experiments we did some hyperparameter tuning for methods like SnP and Width Sca...	Summary: This paper provides a deep dive into the issue of plasticity loss in deep reinforcement learning and introduces a novel approach, "Plasticity Injection". The authors employ this technique to not only diagnose plasticity loss but also effectively dissect confounding factors, such as exploration mechanisms. Thro...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our submission and for the thoughtful comments. We provide responses to your questions and concerns below. Thank you for raising the point about testing plasticity injection outside of Atari. We have conducted an experiment in a simple non-Atari setting bas...	Rebuttal 1: Rebuttal: We thank all reviewers for their efforts to provide the detailed feedback. We particularly appreciate that reviewers find the paper novel (Reviewers 227D, Foz7), well-written (x17L), comprehensive (227D), thorough (Foz7) as well as highlight that the method is simple (227D), well thought out (P5kx...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Simple and Controllable Music Generation	Accept (poster)	Summary: This paper introduces MusicGen, an innovative single-stage transformer language model for conditional music generation, using either text or melody as a conditioning factor. The authors focus on exploring different approaches of codebook interleaving patterns and propose an efficient approach of Delay Pattern....	Rebuttal 1: Rebuttal: _Regarding the VALL-E codebook pattern_: Indeed, we incorrectly describe the “VALL-E” codebook patterns as corresponding to VALL-E. We renamed it to “flat 1st codebook then parallel”. _Regarding text augmentations_: During model development, we experimented with condition merging, word dr...	Summary: MusicGen is an auto-regressive architecture for music audio generation conditioned on textual descriptions and an optional melody. The key proposal is a generic formulation of audio codebook interleaving strategy, which enables parallel code streams to be processed with a simple single-stage Transformer decode...	Rebuttal 1: Rebuttal: _Regarding long-range musical structure_: We agree that evaluating musical structure in the generated music is interesting and important for music generation. However, it is far from trivial as the generated output is audio samples and not interpretable discrete representations (like midi). De...	Summary: The paper proposes a single-stage music generation model that can input text or melody. The proposed approach uses tokens from pre-trained neural audio codec tokens with multiple residual vector quantizers and investigates efficient language modeling to reduce the length of autoregressive steps. Experiments on...	Rebuttal 1: Rebuttal: _Regarding the contribution of the proposed method_: The novelty and contribution of our work are designing a simple and efficient auto-regressive model to perform text-to-music generation. Unlike prior work, which consists of a cascade of models using either super-resolution or semantic token...	Summary: This paper introduces MUSICGEN, an approach for generating music conditioned on either text or melody representation. MUSICGEN consists of a single-stage transformer language model (LM) augmented with efficient token interleaving patterns, eliminating the requirement of employing multiple cascaded models. The ...	Rebuttal 1: Rebuttal: _Regarding novelty and motivation:_ The novelty and motivation of the proposed method (MusicGen) are designing a simple and efficient auto-regressive model to perform text-to-music generation. Unlike prior work, which consists of a cascade of models using either super-resolution or semantic to...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their detailed reviews and valuable feedback. We are happy the reviewers found our experimental setup and results robust and persuasive. We are also glad the reviewers found our method to have both strong performance and efficient modeling. We address each ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Fine-grained Expressivity of Graph Neural Networks	Accept (poster)	Summary: With graphon theory, this work quantifies which distance MPNNs induce and thus provides a deeper understanding of MPNNs’ capacity to capture graph structure, precisely determining when they can and when they cannot assign similar and dissimilar vectorial representations to graphs. Strengths: 1. Strong univers...	Rebuttal 1: Rebuttal: We thank the reviewer for their fair and constructive review. > Graphon focus on graph limit. While in real world settings like graph classfication, the graph is small and MPNN still lacks expressivity. You are correct that graphons are used in the GNN literature via large graphs. These works us...	Summary: This work aims at a fine-gined metric to characterize representation differences from MPNNs. Specifically, they first generalize MPNN to iterative degree measures and prove that Prokhorov metric and unbalanced Wasserstein metric can be used to bound the node/graph representation difference. This relation is va...	Rebuttal 1: Rebuttal: We thank the reviewer for their fair and constructive review. > The motivation and the conclusion of untrained GNNs experiments are not very clear to me. How is "an untrained GNN can outperform trained GNN" related to the previous conclusions and what message does this part try to convey? Our th...	Summary: The paper considers the continuous variant of the 1-WL test and leverages it to characterize the expressive power of MPNNs on graphons. The authors show that if two graphons have similar MPNN outputs then they are close in their metric, extending the existing result proving the opposite implication (graphons ...	Rebuttal 1: Rebuttal: We thank the reviewer for their fair and constructive review. > The paper is hard to read, as it introduces a lot of existing concepts and builds on them. Maybe it is inevitable due to intrinsic theoretical nature of the paper. However, I believe some extra effort is needed to remind the reader t...	Summary: The paper studies the classes of MPNNs on graphons, ultimately showing that the MPNN representations are sufficiently close (up to constants depending on Lipschitz regularity and layers) _if and only if_ the graphons are close according to several metric distances, mainly the Prokhorov metric and the unbalance...	Rebuttal 1: Rebuttal: We thank the reviewer for their fair and constructive review. > The fact that MPNNs exhibit a Lipschitz property compared to distances on graphons does not seem surprising to me (i.e. Lemma 3) given their regularity. The converse statement (Theorem 6) is perhaps less obvious, although I fail to s...	Rebuttal 1: Rebuttal: We thank the reviewers for their fair and constructive reviews and appreciate that they recognize that we present a beautiful theory for graph similarity. Combined with a novel generalization of message-passing graph neural networks (MPNNs) to graphons, our theory allows us to prove that graph(on)...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a novel way to generalize the expressivity of graph neural networks (and other general message passing algorithms) to the graphon case. Furthermore, they identify metrics on graphons (and, consequently graphs) which allow to bound the distance of any MPNN representation of the graphons. S...	Rebuttal 1: Rebuttal: We thank the reviewer for their fair and constructive review. > It seems to me that the definition of GNNs in Line 212 1/2 is a rather severe deviation from most MPNN formulations, as it requires activations to grow linearly with the graph size to offset the normalization. This seems to be genera...	null	null	null	null	null	null
Domain Re-Modulation for Few-Shot Generative Domain Adaptation	Accept (poster)	Summary: This work studies generative domain adaptation (GDA), on the StyleGAN2 architecture. Methods in this setting are commonly evaluated in terms of quality, diversity, and cross-domain consistency. This work claims to also be the first to explore the abilities of memory (“retain knowledge from previously learned d...	Rebuttal 1: Rebuttal: Q1. The claimed contributions are incorrect, and the evaluation is severely lacking We apologize for any oversight and plan to enhance the quality of our paper through the following steps: 1. Thorough Comparison and Differentiation from Prior Works. It has demonstrated in the [General Re...	Summary: This paper focuses on the concept of few-shot generative domain adaptation. Drawing inspiration from the human memory mechanism, the authors introduce a novel approach called DoRM (Domain-Adaptive Mapping and Affine Modules) to adapt the generator to a new domain. By incorporating new mapping networks and affi...	Rebuttal 1: Rebuttal: Q1. It would be beneficial if the authors provide a more comprehensive explanation highlighting the distinct features and advancements of their approach in comparison to [1]. Thank you for your valuable feedback regarding the discussion of related works. We fully understand the importance of ...	Summary: This paper proposes two advanced criteria for few-shot Generalized Domain Adaptation (GDA) inspired by the way human brains acquire knowledge in new domains: memory and domain association. To fully realize the potential of few-shot GDA, an innovative generator structure called Domain Re-Modulation (DoRM) is ...	Rebuttal 1: Rebuttal: Q1. It would benefit from including some recent studies on one-shot image generation [1]. Thank you for providing valuable feedback on our work. We sincerely appreciate your suggestion to include recent studies on the one-shot image generation. Staying up-to-date with the latest research in...	Summary: The paper introduces a novel approach for domain adaptation of StyleGAN2 called Domain Re-Modulation, which is a few-shot technique. The authors argue that this method draws inspiration from the workings of the human brain. To achieve the desired domain shift, the paper utilizes the stylespace of StyleGAN alon...	Rebuttal 1: Rebuttal: Q1. The paper lacks substantial novelty We are committed to conducting a detailed and comprehensive comparative analysis that elucidates the specific advantages of our current method over existing works. 1. StyleCLIP only demonstrates the capability of performing image editing using the "s" ...	Rebuttal 1: Rebuttal: We extend our heartfelt gratitude to all reviewers for their dedicated efforts and invaluable suggestions. We have meticulously addressed the specific concerns raised by each reviewer. For a more detailed breakdown of our responses, including supporting tables and figures, please refer to the atta...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a novel approach for few-shot generation using a lightweight GAN architecture and a new loss function. The method is capable of handling multi-domain and hybrid domain tasks with a single model. The experiments demonstrate the superior performance of the proposed method in terms of both qua...	Rebuttal 1: Rebuttal: Q1. The paper should provide more analysis or improvement on the potential issue of unrealistic results due to domain association. As highlighted, our paper represents the first systematic attempt at domain association in few-shot Generative Domain Adaptation (GDA). We recognize the signific...	null	null	null	null	null	null
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Accept (poster)	Summary: The paper presents a method for incorporating outlier detection into an end-to-end learnable framework. Essentially the paper shows how it is possible to differentiate efficiently through a per-instance Gaussian mixture model using either unrolling, implicit differentiation or Jacobian-free back-propagation as...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We appreciate your constructive suggestions to illustrate forward pass and backward pass execution time separately in Table 1. We will fix all the typos including in math expressions and keep the references of tables and figures consistent in the ...	Summary: This paper introduces a implicit Differentiable Out-of-Distribution (OOD) detection layer. This layer addresses outlier detection by solving for fixed points of a differentiable function and using the last iterate of fixed point solver to backpropagate. Strengths: This is a well-organized and written paper. F...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper. We are glad that you found our paper to be well-written and the proposed JFB for outlier detection idea to be interesting. Please check our general responses for the clarification on the model parameters and performance firstly. Then, we...	Summary: The paper presents an approach that combines the features from pre-trained deep networks and freely available semantic explicit knowledge. It proposes an implicit out-of-distribution (OOD) detection layer to address outlier detection and thus further improve understanding and generalization performance in larg...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper. We will address your concerns/questions regarding the training time and model performances as follows: Q1: Please refer to general response 1 and 2, and check the updated Table 4 in the rebuttal PDF. During training, we augmented each captions with 5 externa...	Summary: This paper introduces a novel approach to integrating explicit knowledge graphs into deep networks for multimodal analysis. To filter noise brought by external knowledge, the authors propose an implicit differentiable Out-of-Distribution (OOD) detection layer with efficient backpropagation. This implicit layer...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper. We are glad that you found our proposed method with OOD detection implicit layer novel and has efficiency benefits. We have started working on integrating suggested previous related research in the manuscript and will incorporate all the ...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper. We appreciate the reviewers unanimously agree that our submission is sound, well-presented with contributions clearly written. We are committed to fix all the typos and incorporate all other suggested modifications into the revised manusc...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a framework for multi-modal (text and image) analysis subject to a differentiable framework for out-of-distribution detection in the input space. The method proposes a pipeline to predict the modes of the in-distribution data using a Gaussian mixture model and then leverages it to predict a...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and review our paper. We would like you to kindly read our general responses for the clarifications on the model parameters and performance. Here we will address your specific concerns and/or questions: >One of the motivations of the paper in the abstract is t...	null	null	null	null	null	null
Learning Reliable Logical Rules with SATNet	Accept (poster)	Summary: The authors propose a new framework to generate interpretable and verifiable logical rules through differentiable learning. The framework is built upon SATNet, but the paper proposes a new interpretation method called maximum equality to decode the weights of SATNet into logical rules. The paper also proposes ...	Rebuttal 1: Rebuttal: Thank you for the positive feedback and comments! In the following, please let us address your raised concerns and questions. > The 100% test accuracy was achieved by adding symbolic constraints that represent domain knowledge (e.g. rules of Sudoku). We want to clarify that our approach could ac...	Summary: This paper builds on the SATNet framework on MaxSAT problems, adds an interpretation method that allows the conversion between its weight and propositional logical rules. Effective verification methods are proposed to see if the decoded rules from SATNet are functionally equivalent to the ground truth. Experim...	Rebuttal 1: Rebuttal: Thanks a lot for your positive review and valuable feedback! We would elaborate on our propositions in our revision. In this response, we discuss the challenges we anticipate when extending our work to more expressive logic like FOL and HOL: * Representing logic rules in FOL/HOL using a simple ma...	Summary: The authors show that interpreting the SATNet model is not reliable. They reformulate the SATNet objective to a 'maximum equality specification' on matrix $C$. Assuming $C$ is ternary instead of continuous, the objective can be interpreted as a MaxSat problem. This problem can then be verified. Furthermore, th...	Rebuttal 1: Rebuttal: Thank you for the detailed and in-depth comments and questions! In the following, we hope to address the stated weaknesses and questions of our paper. > The experiments are somewhat simple and could be presented more clearly or on more tasks like ILP benchmarks in $\partial$ILP. We want to clari...	Summary: This paper builds on SATNet, a differentiable MaxSMT solver that was proposed in the past to learn logical rules from input-output examples. SANet is based on a "low rank semidefinite programming approach" and uses a learnable matrix S to capture the logical rules. SATNet was shown to learn to solve e.g. Sudok...	Rebuttal 1: Rebuttal: Thanks a lot for your positive comments and feedback! If you have further questions, we are happy to answer them!	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The work investigates the problem of generating interpretable and verifiable logical rules through differential learning. Specifically, a deep network layer for satisfiability solving (SATNet) in a differentiable maximum satisfiability (MAXSAT) solver that learns logical rules from input-output examples was u...	Rebuttal 1: Rebuttal: Thanks for your comments and feedback. In the following, we will address your questions point by point. > The work builds upon a specific satisfiability solving method (SATNet), which limits the impact of the work. Indeed, SATNet is an award-winning architecture (ICML 2019 Best paper Honorable ...	null	null	null	null	null	null
D-CIPHER: Discovery of Closed-form Partial Differential Equations	Accept (poster)	Summary: The paper falls in the realm of data driven discovery of dynamical systems, PDEs to be specific. It proposes a framework for a class of PDEs which are termed as variation ready PDEs. These are claimed to be less restrictive than existing methods, which make stronger assumptions on the form of the PDE to be dis...	Rebuttal 1: Rebuttal: Dear Reviewer ubhm, We appreciate your thorough feedback and encouraging comments. We summarize the improvements we have made to the paper based on your review and we answer your questions below. ### Actions taken 1. Elaborated on the requirements and other choices of testing functions in Append...	Summary: This paper proposes a framework (D-CIPHER) to discover closed-form PDEs and ODEs. The framework is more general than some of the previously existing methods, and in particular can handle a class of PDEs defined as variational-ready PDEs in the paper. The empirical experiments evaluated the discovery performanc...	Rebuttal 1: Rebuttal: Dear Reviewer Znhz, We appreciate your thorough feedback and encouraging comments. We are particularly grateful for your suggestion on discussing how D-CIPHER can be applied in real-world scenarios. As a result, we have added two sections to Appendix F that discuss it in more detail. We firmly be...	Summary: The paper proposes a new way of discovering closed-form Partial Differential Equations (PDEs) from data. This especially aims at high-order PDEs, especially when the specific form is not pre-assumed and there is a lack of observations on derivatives. The key idea is to represent the unknown PDE with terms that...	Rebuttal 1: Rebuttal: Dear Reviewer kC9e, We appreciate your thorough feedback and kind remarks. Your questions have undoubtedly strengthened our work. In particular, we want to thank you for your inquiry about taking advantage of the derivative data should it be available. As D-CIPHER can in fact very naturally use s...	null	null	Rebuttal 1: Rebuttal: ### Additional PDF Table 1 in the attached PDF shows 17 different differential equations and what terms they need in a dictionary. Figure 1 in the attached PDF shows how the computation time increases when the dictionary is gradually increased. Pdf: /pdf/f6cf6649829fd862cc88c00944261f0d77b34b23....	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Energy-based learning algorithms for analog computing: a comparative study	Accept (poster)	Summary: This paper aims to investigate existing Energy-based learning algorithms on equal footing with same models and datasets. Energy-based algorithms include contrastive learning (CL), equilibrium propagation (EP) and coupled learning (CpL) have been carried out for comparison. The experiments conducted based on de...	Rebuttal 1: Rebuttal: We have explained in the common rebuttal the novelties in our work. In particular, our work introduces a novel asynchronous update scheme for accelerating the convergence of the energy-minimization process in DCHNs. This asynchronous update scheme allows us to achieve a 13.5x speedup compared to L...	Summary: The paper focuses on exploring and comparing various energy-based training methods for deep convolutional Hopfield networks. The performance of contrastive learning, positively-perturbed, negatively-perturbed, and centered versions of Equilibrium propagation and Coupled learning algorithms are evaluated. The p...	Rebuttal 1: Rebuttal: First, we thank the reviewer for their remark on recent works on energy-based models (EBMs). We understand that the term “energy-based model” is ambiguous and refers to different lines of works with very different motivations, which we would like to clarify here. For clarity, we start by highlight...	Summary: This work conducts an extensive comparison of several energy-based learning (EBL) algorithms, including contrastive learning (CL), equilibrium propagation (EP) and coupled learning (CpL). Depending on the type of perturbation used, 9 variants of EP and CpL are examined. Deep Hopfield networks (DHNs) on five vi...	Rebuttal 1: Rebuttal: Regarding the question on the novelty of the theorems, we would like to clarify that Theorem 1 isn’t new ; it is presented and proved e.g. in Movellan (1991). On the other hand, Theorems 2 and 3 are new in the literature: no prior work had shown that EP (resp. CEP) performs gradient descent on the...	Summary: This work reviews and compares recent EBL methods on classic image classification benchmarks. The paper first reviews a variety of EBL methods including CL, EP, P-EP, N-EP, C-EP, CpL, P-CpL, N-CpL, and C-CpL. Next, two theorems are presented which show that P/N/C EP approximate gradient descent on the cost fun...	Rebuttal 1: Rebuttal: We have addressed the novelty factor in the common rebuttal (see above). We would like to emphasize that our asynchronous update method to compute equilibrium states of DCHNs (i.e. to compute minima of the energy function) is novel, and that this technique unlocked significant speedup – indeed, a ...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and comments. Several criticisms of the manuscript by the reviewers fall into the following two categories: 1. Lack of novelty 2. The choice of DCHNs for comparing the seven EBL algorithms, and the lack of discussion of the limitations of our study We addre...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory	Accept (spotlight)	Summary: The paper introduces generalization bounds for variational autoencoders (VAEs) by employing PAC-Bayesian bounds. Initially, the authors derive a PAC-Bayesian bound utilizing a posterior distribution (Theorem 3.1). This result is subsequently utilized to establish a generalization bound for the reconstruction l...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful review and positive assessment of our work. > Given its strong theoretical content, conducting experiments on synthetic problems could be valuable in assessing the asymptotic behavior of the bound Given the technical nature of the results, our focus was...	Summary: This paper derives novel PAC-Bayesian bounds for VAEs by treating the variational posterior as the PAC-Bayes posterior. To do this, the authors must adapt the PAC-Bayes theorem to also hold in the case where the posterior is conditioned on a learning sample. Strengths: - Well-written paper, with well presente...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and their appreciation of our theoretical contributions. > Bound unfortunately needs to be computed using samples different from those used to train the VAE. This runs counter to one of the most interesting and useful aspects of PAC-Bayes bounds...	Summary: In this paper, the authors provide a novel general PAC-Bayesian bound for a posterior distribution conditioned on individual elements of the instance space (and not only on observed samples). They then use it to derive generalization bounds for reconstruction loss, regeneration, and generation in VAE (both for...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and thoughtful suggestions, which will help us improve the paper. > Perhaps the main weakness is the lack of experimental results. However, I am not sure they are necessary (compare, e.g., with the related work [1]) Indeed, the main objective o...	Summary: 【Post-rebuttal Comments】 I thank the authors for the discussions after the authors' rebuttal. My questions about variance estimation are appropriately answered. So, I want to keep my score and vote for acceptance. 【Original Comments】 This paper derives the PAC-Bayes bound on the hypothesis set by conditional ...	Rebuttal 1: Rebuttal: First, we thank the reviewer for their thoughtful review and insightful comments. > By applying uniform concentration inequality with respect to the decoder parameters (more specifically, the family of loss functions parametrized by the decoder), can we give uniform bound with respect to the deco...	Rebuttal 1: Rebuttal: We are extremely grateful to all the reviewers for taking the time to read our work and make thoughtful comments and suggestions. All the reviewers seem to agree that the subject of this work (extending PAC-Bayes theory to conditional posteriors and deriving statistical guarantees for VAEs) is i...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Adaptive Online Replanning with Diffusion Models	Accept (poster)	Summary: The authors address the online replanning problem within diffusion-based models by introducing their method, RDM, which uses the likelihood of trajectories to decides when and how to replan. Strengths: - The topic of when and how to replan is an important one, particularly within the diffusion model communit...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the reviews and insightful suggestions. Q1. Comparison. > The experimental results are lacking in terms of comparison with other simple baselines that are able to replan. We show the results of the comparison between our RDM algorithm and the replann...	Summary: The submission describes a method for deciding when and how to "replan" when using diffusion models for inferring plans, as in Decision Diffuser [1]. The task is essentially imitation learning (IL): given a training dataset of plans and features, the task is to predict a new plan given novel features. As in [1...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the detailed comments and questions. Q1. Comparison. > It would make more sense to me if the experiments were focused more on comparisons between RDM and other replanning methods. > What trade-off do we see between solution time and quality compared t...	Summary: The manuscript introduces a technique for enhance the motion planner rooted in diffusion models, encompassing decisions concerning the timing of replanning and plan trajectories upon the existing path. The strategy for timing replanning utilizes the inherent estimated likelihood of the trajectory in diffusion ...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the detailed reviews and insightful suggestions. Q1. Replanning baselines. > For long horizon planning tasks and robotic control tasks, is using likelihood better than using state distance deviation as replanning criteria? > > Comparing baselines wit...	Summary: This paper introduces Replanning with Diffusion Models (RDM), which utilizes an internally estimated likelihood of the current plan to determine when to perform replanning. The authors propose various strategies for replanning in different scenarios. Strengths: 1. The introduction of Replanning with Diffusion...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the comments and insightful suggestions. Q1. Estimation of likelihood. > In RDM, a key aspect is the estimation of likelihood, but the authors did not discuss the accuracy of the estimation or its impact on final performance. The accuracy of the esti...	Rebuttal 1: Rebuttal: We thank all the reviewers for taking the time to review our paper and providing insightful and detailed feedback. We appreciate that the reviewers recognize the following contributions. * Significance of our problem. Replanning is a crucial problem in many planning problems. > Online re-plan...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies how to effectively replan with diffusion models. The authors propose an adaptive online replanning strategy using diffusion models. This strategy employs the estimated likelihood of a plan's success to determine when replanning is needed, avoiding frequent, computationally expensive replanni...	Rebuttal 1: Rebuttal: Dear Reviewer, We appreciate the reviewer for the detailed comments and insightful suggestions. Q1. About replanning at every time step. > It is still unclear to me why replaning at every time step does not work. We investigate different intervals $I$ for replanning for Diffuser and Decisio...	null	null	null	null	null	null
Finite-Time Analysis of Single-Timescale Actor-Critic	Accept (poster)	Summary: The paper studies finding the optimal policy in an infinite-horizon average-reward MDP with an online, sample-based algorithm. The state-of-the-art algorithm in this setting uses two different timescales and is known to have a sample complexity of $\widetilde{\mathcal{O}}(\epsilon^{-2.5})$. This paper shows th...	Rebuttal 1: Rebuttal: # Thanks for reviewing our paper! (W1: On general MDP) Thanks for your comments. In this paper, we only consider the general MDP with a non-convex objective function. The single-timescale approach is superior to the two-timescale approach because the latter updates the actor slower than the cr...	Summary: The work studies the actor-critic algorithm’s convergence under the single-timescale update where the step-size of actor and critic variables are only proportional by a constant. Authors suggest the epsilon-optimal solution with a sample complexity $\tilde{O}(\epsilon^{-2})$ under standard assumptions and $...	Rebuttal 1: Rebuttal: # Thanks for reviewing our paper! (W1: On single trajectory and condition $T>2\tau_T$) Thanks for your comment. We are afraid that there is a misunderstanding concerning our work. Assumption 3.2 does not conflict with the statement "the transition tuples are generated from a single trajectory....	Summary: This paper studies the actor-critic algorithm with linear function approximation. The authors provide a single-time-scale analysis for the AC and achieve $\epsilon^{-2}$ sample complexity. Strengths: - The paper is overall well written and easy to follow - The single-time-scale analysis with markovian noise f...	Rebuttal 1: Rebuttal: # Thanks for reviewing our paper! (Q1: On the contribution of $L_\mu$ to the final results) Thanks for seeking clarification. In the current analysis, our final results are linear to the parameter $L_\mu$. To keep $L_\mu$ in the final results, the convergence rate in Theorem 3.5 can be kept as...	Summary: This paper provides a finite-time analysis of single-timescale, single-sample, average-reward actor-critic with a linear critic under Markovian sampling. It is established that the standard scheme achieves $\epsilon$-approximate stationarity with $\widetilde{O}(\epsilon^{-2})$ sample complexity. The analysis i...	Rebuttal 1: Rebuttal: # Thanks for reviewing our paper! (Q1: Key innovation in the analysis) Thanks for seeking clarification. The key innovation lies in conducting a comprehensive analysis of each error term. In two-timescale actor-critic [Wu et al., 2020], convergence is deduced relying on multiplying the error t...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This works provides finite sample analysis for single timescale actor critic. Strengths: A finite sample analysis for single timescale actor critic under Markovian noise and infinite state space is definitely a notable contribution to the community. Weaknesses: My biggest concern is the correctness of this w...	Rebuttal 1: Rebuttal: # Thanks for reviewing our paper! (Q1: What error was pointed out in the ICML submission) Thanks for reviewing our work again. We are glad to show our refined results. The original comment in the ICML round is: "However, the authors utilize a very quick but wrong inequality to extend the analy...	null	null	null	null	null	null

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