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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
Safety through feedback in Constrained RL	Accept (poster)	Summary: This paper studies the safe RL problem with an unknown cost function. As an on-policy approach, this work tries to learn the cost function with safety feedback from an evaluator with novelty sampling and conducts policy optimization at the same time. The authors propose that their approach can deal with feedba...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and constructive feedback. Your insights and suggestions are appreciated, and we look forward to addressing your questions below: 1) In binary classification problems, the ground truth is often represented as a Dirac measure (indicator function), and maximum l...	Summary: This paper presents a method for using evaluator feedback as safety constraints in a constrained RL framework. The novelty comes from the nature of the feedback and the sampling scheme. It is motivated by the fact that previous approaches have various limitations: designing cost, especially comprehensively, is...	Rebuttal 1: Rebuttal: Thank you for your thorough and positive review of our paper. We look forward to addressing your questions and suggestions below: 1) By sparsity we mean the density of violations within the segment is low. This makes distinguishing safe states from unsafe states challenging, as it becomes difficu...	Summary: This paper proposes a surrogate loss function, which instead of collecting a feedback over trajectory-based evaluator, it breaks long trajectories into segments and the evaluator classifies and assigns labels to segments unsafe if any individual state is unsafe during the segments. This paper also proposes a n...	Rebuttal 1: Rebuttal: Thank you for your detailed review and constructive feedback. We appreciate your insights and will address your questions and concerns below: 1) Please note that $p_{gt}^{safe}(s,a)$ is the ground truth probability. Since ground truth is exactly known for a state on whether it is safe or unsafe, ...	Summary: Authors propose a new way of estimating the cost function for constrained RL through user feedback. They propose a surrogate loss that is used to train a model that estimates the probability that a state-action pair is safe. They then use the model to estimate the cost of a policy and adhere to the constraint ...	Rebuttal 1: Rebuttal: Thank you for your detailed and constructive review of our paper. We appreciate your positive assessment of our work and the valuable feedback you provided. We hope to address the weaknesses and questions you raised below: 1) We use a regular Multi-Layer Perceptron (MLP) to estimate the probabili...	Rebuttal 1: Rebuttal: We thank all the reviewers for their thoughtful and insightful reviews. We're happy to see that reviewers E6xa, 1oHB, and w6J6 found our paper well-written and easy to follow, and that all reviewers recognized the importance of our work in the context of safe reinforcement learning. Pdf: /pdf/3acb...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
General Articulated Objects Manipulation in Real Images via Part-Aware Diffusion Process	Accept (poster)	Summary: This paper focuses on manipulating articulated objects in 2D images by leveraging a 2D-3D-2D approach with a diffusion model. Specifically, the authors propose the abstract 3D model to represent the articulated objects and propose dynamic feature maps to transfer seen regions while generating novel areas. The ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thanks for your comments and questions. In the following, we answer all of your questions carefully. Q : Could the authors please provide a more detailed description of how to build an abstract 3D model regarding my major concern? I would consider raising or lowering the score...	Summary: This paper presents a pipeline to directly manipulate articulated objects in real images and generate corresponding images in different articulated poses. The proposed method adopts a 2D-3D-2D approach with a heuristic model to obtain 3D information from source images and generate new images based on the diff...	Rebuttal 1: Rebuttal: Dear Reviewer, In the following, we answer all your questions in detail. Thanks very much! Q : Difficult to follow in Sec. 4.6. Lack of context for the reference work [1] (which is [32] in the 187 paper) in this subsection, making it difficult to understand and validate. As the author mentio...	Summary: This paper introduces a novel diffusion-based method for manipulating articulated objects in real-world images from input text guidance or human interaction. There are three main contributions in this paper: (i) the authors introduce the concept of an Abstract 3D Model, which eliminates the requirement for a 3...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for all of your kind suggestions and questions. In the following, we answer all of your questions in detail. Q : Can the proposed methods effectively handle input objects whose shapes do not match the available primitives? A : Thank you for your valuable questio...	Summary: The paper proposes a method for accurately generating edited images for manipulating articulated objects, effectively avoiding hallucinations. The overall idea and approach are very interesting. The implementation of the method is particularly impressive, presenting a novel articulated object manipulation tech...	Rebuttal 1: Rebuttal: Dear Reviewer, Thanks very much for your valuable suggestions. In the following, we answer all of your questions in detail and carefully. Q : The description of section discussing manipulation in L122 is unclear. For instance, if an object has many movable parts, how is it determined which p...	Rebuttal 1: Rebuttal: Dear Reviewers, We sincerely appreciate all of your insightful and valuable questions and suggestions. We have made a concerted effort to address each query thoroughly and have revised the paper comprehensively following your recommendations. Your perceptive insights have undeniably enriched our ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Query-Efficient Correlation Clustering with Noisy Oracle	Accept (poster)	Summary: The paper considers a correlation clustering setting with an unknown similarity function but with the ability to query a noisy oracle, i.e., the oracle may yield noisy feedback instead of the true similarity of the queried pair of objects. In this scenario, the goal is to achieve a reasonable trade-off between...	Rebuttal 1: Rebuttal: We sincerely appreciate your review. W1 and Q1: Thank you for your insightful question, which is precisely aligned with the critical discussions we had during the process of this work. As you mentioned, our offline problem (minimizing the cost function (1)) is one of the most basic formula...	Summary: [Setting] This paper studies correlation clustering using a noisy oracle. The learner queries a pair of items and receives a noisy estimate of their similarity in the range [0, 1] from an oracle. The goal is to either: 1. [Fixed confidence setting] Minimize the number of queries and return a clustering wit...	Rebuttal 1: Rebuttal: We sincerely appreciate your review. > I am not entirely convinced by the strength of the theoretical results in the fixed confidence setting. Why not move sampling inside the clustering loop in Algorithm 1? Keep sampling $\{u,v_r\}$ for all unclustered nodes $u$ and the chosen pivot $v_r$ until...	Summary: This paper introduces algorithms for correlation clustering with noisy, expensive similarity functions. The authors present two formulations in the PE-CMAB framework: fixed confidence and fixed budget. Their proposed algorithms, KC-FC and KC-FB, combine sampling with KwikCluster approximation. Strengths: The ...	Rebuttal 1: Rebuttal: We sincerely appreciate your review. > The paper could benefit from a more detailed discussion of the limitations of the current to achieve 3-approximation. > What is the main challenge to extend this approach for 3-approximation algorithms? As stated in Lines 35–39, for our offline problem (i....	Summary: This paper copes with the problem of active (weighted) correlation clustering when oracle queries are corrupted by random noise. Authors frame two variants of the problem where high probability guarantees are required: the fixed-confidence and the fixed budget settings; algorithms for the these settings are ...	Rebuttal 1: Rebuttal: We sincerely appreciate your review. > One aspect that should be clarified is the precise runtime of KC-FC which I recommend to include in the statement of Theorem 1. As stated in Lines 224–227, each iteration of the while-loop of TB-HS takes $O(m)$ steps in a naive implementation or amortized ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Estimating Epistemic and Aleatoric Uncertainty with a Single Model	Accept (poster)	Summary: The work applies hypernetworks to estimate both aleatoric and epistemic uncertainty in the context of diffusion models. The authors leverage the distribution on weights created by the hypernetwork to estimate uncertainty via the Total Uncertainty (TU) decomposition into Aleatoric Uncertainty (AU) and Epistemic...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We have summarized and responded to your questions and concerns below. Please let us know if you have any additional questions or comments and we will do our best to respond promptly. **1. Missing citation where diffusion models are used to estimate epistem...	Summary: The paper proposes a approach, HyperDDPM, to estimate both aleatoric and epistemic uncertainty with an ability to disentangle them. This result is achieved by combining diffusion networks (DDPM) to sample predictions from single set of model weights, and Bayesian Hyper Network to generate sets of weights for m...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We have summarized and responded to your questions and concerns below. Please let us know if you have any additional questions or comments and we will do our best to respond promptly. 1. The paper doesn’t have a dedicated contributions section. Thank y...	Summary: This paper proposes HyperDDPM, a novel uncertainty quantification method for generative tasks that uses a hypernetwork and a denoising diffusion probabilistic model (DDPM) and outputs both aleatoric and epistemic uncertainty estimates. HyperDDPM is evaluated on a toy task with available ground-truth uncertaint...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We have summarized and responded to your questions and concerns below. Please let us know if you have any additional questions or comments and we will do our best to respond promptly. **1. The paragraph starting at L29 is misleading and vague. A useful unce...	Summary: Diffusion has been applied in many domains beyond image generation, e.g. weather forecasting and CT reconstruction. Many of these applications are safety-critical as such the model should be capable of expressing uncertainty. As such the submission proposes a method based on the idea of hypernet for uncertaint...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We have summarized and responded to your questions and concerns below. Please let us know if you have any additional questions or comments and we will do our best to respond promptly. 1. Provide error bars / standard deviations. We have trained five se...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful and constructive feedback. We are glad they found our proposed method novel (YZoM, cmte), performant against the current state-of-the-art (pwCt, YZoM, cmte), easy-to-understand (pwCt, YZoM, zgME), and well-supported by experiments (pwCt, YZoM). We have s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
IPO: Interpretable Prompt Optimization for Vision-Language Models	Accept (poster)	Summary: This paper introduces a method called Interpretable Prompt Optimization (IPO) for vision-language models. The goal of IPO is to improve the performance and interpretability of vision-language models by dynamically generating and optimizing text prompts. The paper addresses the limitations of current prompt opt...	Rebuttal 1: Rebuttal: (1) Performance on Base vs. Novel Classes: Previous prompt learning methods like CoOP and CoCoOP, which rely on gradient-based optimization, tend to overfit to base classes, resulting in performance loss on novel classes. Our IPO, however, uses LLMs to optimize prompts with a focus on learnin...	Summary: This paper presents an Interpretable Prompt Optimizer (IPO) designed to improve the performance and interpretability of pre-trained visual language models such as CLIP. By dynamically generating textual cues using a large language model (LLM) and combining it with a large multimodal model (LMM) to generate ima...	Rebuttal 1: Rebuttal: (1) Line 150: Fixed. Thank you. (2) Table 6: We will add citations for each method in Table 6 in the revised manuscript. (3) Cross-Dataset Experimental Evaluation: We conducted a cross-dataset experimental evaluation, following the traditional setting, and found that our IPO ou...	Summary: This paper proposes an interpretable prompt optimizer (IPO) which uses an LLM to iteratively optimize prompt templates that lead to improved zero-shot visual classification performance on CLIP. Strengths: 1) The proposed method outperforms baselines on the novel classes in the evaluation on base-to-novel gene...	Rebuttal 1: Rebuttal: (1) Missing Important References: We thank Reviewer 4MWY for bringing the CVPR 2024 paper by Liu et al. and the forthcoming ECCV 2024 paper by Mirza et al. to our attention. Both works are indeed relevant and will be discussed in the related work and experimental sections of our revised manu...	Summary: The paper addresses the challenge of optimizing text prompts for vision-language models, specifically focusing on the interpretability of these prompts. Traditional methods for prompt optimization rely on gradient descent, which often results in overfitting and produces prompts that are not human-readable. Thi...	Rebuttal 1: Rebuttal: (1) Comparison with Knowledge Bank-Based Prompt Learning Methods: We sincerely thank the reviewer for pointing out these three interesting works. First, we would like to clarify the differences between our IPO and these methods. The works mentioned (L2P, AttriCLIP, DualPrompt) are based on vi...	Rebuttal 1: Rebuttal: We would like to extend our sincere thanks to all the reviewers for their valuable feedback and suggestions. Your insights have been instrumental in refining our work, and we have addressed your concerns in the revised manuscript. Below, we highlight the most significant updates and improvements b...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces a method named IPO for VLMs which uses LLMs to dynamically generate and refine text prompts. The method is to improve the accuracy and interpretability of prompts. Experiments show that it can address issues like overfitting and lack of human comprehension in traditional gradient descent-b...	Rebuttal 1: Rebuttal: (1) Impact of LMM: We thank Reviewer U899 for the insightful suggestion. Indeed, when we replaced the 2.8B parameter MiniCPM-V-2 LMM with the higher capacity GPT-4o (estimated 500B~1T parameters), we observed a performance improvement for 10 out of 11 datasets. On average, the performance acr...	null	null	null	null	null	null
Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss	Accept (poster)	Summary: In this paper the authors propose a graph based network that combines feature extraction with test time optimization to perform two-frame particle tracing. The core problem is that given two sets of points, e.g., point cloud data, to find the correspondences between the point clouds and estimating their veloci...	Rebuttal 1: Rebuttal: ### Q1: 1% chosen consistently across class? Answer: Yes, the sub-sampled dataset is chosen consistently. ### Q2: Statistical Significance Answer: We control experiment randomness with seeds, conducting multiple runs to ensure variability while maintaining consistency with the same seed. R...	Summary: The paper proposes a self-supervised method to learn 3D particle tracking and modelling turbulent fluid flow. They regularize their method with a zero-divergence loss function and inspired by the splat operation they propose a splat-based implementation for this loss. They also incorporate a GCNN feature extra...	Rebuttal 1: Rebuttal: ### Q1: Test-time optimisation will make the result a bit questionable Answer: We note that the test-time optimization is self-supervised without accessing ground truth labels, making the setting realistic. This approach has garnered significant attention recently, as seen in references intro...	Summary: In this paper, the author proposes a self-supervised learning based 3D particle tracking velocimetry (PTV) technique for dual-frame fluid motion estimation. The proposed method surpasses its supervised counterparts while utilizing only 1% of the training samples (without labels) compared to previous methods. A...	Rebuttal 1: Rebuttal: ### Q1: How will the proposed method perform stand alone compared with being integrated into PTV? Answer: We cannot perform this evaluation because the task of PTV is a superset of dual-frame motion estimation. Here, we clarify these differences: Our method targets particle motion vector est...	null	null	Rebuttal 1: Rebuttal: We present all tables and figures mentioned in the rebuttal. Pdf: /pdf/5854c9864f8be4675da5ec2367572c8399750e93.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LLaMo: Large Language Model-based Molecular Graph Assistant	Accept (poster)	Summary: The paper introduces LLaMo, a Large Language Model-based Molecular graph assistant. LLaMo is a model that integrates a GNN encoder, a multi-level graph projector, and a language model. The projector uses a cross-attention mechanism to convert graph representations into graph tokens by abstracting outputs from ...	Rebuttal 1: Rebuttal: [W1] Evaluation of GPT-generated quality. Thank you for your constructive feedback. During the generation, we implement a multi-step assessment process, detailed below: Step 1: We prompt GPT-4 to generate multi-turn conversation instruction data about molecules using captions and IUPAC names...	Summary: This paper proposes a molecule-text model, LLaMo, which aligns text and molecule modalities to tackle diverse downstream tasks with a language interface. Through two-stage alignment: training a graph encoder and then tuning the LLM through instructions, LLaMo aligns text and molecular representations well, and...	Rebuttal 1: Rebuttal: [W1] Details of functional group representations. We appreciate your feedback and provide a comprehensive explanation regarding functional representations. We use simple hand-crafted features derived from the molecular graph as functional group information. To construct functional group repr...	Summary: This paper proposes a Large Language Model-based Molecular graph assistant (LLaMo), which can enhance the molecular graphs's general-purpose understanding and generation capabilities. By integrating molecular graph encoders with large language models, LLaMo enables instruction-following responses in the molecu...	Rebuttal 1: Rebuttal: [W1] Discussion of how multi-level graph projector differs from JKNet and Graph Transformers. We appreciate your constructive comment. Our multi-level graph projector differs from JKNet and Graph Transformers (GTs) in two aspects: main purpose and architecture. Main purpose As shown in Fi...	Summary: This paper presents LLaMo, a novel enabling LLMs and instruction tuning in molecular domain. To bridge the gap between different modalities, the paper also proposes a projector that transforms the graph representations into graph tokens level by level. The authors conduct extensive experiments and compare LLaM...	Rebuttal 1: Rebuttal: [W1] More performance comparison based on different GNN and LLM backbones. Good question. We conduct additional experiments to evaluate performance based on different GNN and LLM backbones. Specifically, we compare pretrained Graph Convolutional Networks (GCNs) with our base GNN backbone (GI...	Rebuttal 1: Rebuttal: We appreciate all the reviewers for their time and efforts in reviewing our paper and insightful comments and questions. We are encouraged that the reviewers recognize multiple strengths in our paper, including: - Clear and effective design that enables LLMs to operate within the molecular do...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exploiting Representation Curvature for Boundary Detection in Time Series	Accept (poster)	Summary: This paper proposes a novel approach called RECURVE (Representation trajEctory CURVaturE) for boundary/change point detection based on time series representations. RECURVE measures changes in representations of time series windows over time, based on curvatures instead of distances in the representation space ...	Rebuttal 1: Rebuttal: Thank you so much for acknowledging the impact and intuitiveness of our new boundary detection method. We hope that our responses have addressed your concerns and that you are able to raise your rating. --- `W1` Theoretical analysis with class-separated representations. (1) You a...	Summary: The authors propose a novel boundary detection method for time series based on measuring the curvature of the local trajectory of a learned per-timepoint embedding/representation. Some theoretical justifications are provided for the proposed method. Empirically, it is shown to work better than a number of exis...	Rebuttal 1: Rebuttal: Thank you so much for acknowledging the novelty, evaluation result, and intuitiveness of our new boundary detection method. --- `W1` Locality assumption on the learned representations. Thank you for your insightful comment. Yes, RECURVE is built upon class-separated representations. *...	Summary: This paper proposed to use curvature as the metric to detect the boundaries. Empirical and experimental results also show that the confining box is different for inter- and intra- variable data points. Strengths: - The idea of using curvature for boundary detection is novel in time series forecasting. - The p...	Rebuttal 1: Rebuttal: Thank you so much for acknowledging the novelty and intuitiveness of our new boundary detection method. --- `W1`, `Q2` Lack of a strong foundational motivation and interpretation of Figure 5. (1) We anticipate that our clarification will effectively address your concerns. * **...	Summary: This paper proposes a novel boundary detection method based on the curvature of a representation trajectory. The feasibility of the proposed algorithm is analyzed intuitively and theoretically, and the proposed method is experimentally shown to have good performance. Strengths: 1. The proposed method is simpl...	Rebuttal 1: Rebuttal: Thank you so much for acknowledging the effectiveness and intuitiveness of our new boundary detection method. We hope that you can support our work during the discussion period. --- `W1`, `Q1` Categorization of the boundaries and evaluation with the categorization. *(1) Categ...	Rebuttal 1: Rebuttal: We deeply appreciate your considerate feedback on our paper. Overall, we are delighted that most of the reviewers agreed with three main contributions: (1) novelty — "the idea of leveraging the curvature of a representation trajectory is novel and innovative" (Reviewers EoQb, ZR9k, v...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces a boundary detection method called RECURVE for time series data, which leverages the curvature of representation trajectories as a novel change metric to accommodate both gradual and abrupt changes. Experiments on diverse real-world datasets demonstrate that RECURVE outperforms state-of-th...	Rebuttal 1: Rebuttal: Thank you so much for acknowledging the innovation of our new boundary detection method. We really hope that our responses are satisfactory to you. --- `W1.1` Similarity to the distance-based metrics. The three points used for calculating the curvature are $t-w$, $t$, and $t+w$. Here...	null	null	null	null	null	null
A Functional Extension of Semi-Structured Networks	Accept (poster)	Summary: This paper extends the application of semi-structured networks to functional data. The orthogonalization technique makes this method more scalable than existing methods. Strengths: This paper is well-structured and well-written. The idea is easy to follow despite the experiment being conducted in an abstract ...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful and detailed comments. Below we address the mentioned weaknesses and questions. ----- ### Weaknesses > [...] interpretability of the proposed [...] not demonstrated in their experiments We agree that this is an important part of our work. We would like...	Summary: In this paper, the authors develop a semi-structured model for functional data, summing an interpretable linear model with a more general nonlinear functional neural network. The authors validate the improved performance of the combination relative to the individual components on a variety of biomechanics data...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comments and pointing out ways to improve our manuscript. Below we address the mentioned weaknesses and questions. ----- ### Weaknesses > Can the authors comment on whether this component is original to their work [...] We thank the reviewer for bringin...	Summary: An extension to semi structured networks is introduced that combines a traditional linear and interpretable model component $\lambda^+$ with a flexible $\lambda^-$ neural network component to better approximate relations in biomechanical application. The model performs equally to traditional methods but requir...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thorough review of our manuscript. ----- ### Weaknesses > improvement [...] rather mediocre [...] would be appreciated if error bars were reported We only report results for a single train/test split as the application fixes this. To still provide a...	Summary: The paper proposes a hybrid approach that combines the benefits of neural networks with those of more structured models (generalised additive models). They show benefits on real and simulated data. Strengths: - The proposed idea makes sense and is novel AFAIK - The empirical results show good performance comp...	Rebuttal 1: Rebuttal: The reviewer raises two points that we answer below. We thank the reviewer for these thoughtful comments, but in both cases would like to point out that these "weaknesses" have already been addressed in our original submission. ----- > How much tuning has been done for the pure deep-network bas...	Rebuttal 1: Rebuttal: We thank reviewers DyRG, SfMu, 9i2N, and Ta2r for their detailed and thoughtful comments. We appreciate your efforts and believe your reviews have allowed us to further improve our paper. We think that have addressed all points raised and eliminated all uncertainties. In detail: 1. **[Reviewer Dy...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers	Accept (poster)	Summary: This paper demonstrates the phenomenon of "context hijacking" in LLMs, where repeated mentions of sentences in the context could negatively influence a model's factual recall. Motivated by this, the authors then formulate an associative memory recall task and prove theoretically certain properties of one-layer...	Rebuttal 1: Rebuttal: Thanks for your support and useful comments! Your suggestion is really valuable in helping us clarify our paper. _Questions on larger models like GPT-4 and limitations of single-layer transformer_ This is a great question! First of all, we didn’t test this on GPT-4 because, as a closed-sou...	Summary: This paper studies the mechanics of factual recall in transformer-based models, framing the problem as next token prediction. In particular, the authors focus on the brittleness of language models, which can be elicited to provide different answers to factual queries by adding distracting information in the pr...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and questions. We're glad that you found the paper to be important, interesting, and solid! _Limitations_ Sorry for the confusion! We don't assume that each latent concept is associated with only one token. Instead, the latent concept space consists of m...	Summary: This paper investigates the mechanisms underlying factual recall in transformer language models. First, the paper demonstrates a "context hijacking" phenomenon, where distractor sentences lead language models to output the wrong answer to factual questions. The paper conducts a theoretical analysis of a one-la...	Rebuttal 1: Rebuttal: Thank you for the support and thorough review! We’re pleased that you think the paper makes a "useful contribution”. _Clarification on associative memory_ This is a really good question! In the literature, the definition of associative memory is usually quite broad. While any prediction can...	Summary: The paper presents a way to study associative memory in Transformer blocks. Specifically, the author presents a method to construct a value matrix representing associative memory and suggests its equivalence to self-attention’s value matrix. Through experiments based on synthetic data, the author proposes that...	Rebuttal 1: Rebuttal: Thanks for your thoughtful review and questions! We are glad that you find the problem of the paper matches the field's concern. _Solving context hijacking_ We apologize if the main objective of our paper was not communicated clearly. Our primary interest lies in understanding the inner mech...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Interpretable Concept-Based Memory Reasoning	Accept (poster)	Summary: This paper presents an extension of Concept Bottleneck Models by modeling task predictions as neural selections over a learnable memory of rules, that is jointly learned during the training phase together with all the other model parameters. The resulting model is fully differentiable and can be exploited not ...	Rebuttal 1: Rebuttal: We first want to sincerely thank the reviewer for taking the time to read and review our paper. We are pleased that the reviewer considered CMR an interesting and novel extension of CBMs, and considered the presentation clear.   **Question: The proposed approach could be compared against s...	Summary: Concept learning is a very important and current area of research. The paper is motivated from this perspective of neurosymbolic concept learning. However, there are a number of flaws. The paper refers to rules and "reasoning" informally; it does not define syntax and semantics neither does it define the reaso...	Rebuttal 1: Rebuttal: We first want to thank the reviewer for taking the time to read and review our paper.   Question: "The rules provided are difficult to interpret and unrelated to the motivation of the paper, e.g. instead of concepts some rules refer to different noises." **In all rules, we only refer ...	Summary: The authors presented a novel framework to explain image classification models, specifically with an explainable-by-design deep model. This model is built to provide interpretability through discovering logic rules that matches ground truths. Intervention towards modifying the rules that changes the interplay ...	Rebuttal 1: Rebuttal: We first want to sincerely thank the reviewer for taking the time to read and review our paper.   **Question: Lines 49-52 explain some example rules for an image classification task, which seem very simple. How are they going to work in practice for more complex scenarios? For example, one...	Summary: In this paper, the authors propose Concept-based Memory Reasoner (CMR), consisting of (a) a concept encoder, (b) a rule selector, and (b) a task-predictor. CMR is tested on a few different datasets-- easy (MNIST+, MNIST+∗, C-MNIST), medium (CEBAB), and hard (CelebA, CUB) for task and concept prediction accurac...	Rebuttal 1: Rebuttal: We want to sincerely thank the reviewer for taking the time to read and review our paper.   **Question: "It is not really clear to me what exactly [...] the concepts are and where they come from. [For] concept accuracy, where are the ground truth concepts for this obtained from, and can yo...	Rebuttal 1: Rebuttal: We first thank the reviewers for their insightful feedback. It has certainly improved the quality of our manuscript, and we hope we have been able to address all the raised concerns in this rebuttal. We reply to questions shared by multiple reviewers in this comment, and reply to specific question...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Provable and Efficient Dataset Distillation for Kernel Ridge Regression	Accept (poster)	Summary: The paper presents provable algorithms for computing distilled sets for various problems. Starting with LRR, they prove that it is enough to have m = number of classes to compute an exact solution as on the whole data. Then they directly show how to make this distilled point "Realistic" by initializing m poi...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback, thoughtful comments, and for appreciating the novelty and value of our work! --- Q1. Why is $\lambda_S$ not equal to $\lambda$? A1. Our goal is to find $X_S$ such that $W_S = W$. $\lambda_S$ and $\lambda$ are predefined hyperparameters in our...	Summary: The authors study theoretical aspects of dataset distillation for kernel ridge regression. They first provide analytical results for KRR with a k-dimensional label and linear features. They then extend these results to certain types of finite-dimensional feature maps, including feature maps which can be comput...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed comments. We believe there are some misunderstandings. Please allow us to address your concerns point-by-point. --- Q1: The main claims seem to be somewhat trivial extensions of [1]. A1. Our results are totally different from [1]. As discussed in the pa...	Summary: This work presented a theoretical framework of dataset distillation for KRR, showing that (slightly more than) one data point per class is often necessary and sufficient to recover the performance of the original model. The theory led to a provable and efficient algorithm for dataset distillation, whose effect...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback, thoughtful review, and for appreciating the merits of our work! --- Q1: intuitions on where significant theoretical improvement comes from. A1. Thanks for the great suggestion! The improvement mainly comes from new techniques of solving distil...	Summary: This paper studies the number of synthetic data required in dataset distillation to ensure that the optimal parameter learned on the synthetic dataset is the same of that learned on the original dataset. The task considered is kernel ridge regression (KRR), where the labels contain $k$ classes. The goal is to...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful reading of our paper and constructive comments. We believe there are some misunderstandings. Please allow us to address your concerns point-by-point. --- *Q1. Proof idea and techniques of Theorem 4.1: The proof works by essentially setting $X_S = W^+Y_S$....	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for taking the time and effort to review our paper! We are delighted that the reviewers found: - The paper provides a theoretical analysis on the minimal number of samples required for a synthetic dataset to preserve the parameter to a ridge regression pro...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Large Scale Transfer Learning for Tabular Data via Language Modeling	Accept (poster)	Summary: This paper presents a large tabular prediction model based on large language models (LLMs). The model was trained on 800M rows from 1.5M tables, hence achieves high zero-shot accuracy on unseen tables. To make it work, the authors made extensive data collection, cleaning, and filtering to build a large-scale t...	Rebuttal 1: Comment: Thank you for taking the time to read our paper, provide constructive comments and insightful connections to related works that will improve the quality of our manuscript. We’re very encouraged to see you appreciate the value of our scaling efforts to build models that can achieve “high zero-shot a...	Summary: The paper introduces TABULA-8B, a specialized large language model for tabular data prediction tasks. It details a comprehensive process for extracting a high-quality, large-scale dataset from the TabLib corpus, labeled as T4, which comprises 1.5 million unique tables and over 800 million rows of data. By fine...	Rebuttal 1: Comment: Thank you for reading our manuscript in detail and providing a constructive review. We’re very encouraged to hear how you believe that our methodology and results “will likely set a new standard for future research” in this area. Please see our responses to your concerns below, and thank you in adv...	Summary: The paper presents a framework in which it curates a large collection of tabular datasets, fine-tunes a language model that can readily be used for few-shot and zero-shot learning. Strengths: - The huge collection of tabular data for pretraining can be very useful. - The proposed method show strong performanc...	Rebuttal 1: Comment: Firstly, thank you for taking the time to review our paper and bringing up a number of interesting comments that we respond to below. We appreciate your dedication to the discussion period. _Larger train size._ Our model is trained on 8b tokens, which after various performance optimizations,...	Summary: The paper introduces TABULA-8B, a language model designed for tabular data prediction. The authors detail the creation of a large, high-quality dataset (T4) from the TabLib corpus, containing over 800 million rows from 1.5 million unique tables. TABULA-8B is fine-tuned from the Llama 3-8B model using technique...	Rebuttal 1: Comment: Thank you for taking the time to carefully read our paper and provide constructive comments that will improve the quality of our manuscript. We also appreciate your recognition of how “TABULA-8B demonstrates superior performance in zero-shot and few-shot learning scenarios”, as well as your belief ...	Rebuttal 1: Rebuttal: Thank you to all the reviewers and the AC for their time and dedication to review our paper, we have responded to all the reviewers individually. However, we have run a number of new experiments and we report the results in the figures attached in the pdf here. Update August 4th: We have included...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ALPINE: Unveiling The Planning Capability of Autoregressive Learning in Language Models	Accept (poster)	Summary: This paper addresses the question of whether or not a transformer-based language model can learn to plan based on next-token prediction. The authors analyse the ability of a transformer-based language model to learn in an abstracted path planning domain, and show that the model can some of the underlying struc...	Rebuttal 1: Rebuttal: Weakness 1: Need to Improve Logical Connections and Clarity Answer: Thank you for highlighting the need for a clearer introduction and improved logical flow. We will revise the paper accordingly. Your comment suggests that the inability of LLMs to deduce transitive reachability is reaso...	Summary: The paper investigates planning capabilities in Transformer-based language models by framing planning as a network path-finding task. It reveals that while Transformers can successfully embed adjacency and reachability matrices to perform path-finding, they struggle with transitivity in reachability, limiting ...	Rebuttal 1: Rebuttal: We greatly appreciate your valuable input and helpful suggestions. Below, we will address your questions and concerns regarding potential weaknesses in this paper. --- Weakness 1: About logical connections and the posed broad question: "Why does next-word prediction generate intelligence?" ...	Summary: This paper studies the planning capabilities of language models and provides a theoretical foundation for understanding it. The paper investigates the problem by abstracting it as a path-finding problem, showing both theoretically and empirically that transformers can embed adjacency and reachability matrices ...	Rebuttal 1: Rebuttal: Thank you for your valuable input. Below, we will address your questions and suggestions concerning potential weaknesses in this paper. --- Weakness 1: Broader Validation across Diverse Planning Datasets Can be more Beneficial Answer: As mentioned in Section 5, under Future Works (c), w...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing	Accept (poster)	Summary: The paper introduces Annealed Multiple Choice Learning (aMCL), a method that integrates simulated annealing with Multiple Choice Learning (MCL), in applications where the output label may be ambiguous, and many values may be plausible given the same input. The authors show that problems arise with the use of ...	Rebuttal 1: Rebuttal: We thank the reviewer for their suggestions to improve the quality of this manuscript. --- ## Comparison with baselines > From the results in seems that epsilon-MCL perform better, overall. We compare epsilon-MCL (refered to as Relaxed-WTA in the following), MCL and aMCL on the UCI datasets for...	Summary: I read the response and found it convincing, and I appreciate the new results. I decided to raise my score. -- This paper aims to tackle the local minima problems in MCL optimization. Inspired by simulated annealing, a temperature-controlled soft assignment is used and then soft-cells are directly optimize...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. We provide here a detailed answer to the raised concerns. --- ## Performance of aMCL > The experimental gains on real datasets are quite small. For the speech separation datasets, the improvement over MCL seems quite small. To compare MCL and...	Summary: The paper proposes to apply deterministic simulated annealing to multiple choice learning (MCL) as a means to mitigate some of the drawbacks associated with the winner-takes-all (WTA) scheme used to train MCLs, such as sensitivity to initialization and hypothesis collapse. They demonstrate that the proposed an...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed suggestions. --- ## Experimental validation > It is surprising that aMCL does not outperform epsilon-MCL in many cases. RMSE (line 239) compares the barycenter of the predicted distribution with the target positions. For this metric, epsilon-MCL (refered...	null	null	Rebuttal 1: Rebuttal: We thank reviewers WAas [R1], WQVX [R2] and qUke [R3] for their precise and detailed comments. We summarize hereafter the main changes in the submission, in accordance with the reviewers' feedbacks. * We provide additional experimental validation on the UCI benchmark [R1, R2, R3]. We demonstrate...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Efficient Reinforcement Learning by Discovering Neural Pathways	Accept (poster)	Summary: The paper proposes a heuristic approach to learn masks so only parts of the neural networks are activated to save energy in RL problems. They design an algorithm by applying the masking method to SAC and provide experimental results. Strengths: The paper considers an important problem of saving energy in RL t...	Rebuttal 1: Rebuttal: # Author's Response to Reviewer ddsT: Thank you for taking the time to review our paper and for the valuable feedback. We are thrilled that you recognize the importance of our work and found our paper well-written, easy to follow, and our experimental presentation clear. In response to your sugge...	Summary: Motivated by the large energy consumption needed to train modern machine learning methods, the authors propose a novel training method to find energy-efficient architectures. To demonstrate this, the authors test their method in single and multi-task reinforcement learning scenarios, showing that the architect...	Rebuttal 1: Rebuttal: # Author's Response to Reviewer wTeM: Thank you for taking the time for such a thorough review of our paper and for the valuable feedback. We appreciate your recognition of the paper's relevance, clarity, and thorough analysis of our experiments. We are elated to hear that the simplicity and effe...	Summary: This paper presents an approach to network pruning in the context of deep reinforcement learning. DRL poses the interesting challenge of non-stationarity in the data distribution as the policy improves and samples states/rewards differently. The idea is to learn a bitmask to selects specific parameters in a ...	Rebuttal 1: Rebuttal: # Author's Response to Reviewer HQmW: Thank you for taking the time to review our paper and for the valuable feedback. We are delighted that you found our approach well-grounded and effective, the paper easy to read, and the experiments informative. In response to your feedback, **we include addi...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Adversarially Robust Decision Transformer	Accept (poster)	Summary: The paper tackles the worst-case-aware RL problem, revises the conventional DT formulation via minimax returns, and proposes the adversarial robust DT to enhance robustness against test-time adversaries. Strengths: - The formulation of adversarial robust DT with minimax return is sound and clear. - Improving...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and suggestions. We address the concern as follows: ------ Q: Two additional returns-to-go networks are needed for return re-labeling, which increases the computation burden. A: The increase in computation is relatively low. And it is wo...	Summary: This paper introduces Adversarial Robust Decision Transformer (ARDT), a novel approach enhancing robustness in sequential decision-making. ARDT aligns policies with worst-case scenarios learned through minimax expectile regression, outperforming DT in robustness against powerful adversaries across different da...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and suggestions. We address the concern as follows: ------ Q: Several attacks on reinforcement learning have been proposed, such as [1,2], why have these not been applied in experimental settings? A: Thanks for the suggested related work...	Summary: This paper consider zero-sum two player Markov game that involves a protagonist and an adversary. The protagonist aims to maximize reward while the adversary aims to minimize it. This paper proposes Adversarial Robust Decision Transformer(ARDT), which is an algorithm that is based on Decision Transformer archi...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and helpful suggestions. We address the concerns as follows: ------ **Q: Lack of ablation study. e.g. how \alpha can effect the performance? It seems when \alpha is closer to 1, the learned return to go is closer to the maximum conditioned value,...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful comments and insights. We have revised our manuscript based on your comments and suggestions, and we have responded to each of your individual comments. ## Manuscript Revision Summary - Typo fixed suggested by reviewer JwFn. - Added related works sugge...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context	Accept (poster)	Summary: This paper studies the gradient descent dynamics of a softmax-activated self-attention unit trained on a population loss over in-context learning (ICL) tasks. Each task entails predicting the binary label of a query input, where the true label is the label of the 1-nearest neighbor (1-NN) in the context, and t...	Rebuttal 1: Rebuttal: >Q1: Can the message of the paper be generalized beyond this specific training distribution or even kNN? A1: We confirm that the data lies on a uniform hypersphere is a key assumption for our analysis, as it allows us to transform a metric comparison problem to an inner-production compari...	Summary: This paper investigates the ability of single-layer transformers to learn the one-nearest neighbor (1-NN) prediction rule through in-context learning. It focuses on how transformers can handle nonparametric methods like 1-NN classification, moving beyond simpler tasks like linear regression that previous studi...	Rebuttal 1: Rebuttal: Thank you for your detailed comments and suggestions. In the following, we will try our best to address your concerns. To accommodate the extensive character count in equations, we will provide our response in multiple parts. >Q1: Assuming that the data lies on a hypersphere and assuming no...	Summary: This submission considers learning to implement 1-NN in-context with a single self-attention layer. In particular, they consider training on in-context learning (ICL) sequences of form $(x1, y1), (x2, y2), … (x_N, y_N), x_{N+1}$, where $x_i$ are sampled from the $d$-dimensional unit sphere independently and wi...	Rebuttal 1: Rebuttal: Thank you for your detailed comments and suggestions. In the following, we will try our best to address your concerns. To accommodate the extensive character count in equations, we will provide our response in multiple parts. >Q1: It would be great to see a discussion on how the technique emp...	Summary: This paper studies the theoretical ability of attention layers to implement a 1-nearest-neighbor (1-NN) classifier via in-context learning (ICL). While prior work has studied the ability for transformers to implement algorithms such as linear regression, this paper is the first to establish that attention laye...	Rebuttal 1: Rebuttal: Thank you for your detailed comments and suggestions. In the following, we will try our best to address your concerns. To accommodate the extensive character count in equations, we will provide our response in multiple parts. >Q1: In-context learning with attention, seems to do a form of non-...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Assist Humans without Inferring Rewards	Accept (poster)	Summary: This paper presents Empowerment via Successor Representations or ESR, a technique that builds an assistive agent that maximizes the human collaborator's ability to influence the world. The key motivation the authors provide for building an assistive agent that seeks to empower the human rather than explicitly ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for the detailed review, and for all the suggestions for improving the paper. Based on the reviewer's feedback, we have run experiments with additional baselines and clarified several parts of the writing. **Together with the discussion below, does this fully address the...	Summary: This paper introduces a method for assistance via empowerment based on contrastive successor representations, while also introducing a number of theoretical results about the relationship between assistive empowerment (understood as maximizing the mutual information between human actions and future states) and...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for the detailed review, and for the suggestions for improving the paper. It seems like the reviewer's main concern is about baselines and presentation. We have attempted to address these concerns by adding XX additional baselines, and by significantly revising the paper ...	Summary: The authors propose a new training objective that motivates the agent to assist humans by maximizing their empowerment, agnostic of their rewards. The proposed empowerment objective is derived from mutual information between human actions and future states, which is estimated via contrastive representation lea...	Rebuttal 1: Rebuttal: Dear Reviewer, Thanks for the detailed review and suggestions for improving the paper. To address the main concern about baselines, we have added two additional baselines (see below). We have also run additional ablation experiments, and tried to address the other concerns in the discussion below...	Summary: The paper addresses the problem setting of human-agent collaboration where the agent learns to empower human decision-making to exert greater control over the environment. By connecting empowerment to reward maximization, the paper proposes the ESR method, which learns an intrinsic reward function based on lea...	Rebuttal 1: Rebuttal: Dear Reviewer, Thanks for the review, and the suggestions for improving the paper. As suggested by the reviewer, we have evaluated the proposed method on a number of additional tasks, and compared with a number of additional baselines. **Together with the responses below, does this fully address ...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their feedback. Reviewers mentioned concerns about baselines and presentation, which we have responded to in detail below. Based on this feedback, we have run additional ablations and baselines for our method and conducted additional qualitative analysis (s...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studies the problem of human-AI collaboration. The goal is to train an cooperative policy that can work with human together in the environment to achieve a shared goal. The key idea of this paper is to maximize the influence of the human's actions on the environment, which is called empowerment. Th...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the comments and suggestions for improving the paper. It seems like the reviewer's main suggestions were to add additional baselines, which we have done by adding comparisons with a goal inference method and a reward inference method. **Together with the d...	null	null	null	null	null	null
Understanding Transformers via N-Gram Statistics	Accept (poster)	Summary: The paper studies how transformer-based large language models (LLMs) use context when predicting the next token by approximating these predictions with N-gram-based statistical rules. The authors propose a method to describe transformer predictions using simple N-gram rules and study how well these rules appro...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and concerns. Weaknesses 1. “The study provides descriptive approximations without offering explanations into why certain rules work, not going deep enough in understanding the transformer behavior.” While it is true we do not provide explanations (as expl...	Summary: The authors use n-gram statistics and regular expression templates to study how well they describe the predictions of Transformers-based models. They craft rules that vary in context length and/or the number of marginalised context variables to predict the next word. They use their framework to study overfitti...	Rebuttal 1: Rebuttal: We thank the reviewer for their time in reviewing our work and appreciating the soundness of using n-gram statistics to understand LLMs. Regarding the 8-gram model vs rules $R_7^{suffix}$ or more generally $R_7$ using up to 7 tokens of context: We are not entirely sure we fully understand the rev...	Summary: The authors use 160M parameter models trained on the TinyStories dataset (artificially generated short stories, 480M tokens, made up of "vocabulary consisting of about 1500 basic words") to study transformers by comparing their predictions to the predictions of n-gram models. This leads to a few observations...	Rebuttal 1: Rebuttal: We thank the reviewer for their time in reviewing our work and appreciating its novelty. Scale The reviewer noted we only trained a small 160M model on TinyStories. Perhaps the reviewer overlooked that we also trained on Wikipedia using 1.4B models? LLaMa 3 70B would not have been an appropri...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable feedback. Attached is a pdf of Figures 1 and 2 relevant to the individual author rebuttals. Some high level comments to reiterate some overlapping feedback: 1) We have substantial evidence that our results hold with scale. This is justified via additi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients	Accept (poster)	Summary: This paper proposes a fundamental validation to understand the relationship between local models and the global model to mitigate the impact of adversarial clients. The level of collaboration needs to be chosen carefully because of the existence of adversarial clients. The theoretical analysis is provided to v...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and address the reviewer's comments point by point. 1- We thank the reviewer for pointing out the typos. 2- We did use different attacks in our experiments but did not notice any important differences. For the defense, we only considered the most...	Summary: This paper studies fine-tuning personalization in federated learning (FL) to mitigate the impact of adversarial clients. The authors leverage interpolation techniques for personalization, and they derive the closed-form approximation of the interpolation parameter $\lambda$. The study comprehensively considers...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and address the reviewer's questions below. On the datasets. Our contribution is mainly theoretical, and we provide some experimental results (covering mean estimation, binary, and multi-class classification) to convey the meaningfulness of our ...	Summary: This paper considers an FL setting where some clients can be adversarial, and we derive conditions under which full collaboration fails. Specifically, they analyze the generalization performance of an interpolated personalized FL framework in the presence of adversarial clients. The authors claim that they pre...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and address the reviewer's questions below. On Section 2. We are not certain what exactly bothers the reviewer in Section 2. Perhaps the relation to the general problem was unclear, in which case the following paragraph might help make it clearer. We wil...	Summary: This paper presents theoretical analysis and experimental validation results of the allowed level of collaboration in personalized FL with the presence of a fraction of Byzantine adversaries. Strengths: + This paper targets a very important and challenging problem in the personalized FL settings. + The theore...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and constructive comments. We address below the reviewer's comments. On the experiments. Since our contribution is mainly theoretical, we provide some experimental results mainly to convey the meaningfulness of our bounds. In Section 2, we restr...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Derivative-enhanced Deep Operator Network	Accept (poster)	Summary: The authors propose incorporating derivative information into the training of DeepONets to improve predictive accuracy on PDE problems. Instead using an encoder-decoder neural operator architecture as in prior work, the authors use the DeepONet architecture as well as incorporate spatial derivative information...	Rebuttal 1: Rebuttal: Thank you for your recognition of our work and for your constructive feedback. They are valuable to us. 1. We address the concerns on computational cost and dfference between our method and DINO in the general response. Additionally, we provide the convergence plots in the pdf file. We hope the ...	Summary: This manuscript introduces a derivative-enhanced deep operator network that utilizes derivative information to improve prediction accuracy. Strengths: S1) This paper presents a new method for improving the accuracy of approximating the output function for DeepONet, along with its directional derivative relati...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments. Regarding the weaknesses: 1. Most of the benchmarks you mention are improved by changing the architecture (or essentially the parameterization method) rather than focusing on adding regularization terms to the loss function, which is the focus of our paper...	Summary: The paper proposes an extension of deep operator networks (DeepONets) enhanced by matching the derivatives of the output function with respect to the input function, for example, the derivatives of the PDE solution w.r.t. the input coefficient. To make the computation tractable, the dimensionality of the input...	Rebuttal 1: Rebuttal: We appreciate your thoughtful comments. Regarding the weaknesses: 1. We updated the experimental results as shown in the general response. Our method have much lower test relative errors compared to FNO when the training samples are scarce (N_train = 16 or 64). The main reason our method does no...	Summary: The paper introduces a modified version of DeepONet, termed Derivative-Enhanced DeepONet, by incorporating derivative terms relative to the input function and spatial domain into the loss function. It outlines a practical method for calculating these derivatives, which serve as supplementary supervision terms ...	Rebuttal 1: Rebuttal: Thank you for your recognition of our work. We'd like to address the questions: 1. The compution of derivative labels $p=du(m;\psi)$ is equivalent to solving Eq.(13). The reviewer can find more details in the former part of the proof Theorem 1. The discretized derivative labels $p(x_i)$ are obtai...	Rebuttal 1: Rebuttal: We are sincerely grateful to the reviewers for dedicating their time and effort to reviewing our work and providing helpful feedback. Compared to DINO, although the DeepONet architecture (and its formulation of dm loss) requires longer training time, it offers the following advantages - Much sh...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Cardinality-Aware Set Prediction and Top-$k$ Classification	Accept (poster)	Summary: This paper proposes a method to handle cardinality-aware top-k classification. It designs an optimization problem where the cardinality of set selectors is also considered. The authors then generalize it to two types of surrogate losses. Given certain assumptions on the hypothesis set H, the authors theoretic...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions. **Weaknesses: No error bar, very limited evaluation, very strong assumption on the hypothesis set, which is usually not easy...	Summary: This paper introduces a new loss function for top-k set prediction where k may vary as a function of the input, which the authors call cardinality-aware top-k classification. However, this loss is intractable to optimize in all but trivial cases, so the authors introduce surrogate loss functions for learning t...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback and suggestions. We will take them all into account when preparing the final version. Below please find responses to specific questions. Weaknesses: **1. It isn't immediately clear why the cost-constrained surrogate loss is tractable to optimize, while ...	Summary: The paper introduced a new cardinality-aware set prediction algorithm with cost-sensitive comp-sum and constrained surrogate losses. Additionally, the paper established theoretical guarantees for top-k classification with fixed cardinality k using the H-consistency bounds. Finally, experiments on linear classi...	Rebuttal 1: Rebuttal: Thank you for your encouraging review. We will take your suggestions into account when preparing the final version. Please find responses to your specific questions below. Weaknesses: **1. Since the theoretical results include the neural network class as a special case, the experiments shoul...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful comments. We share additional experimental results in the attached PDF, as suggested by Reviewer 5ki4. Figure 1 illustrates the cardinality distribution for top-$k$ experts $\mathcal{K} = \\{1, 2, 4, 8\\}$ for the CIFAR-10 and CIFAR-100 datasets, analy...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MambaTree: Tree Topology is All You Need in State Space Model	Accept (spotlight)	Summary: This paper integrates tree structures into SSMs, enabling hierarchical data processing instead of the traditional sequential approach. It identifies a minimum spanning tree that considers locality, utilizing dynamic programming to prune edges from the grid graph. By adopting this tree structure, the length of ...	Rebuttal 1: Rebuttal: Q1: Analysis of the learned structures. Ans: Thanks for the valuable suggestions! We acknowledge that input-aware tree structures provide significant interpretability benefits, including preserving the intricate structural details in the vision content and enhancing the long-range modelin...	Summary: This paper proposes a tree state space model (SSM) to perform feature propagation on an input-aware topology. The author explores tree topology in SSM from both vision and language sides, leading to GrootV and GrootL respectively. The proposed method exhibits strong empirical performances on mainstream tasks. ...	Rebuttal 1: Rebuttal: Q1: Missing efficiency performance. Ans: Thanks for the suggestion. For inference throughputs, please refer to the "All Reviewer" section at the top of this rebuttal page. Besides, we provide the training throughputs of our method in Table 10, which are measured on a Nvidia V100 GPU with ...	Summary: The paper introduces a tree scanning algorithm for state space models specifically for Mamba. The naive and fixed scan patterns like raster or local scans commonly used for vision tasks do not consider the topological structure of 2D image input. The proposed algorithm generates a minimum spanning tree which c...	Rebuttal 1: Rebuttal: Q1: The traverse time of all vertices using dynamic programming algorithm. Ans: Thanks for the comments, but we believe there exists some misunderstanding. Given a sequence with the length of $L$ with an established corresponding minimum spanning tree, for the case of single-vertex settin...	Summary: This paper studies the optimization of selective stat space modeling by particularly proposing the GrootVL model. Specifically, it firstly constructs the tree topology based on spatial information and then aggregates the features to enhance the representation informativeness. The proposed methods are versatile...	Rebuttal 1: Rebuttal: Q1: More efficiency analysis. Ans: Thanks for your valuable suggestions! We have introduced the complexity and optimized version of our method in Section 3.1 and Section B of our manuscript. For the comparison of inference time, please refer to "All Reviewer" section at the top of this re...	Rebuttal 1: Rebuttal: To All Reviewers (Reply about efficiency performance): We sincerely appreciate all reviewers and ACs for their precious time and valuable feedback. Given that reviewers NQEG, xmLw, and g9ag have raised concerns regarding efficiency comparison, we will respond to this issue in this section. Al...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Universal Neural Functionals	Accept (poster)	Summary: This paper develops an algorithm for constructing functions that take neural network parameters / parameter-derived values as inputs but are equivariant / invariant to the inherent permutation symmetries in neural network weights. More specifically, this work is applicable to a larger class of neural network a...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful comments. > The RNN generalisation prediction experiment only compares against one baseline, which is not far behind the proposed method. It is therefore difficult to judge the significance of these numbers. We believe this scale of improvement is typic...	Summary: The paper proposes Universal Neural Functionals that are models operating on neural network weights in a permutation equivariant way. Compared to previous works, UNFs are more general and can be more easily applied to architectures such as RNNs and Transformers. The UNFs show good results in generalization pre...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed feedback. We will also include the suggested relevant works in our discussion. > it does not automatically constructs the specifications which is actually a quite tricky part for some complicated models We agree that constructing specifications can be tri...	Summary: The paper proposes Universal Neural Functionals (UNFs), which are models that process the weights of other neural networks. UNFs are equivariant to permutation symmetries of neurons and applicable to general architectures. The authors formalize neural network weights as sets of tensors, and develop an algorith...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful analysis and feedback. We will incorporate the suggestions and fixes proposed here. > Despite the claims in the abstract and conclusion, there has been other work … We agree that the novelty of this work lies in the construction of maximally expressive equ...	Summary: Extending from recent works, the authors propose a new neural network layer architecture that enforces permutation equivariance in the weight space. The proposed architecture is used for learned optimizers and compared with other existing methods in a series of tasks, archiving improvement over the state-of-th...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and suggestions, which we have incorporated into the draft manuscript. > presentation of the methodology is not very clear (specifically around Eq. 9 and 10) We apologize for any confusion, and have polished the presentation throughout, including more intui...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The study develops permutation equivariant models, developing an algorithm for defining a permutation eqivariant map of tensors with arbitrary rank, applying them in training learned optimizations and generalization prediction and finds that these class of models have improved performance on weight space tasks...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful assessment--we are glad that the review recognized the novelty of the work and the challenging nature of the problem space. > There is a limited number of datasets evaluated upon. We will expand the evaluation in a few ways, based on suggestions from re...	null	null	null	null	null	null
Relational Verification Leaps Forward with RABBit	Accept (poster)	Summary: The paper proposes bound-tightening techniques for verifying the absence of universal adversarial perturbations for a neural network. The tightened bounds are leveraged in a MILP encoding to perform verification. Strengths: - The paper addresses an interesting problem: verifying the absence of universal adver...	Rebuttal 1: Rebuttal: Q1: Comparison with SOTA non-relational baseline MNBaB. R1: We compared the performance of RABBit with the proposed baseline MNBaB across all ConvSmall CIFAR10 networks listed in Table 1 of the paper. The comparison used the same $\epsilon$ values, hardware, and timeout values as mentione...	Summary: The paper presents RABBIt, a general framework for improving the precision of relational verification of DNNs through BaB methods and cross-execution dependencies. RABBIt uses strong bounding and strong branching methods, outperforming BaB methods like $\alpha$-$\beta$-CROWN and RACoon, which only leverages cr...	Rebuttal 1: Rebuttal: Q1: In lines 221-224, I cannot follow how to derive $m^{1/n}$ and $m/n$. Please give me an example. R1: Please refer to the answer to Q3 in the common response. Q2: In the MILP encoding of RABBIt, What are the new terms/constraints compared to RACoon? R2: The MILP encoding i...	Summary: The paper proposes a branch and bound technique for the relational verification of neural networks. To this end, the authors build upon RACoon (Banerjee and Singh [2024]) and $\alpha,\beta$-CROWN (Wang et al. [2021b]). The work describes two branching mechanisms (strong bounding and strong branching) that form...	Rebuttal 1: Rebuttal: Q1: Do you have experiments w.r.t. other relational properties? If not, would you be willing to adjust the title and content of the paper to focus on UAP properties? -- In either case, I would be willing to raise my score to Accept. R1: Please refer to the answer to Q1 in the common respo...	Summary: This paper addresses the problem of verifying certain DNN properties that depend on multiple executions of the DNN, known as “relational verification.” The example used throughout the paper is verifying the k-UAP problem, which aims to confirm the absence of a universal adversarial perturbation for a given DNN...	Rebuttal 1: Rebuttal: Q1: In Table 1, how long does it take to run each verification? R1: Please refer to the answer to Q2 in the common response. **Q2: In Table 1, why the non-relational verifier $\alpha,\beta$-CROWN outperformed the SOTA relational verifier RACoon? Is it because $\alpha,\beta$-CROWN uses ...	Rebuttal 1: Rebuttal: Dear Area Chair and Reviewers, We appreciate the constructive feedback from the reviewers and are encouraged by their acknowledgment of the paper's theoretically sound contributions, and detailed experimental validation. *Q1: Evaluating RABBit on other relational properties (eacX, uEoH)* ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning	Accept (poster)	Summary: This paper proposes a novel MEDIQ framework to simulate clinical interaction between doctor and patient. The proposed MEDIQ framework is capable of proactively asking questions and collecting information to make the diagnosis results. Also, the MEDIQ framework incorporates two agents—expert and patient, to for...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s valuable feedback and insights. Thank you for highlighting the strengths of MediQ, including the novel design of the Patient and Expert two-agent conversation system, proactive information seeking feature in the Expert, and the robust Patient system to access patient r...	Summary: This paper introduce MediQ, a framework to simulate realistic clinical interactions, which incorporates a Patient System and an adaptive Expert System. The Patient system that simulates a patient and responds to follow-up questions, and an Expert system that serves as a doctor's assistant and asks questions to...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough review and feedback. We agree with the reviewer that our paper proposes a more realistic scenario for clinical interactions, and we conduct extensive experiments to validate each component of the framework. We hope to address the reviewer’s concerns below: ...	Summary: The paper proposes MediQ, a two-LLM-agent-system for interactive patient diagnosis. The authors argue that vanilla LLMs do not autonomously engage in an information extending discussion, but rather directly try to diagnose the patient and therefore oftentimes hallucinate. The proposed system specifically aims ...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s feedback and for highlighting our strengths, including the relevance of the topic, the efficacy of the proposed interactive system, and our thorough evaluation and analysis. > Is the purpose then to evaluate other approaches through the simulation system rather than ...	Summary: The goal of the paper is to develop a dataset on which models can be trained for interactive LLM-patient dialogues that require follow-up questions. The paper adapts MedQA and CraftMD datasets into an interactive setup, uses an LLM to mimic a patient's questions and trains LLMs to ask the necessary follow-ups ...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful feedback and thorough review for our paper. We appreciate the reviewer highlighting the strengths of our paper, including adapting existing datasets into conversation format, clarity, interesting findings and thorough experiments and models. We address the ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MILP-StuDio: MILP Instance Generation via Block Structure Decomposition	Accept (poster)	Summary: In this paper, the authors note the specific block structures in the constraint coefficient matrices (CCMs) of MILP instances that are closely related to problem formulations, and propose a novel MILP generation framework, called MILP-StuDio. MILP-StuDio identifies blocks in CCMs and decomposes instances into ...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive and insightful comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and...	Summary: This paper presents a method for generating instances for mixed integer programming that mimic the characteristics of existing instances. The main insight is that the performance characteristics of MIP solvers relate to the structure of the coefficient matrix of the instance, with a few examples of such struct...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive and insightful comments. Our rebuttal includes Tables C1-2 in the attached PDF. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your s...	Summary: This work presents a method for generating MILP instances by leveraging block structure decomposition. The primary aim is to address the challenges of generating high-quality MILP instances that preserve computational properties and structures of the original problems, thereby supporting the study and developm...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. We provide more evidence to show the applicability of MILP-StuDio Global Response. Our rebuttal includes Tables A1-6 and B1-5 in the attached PDF. We sincerely hope that we could properly address your concerns. If so, we would deeply appreciate it if y...	Summary: The paper presents a novel framework for generating high-quality MILP instances. The proposed method, MILP-StuDio, leverages the block structures in constraint coefficient matrices (CCMs) of MILP instances to preserve computational hardness while allowing scalable and efficient generation. The framework consis...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. We show the applicability of MILP-StuDio Global Response. Our rebuttal includes Tables A1-6 in the attached PDF. We sincerely hope that we could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not,...	Rebuttal 1: Rebuttal: Dear reviewers, We sincerely thank all reviewers' insightful and constructive comments, which helped to significantly improve our work. We have responded to the comments given by each reviewer in detail. In this global response, we provide a review on the MILPs with block structures and generaliz...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Contextual Bilevel Reinforcement Learning for Incentive Alignment	Accept (poster)	Summary: This paper proposes a general approach for bilevel optimization where the lower-level component is modeled as a Markov Decision Process (MDP). Traditional solvers for bilevel optimization often involve the computation of the Hessian term, which can be complex and computationally intensive. The main strategy em...	Rebuttal 1: Rebuttal: We thank the reviewer for their helpful comments. Q1: Entropy Entropy regularization allows us to compute the hypergradient explicitly without resorting to implicit differentiation. Without the entropy term, the lower level admits multiple solutions. The problem thus becomes ill-posed as one ...	Summary: This paper introduces the framework of bilevel optimization with lower-level contextual MDPs (BO-CMDP), which covers a wide range of practical decision-making applications. The authors of this paper propose the Hyper Policy Gradient Descent (HPGD) algorithm for solving BO-CMDP. They prove the convergence of HP...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to evaluate our work and appreciate their comments. For Assumption 3.1, we will clarify that the statement is with respect to the infinity norm. We will also add the assumption on the stepsize from [30] and fix the typo in line 551, where the extra 2 is n...	Summary: This paper considers the bilevel reinforcement learning problem with lower-level contextual MDPs. As compared to existing works, the problem considered is more general. The paper proposes a hyper-gradient based method. The hyper-gradient can be directly evaluated by leveraging the close form optimal policy in ...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to evaluate our work and appreciate their comments. Experiments. Thank you for the nice suggestions. The current experiment already covers an important economic application of Principal-Agent reward shaping, as initially motivated. We will clarify thi...	Summary: The paper introduces BO-CMDPs, a form of two-level optimization problems in which the inner optimization is a contextual MDP and the outer optimization problem selects the injected context based on some objective. The authors connect this formulation to a range of interesting domains (meta-RL, economics & rewa...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful evaluation and valuable comments. Experiments. Thank you for the nice suggestions. The current experiment already covers an important economic application of Principal-Agent reward shaping, as initially motivated. We will clarify this connection in t...	Rebuttal 1: Rebuttal: We thank all reviewers for their efforts and helpful comments. We appreciate that all reviewers recognized our contributions and efforts. Specifically, they highlighted: - Our "well-motivated" (UZX1) problem formulation (BO-CMDP) and “interesting framework” (miD3) that is "crucial for the comm...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ContextGS : Compact 3D Gaussian Splatting with Anchor Level Context Model	Accept (poster)	Summary: This paper presents Context-GS, a method designed to reduce the memory overhead of 3D Gaussian Splatting (3DGS). Inspired by context modeling in image compression, the authors introduce a similar concept into Scaffold-GS, which uses anchors to predict 3D Gaussian distributions. The method encodes anchors at a ...	Rebuttal 1: Rebuttal: We sincerely thank you for your thorough review and valuable suggestions    ### "The primary contribution to compression is adopted from Compact-3DGS" Our method is significantly different from Compact-3DGS. We only adopt the masking loss from Compact-3DGS. We also have significant...	Summary: The paper aims to compress Gaussian Splatting-based neural rendering representations. To achieve higher representation performance with a smaller size, the paper proposes hierarchical anchors, where coarser level anchors work as context to achieve a higher compression rate. Additionally, the paper introduces h...	Rebuttal 1: Rebuttal: We sincerely thank you for your review and valuable suggestions on our paper.    ### Typos Thank you for pointing out the typo; we have corrected it.    ### Ablation of Different Number of Levels Thank you for your suggestions. We conducted an ablation study on the Rome-Bunge...	Summary: In this paper, the authors propose ContextGS to reduce spatial redundancy among anchors using an autoregressive model. Specifically, the authors divide anchors into three levels, performing entropy coding from the top (coarse) level to the bottom (fine) level. Anchors from coarser levels are utilized as contex...	Rebuttal 1: Rebuttal: We deeply appreciate your thorough review and valuable feedback on our submission. Here are our detailed responses to your comments and suggestions:     ### Performance Improvement As illustrated in the summary of the rebuttal, we argue that the proposed main components indeed brin...	Summary: This paper proposes ContextGS, a compact 3D Gaussian Splatting (3DGS) framework that requires only a minimal amount of storage size while demonstrating high rendering quality. Upon the neural Gaussian-based 3DGS framework Scaffold-GS, the authors construct a multi-level anchor structure to reduce the spatial ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments!   ## Novelty We want to highlight that our method has significant and essential differences from both HAC and ScaffoldGS. The context model, in the generally accepted sense, does not require additional storage. We use already coded anchors (whi...	Rebuttal 1: Rebuttal: # Thanks to All the Reviewers for the Insightful Comments We would like to thank the reviewers for their efforts and insightful comments. We appreciate the reviewers’ acknowledgment regarding the novelty/motivation and performance of the proposed method. For example: **Novelty/motivation...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning	Accept (poster)	Summary: The authors propose a method for adaptive parameter sharing in Multi-Agent Reinforcement Learning (MARL), by using learnable weight masks for each agent. They combine this with a regularization method to encourage diversity in the masks and a resetting mechanism to reuse masked parameters with a certain probab...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback. We appreciate your questions and suggestions and have provided clarifications below. Please let us know if you have any follow-up questions or comments; we would be happy to discuss further. Q1: The authors do not include FuPS+ID ... a baseline in prior...	Summary: This paper presents an approach to multi-agent reinforcement learning. In this approach, the agent network has an overall set of parameters. These parameters are transformed by agent-specific masks. The masks are learnable. Agent policies are encouraged to be diverse through a diversity-regularisation term...	Rebuttal 1: Rebuttal: Thank you for your positive review. Regarding your questions and suggestions, we would like to provide clarifications below. If you have any follow-up questions or comments, please let us know, and we will be happy to discuss further. Q1: The current limitations of the work are presented in a...	Summary: The paper introduces a novel adaptive partial parameter sharing scheme in multi-agent reinforcement learning (MARL) to enhance policy heterogeneity while maintaining high sample efficiency. The key innovation, Kaleidoscope, employs a set of common parameters and multiple sets of distinct, learnable masks for d...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback. Here are our clarifications. If you have any follow-up questions or comments, please let us know and we will be happy to have further discussions. Q1: The proposed method is similar to SNP... A1: As listed in Table 1, our proposed method differs ...	Summary: This paper introduces Kaleidoscope, an adaptive partial parameter sharing method for multi-agent reinforcement learning (MARL). Kaleidoscope aims to balance the benefits of full parameter sharing (sample efficiency) with the flexibility of non-parameter sharing (policy diversity). It achieves this by using lea...	Rebuttal 1: Rebuttal: Thank you for your positive review. Regarding you questions and suggestions, we would like to provide clarifications and additional results below. If you have any follow-up questions or comments, please let us know and we will be happy to have further discussions. Q1: The paper primarily focu...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful comments and valuable feedback. In our work, we propose a novel adaptive partial parameter sharing scheme that fosters policy heterogeneity while maintaining high sample efficiency in MARL tasks. This approach leads to superior policies in terms of perfo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning	Accept (poster)	Summary: This work presents MTV to enhance the in-context learning (ICL) capabilities of LMMs, which typically have limited context length, especially when dealing with multimodal data that includes both text and images. MTV addresses this by compressing many-shot examples into compact implicit representations within t...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable comments. In the following, we provide a response to the questions raised in the review: Additional MANTIS results. As suggested by the reviewer, we present some additional results of our method using the MANTIS-LLama3 model on OKVQA, Vizwiz, Flower, a...	Summary: This paper should be desk rejected as the single PDF submission does not have the paper checklist. It would be unfair to make an exception as this requirement has been clearly stated at https://neurips.cc/Conferences/2024/CallForPapers and the latex template file. Strengths: NA Weaknesses: NA Technical Qua...	Rebuttal 1: Rebuttal: Please note that our checklist is provided in our Supplementary pdf, and thus, the PCs have decided not to desk-reject this paper. As this has been universally applied to all submissions, our submission is thus in accordance with guidelines, and fairness should not be adversely affected.	Summary: In the context of multimodal understanding, this work studies the area of many shot, long-context ICL (in context-learning) in natively multimodal models (large multimodal models i.e. LMMs, where images and text are interleaved). The premise proposed is that the pretrain time context of existing LMMs is prohib...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and respond to all points in the following, incorporating all clarifications and additional results into the final paper: MTVs for Text-only domain. Using LLaMA3, we evaluate MTV on text-only tasks. The two tasks are English-Spanish translatio...	Summary: In-context learning with many examples can be effective for learning new tasks. However, there are challenges with many-shot multimodal in-context learning (ICL), such as the limitation caused by the model’s context length. This issue is more challenging in the multimodal setting because it processes both imag...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and respond to all points in the following, incorporating all suggested changes into the paper: Comparison with “Many-Shot In-Context Learning in Multimodal Foundation Models”. We thank the reviewer for sharing this concurrent work with us and...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation	Accept (poster)	Summary: The authors propose a technique that utilizes unlabeled 360-degree data to improve previous methods, which includes two main stages: offline mask generation for invalid regions and an online semi-supervised joint training regime. Experimental results indicate that the proposed method outperforms previous meth...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and for recognizing the strengths of our paper. We appreciate your feedback and would like to address your concerns: ### `Q1. Technical contribution and novelty:` While we acknowledge that utilizing unlabeled data is not a new concept, our work makes several n...	Summary: This paper proposes a method to improve 360 monocular depth estimation using perspective distillation and augmentation with unlabeled data. It introduces the concept of "perspective distillation," which leverages the available 360 monocular depth maps and their corresponding equirectangular images to generate ...	Rebuttal 1: Rebuttal: Thank you for your thorough review and constructive feedback. We appreciate your recognition of our method's strengths and innovative techniques. We'll address your concerns and questions point by point: ### `Q1. Applicability to non-dual-projection fusion models:` We acknowledge this limitation ...	Summary: This paper effectively utilizes unlabeled data by employing the SAM and DepthAnything models to generate masks and pseudo-labels respectively. When projecting data onto a cube, the authors use random rotation techniques to minimize cube artifacts, thereby enhancing the accuracy of 360-degree monocular depth es...	Rebuttal 1: Rebuttal: Thank you for your detailed review and constructive feedback. We appreciate your recognition of our method's strengths and will address your concerns point by point: ### `Q1. Indoor/Outdoor Data Clarification:` We apologize for the confusion. While Figure 2 in the original paper shows outdoor exa...	Summary: This paper introduces a novel depth estimation framework specifically designed for 360-degree data using an innovative two-stage process: offline mask generation and online semi-supervised joint training. Initially, invalid regions such as sky and watermarks are masked using detection and segmentation models. ...	Rebuttal 1: Rebuttal: Thank you for your detailed review of our paper. We appreciate your recognition of our method's novelty and practical utility. We'd like to address your concerns and questions: ### `C1. Regarding the straightforwardness of our method:` While our approach may appear straightforward, we believ...	Rebuttal 1: Rebuttal: Dear Reviewers and Area Chair, We sincerely thank all reviewers for their thoughtful feedback. We are encouraged that reviewers found our work to be innovative, well-motivated, and impactful for 360° depth estimation. We appreciate the constructive comments and will address the main points below....	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors present a training strategy for single-image depth estimation on 360-degree equirectangular images. The strategy centers around leveraging strong pre-trained models for perspective images as teacher networks. It does not depend on any particular network architecture and therefore can benefit any 36...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. We appreciate your positive assessment of our work's originality, significance, and clarity. We're glad you found our approach well-motivated and effective. We'll address your questions and concerns point by point: ### `Q1. Comparison to FoVA-Depth (Lichy et ...	null	null	null	null	null	null
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models	Accept (poster)	Summary: This paper introduces WAGLE (Weight Attribution-Guided LLM unLEarning), a novel method for enhancing the effectiveness of large language model (LLM) unlearning. The key contributions in my view are: 1) A bi-level optimization framework for weight attribution in LLM unlearning. 2) A closed-form solution for ...	Rebuttal 1: Rebuttal: Thank you for the constructive feedback. Please find our response to each weakness (W), question (Q), and limitation (L) below. References (in the format of [Rx]) can be found in the general response. Response to W1: Our work, while lacking formal theoretical guarantees on how weight attribut...	Summary: This paper presents WAGLE, a weight attribution method for unlearning. The main goal of WAGLE is to augment existing unlearning methods by identifying influential parameters, and restricting existing methods to the influential parameters. The authors focus their attention to approximate unlearning for large la...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback. Below, we provide detailed responses to each weakness (W) or question (Q). References (in the format of [Rx]) can be found in the general response. Response to W1: Thank you for your comments on the importance of locality in effective unlearning. Her...	Summary: This paper explores the correlation between model weights and the efficacy of unlearning processes in large language models (LLMs). It introduces a framework called WAGLE (Weight Attribution-Guided LLM Unlearning), which elucidates the relationship between the influence of specific weights and the LLM unlearni...	Rebuttal 1: Rebuttal: Thank you for your thorough review, positive assessment, and insightful feedback on our submission. Below, we provide detailed responses to each identified weakness (W) and question (Q). References (in the format of [Rx]) can be found in the general response. **W1/Q1: Could the authors provide ad...	Summary: This paper introduces WAGLE (Weight Attribution-Guided LLM Unlearning), a novel method for large language model (LLM) unlearning that identifies influential weights for the unlearning process while considering the retain loss. The authors evaluate WAGLE on a diverse set of unlearning benchmarks, demonstrating ...	Rebuttal 1: Rebuttal: Thank you for your insightful comments. Below, we address each identified weakness (W) in detail. References (in the format of [Rx]) can be found in the general response. W1: Discuss limitations and future work in the main paper rather than the appendix. A: We will follow your suggestio...	Rebuttal 1: Rebuttal: We appreciate the detailed feedback from all reviewers. Below is a general response addressing common concerns highlighted in your comments. Refer to the attached PDF for figures and tables labeled as Figure Rx and Table Rx, where 'R' denotes 'rebuttal'. **GR1: Why weight attribution he...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module	Accept (poster)	Summary: The paper proposes to address over-smoothing through the lens of overlapping neighborhoods and revolves around addressing it in residual GCN line of work. It highlights the lack of adaptability of higher-order neighborhood information as the limitation of previous residual methods. To overcome these drawbacks,...	Rebuttal 1: Rebuttal: _We thank the reviewer for the valuable feedback. We are glad that the reviewer appreciates the idea and technical contributions of our work. Below, we address the reviewer’s concerns one by one._ > Q1:The discussion of related works is limited ... A1: Thank you for the suggestion. At line 3...	Summary: This manuscript focuses on the issue of over-smoothing in Graph Neural Networks (GNNs), which occurs when increasing the number of layers causes node representations to become indistinguishable. The authors explore this problem from the perspective of overlapping neighborhood subgraphs and propose a novel Post...	Rebuttal 1: Rebuttal: _We thank the reviewer for the valuable feedback. We are glad that the reviewer appreciates the idea and technical contributions of our work. Below, we address the reviewer’s concerns one by one._ >**Q1: Relation to other subgraphs: What is the difference between the neighborhood subgraphs propo...	Summary: The authors revisit the problem of over-smoothing in graph neural networks from the perspective of overlapping neighbourhood subgraphs, and propose a node adaptive residual module based on a posteriori sampling to demonstrate the effectiveness of this method from both theoretical and experimental perspectives....	Rebuttal 1: Rebuttal: _We thank the reviewer for the valuable feedback. We are glad that the reviewer appreciates the idea and technical contributions of our work. Below, we address the reviewer’s concerns one by one._ >Q1: The paper needs further polishing, e.g. some formulas are numbered and some are not. A1: T...	Summary: This paper proposes a PSNR module to alleviate the over-smoothing problem faced by graph neural networks when the number of layers increases. The effectiveness of this method is demonstrated through both theoretical analysis and experimental results. Strengths: 1. The motivation is reasonable and the method i...	Rebuttal 1: Rebuttal: _We thank the reviewer for the valuable feedback. We are glad that the reviewer appreciates the idea and technical contributions of our work. Below, we address the reviewer’s concerns one by one._ > **Q1: The paper assumes that the posterior distribution of residual coefficients is Gaussian, but ...	Rebuttal 1: Rebuttal: # General Response We thank the reviewers for their insightful and constructive reviews of our manuscript. We are encouraged to hear that the reviewers found our motivation and theoretical proofs to be reasonable (Reviewers 61ZY, ZBJB, Uv7R) and appreciated the **comprehensiveness and sup...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Generalized Linear Programming Value Functions	Accept (spotlight)	Summary: This article presents some contribution on the use of neural network to learn what is called the Generalized Linear Programming Value Function (GVF), that evaluates the optimal value of a linear program given as inputs the objective vector and the matrix constraints. This function is useful in many algorithms ...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s comments, especially for highlighting our contributions and the clarity of the paper. Please see the global rebuttal for expanded computational results. We address the specific comments (both from the Weaknesses and Questions section) below. 1. **Effectiveness of the ...	Summary: This paper proposes a novel, theoretically grounded approach to learning values function (VF) of linear programs (LP). Specifically, a Dual-Slack Model (DSM) is proposed to approximate the value function of linear programs with varying right-hand sides and objective coefficients. The authors prove several ke...	Rebuttal 1: Rebuttal: Thank you for the detailed review. We are happy to hear that the reviewer appreciates the significance and clarity of the paper. We now will address each comment in both the Weaknesses and Questions section. 1. Limited computational results: As proposed, we will add both a new training baseli...	Summary: ### Summary Traditional LP Value Function (LPVF) represents the optimal value of a linear program as a function of its parameters, typically objective coefficients and constraint bounds. It is piecewise linear and convex, often used in sensitivity analysis and parametric programming. However, computing the LP...	Rebuttal 1: Rebuttal: Thank you for the review. Our new computational experiments, described in detail in the global rebuttal, expand our computational study with another family of problem instances and two new baselines (one for the model and one for the heuristic). We hope that this can address the reviewer’s concern...	Summary: The paper presents a novel learning method for the Generalized Linear Programming Value Function (GVF), which models the optimal value of a linear programming (LP) problem as its objective and constraint bounds vary. The authors develop a neural network architecture, the Dual-Stack Model (DSM), that can effici...	Rebuttal 1: Rebuttal: Thank you for the review. Please see the global rebuttal for our response on the limitation of the computational results raised in the “Weaknesses” section. We hope that it addresses your concerns regarding how our approach generalizes to other types of optimization problems, and regarding how it ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their feedback. We wrote this paper with the goal of presenting a “theoretically motivated architecture” for what we view as an important and interesting setting in mathematical optimization, and we are happy to see that this vision appears to have resonated with the...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm	Accept (poster)	Summary: This paper studies learning in MDPs with the long-run average reward objective, assuming that the MDP satisfies some kernel assumptions. A UCB-type of learning algorithm is proposed and is proved to achieve sublinear regret when the eigenvalues of the kernel operators decay at the rate of a p-th degree polynom...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper. We appreciate your positive feedback on the strength of the results. Here, we address your comments in detail and hope this will enhance your evaluation of the paper. > The use of $O$ notation: As mentioned by the reviewer all of our proofs are non-asymptoti...	Summary: The authors consider the setting of reinforcement learning in infinite horizon average reward MDPs. In particular, they consider a kernel based function approximation to represent value functions. Most of the prior work involving kernel regression has been in the context of bandits, where the state cardinality...	Rebuttal 1: Rebuttal: Thank you for your detailed review of our paper. We appreciate your positive feedback regarding the generality of our setting and results, as well as the potential independent interest in the confidence bounds. We will address your comments and questions in detail, hoping this will enhance your ev...	Summary: The paper proposes a kernel-based optimistic algorithm for the average reward setting and corresponding regret analysis. As described, the kernel-based setting is a more general extension of linear structure to an infinite-dimensional linear model in the feature space of a positive definite kernel. Strengths:...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper. We appreciate your positive feedback. Following your comment, we will enhance the main text with more detailed technical content. We have provided a proof sketch in the paragraph following Remark 1, which we will expand to further detail the proof of Theorem 1. ...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Satformer: Accurate and Robust Traffic Data Estimation for Satellite Networks	Accept (poster)	Summary: This paper proposed a satellite network traffic data estimation method which is called Satfomer. The proposed method uses the adaptive sparse spatio-temporal attention mechanism to capture nonlinear spatio-temporal relationships. The proposed method is assessed on different satellite network datasets and comp...	Rebuttal 1: Rebuttal: ## Dear Reviewer ZGQc: First and foremost, we would like to express our heartfelt gratitude for your diligent review of our research work and for providing valuable feedback. Your expertise and constructive comments are instrumental in enhancing the quality of our paper. Based on your comments,...	Summary: The paper introduces Satformer, a novel method for accurate and robust traffic data estimation in satellite networks. It addresses the challenges of large-scale and dynamic nature of satellite networks by proposing an adaptive sparse spatio-temporal attention mechanism that focuses on specific local regions of...	Rebuttal 1: Rebuttal: ## Dear Reviewer 8BTK: Initial and foremost, we would like to sincerely thank you for your thorough analysis of our study and insightful comments. Your knowledge and insightful criticism have been very helpful in improving our paper's quality. We have had in-depth talks and implemented the requ...	Summary: This paper introduces novel network designed for satellite traffic data estimation. Proposed approach includes several novelties and the experimental results demonstrate that the propsoed methods outperformed several alternative mehtematical and neural baseline methods. Strengths: The paper proposes a new met...	Rebuttal 1: Rebuttal: ## Dear Reviewer RAyE: We would like to thank the reviewer forcareful and thorough reading of this paper and for the thoughtful commentsand constructive suggestions, which help to improve the quality of our paper. Based on your comments, we have engaged in thorough discussions and made the neces...	Summary: This paper presents to make use of the transformer for spatio-temporal data imputation, and the application is for satellite networks. Strengths: - The paper is well-written and the model descriptions are clear - It is interesting to see the spatio-temporal imputation can be used in satellite networks Weakne...	Rebuttal 1: Rebuttal: ## Dear Reviewer LZHh: Thank you very much for your time involved in reviewing the manuscript and your very encouraging comments on the merits. We also appreciate your clear and detailed feedback and hope that the explanation has fully addressed all of your concerns. In the remainder of this l...	Rebuttal 1: Rebuttal: We appreciate the reviewers thoughtful and detailed comments, and agree with the majority of the comments and suggestions. In terms of the overall identified weaknesses,the reviewers' concerns can be roughly grouped into: 1. The need for a clearer explanation of the significance and unique challe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Pipeline Parallelism with Controllable Memory	Accept (poster)	Summary: The paper addresses the inefficiencies in current pipeline parallelism schedules used for training large-scale models, particularly focusing on high activation memory and pipeline bubbles. The paper proposes a new framework to decompose pipeline schedules into repeating building blocks and introduces V-shape b...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and suggestions for improvement. We respond to individual points from your review below. >It would be helpful to have experimental results for long sequence lengths. 1024 and 3072 sequence lengths are too short compared to what SOTA LLMs can handle....	Summary: This work proposes systematic methodology for designing pipeline parallelism schedules and analyzing their performance (e.g. peak memory and pipeline bubbles). The major observation is that a pipeline schedule can be viewed as repeating a building block, and the peak memory critically depends on the lifespan o...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and suggestions for improvement. We respond to individual points from your review below. >There are quite some typos, some of which are listed here: Line 22 "tensor" -> "Tensor"; Line 189, "V-Min" -> "V-ZB" (?); Line 193, "serious" -> "series". Line...	Summary: Authors propose a way to identify the repeated building block a pipeline schedule is built from. By relating the peak activation memory of the schedule to the lifespan of the building block the authors show that existing schedules do not optimally use memory, and design higher-throughput schedules that use the...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and insightful suggestions for improvement. We have revised the manuscript to address most of the mentioned issues. We respond to individual points from your review below. >grammatical errors, a lack of useful captions in figures, Some plots are har...	null	null	Rebuttal 1: Rebuttal: Thanks for all reviewers for the valuable feedback and insightful suggestions. We updated our PDF accordingly. The changes mainly include: - We change the title from "Efficient Pipeline Parallelism with Controllable Memory" to "Pipeline Parallelism with Controllable Memory. - We've corrected the M...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Online Budgeted Matching with General Bids	Accept (poster)	Summary: This paper addresses the Online Budgeted Matching (OBM) problem with general bids, which is fundamental in applications like online advertising and resource allocation. Traditional algorithms typically assume a very small bid-to-budget ratio κ, limiting their practicality. The authors remove the Fractional Las...	Rebuttal 1: Rebuttal: We thank the reviewer for the questions. `How does the contribution fundamentally differ from prior work?` Beyond the removal of the small-bid and FLM assumptions, we present a novel meta algorithm (MetaAd) for general discounting functions. MetaAd can reduce to different competitive algorit...	Summary: This paper studies the online budgeted matching problem without the small-budget or Fractional Last Matching (FLM) assumption. The authors propose a meta algorithm called MetaAd, which uses a discount function to assign each node with a score. The authors perform competitive analysis for their meta algorithm a...	Rebuttal 1: Rebuttal: We thank the reviewer for the questions. `How to choose a discounting function $\phi$?` Our results (Theorem 4.2 and 4.3 ) are instrumental to design a discounting function $\phi$. Specifically, Theorem 4.2 and 4.3 enable us to identify appropriate $\phi$ by maximizing the derived compe...	Summary: The paper studies the classic online budgeted matching problem (OBM), relaxing the small bid assumption and the FLM assumption. Precisely, an upper bound on the competitive ratio is proven for any deterministic algorithms. Then a framework of algorithms for OBM is proposed to solve OBM with general bids, which...	Rebuttal 1: Rebuttal: Thank you for your question regarding the motivation of general `non-small` bids. Relaxing the small bid assumption is crucial to providing provable algorithms for many useful online bipartite matching (OBM) scenarios. We explain the significance from both application and theory sides. + **Appl...	Summary: Problem Studied This paper studies the online budgeted matching problem (also known as AdWords). The input to the problem is a bipartite graph, where one side of the graph (the advertisers) is known in advance. Each advertiser has a budget, which is the maximum amount of money they are able to spend. The ...	Rebuttal 1: Rebuttal: Thank you for your questions. We answer them as below. `Why is it interesting to study the setting with general bids and no FLM?` OBM with general bids covers a wide range of online bipartite matching problems [1], so it models many applications where FLM does not hold. Two examples are giv...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments and questions. We've included results for a new set of experiments based on cloud resource management. The results are available in the attached PDF. `Experiment setup` In the experiment, the cloud manager allocates 100 virtual machines ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stepwise Alignment for Constrained Language Model Policy Optimization	Accept (poster)	Summary: The paper studies constrained policy optimization for the language model alignment problem. The authors propose a stepwise alignment method that involves two separate steps for fine-tuning a language model: first with a reward and second with constraints. Several advantages of the proposed method are illustrat...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer’s encouraging comments. We will answer the Questions first and then address the comments in Weaknesses. ### Questions Unpaired vs paired. In our paper, we call a dataset without paired preference labels {$y_w, y_l$} unpaired. Since this terminology may be c...	Summary: The paper presents the SACPO, a method that optimizes LM policies by sequentially aligning them to maximize helpfulness and harmlessness in either order. By selecting appropriate hyperparameters, the method enables balancing these criteria according to contextual needs. The authors leverage DPO and KTO in vari...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer’s encouraging comments. The thoughtful comments of Reviewer w7hx are valuable and help us improve the manuscript's quality. We will answer the Questions first and then address the comments in Weakness. Q1 (ELO scores). In Figure 3 - 5, when we compute the ELO...	Summary: From the perspective of safe reinforcement learning, the author formulates human value alignment as an optimization problem of the LM policy to maximize reward under a safety constraint, and then proposes an algorithm, Stepwise Alignment for Constrained Policy Optimization (SACPO). Strengths: The author intro...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer’s encouraging comments. The thoughtful comments of Reviewer z9np are valuable and help us improve the manuscript's quality. We will answer comments in Weaknesses. Weakness 1. We appreciate the reviewer's feedback. Given the amount of content and the strict pa...	Summary: This paper proposes a new alignment algorithm SACPO to improve the both helpfulness and safety (harmlessness) of language model. SACPO separates the two objectives into two alignment steps. The second step of optimization is equivalent to be an optimization with the policy from first step as the reference poli...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer’s encouraging comments. The thoughtful comments of Reviewer VoMk are valuable and help us improve the manuscript's quality. We will answer the Questions first and then address the comments in Weakness. Questions > Q1. Can SACPO be generalized to the setti...	Rebuttal 1: Rebuttal: Dear reviewers and AC, We deeply thank all the reviewers for their insightful comments and constructive suggestions. - We have conducted new experiments based on the reviewers' comments. Additional experimental results are provided in a one-page PDF containing new figures attached in this "globa...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper addresses the challenge of fine-tuning a language model (LM) policy to maximize reward while adhering to safety constraints. Building on the concept of Safe RLHF, which introduces a constrained safe RL paradigm for aligning LLMs, the authors propose a novel approach: Stepwise Alignment for Constraine...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer KBZp for helpful and thoughtful comments and questions. We will first answer the Question and then address the comments in Weakness. Questions >In the experiments, you have tested only four combinations (DPO (H) $\rightarrow$ DPO (S), DPO (H) $\rightarrow$ K...	null	null	null	null	null	null
Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients	Accept (poster)	Summary: This paper studies the nonparametric instrumental variable (NPIV) regression problem. The authors present a new algorithm, SAGD-IV, which utilizes stochastic gradient descent in a function space to directly minimize the populational risk. The gradient can be computed via computing the estimating the conditiona...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful observations. Below, we separately address each weakness: 1. The referee is correct in pointing out that estimating conditional densities in high dimensional settings poses nontrivial difficulties, being a limitation which applies in our case, as well as i...	Summary: The authors propose an estimator for nonparametric instrumental variable regression (NPIV), with an extension to the binary outcome case. They prove that the excess risk of the projected estimator is controlled by rates of the density ratio, a regression, and the conditional expectation operator. Extensive com...	Rebuttal 1: Rebuttal: We thank the reviewer for the attentive analysis and relevant suggestions. Below, we separately address the weaknesses and questions. ### Weaknesses Thank you very much for pointing out such an interesting paper. The cited work explicitly uses NN classes as approximations to $L^2$ spaces and...	Summary: This paper considers the standard problem of non-parametric instrumental variable estimation (NPIV) and proposes a new approach of functional stochastic gradient descent to solve it (SAGD–IV), where the gradient estimator can be implemented and adapted using certain machine learning or deep learning techniques...	Rebuttal 1: Rebuttal: We thank the reviewer for providing a careful assessment of our work. In what follows, we address the weaknesses and questions in a point by point fashion. ### Weaknesses 1. We are assuming that "consistency" here means convergence of $\|\|\hat{h} - h^*\|\|$ to $0$ as the number of data points g...	Summary: This paper introduces a novel IV method, called the SAGD-IV, which is more efficient and stable in the NPIV regression. Two different variants of the SAGD-IV are given, and a range of comparisons between these variants with the existing methods are given. Moreover, the performance of SAGD is not only shown in ...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive comments! Concerning the weakness, we appreciate the reviewer's input on our exposition and we will improve the discussion on IV and the comparison with current approaches. Regarding your question, it is possible to use SAGD-IV for other models. However...	Rebuttal 1: Rebuttal: We thank all reviewers for reading our paper and providing valuable feedback which will certainly result in improvements to the final version. We have uploaded a PDF containing experiment results which address points raised by reviewer 1tRc. Pdf: /pdf/da8805d95e17b4da5d263be27038346216f1b2ed.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SGLang: Efficient Execution of Structured Language Model Programs	Accept (poster)	Summary: This paper proposes SGLang, a system with frontend programming interface and run-time optimization for efficient execution of LLM-based applications. For frontend programming interface, SGLang provides primitives for generation and parallelism control. For run-time optimization, SGLang has three key optimizat...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback. We will incorporate the clarifications and address the issues in the next draft. Here is our response to your questions: > Weaknesses 1: No modular sensitivity study. Section 6.3, "Ablation Study," in the original paper did what you asked for. In line 306...	Summary: This paper introduces SGLang, a programming language for large language models (LLMs) that enables users to write programs specifying prompts in natural language and control flow with Python and execute them to call LLMs as needed. SGLang provides a set of language constructs and functions that allow users t...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback. We will incorporate the clarifications and address the issues in the next draft. Here is our response to your questions: > For example, optimizing for API-based model calls is not thoroughly explained or explored, but that is also not the main goal of the pa...	Summary: The paper introduces SGLang, a system designed for efficient execution of complex language model (LM) programs. SGLang consists of a frontend language that simplifies programming with generation and parallelism primitives, and a runtime that boosts execution speed through several optimizations. The paper descr...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback. We will incorporate the clarifications and address the issues in the next draft. Here is our response to your questions: > Providing more details on optimizing API calls to public API models and including more examples in the evaluation would strengthen this ...	Summary: The authors introduce and evaluate SGLang, a LLM language and implementation that can perform inference or use an external API. They claim significant performance improvements mostly though cache redesign. Strengths: I liked the Python-embedded language. It seems relatively straightforward to use. The results...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback. We will incorporate the clarifications and address the issues in the next draft. Here is our response to your questions: > The authors provide a nice example of their language, but very informal. Is the language just what you mention here.? We introduced th...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper introduces SGLang, a comprehensive framework designed to enhance frontend LLM-based prompting/programming and to accelerate the runtime inference of LLM programs. In the frontend component, SGLang employs a meticulously structured primitive to streamline LLM prompting and support parallel execution....	Rebuttal 1: Rebuttal: We appreciate your valuable feedback. We will incorporate the clarifications and address the issues in the next draft. Here is our response to your questions: > For the frontend component, the selection of primitives such as [gen], [fork], and [select] for building language programs is intriguing...	null	null	null	null	null	null
When is an Embedding Model More Promising than Another?	Accept (poster)	Summary: The authors propose a task-agnostic method for the evaluation of embedding models called "information sufficiency". The general notion is to generate a pairwise matrix that effectively measures how well each embedding model can be used to generate the information content of the other. They compute an overall "...	Rebuttal 1: Rebuttal: We warmly thank Reviewer 4AaU for their detailed review and the effort they put into evaluating our work. Questions. 1. Orthogonal models and unique information. Yes, a model could contain unique information and still be competitive while not predicting the others. Our method allows th...	Summary: This article introduces a novel framework, grounded in information theory, for assessing the relevance of embedding models (embedders). The authors begin by introducing the notion of sufficiency of a model A relatively to a model B, which can be used to rank embedders. They prove: 1- sufficiency implies (...	Rebuttal 1: Rebuttal: We would like to thank Reviewer UcaH for their detailed account of our work and the efforts they put into their review. Weaknesses. 1. Estimation of $I_S(U \longrightarrow Z)$ (See PDF in general comment). For a given dataset $D$, we generate the embeddings $(u_i, z_i)$. We then fit a Ga...	Summary: Evaluating embedding models is challenging because it typically relies on various downstream task data, despite the embedding models being trained for general purposes. This paper introduces an information-theoretic metric for comparing embedding models, eliminating the need for labeled datasets in their evalu...	Rebuttal 1: Rebuttal: We thank Reviewer o2cb for their review and the interesting questions they raised. We do our best to answer them below. Weaknesses 1. Information sufficiency and deficiency. (See Official comment below for the proof) We can establish the following connection between deficiency and mutual...	Summary: This paper proposes a new metric to compare embedding models without relying on labeled data. The approach involves embedding data using two separate neural networks Z and U, and then trying to use embedding model U to simulate/match the output of embedding Z. They then use this to calculate an information suf...	Rebuttal 1: Rebuttal: We warmly thank Reviewer id3c for their reviews. Weaknesses: 1. We refer the reviewer to Questions below. 2. Computational costs. Our method is cheap computationally. We quickly discussed the computational cost of evaluating our method in Sec. E.5. Computing the informativeness of a mod...	Rebuttal 1: Rebuttal: We appreciate that all the reviewers have recognized our work's novelty, significance, and clarity, as well as its comprehensive empirical analysis. The reviewers raised 3 main concerns: a lack of details about the estimation procedure, its practical usage, and computational cost, and requested a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents	Accept (poster)	Summary: The paper proposes a method for including the influence of other agent’s into an agent’s own reward function, which is shown to promote cooperation in SSDs like IPD and the temporal version Coins. It also highlights that the method only requires first order RL algorithms, does not require access to privileged ...	Rebuttal 1: Rebuttal: Weaknesses W1 - One key issue with LOLA and IPD-Analytic is that the original LOLA implementation assumed that the opponent takes vanilla gradient steps directly through the closed-form analytic solution to the IPD return. LOLA then directly computes the gradient of the opponent's return w.r.t...	Summary: The authors develop a new intrinsic reward that tracks the balance of influence compared to a counterfactual baseline and provides positive/negative rewards to incentivize naive learners to cooperate. They test this algorithm in an iterated prisoner dilemma and a simplified coin game, showing positive results ...	Rebuttal 1: Rebuttal: Weaknesses W1 - We have added these details in the Appendix: each experiment was run with 8 random seeds, and results were averaged and plotted with SEM. W2-3 - To the best of our knowledge, previous works in the opponent shaping literature have conducted evaluations only on simple iterated ma...	Summary: The paper discusses an analysis of the effects of a combination of methods (1-step influence, debt mechanism and intrinsic reciprocal reward) on the emergence of cooperation in a society of artificial agents. The authors evaluate their approach for the case of n=2 using an IPD and the Coin game. Strengths: - ...	Rebuttal 1: Rebuttal: Weaknesses W1 - 2 - We apologize for our lack of clarity and hope the following provides intuition: reciprocating strategies (such as TFT) are known to induce cooperation [1]. Reciprocation requires the ability to distinguish cooperation from defection, which is difficult to define a priori in...	Summary: The authors introduce Reciprocators, RL agents that are intrinsically motivated to reciprocate the influence of other player's actions on the agent's returns. They show that this promotes cooperation in social dilemmas. Strengths: Originality: - I do not believe this method has been proposed before Quality:...	Rebuttal 1: Rebuttal: Weaknesses W1 - Although the central finding of this paper is that we are able to produce cooperative behavior through our intrinsic reward, we emphasize that the underlying mechanism of the reciprocal reward is one of opponent shaping rather than a prosocial inclination to cooperate. The intr...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems	Accept (poster)	Summary: The paper presents a novel approach by ingeniously leveraging large language models (LLMs) to generate data augmentation strategies for long-tailed problems. Given the recent rapid advancements in LLMs and their potential applications beyond natural language processing, this integration of LLMs with long-taile...	Rebuttal 1: Rebuttal: Thank you very much for your valuable comments and questions. Let me respond to them one by one: W1: In our framework, different prompts play complementary roles. For example, some prompts are responsible for generating new augmentation algorithms, while others mutate existing algorithms. Thr...	Summary: This paper proposes to leverage large language models (LLMs) to help automatically facilitate data augmentation for long-tailed learning. It first discusses the limitations of traditional re-balancing or data augmentation methods. Then it proposes a novel LLM-based augmentation framework LLM-AutoDA. LLM-AutoDA...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. These suggestions are very helpful for improving the quality of the paper. I will respond to your questions one by one: W1: Your suggestion is very pertinent. In the revised version, we will supplement some examples of augmentation strategies generated by...	Summary: The paper proposes LLM-AutoDA to select the optimal augmentation strategies with the help of pre-trained LLMs. Specifically, the authors carefully designed various prompts to instruct an LLM to design new algorithms or mutate existing algorithms with the goal of improving the validation performance on long-tai...	Rebuttal 1: Rebuttal: Thank you very much for your detailed comments and questions. These feedbacks are invaluable for improving our work. Let me respond to your questions one by one: First, from your review comments, I noticed that our work may have caused some misunderstandings. For example, you thought our method "...	Summary: This work introduces gradient-free black-box optimization algorithms to formulate appropriate data augmentation methods, achieving some performance improvements. Utilizing LLM for evolutionary strategies is interesting, but the final augmentation strategy remains a black box. This makes it difficult to validat...	Rebuttal 1: Rebuttal: Thank you very much for these pertinent and in-depth comments, which are very helpful in improving our work and clarifying our contributions. Let me respond to your comments one by one: W1: Thank you for your advice. We have done our best to supplement the comparative experiments of our metho...	Rebuttal 1: Rebuttal: Dear reviewers, We sincerely appreciate your valuable comments and suggestions. We are encouraged by the positive feedback highlighting the novelty, significance, and potential impact of our work in the field of long-tailed learning. We are delighted to receive many favorable assessments. Review...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Topology-aware Graph Coarsening Framework for Continual Graph Learning	Accept (poster)	Summary: This paper proposes a novel rehearsal-based continual graph learning method, TACO, which stores the information of previous tasks as a reduced graph. TACO performs graph coarsening based on node representation proximities to reduce a graph while preserving essential topological information. TACO shows signific...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback! We are happy to address your concerns and questions. Detailed responses to your comments are provided below, along with new experimental results. --- ## W1. The paper is not well structured A: We are committed to making these improvements to enhance...	Summary: The paper proposes a continual learning framework for the node classification task. The key idea is to learn a compressed form of graph from previous task and train the model by combining (and further compressing) the reduced graph from previous stage and the new graph. The graph compression step solves the ...	Rebuttal 1: Rebuttal: Thank you for your positive feedback. We are happy to address your concerns and questions. Detailed responses to your comments are provided below. --- ## Q1. Clarification on the setting of task splitting A: We understand your concern. To clarify, **$G_t$ contains nodes from task $t$ and...	Summary: This paper presents TACO, a novel framework designed to address the issue of catastrophic forgetting in Graph Neural Networks (GNNs) during continual learning. The framework proposes a method to store information from previous tasks as a reduced graph. This graph expands with new tasks and undergoes a reductio...	Rebuttal 1: Rebuttal: Thank you for your positive feedback! We are happy to address your concerns and questions. Detailed responses to your comments are provided below. --- ## W1. Additional related studies A: Thank you for pointing it out. **We have conducted additional literature review and compared with mor...	Summary: This paper studies the continual learning of Graph Neural Networks. Specifically, the de facto rehearsal based methods fail to adequately capture the topological information. Accordingly, in this work, the authors propose to develop a graph coearsening based method, which stores the topological information int...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback. We are happy to address your concerns and questions. Detailed responses to your comments are provided below. --- ## W1. How to split datasets into different tasks A: We split the original graph into different tasks (subgraphs) based on the timestamp...	Rebuttal 1: Rebuttal: We appreciate the thorough reviews provided for our paper. We are encouraged by the positive comments. **All four reviewers recognize the novelty of our work. They also concur that our extensive experimental evaluation serves as strong evidence to support the effectiveness of our method. Addition...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback	Accept (poster)	Summary: The authors consider stochastic bandit submodular maximization. For this problem, there are two known regret upper bounds: a $\sqrt{T}$ regret upper bound with large coefficients obtained by naively considering the problem as the multi-armed bandit problem, and a $T^{2/3}$ regret upper bound with a relatively ...	Rebuttal 1: Rebuttal: We are grateful for your review and suggestions. The mention of two types of algorithms in the lower bound section is intended to provide more intuition for the $ T^{2/3} $ term of the lower bound, which is uncommon in the bandits literature. To avoid any confusion, we will move this intuition to...	Summary: The authors investigate lower bounds for regret for combinatorial multi-armed bandit (CMAB) problems under submodular rewards and bandit feedback. For stochastic environments, there has been an open question about the gap between $O(\sqrt{T})$ dependence typically seen in MAB problems (and submodular CMAB wi...	Rebuttal 1: Rebuttal: We appreciate your thorough review and valuable suggestions. In [21], Theorem 7 provides a lower bound on the convergence rate of bandit Blackwell games. In our setting, as you correctly pointed out, this reduction implies that all explore-then-commit greedy algorithms in adversarial setting have...	Summary: The paper addresses the problem of stochastic submodular bandits. The main contributions are twofold: it provides a new lower bound for the problem and proposes a novel UCB-type algorithm whose regret matches this lower bound. Strengths: - The paper introduces notation clearly and provides a sufficiently thor...	Rebuttal 1: Rebuttal: Thank you for your review. We noticed that this review appears to be copied from a previous conference. We would like to highlight that the concerns raised have already been addressed in the current submission. Specifically: * In the introduction section (from line 119), we discuss the indirect ...	Summary: This paper studies the stochastic submodular bandit problem. In this problem, the decision-maker needs to select a subset with size at most k from a known ground set, and then the decision-maker gains a stochastic reward associated with the subset. The expectation of the reward is a submodular function. This p...	Rebuttal 1: Rebuttal: We appreciate your thorough review and valuable suggestions. We agree that the statement in line 132 that $ R_{gr}$ is always more appropriate than $ R_{\alpha}$ is too strong. We will change it to: "In studying regret against approximations attained by an offline step-wise greedy procedure, $R_{...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors of this paper study the submodular maximization problem with bandit feedback. By adopting regret as the metric, the authors prove the minimax bound of the regret for their problem. A UCB-based algorithm is devised to tackle the submodular optimization problem and the authors prove that this algorit...	Rebuttal 1: Rebuttal: We appreciate your review and valuable suggestions. Regarding your question on the applications of this setting, one example is antibiotic prescription, as mentioned in the introduction section. The reason for noisy feedback in this context is that patients come from a population with an unknown ...	null	null	null	null	null	null
Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach	Accept (poster)	Summary: This work focuses on an important and realistic visual task called visual entity recognition (image, query --> entity name). This work proposes a LLM driven technique to generate and refine large scale training data by modifying incorrectly labeled entity names from Entity-WebLI. Several errors are identified ...	Rebuttal 1: Rebuttal: **releasing data and code: Although the data will not be released, I think the reproducibility of experiments are supported by the experiments with 5 random seeds and also using open-sourced data such as LAION. The community will benefit from reproducing such large-scale entity recognition image d...	Summary: The work proposes a data-centric approach, that leverages MLLMs to first verify the correspondence of the existing pre-training dataset (Entity-WebLI) and correct errors, ask them to produce rationales, and generate novel, query split-oriented QA pairs to train a language model. The refined dataset REW is cura...	Rebuttal 1: Rebuttal: In L241, authors state that the trained model is better than MLLM itself which is used to produce filtered datasets. However, I did not see the results for Gemini Pro (in L226 they mention that) in Table 1. This result is shown in the inline text in Section 4.2: “Finally, we report the zero-s...	Summary: The paper deals with web-scale visual entity recognition, which consists of math a question(text)-image query to one of the 6M entities (wikipedia page title) of a base of reference. In the vein of previous works, the task is addressed with a generative text-to-image model, the challenge lying in building the ...	Rebuttal 1: Rebuttal: **It is regrettable to rely on a private model (Gemini-pro) to build the main contribution of the paper, which is the training dataset. There's no guarantee that this model will be stable over time, such that the proposed method (to build the dataset) may not be reproducible in a couple of months....	Summary: The paper presents a method to curate datasets for visual entity recognition tasks. They rely on a multimodal LLM (Gemini Pro), which employs metadata information about the image (the caption) and the content of the Wikipedia page to improve the quality of the Entity Web-Li dataset. They further enrich the res...	Rebuttal 1: Rebuttal: **The main contribution of the paper is the creation of an enriched version of the Entity Web-Li dataset, named REW, which however is not released to the public together with the paper (this is mentioned in the Checklist). I find this a major weakness of this project, given the complexity of the c...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive comments. The reviewers especially appreciate the “thorough experiments and ablation studies”, that our work “could be of great help for future works that focus on synthetic datasets towards entity-specific domains”, and better results compared to the ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Robust Fine-tuning of Zero-shot Models via Variance Reduction	Accept (poster)	Summary: This paper addresses the ID-OOD trade-off of the ensemble-based method under the pretrain-finetune regime. The authors propose a simple yet effective adaptive ensemble method that determines the ensemble coefficient based on the feature distance between a test sample and the zero-shot failure set. The proposed...	Rebuttal 1: Rebuttal: Q1: Weaknesses on computation costs. A1: For efficiently performing k-NN search, we use Faiss library [11], which can perform billion-scale similarity search with GPUs. For our ImageNet experiments, the inference speed of kNN search for a single image is averaged 3.2 ms. For our ImageNet...	Summary: This study examines the robust fine-tuning of CLIP models. The proposed method, Variance Reduction Fine-tuning (VRF), employs a sample-wise ensembling technique to enhance both ID and OOD accuracy, reducing trade-offs between them. Experimental results on ImageNet and associated distribution shifts empirically...	Rebuttal 1: Rebuttal: Q1: Applying VRF to other robust fine-tuning methods. A1: To further demonstrate the versatility and effectiveness of our VRF method, we applied it to another robust fine-tuning method, FTYP, and conducted experiments on ImageNet and its variants. As expected, our VRF framework further im...	Summary: This paper studies the trade-off between in-distribution (ID) and out-of-distribution (OOD) performance of pre-trained models before and after fine-tuning. The authors observed that the sample distance is inversely proportional to $\frac{Acc_{ft}}{Acc_{zs}}$. After modeling the residual error of the model, the...	Rebuttal 1: Rebuttal: Q1: VRF requires identifying and saving the zs classification error samples for subsequent use, which presents certain limitations. A1: For our ImageNet experiments, the storage size for the ZSF set features is 289 MB. While this does require additional storage compared to some traditiona...	Summary: This paper aims to tackle the ID-OOD trade-off in the fine-tuning of pre-trained models with zero-shot abilities like CLIP. The proposed Variance Reduction Fine-tuning (VRF) is a sample-wise ensembling method concerning the zero-shot and fine-tuned models. The ensemble weights are determined by the distance fr...	Rebuttal 1: Rebuttal: Q1: There should be a discussion on the theoretical and practical computational complexity regarding space and time. A1: To address the efficiency of k-NN search, we leverage the Faiss library [11], which is optimized for large-scale similarity searches using GPUs. In our ImageNet experi...	Rebuttal 1: Rebuttal: We have uploaded ID-OOD frontier curves for WSE in the attachment. Pdf: /pdf/bb943f3a3a6816cfad277887a58da672f167f6bc.pdf	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper addresses the issue of ID-OOD (In-Distribution vs. Out-of-Distribution) in the context of robust fine-tuning techniques commonly used in ensemble methods. The authors propose a sample-wise mixing approach to resolve this problem. The method involves creating a Zero-Shot Failure set, which contains s...	Rebuttal 1: Rebuttal: Q1: The main drawback of the proposed method is the reliance on multiple hyperparameters. A1: We understand and acknowledge the reviewer’s concern regarding the complexity of tuning multiple hyperparameters. However, we would like to provide further clarification and context to address t...	null	null	null	null	null	null
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs	Accept (oral)	Summary: This paper presents extensive studies on existing MLLM benchmarks addressing the difficulties involved in consolidating and interpreting results from various tasks for MLLM designs. Moreover, the authors also propose Spatial Vision Aggregator (SVA), a dynamic and spatially-aware connector to fuse vision featur...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and acknowledgments. We appreciate that you find our work is “well-written”, contains “extensive experiments” and “good performance”, and will “definitely benefit the community” when open-sourced. We summarize your questions and provide responses below...	Summary: This paper introduces a multimodal large language models (MLLMs), named Cambrian-1, designed with a vision-centric approach. In current MLLM researches, the choices of visual encoder are not sufficiently explored. This study utilizes MLLM performance as a visual representation evaluator, showing different char...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and acknowledgments. We appreciate that you find our work “notably well-written”, “shows the limitations of MLLM benchmarks”, and “a great contribution to this field” via our fully-open approach. We summarize your questions and provide our explanations...	Summary: The paper conducts a comprehensive study of multimodal LLMs from a vision-centric perspective. Different from the lines of previous literature which aim to propose new architectures/algorithms for multimodal LLMs, this paper carefully splits the design space of visual parts of multimodal LLMs into several indi...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and acknowledgments. We appreciate that you find our work “insightful and well-motivated”, contains “rigorous and carefully designed experiments”, and “can be valuable for the future development of multimodal LLMs”. > **W1 & Q1: Discussion around nati...	Summary: The paper explores Multimodal Large Language Models (MLLMs) and constructs the Cambrian-1 series models. This approach builds a series of advanced MLLMs through five key pillars, achieving exceptional performance in vision-centric tasks and diverse benchmark tests. By exploring different visual encoders, the m...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and acknowledgments. We appreciate that you find our work “bridges the gap in visual understanding”, “offers critical assessment and enhancement of MLLM benchmarks”, “introduces an innovative spatial-aware connector”, and “achieves top performance”. We...	Rebuttal 1: Rebuttal: We thank all reviewers for their thorough review and valuable feedback on our paper. We appreciate that you find our work "bridges the gap in visual understanding" (Reviewers BwuE, xvQn), "offers assessment and enhancement of MLLM benchmarks" (Reviewers BwuE, K2Un, ED7N), "well-written" (Reviewer...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP	Accept (poster)	Summary: The authors extend the idea proposed in [10] of decomposing CLIP’s image representation and interpreting the decomposed components via text, to models other than CLIP via learning a set of mapping functions, one for each decomposed component, to map the representation of a model to be analysed (e.g., DINO) to ...	Rebuttal 1: Rebuttal: Thank you for your detailed comments on our paper, we appreciate the feedback. We are glad that you find the direction of our work important and interesting. However, we disagree with your characterization of our work on multiple points. We answer your concerns below: > [W1] In my opinion, the a...	Summary: This paper introduces a framework for identifying the roles of various components in Vision Transformers (ViTs), extending beyond CLIP models. The framework consists of two key components: RepDecompose, which automates the decomposition of representations into contributions, and CompAlign, which maps these con...	Rebuttal 1: Rebuttal: Thank you for your favorable review. We are happy to hear that you found our work clear and coherent, with comprehensive evaluation. We answer your questions below: > Ambiguity in Introduction Thank you for the feedback. We will take care to expand and rephrase the introduction to remove this am...	Summary: This paper proposed a novel representation decomposition method for general ViT models. Then, with aligning the component representations to CLIP space, the decomposed contribution vectors can be interpreted through text using CLIP text encoder. Moreover, a scoring function is also proposed to assign an import...	Rebuttal 1: Rebuttal: Thank you for the favorable review. We answer your questions below: > Lack of qualitative or quantitative comparison with the related works, such as the previous image representation decomposition method mentioned in Sec.3. We would like to point out that our work can be viewed as an extension o...	Summary: The paper analyzes the direct contributions of individual layers and attention heads in vision models. Based on a similar idea to Gandelsman at al. that was applied to CLIP, this method decomposes the final representations of other models into individual contributions. To interpret these contributions, the pap...	Rebuttal 1: Rebuttal: Thank you for your favorable review. We are glad that you found our work novel, clear and convincing. We address your questions below: > A brief explanation of the TextSpan algorithm can be useful to understand the paper without the need for reading Gandelsman et al. We agree and apologize for t...	Rebuttal 1: Rebuttal: We thank the reviewers for their extensive and insightful comments. We are encouraged that the reviewers found our work novel, clear, and impactful. We address some common concerns in this global rebuttal: ## How does RepDecompose work? To illustrate the workings of our algorithm, we describe th...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper proposes a novel method for evaluating the contribution of individual components in arbitrary vision transformers and mapping these contributions to the CLIP space for text interpretation. To avoid rigid matching of each component, the paper introduces a continuous scoring function for component-feat...	Rebuttal 1: Rebuttal: Thank you for your positive review. We are encouraged that you found our work novel with comprehensive experiments. We reply to your questions below: > The paper does not include comparisons with any baseline methods. Although previous methods may not extend to arbitrary Vision Transformer models...	null	null	null	null	null	null
$\textit{Bifr\"ost}$: 3D-Aware Image Compositing with Language Instructions	Accept (poster)	Summary: This paper proposes a 3D-aware framework for generative image compositing. It first fine-tune a MLLM with custom counterfacual dataset consisting of image-instruction-answer triplets to predict object bounding box and depth value. In the second stage, a pretrained diffusion model is fine-tuned with large-scale...	Rebuttal 1: Rebuttal: # Response to Reviewer dLCn We thank the reviewer for the positive feedback and insightful comments. We address the questions and comments below. > Q1: ...image-instruction-answer triplet data generation… As we described in the Dataset Generation section. We have gotten the names of the selecte...	Summary: This paper presents a 3D-aware image compositing framework to guide the depth-aware placement of objects within images. The method leverages a multi-modal LM which proposes a bounding box and depth of the object to be inserted. This is followed by fusion reference object depth with the background's depth map. ...	Rebuttal 1: Rebuttal: # Response to Reviewer CdZU We thank the reviewer for the positive feedback and insightful comments. We address the questions and comments below. > Q1: ...clarity… R1: Thanks, we will proofread the paper and correct the writing errors. Regarding our depth fusion algorithm, we briefly introduce...	Summary: This paper deals with a “generative fill” task where the generation model aims to fill the reference object to the target location of a background image in a reasonable flavor. The first difficulty is to figure out the exact target location and the relative depth for the reference object to appear in the gener...	Rebuttal 1: Rebuttal: # Response to Reviewer wo4W We thank the reviewer for the positive feedback and insightful comments. We address the questions and comments below. > Q1: ...lacks technical novelty… R1: Many thanks for acknowledging that our method obtains good results than other models and for the contribution o...	Summary: ## Summary of the Paper: Problem Statement: Given a single-object image I_ref with object O_ref , a background image I_bg containing an object O_bg, and a text prompt P_text, the paper proposes a method for composing O_ref onto I_bg in such a way that the position of O_ref in the resulting image adher...	Rebuttal 1: Rebuttal: # Response to Reviewer 1M1TS We thank the reviewer for the positive feedback and insightful comments. We address the questions and comments below. > Q1: …incorporating depth…reasoning behind this… R1: Many thanks for acknowledging our novelty in the context of image composition. We achieve 3D-a...	Rebuttal 1: Rebuttal: # Response to all Reviewers We thank the reviewers for their valuable time and feedback, and for acknowledging the well writing (Reviewer M1TS, dLCn), interesting ideas (Reviewer M1TS), solid results (Reviewer M1TS, CdZU, dLCn), and novel contribution (Reviewer M1TS, CdZU, dLCn). To our best know...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models	Accept (poster)	Summary: The paper introduces a novel training framework and novel sampling technique to enhance the state-of-the-art personalized and style-preserving adaptation of text-to-image diffusion models in the low-shot fine-tuning regime. Strengths: * The training objective introduced in the paper is implicitly incentivized...	Rebuttal 1: Rebuttal: Dear reviewer XWYq, Thank you for valuable comments and suggestions in reviewing our work. We address each of your questions and concerns individually as follows. --- ### [W1] Regarding computational cost We will move the limitation into the main text in our final revision. --- ### [W2] Referen...	Summary: Current personalized T2I models, like DreamBooth, are capable of generating personalized images by fine-tuning on a small set of reference images. However, these fine-tuned models often suffer from robustness issues, such as poor compositional capabilities with the pretrained model concepts and other fine-tune...	Rebuttal 1: Rebuttal: Dear reviewer ayXu, Thank you for valuable comments and suggestions in reviewing our work. We address each of your questions and concerns individually as follows. --- ### [W1] Improvement by using DCO. We claim that DCO uses a novel fine-tuning that pushes the frontier of the pareto curve of ima...	Summary: This paper introduces a novel fine-tuning objective called Direct Consistency Optimization, which regulates the deviation between fine-tuning and pre-trained models to preserve pre-trained knowledge during the fine-tuning process. Models fine-tuned using this method can be merged seamlessly without interferenc...	Rebuttal 1: Rebuttal: Dear reviewer aXKh, Thank you for valuable comments and suggestions in reviewing our work. We address each of your questions and concerns individually as follows. --- ### [W1] Experiment with LoRA We clarify that all our experiments were conducted using LoRA. This has been explicitly mentioned i...	Summary: This paper studies the catastrophically forgetting issue in personalizing text-to-image diffusion model. The main comparable baseline is DreamBooth, which prevents forgetting by finetuning the model on a subsample of original training data while learning new concepts. The proposed method, on the other hand, di...	Rebuttal 1: Rebuttal: Dear reviewer HfJW, Thank you for valuable comments and suggestions in reviewing our work. We address your questions and concerns as follows. --- ### [W1] Human evaluation Following your suggestion, we conduct a user study to compare DCO (ours) against DreamBooth (DB) and DB with prior preservat...	Rebuttal 1: Rebuttal: Dear reviewers and AC, We sincerely appreciate your valuable time and effort spent reviewing our manuscript. As reviewers highlighted, we believe our work presents a novel training objective that is simple and effective (HfJW, aXKh, ayXu, XWYq) that is supported by qualitative and quantitative e...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Robust Preference Optimization through Reward Model Distillation	Reject	Summary: Based on the analysis of the shortcomings of DPO (Section 2.3), the authors proposed a simple reward distillation approach (Section 3.1) to align language models and a pessimistic variant (Section 3.2). These approaches outperform the vanilla DPO. Strengths: - The analysis in Section 2.3 extends the result in...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We were pleased to see that the reviewer appreciated the technical contribution of this work, and that the reviewer believed that the paper offered intriguing insights. We also appreciate the concerns and questions raised in t...	Summary: This paper addresses the limitations of DPO in LM alignment by proposing a reward model distillation approach. DPO, while efficient, often leads to overconfident and degenerate policies due to limited preference annotations. The authors introduce a method that trains LMs to match the distribution from a reward...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We were happy to see that the reviewer viewed our work as a considerable theoretical contribution towards understanding, and mitigating, degeneration in DPO, and that the reviewer thought that our analysis was theoretically so...	Summary: The authors discuss and give formal results on the limitations of DPO that have been observed in practice, and investigate reward model objectives for 1) distilling reward differences into the generator (eq. 7), and 2) pessimistic "minimax" distillation over a family of reward models, to mitigate these limitat...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We were delighted to see that the reviewer finds our work to be well-written, insightful, well-formulated, and novel. Per the reviewer’s suggestions, we have added new experimental results in our supplemental section for this ...	null	null	Rebuttal 1: Rebuttal: Thank you to all the reviewers for taking the time to read and comment on our work. We were delighted to see that overall the reviewers found our work to be well-written, insightful, and a considerable technical contribution. We were also pleased to receive several good questions and suggestions: ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AlphaMath Almost Zero: Process Supervision without Process	Accept (poster)	Summary: The paper proposes an innovative framework named AlphaMath, aimed at enhancing the mathematical reasoning capabilities of large language models (LLMs) without relying on expensive human annotations from domain experts or GPT-4. The authors leverage Monte Carlo Tree Search (MCTS) to allow a pre-trained LLM to a...	Rebuttal 1: Rebuttal: Dear Reviewer 5iMV, We are grateful for the time and effort you have invested in providing detailed and insightful feedback. We appreciate your recognition of the key contributions of our method, including eliminating the need for human annotation, the commendability of trajectories generated via...	Summary: The authors propose the AlphaMath method, which leverages Monte Carlo Tree Search (MCTS) to improve the mathematical reasoning capabilities of large language models (LLMs) without requiring expensive human or GPT-4 annotations for process supervision. The framework integrates a value model with the LLM to gene...	Rebuttal 1: Rebuttal: Dear Reviewer 15WC, We sincerely appreciate your efforts in evaluating our manuscript. We have carefully considered your insights and critiques, then address each of your comments. > W1: Distinctions to [1,4] Thanks for raising this issue. We will avoid over-claiming in future version. Highligh...	Summary: This paper leverages the Monte Carlo Tree Search (MCTS) to iteratively train policy and value models by automatically generating process supervision and step-level evaluation signals, eliminating the need for human-annotated process supervision data. Specifically, the method combines the inner capabilities of ...	Rebuttal 1: Rebuttal: Dear Reviewer otxC, We greatly appreciate your valuable comments and are pleased that you recognize the novelty and efficiency of our proposed method. Below, we address your comments in detail. > W1: Although the method does not rely on human-annotated process data, it still requires actual answ...	Summary: The paper introduces a novel approach leveraging the Monte Carlo Tree Search (MCTS) framework to generate process supervision and step-level evaluation signals automatically, thus enhancing the mathematical reasoning capabilities of LLMs. This method bypasses the need for costly and labor-intensive manual anno...	Rebuttal 1: Rebuttal: Dear Reviewer EVyZ, We greatly appreciate your detailed review and the recognition of the strengths of our paper. Due to word limit, we apologize for shortening your questions. We will address each of your comments in detail, hoping to alleviate any concerns you may have. > W1: Duplicate citatio...	Rebuttal 1: Rebuttal: > G1: Why our value model training works better? We agree with previous works [1,4] (by Reviewer 15WC) may learn a reasonable policy model if they applied some autonomous annotation method, even they actually didn't do it. However, we respectfully argue that the value model learning in [1,4] may ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper utilizes Monte Carlo Tree Search (MCTS) to sample high-quality process-supervised data for iterative training, effectively reducing the dependency on GPT-4 and thus lowering the associated costs. Additionally, the authors propose an inference strategy, step-level beam search, which leverages value m...	Rebuttal 1: Rebuttal: Dear Reviewer 2HCk We sincerely thank you for your thorough evaluation and for acknowledging the clarity of our paper, as well as the effectiveness and significant performance improvements we've demonstrated. Due to word limit, we apologize for omitting your question and replacing them with numbe...	null	null	null	null	null	null
LoRA-GA: Low-Rank Adaptation with Gradient Approximation	Accept (poster)	Summary: This paper proposes LoRA-GA, which uses an adapter to approximate the gradient update of weights. This method achieves a 2-4 times improvement in convergence speed compared to vanilla LoRA and offers better accuracy than other LoRA-based methods. Strengths: 1. This paper provides a novel perspective on the in...	Rebuttal 1: Rebuttal: Thank you for your valuable questions and suggestion! ### Weakness Weakness: The difference between LoRA-GA and LoRA reparameterization is not shown in Figure 1. Answer: The parameterization of LoRA and LoRA-GA is identical, with both methods utilizing low-rank matrices $A$ and $B$, as ...	Summary: LoRA has a slower convergence rate compared to full fine-tuning. This paper proposes a novel initialization method, LoRA-GA (Low-Rank Adaptation with Gradient Approximation), which aligns the gradients of the low-rank matrix product with those of full fine-tuning from the first step. Numerical experiments demo...	Rebuttal 1: Rebuttal: We sincerely thank you for the valuable feedback and insightful comments. ### Question 1 Question1: The concept of using eigenvectors for initialization might not be entirely new, but its application in this specific context is original. Answer1: Indeed, the idea of utilizing eigenvect...	Summary: This paper proposes a novel initialization method for LoRA based on detailed theoretical analysis. The experimental results illustrate that the proposed method can achieve a great performance on the most tasks. Strengths: Strength: 1. The paper provide a beautiful theoretical analysis about the initializatio...	Rebuttal 1: Rebuttal: We appreciate your valuable suggestions and have conducted additional experiments in response to your feedback. ### Dataset Selection Regarding your concern about the complexity of tasks, we acknowledge that GULU may have been effectively addressed by current PEFT methods. Consequently, we expan...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Balancing Context Length and Mixing Times for Reinforcement Learning at Scale	Accept (poster)	Summary: This paper studies the interaction between policies that operate given some context and the mixing time of the policies. It provides a novel connection between the context length and the mixing time, indicating that increased context length might lead to increased mixing times and thus, need for longer, more e...	Rebuttal 1: Rebuttal: Thank you for your comprehensive review of our paper. We really appreciate your kind words about the importance of our problem statement, the clarity of our writing, the quality of our theoretical results, and the value of our work to the research community. We have tried to address each point of ...	Summary: This work presents theoretical and empirical evidence that increasing the context length informing a policy also increases the amount of time required to evaluate the capability of the policy accurately (in other words for the distribution over states independent of initial state to stabilize). Theorem bounds ...	Rebuttal 1: Rebuttal: Thank you for your detailed review of our paper. We very much appreciate your kind words regarding the originality, quality, clarity, and significance of our work. We have attempted to address your key concerns and all points of confusion mentioned in your review below. **Definitions of aperiodi...	Summary: This work analyzes the mixing time of a policy in average-reward reinforcement learning problems. It shows that the mixing time is related to the structure of the underlying dynamic Bayesian network (DBN), specifically how the state variables are grouped into strongly connected components by the policy. Additi...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review of our paper. We appreciate that you highlighted our theoretical contribution and want to thank you for your praise regarding the writing quality and empirical verification of our theoretical results. Below we have tried to provide clarity regarding each of you...	Summary: In the quest for applying RL to more realistic tasks in the long term, the authors interrogate the relationship between context length and mixing times. They provide an extensive theoretical analysis leading to a novel finding regarding the trade-off between learning with large context and slower evaluation. T...	Rebuttal 1: Rebuttal: We wanted to begin by thanking you for the kind words in your review regarding a number of aspects of our paper including the novelty, comprehensive empirical analysis, and writing quality. We really appreciate this validation of our work. We also wanted to make sure to engage with all of your con...	Rebuttal 1: Rebuttal: We wanted to begin by thanking all six reviewers assigned to our paper. We were very happy to see so many positive comments about our work. We were also grateful to see so many constructive comments that will help make our work even better in the final draft. We have provided a detailed rebuttal f...	NeurIPS_2024_submissions_huggingface	2,024	Summary: Authors analyzed the relationship between mixing time (policy evaluation time) with increase in context length for non-markovian, POMDPs. Certainly, increasing context, increases mixing time. Strengths: Authors propose a tighter upper-bound for mixing times of multi-dimensional mdp with longer contexts Weakn...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We are sorry to see that you are so negative about the value of our contributions to the community. We believe that there are some key points of confusion that may have led you to this perspective that we will attempt to address below. We will als...	Summary: I am not a theorist and so did not feel able to provide a detailed review of the theoretical parts of this work. The authors discuss the trade-off between context length and mixing time of MDPs, demonstrating that a longer context length leads to longer mixing times and hence greater difficulty in evaluating...	Rebuttal 1: Rebuttal: We wanted to begin by thanking you for your thoughtful review of our paper. We really appreciate your praise of the implications of our theoretical findings, the evaluation of these findings, and the writing quality of our paper. We also found your intuitive explanation of our findings to be quite...	null	null	null	null
SafeWorld: Geo-Diverse Safety Alignment	Accept (poster)	Summary: - An open question is ensuring LLM outputs abide to content policies created by their engineers (“safety”) - One challenge is geographic variation in these requirements–certain outputs may be acceptable in one region but not another. - This paper makes three contributions - It introduces a benchmark design...	Rebuttal 1: Rebuttal: Dear Reviewer `Fmfm`, Thank you for your thoughtful review and recognizing the novelty and compelling focus of our paper. We appreciate your positive feedback on our benchmark construction process and are glad you found our empirical studies substantial, interesting, and helpful. Below, we answer...	Summary: This paper describes the challenge of geo-diverse safety standards, where the legally compliant and cultural sensitive responses vary by context (cultural, geographic, etc). The paper describes a method for creating a dataset of test queries, uses them to benchmark LLMs and finetune (w/DPO) an LLM. They find...	Rebuttal 1: Rebuttal: Dear Reviewer `NsF2`, We thank the reviewer for their thoughtful engagement with our work. We appreciate their recognition of the importance of geo-diverse safety challenges and their acknowledgment of SafeWorld as a well-designed and impactful benchmark. We would like to address your concerns in...	Summary: In this paper the authors study how LLMs incorporate global cultural norms and laws into model responses. They provide three main contributions: (1) a dataset of questions relating to global cultural norms and laws in different ways, (2) an autoeval setup for this dataset and (3) empirical evidence that train...	Rebuttal 1: Rebuttal: Dear Reviewer `xqmN`, Thank you for engaging with our work! We’re particularly excited that our paper is “an important paper that you are glad to see is being worked on, especially at NeurIPS”. We're pleased our geo-diverse safety research question is seen as critically important. Below are our r...	null	null	Rebuttal 1: Rebuttal: Dear reviewers, We thank for all your thoughtful reviews and pleased that you recognize the strengths of our work. The reviewers mentioned multiple merits: - An important paper that I am glad is being worked on, especially at NeurIPS: `xqmN` - Compelling paper focus: `Fmfm` - **Very int...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reproducibility of predictive networks for mouse visual cortex	Accept (spotlight)	Summary: This work demonstrates that overparameterized neural network models, which have many non-unique solutions, can lead to inconsistencies in representing the mouse visual cortex. It suggests a novel approach called "adaptive regularization," where the regularization parameters in the loss term are learnable rathe...	Rebuttal 1: Rebuttal: Thank you very much for your rigorous review. We highly appreciate your feedback, which helps us to improve our paper. Regarding the weaknesses you mention: * “The study is limited to only one type of model, the Rotational Equivariance CNN” The motivation behind the rotation-equivariant core...	Summary: The authors present a systematic investigation of the use of deep neural network fits to biological neurons as the basis for neuron cell type classification. The authors explore how various factors such as regularization and model pruning can influence both the predictive model fit and the consistency of the n...	Rebuttal 1: Rebuttal: Thank you for appreciating our paper stating that “with increasing interest in this area and increasingly bold claims, work like this is a breath of fresh air.” Regarding your questions: * **“Why is the adaptive model not pruned in the experiments shown in fig 4? I feel like it makes sense to inc...	Summary: This work studies models trained to predict the responses of neurons in visual cortex. The model has a shared multilayer network core followed by a final layer which maps the core features into individual neuron responses. The key question that this paper asks is, How reproducible are the individual neuron pro...	Rebuttal 1: Rebuttal: Thanks a lot for your time and effort for the review. We are very happy to see your feedback and high evaluation of our work. We will fix the typos, clarify details and address the style comments in the final version. You basically raise two main concerns about the t-SNE plot in Fig. 2 and the s...	Summary: The paper "Reproducibility of predictive networks for mouse visual cortex," explores the reproducibility of neuronal embeddings in the mouse visual cortex using deep predictive models. By introducing adaptive regularization and iterative feature pruning, the authors address key issues related to model overpara...	Rebuttal 1: Rebuttal: Thank you for thoroughly reviewing our paper and providing valuable feedback. We are happy that you found the adaptive regularization scheme “a significant novelty”, iterative pruning as “another novel contribution” and our consistent evaluation procedure to “highlight the robustness and reproduci...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive and positive feedback on our paper, highlighting its novelty and importance. One concern was that our analysis might not generalize beyond our architecture choice of a rotation-equivariant neural predictive model. We now have performed additional exp...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Data Acquisition via Experimental Design for Data Markets	Accept (poster)	Summary: The paper focuses on the problem of data acquisition in decentralized data marketplaces. The paper claims that acquiring training data using validation-based data valuation methods can be overfitting. Thus, the paper proposes DAVED, which directly selects training data by optimizing test loss on a given test s...	Rebuttal 1: Rebuttal: We greatly thank the reviewer for their close reading of our work, their detailed feedback, and their enthusiastic support. We address the comments raised below. > [...] compare its performance with methods that focus on data acquisition problems First, we want to emphasize that our method uses ...	Summary: This paper uses the linear experimental design to tackle the data acquisition problem. Strengths: This paper tackles a very challenging but important problem in the data marketplace : evaluating data quality before data transactions as well as minimizing the test error. The main contribution is the Federated...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. We agree that our exposition is dense and some derivations may be inaccessible to reader unfamiliar with experiment design. We will add a more detailed derivation in the Appendix where we will go through all the steps in more detail. Further, we will aim t...	Summary: This paper introduces a novel method for acquiring training data in a decentralized data market without requiring labeled validation data. Based on detailed and reliable theoretical support, the authors comes to a promising conclusion that the selection methods based on validation set has the performance as ba...	Rebuttal 1: Rebuttal: We greatly thank the reviewer for their close reading of our work, their detailed feedback, and their enthusiastic support. We address the comments raised below. > W1: The input for data acquisition task is too idealistic ... We absolutely agree that a truly practical implementation of our metho...	Summary: The paper introduces a new data valuation algorithm based on linear experimental design. The technique does not require a validation set and can be used in a federated setting. Strengths: The paper studies a well-motivated trending topic. Data valuation and data selection are important subfields in data-centr...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and the positive evaluation of our work. We agree that the choice of feature extractor is very important. In short, how well our linear proxy model works depends on the quality of our feature extractor. If there are features that are relevant to the task bu...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learner	Accept (poster)	Summary: This paper proposes to integrate MoE and LoRA into pre-trained ViT to construct a multi-task model. Specifically, the experts are formed in the FFN of the transformer blocks based on the channel similarity, and the LoRA is used for each expert to fine-tune the parameters. On top of that, the paper further intr...	Rebuttal 1: Rebuttal: Thank you for your detailed review, we will try to address any concerns encountered in the paper. > Q1: Is the router truly necessary? It is an insightful question regarding the role of the router in our framework. Initially, we followed to the conventional MoE framework which includes a router....	Summary: Considering recent trends in multi-task learning (MTL) that design Mixture-of-Experts (MoE) structures and integrate Low-Rank Adaptation (LoRA), this paper proposes Efficient Multi-Task Learning (EMTAL) to address the sub-optimal performance resulting from the rigid combination of MoE optimization and LoRA's r...	Rebuttal 1: Rebuttal: Thanks a lot for your insightful comments and suggestions. Our responses are summarized as below: > W1. On grouping strategies. A1. To the best of our knowledge, our work is the first one that employs the idea of grouping similar weights to establish the LoRA experts, and there lack grouping st...	Summary: The paper introduces a method to decompose ViT MLP matrices into a mixture of experts based on channel similarity, training each expert with LoRA. The authors also use a quality-retaining loss to effectively optimize during training for multi-task learning. Strengths: (S1): The idea of decomposing the wei...	Rebuttal 1: Rebuttal: Thanks for your insightful suggestions. Our point-to-point responses are summarized as below. > W1. On ablation study. Firstly, the setting alpha=0 from the very beginning is not equivalent to the vanilla LoRA without clustering. We refer to this as Cluster+Finer LoRA, and provide extra ablation ...	null	null	Rebuttal 1: Rebuttal: According to the reviewer's suggestions, we have provided a more fair comparison in Table A in 'Rebuttal. pdf' and compared the inference time of different methods Pdf: /pdf/ae2b3f7969ab41174f75cbef011ba4788da194f3.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning from Offline Foundation Features with Tensor Augmentations	Accept (poster)	Summary: The paper discusses leveraging the large foundational models in resource-scare settings to fine-tune an image classification task. The authors propose saving the features from the frozen foundational model and then using them to train/fine-tune a smaller classifier model. They introduce tensor augmentations to...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. 1 - This is the main limitation of our study that we highlighted in the Limitations subsection - much like any caching strategy, there is a trade off. However, we note that foundation models are inherently less suitable for low latency inference tasks due...	Summary: This paper introduced a training scheme considering offline foundation features with tensor augmentations LOFF-TA, focusing on limited resource settings. They basically trained a classifier on cached features from frozen foundation models with an augmentation process to these cached embedded features. Their pr...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. Weaknesses W1 - We do not think spatial tensor augmentations will harm anatomical features more than voxel space augmentations would. However, some care must be taken in the selection of the tensor augmentations. In Appendix C, we pointed out that co...	Summary: The authors propose to store low dimensional representations of images that are obtained after passing them through pretrained (foundation) models. Only afterwards augmentation strategies ("tensor transformations") for training the downstream classifier are employed. This has the benefit that the foundation mo...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. 1- We have made efforts to choose our wording carefully and clearly outline the limitations of our study. We welcome any suggestions for improvement and would be grateful if the reviewer could identify specific sections of the manuscript that might need f...	Summary: This paper proposed a framework to efficiently use foundation features for online serving. The idea (offline feature extraction from foundation model and online inference with lightweight model) is simple but interesting/effective. Strengths: 1. The paper is well written and easy to follow. 2. The paper propo...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. Weaknesses 1 - Reporting such an accuracy is not possible. Generally, the classes included during pre-training and fine tuning are different, e.g. self-supervised pre-training of Dino vs. supervised fine-tuning on retinopathy images. Furthermore, the...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features	Accept (poster)	Summary: In this paper, the authors design a feature-universal graph contrastive pre-training strategy for model rebuiding and data reshaping. They focus on the inherent complex characteristics in graphs, and introduce a theoretical analysis for their method. The gloabl uniformity constraint reduces the time complexity...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. Our responses are as follows. # R1 for W1. While there have been some LLM4graphs works which seems achieve feature diversity problem, as highlighted in our paper, they encounter many challenges which is correctly our focus. - Restricted Feature Input. LLMs re...	Summary: In this paper, it is discovered that contrastive learning and PCA follow the same framework. By combining the two, the authors propose a graph pre-training model that offers both the generalization capability of PCA for arbitrary node shapes and the powerful embedding ability of GCLs. This approach enhances th...	Rebuttal 1: Rebuttal: Thanks you very much for your comments. The following are our response. # R1 for W1. The works related to Graph Domain Adaptation (GDA) primarily focus on knowledge transfer between domains. They differ significantly from FUG in several ways. - First, GDAs are typically supervised in the sourc...	Summary: The paper is generally well-presented. It aims to address the generalizability of graph pre-training models to graph data with arbitrary node feature shapes. By comparing the Principal Component Analysis (PCA) with existing graph contrastive learning methods, the authors discovered that PCA follows the same fr...	Rebuttal 1: Rebuttal: Thanks for your comments, which are incredibly helpful for improving our paper. # R1 for W1. We apologize for not explaining this clearly. Here, we provide a formal definition of $Dim(\cdot)$ and $Fea(\cdot)$. $Dim(\cdot)$ is defined as a dimension-specific encoder, formalized as $DimEmb_i = Enc...	Summary: The authors propose a universal graph pretraining framework called FUG. Inspired by PCA, FUG includes a parameterized dimension encoding component to unify different forms of node attributes, thereby preventing the loss of important semantics. The authors design an optimization objective based on relative sema...	Rebuttal 1: Rebuttal: Thank you very much for recognizing our work. Our responses are blow. # R1 for W1. The robustness improvement brought by data augmentation mainly stems from its ability to encode perturbed data into the same representation as clean data. Although FUG does not use data augmentation, it retains th...	Rebuttal 1: Rebuttal: We sincerely thank the five reviewers for their constructive suggestions and comments, which have greatly helped us improve our paper. We also extend our gratitude to the NeurIPS Chairs for reviewing our paper. We have noticed that some reviewers raised concerns regarding Theorem 3.1. In our pape...	NeurIPS_2024_submissions_huggingface	2,024	Summary: In this paper, the authors try to answer the question: Could a graph contrastive pre-training principle based on PCA theory be developed to address inherent issues of PCA and broadly apply to node features of different shapes? Then the authors start with some theoretical analysis and then propose a method name...	Rebuttal 1: Rebuttal: Thanks for your thorough review of our paper and your comments. Here are our responses. # R1 for W1. (a) We first give the definition of $Dim(\cdot)$ and $Fea(\cdot)$. Given a data matrix $X\in\mathbb{R}^{N\times D}$, $N$ represents the number of samples and $D$ represents the number of dimensi...	null	null	null	null	null	null
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces	Accept (poster)	Summary: This paper addresses inverse RL in discounted MDPs with continuous state and action spaces. First, the paper delves on the design of a suitable solution concept, which results in learning a reward that makes the expert at least approximately optimal plus a linear normalization constraint. Then, the paper studi...	Rebuttal 1: Rebuttal: We thank the reviewer for the time spent in evaluating our work. In the following, we address your comments. ## Weaknesses > The presentation is confusing ... We would greatly appreciate it if you could specify which parts of the paper you find confusing. We put significant effort into presenti...	Summary: The paper proposes an optimization formulation of the inverse RL problem. The paper discusses the properties of the solution to the optimization problem. It further discusses how to reduce of the raw problem to a computation feasible one. It gives the theoretical analysis on the approximation error and sample ...	Rebuttal 1: Rebuttal: We thank the reviewer for the time spent in evaluating our work. In the following, we address your comments. ## Weaknesses > The paper's result lacks benchmark ... This work focuses on theoretical aspects of inverse RL, specifically on learning a cost function in continuous state and action spa...	Summary: This paper investigates the problem of inferring cost functions from observed optimal behavior in continuous state Markov Decision Processes. The authors develop their theoretical framework initially assuming full access to the expert policy. To address the issue of trivial solutions, they introduce a linear n...	Rebuttal 1: Rebuttal: > The paper appears to present a series of theorems without providing sufficient intuitive explanations or justifications. This approach can make the work difficult to understand for reviewer. We put significant effort into presenting the material in an accessible and comprehensible manner with...	Summary: The paper 1) establishes a formal framework for studying the problem of continuous state- and action-space inverse reinforcement learning (i.e. given an expert policy, recovering a set of cost functions for which the policy is optimal) 2) provides theoretical results characterizing the set of cost functions th...	Rebuttal 1: Rebuttal: We thank the reviewer for the time spent in evaluating our work. In the following, we address your comments. > The paper certainly contains enough material for a substantially longer journal paper - some of the sections could benefit from being longer, trying to convey more intuitions and context ...	Rebuttal 1: Rebuttal: We thank all reviewers for their helpful feedback. Below, we provide our responses addressing each point raised by the reviewers. Pdf: /pdf/86cb6f4926efde8fcf49565ef22aa9025b2ffb49.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis	Accept (poster)	Summary: This paper introduces a new method, Kernel PCA Attention (RPC-Attention), designed to enhance the self-attention mechanisms in transformers widely used in sequence modeling for both natural language processing and computer vision. The method applies principal component analysis to derive self-attention kernels...	Rebuttal 1: Rebuttal: Q1. My main concern is about the practicality of the novel RPC-Attention mechanism. In your experiments, RPC-SymViT can only scale to a small size, and it does not show a significant performance advantage compared to ViT-Small. **I am also concerned about the scalability of this model, which ...	Summary: This paper studies the self-attention mechanism. The authors recover self-attention from kernel principle component analysis (kernel PCA). The authors empirically verify that the projection error is being minimized during training, suggesting that the transformer model is actually training to perform PCA durin...	Rebuttal 1: Rebuttal: Q1. The RPC-Attention uses an iterative algorithm PAP to solve the convex formulation of principal component pursuit. How is back-propagation via PAP algorithm calculated? Does it lead to any instability? Answer: As shown in Algorithm 1 in the main text, the Principal Attention Pursuit (...	Summary: The paper proposes a new perspective for understanding the underlying operation learned by scaled dot-product self-attention from a kernel PCA perspective. The paper recovers the self-attention formulation starting from PCA over feature projections of data points. In particular, the authors show that different...	Rebuttal 1: Rebuttal: Q1. [minor] The experiments use relatively small-scale models and short training runs (e.g. 50 epochs on Imagenet). While not a core issue given the objective of the paper, it would be nice to see results in more standardized settings (e.g. ViT-Base with 300 epochs) Answer: Thanks for you...	Summary: The paper derives self-attention from kernel principal component analysis (kernel PCA), showing that the attention outputs are projections of query vectors onto the principal component axes of the key matrix in a feature space. Using this kernel PCA framework, the authors propose Attention with Robust Princip...	Rebuttal 1: Rebuttal: **Q1. The results in Table 1 show that the best performance on different tasks is achieved with varying settings. For example, RPC achieves the best performance with 6 iterations/layer 1 on IN-1K, while RPC achieves the best performance with 2 iterations/all layers on IN-A. Moreover, for IN-1K top...	Rebuttal 1: Rebuttal: ## Global Rebuttal Dear AC and reviewers, Thanks for your thoughtful reviews and valuable comments, which have helped us improve the paper significantly. We are encouraged by the endorsements that: 1) The perspective for understanding self-attention from the lens of Kernel PCA provided by the pa...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This work introduces a method for understanding attention mechanisms using kernel principal component analysis (kernel PCA). From this perspective, self-attention is viewed as projecting the query vectors onto the principal components of the key matrix. Building on this framework, this work develops a new robu...	Rebuttal 1: Rebuttal: Q1. Related works. The manuscript omits several relevant works such as those below [1] Is Attention Better Than Matrix Decomposition? [2] Graph Neural Networks Inspired by Classical Iterative Algorithms [3] OptNet: Differentiable Optimization as a Layer in Neural Networks **[4]...	null	null	null	null	null	null
A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking	Accept (poster)	Summary: This work proposes a trajectory-aware spatial-temporal graph for video salient object ranking. The proposed model includes a spatial correlation graph and a temporal correlation graph. Unlike previous VSOR methods, this work suggests to modeling instance-level temporal relations. They conduct experiments to de...	Rebuttal 1: Rebuttal: ###1 Weakness 1. _Inaccurate claim of "explicitly model the motion trajectories of each instance."_ Response Yes, thanks for pointing out this problem. Actually, according the the human visual mechanism, our motivation is to measure the magnitude of instance motion across adjacent fra...	Summary: This paper proposes a graph-based video salient object ranking method. It introduces a spatial-temporal graph to integrate trajectory-wise spatial and temporal saliency cues. Based on VSOR, this paper proposes a video retargeting method to adjust the videos to different aspect ratios adaptively. Extensive expe...	Rebuttal 1: Rebuttal: ###1 Weakness 1. _Similar ablation visual results in the first and second examples in Figure 4. I cannot see the effectiveness of different ablation models according to these visual results._ Response 1) Yes. The methods 'w/o TRM', 'w/ GTRM', and 'w/ ITRM' will indeed share similar re...	Summary: This paper introduces a graph model for the video salient objects ranking task. Distinguishing itself from prior research, this study incorporates instance trajectory modeling to amplify temporal saliency cues. Additionally, the authors present a cohesive optimization approach that seamlessly integrates spatia...	Rebuttal 1: Rebuttal: ###1 Weakness 1. _Some failure cases._ Response As shown in Fig. R2, the final saliency inference is highly influenced by the object detector performance. Poor detector output leads to inaccurate instance features and wrong saliency ranking. In future, we plan to address this by two w...	Summary: This paper proposes a video salient object ranking approach based on spatio-temporal graph, leveraging instance trajectories and spatio-temporal saliency cues to improve SOR accuracy. Experiments demonstrate the superiority of the proposed model. Strengths: Originality: The proposed approach has some original...	Rebuttal 1: Rebuttal: ###1 Weakness 1. _It seems that the proposed approach is an temporal extension version of [1]._ Response Unlike [1] that focuses on spatial saliency cues for static images, we focus on two new key problems for video SOR: 1) Modeling diverse temporal saliency cues, especially instance-...	Rebuttal 1: Rebuttal: The figures and tables can be seen in PDF. Pdf: /pdf/0de3dafdf4a46d8d928087541e56ee6996270dc0.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization	Reject	Summary: This paper proposes using Bayesian Kernelized Tensor Factorization (BKTF) as a new surrogate model for Bayesian optimization (BO). BKTF approximates the objective function using a low-rank tensor factorization, with Gaussian process priors placed on the latent factors to capture dependencies and enable uncerta...	Rebuttal 1: Rebuttal: Thank you for your review. We reply to each comment below. For Weaknesses: - 1. We have compared BKTF with a fully Bayesian deepGP model as the baseline in the experiments on test functions (Section 5.1, refer to Fig. 2). We clarified the conducted experiments in General Response; please ref...	Summary: This paper proposes the Bayesian Kernelized Tensor Factorization (BKTF) as a surrogate for Bayesian optimization (BO). This model uses a CP decomposition to define a set of random basis functions drawn from a GP prior. These latent functions are then weighted by another set of random variables. This defines a ...	Rebuttal 1: Rebuttal: Thank you for your detailed review and thoughtful concerns. We address each comment below. For Weaknesses: - 1. Comparison to other methods: (1) The separability of ARD kernel. We re-checked definitions of ARD, SE, and Matern kernel functions, we agree that we should not directly state A...	Summary: The paper introduces a new surrogate model for Bayesian optimization, based on a functional tensor factorization. The approach discretizes the model to a pre-specified grid and uses MCMC sampling for inference. Bayesian optimization is carried out by selecting promising points from the pre-specified grid, as q...	Rebuttal 1: Rebuttal: We appreciate the reviewer's detailed comments and feedback. Please find our response to each concern below. For Weaknesses: - 1. Optimization method appears to be restricted to an a-priori defined grid of possible candidate points. `[reply]:` Yes, to apply BKTF for BO, we first need to intr...	Summary: This paper proposes a method for Bayesian optimization where the prior is a low-rank sum of tensor products of GPs. An MCMC scheme is developed for approximate updating and UCB sampling. Several experiments show strong performance relative to baselines on artificial function optimization and ML hyperparam tuni...	Rebuttal 1: Rebuttal: Thank you for your review. We appreciate the reviewer's detailed comments and relatively positive feedback on this work. We reply to each concern under Weaknesses and Questions below. For Weaknesses: - 1. It's not clear what is new relative to previous BKTF papers [10,11], other than ...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you for your time and for providing detailed and valuable feedback. In this general response, we aim to address and clarify several common concerns from the reviewers: - 1. The Contribution of this work. One concern raised is the contribution of this work. To the best of ou...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics	Accept (poster)	Summary: The author(s) of the paper provides a theoretically sound method of a Dynamics-aware SDE Diffusion Generative Model (D^3GM) to enhance the stability and generalizability of inverse problem diffusion models. The authors provide a rigorous mathematical examination of the temporal distribution discrepancy for the...	Rebuttal 1: Rebuttal: We thank Reviewer z6fz for the excellent summary of our paper and for capturing our core contributions so well. We appreciate the positive comments on our clear logic and detailed mathematical analysis. >Q. Add explanations for non-math background readers: We acknowledge that combining theoretic...	Summary: The paper addresses the use of diffusion models in solving inverse problems, which involve estimating causal factors from degraded data. Traditional methods often fall short in real-world scenarios due to accumulated errors and biases. To tackle these issues, the authors propose a new theoretical framework bas...	Rebuttal 1: Rebuttal: We would like to thank Reviewer J29t for your valuable feedback and insightful comments. Your suggestions have significantly contributed to highlighting the distinctiveness and clarity of our work. >Q. Difference from DDBM and Augmented Bridge Matching: We thank the reviewer for mentioning these...	Summary: Given that existing diffusion models are limited to linear inverse problems, this paper proposes to use measure-preserving dynamics of random dynamical systems to formulate a theoretical framework for SDE diffusion models. They uncover several strategies that inherently enhance the stability and generalizabili...	Rebuttal 1: Rebuttal: We thank Reviewer APgH for the detailed review and recognition of our theoretical contributions and innovative use of measure-preserving dynamics. >Q. Connection between measure-preserving property and D3GM: The measure-preserving property ensures that despite complex degradations, the distribut...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers (APgH, J29t, z6fz) for their insightful comments and recognition of the novelty and strong theoretical foundations, comprehensive experiments and convincing results, and applicability across areas. We greatly appreciate the recognition that D3GM opens up many potential pat...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PageRank Bandits for Link Prediction	Accept (poster)	Summary: The authors propose PRB, a new method that blends exploration, exploitation from previous neural bandit literature into an architecture that effectively considers graph connectivity in order to boost the performance for both the node classification and link prediction tasks. The authors demonstrate the soundne...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and precious time. Here, we try our best to address the questions and concerns in the form of Q&A. Similar questions raised by other reviewers are addressed in the Global Response. Additional content has been added to the 1-page PDF to better address review...	Summary: This paper introduces PRB (PageRank Bandits), a novel algorithm for link prediction in graph learning that combines contextual bandits with PageRank to balance exploitation and exploration. Framing link prediction as a sequential decision-making process, the paper provides a new reward formulation and theoreti...	Rebuttal 1: Rebuttal: Thank you very much for your constructive feedback and precious time. Here, we will try our best to address the questions and concerns in the form of Q&A. Since some of your questions are also raised by other reviewers, we have moved some answers to the Global Response. We also include additional ...	Summary: This paper reformulates link prediction as a sequential decision-making process and propose a algorithm that combines contextual bandits with PageRank for collaborative exploitation and exploration. The experiments validate the effectiveness of the method. Strengths: 1. The problem is interesting and the pape...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for your constructive feedback and precious time. Here, we try our best to address the questions and concerns in the form of Q&A. Since some of your questions are also raised by other reviewers, we have moved some answers to the Global Response. We also include addi...	Summary: In light of the dynamic and evolving nature of real-world graphs, PageRank Bandits is proposed to make the prediction task consistently meet the context and adapt over time. Both experimental results and theoretical analysis are solid. But the paper organization is not so well. Strengths: --It is novel to com...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for your constructive feedback and precious time. Here, we try our best to address the questions and concerns in the form of Q&A. Since some of your questions are also raised by other reviewers, we have moved some answers to the Global Response. We also include ...	Rebuttal 1: Rebuttal: --- ## Q1: Advantages of solving link predictions in the contextual bandit setting (1) Adaptation over Time. As links in real-world graphs are typically formed sequentially, it is natural to frame link predictions as a sequential decision-making process. Each link prediction can be view...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
NoiseGPT: Label Noise Detection and Rectification through Probability Curvature	Accept (poster)	Summary: This paper proposes NoiseGPT, which utilizes a token-wise Mix-of-Feature (MoF) technique and an In-Context Discrepancy (ICD) measure to determine the noisy samples and find best candidate labels with CLIP and MLLM. The effectiveness of this approach is demonstrated through experiments, particularly on the ILSV...	Rebuttal 1: Rebuttal: Thank you for pointing out perspectives that could be further improved. - Claims of NoiseGPT: - Although there is a lack of works leveraging MLLMs to mitigate label noise, prior researchers have explored its capability in different fields. Huang [1] proposes MVT to supervise vision models using...	Summary: This paper proposes to use large multimodal models (LMM) to detect noisy annotations in image datasets. The NoiseGPT method consists in observing whether a controlled perturbation in the latent representation of the LMM leads to a large modification of the LMM response. Experiments in the paper demonstrate tha...	Rebuttal 1: Rebuttal: Thank you for your suggestions. - Complementary of noise detection & Baseline of noise detection using CLIP: - To directly compare the effectiveness of our noise detection over the baseline methods such as Pro-Mix and DividMix, we conduct experiments on CIFAR-10 sym. 80% dataset and show the r...	Summary: The paper introduces NoiseGPT, a method that leverages Multimodal Large Language Models (MLLMs) for label noise detection and rectification in datasets. The approach exploits the probability curvature effect observed in MLLMs, where clean and noisy examples exhibit different curvature smoothness under perturba...	Rebuttal 1: Rebuttal: Thank you for your suggestions. - Relationship to previous loss curvature works: - The curvature of loss function is a common phenomenon in deep networks, existing works have studied in some fields but none of them have been conducted in LNL. Our work is the first to discover the effectiveness...	Summary: This paper employs Multimodal Large Language Models to detect noise samples. The In-Context Discrepancy is utilized to quantify the discrepancy between the original and perturbed samples. Additionally, the identified noise samples are integrated with the Pro-Mix and M-correction noise label learning framework....	Rebuttal 1: Rebuttal: Thanks for your constructive comments. - Limited novelty: - We make the first contribution to effectively detect and rectify label noise without the need for pre-training, providing a novel direction for learning with label noise, which has never been previously studied (Reviewer 81A3), and...	Rebuttal 1: Rebuttal: We thank all reviewers for reading and highlighting our paper, including - 1)”The paper presents a new application of MLLMs for label noise detection and rectification, introducing the concept of probability curvature…” (R1); - 2)”The observation about the sensitivity of noisy samples to noise i...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems	Accept (poster)	Summary: This paper aims to address the limitations of existing methods that require distinct prompt designs for different mathematical problems. The authors propose a general Multi-Agent System for conditional Mining (MACM) method that uses three LLM agents (Thinker, Judge, Executor) to iteratively propose the conditi...	Rebuttal 1: Rebuttal: Thank you for reviewing our work. Here are our responses to your concerns: --- *For Weakness 1* and Question 1** > why do the LLM gets two different “New condition” (one correct, one incorrect) under the same prompt (prompt 2)? Thanks for the question! In MACM, the conversation takes plac...	Summary: The paper proposes a prompting method called Multi-Agent System for conditional Mining (MACM). MACM involves three agents, Thinker, Judge, and Executor, who maintain a condition list and try to solve the problems when the conditions are sufficient. The paper conducts experiments with GPT and Llama series on M...	Rebuttal 1: Rebuttal: Thank you for reviewing our work. Here are our responses to your concerns: --- *For Weakness 1:*** MACM and Cumulative Reasoning differ significantly, especially in the rechecking and voting processes. Intuitively, Cumulative Reasoning has an accuracy of only $72.2$% on MATH. According to Op...	Summary: The paper presents a novel prompting technique MACM, which utilises multiple agents to cooperate and perform backtracking for mathematical reasoning problems. Strengths: The prompting method seems to work well for mathematical reasoning tasks and shows a degree of generalisation. Weaknesses: My main concern ...	Rebuttal 1: Rebuttal: Thank you for reviewing our work. Here are our responses to your concerns: --- For weakness (a) > MATH has its own evaluation protocol, and the Minerva paper [1] also gave a good evaluation protocol. The reliance on GPT4 Turbo as a judge seems unjustifiable. Thank you for your question! G...	Summary: This paper proposes a universal prompting method for solving complex reasoning problems such as mathematical problems and the 24-point game. The method first abstracts the conditions and objectives of the problems and then progressively discovers new conditions until enough information is gathered to solve the...	Rebuttal 1: Rebuttal: Thank you for your valuable suggestions regarding our work. Here are our responses to the comments you raised: --- *For Weaknesse 1 & Question 1*** > What is the number of responses for each model (including baselines and the proposed MACM) in Table 1 and Table 2? Thanks for the question! In...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning	Accept (poster)	Summary: This paper introduces KG-ICL, a model that facilitates generalized reasoning over knowledge graphs via in-context learning. KG-ICL first extracts example facts relevant to the query from the knowledge graph to generate prompt graphs. These prompt graphs are then encoded using a unified tokenizer and message pa...	Rebuttal 1: Rebuttal: Dear Reviewer JyNi, We deeply appreciate your valuable comments and dedication during the review period. We sincerely hope that our response can ease all your concerns. Please feel free to contact us with any further comments or require additional clarification. **W1: Details about in-context se...	Summary: The paper studies inductive KG reasoning with unseen entities and relations at inference time and introduces KG-ICL, an in-context learning model for KG completion. Following Ultra [1], KG-ICL employs a two-stage approach: (1) obtaining relational representations based on the given graph; (2) performing entity...	Rebuttal 1: Rebuttal: Dear Reviewer 4T5d, We sincerely appreciate your invaluable time and positive comments. Your insightful suggestions give us a great opportunity to improve our paper. We conduct extra experiments and provide further analyses, which we will incorporate into the paper. We sincerely hope these enhanc...	Summary: This paper aims to build a foundation model for knowledge graphs, to have a universal reasoning ability across diverse knowledge graphs including the unseen entities and relations. Specifically, given the query, the proposed approach first extracts its relevant prompt graphs and then map their entities and rel...	Rebuttal 1: Rebuttal: Dear Reviewer xJpJ, We sincerely thank you for your positive comments and valuable suggestions. If you have any further suggestions, please let us know. We would be happy to continue the discussion. Q1: Key factor for performance improvement. The proposed local context prompt graph for rel...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers, We express our sincere gratitude for your invaluable time and dedication throughout the review period. We sincerely appreciate your positive comments acknowledging that * The foundation model for KG reasoning on any unseen graph is a timely and important topic. New non-trivial app...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PrivCirNet: Efficient Private Inference via Block Circulant Transformation	Accept (poster)	Summary: Proposing the use of cyclic blocks for matrix multiplication to accelerate privacy-preserving inference in neural networks. Strengths: PrivCirNet features a novel encoding algorithm optimized for block circulant weight matrices, dubbed CirEncode, that reduces the HE computation in proportion to block size. Pr...	Rebuttal 1: Rebuttal: We appreciate Reviewer mVR6's feedback and we will address each of the questions as follows. * [To weakness, lack of communication cost comparison] First, we do have a communication comparison for linear layers** in Table 3 (see "# Ciphertexts"). CirEncode minimizes the communication of...	Summary: This paper proposes an optimization for linear layers of secure inference systems. The authors propose a linear layer evaluation protocol that can efficiently compute linear layers when the model weights are circulant (diagonally repeating). They also propose optimizations to determine the optimal block size t...	Rebuttal 1: Rebuttal: Thank you very much for your support and professional, detailed feedback! We are also grateful to Reviewer wNvj for evaluating our work as "an interesting paper". See below for the answers to your questions and comments. * [To Weakness1 and Question2, misleading notation of GEMM]** We highl...	Summary: The paper introduces PrivCirNet, a framework designed to enhance the efficiency of private DNN inference using homomorphic encryption (HE) and MPC schemes. The key contributions are as follows. 1. By converting DNN weights into block circulant matrices, the framework transforms general matrix-vector multiplica...	Rebuttal 1: Rebuttal: Thank you very much for your support and the thorough comments, which have greatly helped us improve our work! We appreciate that the novelty, effectiveness, and importance of PrivCirNet are acknowledged. Below we list our responses to each of the comments: * [Our implementation code of pri...	Summary: The authors have proposed a co-design method for making the Homomorphic encryption (HE) evaluation faster in private inference. Specifically, they used a new HE encoding mechanism to leverage the properties of the black-circulant matrix to reduce the HE rotations and ciphertext-plaintext multiplications. The...	Rebuttal 1: Rebuttal: We appreciate Reviewer eRSG's constructive feedback, which has helped us improve our work! We have conducted all the additional experiments mentioned in the review and will be added to our final version. The results are included in the PDF attached to the global response, including comparisons...	Rebuttal 1: Rebuttal: [1. Implementation details and latency for nonlinear layers] We utilize HE to evaluate linear layers and MPC to evaluate nonlinear layers. For nonlinear layers, we follow Cheetah [1] to evaluate the ReLU function (Section 4 in Cheetah) and Bolt [2] to evaluate GeLU, LayerNorm, and Softmax fun...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Weak Supervision Performance Evaluation via Partial Identification	Accept (poster)	Summary: This paper proposes solutions via convex programs to estimate Frechet bounds for Programmatic Weak Supervision (PWS). This approach uses estimates of the true labels via labelmodels (i.e., different aggregation schemes that exist in the literature). With these estimates of the labels, they provide an approach ...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your dedication to our paper. We addressed the issues you raised below. Please let us know if you have any other questions. - Regarding weak label pruning: Thank you for raising this point. In the following, we explain our point of view, which we will make clearer...	Summary: The authors propose a method for bounding the performance of models obtained using weak-supervision, without the need for a gold-standard label set. The proposed approach starts with Frechet bounds, and casts them as dual optimization problems. From there, the dual optimization problems are relaxed to a smooth...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your work on our paper. We addressed the issues you raised below. Please let us know if you have any questions. - Clarity and appendix material: Thanks for your suggestion. We will revise to reference the appendix where appropriate and give some ideas on the basic...	Summary: Programmatic weak supervision is a machine learning approach where labeled training data is generated using heuristic rules, domain-specific functions, or other programmatic methods, rather than manual annotation. This technique allows for the creation of large training datasets quickly and cost-effectively by...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your dedication to our paper. We realized that the main issues you raised are related to clarity and presentation. We revised the main parts you brought up and are willing to further improve other parts depending on your feedback. Please let us know if you have more is...	Summary: The paper considers the problem of Frechet bound which is the task of determining the infimum and supremum of a function g(X,Y,Z) over the set of joint distribution with fixed marginals. Asymptotic behavior is derived when estimation of condition distribution P_{Y\|z} is available with vanishing total variation...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your work on our paper. We addressed the issues you raised below. Please let us know if you have any other questions. - Why not estimate performance metrics directly? In fact, we work under the assumption that no true labels are observed, which is a realistic ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons	Accept (poster)	Summary: This paper investigates how canonical recurrent circuits in the cortex can implement sampling algorithms. The authors show that circuits with only E and PV neurons implement Langevin dynamics, while including SOM neurons enables a mixture of Langevin and Hamiltonian sampling. Strengths: The paper demonstrates...	Rebuttal 1: Rebuttal: We appreciate the reviewer's feedback and positive comments about the math rigor and biological plausibility of our work. > Two major limitations of the sampling circuit proposed in the paper are its low-dimensionality and uniform prior. Overall, the authors only demonstrate sampling of 1-D Gauss...	Summary: The paper is build on previous work on Bayesian sampling to provide incremental but novel analytical insights about a neuronal circuit implementing bayesian inference. The circuit comprises excitatory neurons and two types of inhibitory neurons, PV and SOM, and uses the ring architecture. Authors find that the...	Rebuttal 1: Rebuttal: We appreciate the reviewer's feedback and positive comments about math analysis, and the insight of SOM neuron's role in inference presented in our manuscript > The biggest weakness of the paper... Thanks for your suggestions; we will revise our manuscript accordingly by adding more content abou...	Summary: The paper proposes a dynamical model for how neurophysiologically realistic circuits of pyramidal excitatory neurons, with two types of inhibitory interneurons, could perform Bayesian inference in a generative model of a uniform prior (over a bounded domain such as orientation angles) and a Gaussian likelihood...	Rebuttal 1: Rebuttal: Thanks for the reviewer's appreciation of our math analysis and the biological plausibility of the model. > The paper is quite dense, often unclear for a machine-learning audience, and ultimately only addresses a uniform-Gaussian joint density that cannot capture natural stimuli or behavior. We ...	Summary: This paper studied how the introduction of two inhibitory neuron population affects Bayesian inference in firing rate models with additive noise terms. Analytical derivation and simulations are done with circular 1D input variable (orientation). They found the inclusion of SOM leads to faster inference. Stren...	Rebuttal 1: Rebuttal: We appreciate the reviewer's positive feedback on our writing and math analysis! > My main reservation with the paper is on its significance - I think the paper (at least in the way it is written) interests people within the specific subfield of Bayesian sampling in simplified circuit models for...	Rebuttal 1: Rebuttal: ## Global rebuttal We thank all reviewers' efforts in reading our manuscript! ### Neural circuit sampling of complex posteriors Three Reviewers (6xXx, K6Di, and RifV) wondered whether the circuit model can sample more complex posteriors, e.g., multi-modal and/or high-dim posteriors. Yes, the pro...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations	Accept (poster)	Summary: The authors introduce the CoVoMix framework for human-like monologue and dialogue generation. They point out the shortcomings of the previous multispeaker dialogue systems that they are less explored, and there are lack of high-quality, spontaneous conventional datasets. The proposed CoVoMix achieves high natu...	Rebuttal 1: Rebuttal: **We sincerely appreciate your efforts in reviewing our paper and providing valuable and constructive feedback. We have implemented the Soundstorm model for dialogue synthesis as previous work to compare with our CoVoMix model, which will be included in the revised version. The detailed responses ...	Summary: This study proposes a personalized speech synthesis model capable of generating monologues or dialogues. The study achieves this goal through the development of a text-to-semantic token model and the conversion of semantic tokens into mel-spectrograms. By utilizing the Fisher dataset, which contains natural sp...	Rebuttal 1: Rebuttal: **We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. We added a table to highlight the unique aspects of our model in comparison with previous works. Additionally, we implemented the Soundstorm-style baseline for dialogue synthesis to...	Summary: This paper proposed CoVoMix, a zero-short TTS model for multi-speaker conversations. CoVoMix consists of a multi-stream text-to-units model, a flow-matching acoustic model for mixed-speaker spectrogram generation and HiFiGAN vocoder for waveform generation. The major contribution of CoVoMix is that it is one o...	Rebuttal 1: Rebuttal: **We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. We have clarified some unclear writing that caused misunderstandings and added a baseline system for comparison, as well as a speaker similarity subjective test for dialogue, which ...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers, Thank you for your efforts in reviewing our paper. We greatly appreciate your acknowledgment of our contributions, including our first attempt at zero-shot, human-like, multi-talker conversational mixed speech generation, the various metrics to facilitate evaluation, and the good ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Autoregressive Image Generation without Vector Quantization	Accept (spotlight)	Summary: This paper deals with the task of autoregressive image generation using continuous tokenizers. AR image generation has primarily focused on using discrete tokens, and training discrete tokenizers are quite hard. In this paper, the authors train AR models on continuous tokenizers. The idea is to predict continu...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our clean and neat approach as well as our solid empirical results. Here, we address the weaknesses (*W) and questions (Q) raised by the reviewer. Inference speed (W1)* We agree that DiffLoss can introduce overhead to the inference process, ...	Summary: This paper proposes an autoregressive modeling method without the use of vector quantization tokens. By using a diffusion procedure to model the next-token probability, it is able to apply autoregressive models in a continuous-valued space. To model the probability of one token $x_i$ given the condition $X_{<i...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our approach and the comprehensive experimental results. It seems there is a key misunderstanding about our paper that “the proposed method still needs a discrete tokenizer”. In fact, *ALL* experimental results in our paper using Diffusion Loss are conduc...	Summary: The paper introduces an autoregressive image generation method without vector quantisation for visual. The authors observe that while discrete-valued spaces facilitate representing categorical distributions, they are not necessary for autoregressive modeling. They propose modeling the per-token probability d...	Rebuttal 1: Rebuttal: We thank the reviewer for the appreciation of the clear motivations, original contributions, and significant benefits of our work. Here we address the weaknesses (*W) and questions (Q*) raised by the reviewer. We are also happy to discuss further if you have any additional questions or ...	Summary: This work challenges the conventional belief that autoregressive (AR) models implemented by a Transformer are best suited for modeling discrete sequences. Instead, it proposes modeling the per-token probability distribution using a diffusion procedure, enabling the application of AR models in a continuous-valu...	Rebuttal 1: Rebuttal: We thank the reviewer for the appreciation of our motivation and proposed method. Here we address the weaknesses (*W) and questions (Q) raised by the reviewer. Scope of experiments (W1)* We have provided an additional experiment on ImageNet 512x512 in the general response to vali...	Rebuttal 1: Rebuttal: We thank all reviewers for providing lots of insightful and constructive feedback. We will definitely improve our manuscript accordingly. We are glad to see the commonly recognized strengths highlighted by the reviewers: 1. The presentation of the paper is clear and well-structured (xuXX, mV98, V...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper introduces a novel approach to image generation using autoregressive models with continuous-valued tokens, challenging the conventional use of discrete vector-quantized tokens. The authors propose a "Diffusion Loss" function that models per-token probability distributions using a diffusion process, ...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our novel approach, significant contribution, and comprehensive empirical results. Here, we address the weaknesses (*W) and questions (Q) raised by the reviewer. Computational complexity (W1)* \|Methods\| Epochs \|Training Costs (days)\|FID w/o ...	null	null	null	null	null	null
Learning Better Representations From Less Data For Propositional Satisfiability	Accept (spotlight)	Summary: The paper presents NeuRes, a neural symbolic system designed to solve the Boolean satisfiability problem of CNF (Conjunctive Normal Form). NeuRes utilizes a message-passing graph neural network to embed the information contained in CNF and attempts to predict clause pairs for propositional resolution and value...	Rebuttal 1: Rebuttal: Thank you for your review and comments. Please find them addressed below. > The paper does not include a thorough ablation study to evaluate the effectiveness of each proposed component. i.e. It lacks an assessment of the true value assignment task’s effectiveness. - It is true that in the paper...	Summary: The paper integrates attention-based neural networks into the SAT solving process based on resolution. The neural networks aim to predict pairs of disjunctive clauses to merge. The paper proposes several variants of neural networks for this task and conducted experiments to check their performance. Although th...	Rebuttal 1: Rebuttal: Thank you for your feedback and questions. Please find our responses to your questions below. > Q1: How big is the gap between the proposed approach and the highly-engineered SAT solvers? - Generally speaking, the main merit of our approach over traditional SAT solvers is its ability to capture ...	Summary: The paper presents NeuRes, a system to solve SAT/UNSAT problems. In particular, it is able to provide UNSAT certificates via resolution proofs. The system constructs a resolution proof by iteratively selecting two clauses from all clauses, producing the resolvent from the two clauses, and adding it back to the...	Rebuttal 1: Rebuttal: Thank you for your review and comments. Please find your questions addressed below. > Q1: Could you explain how the method applies to SAT problems? - In the case of a satisfiable formula, NeuRes terminates when a satisfying truth assignment is found by the assignment decoder network. Initially t...	Summary: The authors proposed an attention-based architecture that autoregressively selects pairs of clauses for propositional resolution. The framework can generate proofs of unsatisfiability and accelerate the process of finding satisfying truth assignments simutaneously. Emprical evaluation shows that the resulting ...	Rebuttal 1: Rebuttal: Thank you for your feedback and questions; we're glad you enjoyed the paper! We address your comments in the following. > Speed can potentially be an issue.. for large problems, the calls can be quite numerous. - It is true that speed is a limiting factor in scaling deep learning models to very...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper presents a deep-learning-based approach for generating resolution proofs for SAT formulas. The proposed method outperforms NeuroSAT on proving/predicting satisfiability on a family of benchmarks. Strengths: - Instead of directly learning to predict satisfiability, this paper proposes to learn the s...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, and we address their reservations below. > The comparison with NeuroSAT to me is quite apple-to-orange. > The current title can be a little misleading because words like "better" and "less" do not make much sense when the underlying learning task chang...	null	null	null	null	null	null
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization	Accept (poster)	Summary: This paper proposed an augmentation technique in frequency domain, motivated by the phenomenon of frequency bias/frequency shortcut learning. The technique AAUA adversarially perturbs frequency components of images which the models over-rely for classification. As AAUA might encourage shortcut learning in hig...	Rebuttal 1: Rebuttal: ## Merging Contributions Thank you for your valuable comments. We will merge the first two points as suggested. ## Claim as The First (1) [General Robustness vs. Domain Generalization(DG)] We acknowledge the correlation between general robustness studies (i.e. DFM-X) and DG, but respectfull...	Summary: The paper addresses the challenge of domain generalization by focusing on the issue of frequency simplicity-biased learning in neural networks. This phenomenon leads to an over-reliance on specific frequency sets, known as frequency shortcuts, instead of semantic information, thereby impairing generalization p...	Rebuttal 1: Rebuttal: ## More Hyper-Parameter Analysis. (1) [Additional Results] Thank you for your valuable comments. Following your suggestion, we further provide hyper-parameter studies of the intensity of AAUA perturbations and the probability threshold $p$ for AAD in Tab. Re6 and Tab. Re7, respectively. As we...	Summary: The paper proposes a method for single source domain generalization. The method is based on the insight of countering frequency shortcuts learnt during training. The paper proposes to augment images in the Fourier domain to achieve this. Such augmentation is done adversarially, by changing the mean and varianc...	Rebuttal 1: Rebuttal: ## Writing. Thank you for your detailed comments. We will thoroughly revise the manuscript. ## Implementation details and clarity of experiment results. [JS loss] Yes, the JS loss is applied between predictions on augmented and clean samples [Training domains] (1) Kindly note tha...	Summary: This paper aims to develop data augmentation techniques to prevent the learning of frequency shortcuts and achieve enhanced generalization performances. They modify the frequency spectrum of the dataset statistical structure with aggressive frequency data augmentation, aiming to adaptively manipulate the model...	Rebuttal 1: Rebuttal: ## Parameters and Time complexity. (1) [No Additional Parameters] We sincerely thank you for your insightful comments. Notably, as AAUA and AAD are data augmentation methods instead of neural networks, they don’t have any additional parameters. Thus, there won’t be additional parameters to tr...	Rebuttal 1: Rebuttal: We thank the reviewers (uwHe, jMvT, 57HS, szDc) for all the informative and constructive feedback and we appreciate the comments to improve our work. Reviewer uwHe: "Two effective and practical adversarial frequency enhancement modules. Combines data augmentation and frequency analysis to ad...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Latent Neural Operator for Solving Forward and Inverse PDE Problems	Accept (poster)	Summary: The present paper introduces a novel architecture, coined Latent Neural Operator (LNO), made of a sequence of self-attention layers intertwined between two Physics-Cross-Attension (PhCA) encoder and decoders. Experiments conducted on multiple PDE datasets evaluate the efficacy of the proposed approach across v...	Rebuttal 1: Rebuttal: Q1: Reviewer thought our primary novelty lies in the integration of transformer blocks within the latent space and pointed out the incorporation of self-attention layers in neural operators is not new and well-documented but we have missed multiple references. Our main contribution lies in th...	Summary: This paper suggests a neural operator architecture for learning solutions of forward and inverse PDE problems using a sequence of transformer layers. The novelty of the architecture is that after a carefully designed initial transformation step, named “Physics-Cross-Attention”, the inputs to subsequent transfo...	Rebuttal 1: Rebuttal: Q1: Reviewer thought our inverse problem solution method interesting and wondered whether it is a novel solution method. This two-stage method is inspired by the inpainting and outpainting problem in the computer vision field. **Q2: Reviewer thought our definitions of "geometric space" and "...	Summary: The paper introduces Latent Neural Operator for solving forward and inverse problems in latent space. It uses a Physics-Cross-Attention to encode the input to the latent space, uses self-attention to evolve the state in the latent space, and uses another Physics-Cross-Attention to decode back to the real-world...	Rebuttal 1: Rebuttal: Q1: Reviewer thought our paper is somewhat limited in terms of novelty because the usage of cross-attention to embed the input into latent tokens is much like that in [22] and our main modules are based on attention which is a standard neural architecture. We have discussed the difference bet...	Summary: The paper presents the Latent Neural Operator (LNO), a novel approach for solving forward and inverse partial differential equations (PDEs) by operating in a latent space. The LNO introduces a Physics-Cross-Attention (PhCA) module for transforming data from the geometric space to a learnable latent space and d...	Rebuttal 1: Rebuttal: Q1: Reviewer pointed out that our work lacks theoretical proof and expected us to provide a set of theoretical support for the current structure design. Our current structure design is mainly inspired by the kernel integral operator with position-only kernel function. Please refer to the "Th...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable feedback and suggestions from the reviewers. We are delighted to see that the reviewers recognize the overall design of our model as clean and efficient. We are particularly grateful for the acknowledgment of the innovation in our core module, Physics-Cross-Att...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Energy-based Hopfield Boosting for Out-of-Distribution Detection	Accept (poster)	Summary: The authors introduce Hopfield Boosting for addressing the out-of-distribution detection task. This algorithm trains a model with two heads: one for normal classification and the other for assigning an out-of-distribution score. The OOD score head maintains a list of in-distribution samples and out-of-distribu...	Rebuttal 1: Rebuttal: Response to Weaknesses: 1. For the experiments comprising ImageNet-1K as the ID data set, we use images with size 224x224. This misunderstanding arises because we missed to report the resolutions we use for the ImageNet benchmark. We apologize for this error. We will make sure that the updated...	Summary: A novel method for identifying OOD samples is presented using Hopfield boosting. A classifier is trained using a loss function which sharpens the decision boundary between inlier and outlier data samples, where outlier data samples are purposefully drawn from an auxiliary dataset. By sampling examples close ...	Rebuttal 1: Rebuttal: Response to Weaknesses: - We agree with this. To demonstrate how Hopfield Boosting can interact in a classification task we created a toy example that resembles the ID classification setting (Figure 4 in the PDF attached to the response on the top of the page): The example shows the decision b...	Summary: - The paper introduces a novel approach to improve OOD detection by leveraging modern Hopfield energy. The proposed method, called Hopfield Boosting, utilizes auxiliary outlier data to refine the decision boundary between ID and OOD data. By focusing on hard-to-distinguish auxiliary outlier samples near the de...	Rebuttal 1: Rebuttal: Response to Weaknesses: - Because of the highly competitive nature of the CIFAR-10 benchmark, we view every improvement as important (e.g., the previous methods' AUROC were already above 99%; e.g., POEM achieves an AUROC of 99.21%). That said, we agree that more evaluations w.r.t. the individ...	Summary: This paper addresses the crucial task of out-of-distribution (OOD) detection, essential for the safe deployment of machine learning models in real-world scenarios. Within the domain of OOD detection, outlier exposure methods, which use auxiliary outlier data during training, have shown significant improvements...	Rebuttal 1: Rebuttal: Response to Weaknesses: - We respectfully disagree with the claim that Hopfield Boosting is an incremental extension. We take it as a hint that we did not emphasize the novelty enough. Hopfield Boosting not only combines existing concepts in a novel, principled, and theoretically well-motivat...	Rebuttal 1: Rebuttal: We thank all reviewers for taking the time to provide high-quality feedback. It allowed us to significantly enhance our paper. In summary, the reviewers appreciated the technical quality and the theoretical depth of our contribution. They also positively acknowledged that Hopfield Boosting obtains...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spiking Graph Neural Network on Riemannian Manifolds	Accept (poster)	Summary: The authors generalize spiking GNN to Riemannian manifold, and design a new architecture of parallel forwarding so as to boost model training. Then, the proposed MSG is evaluated with 12 baselines on real graphs. Strengths: S1. A technical strong paper, and No error is detected. It presents a Riemannian optim...	Rebuttal 1: Rebuttal: W1: Performance on GAT backbone. The proposed MSG is applicable to any backbone GNN, where the incoming current $x$ is given by the corresponding backbone. We show the results of GAT backbone in the sphere manifold on Computers, Photo, CS and Physics datasets as follows. (The mean with stand...	Summary: This work proposes a spiking graph neural network on Riemannian manifolds, named Manifold-valued Spiking GNN (MSG). This work also develops a new training algorithm of differentiation via manifold (DvM) that avoids the high training overhead of BPTT methods and proves that the MSG is a neural ODE solver. Exper...	Rebuttal 1: Rebuttal: W1: On Figure 4(a) and Training Time Sorry for the typo in Figure 4(a) where we mislabeled the legend. We list the training time as follows. The gradient backpropagation (BP) time of DvM and Surrogate on Lorentz model is shown below. \| Time steps \| BP Times (DvM) \| BP Times (Surrogate) ...	Summary: In order to improve energy efficiency and performance in graph learning, the research presents a unique Manifold-valued Spiking Graph Neural Network (MSG), which combines spiking neurons with Riemannian manifolds. Differentiation via Manifold (DvM), a unique training approach, is proposed by the authors to sol...	Rebuttal 1: Rebuttal: W1: It is not the first time the Riemann manifold has been introduced into spiking neural networks. In this paper, the geometric concept of Riemannian manifold is a smooth manifold endowed with a Riemannian metric. To the best of our knowledge, this is the first time that Riemannian manif...	Summary: This paper first analyzes the limitations of spiking GNN, representation space and model training, and then present a Riemannian model called MSG, which is connected to manifold ODE. Finally, the authors conduct experiment to show the effectiveness and energy efficiency. Strengths: On the significance, 1.This...	Rebuttal 1: Rebuttal: W1: In Table 1, some link prediction results of SNN baselines are lacked. For the SNNs generating spiking representations (i.e., SpikeGCN and SpikeGCL), we feed the spiking representation into Fermi-Dirac decoder, obtaining the pairwise similarity for link prediction. For the SNNs that canno...	Rebuttal 1: Rebuttal: Thanks for the appreciation and detailed comments of all the reviewers! Tables in the rebuttal are also given in the PDF for better readability. All authors of submission 6586. Pdf: /pdf/a2ff4f8c2f1d11481341ff0cd79b41fc039ef8c3.pdf	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper focuses on the theoretical aspects of spiking neural networks, which are a variant of neural networks closer to the human brain, particularly incorporating a time component. One of the hopes for this type of networks is that they are much more energy efficient. In this paper, the authors extend the ...	Rebuttal 1: Rebuttal: Q: Argument, why Riemannian manifolds are required, could be strengthened, and also what about other types of manifolds? On the one hand, Riemannian manifolds are well aligned with graph structures (e.g., the hierarchical/tree-like and cyclical structures of graphs correspond to hyperbolic an...	null	null	null	null	null	null
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning	Accept (spotlight)	Summary: The authors introduce BPQP, a differentiable convex optimization framework designed for efficient end-to-end learning. The core of this work lies in simplifying and decoupling the KKT matrix for the backward pass and solving it with a first-order solver to improve the overall efficiency of the module. Strengt...	Rebuttal 1: Rebuttal: Thank you for your review. We will address each of the weaknesses and questions you have raised in detail. ## Q1&Q2. Differnece between BPQP and OptNet & The motivation of using ADMM > The differences between BPQP and OptNet seem to be only reflected between Equation 3 and Equation 5. > For Eq...	Summary: This paper develops a technique to use deep learning models to solve convex optimization problems that offers speedups and space benefits over the current state of the art. Rather than using conventional implicit layers to predict optimal solutions, the authors consider the Karush-Kuhn Tucker (KKT) conditions ...	Rebuttal 1: Rebuttal: Thank you for your review. We will address each of the weaknesses and questions you have raised in detail. ## Q1. Infeasible input > During inference, if the input to the model is actually infeasible (no solution exists that satisfies the corresponding KKT conditions), is there any indication of...	Summary: The paper provides a novel approach for handling differentiable optimization layers when their forward corresponds to the solution to a convex constrained optimization problem. The authors show that the gradient of such a layer corresponds to the solution of a QP, thus enabling a tractable backward through the...	Rebuttal 1: Rebuttal: Thank you for your review. We will address each of the weaknesses and questions you have raised in detail. ## Q1. Effectiveness in less well-behaved problems > Only QPs are considered in the experiments section. Does the proposed algorithm provide as significant a speedup when the considered pro...	Summary: This paper proposes BPQP, a differentiable convex optimization framework to perform efficient end-to-end learning on convex problems using KKT condition. Compared with existing OptNet baselines, BPQP achieves similar performance but much faster speed. Strengths: 1. This paper proposed BPQP, which utilizes the...	Rebuttal 1: Rebuttal: Thank you for your review. We will address each of the weaknesses and questions you have raised in detail. ## Q1. Extension to non-convex problems > most real-world decision-making problems are more complex and non-convex Thank you for highlighting this important limitation. While it is true th...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their constructive feedback. Below, we summarize the major concerns raised and provide our explanations. ## Q1. Can the proposed algorithm be extended to non-convex problems? BPQP can provide a viable approach even in non-convex scenarios. When addressi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models	Accept (poster)	Summary: Introduces a new technique, WISE, to edit/insert facts in a autoregressive language model. The proposed approach gives a balanced performance in terms of reliability, generalization, and locality; improving upon the existing methods. WISE edits facts in a side-memory that is equivalent to one of the MLP down p...	Rebuttal 1: Rebuttal: # Response to Reviewer fviW Thanks for your valuable feedback. We appreciate the opportunity to address your concerns. > 1. Response to “The activation $\mathbf{a}$ used in routing corresponds to which token in the prompt?” > As shown in Equation 3, the activation used for classification is de...	Summary: The paper studies the problem of editing (updating) knowledge of LLMs in a lifelong learning scenario. Authors design WISE, a multi-level memory system designed to store updates to the model. The proposed design contains of the main memory and a number of side-memories, with a mixture-of-expert-like router to ...	Rebuttal 1: Rebuttal: # Response to Reviewer pT3T Thanks for your valuable feedback. We appreciate the opportunity to address your concerns. > 1. Response to “many of ablations are missing.” > Thanks for the comment. All components of WISE can be summarized as router, merging strategy, locating side memory, and sid...	Summary: This paper proposes WISE which uses a side memory and some model merging techniques to perform lifelong knowledge editing. Strengths: 1. The paper is well-written and easy to follow. 2. The experiments are somewhat comprehensive. 3. The paper is working on the continual editing problem, which is important. ...	Rebuttal 1: Rebuttal: ## Response to Reviewer 5h8i We thank the reviewer for acknowledging that our paper is well-motivated and the experiments are comprehensive. We kindly address your questions as follows. > 1. Response to “Why not store the knowledge in the form of raw text and perform knowledge retrieval (RAG...	Summary: A fundamental question in lifelong model editing of large language models (LLMs) is where the updated knowledge should reside in the model's memory. This paper identifies an inherent challenge in editing either long-term memory (direct model parameters) or working memory (non-parametric knowledge through neura...	Rebuttal 1: Rebuttal: # Response to Reviewer KKAN Thanks for recognizing the value of our work. Your comments are highly aligned with our paper, and we hope the following comments could address your questions: > 1. Response to “During the training phase, the model requires excessive memory …” Thanks for the comment....	Rebuttal 1: Rebuttal: # General Response We thank all the reviewers for their time and for providing constructive comments to enhance the paper. We appreciate that reviewers recognized: - Our paper is well-written, easy to follow, and has clear motivation (balancing Reliability, Generalization, and Locality in lifelo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters	Accept (poster)	Summary: This paper proposes a method to improve the quality of pseudo labels for unlabeled target data. Specifically, it designs different strategies for using SAM to handle 'stuff' and 'things,' which are classified based on the initial pseudo labels. Subsequently, it fits a Gaussian Mixture Model (GMM) on the per-co...	Rebuttal 1: Rebuttal: ### Thank you for recognizing our work and providing constructive feedback. ### Q1: SCC vs DivideMix Although our SCC is partly inspired by DivideMix, it is different from DivideMix from four aspects. #### Address Different Tasks: - SCC: Cross-domain semantic segmentation. - DivideMix: ...	Summary: This paper proposed an effective method to generate reliable high-quality pseudo-labels for cross-domain semantic segmentation. It introduces a novel method, Semantic Connectivity-driven Pseudo-labeling (SeCo), which addresses these issues by formulating pseudo-labels at the connectivity level, thereby improvi...	Rebuttal 1: Rebuttal: ### Thank you for your recognition of our work and your constructive suggestions. ### Q1: Quantitative Analysis on "Prompting Only" and "Semantic Alignment" We conduct ablation studies on "Prompting Only" (PO) and "Semantic Alignment"(SA) across multiple tasks in GTA $\rightarrow$ Cityscape. We ...	Summary: This paper tackles the cross-domain semantic segmentation problem. It incorporates two modules, including the Pixel Semantic Aggregation and the Semantic Connectivity Correctio modules. The former adopts the SAM model to separately refine pseudo-labels for thing and stuff classes. The latter adopts the refined...	Rebuttal 1: Rebuttal: ### Thank you for your recognition and your useful suggestions. ### Q1. The work may be limited to a relatively narrow field. Good suggestion. We explore SeCo's performance in more segmentation scenarios, including indoor scenes, cross-domain medical images, and cross-domain remote sensing image...	null	null	Rebuttal 1: Rebuttal: We sincerely thank the AC and the reviewers for their tremendous effort in handling our paper. We have adequately addressed all the issues raised by the reviewers. These include providing validation in more segmentation scenarios (Reviewer #SXcd), explaining fairness concerns(Reviewer #SXcd), dis...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Where to Edit Vision Transformers	Accept (poster)	Summary: The paper presents a novel approach for editing Vision Transformers (ViTs) to correct predictive errors, specifically addressing subpopulation shifts. It introduces a learning-to-learn methodology utilizing a hypernetwork that identifies and modifies a small set of critical parameters in response to erroneous ...	Rebuttal 1: Rebuttal: We sincerely appreciate your constructive comments on improving our paper. We detail our response below point by point. Please kindly let us know if our response addresses the questions you had for this paper. ##### [W1] Reliability of the hypernetwork > > We argue that identifying the 'correct'...	Summary: The paper explores and discussed the VIT editing, which is the first work in this field. The paper proposes a learning-to-learn approach that identifies a small set of critical parameters to edit to correct the prediction of an error example. The paper considers the reliability and generalization during the Vi...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. We address your concerns below point by point. Please kindly let us know whether you have any further concerns. ##### [W1] Motivation and influence of CutMix > We appreciate the reviewer's insightful question regarding the motivation and influence ...	Summary: The paper explores a model-editing task for ViT that is similar to model-editing in LLMs. For ViT model-editing, the paper proposes a meta-learning-based approach to selecting parameters for fine-tuning updates. The proposed selecting method trains a hyper-network that selects learning parameters using episode...	Rebuttal 1: Rebuttal: We sincerely appreciate your comments on our paper. You may find our responses below for your concerns. If you have any further concerns, we would be grateful if you could let us know. ##### [W1 & Q1] Explanation and justification for the necessity of model editing in the vision domain > - We wou...	Summary: The paper investigates a novel method for editing Vision Transformers (ViTs) to enhance their performance by rectifying predictive errors. Specifically, it proposes training an additional ViT to locate specific rows in the weight matrices of several feedforward network (FFN) layers for efficient fine-tuning. T...	Rebuttal 1: Rebuttal: We sincerely appreciate your constructive comments on this paper. We detail our response below point by point. Please kindly let us know if our response addresses the issues you raised in this paper. ##### [W1] Application to hierarchical ViTs > We greatly appreciate the reviewer's suggestion to...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers and ACs for your diligent efforts and high-quality reviews. If there are any additional questions or if further clarification is needed, please feel free to let us know. Your insights are highly valued. We are delighted to note that reviewers find that: - ou...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena	Accept (poster)	Summary: This paper introduces an offline WizardArena in a two-step process. It selects diverse and hard prompts from the lmsys-chat-1m dataset as a test set and divides the remaining samples into nine parts for the training set. Then, a judge model based on LLAMA3-70B-Chat is also constructed with the designed prompt....	Rebuttal 1: Rebuttal: Thank you for your meticulous review and insightful questions and the time you spent reviewing our work. We sincerely apologize for any confusion this may have caused. In the revised version of the paper, we will provide more detailed and clearer implementation in Sections 3 and 4. Below, we will ...	Summary: This paper proposes a Simulated Chatbot Arena named WizardArena to efficiently evaluate and train large language model (LLM) chatbots without human intervention. WizardArena is based on Elo rankings similar to LMSys Chatbot Arena but replaces human judges with powerful open-source LLMs, e.g. Llama-3. For evalu...	Rebuttal 1: Rebuttal: Dear Reviewer, we thank you for your valuable comments and the time you spent reviewing our work! Please find below a detailed discussion of the points you have raised: > Weaknesses-1: The iterative training data generation needs multiple training and generation rounds... It can be costly a...	Summary: This paper introduces WizardArena, an AI-based simulated offline chatbot arena that generates high-quality synthetic data and uses a fine-tuned Llama 70B as a judge model for automated evaluation. This approach significantly reduces the labor and time costs of post-training large language models while maintain...	Rebuttal 1: Rebuttal: Dear Reviewer, thank you for your valuable comments and the time spent reviewing our work! Your feedback is invaluable for improving the quality and competitiveness of our paper. Please find below a detailed discussion of the points you have raised: > Weaknesses-1: The approach of this work ...	Summary: The paper proposes a new framework for improving large language models (LLMs) post-training through a simulated environment called WizardArena. This environment aims to avoid the costly and time-consuming manual interventions typically required for training and evaluating chatbots. Strengths: - This paper pro...	Rebuttal 1: Rebuttal: Dear Reviewer, we thank you for your valuable comments and the time you spent reviewing our work! Your professional feedback provides valuable guidance for writing a more comprehensive and competitive paper. Please find below a detailed discussion of the points you have raised: > *Weaknesses-1...	Rebuttal 1: Rebuttal: We express our gratitude to all reviewers for their thorough evaluations. We have included the supplementary experiments in the newly uploaded WizardArena_rebuttal PDF as follows. 1. In Table 1 shows the Chinese ELO rankings results of 19 models on LMSYS ChatBot Arena-CH, WizardArena-Diverse-...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation	Accept (spotlight)	Summary: This paper presents StoryDiffusion, a framework based on diffusion models designed to generate consistent images or videos. StoryDiffusion comprises two key components. The first is a novel self-attention mechanism, named Consistent Self-Attention, which enhances the consistency between generated images and ca...	Rebuttal 1: Rebuttal: We wish to express our sincere gratitude to the reviewer for the thorough review and constructive feedback. Following the suggestion of the reviewer, we provide detailed responses in the hope of addressing the reviewer's concerns. Q1: Additional Comparison Methods. We are thankful to the r...	Summary: The paper proposes a diffusion-based generative model designed to create subject-consistent images and videos that correspond to given story texts. The paper presents both qualitative and quantitative results showing enhanced subject consistency in generated images and videos compared to existing work. Streng...	Rebuttal 1: Rebuttal: Firstly, we would like to thank the reviewer for acknowledging our efforts and contributions. Additionally, we deeply appreciate the reviewer's constructive feedback and would like to reply to the question raised by the reviewer in detail. Q1: "The Method section lacks clarity" We are very t...	Summary: Aiming to generate a story-based images or videos, this paper introduces StoryDiffusion, and proposes to use the following methods: 1. Consistent self-attention, which is a training-free way that modifies existing self-attention to maintain the between frames. 2. Semantic motion predictor, which is additional ...	Rebuttal 1: Rebuttal: Firstly, we sincerely thank the reviewer for the thorough review and constructive feedback. We also appreciate the positive rating provided by the reviewer. After carefully reading the comments, we hope to address the reviewer's concerns in the following: **Q1: "(whether) consistent self-attenti...	Summary: This paper proposes story diffusion which is a video generation model encapsulating two contributions: * First, the authors show how to generate a sequence of image that are self-consistent (e.g. consistent appearance for characters, consistent attire, etc) but obey different prompts. This is done using ordin...	Rebuttal 1: Rebuttal: First, we are extremely grateful that the reviewer recognizes our efforts and contributions. At the same time, we also appreciate the reviewer's constructive feedback and would like to respond specifically to the following aspects. Q1: Improve ablation experiments of Random Sampling. Special...	Rebuttal 1: Rebuttal: We would like to sincerely thank all the reviewers for their thorough review, and we are deeply grateful for their recognition of our efforts in this work. After carefully reading their review comments, We are very pleased to reply to the reviewer regarding the issues they are concerned about. ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Slight Corruption in Pre-training Data Makes Better Diffusion Models	Accept (spotlight)	Summary: The authors find that slight corruption in the conditioning of text-to-image diffusion models improves the performance. The authors theoretically analyze this empirical finding in a toy model where the goal is to learn to sample from a Gaussian Mixture and the network is piece-wise linear function. Inspired by...	Rebuttal 1: Rebuttal: We thank the reviewer's efforts on this paper and his constructional suggestions on the ablation study of CEP. We now address the concerns as the follows. --- > I believe that the authors should emphasize... Thanks for this suggestion. We will make condition perturbation more clear in our titl...	Summary: The paper investigates the impact of slight corruption of conditioning information in pre-training data on the performance of diffusion models (DMs). By introducing synthetic corruption to ImageNet-1K and CC3M datasets, the study evaluates over 50 conditional DMs. Empirical and theoretical analyses reveal that...	Rebuttal 1: Rebuttal: We thank the reviewer's acknowledgment on this paper. We now address the weakness raised as follows. --- > Notion of significance: The paper claims that slight pre-training corruption yield significantly better performance in terms of FID and IS metrics. Nevertheless, this notion of significance...	Summary: In this paper, the authors study the effect of slight corruptions to the training data during pretraining of conditioned diffusion models. They introduce the perturbations to the "condition" in the diffusion models and show that this leads to a better model (via FID and other metrics). They also provide an in...	Rebuttal 1: Rebuttal: Thanks for the reviewer's constructional feedback on this paper. We now address the weaknesses and questions as follows. --- > While authors showed extensively that adding noise can help, they missed to show "when does it help"? For example, if I'm a practitioner who wants to train a large model...	Summary: This paper claims that slight corruption of the condition is beneficial for conditional diffusion models. Experimentally, they conducted experiments with label flips or text swaps in the dataset and observed the performance improvement over zero noise at noise levels of 2.5% to 5%. Theoretically, they used a G...	Rebuttal 1: Rebuttal: Thanks for your time reviewing this paper. We now address your questions as follows. --- > From my understanding, in Theorem 2...It would be beneficial to address this explicitly in the main text. Thank you for your suggestion. We have already mentioned that the order of $\gamma$ is $O(\frac{1...	Rebuttal 1: Rebuttal: We first would like to thank all reviewer and AC's efforts and time reviewing this paper and suggestions for making it better. According to the reviewers' feedback, we summarize several critical points and make a general response here. --- > Motivation for corruption in pre-training. - First o...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment	Accept (poster)	Summary: This paper aims to alleviate the reward-misalignment issue by introducing task-related objective. The authors theoretically derive sufficient conditions to mitigate the task-reward misalignment issue and design algorithms accordingly. Theoretical analysis of the algorithm is provided to guarantee the reward le...	Rebuttal 1: Rebuttal: > ## ... continuous environments are missing, e.g., MuJoCo. We have included continuous control tasks in Appendix C.2. > ## ... environment setups are too simple ... fewer than 100 states The MiniGrid environments are challenging benchmarks for RL and IRL although they may look deceptively simp...	Summary: This paper introduces a novel approach to inverse RL (IRL) by identify weaknesses in current IRL algorithms and proposing the respective solution: optimizing for task-related reward functions. The authors provide a clean theoretical framework for task-related rewards, but lack applicability in practice from...	Rebuttal 1: Rebuttal: > ## The experimental campaign is concentrated on simple tasks... more complex tasks could prove scalability. The MiniGrid tasks we employ are challenging benchmarks for RL and IRL although they may look deceptively simple. The agent only makes partial observation of the environment -- the 7x...	Summary: This paper addresses the reward misalignment in inverse reinforcement learning and introduces a novel approach to tackle this problem: PAGAR. It introduces a protagonist and an antagonist policy and treats the expert demonstrations as weak supervision to derive a set of reward functions rather than a single re...	Rebuttal 1: Rebuttal: > ## the basis of UED should be explained more. Our Eq. 3, which minimizes the worst-case Protagonist Antagonist Induced Regret $Regret$, is inspired by UED in [1]. However, the core of our approach focuses on learning policy with a set of candidate reward functions rather than searching for div...	Summary: This work investigates the inverse reinforcement learning problem. Previous methods suffer from the weakness: fail to capture the true task objectives from demonstrations. This study proposes deriving a set of candidate reward functions that align with the task rather than merely the data. Using an adversarial...	Rebuttal 1: Rebuttal: > ## MiniGrid includes more challenging environments As our approach builds upon existing IRL/IL methods (PAGAR-GAIL, PAGAR-VAIL), its performance is influenced by the integrated IRL/IL algorithms. We found that the more complex MiniGrid environments, such as ObstructedMaze and KeyCorridor, are t...	Rebuttal 1: Rebuttal: We appreciate the reviewers' insights and suggestions. In this general response, we would like to reiterate the motivation of this paper and share our additional experimental results. > ## PAGAR-Based IL for Task-Alignment In this paper, we focus on aligning with tasks that fit the format descr...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PRODuctive bandits: Importance Weighting No More	Accept (poster)	Summary: This paper first considers incentive-compatible online learning problem, which is first considered in [Freeman et al., 2020]. Specifically, the original WSU-UX algorithm only achieves $O(T^{2/3})$ regret shown in [Freeman et al., 2020]. By injecting a bias in the loss estimator constuction to the original WSU-...	Rebuttal 1: Rebuttal: We address weaknesses and questions below: Weaknesses: --Difficulty understanding the form of the bias: The exponentiated weights/hedge algorithm update can be written as the first equality after line 149, where $\lambda_t$ is the normalization factor which makes $\pi_{t+1}$ a probability distri...	Summary: This paper revisits the simple PROD-type algorithms, originally introduced by Cesa-Bianchi et al. in 2007 for online learning under full feedback, and does an excellent job in doing so. Specifically, the authors demonstrate that some version of these algorithms can achieve optimality even for the $K$-armed ban...	Rebuttal 1: Rebuttal: We address weaknesses and questions below: Weaknesses: --The presentation targets a specialized audience: Thank you for the suggestion. We agree that the presentation in the main paper is quite technical. We are happy to add an appendix which describes the standard analysis of OMD and gives a qu...	Summary: Adversarial bandits have typically been solved through techniques such as online mirror descent or variants of multiplicative weight updates, but these might rely on some notion of importance-based weighting. To develop solutions free of such importance-weighting, the authors propose using the Prod algorithm, ...	Rebuttal 1: Rebuttal: We address weaknesses and questions below: Weaknesses: --Unclear rationale behind using Prod: As we have already stated the Prod updates are simple and closed form, that is they do not require solving an optimization problem at every step. Prod type algorithms have been previously used for Online...	Summary: This paper presents novel algorithms for the multi-armed bandit (MAB) problem that are shown to achieve optimal regret bounds even without relying on importance weighting. The authors introduce variants of the well-known Prod algorithm that are effective for both adversarial and stochastic settings, presenting...	Rebuttal 1: Rebuttal: We address weaknesses and questions below: Weaknesses: --More details on the setting of incentive-compatible online learning: We are happy to include more details for the incentive-compatible setting. We propose adding a quick overview which includes what constitutes a proper scoring rule togeth...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their careful reviews and suggestions on how to improve our work. We address each of the comments regarding weaknesses and each of the questions separately under the respective reviewer.	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper studies the multi-armed bandit (MAB) problem. The main focus is on the Prod algorithm, which is fundamentally considered to be sub-optimal for MAB settings. The authors challenge this conjecture by leveraging Prod's interpretation as a first-order Online Mirror Descent (OMD) approximation. They make ...	Rebuttal 1: Rebuttal: We address weaknesses and questions below: Weaknesses: --No experimental results: This is a purely theoretical paper. Our goal was to develop new variants of Prod which enjoy min-max optimal regret in the adversarial setting and instance-dependent optimal regret bounds in the stochastic setting....	null	null	null	null	null	null
A Turing Test for Self-Awareness	Reject	Summary: This paper introduces a simple test, along the lines of the Turing test, to determine whether or not an LLM (or an generative text model) is _self-aware_ (or _self-conscious_). The general structure of the test is that the LLM participates as one of the interlocutors in a dialogue and then is asked aster the f...	Rebuttal 1: Rebuttal: I sincerely thank reviewer deXV for their time and thoughtful feedback. I wish to appreciate the serious effort this reviewer demonstrated to understand and engage with the work. Starting with your primary concern: Despite appearances, the distinction between a sensational, philosophical sense o...	Summary: This paper aims to answer the profound philosophical question of whether the state-of-the-art, transformer-based large language models (LLMs) pose self-awareness. As the author points out in this paper, this question, which I agree to be legitimate and important, is rarely addresses in a rigorous, academic man...	Rebuttal 1: Rebuttal: I sincerely thank z1UP for your time and careful consideration. Your feedback is clear, well thought out, and very warmly received! Taking each of your points in order: W1 Giving names, let’s say Charlie (a close friend) listens to a conversation between Jane and John, and Charlie can easily id...	Summary: This paper proposes a test of self-awareness similar to that of the Turing test. It starts by motivating the need for an "objective measure" of AI progress, given that the Turing test has been passed by LLMs. There is a brief discussion of literature on self-awareness and related topics in philosophy. The tes...	Rebuttal 1: Rebuttal: Thank you to reviewer rLt4 for your consideration of the manuscript. To your points about the paper being both verbose and drawn out—I argue that providing the necessary philosophical background to the test is both a necessary and indispensable part of the paper. I believe it would seriously weak...	Summary: This work proposes an objective inventory for testing the self-awareness of an artificial intelligence agent. The core idea is to test whether an agent can distinguish content it has produced from content originating from the external world. The experiments show that no large language models have demonstrated ...	Rebuttal 1: Rebuttal: Thank you to reviewer Mykv for your time considering the paper. Is there any reasoning you are able to share for your assessment that the test is unsound? So far, no explanation or rationale has been given. Likewise, it’s claimed there are obvious flaws in the proposed test, but no flaws have ac...	Rebuttal 1: Rebuttal: Global Rebuttal I thank all the reviewers for their time and consideration of the manuscript. I must start by clarifying two confusions I take responsibility for in the reviewer’s initial reading: First, there was a repeated confusion that the single decision problem of role-identification (sec ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport	Accept (poster)	Summary: The paper proposes a new Distributionally Robust Optimization (DRO) framework based on Unbalanced Optimal Transport (UOT) distance. Under some conditions, the authors establish strong duality results for a Lagrangian penalty variation of the proposed problem. The designed algorithm is tested in linear, logisti...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful feedback. We address the concerns below. --- > Quality 1: Chosen divergence and impact of $\beta$ Thank you for highlighting the chosen divergence in UOT. This work uses the well-studied KL divergence, which finds diverse applications in information...	Summary: This paper introduces a novel outlier-robust Wasserstein Distributionally Robust Optimization (WDRO) framework based on unbalanced optimal transport (UOT). For the UOT-DRO, the authors establish strong duality results under specific smoothness assumptions. To enhance computational efficiency, they propose a La...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful feedback. We address the concerns below. --- > W1: Excess risk Thank you for raising this point. Analyzing the excess risk of UOT-DRO is quite challenging due to the existence of unbalanced optimal transport. Moreover, while [17] gives the analysis ...	Summary: The authors leverage unbalanced optimal transport (UOT) to build a new DRO model. Strong dual reformulations together with efficient algorithm design have been proposed. Numerical studies on convex loss function demonstrate the superior performance of this framework. Strengths: Overall this is a good paper. T...	Rebuttal 1: Rebuttal: We thank the reviewer for your thoughtful feedback. Please see the detailed response below. --- > W1(Q1): Reference [JGX2023] We thank the reviewer for bringing our attention to the reference [JGX2023] and other related references about regularized optimal transport distance. We will add the...	null	null	Rebuttal 1: Rebuttal: # Common Response > C1: How does our method deal with outliers? Consider the Lagrangian penalty problem of minimizing $F(\theta) = \sum_{i=1}^n \exp\left(\frac{f _ {\lambda}(\theta,\hat{\zeta}_i)} {\lambda \beta} \right)$ in Eq (13), where $\hat{\zeta}_i$ is the $i$-th sample that may be ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Gradient-Variation Online Learning under Generalized Smoothness	Accept (poster)	Summary: This paper contributes gradient-variation extensions of several online learning guarantees to a generalized smoothness setting. Under a more general smoothness assumption, the paper first provides an algorithm which achieves a $O(\sqrt{V_T})$ gurantee, where $V_T=\sum_{t} \sup_{x}\\\|\nabla f_t(x)-\nabla f_{t-1...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! In the following, we will answer your question and respond to your concerns regarding the assumptions. --- Q1. "However, to ensure the theoretical results are valid, we assume that there exist finite but unknown upper bounds G and L for the Lipschitzness...	Summary: Problem: The paper studies the OCO problem under the generalized smoothness assumption, i.e. at each time $t$, $f_{t}$ satisfies $\\|\| \nabla ^ {2} f_{t} (x) \\|\| \le \ell_{t}(\\|\| \nabla f_{t}(x) \\|\|)$ for all $x \in \mathcal{X}$, where $\ell_{t}$ is a positive non-decreasing function. In addition to the informa...	Rebuttal 1: Rebuttal: Thank you for your careful review and very constructive comments. We will revise the paper according to your suggestions. In the following, we address each of your technical questions. --- Q1. "The discussion towards the end of page 3 is confusing to me....the bounds presented in the Theorem...	Summary: The paper aims to provide gradient-variation regret bound when only a generalized smoothness condition holds. It considers the optimistic mirror descent algorithm. It further designed a meta-algorithm, which can be Lipscthiz adaptive. Lastly, it considers adaptive regret and dynamic regret. Strengths: (1) Com...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. We will revise our paper to highlight the contributions according to your suggestions. Below, we will address your questions. --- Q1. "However, Assumption 2 posits a bounded domain. Does this imply that the gradients and smoothness are also bounded? In ...	Summary: This paper studies adaptive online learning under the generalized smoothness assumption. The authors proposed optimistic OMD based algorithms that achieves the first gradient variation bounds under this general setting. Under this assumption, they also provide uninveral algorithms which can adapt to different ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback and appreciation of our work! We will respond to your comments below. --- Q1. "The base learner proposed in Section 2 needs to know the Smooth constant of the previous observed loss functions (at round $t-1$), which might be difficult to compute if the lo...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing	Accept (poster)	Summary: This paper aims to fine-tune a visual foundation model for visual place recognition, with an innovative focus on the probing stage. Specifically, it makes three contributions: 1. It proposes a probing method called CFP and introduces a normalization method named DPN within the CFP. 2. It provides theoretica...	Rebuttal 1: Rebuttal: Thanks for acknowledging our paper, and we appreciate your valuable suggestions. 1. As discussed in our response to reviewer psQJ, LP is a kind of first-order feature and less accurate compared to second-order features in the fine-grained VPR task. MP is proposed solely for coarse-grained classif...	Summary: Existing VPR models often need to be trained from scratch on environment-specific data, resulting in insufficient generalization performance. The paper aims to improve environment generalization by fine-tuning a visual foundation model. Specifically, it uses DINOv2 as the foundation model and enhances its feat...	Rebuttal 1: Rebuttal: Thank you for acknowledging our motivations and experimental results. 1. The VPR task involves a more fine-grained classification, as two images of the same location may have minimal overlap and require identification based on features from a small region. As discussed in the introduction of th...	Summary: The paper presents a method for parameter-efficient fine-tuning of Visual Foundation Models (VFMs) for the Visual Place Recognition (VPR) task. The method includes a DPN module, which is placed between frozen VFM blocks to recalibrate features for the VPR task, and an aggregation (probing) module named Centroi...	Rebuttal 1: Rebuttal: Thanks for your insightful comments, we provide the feedback as follows: Q1&W2. Detailed information on the training procedure. To ensure a fair comparison, we have kept the details unrelated to the innovations of this paper consistent with previous SOTA methods (i.e., Conv-AP [12], MixVPR [1...	Summary: This work presents a novel pipeline, i.e., EMVP, for visual place recognition based on foundation models. In this pipeline, a Centroid-Free Probing (CFP) method is used to process the output of the foundation model and get the global features of place images. Besides, the authors also propose a Dynamic Power N...	Rebuttal 1: Rebuttal: Thanks for carefully reading our paper and recognizing its writing and novelty. We appreciate the opportunity to address your questions: 1.a. We posit that the second-order statistics (bilinear features) employed in NetVLAD are implemented through the outer product of two vectors corresp...	Rebuttal 1: Rebuttal: Dear Reviewers, Thanks for your hard work. Your constructive comments will help us continuously improve this paper. We list all the references mentioned in our rebuttals for your convenience. [1] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Continual Counting with Gradual Privacy Expiration	Accept (poster)	Summary: This paper studies the continual counting problem, under a “relaxed” privacy notion, where the privacy budget is a function of time, aka differential privacy with expiration functions. This notion, proposed by Bolot et al.[2], captures the settings where data becomes less sensitive with time. The paper propose...	Rebuttal 1: Rebuttal: We thank the reviewer for their reading and questions. Q1: how to set $\lambda$? $\lambda$ is provided by the user together with the input, and should reflect their privacy goals. The larger we make $\lambda$, the weaker the asymptotic privacy guarantee becomes to the benefit of the error, as ...	Summary: The paper studies differential privacy with gradual expiration models, gives upper and lower bounds for a large set of expriation functions $g$. The proposed algorithm can achieve an additive error of $O(log(T) / \epsilon)$, matching the lower bound. Empirical evaluationshows that the algorithm can achieve sma...	Rebuttal 1: Rebuttal: We thank the reviewer for their reading and question. W1: Section 3.2 is too abstract We will include examples to make the related concepts in Section 3.2 more intuitive. Q1: matrix mechanisms with privacy expiration It is possible that techniques related to matrix mechanisms can be levera...	Summary: Modern DP solutions are often working in the setting where the new data is coming daily. However, DP doesn't allow querying the data indefinitely; therefore, all of these solutions are either treating database as partitioned by time, or refresh the budget on some schedule. Both of these approaches are not id...	Rebuttal 1: Rebuttal: We thank the reviewer for their reading and their exact questions/comments. We will incorporate the comments into the final version, and address a subset of them below. line 42: refreshing budgets means linear expiration We agree; we will state this explicitly in the introduction. *line 54: n...	Summary: This paper studies a variant of the continual release model of differential privacy where the privacy parameter degrades (potentially smoothly) with time, i.e., the $\epsilon$ associated with changing a datapoint at time $t$ is $\epsilon g(T-t)$ (the model was introduced by Bolot et al.). This model makes sens...	Rebuttal 1: Rebuttal: We thank the reviewer for their reading and questions. Q1: why not use the vanilla binary mechanism It can be shown that the vanilla binary mechanism satisfies the same error/privacy expiration as our algorithm for $\lambda=1$. We however do not believe that the vanilla binary mechanism can ...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studies the DP counting problem in the continual release setting. The difference between the problem studied by this paper and the standard problem is that it allows earlier change of the element has a more relaxed privacy budget. The high level idea is to follow the previous pan-private continual c...	Rebuttal 1: Rebuttal: We thank the reviewer for their reading and question. Q1: error bound for expiration growing as $\log^{o(1)}(d)$. Our algorithm also supports privacy expiration $g(d) = O(\log\log(d))$ when $\lambda = 0$, which is shown in Appendix C.3.1. The accuracy guarantees for this case is given by setti...	null	null	null	null	null	null
CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming	Accept (poster)	Summary: This work intorduces an encoder-decoder transformer model, CodeRosetta, translating between programming languages and also their high-performance computing (HPC) extensions parallelly. The authors claim that CodeRosetta outperforms baselines and general LLMs on C++ to CUDA and Fortran translation tasks. Stre...	Rebuttal 1: Rebuttal: Metric issue and Formal definition of compilation We understand the metrics used may not be comprehensive enough, as noted in [1]. To address this, we manually analyzed and executed the translated code. Please reference to global response. Moreover, as it was mentioned in Out of the BLEU [1] ...	Summary: This paper introduces CodeRosetta, an encoder-decoder transformer model for unsupervised translation between programming languages and their high-performance computing (HPC) extensions. The main contributions include unsupervised code translation: CodeRosetta translates between programming languages (e.g., C++...	Rebuttal 1: Rebuttal: Evaluation metrics and runtime correctness Evaluation metrics were selected for comparison as they have been utilized in the baselines. We understand that these metrics can have limitations. We tried to address this point by manually analyzing the translated code and executing it. Please refe...	Summary: The paper presents CodeRosetta, a transformer model designed for unsupervised code translation and their high-performance computing extensions, such as C++ to CUDA and Fortran. By introducing novel pre-training objectives like AST Entity Recognition and customized Denoising Auto-Encoding, CodeRosetta effective...	Rebuttal 1: Rebuttal: Difference with relevant works, CodeT5[1] and PLBART[2] Thank you for providing us with these references. Indeed, Denoising Auto Encoding (DAE) is a popular technique when it comes to training Encoder-Decoder models, as both CodeT5 and PLBART use it, too. Some of the noising strategies, such ...	Summary: The paper looks into the problem of unsupervised code translation with a particular focus on parallel programming languages like C++ to CUDA. It trains (relatively) small encoder-decoder models compared to current LLMs while achieving very strong performance on translation tasks. It incorporates program-specif...	Rebuttal 1: Rebuttal: Deduplication analysis The C++ to CUDA dataset was obtained from BabelTower [26]. This dataset has already gone through a rigorous deduplication and cleaning process. Moreover, there is no paired trained data available in the training set. This means the model can not see a C++ code and its C...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you very much for your invaluable feedback and comments. We acknowledge that evaluation metrics may not capture all nuances and that systematically evaluating the generated code against references is challenging. However, these metrics have been used widely in the baseline ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Potts Relaxations and Soft Self-labeling for Weakly-Supervised Segmentation	Reject	Summary: This paper proposes a method to improve weakly-supervised semantic segmentation using sparse annotations. The authors introduce a Potts relaxation method, which is an extension of the traditional CRF-like methods. The experiments are conducted on the PASCAL dataset. Strengths: Pros. 1. The proposed method ap...	Rebuttal 1: Rebuttal: 1. The comparisons with other methods are not comprehensive\ Compared to the scribble-supervision results in [1] (76.4%), [2] (76.6%), and [3] (77.5%), see citations by the reviewer, our result (78.1%, see Table 5) using the same architecture (Deeplab + ResNet101 backbone) is better. We will i...	Summary: This paper considers semantic segmentation under scribble supervision. The paper studies relaxations of the Potts model and proposes a framework for generating soft pseudo-labels, which benefit over hard pseudo-labels in that they can represent uncertainty. The paper highlights problem cases with two standard ...	Rebuttal 1: Rebuttal: Note: We can provide only brief answers to the first two questions. We fully understand that NeurIPS reviewers may not be familiar with CRF methodology/terminology. However, please note that it has been standard for image segmentation at least since 1980's with numerous textbooks on the subjec...	Summary: The work proposes a soft self-labeling framework for weakly supervised semantic segmentation using scribbles. This model-agnostic framework requires only the joint optimization of network predictions and pseudo labels, guided by specific loss functions: collision cross-entropy and log-quadratic Potts relaxatio...	Rebuttal 1: Rebuttal: Does the same conclusion hold for other segmentation models from an experimental perspective?\ We do not see any technical reasons to expect significant differences compared to current results, which are consistent with their theoretical properties shown in our paper. **Results for other segm...	Summary: This paper proposes a new framework for Weakly-Supervised image Segmentation. The main contribution is to use a soft-labelling approach which is considered superior to classic hard labelling because it can keep track of the centanty of the label. Then different forms of second order potts relaxation and cross-...	Rebuttal 1: Rebuttal: The motivation for using soft pseudo-labels is not clear\ First, soft pseudo-label (distribution) is a generalization of the standard hard pseudo-label (one-hot distribution). Softness can represent more information, which can be naturally interpreted probabilistically as the uncertainty of th...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stochastic Newton Proximal Extragradient Method	Accept (poster)	Summary: The paper develops an accelerated scheme for strongly convex problems. As feedback it requires a deterministic first order oracles, but only an inexact Hessian estimator –– the main assumption being that the Hessian noise is mean zero and sub-exponential (implied by e.g. Hessian subsampling and $\Vert\nabla^2 ...	Rebuttal 1: Rebuttal: Q1 Running the accelerated version in the experiment. A1 We added numerical results for the original SNPE with the extragradient step and compared it with the variant without it (Figure 3 in the shared PDF). We observe that the modified variant outperforms the original, suggesting the ext...	Summary: This work proposes a novel algorithm called the Stochastic Newton Proximal Extragradient method. Authors claim that their method reaches a superlinear convergence rate after $\mathcal{O}(\kappa)$ iterations, in contrast to the $\mathcal{O}(\kappa^2)$ iterations proved in previous work. Strengths: - This paper...	Rebuttal 1: Rebuttal: Q1 The assumptions are very restrictive. e.g., Assumptions 3 and 5. A1 We note that our assumptions are standard in the study of (stochastic) second-order methods, including Subsampled Newton (Erdogdu & Montanari, 2015; Roosta-Khorasani & Mahoney, 2019), Newton Sketch (Pilanci & Wainwrig...	Summary: This paper uses the hybrid proximal extragradient framework to accelerate the convergence of Hessian average. The theoretical results significantly reduce the number of iterations to enter the linear phase, initial superlinear phase, and final superlinear phase when compared to the initial Hessian average meth...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback. Q1 Empirical comparison with AGD. A1 Following your suggestion, we compared the performance of our SNPE method against AGD in our new experiment; please see Figures 1 and 2 in the shared PDF file. From Figure 1, we observe that our SNPE...	Summary: Newton method is well-known for its local quadratic convergence. However, the use of Hessian introduces additional computation challenges. One approach to tackling this issue is an inexact approximation of the Hessian. In this paper, the authors consider the finite sum minimization problem. They propose a stoc...	Rebuttal 1: Rebuttal: Q1 Authors consider only a strongly convex setup and this seems to me as a major limitation of this work. A1 Thank you for raising this point. To begin with, we note that the strong convexity assumption is common in the study of stochastic second-order methods, including Subsampled Newto...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful feedback. Overall, Reviewers qDEj, m5uq and TogL provided largely positive comments, highlighting the strength of our theoretical analysis and the clarity of the presentation. Reviewer BRp1 raised some concerns regarding our complexity re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null

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