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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
TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering	Accept (poster)	Summary: This paper proposes a novel framework named TFGDA for the semi-supervised graph domain adaptation. Considering that existing graph domain adaptation works overlook the utilization of graph structure information, this paper innovatively proposes a STSA strategy that fully leverages graph structure information t...	Rebuttal 1: Rebuttal: We appreciate Reviewer R5oT for the thorough review of the manuscript and the valuable comments that will aid in enhancing our paper. Q1: Include some discussions on practical applications of graph transfer learning in the Sec.1. A1: Graph Transfer Learning can be applied in various pr...	Summary: This paper focus on the semi-supervised graph domain adaptation, and introduces a new framework called TFGDA. Graph usually contains complex structure information, while existing GTL studies often overlooks the importance of structure information when extracting transferable node features. TFGDA thus proposes ...	Rebuttal 1: Rebuttal: We thank the Reviewer byDA for the careful reading of the manuscript and the related comments, which are helpful to improve our paper. W1: Further exploration can be conducted on other types of graph datasets. A1: Based on your suggestion, we conduct additional experiments on a real-wor...	Summary: This paper presents a framework called TFGDA for semi-supervised graph domain adaptation (SGDA). It addresses the challenge of annotating unlabeled target graph nodes by utilizing knowledge from a source graph with limited labels. The framework incorporates three key strategies: Subgraph Topological Structure ...	Rebuttal 1: Rebuttal: We extend our heartfelt gratitude to Reviewer aMJG for the careful reading of the manuscript and the valuable feedback provided, which are helpful to improve our paper. Our detailed point-by-point responses are provided below. **W1: While the authors claim to be the first to consider graph struct...	Summary: This paper introduces a graph transfer learning framework called TFGDA, which leverages the structure information to enhance model’s generalization performance. The TFGDA framework includes the structure alignment strategy STSA, the feature distribution alignment strategy SDA, and the RNC strategy to address t...	Rebuttal 1: Rebuttal: We would like to express our gratitude to Reviewer 9hdx for their comprehensive evaluation of the manuscript and the insightful feedback provided, which will greatly contribute to the improvement of our paper. Kindly refer to our specific responses outlined below. **W1: This paper proposes to ext...	Rebuttal 1: Rebuttal: 1) To Reviewer 9hdx: Dear Reviewer 9hdx, based on your insightful advice, we provide a theoretical analysis of our method. The theoretical analysis of our method is based on the theory of domain adaptation (DA) [Y4-Y5]. Formally, let $\mathcal{H}$ be the hypothesis space. Given two domai...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed	Accept (poster)	Summary: This is a theoretical paper that studies graph neural tangent kernels in the context of temporal graphs. The authors propose a temporal GNTK model and proves rigorous error bounds. This work also links the convergence of the temporal GNTK to the graphon GNTK. Strengths: To the best of my knowledge, this is th...	Rebuttal 1: Rebuttal: Thanks very much for the review! We greatly appreciate your enjoy on our paper's insights, theoretical analysis, and experiments. We address your questions in the form of Q&A below. Moreover, for the length limit, we distillate our answer and provide brief but important answers, more detailed int...	Summary: This paper proposes a graph neural tangent kernel method that extends the advantages from static graphs to temporal graphs. Theoretical analyses are conducted to prove the transferability and robustness of the model. Experiments are performed on graph-level tasks as well as node-level tasks. Strengths: 1: The...	Rebuttal 1: Rebuttal: Thanks very much for the review! We greatly appreciate your acknowledgment of our paper's motivation, theoretical analysis, and experiments. We address your questions in the form of Q&A below. Moreover, for the length limit, we distillate our answer and provide brief but important answers, more d...	Summary: The proposed Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed or Temp-G3 NTK method is a novel temporal graph learning method with provable error bounds. It extends the the simplicity and interpretation ability of Graph Neural Tangent Kernel to the temporal graph setting leading to rigorous error b...	Rebuttal 1: Rebuttal: Thanks very much for the review! We greatly appreciate your acknowledgment of our paper's novelty, presentation, and model performance. We address your questions in the form of Q&A below. Moreover, for the length limit, we distillate our answer and provide brief but important answers, more detail...	Summary: This paper introduces the graph tangent kernel for temporal graph, thus enabling the learning task on time-evolving graph structures. The authors generalized the concepts of graph tangent kernel to incorporate the time domain information. They derived the generalization bound for the corresponding kernel predi...	Rebuttal 1: Rebuttal: Thanks very much for the review! We greatly appreciate your acknowledgment of our paper's theoretical analysis. We address your questions in the form of Q&A below. Moreover, for the length limit, we distillate our answer and provide brief but important answers, more detailed intermediate steps, r...	Rebuttal 1: Rebuttal: First of all, we want to sincerely thank the time and review of all reviewers and chairs, and we are very glad to learn the reviewers' appreciation for this paper. Also, the reviewers' raised questions are very actionable and helpful to improve our paper. We have uploaded the answers individually...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
You Only Cache Once: Decoder-Decoder Architectures for Language Models	Accept (oral)	Summary: This paper proposes YOCO, a hybrid model that combines gated linear attention with standard attention (SA). The model stacks efficient self attention (ESA) in the first $L/2$ layers, succeeded by another $L/2$ cross-attention layers. Notably, the output of the last ESA is shared across subsequent CA layers, t...	Rebuttal 1: Rebuttal: Thanks for your positive comments. >Q1: add results of exisiting linear-time / hybrid models trained on trillions of tokens, e.g., RWKV6 and TransNormer A1: We focus on the evaluation using the same training data for fair comparisons. So we use OpenLLaMA and StableLM in Table 3. We also compare ...	Summary: The authors propose a new architecture for language models, where the top half of the transformer layers uses the KV from the bottom layer, while the bottom half applies efficient self-attention. The proposed architecture effectively reduces the KV cache size while maintaining the performance of the model, esp...	Rebuttal 1: Rebuttal: >Q1: insights and explanations for the effectiveness of the proposed architecture A1: The key insights are summarized as follows. First, KV cache can be shared across layers without significantly affecting language modeling performance. Most previous work focuses on compressing KV cache along wit...	Summary: The paper introduces YOCO, a decoder-decoder architecture designed for large language models. This architecture comprises a cross-decoder stacked upon a self-decoder, efficiently encoding global key-value caches reused by the cross-decoder. YOCO aims to reduce GPU memory demands and improve prefill latency and...	Rebuttal 1: Rebuttal: >Q1: The comparison and discussion with FlashDecoding. A1: Flash-Decoding and kernel fusion have been used in comparison (described in L240, L121, L25), i.e., the Transformer results have been based on FlashDecoding. The contributions of YOCO and FlashDecoding are orthogonal. YOCO optimizes the p...	Summary: The paper introduces YOCO (You Only Cache Once), a novel decoder-decoder architecture for large language models. YOCO uses a self-decoder to generate global key-value (KV) caches, reused by a cross-decoder, reducing GPU memory usage and improving inference efficiency. The architecture achieves comparable perfo...	Rebuttal 1: Rebuttal: Thank you for the positive review and insightful feedback. >Q1: In the ablation study, does Unstacked YOCO refer to the model without the self-decoder? A1: The input of Unstacked YOCO's cross-decoder is the output of embedding layer. In comparison, the input of YOCO's cross-decoder is the ou...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
GREATS: Online Selection of High-Quality Data for LLM Training in Every Iteration	Accept (spotlight)	Summary: This paper proposes a novel online batch selection algorithm called GREedy Approximation Taylor Selection (GREATS) for training large language models (LLMs). The algorithm aims to improve training convergence speed and generalization performance by selecting informative and diverse examples for model updates. ...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback! Q. [Additional Baseline Comparison.] "Why are uniform selection and selection-via-Proxy not included as baselines?" A. We thank the reviewer for bringing up potentially additional baselines. If we understand correctly, the “uniform selec...	Summary: The paper introduces a well-motivated online data selection method based on Taylor series expansions (with additional approximations) and apply it to training/fine-tuning LLMs. The paper's results show good performance improvements on fine-tuning tasks, but little gains on pre-training tasks. Strengths: The p...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments! Q. [Additional Baseline Comparison.] A. We sincerely appreciate the reviewer's feedback and suggestions for additional baselines. We have taken these recommendations seriously and have expanded our comparisons accordingly. **(1) Reference ...	Summary: The paper presents an online adaptive subset selection framework GREATS which acts as a principled and efficient online batch selection. The authors further showcase their proposal's utility towards training for Large language models training thereby better performance during training Strengths: - The paper f...	Rebuttal 1: Rebuttal: Q. [Notation] A. We thank the reviewer for the suggestions on the notations, which we will incorporate into our revision. We note that the utility function $U^{(t)}: R^d \rightarrow R$ maps to the model performance change in $t$th step instead of the optimal batch. **Q. [Is the proposed...	Summary: The paper proposes a new online batch selection algorithm, GREATS, which frames batch selection as an optimization problem: specifically, selecting a subset of the training data that maximally reduce the validation loss when used as part of training. It uses a Taylor approximation of the effects of training on...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments! Q. [Relationship between online batch selection and offline data selection / data mixture optimization techniques [1, 2]? & Additional baseline comparison] A. We appreciate the reviewer highlighting the relevant literature on offline da...	Rebuttal 1: Rebuttal: We thank all the reviewers for the positive assessments! Q1 [Additional baseline comparisons & hyperparameter choices] A. We appreciate the reviewers' suggestions for additional baseline comparisons and hyperparameter sensitivity analysis. In response, we have conducted extensive additi...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposes GREATS (GREedy Approximation Taylor Selection), a novel online batch selection algorithm for large language model (LLM) training. GREATS aims to improve training efficiency by dynamically selecting the most informative data points from each training batch, based on their potential to reduce...	Rebuttal 1: Rebuttal: We thank the reviewer for the nice words! Q [Requirement of validation set] A. We acknowledge that the availability of a high-quality validation set is a limitation of GREATS. However, most state-of-the-art data selection (e.g., [1, 2]) and online batch selection techniques (e.g., [3]) r...	null	null	null	null	null	null
G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models	Accept (poster)	Summary: In the paper, authors propose a novel framework, G3, for worldwide geolocalization of a given photograph anywhere on Earth. The authors address the challenges of capturing location-specific visual cues and handling variations in image data distribution across the globe. G3 utilizes a three-step process: Geo-al...	Rebuttal 1: Rebuttal: W1: No mention of how much compute time and memory (in number) is required to geo-localize a given input image. Response: Thanks for your comment. We gather the compute time and memory cost in Geo-diversification and Geo-verification, as Geo-alignment is not directly used in inference. W...	Summary: This paper introduces G3, a RAG framework for geo-localization. By introducing a three-step process, the G3 framework achieves superior performance against other SoTA methods. To improve the expressiveness of the image embeddings, the paper proposed a new dataset, MP16-Pro, which adds textual descriptions to t...	Rebuttal 1: Rebuttal: W1: The necessity of the Geo-alignment module in the G3 framework has been indicated by experiments results in Table 2. However, the authors did not address the motivation for their particular design choice of the alignment module. Why do the image features have to align with both text feature...	Summary: This paper proposes three steps, i.e., geo-alignment, geo-diversification, and geo-verification to optimize both retrieval and generation phases of word-wide geo-localization. Strengths: 1. The motivation is clearly stated. 2. The experimental results show the effectiveness of the proposed method. 3. The prop...	Rebuttal 1: Rebuttal: W1: The experiments are not insufficient. The model seems too large, so the author should provide the number of parameters and gflops experiments. Response: Thanks for your question. We compile the data of model's parameters and computational load, as shown in the table below. \| Total p...	Summary: This work focuses on the task of "worldwide geolocalization" with an effective and adaptive framework based on large multi-Modality models. A novel framework, i.e., G3, is proposed, including Geo-alignment, Geo-diversification, and Geo-verification. This work also releases a new dataset MP16-Pro. The experimen...	Rebuttal 1: Rebuttal: W1: In the Geo-diversification part, there is no ablation study on different LMMs and different RAG templates. I wonder whether it still works on open-source LLMs. Reponse: Thanks for your advice. For the ablation study on different LMMs, please refer to the Open-source LMM (LLaVA) exp...	Rebuttal 1: Rebuttal: # Global Response We would like to extend our sincere gratitude to all the reviewers for your valuable comments and constructive feedback on our manuscript. Your insights have been instrumental in improving the quality of our work. We find two common concerns raised by multiple reviewers, which ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposes a RAG-based framework for worldwide geo-localization. The first Geo-alignment stage projects input images to embedding spaces to align with GPS coordinates, and text description with contrastive learning. Given new input image, the system is able to retrieve similar GPS and text description...	Rebuttal 1: Rebuttal: W1: My major concern is that the current pipeline is highly dependent on the LMM which is the powerful closed-source model, GPT-4V. This model is expensive for large-scale applications and also hard to reproduce due to unannounced updates for API across time. It would be better to provide the ...	null	null	null	null	null	null
Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond	Accept (poster)	Summary: This paper presents a collaborative framework for multi-drone object trajectory prediction, named DHD, which consists of two specifically designed components: the GBG module, aimed at generating more accurate BEV representations in aerial scenes, and the SISW module, which adaptively selects regions for collab...	Rebuttal 1: Rebuttal: ### [Weakness 1]: Grammar mistakes We have carefully reviewed and corrected the grammar mistakes, typos, and formatting errors. ### [Weakness 2]: Transmission volumes. Why the results of DHD in Table 2 bold? Based on the V2V communication protocol [1], data broadcasting can achieve a bandwidth of...	Summary: This paper studied the problem of collaborative multi-drone object trajectory prediction. The authors proposed a framework named "Drones Help Drones" (DHD) that can improve accuracy compared to existing methods while reducing the required communication bandwidth. To be more specific, the authors leveraged a Gr...	Rebuttal 1: Rebuttal: ### [Weakness 1]: Questions about the framework's performance in more complex, real-world scenarios with sensor noise, communication delays, environmental variability, and other common difficulties in real-world experiments. To address the reviewer’s concerns about the gap between real-world scen...	Summary: The paper presents the "Drones Help Drones" (DHD) framework for improving collaborative trajectory prediction in multi-drone systems. It addresses challenges with aerial observations and communication bandwidth by: (i) Using ground priors from inclined drone observations to enhance Bird's Eye View (BEV) accura...	Rebuttal 1: Rebuttal: ### [W1 & Q1]: Differences with existing methodologies. Our DHD builds on existing solutions, but we make significant improvements to address unique challenges in multi-drone collaborative prediction. For instance, long-range aerial observations can lead to substantial depth estimation errors, af...	Summary: The paper proposes a ground-prior-based BEV generation for drone trajectory prediction. The methods also propose an efficient information interaction via a Sliding Windows module. It accesses information volume across different areas through sliding windows. The authors provide experiments on the simulator-bas...	Rebuttal 1: Rebuttal: ### [W1 & L1]: Consider variations in terrain, drone stability, and other environmental factors. Please refer to Answer 1 in the overall rebuttal. ### [W2]: Analysis of the computational efficiency and scalability of the overall framework As seen in Table VI of the PDF, the differences in trainabl...	Rebuttal 1: Rebuttal: ## Answer 1: Investigate the effects of sensor noise, flight turbulence and rough terrain on the performance of DHD. ### Flight Vibrations and Uneven Terrain. We acknowledge that flight vibrations and uneven terrain can interfere with the drone's relative height to the ground, affecting the BEV g...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposes a framework, Drones Help Drones (DHD), which tackles trajectory prediction of objects in the scene. DHD consists of a Ground Prior Based Bird’s Eye View (BEV) Generation (GBG) module, which provides depth estimation from the drone to an object using ground priors to create an accurate BEV r...	Rebuttal 1: Rebuttal: ### [W1]: We envision a scenario where vehicles are centrally coordinated. In this setting, collaborative drones predict abnormal trajectories that may lead to accidents. Upon identifying the risks, the drones use wireless communication to alert nearby vehicles, prompting them to take evasive acti...	null	null	null	null	null	null
Reparameterization invariance in approximate Bayesian inference	Accept (spotlight)	Summary: The paper studies the effects of the invariance of Bayesian neural networks under reparametrization on their approximate posterior inference. The primary effect is the ambiguity on the uncertainty of the inferred function as it can be represented by multiple reparameterizations, each of which may be assigned a...	Rebuttal 1: Rebuttal: We thank the reviewer for the excellent review and we are very grateful for a thorough engagement with our work and for pinpointing its highlights. We acknowledge your point (under Weaknesses) about providing a bit more explanation of unconventional terms such as kernel (that in machine learning ...	Summary: The paper addresses the problem of maintaining invariance under reparameterization of Bayesian Neural Networks (BNNs). This issue undermines the reliability of BNNs since different parameterizations of identical functions produce different posterior densities. Through theoretical analysis and empirical validat...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review. - “Although the proposed method is theoretically sound, its computational complexity is higher than simpler approximations. [...]” While this is a valid comment we believe it stems from a slight misunderstanding of the main point that is being ma...	Summary: The authors provide theoretical analysis of the underfitting problem of Laplace approximation. Specifically, they show that the underfitting of Laplace approximation is due to the approximate posterior covariance is not invariant under reparameterization. Moreover, they propose a reparametrization invariant di...	Rebuttal 1: Rebuttal: Thanks for the excellent review, which we will reply to in parts. - “The paper provides, to my knowledge, the first theoretical justification of why Laplace approximation sufferes from underfitting: the approximate posterior covariance is not reparameterization invariance.” This is correct. Addi...	Summary: This work questions why the linearized Laplace approximation can be effective for uncertainty estimation, whereas the vanilla Laplace approximation can lead to poor performance, such as underfitting. This work argues that this degradation is attributed to the fact that the random function derived from each par...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and questions, which will further improve the paper. - “The lack of explanation of background knowledge, such as quotient space and Riemannian manifold, makes the paper difficult to understand.” We acknowledge this issue, which is due to the space ...	Rebuttal 1: Rebuttal: We are grateful to the four reviewers who all argue in favor of acceptance. We observe a general agreement that the developed theory is both novel and sheds significant light on the difficulties of Bayesian deep learning induced by reparametrizations. Some concerns have been raised about the expe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods	Accept (poster)	Summary: The paper presents a new method for shape correspondence that extracts descriptors that are both smooth over the manifold and distinctive enough to find high precision point matching. One can view the presented method as an improvement over the 2D-GEM method, that, as opposed to the latter, does not use the ei...	Rebuttal 1: Rebuttal: We thank the Reviewer for the insightful and constructive comments. We address some of the Reviewer’s comments and suggestions below: # Weaknesses: - W1: The new method to some extent resembles the 2D-GEM method, which limits the novelty. As presented in the limitation, since it uses the vertex n...	Summary: This paper introduces HOPE, a method that leverages k-hop neighborhoods as pairwise descriptors to achieve both accuracy and smoothness in shape matching. This approach aims to overcome the limitations of existing methods that often struggle to balance these two crucial aspects. The k-hop neighborhood concept ...	Rebuttal 1: Rebuttal: We thank the Reviewer for the constructive and insightful comments. We will strive to the best of our ability to address all concerns in hopes that the Reviewer can please reconcider the reviews . # Weaknesses: - W1: Novelty: The methods in the paper lack overall novelty, as the proposed approac...	Summary: This submission proposes a descriptor utilizing k-hop neighborhoods for non-rigid 3D shape matching. The descriptor is used jointly with local map distortion for map refinement. Overall, I am highly frustrated during reviewing this submission. While I have tried my best to parse and understand the technical ...	Rebuttal 1: Rebuttal: We thank the Reviewer for the clarification seeking comments. We sincerely apologize to the reviewer for any confusion or misunderstanding. We will strive to the best of our ability to address all concerns. However, considering the good scores of other Reviewers, we strongly plead with the Reviewe...	Summary: The paper focuses on the shape-matching tasks and proposes a shape-matching refinement technique based on the K-hop neighborhood descriptor and local map distortions. The experiments show improved results compared to existing approaches in isometric and non-isometric shape registration. Strengths: - The paper...	Rebuttal 1: Rebuttal: We thank the Reviewer for the insightful and constructive comments. We address some of the Reviewer’s comments and suggestions below: # Weaknesses: - W1: While there is existing work on deep and modern feature extraction, the experiments in this paper are limited to the SHOT descriptor. Incorpora...	Rebuttal 1: Rebuttal: # Comment To All Reviewers - First, we thank the Reviewer for their comments and please direct each Reviewer to specific responses to each as well as to the general responses here and the attached PDF. - We added experiments on intra-class matching on the SMAL_r dataset as suggested by Reviewer NA...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Graph Classification via Reference Distribution Learning: Theory and Practice	Accept (poster)	Summary: Instead of compressing node embedding matrix into a graph-level vector, this paper proposes a reference distribution learning method GRDL especially designed for graph classification task. GRDL achieves graph classification by measuring the dissimilarity between distributions of the input graph and the referen...	Rebuttal 1: Rebuttal: Response to Weakness 1: Thanks for your comment. This is a misunderstanding. Almost all papers studying the theory of classification use the i.i.d assumption. The identical distribution does NOT contradict the multiple classes scenario. Here the identical data distribution $\mathcal{D}$ i...	Summary: The paper introduces a novel algorithm called GRDL for graph classification. GRDL treats each graph’s latent node embeddings given by GNN layers as a discrete distribution and directly classify distributions without global pooling. The authors derived generalization error bounds for GRDL and verified them num...	Rebuttal 1: Rebuttal: Response to Question 1: Thank you for your question. We chose GIN because it is a highly representative model within the class of Graph Neural Networks (GNNs) that utilize neighbor aggregation schemes. GIN is probably the most expressive model in this category, and such schemes are widely emp...	Summary: This paper proposes to make graph-level predictions via distribution comparison between node-level representations and discrete reference distributions. The authors claim that their proposed method avoids the requirements of graph pooling for graph-level tasks. and reduce the risk of information loss. Theoreti...	Rebuttal 1: Rebuttal: Response to Weakness 1: Thank you for your feedback. The equation in lines 138-139 stems from the definition of the Maximum Mean Discrepancy (MMD). MMD is a measure between two discrete distributions, where the inputs are two matrices and the output is a scalar. This means that in the computa...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' comments. In this rebuttal, we added the following experiments. 1. Training time per epoch compared to two latest baselines (WitTopoPool and MSGNN both proposed in 2023) mentioned by reviewer anD1 on both experimental datasets in our paper and three synth...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?	Accept (poster)	Summary: The paper investigates the reversal curse a phenomena where LLM trained on documents on the format “A is B” are unable to improve their likelihood on statements of the format “B is A”. The authors extend the work to consider documents of the format “Name is Description” and “Description is Name”, whilst also c...	Rebuttal 1: Rebuttal: We are greatly encouraged by the reviewer's acknowledgement of the extensiveness and compellingness of our experiments. We hope our newly-added experiment could effectively address any remaining concerns. > Q: For these results, my fundamental question is if this is just a symptom of the model si...	Summary: The paper investigates the reversal curse, which is the finding that LLMs fail to generalize from seeing “A=B” to “B=A”, in a broader range of settings. They reproduce the reversal curse findings from Berglund et al (2023). They also investigate a new setting, multiple choice Q&A (rather than free form questio...	Rebuttal 1: Rebuttal: We are greatly encouraged by your acknowledgment of the novelty and solidness of our findings. We hope our rebuttals can adequately address your concerns. > W1: My main criticism is about the presentation of the results. Ans: Our main contribution is to provide new interpretations and insigh...	Summary: Building off prior work that studies the “reversal curse” in LLMs, the present paper provides additional analysis on 1) characterizing the limitations of LLMs on the reversal curse through more detailed experimentation (e.g., limitations with chain of thought prompting or providing multiple choice questions), ...	Rebuttal 1: Rebuttal: Thank you for your constructive suggestions and thoughtful comments. We hope that our response will effectively address your concerns. > W1 & Q2: What are the statistics/proportions that show a bias in the pretraining corpus of "A is B" over "B is A"? Ans: We conduct a statistical analysis o...	Summary: The authors extend the original reversal curse dataset to two tasks: open ended QA and MCQ. The authors analyze the generalization capabilities of LLMs on reversal tasks and provide several experiments towards their claims. They show that LLMs can generalize from A is B training to B to A, when both B and A ar...	Rebuttal 1: Rebuttal: # Part 1 > W1: Is the paper analyzing reversal curse? Was that not already done? A: Yes, we thoroughly analyze the reversal curse. As discussed in Global Rebuttal, we find previous discussions on this problem are rather vague and arbitrary. We provide conceptual clarity and an explanatio...	Rebuttal 1: Rebuttal: We thank all the reviewers for their time, insightful suggestions, and valuable comments. We also notice that there may be concerns regarding the comparison of our work to previous studies on the reversal curse and the clarity of our contributions. Here, we provide a comprehensive summary of the r...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MotionBooth: Motion-Aware Customized Text-to-Video Generation	Accept (spotlight)	Summary: The authors propose MotionBooth, a method to fine-tune a pre-trained text-to-video model on a collection of images of a specific object to enable the model to generate controlled videos of that object. Fine-tuning incorporates three losses: diffusion loss on image data, restricted to the object's region; video...	Rebuttal 1: Rebuttal: Thanks very much for the review! Here are our replies. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # Novelty of our method Thanks for the suggestion. We appreciate that most reviewers have acknowledged the novelty of our method a...	Summary: The paper proposes a video generated method - for customizing a subject, along with its motion, provided in the form of bounding boxes, and camera movement provided in the form of camera pose. To do this, the training method has a subject region loss to prevent bias from background and other details in the ima...	Rebuttal 1: Rebuttal: Thanks very much for the review! Here are our replies. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # Separate results for subject motion control, camera motion control, and both Thank you for your feedback and suggestion. It's a ...	Summary: This paper addresses the challenge of how to control the identity and the motion of the subject while generating video, in a text-to-video setup. Specifically, in addition to the standard text prompt, it allows the user to control: the subject's identity by providing a few images of it (e.g. 5 photos of my dog...	Rebuttal 1: Rebuttal: Thanks very much for the review! Here are our replies. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # Metrics for static image crop of the subject We apologize for any confusion. We believe your suggestion involves cropping a vide...	Summary: This paper primarily addresses the problem of motion-aware customized video generation. Traditional customized generation methods either tend to lose the motion information of the video or require additional training of motion modules. To enhance subject fidelity and video dynamics, this paper introduces subje...	Rebuttal 1: Rebuttal: Thanks very much for the review! Here are our replies. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # Details about video data used for training Thanks for the question, during training, we randomly download 500 videos from th...	Rebuttal 1: Rebuttal: Thanks very much for the reviews! In the general response, we mainly reply to the most frequently asked questions. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # Novelty of our subject and camera motion control methods We apprecia...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper targets at animating customized subjects with both motion and camera control. To achieve that, they first customize t2v models with subject images finetuning without hurting video generation capability. Then, they propose a training-free approach to manage subject and camera motions. Extensive exper...	Rebuttal 1: Rebuttal: Thanks very much for the review! Here are our replies. Note: Please refer to the one-page PDF for the mentioned figures and tables with an "R." in their names. # The definition of hyper-parameters We use different sets of hyper-parameters for the Zeroscope and LaVie models, as detailed in ...	null	null	null	null	null	null
Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information	Accept (poster)	Summary: In this paper, the authors present a memory-efficient way of computing the uncertainty score of a model (the variance of the model's prediction w.r.t. distribution over the model's parameters). Specifically, they combined Lanczos method and sketching to obtain a low-rank approximation of the Generalized Gauss-...	Rebuttal 1: Rebuttal: - "For the claim that "the disadvantage of introducing noise through sketching is outweighed by a higher-rank approximation" (lines 55~57), the authors should add a simple experiment with synthetic data to verify it." We thank the reviewer for the valuable comment. We run an experiment on synthet...	Summary: This paper provides a new algorithm for approximating the Fisher information matrix. Approximation of the Fisher information matrix is an important task whenever the amount of parameters is very large, such as the case for neural networks. This paper makes use of Lanczos algorithm to find an approximate spectr...	Rebuttal 1: Rebuttal: - "I do not see significant weaknesses with this paper. I think it would be nice if the choice of embedding procedure was better described in the main text. I believe that this might be a problem dependent choice, and whilst the authors have provided a good default choice, it would be good to know...	Summary: ## Summary - This paper presents an architecture agnostic technique to compute uncertainty scores for pre-trained neural networks. The space complexity (memory usage) of their proposed technique SLU (Sketched Lanczos Uncertainty) grows logarithmically in the size of model parameters. The experiments reported ...	Rebuttal 1: Rebuttal: - "As I alluded to earlier, the models used in experiments are primarily vision models ranging from 40K params to 300K params, it's not clear whether this approach would work for NLP pre-trained models typically ranging in millions of parameters." We made additional experiments, specifically, we ...	null	null	Rebuttal 1: Rebuttal: We are grateful for the positive and constructive reviews that we reply to individually below. Some of the individual replies refer to figures and tables that are provided in the PDF attached to this message. These figures provide the requested additional experiments, including demonstrating resul...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective	Accept (poster)	Summary: The authors analyze the training dynamics of LP-FT for classification models based on NTK theory. They decomposed NTK matrix and highlight the importance of linear head norm. Additionally, they highlight the increased linear head norms can negatively affect the model calibration and can be fixed by temperature...	Rebuttal 1: Rebuttal: Thank you very much for taking the time and effort to review our paper. We appreciate your insightful question. > Q1: The experiments primarily focus on the classification tasks. Since the authors also extend their analysis to LoRA, exploring additional domains and harder tasks (reasoning, math, c...	Summary: The authors present a theoretical analysis of the linear probing and fine-tuning framework based on neural tangent theory, supported by experiments with transformer-based models on natural language processing benchmarks. Their analysis decomposes the NTK matrix into the FT-effective component and the pre-train...	Rebuttal 1: Rebuttal: Thank you very much for taking the time and effort to review our paper. We appreciate your valuable suggestions for improvements. > W1: Although the experiments included an exhaustive list of benchmarks, I noticed that you only implemented one transformer model in your setup. I believe it would be...	Summary: This paper studies the dynamics of Linear probing and fine tuning by means of NTK theory. The authors provide a connection between the Frobenius norm of linear probing weights and FT-effective component of the NTK matrix. Strengths: The NTK sees the model as an Gaussian process, making it a powerful tool...	Rebuttal 1: Rebuttal: Thank you very much for taking the time and effort to review our paper. We appreciate your valuable advice and suggestions for improvements. > W1: The organization of material can be improved. for instance, you used the linear model in Proposition 1, however it is introduced later in Definition ...	Summary: The paper presents a novel application of neural tangent kernel (NTK) theory to analyze the training dynamics of the linear probing then fine-tuning (LP-FT) method for large language models, demonstrating its effectiveness and extending the analysis to include the low-rank adaptation (LoRA) method. Strengths:...	Rebuttal 1: Rebuttal: Thank you very much for taking the time and effort to review our paper. We appreciate your valuable advice and suggestions for improvements. > W1: One weakness of the paper is that while it presents a solid theoretical foundation and empirical evidence, it could benefit from more detailed explanat...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts	Accept (poster)	Summary: This paper investigates the problem of multi-output regression under combinatorial covariate shift, namely when the input domains in the testing set differ significantly from the training set. The authors view the functional mapping from the inputs to the vector-valued output, which is evaluated discretely in ...	Rebuttal 1: Rebuttal: Weakness & Limitations: Lack comparisons with some other benchmark approaches Re: We appreciate the suggestion. However, we must emphasize that our work is the first, to our knowledge, to address MOR under CDS. Existing tensor decomposition methods are not designed for this specific probl...	Summary: This paper proposes function t-SVD for multi-output regression under combinatorial shifts. Excess-risk bounds have been derived. Using simulation experiments risk bounds with combinatorial shifts has been compared with regular risk bounds. Strengths: 1) Proposal of functional t-SVD, which is a descent contrib...	Rebuttal 1: Rebuttal: Thank you for your thorough and constructive feedback. We address your concerns as follows: W1 & Q1: Lack of real-data experiments Re: We acknowledge the importance of real-world data experiments. However, the primary contribution of this paper is theoretical, as we provide the first for...	Summary: The paper introduces a novel approach to multi-output regression (MOR) under combinatorial distribution shifts (CDS) using a generalized tensor decomposition framework. The proposed Functional t-Singular Value Decomposition (Ft-SVD) extends classical tensor SVD to infinite and continuous feature domains, provi...	Rebuttal 1: Rebuttal: Many thanks for the thoughtful and constructive feedback. W1: Insufficient introduction of CDS in MOR Re: We acknowledge the need to better define and highlight the importance of Combinatorial Distribution Shift (CDS) in the context of multi-output regression (MOR). CDS occurs when train...	Summary: The authors propose a new model for decomposing vector-valued functions of two vector arguments. The proposed model considers a SVD decomposition of these functions in Hilbert space, effectively extending t-SVD to Hilbert spaces. Here t-SVD, short for tensor-SVD, consists of applying SVD to each matrix slice o...	Rebuttal 1: Rebuttal: Thanks for the constructive feedback. W1: Lack of numerical experiments Re: While our main contribution is theoretical, we've included proof-of-concept experiments on Page 9 in the submission. Additional results, including synthetic and real-world data, are in the attached PDF. **W2...	Rebuttal 1: Rebuttal: We appreciate the reviewers' constructive feedback and provide a summary of contributions, the feedback, and responses. Contributions. Our work proposes the novel Functional t-Singular Value Decomposition (Ft-SVD) framework, addressing the challenges of multi-output regression (MOR) under com...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Language-Driven Interactive Traffic Trajectory Generation	Accept (poster)	Summary: This paper proposes a large language model-based traffic trajectory generation method. Due to the designed interaction interpretation mechanism, the proposed method can generate a better corresponding trajectory. In addition, to improve the generation quality, the authors also proposed a well-designed prompt a...	Rebuttal 1: Rebuttal: ## Response to Reviewer awjY $\textbf{Question1:}$ Since the training of the proposed method also still relies on pre-collected ground truth data, I think the authors need to compare it with CTG and CTG++. Another reason is these two methods are also based on language conditions. $\textbf{An...	Summary: This work proposes a novel framework for traffic trajectory generation with natural language description of interactions, called InteractTraj. Specifically, the proposed framework consists of two main components: a language-to-code encoder and a code-to-trajectory decoder. The language-to-code encoder is desig...	Rebuttal 1: Rebuttal: ## Response to Reviewer xSeP $\textbf{Question1:}$ It seems the proposed framework is computationally expensive; the authors should provide the details about the efficiency of the model. $\textbf{Answer1:}$ The overall resources needed for model training and inference are not expensive. On...	Summary: The paper introduces a novel method called InteractTraj, which is the first language-driven traffic trajectory generator capable of producing interactive traffic trajectories. This method is critical for advancing autonomous driving technology because it can generate realistic and controllable vehicle interact...	Rebuttal 1: Rebuttal: ## Response to Reviewer YJzW $\textbf{Question1:}$ Clarify how the model performs when dealing with emergent or less common interaction types, like parallel driving or platooning? Would the model generalize well to such scenarios, or would it require retraining? $\textbf{Answer1:}$ Our mode...	null	null	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We have responded to questions based on the opinions of each reviewer. The attached PDF contains various figures. Please review it. Pdf: /pdf/1ce1b0950689f8692221b286f92723dbefb5c698.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Can Models Learn Skill Composition from Examples?	Accept (poster)	Summary: The paper studies the extent to which LLMs can learn compositional generalization over skills by finetuning with suitable training examples. Based on abundant experimental results, the paper concludes that finetuning using a dataset with skill composition examples helps the model build a “meta-skill” that allo...	Rebuttal 1: Rebuttal: Dear Reviewer vwLV, Thanks for your time and effort in reviewing our paper. Below we address your questions and suggestions. > Q: Why use [prompt 1, answer 1, prompt 2, answer 2] instead of [prompt 1, answer 1]? A: This prompt template aims to improve the Skill-Mix performance of the model ...	Summary: This article studies whether or not LLMs can be fine-tuned to compose skills for text generation. The authors generated training data from GPt-4 in the style of the Skill-Mix benchmark, asking the model to generate text about a topic while using a set of k skills (e.g., sympathy, temporal reasoning, syllogism,...	Rebuttal 1: Rebuttal: Dear Reviewer 4iaf, Thanks for your review and suggestions. We will add more visualization about the results in the next version. > Q: Skill-Mix seems very artificial, and there are very few examples in the paper. One example in the paper (among 2) has a weird syntax in the prompt. A: Thank...	Summary: The paper explores the capacity of smaller language models to learn compositional generalization from finetuning. Utilizing the Skill-Mix set-up, the study delivers comprehensive experiments to assesse how small language models can improve their performance on both in-distribution and out-of-distribution compo...	Rebuttal 1: Rebuttal: Dear Reviewer ZBU1, Thanks for your time and effort in reviewing our paper. Below we address your questions. > Q: The pipeline has already been introduced by Yu et al. 2024 and there is limited novelty. A: Thanks for asking this question. We want to point out some differences between the Sk...	Summary: The authors present a (family of) fine-tuned LLMs, trained using the SKILL-MIX dataset. They show that models trained on composite tasks where each instance involves a sequence of different skills improves the model in similar composite tasks. They demonstrate generalization by fine-tuning the models on subset...	Rebuttal 1: Rebuttal: Dear Reviewer bbsi, Thanks for your time and effort in reviewing our paper. Below we address your questions and concerns. > Q: Concerns about Skill-Mix A: The reviewer is correct that there could be “cheating” ways to pass the Skill-Mix eval that fool the GPT4 grader, since “Make sense” is ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation	Accept (poster)	Summary: This paper proposes a new algorithm for knowledge distillation by replacing KL-divergence loss with Wasserstein distance loss. The proposed algorithm contains two parts: (1) Logits distillation with Wasserstein distance loss, implementing with an entropy regularized linear programming; (2) Assum...	Rebuttal 1: Rebuttal: Dear Reviewer F2zQ, Thank you for reviewing our paper and for providing constructive and insightful comments. We appreciate your acknowledgement of good soundness, presentation \& contribution of our paper. We hope our detailed responses can address your concerns, and could you please consider in...	Summary: This paper proposes a Wasserstein distance based knowledge distillation method for both the logit distillation and feature distillation settings. The logit based version uses discrete WD to model the discrepancy beteween the prediction probabilites of student and teacher networks. It further uses the separatio...	Rebuttal 1: Rebuttal: Dear Reviewer 5GY4, Thank you for reviewing our paper and for providing constructive and insightful comments. Particularly, we appreciate your acknowledgement that our paper has excellent contribution, strong performance and good presentation. We hope our detailed responses can address your conce...	Summary: The paper introduces a novel methodology for knowledge distillation using Wasserstein Distance (WD) instead of the traditional Kullback-Leibler Divergence (KL-Div). The proposed methods include a logit distillation approach (WKD-L) that leverages cross-category comparisons and a feature distillation method (WK...	Rebuttal 1: Rebuttal: Dear Reviewer Zit9, Thank you for reviewing our paper and for providing constructive and insightful comments. We are grateful for your positive comments on novelty and contribution of WD based methods, such as "a fresh perspective and addresses the limitations of KL-Div" and "is innovative and ef...	Summary: The paper proposes the utilization of Wasserstein Distance based distillation as opposed to KLD, as is common in practice. This is because the latter does not facilitate cross-category comparisons. Both logit and feature based variants have been proposed. The comparisons have been shown for both classification...	Rebuttal 1: Rebuttal: Dear Reviewer ScyW, Thank you for reviewing our paper and for providing constructive and insightful comments. Particularly, we appreciate your positive comments on novelty ("a fresh perspective of WD in distillation"), on soundness ("theoretically sound"), and presentation \& contribution ("good"...	Rebuttal 1: Rebuttal: Dear Reviewers, Area Chairs and Program Chairs, We thank all reviewers again for their thoughtful and constructive comments, which are helpful in improving the quality of our paper. After carefully reading all comments and questions, we conducted additional experiments and discussions to address ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation	Accept (poster)	Summary: This paper introduces a novel framework that incorporates closed-loop feedback into vision-based control systems for robot manipulation tasks. The author proposes a text-conditioned video diffusion model for high-level reference path planning. The error measurement and feedback policy are obtained through an e...	Rebuttal 1: Rebuttal: Thanks for your valuable review. We address your concerns below. >${\color{BrickRed}Question 1:}$ Generalization: how this framework can be generalized across different task/robot platforms/datasets? Especially for the sub-goal replan/transition, which seems to be very specific for each task. C...	Summary: This paper proposes CLOVER, a closed loop visuomotor control framework that incorporates feedback mechanisms to improve adaptive robotic control. The framework consists of a text-conditioned video diffusion model for generating visual plans as reference inputs, an error measurement module to model the discrepa...	Rebuttal 1: Rebuttal: Thanks for your careful review and valuable comments. We address each question below. >${\color{BrickRed}Question 1:}$ Limited evaluation to real robot environment. We conduct new real-world experiments to further validate the effectiveness and generalizability of CLOVER. Please refer to th...	Summary: The paper presents a novel framework named CLOVER. The proposed system aims to enhance the adaptability and robustness of robotic manipulation in long-horizon tasks by incorporating closed-loop control principles. CLOVER consists of three main components: a text-conditioned video diffusion model for generating...	Rebuttal 1: Rebuttal: Thanks for your detailed review. We address your questions below. > ${\color{BrickRed}Question 1:}$ Novelty of the error measurement approach. Comparison with other state-of-the-art error measurement methods. As discussed in Sec. 2, existing works rely on additional detection models with manua...	Summary: The authorsThe authors introduce CLOVER, a generalizable closed-loop visuomotor control framework that incorporates a feedback mechanism to improve adaptive robotic control. The method uses a text-conditioned video diffusion model to generate reference visual subgoals, an error measurement to quantify the diff...	Rebuttal 1: Rebuttal: Thanks for your careful review and we really appreciate your comments. We address your questions below. >${\color{BrickRed}Question 1:}$ How are you different from AVDC? Our work differentiates from AVDC in the following aspects: - Model structure: For the visual planning part, we introduc...	Rebuttal 1: Rebuttal: Dear Area Chairs and Reviewers, We thank all the Reviewers for their detailed and helpful comments on our work. We appreciate the Reviewers for acknowledging our strengths and contributions, such as a creative and novel feedback policy and cohesive system design (DjFL, hKyy, eUsr), a useful ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample-Efficient Constrained Reinforcement Learning with General Parameterization	Accept (poster)	Summary: The paper studies constrained Markov decision processes. Using momentum acceleration, the paper develops a primal-dual natural policy gradient-based algorithm and shows that it achieves an \epsilon global optimality with O(\epsilon^-3) samples. The policy class considered in the paper is the generic Fisher non...	Rebuttal 1: Rebuttal: On the Generalizability of [25]: The approach explored in [25] does not generalize to CMDPs. To understand the reasoning, note that one of the preliminary steps in [25] is showing a descent-like inequality. For example, Lemma 6 in [25] reads as follows. \begin{align} \delta\_{t+1} \leq (1...	Summary: The paper considers the constrained MDP setting. Given a target error of $\epsilon > 0$, the paper provides a primal-dual algorithm on that will return an epsilon optimal policy making at most epsilon constraint violations. The claim is that the algorithm solves such problem with O($\epsilon^{-3}$) sample com...	Rebuttal 1: Rebuttal: On Slater's Constant: We would like to clarify that, for a given problem instance, Slater's constant is fixed and outside of the control of the learner. On the other hand, the target optimality error $\epsilon$ can be chosen by the learner to be arbitrarily small. Therefore, it would be inappr...	Summary: This paper studied the sample complexity of learning a discounted-reward MDP with a discounted cost constraint under a general policy parameterization. It proposes a policy-gradient-type of algorithm that combines natural policy gradient and an accelerating stochastic gradient descent algorithm proposed in [6...	Rebuttal 1: Rebuttal: Response to Weakness 1: The term "sample-efficient" simply acknowledges the fact that the sample complexity of our algorithm is better than that of all existing algorithms in the general parameterized CMDP setup. Note that we refrain from using the term "sample optimal" since there is still a ...	Summary: The paper studies the sample complexity of the constrained Markov Decision Process (CMDP), and derives an algorithm that attains the $O(\epsilon^{-3})$ that improves the SOTA sample complexity bound for CMDP by a factor of $O(\epsilon^{-1})$. Strengths: CMDP with general parameterization is a very challenging...	Rebuttal 1: Rebuttal: Fairer Comparison of Sample Complexities: We would like to point out that the sample complexity of $\tilde{\mathcal{O}}((1-\gamma)^{-6}\epsilon^{-4})$ reported by [1] in their AAAI version is erroneous. The authors have subsequently corrected their result and the sample complexity has been mod...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ANT: Adaptive Noise Schedule for Time Series Diffusion Models	Accept (poster)	Summary: Paper introduces the new method, adaptive noise schedule for time series diffusion model(ANT) . This is a new methodology as per my knowledge and hence extends the boundaries of the field. Diffusion models are highly effective in generating data but their inclusion in Time Series tasks overlooks the significa...	Rebuttal 1: Rebuttal: ### W1. Details on the evaluation of experiments We agree that some details on the evaluation of experiments were omitted due to space limitations. Here, we provide the additional details and will incorporate them into Appendix.   > 1. Metric for TS forecasting task Followin...	Summary: This paper proposes ANT, which adaptively selects the optimal noise schedule for time series diffusion models. The ANT score is computed for each schedule offline based on the datasets' statistics, which is the basis for selection. Extensive experiments demonstrate the method's superior performance in multiple...	Rebuttal 1: Rebuttal: ### W1, Q1. Candidate schedules do not guarantee to include the optimal schedule. As the reviewer eJYD mentioned, the candidate schedule may not include the optimal schedule. However, we note that we do not aim to find the optimal schedule; our proposed ANT is a **criterion for choosing a...	Summary: This paper addresses the non-stationarity in time series data by proposing an ANT score to enable an adaptive noise schedule for diffusion models. It provides extensive experimental results on time series forecasting and generation tasks. Strengths: 1. The idea of adaptively select noise schedule in diffusion...	Rebuttal 1: Rebuttal: ### W1, Q1. Theoretical analysis on the ANT score Previous works on noise schedules have demonstrated their effectiveness primarily through empirical justification rather than theoretical analysis [4,21,24]. In line with this approach, we have conducted extensive experiments to support ou...	null	null	Rebuttal 1: Rebuttal: # General Comments First of all, we deeply appreciate your time and effort in reviewing our paper. Our work introduces ANT (Adaptive Noise Schedule for Time Series), a method for automatically predetermining proper noise schedules based on the characteristics of the dataset. As a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks	Accept (poster)	Summary: The authors consider the problem of degree bias for node classification using graph neural networks (GNNs). Much prior work has shown that GNNs tend to be much more accurate for nodes with higher degree than for lower degree. The authors survey 38 papers that pose hypotheses, sometimes contradictory, for why t...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and positive reception! Regarding the weaknesses: - This is a great and interesting question! We agree that it would be beneficial to close this missing link. Via some preliminary analysis, we find that it is possible to express the inverse collision probabilit...	Summary: The paper provides a comprehensive analysis of the origins of degree bias in message-passing GNNs, proving that high-degree test nodes have a lower probability of misclassification, regardless of how GNNs are trained. They surveyed 38 papers on degree bias and found that existing hypotheses are often not rigo...	Rebuttal 1: Rebuttal: Thanks for your thoughtful feedback and questions! Regarding the weaknesses and your questions: - Our findings do extend to heterophilic graphs. For example, in Lines 220-222, we explain that high-degree nodes in heterophilic networks do not have more negative L-hop prediction homogeneity levels d...	Summary: The paper explores the causes and characteristics of degree bias in graph neural networks (GNNs), particularly in node classification tasks. It contributes to the field by providing a rigorous theoretical analysis supported by empirical evidence across multiple datasets, revealing that degree bias is influence...	Rebuttal 1: Rebuttal: Thanks for your feedback and the helpful resources! Regarding the weaknesses, we will definitely include and discuss the recommended references on homophily in our paper: [1] provides a complementary perspective on the possible performance issues of GNNs that arise from degree disparities in grap...	Summary: Given the wide-adoption of GNN-based node classification and the potential risk of degree-related bias, this paper systematically reviews the degree-bias paper in previous literature and proposes a theoretical framework to analyze the degree-related bias. Several insightful observations have been drawn with em...	Rebuttal 1: Rebuttal: Thanks for your comments and thorough questions! Regarding your questions: (1) We will make the scope of the paper clearer by indicating in the title, introduction section, and limitations section that we focus on node classification. Our findings readily lend insight into the origins of degree b...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful and helpful comments! We are pleased to read that: - Reviewer Ao8B finds our paper constitutes a “systematic investigation of a long-standing yet not addressed research problem.” - Reviewer Xp6E finds our paper has good quality and clarity. - Reviewer US...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance	Accept (poster)	Summary: This work proposes a self-guidance method for masked generative models to improve the diversity and quality of class conditional image generation. The main challenge is to design a semantically meaningful smoothing for the discrete VQ token space such that coarse scale information can be extracted. The authors...	Rebuttal 1: Rebuttal: We appreciate your thorough review. We address your concerns and questions below. All visual results can be found in the submitted PDF. Please zoom in on the figure of the submitted PDF for the best view. $\\textbf{Clarified visual comparison.}$ In Fig. 6 of the submitted PDF, we directly compar...	Summary: This paper explores the use of masked generative models for image generation. While these methods are typically efficient, they sometimes fall short in terms of quality compared to diffusion models. To reduce this gap, this paper proposes a self-guidance algorithm, which is further improved with semantic smoot...	Rebuttal 1: Rebuttal: Thank you for your review. We address your concerns below. All visual results can be found in the submitted PDF. Please zoom in on the figure in PDF for the best view. Clarifications of implementation details, figures, and code release. Thank you for your suggestion to make our research clea...	Summary: The paper focuses on enhancing Masked Generative Models (MGMs) for image synthesis. The authors identified several reasons for the underperformance of MGMs: 1) lack of sequential dependencies, 2) multi-modality problem, 3) compounding decoding errors, 4) limitations of low-temperature sampling and 5) ineffecti...	Rebuttal 1: Rebuttal: Thank you for your comprehensive review and for identifying the ambiguous aspects of our experimental designs. We address your concerns and questions below. All visual results can be found in the submitted PDF. Please zoom in on the figure of the submitted PDF for the best view. **Include more vi...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers and Area Chairs, We thank the reviewers for their constructive feedback. We are glad to take various helpful reviewer comments to clarify and complete our work. Reviewers agreed on the originality, motivation, soundness, and significance of the paper. At the same time, reviewers ar...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors	Accept (poster)	Summary: The paper builds on a previous model of FL to study trade-offs between core stability and egalitarian fairness when agents exhibit altruistic behaviors. The authors provide egalitarian fairness bounds under core stability solutions across different cases of altruism. There is also a small experimental part whe...	Rebuttal 1: Rebuttal: We thank the reviewer for finding our work interesting and well-motivated. We also appreciate the detailed comments posed by the reviewer. Please find below the point-to-point responses to the reviewer's comments. > W1 (Question addressing): We'd like to clarify how the obtained egalitarian ...	Summary: The authors explored the impact of egalitarian fairness on the stability of Federated Learning (FL) systems. FL allows multiple clients to train a global model collaboratively without sharing their local data, thus preserving privacy. The authors addressed the concern that achieving egalitarian fairness, which...	Rebuttal 1: Rebuttal: We are delighted that the reviewer found our work novel and valuable, with robust theoretical support. Thank you for your positive opinions and insightful comments. > W1 & Q1 (More complex and diverse FL scenarios): Thank you for your comment. Following your suggestion, we first add more ex...	Summary: This paper examines the relationship between egalitarian fairness concepts and stability in federated learning, where multiple clients collaboratively train a shared model while retaining local data privacy. Egalitarian fairness promotes uniform model performance across clients, but this can reduce performance...	Rebuttal 1: Rebuttal: We are delighted that the reviewer found our motivations and methods innovative and significant towards an important research question. Thank you for your positive opinions and insightful comments. Weakness: Thank you for your thoughtful comments. We provided responses to the points in Q4 a...	Summary: This paper rigorously answered a critical question regarding fair federated learning: Does egalitarian fairness lead to instability? It analyzed and presented the influence of clients’ altruistic behaviors and the configuration of the friend-relationship network on the achievable egalitarian fairness within a ...	Rebuttal 1: Rebuttal: We are delighted that the reviewer recognized the significance of our research and its novel key insights. Thank you for your positive feedback and insightful comments. > W1 (Illustration): Thank you for your comment. In Sections 4.2 and 5.1, we utilize color text to better illustrate the res...	Rebuttal 1: Rebuttal: Dear Chairs and Reviewers, We kindly thank all the reviewers for their time and for providing valuable feedback on our work. We appreciate that reviewers have pointed out that our work is novel (Reviewer LduT, r5t6), significant (Reviewer LduT, CtFP) with valuable insights (Reviewer LduT, r5t6),...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner	Accept (poster)	Summary: This paper introduces FlowTurbo, a framework to accelerate flow-based generative models. The key contributions are: 1) a lightweight velocity refiner to estimate the offset of velocity efficiently during sampling; 2) a pseudo corrector to reduce the number of model evaluations while keeping the original second...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below. Q1: About experiments on other models. [Reply] Thanks for your advice. Since SD3 is open-sourced a few months later than the submission, ...	Summary: This paper explores enhancing flow-based generative models for image generation by accelerating the sampling process while maintaining or improving image quality. The key contribution, FlowTurbo, is a new framework that introduces a lightweight velocity refiner to adjust velocity predictions during sampling, r...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below. Q1: About the design of sampling blocks. [Reply] Sorry for the confusion. As illustrated in Figure 1, the velocity during the sampling pr...	Summary: The authors propose FlowTurbo, a method to adapt pre-trained flow-based generative models for faster sampling. The method is based on the observation that the predicted velocity field is quite stable throughout the integration time and hence at each timestamp only small refinement from the previous step is req...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the positive comments on our work! We address the questions and clarify the issues accordingly as described below. Q1: About prior works on flow-based methods. [Reply] Thanks for your suggestions. We agree that these works are important in the area of ...	Summary: The paper presents a new approach to accelerate the sampling process in flow-based generative models. Unlike diffusion models, flow-based models, which are based on learning velocity fields through flow-matching, have not seen extensive development in efficient sampling techniques. The authors introduce FlowTu...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We address the questions and clarify the issues accordingly as described below. Q1: About comparisons to distillation-based methods. [Reply] Thanks for your suggestions. We have detailedly discussed the difference between our FlowTurbo and...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for the positive feedback and valuable comments on our work. In the attached one-page PDF, we provide more visualizations and qualitative results to better illustrate the motivation and effectiveness of our FlowTurbo. In Figure A1, we compare the curvature (as sugg...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation	Accept (poster)	Summary: The paper proposes an LLM-based personal activity generation by utilizing existing OpenAI LLM through API, named as LLMob. The problem that authors are time and important research topic in the area of human mobility simulation. While existing generative solely trained on the given dataset, LLM agent has alread...	Rebuttal 1: Rebuttal: Thank you for your insightful review and acknowledgment of our work. We would like to address the concerns as follows. > Question 1 Did you utilize the whole dataset in Phase 1? Answer: Phase 1 is targeted at identifying the persona of each person. For the sake of fair comparison, for ...	Summary: This paper proposes an LLM-based agent framework for generating personal mobility. It tries to address several problems in the domain of personal mobility generation, including aligning LLMs with the urban data in the real world, developing strategies with reliable activities, and exploring further application...	Rebuttal 1: Rebuttal: Thank you for your insightful comments. We would like to address your concerns as follows. > Weakness 1 What about other LLMs performing as the core, besides GPT-3.5-turbo? I think GPT-4/GPT-4o can be utilized to generate data of higher quality. Answer: We conducted experiments using G...	Summary: This paper introduces LLMob, a framework for personal mobility generation using a large language model. The framework aims to leverage urban activity patterns for emulating urban residents, facilitating the human mobility trajectory generation. Using the Tokyo personal activity dataset, the effectiveness of th...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and time. We would like to address your concerns as follows. > Weakness 1 The proposed framework has only been validated in GPT-3.5. It remains unclear whether the performance would vary with different backbone models, such as Llama-2. Answer: We ...	Summary: This paper presents a prompt engineering framework for generating synthetic human trajectories using LLMs. The framework is guided, in an overall manner, by the observation that human movement is affected by habitual activity patterns and motivations. In this way, the framework has two phases for considering ...	Rebuttal 1: Rebuttal: Thank you for your insightful review and valuable feedback on our work. We would like to address the concerns as follows. > Weakness 2: It is unclear which method actually yields the best performance across different metrics. Answer: In our evaluation, we employ four metrics to compreh...	Rebuttal 1: Rebuttal: Thank you to all the reviewers for your comments. We have addressed each reviewer's feedback in the individual response page. Additionally, we have attached a file that provides more detailed information about the experimental data we used. Pdf: /pdf/1dce1964bb827e1dd530c4e90781e890168381e1.pd...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf	Accept (poster)	Summary: The authors take a specific natural language game (One Night Ultimate Werewolf) and study the performance of RL-trained LLM agents with respect to the mathematically derived (by the authors) Nash equilibrium solutions. Strengths: [1] The paper chooses ONUW as a game that has the Nash equilibrium solution to b...	Rebuttal 1: Rebuttal: Thank you very much for your positive comments. Response to W1: The transferability of proposed algorithms and proofs to different games or game-like environments like financial markets is not demonstrated. Thank you for your comment. As this work is initially motivated by our analyses of th...	Summary: This paper delves into the strategic aspects of discussion in the context of a population social deduction game, "One Night Ultimate Werewolf" (ONUW) game. By analyzing Perfect Bayesian Equilibria in scenarios with and without discussion, the authors highlights the pivotal role of discussion tactics in influen...	Rebuttal 1: Rebuttal: Thank you very much for your insightful review. Response to W1: Differences from related work [1]. Thank you for your comment. We have discussed the differences between our work and related work [1] (line 80-82). Here are more detailed differences. And these will be added to the main paper i...	Summary: The authors investigate the social game ‘One Night Ultimate Werewolf’ (ONUW) as a testbed for their framework on RL-instruct-tuning an agent to select optimal discussion tactics. They prove the existence of a Perfect Bayesian Equilibria in the game ‘Werewolf’ when the game consists of a single round and show t...	Rebuttal 1: Rebuttal: Thank you very much for your positive feedback. Response to W1: About the oversimplification and manual discretization of the discussion tactic space. Thank you for highlighting this limitation. As acknowledged in our work, the six discussion tactics are manually identified and simplified. T...	Summary: The paper presents an innovative framework for enhancing the discussion capabilities of language agents in the game "One Night Ultimate Werewolf" (ONUW) using reinforcement learning (RL). The authors propose a multi-phase extensive-form Bayesian game formulation for ONUW, analyze perfect Bayesian equilibria in...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. Response to Q1&W1: Detailed comparison with related work [1] and [2]. Thank you for your constructive comment. We have discussed the differences between our work and related work [1] (line 80-82). Here are detailed differences between our work ...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful feedback! Your reviews have greatly improved the paper. And we are grateful for the appreciation of our theoretical analysis (`uoU5`, `ADZR`, `Tkt3`, `b5Fq`), the innovation of our framework (`ADZR`, `Tkt3`, `b5Fq`), and the recognition of our research s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization	Accept (poster)	Summary: This paper proposed MAGNET, a gradient-based tokenization method, to address an over-segmentation issue when handling multilingual text data written in different scripts. MAGNET learns to predict segment boundaries between byte tokens in a text sequence. MAGNET has a customizable architecture where byte-level ...	Rebuttal 1: Rebuttal: We thank reviewer 2xYq for taking the time to review our work and noting that our experiments and results are extensive with a successful reduction in inference time. We thank you for your suggestions and address your concerns below. Sufficient language coverage - Our choice of languages ...	Summary: The authors propose MAGNET (multilingual adaptive gradient-based tokenization) to remove the common problem in non-Latin-script languages getting only tokens (subwords assigned in the vocabulary) representing short character sequences as opposed to English getting high-semantic-content tokens. As opposed to p...	Rebuttal 1: Rebuttal: We thank reviewer KW7H for reviewing our work and noting that MAGNET is very impactful and useful in reducing over-segmentation in multilingual language models. They also note that MAGNET results in better performance and lower inference costs. We appreciate your suggestions and address your conce...	Summary: In this paper, the authors propose a multilingual adaptive gradient-based tokenization approach to reduce over-segmentation for non-Latin language texts. In particular, they improve the previous Dynamic Token Pooling method by inferring token boundaries with different predictors for different languages. They c...	Rebuttal 1: Rebuttal: We thank reviewer WSTY for taking time to review our work and noting that our improvements over previous work yield better tokenization. We address your concerns below. Basic token prediction tasks or text generation tasks are not included in the evaluation - We limited our evaluation to te...	Summary: This paper presents multilingual adaptive gradient-based tokenization (MAGNET), which aims to reduce over-segmentation in non-Latin script languages in multilingual settings. MAGNET processes byte-level sequences and routes them through language-script-specific predictors, each optimized for its respective scr...	Rebuttal 1: Rebuttal: We thank reviewer stTy for taking the time to review our work and noting that MAGNET maintains performance on downstream tasks while improving efficiency and combating over-segmentation. We address your concerns below. Sufficient language coverage - Our choice of languages was not influence...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing	Accept (poster)	Summary: This paper presents a comprehensive benchmark for instructional image editing. The benchmark contains a high-quality dataset with over 2000 images and 4000 instructions. In addition, the benchmark presents a new evaluation pipeline that leverages GPT to act as the judge to validate the performance of different...	Rebuttal 1: Rebuttal: We would like to extend our heartfelt gratitude for your thoughtful and encouraging feedback on our paper. We are deeply appreciative of your commendation, recognizing our work as a significant contribution that fills a notable gap in high-quality benchmarks for image editing. Your praise regardin...	Summary: This paper proposes I2EBench, which is a new benchmark for evaluating Instruction-based Image Editing (IIE) models. It offers a large dataset with over 2,000 images and 4,000 instructions across 16 detailed evaluation dimensions. The benchmark is designed to assess image editing quality automatically and align...	Rebuttal 1: Rebuttal: We would like to extend our heartfelt gratitude for your thoughtful and encouraging feedback on our paper. We are deeply appreciative of your commendation, recognizing our work as insightful and valuable. Thank you for acknowledging that our benchmark encompasses a wide range of evaluation dimensi...	Summary: The paper addresses the challenge of evaluating models in the field of Instruction-based Image Editing (IIE) by proposing a comprehensive benchmark called I2EBench. It features: 1) Comprehensive Evaluation: Covers 16 evaluation dimensions for a thorough assessment of IIE models. 2) Human Perception Alignment: ...	Rebuttal 1: Rebuttal: We would like to extend our heartfelt gratitude for your thoughtful feedback on our paper. We sincerely appreciate your recognition of its strengths, including the comprehensive explanation of the evaluation process, the wide array of evaluation dimensions, the provision of evaluation code, and th...	Summary: This paper proposes I2EBench, a comprehensive benchmark designed to automatically evaluate the quality of edited images produced by IIE models from multiple dimensions. I2EBench comprises 16 evaluation dimensions, covering both high-level and low-level aspects. Additionally, through user studies, the authors a...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude for your positive feedback on our paper and for recognizing its strengths. We appreciate your agreement that I2EBench represents a significant advancement over previous works, greatly benefiting the research community. Additionally, we are thankful fo...	Rebuttal 1: Rebuttal: ### Response To All Reviewers and Table data We would like to extend our heartfelt gratitude to the reviewers for their valuable feedback and positive comments on our paper. Their insightful reviews have significantly enhanced the clarity and overall quality of our work. We thank Reviewer riYa ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints	Accept (poster)	Summary: This paper studied an extension of multi-armed bandit problem by introducing infinitely many arms and generalized rotting constraints. It provides explicit regret lower bounds and proposes an algorithm with regret upper bound matching the lower bound when $\beta \geq 1$. It has been claimed that closing the ga...	Rebuttal 1: Rebuttal: We appreciate your feedback and positive evaluation. Below, we address each comment. The paper is an extension of [1]. When $\beta\in(0,1)$, the proposed algorithm can not be proved optimal: First of all, we highlight that we consider rotting constraints with $V_T$ or $S_T$ and initial mean ...	Summary: This paper considers the extension of the classic stochastic multi-armed bandit problem to the case where there is a) an infinite set of arms and b) rotting of the arm means. Specifically the rotting behaviour is of the 'rested bandit' variety, where the mean reward of an arm may fall as an immediate consequen...	Rebuttal 1: Rebuttal: We appreciate your feedback and positive evaluation of our work. Below, we address each comment. **All of the theoretical results are ultimately order results only, without identification of the constants in the appendices. It would be helpful if the bounds could thus be realised for the cases co...	Summary: Authors investigated an adaptive approach for the rotting bandits problem under the infinitely arms assumption. Strengths: Authors introduced a new ucb like policy for the mentioned problem, additionally a lower bound analysis has been carried on. Weaknesses: No clear weaknesses Technical Quality: 3 Clarit...	Rebuttal 1: Rebuttal: We appreciate your feedback and positive evaluation of our work. Below, we address each comment. I wonder how the regret bound behaves with respect to the effective rotting instead of the $V_T$ or $S_T$: In this problem, as mentioned in Assumption 2.1, we consider an adaptive adversary who d...	Summary: The paper studies infinite-armed bandits with rotting rewards. They show a lower bound on regret in terms of the total-variation and number of abrupt changepoints in the change in rewards. They also provide regret upper bounds: (1) a UCB-like algorithm tuned with knowledge of problem parameters gets optimal re...	Rebuttal 1: Rebuttal: We appreciate your time to review our paper and comments. Below, we address each comment. The definition of $V_T$, $S_T$ are a bit unclear and may not be fully rigorous. They are bounds on the total amount and count of rotting, but the rotting depends on the chosen arm and is thus random...	Rebuttal 1: Rebuttal: We appreciate you taking the time to review our paper. We are encouraged by the feedback indicating that our problem is well motivated by many real-world applications, solid theoretical analyses and empirical results are presented, and the paper is written well and concisely. We have attached a p...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes	Accept (poster)	Summary: This paper improves the Policy Optimization methods for learning MDP by eliminating the undesired warm-up phase and replacing it with a simple and efficient contraction mechanism. For linear MDP, it is shown that the proposed Policy Optimization algorithm achieves regret with improved dependence on problem par...	Rebuttal 1: Rebuttal: Thank you for the time and effort put into the review. Regarding synthetic experiments, we are not aware of existing benchmarks that are specific to linear MDPs rather than tabular ones. However, in tabular MDPs the contraction is not necessary and thus we do not expect to see improvement. We agr...	Summary: This paper equips the rare-switching mechanism with a novel feature shrinkage technique to achieve efficient policy optimization (PO) for linear MDPs with adversarial losses or bandit feedback. By shrinking features in directions of high uncertainty, the authors show that the proposed algorithm has its regret ...	Rebuttal 1: Rebuttal: Thank you for the thorough review and helpful comments, we will incorporate them in our revision. The following responds to your individual points: 1. Finite $\mathcal{X}$: You are correct, the regret does not depend on the assumption that $\mathcal{X}$ is finite. In short, the assumption is purel...	Summary: This paper presents a new policy optimization algorithm called Contracted Features Policy Optimization (CFPO) for reinforcement learning in linear Markov Decision Processes (MDPs). The key contribution is eliminating the need for a costly warm-up phase used in previous state-of-the-art methods, while achieving...	Rebuttal 1: Rebuttal: Thank you for the time and effort put into the review. The following addresses the points made in your review: 1. Regarding dependence on $K,d,H$: Are you referring to the additive regret term that is logarithmic in $K$? As you mentioned, most works assume that $K$ is the dominant factor and thus ...	Summary: This paper studies online learning for linear MDPs with stochastic and adversarial full-information losses. The authors propose a new contraction mechanism, avoiding the costly initial exploration phase in previous papers and achieving a better regret bound. Strengths: 1. This paper studies an important probl...	Rebuttal 1: Rebuttal: Thank you very much for the positive comments. We agree that reiterating the explanations on the effect of clipping on the covering number will make the paper more self-contained and we will include it in the final version of the paper.	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Wide Two-Layer Networks can Learn from Adversarial Perturbations	Accept (poster)	Summary: In this paper, the authors theoretically investigate the perturbation learning phenomenon from a future hypothesis perspective. Perturbation learning means that a classifier can be learned from adversarial examples with incorrect labels (the labels used to generate adversarial examples, but seems to be incorre...	Rebuttal 1: Rebuttal: We appreciate the reviewer's constructive comments. > My main concern is whether the kernel regime is a suitable and extensible tool to study the feature hypothesis in adversarial training and explain other interesting phenomena such as the transferability of adversarial examples and the trade-of...	Summary: This work aims to provide an alternative theoretical analysis to justify feature hypothesis and perturbation learning. The analysis is based on approximation theory in the kernel regime (i.e., infinite width). They show that the adversarial perturbation contains sufficient data information, which can be retrie...	Rebuttal 1: Rebuttal: We thank the reviewer's suggestive questions. The reviewer seems to appreciate our technical contributions, and the questions are thus more posed on the high-level understanding of the results. We here address this concern. We are willing to address any feedback and requests for further clarificat...	Summary: Perturbation learning, where classifiers are trained on adversarial examples with their associated incorrect labels, results in non-trivial generalization. This work theoretically tackles the perplexing former phenomenon for wide two-layer networks in the kernel regime. The authors first prove that adversarial...	Rebuttal 1: Rebuttal: We appreciate the reviewer's insightful comments. The reviewer seems to highly evaluate our problem and analysis as interesting but also has concerns about the theoretical principle, which results in a low initial score. Regarding the concerns, the reviewer seems to have several fundamental misun...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multilingual Diversity Improves Vision-Language Representations	Accept (spotlight)	Summary: The paper conducts a systematic study to explore the performance benefits of using translated non-English data for English vision tasks. By translating multilingual image-text pairs from a raw web crawl to English and re-filtering them, the authors show that continual pre-training on this data increases the pe...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. It is very important that we clarify a misconception: our work deals with vision-language models and vision benchmarks. We do not train any LLMs or make claims about the state of multilingual NLP research. **Framing should be more sensitive to multilingua...	Summary: This paper points out an important issue in current training of CLIP models---the push for including more english-centric / english-only data in the pretraining dataset. The paper points out that this is mainly driven by the downstream evaluation test-beds primarily being english-focused and hence the need to...	Rebuttal 1: Rebuttal: We thank the reviewer for all the valuable suggestions! Showcase the diversity for certain concepts. Per your suggestion, we ran additional analysis to compare the diversity of images for the same concept. We sampled 1K images from each data distribution for which the corresponding (translate...	Summary: The authors investigate whether multilingual vision-language data improves the English-only performance on a model in vision-language tasks. They translate captions from DataComp from English to other languages and train a CLIP model on these multilingual captions. They find that this action boosts perfo...	Rebuttal 1: Rebuttal: Thank you for the review and for recognizing the strengths of our work! How the proposed automated translation pipeline ties in to the original motivation (culturally specific items not being captured in English data). We discuss the presence of culturally salient concepts in the Introduction...	Summary: In this paper, the authors conduct a thorough exploration of how multilingual image-text pairs benefit English vision tasks. They first present how to effectively utilize translated data to improve performance on standard vision tasks and derive valuable conclusions through detailed ablation studies. Second, t...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and feedback! No quantitative assessment for the quality of translation. Given your concern, we have performed additional analysis of the translation quality. We sampled 100K multilingual captions from our raw data pool and backtranslated the English-trans...	Rebuttal 1: Rebuttal: We would like to thank all reviewers again for providing thoughtful reviews of our work. Here we highlight several new results/ analyses taking your feedback into consideration: \   1. Reviewer xoEn wondered about the translation quality. In response, we sampled 100K captions from our raw dat...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
KrwEmd: Revising the Imperfect Recall Abstraction from Forgetting Everything	Reject	Summary: This paper introduces the KrwEmd algorithm, a novel approach to hand abstraction in Texas Hold’em-style games. The main contribution is the integration of historical information using K-recall winrate features and earth mover’s distance, addressing the limitations of previous imperfect recall abstraction metho...	Rebuttal 1: Rebuttal: Thank you for your positive evaluation of our work, which has greatly encouraged us. You mentioned that we should provide a more detailed comparison with previous hand abstraction algorithms. We will adjust the hyperparameters and conduct more comparisons. The previous experiments were somewhat l...	Summary: This paper introduces a novel approach to hand abstraction in Texas Hold'em-style poker games, addressing the limitations of current methods that often disregard historical information. The authors make two primary contributions: First, they develop KRWI (K-Recall Winrate Isomorphism), a new abstraction method...	Rebuttal 1: Rebuttal: Thank you for your positive evaluation of my work and for providing many constructive suggestions. These suggestions are very helpful, and we will take them seriously. You and other reviewers mentioned that this paper is somewhat obscure for researchers outside the Poker AI field. We realized tha...	Summary: This paper focuses on the problem of hand abstraction for Texas Hold-Em style poker games. Hand abstraction is the process of partitioning game histories into infosets which still contain enough information to make strategically advantageous decisions. Previous approaches have focused on abstractions that prim...	Rebuttal 1: Rebuttal: We apologize for the difficulties you experienced while reading my paper, and we appreciate that you did not give a very negative score, giving me the opportunity to further present my work. Your suggestions are excellent. In the revised version, I will incorporate more intuitive examples (especi...	Summary: This paper proposes KrwEmd, a novel hand abstraction algorithm for imperfect recall settings in Texas Hold'em poker. The algorithm leverages K-recall winrate features, incorporating historical information in addition to future information for constructing hand abstractions. The authors introduce two new isomor...	Rebuttal 1: Rebuttal: Thank you for your positive evaluation of my work. Your feedback is very constructive and helpful. To address the weaknesses you pointed out, I will make the following improvements: You and other reviewers pointed out that my paper is not very easy to read. I acknowledge this issue and appreciat...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper develops new hand abstraction techniques for Texas Hold'em-style games (in general: games with ordered signals), which fare better than previous methods in both the number of hands identified, and performance (exploitability) in a simplified version of the game. Hand abstraction is a technique aidi...	Rebuttal 1: Rebuttal: Thank you for providing such a thorough review. Your diligence and responsibility are truly impressive, and I am very grateful for your positive evaluation of my paper and the highly constructive feedback. This is extremely helpful for me to improve my work, regardless of whether my paper is accep...	null	null	null	null	null	null
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models	Accept (poster)	Summary: The paper tackles the problem of single-shot fine-tuning of text-to-image models for diverse subject-driven renditions. It first discusses the problem of image-text alignment present in the few-shot fine-tuning paradigm for text-to-image models. It then presents FineStyle. More specifically, it introduces * ...	Rebuttal 1: Rebuttal: Thank you for the insightful comments and suggestions! We appreciate them and will make the necessary revisions to ensure our work is presented with better accuracy and completeness. W1: As detailed in Section 3 of our paper, Muse employs a cascaded design of generative modules. FineStyle only ad...	Summary: This paper proposes a few-shot fine-tuning paradigm called FineStyle for controllability-enhanced style personalization that requires only a single reference image. A concept-oriented data scaling scheme and a parameter-efficient adapter are two key components of the proposed method to achieve this goal. Stre...	Rebuttal 1: Rebuttal: Thank you for your comments, particularly regarding the need for comparison with extra baselines. We appreciate the opportunity to add new baseline results and clarify the advantage of our method. W1: See Figure 1 of the attached PDF. We include results of DreamStyler, StyleAligned, and IP-Adapte...	Summary: Existing style-tuning based style transfer methods often result in content leakage because of the coupled style and content. To address this, FineStyle proposes a decomposition conception of style and content in images and fine-tune a kv adapter in cross-attention on MUSE. FineStyle demonstrates better fine-gr...	Rebuttal 1: Rebuttal: Thank you for your insightful and detailed reviews. We appreciate the opportunity to address the weaknesses and questions raised and to clarify aspects that may have been unclear. W1: The pre-training and human feedback datasets exhibit significant size disparities, which leads to differing meth...	null	null	Rebuttal 1: Rebuttal: Reviewer PDcY, Q5 We agree using an LLM can improve efficiency and reduce human work. Therefore we use an internal multi-modal LLM and prompt it with the image from Figure 3 and a prompt outlined below. The output shows we can use fairly simple prompt to a multi-modal LLM we can automate the ori...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation	Accept (poster)	Summary: The authors note that existing methods assume a uniform time interval among user behaviors. This paper posits that the time intervals in sequential recommendation increase the uncertainty of users' behavior. Therefore, it proposes NSDE to learn users’ fine-grained time-evolving behavior, while evidential learn...	Rebuttal 1: Rebuttal: **Q1: The abbreviation for Normalized Discounted Cumulative Gain is generally NDCG.The combination of metrics chosen in the paper is uncommon and lacks persuasiveness. According to line 294, the metrics are derived from Bert4Rec. However, Bert4Rec's metrics are Hit Ratio (HR), Normalized Discounte...	Summary: This paper investigates the modeling of interaction time intervals in sequential recommendation. Considering both time interval and model uncertainty, this paper formulates E-NSDE to integrate NSDE and evidential learning to model effective time-aware sequential recommendation. Experimental results on four re...	Rebuttal 1: Rebuttal: **Q1: The motivation in the introduction is unclear. As the authors argue, GRU-ODE recommends genres that come from past behaviors, while NSDE recommends genres which have potential future benefits in Table 2. It is not sure if some genres recommended by NSDE are in accordance with long-term inter...	Summary: The paper revolves around enhancing sequential recommendation systems by incorporating time-awareness through the utilization of Evidential Neural Stochastic Differential Equations (E-NSDE). Traditional recommendation systems often overlook the temporal dynamics of user interactions, leading to suboptimal reco...	Rebuttal 1: Rebuttal: Q1: When user-item interactions have uniform time intervals, such as in click-through scenarios, the model's time-aware approach may be less effective in capturing uncertainty. We agree with the reviewer that when interaction intervals are uniform, the time-aware uncertainty component is less...	Summary: This work investigates sequential recommendation, and proposes a new method that utilizes stochastic differential equations (SDEs) to model dynamic time intervals and estimate uncertainty. Overall, this study addresses an engaging problem and provides a novel and reasonable solution. Extensive experiments hav...	Rebuttal 1: Rebuttal: Q1: Diffusion model-based sequential recommendation baselines for comparison. Thanks for suggesting those important related works. We will cite them and add a discussion in the revised paper. More specifically, DiffuRec [a1] models item representations as distributions by corrupting the tar...	Rebuttal 1: Rebuttal: We thank all the reviewers for their constructive comments and suggestions. Here, we provide results for several suggested baselines and top-$N$ metrics with a higher $N$ value as requested by the reviewers: \| Datasets \| Metric \| Bert4Rec \| ResAct \|GRU-ODE \|DiffuRec \|**T...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment	Accept (poster)	Summary: This paper introduces a defence against jailbreak fine-tuning attacks that markedly improves over the baseline suggested by Qi et al. Their method works by implanting a safety backdoor that is subsequently used during inference and show that it is effective on preventing few shot fine-tuning attacks across a v...	Rebuttal 1: Rebuttal: > Question A: “pure_bad” dataset construction details. The “pure_bad” dataset used in our experiments consists of 100 harmful question-answer pairs. These pairs are exactly the same as the harmful samples used in Qi et al.'s work, which were subsampled from the Anthropic red team dataset [1]. In ...	Summary: The authors introduce the Backdoor Enhanced Safety Alignment method, which uses prefixed safety examples with a secret prompt acting as a backdoor trigger to ensure safety responses during inference. This approach aims to maintain the safety alignment of LLMs with minimal safety examples and without compromisi...	Rebuttal 1: Rebuttal: > Question A: Various benchmarks for harmlessness evaluation. To demonstrate the effectiveness of our method across different scenarios, we also apply the AdvBench [1] and HarmBench [2] benchmarks, which are widely used to assess robustness against jailbreak attacks, to evaluate safety alignment ...	Summary: In this paper, the authors present a new approach to defending LLMs against the fine-tuning-based Jailbreak Attack (FJAttack). The FJAttack exploits the fine-tuning process by introducing harmful examples into the dataset, compromising the model's safety alignment. The proposed method, Backdoor Enhanced Safety...	Rebuttal 1: Rebuttal: > Question A: Concerns about the security risks of the secret prompt; improvement is not significant Our defense method is primarily designed for the Language-Model-as-a-Service (LMaaS) based threat model, where attackers are only permitted to upload a fine-tuning dataset to perform the fine-tuni...	Summary: This paper proposes a defense method against fine-tuning-based jailbreaking attacks on close-source LLM services. The main insight is to add a backdoor trigger to safe prompts incorporated during the fine-tuning, and use the trigger as a prefix during inference. Strengths: 1. This paper focuses on a trendy an...	Rebuttal 1: Rebuttal: > Question A: Concerns about the safe triggers affecting the LLM’s performance. Thank you for your question. In our paper, we have evaluated the performance on various widely-used benchmarks, including the ARC Challenge, MMLU, and MT-Bench. The results are shown in Table 1 of our paper. It empiri...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FastDrag: Manipulate Anything in One Step	Accept (poster)	Summary: This paper presents a method that enables fast drag-based image editing using diffusion models. The proposed method uses a latent warpage function to obtain the dragged latent representation. The additional nearest neighbor interpolation and content-preserving strategy further improve the result. Strengths: T...	Rebuttal 1: Rebuttal: We would like to thank you for the positive feedback, helpful comments, and the support of our work. Following are our responses to each individual comment (which are highlighted in italics). # Response for Weaknesses (RfW) >*W1: I think the discussion of limitation is too shallow. I recommend p...	Summary: The paper introduces "FastDrag," a novel one-step drag-based image editing method that significantly accelerates the editing process compared to existing n-step iterative approaches. The core of FastDrag is the Latent Warpage Function (LWF), which simulates the behavior of stretched material to adjust pixel lo...	Rebuttal 1: Rebuttal: We would like to thank you for the positive feedback, helpful comments, and high praise and recognition of our work. Below are our responses to each individual comment (highlighted in italics). # Response for Weaknesses > W1: Theoretical Depth Our one-step optimization strategy is developed by...	Summary: This paper introduces a new one-step drag-based image editing method that significantly accelerates the editing process using a LWF function. It also employs a BNNI strategy to handle null regions and a consistency-preserving strategy to maintain the integrity of the edited image. Experimental results demonstr...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the positive feedback and helpful comments. Following are our responses to each individual comment (which are highlighted in italics). # Responses for Weaknesses (RfW): >*W1: If the drag distance is long, will the BNNI still success to maintain high semant...	Summary: This paper presents a drag-based image editing method that uses the Latent Warpage Function to optimize pixel adjustments in a single step, which is an improvement over previous iterative methods. This approach simulates a stretched material in the latent space to allow for fast and accurate pixel adjustments....	Rebuttal 1: Rebuttal: We would like to thank you for the positive feedback and helpful comments. This rebuttal addresses your comments and suggestions for conciseness. Following are our responses to each individual comment (which are highlighted in italics). # Response for Weaknesses (RfW) >*W1: ... needs to include m...	Rebuttal 1: Rebuttal: # General Response to Reviewers We would like to thank the reviewers for the positive feedback and valuable comments. We are elated that the reviewers found our paper well-written, the presentation clear and excellence in ultra-short drag-based editing time compared to state-of-the-art (SOTA) met...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On the Computational Landscape of Replicable Learning	Accept (poster)	Summary: The authors study the computational relationship between replicable learning algorithms, a recent notion of algorithmic stability [ILPS22] promising two runs of the algorithm over independent data output the same hypothesis with high probability assuming shared randomness, and other classical notions of algo...	Rebuttal 1: Rebuttal: We thank reviewer 9ean for their thorough review, their suggestions regarding the presentation of the manuscript, and the clarification of results from prior work. We apologize for the confusion; indeed there was a misunderstanding on our end about the separation that [BGH+23] provided. We are co...	Summary: In the paper, the authors discuss the connection and differences among replicability, a novel stability condition for learning algorithms proposed by Impagliazzo et al. [2022], online learning, and differential privacy from a computational perspective. Their first contribution is a computational separation bet...	Rebuttal 1: Rebuttal: We thank reviewer iQWb for recognizing the contributions of our paper. The main result of [ILPS22] is that SQ-based algorithms can be made replicable. However, our understanding of replicability beyond SQ algorithms is fairly limited. The main candidate in understanding this question is learning ...	Summary: Replicability is a notion of stability for learning algorithms, recently proposed by Impagliazzo et al. [ILPS22] to address the replicability crisis pervasive in scientific studies using statistical procedures. A learning algorithm $A$ is a function that takes as inputs a dataset $S \in (\mathcal{X} \times \ma...	Rebuttal 1: Rebuttal: We thank reviewer npit for the insightful suggestions regarding the presentation of our paper. The random string in the definition of replicability does indeed model the random seed of a learning algorithm in practice and sharing internal randomness can be easily implemented in practice by sharin...	Summary: This work contributes to the recently evolving area of replicable learning. This work establishes three main results 1. It is known that online learning algorithms can be replicable and replicable learning algorithms yield online learning algorithms. The work focuses on the computational complexity of these t...	Rebuttal 1: Rebuttal: We thank reviewer eJSY for recognizing the importance of our results and their suggestions regarding the presentation of our paper. We believe that the main contribution of our work, as the reviewer acknowledges, is a collection of interesting conceptual results that are missing from prior work. ...	Rebuttal 1: Rebuttal: Following reviewer 9ean's suggestions, we have uploaded a slightly modified figure of the computational landscape of stability to clarify the computational separation between approximate DP and replicability given in [BGH+23], which is not for PAC learning but for some other statistical task. We...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model	Accept (poster)	Summary: The paper introduces CaPaint, a causal structure plugin for spatio-temporal (ST) forecasting, aiming to identify causal regions in data and enable the model to perform causal reasoning. Utilizing a two-stage process and employing a novel image inpainting technique using a fine-tuned unconditional Diffusion Pro...	Rebuttal 1: Rebuttal: W1: Thank you for your careful review. We appreciate your attention to detail and will correct this typo to accurately reflect the use of front-door adjustment in the abstract. W2: Thank you for your valuable feedback. For each original spatio-temporal sequence, we enhance the data by fir...	Summary: The paper focuses on generalizability and interpretability for spatio-temporal predicting. The authors propose a causal structure plugin, named CaPaint, which identifies causal regions in data to generate data for scenarios where data are scarce. Experiments on five datasets demonstrate the effectiveness of th...	Rebuttal 1: Rebuttal: Q1: In line 39-44, the authors lack discussion of why the causality and interpretability of models can improve generalization capabilities when dealing with the uneven, insufficient data collection. Answer: 1. When data is sparse, models may learn shortcut solutions from biased data...	Summary: The paper presents CaPaint to improve spatio-temporal predictions by identifying causal regions and employing diffusion inpainting techniques. The approach addresses the challenges of high computational costs in ST causal discovery. Strengths: 1. CaPaint seamlessly integrates with a variety of existing spati...	Rebuttal 1: Rebuttal: Answer(Q1): Thank you for your feedback. We respectfully disagree with the assessment that our method lacks novelty. Our work presents significant improvements and innovations over NuwaDynamics. 1. Conceptual Difference: - Our proposed method employs a different approach to causal ...	Summary: This paper introduces a groundbreaking framework named CaPaint, which is designed to tackle the critical issues of data scarcity and the absence of causal connections in spatiotemporal (ST) prediction models. The authors have established a robust causal framework that not only identifies regions within data th...	Rebuttal 1: Rebuttal: - Details on computational efficiency or scalability of the proposed method are not provided, leaving it as a potential limitation for practical applications. Thank you for your valuable feedback. We appreciate your concern regarding the computational efficiency and scalability of our proposed me...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to extend our sincere gratitude to all reviewers for their thorough and insightful feedback on our manuscript. We appreciate the time and effort you have invested in evaluating our work. Below, we provide an overall summary addressing the common strengths, weaknesses...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation	Reject	Summary: The paper introduces M3-Impute, a mask-guided representation learning method for missing value imputation. The core idea of M3-Impute is to leverage missingness information as an explicit input to the model through masking schemes. This approach allows M3-Impute to effectively learn both feature-wise and sampl...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our method as a novel approach with innovative masking schemes and that our experiments comprehensively demonstrate superior performance of our method to baseline methods. We also appreciate the constructive comments. Below we provide our response to the conce...	Summary: This study proposes a missing value imputation method. The proposed method tackles the missing value imputation problem as a link prediction task on the bipartite graph. It represents a data matrix with missing values as a bipartite graph, then uses a graph neural network on the bipartite graph to learn the em...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our method as an advanced and novel approach that achieves significant performance improvements, and that our contribution is excellent. We also appreciate the constructive comments. Below we respond to the main concerns raised. **Q1. The size of the biparti...	Summary: This paper addresses the challenge of missing values in data analysis and machine learning by proposing M3-Impute, a novel imputation method. Traditional imputation techniques often neglect 'missingness' information and fail to explicitly model feature and sample correlations, leading to suboptimal results. M3...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing that our FCU and SCU are particularly compelling, our method is methodologically sound, and the experiments are extensive. We also appreciate the reviewer for the constructive comments. Below we provide our response to the concerns raised. **Q1. M3-Impute de...	Summary: The paper proposed a new imputations method called M3-impute. M3-impute follows the basic structure of some recent imputation methods: a undirected bipartite graph is constructed with nodes for features and samples, where edge weights correspond to observed data at the given feature-sample pair. Previous appro...	Rebuttal 1: Rebuttal: We sincerely thank you for recognizing that our paper is well-written and our empirical results are extensive. Below we provide our response to the concerns raised. **Q1. The paper does not support categorical features. This is a big weakness compared to other imputation methods that can handle...	Rebuttal 1: Rebuttal: We appreciate the constructive comments from Reviewer LUcW (R1), JuhZ (R2), LMmS (R3), f6SV (R4), E1Qa (R5), Bqr5 (R6), and WjjR (R7). We are encouraged that they find our approach novel (R1, R2, R3, R5, R6, R7), our masking scheme innovative and effective (R2, R3, R5, R7), our experiments compreh...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposes M^3-Impute, a missing value imputation method that utilizes GNNs to learn embeddings of samples and features. By incorporating feature correlation unit and sample correlation unit, M^3-Impute effectively captures correlations between features and samples for accurate imputation. Strengths:...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for recognizing that our masking schemes are novel, the proposed correlation units are helpful, and our experiments demonstrate good performance. We also appreciate the constructive comments. Below we provide our response to the concerns raised. **Q1. The initiali...	Summary: This is a novel approach for imputing missing data using mask-guided representation learning. The main contributions include the development of an imputation model that leverages both feature and sample correlations. This model improves imputation accuracy and robustness compared to existing methods. The paper...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for recognizing that our work is novel with a unique mask-guided representation learning method and we demonstrate strong empirical performance with comprehensive experiments. Below we provide our response to the concerns raised. **Q1. Weakness: Computational comp...	Summary: This paper presents a novel imputation method, based on a bipartite graph constructed from the data and the missing-data patterns, and two components which allow to measure similarities between the features and samples. The method shows very good results in terms of MAE on several datasets for MCAR, MAR and M...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our paper as well-written and the experiments as well-conducted. We also appreciate the constructive comments. Below we provide our response to the concerns raised. Q1. Although well presented, the method is complicated to understand. The main idea behin...	null	null
CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference	Accept (poster)	Summary: This paper introduces an approach to ab-initio homogeneous reconstruction that handles multi-modal pose distributions with a tailored encoder and accelerates pose optimization with semi-amortization. The approach uses a shared CNN feature extractor with multiple pose predictor heads, predicting several plausib...	Rebuttal 1: Rebuttal: Thanks for the insightful comments and questions. Below we address the concerns. speed comparison and decoder ablation. To clarify, we perform an additional study, detailed in the rebuttal PDF Tab.1. We report reconstruction time per epoch, GPU memory usage, and number of parameters for vario...	Summary: This work introduces a new method for 3D ab initio estimation in cryo-EM using amortized inference. It relies on an existing technique which predicts the 3D rotation of a cryo-EM image using a convolutional neural network. That estimated rotation is then used together with a neural representation of the 3D vol...	Rebuttal 1: Rebuttal: We appreciate your thoughtful feedback. In what follows, we address the main concerns and questions individually. 3D rotation parameterization. In the supplement, Sec. A, we discuss the rotation parameterization and its optimization using PyTorch Autodiff. Specifically, we follow cryoAI and ...	Summary: The submission addresses ab-initio cryo-EM reconstruction, where both image poses and the 3D structure are estimated. The authors adopt a multihead architecture to estimate multiple poses for each image, encouraging the exploration of pose space early in the reconstruction process. They then refine poses in an...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review and questions. Below, we address the concerns raised in the review. "technical contribution limited to a multi-head architecture". Our contributions include the design of a new encoder equipped with multiple heads to mitigate uncertainty in pose auto-encodi...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful feedback. In the attached rebuttal PDF, we include results of additional experiments to clarify and address concerns raised by reviews, including additional baselines and datasets, and including empirical results using semi-amortized inference to estimat...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multi-view Masked Contrastive Representation Learning for Endoscopic Video Analysis	Accept (poster)	Summary: The authors propose a self-supervised learning regime for spatio-temporal data called multi-view masked contrastive learning which combines a frame-aggregated attention guided tube mask and a multi-view mask strategy using a student-teacher framework. The learnt representations are evaluated on multiple downst...	Rebuttal 1: Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below. W1 and Q1. Detail information about downstream tasks. Our downstream tasks include classification, segmentation, and detection, each addressing different aspects of endoscopic vide...	Summary: This work presents M$^2$CRL, a self-supervised learning method for representation learning of endoscopic videos. The method leverages a multi-view masking technique with attention-guided masking of global features and random spatiotemporal tube masking of local features. Both contrastive learning and masked au...	Rebuttal 1: Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below. Q1. Were all methods pretrained on the same union of 7 datasets as M$^2$CRL? Yes, for a fair comparison, all methods were pretrained on the same union of 7 datasets as our M$^2$CRL...	Summary: The paper proposes a representation learning framework that combines masked pretraining strategies with contrastive learning methods. Particularly, this framework aims to generate a representation learning approach that can work with downstream tasks requiring dense pixel-level representations (for image segme...	Rebuttal 1: Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below. W1 and Q1. Downstream tasks are evaluated on a single endoscopic modality. In our study, we used multiple publicly available endoscopic video datasets. These datasets cover 3 types...	Summary: The paper proposes a pre-training approach called Multi-view Masked Contrastive Representation Learning for endoscopic videos. The approach combines self-distillation and masked video modeling under multi-view setting. To consider the characteristics of inter-frame instability and small inter-class differences...	Rebuttal 1: Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below. W1. The novelty. Existing self-supervised pre-training methods for endoscopic videos predominantly rely on contrastive learning. However, using contrast learning alone is not suffi...	Rebuttal 1: Rebuttal: Dear reviewers and AC, We sincerely appreciate your valuable time and effort spent reviewing our manuscript. We thank all reviewers for their useful comments, positive consideration and relevant feedback on our paper. It seems that the reviews are positive in general and acknowledges our main ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies	Accept (poster)	Summary: This paper introduces DP-Attacker, a suite of algorithms designed to generate adversarial attacks against diffusion-based policies (DPs). The paper explores two attack scenarios: (1) hacking the scene camera by adding imperceptible digital perturbations to the visual inputs, and (2) hacking the scene by attach...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and comments. We are glad that the author finds our work novel and impactful for developing more robust DP systems. Below is our response to some of the questions raised by the reviewer. > Lack of Defense Strategies: While the paper demonstrates the...	Summary: The paper proposes an adversarial example construction method targeting Diffusion-based Policies, aiming to create malicious observation inputs to diffusion policies that cause them to fail in generating correct actions, leading to robot errors. The authors present attack methods including both untargeted and ...	Rebuttal 1: Rebuttal: We thank the reviewer for the useful valuable comments and feedback! We are glad that the reviewer liked our writing and acknowledges our comprehensive attacks. We would like to address the reviewer’s questions below. > The proposed method seems to be a straightforward extension of a previous app...	Summary: This paper presents strategies to attack visual-based diffusion policy networks. The authors investigated two attacking scenarios: hacking the scene camera by adding imperceptible digital perturbations and hacking the scene by attaching small adversarial patches to the environments. Strengths: 1. The paper is...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's comments and valuable feedback. We are delighted that the author liked our writing and is convinced by our algorithms. We appreciate that the reviewer is convinced that diffusion-based policy networks are susceptible to adversarial attacks. Here’s our response to...	Summary: This paper studies the adversarial attack to diffusion policy. Two attack scenario settings are introduced. One is to attack the scene camera by adding imperceptible digital perturbations to the visual observation. The other is to attack the scene by adding small adversarial patches to the environment. Experim...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and feedback. Below is our response to address some of the questions raised in the review. We hope that it will address some of your concerns: > W1 The technical novelty is marginal. The proposed framework directly applies the existing attack me...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable comments and feedback on our paper. We are delighted that all the reviews find our method a novel and effective attack against diffusion policies. We are glad that the reviewers kPb5 and 69xg are convinced by our experimental visualizations and results...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper analyzes the security vulnerabilities of the diffusion strategy and proposes possible attack scenarios. A set of algorithms called DP-Attacker is proposed, which can successfully reduce the performance of the diffusion strategy in different adversarial scenarios (including online and offline attacks)...	Rebuttal 1: Rebuttal: We thank the reviewer for the questions raised. Please see our response below. We sincerely ask the reviewer to refer to the general response for possible concerns. > Q1: The method used is not specific to the diffusion model. Novelty is limited. What is unique about this approach compared to a l...	Summary: Diffusion policy is used to generate the action trajectory from a pure Gaussian noise conditioned on the input images, applied in many applications such as autonomous driving. This paper proposes white-box adversarial attacks against diffusion policy, which aim to generate a target bad action or an untargeted ...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments. We are glad that the reviewer found our attack scenario novel and liked our visualizations. Below is our response to some of the question raised. First, we would like to clarify the loss used in our DP-Attacker is not the distance between generated...	null	null	null	null
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?	Accept (poster)	Summary: The paper is concerned with the question of whether LLM can perform causal reasoning on a human-like level by incorporating contextual information in their decision and answer process. The authors argue that many everyday causal inferences are not purely logical but take into account general knowledge and inte...	Rebuttal 1: Rebuttal: Thank you for constructive comments and they are valuable for improving our paper. In the following, I will address your concerns one-by-one. > Q1. The paper remains vague on the particular effect that the additional context might impose in terms of the cause-effect pairs as discussed in Sec. 5 /...	Summary: The authors proposed a new causal reasoning framework, to improve the causal reasoning capacity of LLMs, with inspiration drawn from causal graph theory and human reasoning process. The work utilized "general world knowledge" as a component to take a step closer to making LLMs perform a more human-like causal ...	Rebuttal 1: Rebuttal: Thank you for constructive comments and they are valuable for improving our paper. In the following, I will address your concerns one-by-one. > Q1. The key hypothesis of this work is that LLM is capable of "level-1" reasoning, but lacks the capacity of "level-2" reasoning. Given that this work is...	Summary: The paper investigates the causal reasoning capabilities of LLMs and argues that current LLMs are limited to shallow (level-1) causal reasoning. To support this claim, the authors introduce a new benchmark, CausalProbe-2024, which reveals that LLMs struggle with causal reasoning in fresh and unseen contexts. T...	Rebuttal 1: Rebuttal: Thank you for constructive comments and they are valuable for improving our paper. In the following, I will address your concerns one-by-one. > Q1. The long-term viability of this approach is uncertain. Continuous advancements in model architecture and training techniques might be required to tru...	Summary: This paper studies the autoregressive mechanism of the transformer-based LLMs and their ability to reason causally and introduces a new Q&A-based benchmark named CausalProbe-2024 with around 3k Q&A pairs (believed to be unseen by the existing LLMs). The paper further introduces a reasoning strategy that consid...	Rebuttal 1: Rebuttal: Thank you for your constructive comments and they are valuable for improving our paper. In the following, I will address your concerns one-by-one. > Q1. Weakness (1) A1. Thank you for point out this ambiguity. Here we provide more clear definitions for two causal reasoning levels. - Level-1...	Rebuttal 1: Rebuttal: Here, we mainly present the work we have done and the new efforts added for data quality control. > Current quality control for CausalProbe 2024. We discuss our efforts for ensuring the benchmark's quality here, which have not been discussed in detail in our paper. We have merged them in...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching	Accept (poster)	Summary: The paper investigates the application of flow matching-based generative models for high-resolution image synthesis, particularly as priors for solving inverse problems. A notable challenge addressed is the slow computation of log-likelihoods in high-dimensional contexts, necessitating backpropagation through ...	Rebuttal 1: Rebuttal: We thank you for your helpful review and provide the additional experiments which we hope address your concerns: More baselines: RED-Diff, $\Pi$GDM, and D-Flow See Tab. 2 in the attached pdf above. In addition to the flow-based baselines, we have included two representative diffusion-based ba...	Summary: The paper proposes a flow prior under the MAP structure for solving inverse problems, a theoretical analysis is also given. Strengths: 1. The paper is easy to follow, the presentation from concepts to methods is concise, the motivation of the proposed method on overcoming shortage of flow model is also clear ...	Rebuttal 1: Rebuttal: We sincerely thank you for your helpful and detailed feedback, which significantly helped us improve the paper quality. Dependence on $N$ in Theorem 1 We appreciate your concern regarding our theory. While we agree that it could be beneficial to obtain a non-asymptotic error bound in terms o...	Summary: This paper addresses the challenge of solving linear inverse problems in high-resolution image synthesis using generative models based on flow matching. While these models are appealing due to their ability to compute image likelihoods directly from a learned flow, they suffer from slow log-likelihood computat...	Rebuttal 1: Rebuttal: We appreciate your recognition of our original contributions and will work on enhancing the clarity and coherence of our presentation. In the attached pdf above, more experiments have been added to show our method's capability to handle more challenging conditions ($\nu=1/10$, Tab. 1), highly nois...	Summary: The paper proposes efficiently recovering MAP estimates by utilizing flow-matching priors. The main proposition in the presented method is to break down the MAP objective into a sum of N local MAP objectives, facilitating a computationally feasible approach that runs in reasonable time. The results are compare...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed feedback and comments. We would like to address your concerns regarding our paper's motivation and contribution to the inverse problems community. We will respond to each concern below: The use of log-likelihood and flow prior First, we would lik...	Rebuttal 1: Rebuttal: Dear Reviewers, We first thank you for your valuable feedback and appreciate the recognition of our paper's strengths, such as - concise presentation from concepts to methods and clear motivation (sT2G), - a solid theoretical foundation supporting the method (qjx2), - consistently improved per...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Trajectory Diffusion for ObjectGoal Navigation	Accept (poster)	Summary: This paper tackles the object goal navigation problem, where given visual observations and a target goal object, the task is to plan a navigation path to find said object. The paper proposes a trajectory diffusion model, where the future navigation trajectory is predicted starting from random points. The diffu...	Rebuttal 1: Rebuttal: Thanks to the reviewer for the appreciation and suggestions for our work. We address the concerns in the following lines. ### (W1 & L2) Concerns about small map size limiting the proposed method's generalization to large-scale environments. We evaluate navigation performance with various map size...	Summary: This paper argues that the previous object navigation algorithms generally only consider one-step decision-making, which can lead to temporal inconsistency and shortsightedness. Therefore, the authors propose using diffusion models to learn sequential decision-making. By collecting expert trajectories and then...	Rebuttal 1: Rebuttal: Thanks to the reviewer for the insightful and valuable feedback. We address your concerns below. ### (W1) Streamlining Sec. 3 of the main text. We appreciate the reviewer's suggestion and will adjust the content arrangement to ensure conciseness. --- ### (W2-1 & Q1) Temporal inconsistencies of en...	Summary: The authors propose a diffusion trajectory planner in the context of indoor object navigation that takes current semantic maps (could be partial) and the target object as input to produce a planned future sequential trajectory. Evaluation is done in simulation using the habitat simulator on two datasets. Stre...	Rebuttal 1: Rebuttal: Thanks to the reviewer for the constructive and insightful feedback. We address your concerns below. ### (W1 & L2) Lack of novelty. We discuss the novelty of our work from two perspectives: (1) Comparison with existing diffusion-based planning methods Conditional diffusion models are widely ado...	Summary: The paper "Trajectory Diffusion for ObjectGoal Navigation" introduces a novel method called "trajectory diffusion" for the task of ObjectGoal Navigation (ObjectNav), where an agent is required to navigate to a specified object in an unseen environment based on visual observations. The existing methods for Obje...	Rebuttal 1: Rebuttal: We appreciate the detailed questions and address them in the following lines. ### Comparison with other methods of sequence planning for ObjectNav. We choose Habitat-web [1] and ENTL [2] for comparison, as they also output sequence predictions for ObjectNav task. The comparison is based on the fol...	Rebuttal 1: Rebuttal: # Overall Response We thank all reviewers for their valuable and insightful feedback. We appreciate the supportive comments regarding our well-written presentation (ria9, JCTG, sqUP), novel approach (JCTG, sqUP), precise figures (ria9, k5Ci), sound motivation (ria9), robust performance (ria9, k5Ci...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper presents a novel, diffusion-based modular sequential planning algorithm for image-based goal-conditioned ObjectNav tasks. Concretely, the method performs supervised training to solve the following task: given a partial map constructed from past image observations and a user-specified object, predict...	Rebuttal 1: Rebuttal: Thanks to the reviewer for the appreciation and suggestions for our work. We address the concerns in the following lines. ### (W1) Figure placement. Thanks for the valuable suggestion. We will adjust the figure placement in the final version to enhance readability. --- ### (W2) Post-processing on...	null	null	null	null	null	null
A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding	Accept (poster)	Summary: This paper proposes a depth-range free MVS network that considers the information within the MVS framework. The initial depth is derived from the epipolar geometry of the reference image and the source images. In addition, the disparity features are enhanced by an uncertainty encoding module and a multi-view d...	Rebuttal 1: Rebuttal: > no explanation for $\overrightarrow{f}$ in equations 3 and 4. Similar to DispMVS, $\overrightarrow{f}$ is a 2D flow vector along the epipolar line that provides flow in the x dimension $\overrightarrow{f} _{xr\to xs }(p_r)$ and y dimension $\overrightarrow{f} _{yr\to ys }(p_r)$. > no explanati...	Summary: This paper proposes a depth-range-free Multi-View Stereo (MVS) method, which iteratively updates the depth using a GRU-based approach. To eliminate the dependency on depth priors, the paper improves the depth initialization method of DispMVS. To fully utilize multi-frame information, the paper encodes the obse...	Rebuttal 1: Rebuttal: > the left side of Eq. 5 should be $V_i(p0)$; The $Fd_i$ in Fig. 2, Eq. 8, and $F^d_i$ in Sec 3.4, the $H_i$ in Fig. 2 and $Hd_i$ in Eq. 8. Thanks for your reminder. I will standardize the notation for cost volume as $V_i(p0)$, epipolar disparity features as $Fd_i$, and disparity hidden State a...	Summary: - The author proposes a depth-range-free multi-view stereo framework that simultaneously takes into account all the source images. - The author has specially designed a 3D pose embedding to better encode specific geometric information. - The Multi-View Stereo method proposed in the paper achieves more robust ...	Rebuttal 1: Rebuttal: > The visual results of the method proposed in the article exhibit many floater artifacts. Thanks for your reminder. Artifacts are generated during the depth fusion step. For point cloud fusion, we directly sampled the pcd method from DispMVS. This method selects multiple relevant depth views for...	Summary: The paper proposes a depth-rage free method for MVS. It leverages the transformers for designing a global-aware model, with using pose positional embedding to guide the model and also predict the uncertainty at the same time. The methods demonstrates good performance on diverse datasets and is also robust to d...	Rebuttal 1: Rebuttal: > Using transformers for building global-aware model is very common for 3D reconstruction now [1,2,3]. I would be good if the authors could discuss these related works. Thank you for your suggestion. We will discuss the corresponding references in the related work section. [1] Wang, Peng et al. ...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for valuable feedback and positive comments like "novel and nontrivial"(Reviewer RLMs), "satisfying experimental results"(Reviewer heJr), "exhibits technical novelty"(Reviewer fCJ7), "motivation for the model design is clear"(Reviewer Q3xc), "promising results ... ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper describes an MVS approach that does not depend on given depth ranges, which MVS algorithms typically require when building 3D cost volumes. The solution, previously proposed in DispMVS, is to perform searching and iterative updates in disparity space. Compared to DispMVS, the authors propose several ...	Rebuttal 1: Rebuttal: > "sampling range" on L.168. Thanks for your reminder. Following DispMVS, the "sampling range" on L.168 refers to the set of sampling points uniformly sampled along the epipolar line. > "$t_c$, $t_f$" in Eq.11. on L.163, "$t_c$, $t_f$" are iterations at the coarse and fine stage. > The GRU upd...	null	null	null	null	null	null
Truth is Universal: Robust Detection of Lies in LLMs	Accept (poster)	Summary: This paper provides the analysis of linear subspaces of activations in LLMs in order to detect the truthfulness of the answer. Authors show that: 1) there are at least two-dimensional subspace for two types of false statements (affirmative and negated statements), which is the reason why previous approaches ge...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We are glad that you found our identification of the 2D truth subspace to be a significant contribution and appreciate your constructive feedback. Regarding theoretical background: We agree that a theoretical explanation for why these truth directions emerge ...	Summary: The paper presents a discovery of truth vectors, specifically a general one and a polarized one, present in Large Language Models (LLMs) when “lying”. The paper builds upon previous work by using vectors from intermediate-layer vector presentations to find these two vectors. This paper finds that one needs two...	Rebuttal 1: Rebuttal: Thank you for your positive review! We appreciate the criticism regarding clarity. In the revision, we will improve the writing throughout the manuscript. Regarding code release: We fully agree. In the spirit of reproducible research, with the revision, we will make our code and scripts public, s...	Summary: The authors study LLM lie detection using probes trained on model internals. They show that LLM representations contain a two-dimensional subspace that corresponds to a general truth direction as well as a polarity-dependent truth direction which is sensitive to whether the statement is affirmative or negated....	Rebuttal 1: Rebuttal: Thanks a lot for your review! We are glad you found the 2D subspace explanation convincing and liked the presentation of the results. Regarding Novelty: We agree that our work was directly motivated by the empirical findings of Marks and Tegmark and Levinstein & Herrmann. The novelty and importa...	Summary: The paper considers the problem of detecting whether a statement is true or false from the intermediate activations of an LLM. It starts by introducing a linear functional form with two linear components t_G and t_P which is able to discriminate between true and false statements for both affirmative and negate...	Rebuttal 1: Rebuttal: Thank you very much for your review! We appreciate your comments and hope that our response below can convince you that the contribution of our work goes beyond just showing that training on more diverse and balanced data improves generalization. “The analysis of section 3 is qualitative”. Please...	Rebuttal 1: Rebuttal: In response to reviewer YJ6m's suggestion, we expanded our comparison of classifiers in Section 5. In addition to Logistic Regression (LR) and Contrast Consistent Search (CCS), we now include Mass Mean (MM) probing by Marks and Tegmark [2023] and a new classifier we developed after the original su...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Schrodinger Bridge Flow for Unpaired Data Translation	Accept (spotlight)	Summary: This paper proposes $\alpha$-IMF, which is an incremental version of IMF method in DSBM paper. Specifically, this work demonstrate the convergence properties of $\alpha$-IMF and implement it through an online learning method. Here, $\alpha$ is implicitly reflected. Moreover, the functional flow interpretation ...	Rebuttal 1: Rebuttal: Thank you for your comments, we appreciate your acknowledgements of the paper’s merits. > The experiment on practical data is insufficient and the performance improvement is incremental. The paper only presents real-world I2I on Cat <-> Wild. Moreover, the performance of bidirectional online lear...	Summary: The authors consider developing a new algorithm for a Schrödinger Bridge problem of translating between two probability distributions. Motivated by the fact that current Schrödinger Bridge (SB) approaches either use mini-batch optimal transport techniques or require training diffusion at every iteration, the a...	Rebuttal 1: Rebuttal: Thank you for your comments and thoughtful questions, we are glad you enjoyed the paper. > The paper lacks a study of image quality boost given the same computational budget. > It would be a more solid argument to evaluate the Gaussian setup with a full covariance matrix [6]. > A bigger datase...	Summary: This work introduces $\alpha$-DSBM, a new way of training DSBM-like models, which does not require a Markovian projection at each step and eliminates the need to train multiple models. The main advantage over previous DSBM-based approaches is that $\alpha$-DSBM only needs to train a single model with a single ...	Rebuttal 1: Rebuttal: Thank you for your positive comments and feedback. > While the experimental section of the paper thoroughly analyzes and compares DSBM and its different flavors, including the proposed method, a comparison with other competing methods could further support-DSBM through empirical evidence. > How...	Summary: The paper proposes a novel algorithm for mass transport problems, aiming to compute maps that transport one distribution to another. This paper introduces the Schrödinger Bridge Flow algorithm, a dynamic entropy-regularized version of OT, eliminating the need to train multiple DDM-type models. The algorithm di...	Rebuttal 1: Rebuttal: Thank you for your comments, we appreciate your acknowledgements of the paper’s merits. > It is necessary to state these assumptions in the main body of the paper and provide references to justify their rationality. Due to space limitations, we have detailed most of the technical assumptions for...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their valuable time and insightful feedback. We appreciate the thoughtful questions and the overall positive response. We have provided detailed responses to each reviewer's comments. In summary, the key feedback points we have received are as follows: * **Addi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multiview Scene Graph	Accept (poster)	Summary: The paper introduces a new scene graph format, which regards objects and places as nodes. The edges are intuitive, basically saying which objects are in which places and which places are close to each other. The paper also provides two metrics on which the graph generation method can perform two tasks better t...	Rebuttal 1: Rebuttal: __Q1__: the current scene graph extracted from multiple views is a bit simple __A1__: We acknowledge the fact that the proposed MSG has a rather simple format as it is free of spatial or high-level semantic relationships between objects. However, this does not mean this task is simple, insignifi...	Summary: The paper proposes a novel task: generating scene graphs from unposed RGB images as well as a new benchmark based on ARKitscenes. To achieve this, the paper proposes a novel approach that use off-shelf image encoder and detector that are frozen and only train a decoder that takes in the features learned from f...	Rebuttal 1: Rebuttal: __Q1__:The task is not novel, see EgoSG. __A1__: Thank you for the question. We will cite EgoSG as a related work. However, while both carry “scene graph” in the names, the MSG task is completely different from the EgoSG and other existing 3D scene graphs. Firstly, the definition of the graph is...	Summary: The manuscript proposes the problem of inferring a scene graph from unposed images of a space. The key distinguishing factor to previous work is using multiple frames (as opposed to a single frame) and not requiring poses and depth (like for typical metric 3d scene graphs). The scenegraph is defined as a the s...	Rebuttal 1: Rebuttal: __Q1__: Example of qualitative experiments on some arbitrary video sequences. __A1__: Thank you for the great suggestion! We have self-recorded an unposed video with an iPhone in a household scenario and run our trained AoMSG model with a pretrained Grounding DINO detector on it. In the attached ...	Summary: The paper introduces the novel task of Multiview Scene Graph generation from unposed RGB images, whereas this type of scene graph encodes the notion of 'places', i.e., images from spatially close locations, and detected objects as graph nodes. The motivation is to combine spatial reasoning of object associatio...	Rebuttal 1: Rebuttal: __Q1__: The current task and model do not seem to allow for an adjustable "place" definition beyond the train set constraints __A1__: Thank you for the question. The thresholds are dataset hyperparameters which is a conventional setup in visual place recognition (VPR) tasks and datasets [1, 2]. S...	Rebuttal 1: Rebuttal: We thank our reviewers for their encouraging comments, helpful suggestions, and insightful questions. Acknowledgements. - All reviewers acknowledge our novelty. - R1 qE49: “The paper introduces the novel task…”. - R3 9wC7: “The paper proposes a novel task…” “proposes a new benchma...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection	Accept (poster)	Summary: This paper looks to perform OOD detection via distributional representation learning rather than assume some pre-specified distributional form for the ID data. Their motivations are that there can exist inconsistencies between the assumptions on the ID distribution from prior work and the actual unknown ground...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer's dedicated time and effort in evaluating our work. In response to the insightful comments provided, we have provided detailed responses to each point raised, hoping that our responses can adequately resolve your concerns. Please find our responses below. *Q.1....	Summary: The paper provides a novel approach called DRL to help optimize post-hoc OOD detection methods by explicitly shaping the ID space during pre-training. In particular, DRL is defined through an Expectation-Maximization algorithm with alongside a structured mini-batch setting. The resulting DRL is shown to have s...	Rebuttal 1: Rebuttal: We sincerely appreciate your dedicated time and effort in reviewing our work. In response to the valuable comments provided, we have provided detailed responses to each point raised, hoping that our responses can adequately address your concerns. Please find our responses below. Q.1. More cla...	Summary: The paper proposes an in-distribution (ID) modeling approach, termed distributional representation learning (DRL), which enhances the convergence of ID latent feature learning. The authors include theoretical proof to corroborate the proposed approach. They have conducted a few experiments on standard OOD benc...	Rebuttal 1: Rebuttal: We sincerely appreciate your time and effort in reviewing our work. Below are detailed responses to your valuable comments. Q.1. Although the proposed method is claimed to mitigate the assumptions in previous works, the model convergence still relies on prior works' assumptions (vMF). Even th...	Summary: The paper introduces an innovative learning framework, Distributional Representation Learning (DRL), designed to bridge the gap between network pre-training and density-based scoring strategies. DRL is formulated as a provably convergent Expectation-Maximization algorithm. Key contributions include the introdu...	Rebuttal 1: Rebuttal: We sincerely appreciate your time and effort in reviewing. We have provided detailed responses below, hoping your concerns can be adequately addressed. Q.1. Contradiction in Assumptions A.1. We apologize for the misunderstanding and will provide explicit explanations highlighting that ou...	Rebuttal 1: Rebuttal: We visualize our loss curve in the uploaded PDF file. Pdf: /pdf/9c43e64fe9dfe1eb4a68956e208785c20e2b34f4.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Diff-PCC: Diffusion-based Neural Compression for 3D Point Clouds	Reject	Summary: The paper proposes the first diffusion-based point cloud compression method called Diff-PCC. A dual-space latent representation is devised in this paper, where a compressor composed of two independent encoding backbones is used to extract expressive shape latents from different latent spaces. At the deco...	Rebuttal 1: Rebuttal: Dear Reviewer HJUZ, Thank you for your detailed review and the valuable feedback. We will address your concerns below. Q1: More work on diffusion model for data compression could be discussed;The way of applying diffusion model should be compared with those learned image compression works in...	Summary: In this work, the authors propose a diffusion-based point cloud compression framework. Low frequency and high frequency features are extracted via PointNet and PointPN from input point clouds, which are quantized and encoded for compression. During decompression, the quantized features would be decoded to cond...	Rebuttal 1: Rebuttal: Dear Reviewer U3Bi, Thank you for your detailed review and the valuable feedback. We will address your concerns below. Q1: Some popular methods are not compared, including PCGC, PCGCv2,and 3QNet. The proposed Diff-PCC mainly focuses on small-scale point clouds. The mentioned PCGC, PCGCv2 th...	Summary: In this paper, they introduce the diffusion-based point cloud compression method, dubbed Diff-PCC, to leverage the expressive power of the diffusion model for generative and aesthetically superior decoding. They get better performance than G-PCC and two deep learning methods. Strengths: Encoding point clouds...	Rebuttal 1: Rebuttal: Dear Reviewer QHjS, Thank you for your detailed review and the valuable feedback. In the following, we address all comments in the review. Q1: How to obtain a point cloud with added noise in the decoder? In a word, the decoder do not need any prior knowledge of the original point cloud. Dur...	null	null	Rebuttal 1: Rebuttal: Dear all reviewers, We thank each of you for generously dedicating your valuable time and expertise to reviewing our work. We sincerely appreciate your constructive feedback and are delighted to see the positive comments: 1.Novelty • Reviewer HJUZ: ”diffusion model is used in point cloud compre...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Risk-Averse Fine-tuning of Large Language Models	Accept (poster)	Summary: This paper addresses the challenge of mitigating the generation of negative or toxic content by Large Language Models (LLMs) in response to certain prompts. It proposes an innovative approach that integrates risk-averse principles into LLM fine-tuning, aiming to reduce harmful outputs, particularly rare but si...	Rebuttal 1: Rebuttal: Thank you appreciating and recognizing the strengths of our work. Please find additional details of interest below. Computational resource requirement. The computational resource requirements are mentioned in Appendix E.1. Computational complexity. Our algorithm has the same computatio...	Summary: To mitigate the generation of negative or toxic content by LLMs, this paper proposes a new method "risk-averse" RLHF (RA-RLHF) to finetune LLMs. Their RA-RLHF optimizes the CVaR risk measure with RL to decrease the order of negativity or toxicity. They experiment with two datasets, IMDB-Gen and Jigsaw. Their e...	Rebuttal 1: Rebuttal: Thank you for your insightful questions and comments. Before we begin answering the questions, we wanted to highlight that in our work, we studied inclusion of safety in LLMs using three datasets - IMDB, Jigsaw and RealToxicityPrompts - over GPT-2 (117M parameters) and GPT-J (6B parameters). **Q...	Summary: The paper presents a method for fine-tuning large language models (LLMs) using risk-averse reinforcement learning from human feedback (RA-RLHF). The core contribution is the integration of risk aversion into the RL fine-tuning process to minimize the generation of toxic content, particularly in responses to ha...	Rebuttal 1: Rebuttal: Thank you for your insightful questions and comments. C1. The approach primarily adapts the CeSoR algorithm by Greenberg et al. [2022].\.. A1. Our work is the first to introduce a nuanced understanding of `risk' in the context of LLM content generation, going beyond Greenberg et al. [2022]'...	Summary: The authors propose a fine-tuning method to mitigate text degeneration, such as toxic outputs, in large language models. The proposed method optimizes a Conditional Value at Risk-inspired criterion. Experimental results show that the proposed method outperforms various baselines on two datasets. Strengths: - ...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and questions. C1. The experimental results are limited.\... A2C1. In our work, we studied inclusion of safety in LLMs using three datasets - IMDB, Jigsaw and RealToxicityPrompts - over GPT-2 (117M parameters) and GPT-J (6B parameters). Most of the rel...	Rebuttal 1: Rebuttal: With this work, our goal was to introduce a nuanced understanding of "risk" in the context of LLM content generation to induce safety/non-toxicity in LLM generations. We achieved so by introducing a risk-averse strategy to LLM finetuning, focusing on optimizing Conditional Value at Risk (CVaR), re...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper presents a new way to reduce the generation of toxic content by large language models. The authors introduce a method that integrates risk-averse principles into the fine-tuning process, focusing on minimizing harmful outputs using Conditional Value at Risk (CVaR) as the risk measure. The goal of th...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and questions. Q1. What steps can be taken to ensure the risk-averse fine-tuning process doesn't inadvertently reinforce existing biases in the training data? How can the model be evaluated for potential biases? A1. Since we work in the regime of RLHF ...	null	null	null	null	null	null
Learning-Augmented Priority Queues	Accept (poster)	Summary: The study investigates the integration of learning-augmented frameworks into the design of priority queues, focusing on enhancing worst-case performance using potentially inaccurate predictions. It examines three specific prediction models—dirty comparisons, pointer prediction, and rank prediction—applied with...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and suggestions. We address below their concerns. ### Weaknesses * O1.1. (Limitations of standard priority queues, line 21) The lack of $o(\log n)$ time priority queues is an impossibility result, hence a limitation. We are not quite sure what type of d...	Summary: The paper studies various beyond worst-case models for priority queues, a fundamental data structure. A learning-augmented viewpoint is taken and the authors comprehensively study three different natural prediction models: dirty/clean comparisons where some comparisons between items maybe in correct, pointer p...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and insightful suggestions. Below, we address the concerns and questions raised in the review. ### Weaknesses * Table of results. We will include a table, as suggested by the reviewer, summarizing the complexities of the operations using stand...	Summary: The authors in this paper propose a learning-augmented priority queue data structure which takes advantage of the inaccurate predictions to facilitate the operations. Three different prediction models including dirty comparisons, pointer predictions, and rank predictions have been explored and discussed. The a...	Rebuttal 1: Rebuttal: We express our gratitude to the reviewer for their feedback and insightful comments. We address below the weaknesses they have raised. ### Weaknesses * Comparison with prior learning-augmented algorithms. We are uncertain about the specific data structure the reviewer is referring to and woul...	Summary: The paper considers designing data structures for priority queues that accept predictions / advice to improve the time complexity of common queue operations. The paper considers three different models of predictions - (i) dirty comparisons : cheap but possibly inaccurate comparisons are available. Goal is to u...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and for the time and effort spent on our submission. Weakness. The intuitions behind some algorithmic ideas are indeed inspired by previous work, and we highlighted these connections as much as possible to make the paper and algorithms easier...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FINALLY: fast and universal speech enhancement with studio-like quality	Accept (poster)	Summary: The authors propose FINALLY, a speech enhancement algorithm based on GANs and the Wav LM encoder. They evaluate different feature extractors and then show qualitative and quantitative results on speech enhancement. Strengths: A major strength is the fidelity of the generated outputs. The results are impressiv...	Rebuttal 1: Rebuttal: Dear Reviewer, Firstly, we would like to thank you for your invaluable work. Below, we address your concerns about the paper. W1. Diffusion Models and GANs for Conditional Sampling We generally agree that generative models, such as GANs and diffusion models, are not necessarily used to allo...	Summary: The paper proposes a GAN based method for universal speech enhancement (SE) demonstrating competitive experimental performance. To justify the use of adversarial learning for SE, the authors provide a theoretical insight regarding the effectiveness of the mode-covering property of LSGAN for the SE task under ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you very much for your high assessment of our work. We truly appreciate your valuable comments. Below, we address your concerns. W1. Comparison with HiFi++ & Q1. Advantage of incorporating the WavLM Encoder The original HiFi++ model was proposed for speech denoising and...	Summary: This paper proposes a universal speech enhancement model for real-world recording environments utilizing GAN, referred to as FINALLY. The authors theoretically analyze that using LS-GAN loss leads to finding the point of maximum density within the conditional clean speech distribution. To stabilize the adversa...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your time and consideration. We would like to address your concerns about the paper. W1. It still needs objective metrics such as PESQ and STOI for a solid evaluation Our paper does not include similarity-based metrics such as PESQ and STOI for two reasons. Firs...	Summary: This paper describes a new formulation of GAN-based speech enhancement. It includes an analysis of the convexity in different feature spaces of the distribution of TTS utterances generated from the same inputs, concluding that WavLM's convolutional encoders provide the most convex such space. This representati...	Rebuttal 1: Rebuttal: Dear Reviewer, We are very grateful for the high assessment of our work and your valuable suggestions. Below, we address your concerns. W1. Work by Maiti and Mandel (2019) Thank you very much for pointing out this work. We will add a discussion of it to the related work section in the camer...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to express our sincere gratitude for your thoughtful comments and suggestions. Your appreciation of our insights into GAN training and the analysis of SSL models’ feature spaces is truly encouraging. We have worked hard to address the questions and concerns you raise...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation	Accept (poster)	Summary: This paper presents a novel approach for generating crystal materials using language models. The key innovation lies in the Mat2Seq method, which converts 3D crystal structures into 1D sequences while ensuring SE(3) and periodic invariance. This approach addresses the challenge of representing crystal structur...	Rebuttal 1: Rebuttal: Dear Reviewer vCYe, Thank you for your recognition that our approach addresses the challenge of representing crystal structures in a unique and invariant way under different mathematical descriptions. For your concerns and questions, we provide point-to-point responses below. 1. The paper should...	Summary: In this paper, the authors focus on the application of language models in the field of material generation. Starting from CIF files that represent crystal unit cell structures, they primarily utilize the Niggli reduction method to organize structures under different translations, rotations, and unit cell expan...	Rebuttal 1: Rebuttal: Dear Reviewer Sr8M, Thank you very much for your time invested in reviewing our work. For your concerns and questions, we provide point-to-point responses below. 1. About authors' validation whether Mat2Seq helps LM learns from SE(3) equivariance or if the improved performance is due to the new ...	Summary: There are several challenges when developing LMs for materials: 1) each crystal structure consists of an infinite number of atoms and a unique and invariant unit cell must therefore be selected for each crystal 2) the unit cell can be represented in a one-dimensional (1D) sequence that maintains invariance und...	Rebuttal 1: Rebuttal: Dear Reviewer 5Kst, Thank you for your time invested in reviewing this work. We provide point-to-point responses to your questions and concerns. 1. Weakness 1 Thank you very much for raising this question. However, we kindly disagree with this and we feel there might be a potential misunderstan...	Summary: This article propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq to converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby probably achieving SE(3) and periodic invari...	Rebuttal 1: Rebuttal: Dear Reviewer JJwt, Thank you very much for your recognition of our work in terms of insights and contributions. For your questions and concerns, we provide point-to-point responses as follows. 1. Lack of experimental data validation for the generated data. Need to verify the generated data with...	Rebuttal 1: Rebuttal: Dear Reviewers, ACs, and PCs, We thank all reviewers for your time invested in reviewing our work, and appreciate your valuable suggestions. For all your (reviewers') questions and concerns, we provide detailed clarifications with additional experimental results, and some of these experiments a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks	Accept (spotlight)	Summary: The authors propose a method for debiasing the representations of Vision-Language Models (VLMs) that can be applied at various layers of the image and text encoders/decoders, and can be used for a variety of downstream tasks such as image generation, 0-shot classification, text to image retrieval, and image ca...	Rebuttal 1: Rebuttal: ### Confidence interval in Text-to-Image Generation Thank you for pointing out the lack of confidence intervals in our experimental results. We acknowledge that text-to-image generation is not deterministic. This is why we conduct text-to-image generation 10 times with different seeds and use a u...	Summary: this paper introduces Selective Feature Imputation for Debiasing (SFID), which integrates feature pruning and low confidence imputation (LCI) to effectively reduce biases in VLMs. Strengths: 1.The proposed method utilize feature selection techniques such as RandomForest to identify gender-specific (or race) b...	Rebuttal 1: Rebuttal: ### Novelty of the proposed method We appreciate the reviewer's opinion regarding the simplicity and novelty of utilizing RandomForest in our framework. While the RandomForest algorithm itself is well-known and simple, the novelty of our work lies not in the use of RandomForest alone, but in the...	Summary: The paper introduces a new method to reduce biases in VLMs, which works by using a random forest to identify the bias-related features in model representations and then imputes the values for those features with values from the low-confidence samples. The authors test it on tasks like zero-shot classification,...	Rebuttal 1: Rebuttal: ### How SFID Mitigates Bias in Various Attributes, Including Racial Bias Thank you for your detailed observations on how SFID mitigates different types of bias, which is even more complex. SFID effectively addresses biases when attribute labels are provided such as gender and race in the FairFace...	null	null	Rebuttal 1: Rebuttal: Thank you to the reviewers for the valuable feedback! Here is our global rebuttal, with a PDF file attached for figures and tables. Please refer to the individual rebuttals for specific details and additional information. ### SFID mitigates various types of bias, even in multi-attribute case To a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation	Accept (poster)	Summary: This paper explores the Multi-modal Large Language Model (MLLM) based Referring Expression Generation (REG) task, which aims to generate unambiguous text descriptions for specific objects or regions in images. MLLM-based REG models tend to suffer from hallucination issues, and there is a trade-off between deta...	Rebuttal 1: Rebuttal: Thank you for your attention to our work and your positive acknowledgment of our idea. We have open-sourced the code. We will address your concerns below. 1. low efficiency. We would like to hightlight more about probing-based estimation that is designed to allivate this issue. While the cycl...	Summary: This paper presents an approach for improving the accuracy and richness of referring expression generation (REG) by leveraging the descriptive potential of intermediate layers in Multi-modal Large Language Models (MLLMs). The method employs a cycle-consistency-based decoding strategy to reduce hallucinations a...	Rebuttal 1: Rebuttal: Thanks for your feedback. We have made the code open source at the link attached to the abstract. We will address your concerns below. 1. Increase computational overhead. Thanks for the comments. In the cycle-consistency-based quality ranking, the RES model is incorporated as an auxiliary, le...	Summary: The paper aims to strike a balance between detailed description and precise captioning when using multimodal large language models (MLLM) in the task of referring expression generation (REG). A key observation is that the output of a Referring Expression Segmentation (RES) model should be consistent with the i...	Rebuttal 1: Rebuttal: Thank you for your detailed review and constructive comments. The code is accessible through the link indicated in the abstract. We noticed that there might have been some misunderstandings regarding to our proposed "Probing-based importance estimation" method, and we hope this response will bette...	Summary: The paper addresses the Referring Expression Generation (REG) task using Multi-modal Large Language Models (MLLMs), identifying the key challenge as the trade-off between generating detailed descriptions and accurately targeting the referring objects, which often leads to hallucinations—the inclusion of incorr...	Rebuttal 1: Rebuttal: Thank you for your positive acknowledgment of our work. We are glad to notice your interest in the latent information of the intermediate layers. We hope our responses below will partially address your concerns. 1. Suboptimal compared to the training-based methods. Yes, we proposed an infer...	Rebuttal 1: Rebuttal: We appreciate the time and effort of all reviewers in reviewing our manuscript. Your insightful feedback has been essential in enhancing our work’s quality. We have released our code, and it is available at the link listed in the abstract. In this common response, we would like to explain the f...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Dense Associative Memory Through the Lens of Random Features	Accept (poster)	Summary: This paper introduce an iterative random feature Dense Associative Memory Model (DenseAM). It is a kernel approximation of the standard DenseAMs using random feature decomposition and approximation. The authors term this approach to DenseAMs as distributed representation/formulation of memory (DrDAM). Theore...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough and insightful evaluation of our work. We are glad you found it interesting and beneficial to the ML community! We will add a Related Work section in which we will review the results of - Hu et al., Nonparametric Modern Hopfield Models - Wu et al., Uniform...	Summary: The paper offers a kernel-based approximation of Dense Associative Memory that allows for Hebbian encoding in a space of randomized basis functions. The main advantage when compared with the exact approach is that information concerning all patterns is shared in a single weight tensor, without requiring additi...	Rebuttal 1: Rebuttal: > Improve embedding of this paper into existing literature Thank you for your suggestions, we will update the paper with the Related Work section and will include the discussion of all the papers that you have suggested. As a side note, the reference [4] actually is cited in our submission, pleas...	Summary: This paper studies a method to modify associative memory network weights when introducing new memories. The proposed uses random features and is shown to approximate the energy function of the conventional ones. Strengths: 1. The proposed method and results are novel to me, understanding associative memories ...	Rebuttal 1: Rebuttal: > Why using hamming distance when calculating retrieval error when the theoretical results use L2? > We consider Hamming error since we are storing and retrieving binary memories, e.g. in Fig.3. Our theoretical results operate in the more general continuous $\mathbb{R}^d$ space, and thus bounds ...	Summary: The paper proposes to interpret the energy function of Dense Associative Memory as relying on a kernel that can be approximated by kernel-specific random feature maps. The approximation allows to condensate the stored patterns into a tensor whose size is independent of the number of patterns stored, similarly...	Rebuttal 1: Rebuttal: We are happy to hear that the main message of the paper got across well, and thank the reviewer for their insightful comments and feedback. Below we answer individual questions raised. > … looking at the error bound in eq. (12), it seems that $Y$ has to be taken of the order of $O(K^2 D)$… > It...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their thoughtful feedback. We are honored that our work has been recognized as addressing “the greatest limitation of Dense Associative Memories with non-quadratic energy” [wQ3r] and as making a “significant and solid step forward for the community” [9NBh]. We ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization	Accept (poster)	Summary: The paper considers a new fairness measure, say NSW, lacking Lipschitzness. Unlike previous measures, the multi-armed bandit problem cannot obtain common $O(\sqrt T)$ regret. For stochastic MAB, the authors find an algorithm achieving $\tilde{O}(T^{\frac{N-1}{N}})$ regret and show its tightness. For adversary ...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and valuable comments to our paper. We address the reviewer's questions as follows. Q1: However, why not consider a simple average, which is more intuitive? I recommend that the authors provide some realistic applications for NSW. A: It is well-known th...	Summary: The authors address the problem of maximizing Nash Social Welfare (NSW) in a centralized multi-agent multi-armed bandit setting. In each round, a central entity selects a probability distribution $p_t$ over the $K$ actions, and draw an action $i_t \sim p_t$. Upon selecting an action, the central entity obser...	Rebuttal 1: Rebuttal: Thanks for the reviewer's detailed and valuable comments to our paper. We address the reviewer's questions as follows. Q1: the use of the Nash Social Welfare is not motivated A: We will add more discussion on the significance and relevance of the use of NSW in the next version as suggested. ...	Summary: The paper studies the problem of online social welfare maximization. More precisely, the authors consider the online learning setting where the learner, at each round $t \in [T]$, picks an action $i \in [K]$ that then determines the utility of each of the $n$ agents. The utility of agent $j$ is given by the $(...	Rebuttal 1: Rebuttal: Thanks for the reviewer's appreciation of our paper! We answer the reviewer's question as follows. Q1: A detailed discussion on the potential applications of the model. One important application of the model is fair repeated policy decision making. In fact, we believe that Hossain et al. [20...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners	Accept (poster)	Summary: The paper argues that while diversity among training partners is essential for developing robust generalist cooperative agents, specialization also plays a crucial role. The authors introduce a method for quantifying both diversity and specialization using mutual information. They highlight the limitations of ...	Rebuttal 1: Rebuttal: We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks. Here, we address the questions raised by the reviewer > Can you elaborate on the computational requirements for implementing SpecTRL and SpecTRL DAgger in different environments? The compu...	Summary: This work studies partner diversity in the context of training a generalist agent. The authors observe that XP-min approaches, while capable of producing behavioral diversity in its teammates, generates “handshaking`` behaviors—a kind of overfitting. While MP-reg aims to correct for this overfitting, the autho...	Rebuttal 1: Rebuttal: We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks. Here, we address the question raised by the reviewer > I think I have some intuitive understanding for why distillation instead of regularization during training may help prevent the overfit...	Summary: The submission positions itself within the problem of ad-hoc teamwork: learning to cooperate with teammates previously unseen during training. Indeed, an important aspect of ad-hoc teamwork is the rule of "no prior coordination". Previous work focused on developing a rich enough set of training partners to all...	Rebuttal 1: Rebuttal: We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks. _We have included weaknesses mentioned by the reviewer in the general response. We hope that our responses will resolve the questions and concerns raised by the reviewer. We are happy to fur...	null	null	Rebuttal 1: Rebuttal: # General response to all reviewers We thank all the reviewers for the overall positive sentiment towards the paper and their thoughtful feedbacks. Here, we address common concerns among the reviewers: - __Only evaluating with multi-recipe Overcooked (reviewer sQwt, Vvdc, and G3wa)__ We agree w...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Enhancing Domain Adaptation through Prompt Gradient Alignment	Accept (poster)	Summary: This paper aims to leverage prompt tuning of vision-language models for Unsupervised Domain Adaptation (UDA) tasks. The authors formulate UDA as a multi-objective problem where each objective is modeled by a domain loss. To resolve conflicts between domains, they manipulate gradients by maximizing their cosine...	Rebuttal 1: Rebuttal: >1. why the proposed gradient manipulation is better?: Thank you for your insightful question. We would appreciate further clarification on your reference to the paper discussing the limitations of scalarization, particularly in under-parametrized setups, as it seems to suggest scalarization’s i...	Summary: This paper proposed a novel domain adaptation method that tunes the text prompts based on self-training with pseudo labels. The proposed method treats the source training and target training as multi-objective optimization problems, and it introduces to alignment of the gradients from both training (Prompt Gra...	Rebuttal 1: Rebuttal: > whether the comparison with existing methods is fair As we follow the experimental settings in [6, 15, 22, R4, R5, R6], we use their reported results which we believe have been verified to be fair when comparing old UDA methods and prompt-based methods. Specifically, they share the same vision ...	Summary: This paper proposes a novel approach called Prompt Gradient Alignment (PGA) for unsupervised domain adaptation (UDA). The key contributions are: (1) Formulating UDA as a multi-objective optimization problem with objectives for source and target domains; (2) Aligning gradients between objectives to encourage co...	Rebuttal 1: Rebuttal: 1. In Figure 1 of the attached PDF we provide the complexity for some comparative baselines. Accuracy curve (left): While DANN and CDAN obtain their best performance at approximately 77% after more than 1000s, PGA and MPGA achieve 84% within 100s. Besides, the first stage of pairwise source-targe...	Summary: To enhance both transferability and discriminability for prompt learning based domain adaptation, this paper proposes a Prompt Gradient Alignment (PGA) method. PGA encompasses multiple domain-wise classification objectives, cosine similarity maximization regularizers between prompt gradients of different domai...	Rebuttal 1: Rebuttal: 1. Thanks for the suggestion, we will revise the abstract to reflect our technical contributions more clearly. Regarding the multiple objective optimization viewpoint, our MOO problem consists of per-domain objectives, motivated by the strong performance of the self-training baseline in Section 1....	Rebuttal 1: Rebuttal: We thank all reviewers for the valuable and supportive feedback. We appreciate that our paper is recognized for having strong empirical results (reviewers JYym, dhjM), a generalization bound which adds credibility to our approach (all reviewers), a novel method with *clear intuitions...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Bayesian-guided Label Mapping for Visual Reprogramming	Accept (oral)	Summary: This paper focuses on a technique called label mapping (LM) which is used in visual reprogramming. It finds the relation between pre-trained labels and downstream labels. In conventional fine-tuning, LM is just the backpropogation using the loss function. However, this paper and previous similar papers aim to ...	Rebuttal 1: Rebuttal: W1: Thank you for your question. This paper falls in the scope of Visual Reprogramming (VR). Thus, the experimental setting of this paper aligns with those used in previous VR studies, particularly ILM [1], to ensure comparability and consistency in the field. This setting serves as an establi...	Summary: Visual reprogramming is an interesting way to reuse a pre-trained classifier or an VLM. In previous methods, the way to change the output interface is basically gradient-free and one-on-one mapping. In this paper, the authors found that the previous way is suboptimal and ignores information. Then, from a theo...	Rebuttal 1: Rebuttal: W1: Thanks! The gap between left-hand side (LHS) and right-hand side (RHS) comes from the relationship between Maximum Likelihood Estimation (LHS) and Empirical Risk Minimization (RHS) in statistical learning theory. - LHS: represents the true objective of VR is to maximize the conditional pro...	Summary: Another type of transfer learning approach is considered in this paper: model reprogramming. Different from classical transfer learning approaches, model reprogramming only changes models via changing the input space and output space, which is more efficient to fit a pretrained model to some downstream tasks. ...	Rebuttal 1: Rebuttal: W1/Q1: Thank you very much for your suggestion. In the next version, we will expand our literature review to include relevant transfer learning concepts, particularly focusing on how Visual Reprogramming relates to and differs from traditional transfer learning methods. To better position this...	Summary: Pretrained models play a crucial role in current machine learning and computer vision tasks, and effectively leveraging them in downstream tasks has become increasingly important. This paper explores the research area of visual reprogramming (VR), which diverges from traditional fine-tuning by adjusting the in...	Rebuttal 1: Rebuttal: W1: Thank you. We have added a detailed explanation in Common Question 2 and provided a simple example to illustrate the conditional probabilities. W2: Thanks. We want to clarify how our analysis can be extended to multi-class cases. Expected Accuracy Definition The formula (Eq...	Rebuttal 1: Rebuttal: Common Question 1: Concerns about the required number of epochs and training time for BLM/BLM+ Response 1: Regarding the number of epochs, we initially used 200 epochs as with the original papers to ensure a fair comparison with the baseline methods. However, during the rebuttal stage, we...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?	Accept (poster)	Summary: This paper addresses the challenge of large-scale dataset distillation, specifically focusing on reducing the storage requirements for auxiliary soft labels in ImageNet-level condensation. The authors propose Label Pruning for Large-scale Distillation (LPLD), which aims to achieve state-of-the-art performance ...	Rebuttal 1: Rebuttal: Thank you for your questions and feedbacks. We want to address them one by one. > 1. How sensitive is the method to the choice of hyperparameters, particularly in the label pruning process? Is there a way to automatically determine the optimal pruning rate? Thank you for bringing up this point. ...	Summary: The paper focuses on reducing the size of soft-label storage in large-scale dataset condensation. The authors discussed why the labels are large-scaled and then proposed to prune the labels by increasing the diversity of synthetic images. Extensive experiments are conducted to validate the effectiveness of the...	Rebuttal 1: Rebuttal: Thank you for bring up all these values feedbacks. > 1. The proposed method seems to be a class-wise version of SRe^2L, with a soft label-reuse mechanism during training. Thus the technical contributions seem to be weak. Thank you for your important question. Our approach is not merely a variant...	Summary: This paper discovers that the conventional method generates images with high similarity. To solve this, the authors introduce class-wise supervision during the image-synthesizing process by batching the samples within classes. Thanks to the increase in diversity, the soft labels can be pruned to reduce the sto...	Rebuttal 1: Rebuttal: Thank you so much for raising these questions and concerns. We want to address them one by one. > 1. There are confusing sentences. The authors mention that ”The high similarity of images within the same class requires extensive data augmentation to provide different supervision” and then “To add...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Data Distribution Valuation	Accept (poster)	Summary: This paper starts by highlighting the importance of accurately assessing the value of data distributions, especially in the growing data economy. To address the problem, this paper introduces a data distribution valuation method based on Maximum Mean Discrepancy (MMD) for comparing the value of data distributi...	Rebuttal 1: Rebuttal: We thank Reviewer Sj3P for reviewing our paper, and appreciating the theoretical rigor of our work, the meaningfulness of our studied problem and the novelty and fresh perspective of our method. We would like to address the comments and feedback as follows. > This paper relies on certain assumpt...	Summary: The valuation of data is crucial in data marketplaces. Instead of assessing the value of a specific dataset, this paper focuses on the valuation of data distribution behind the dataset itself. For example, several vendors are trying to sell different or even the same datasets, what is the best distribution to ...	Rebuttal 1: Rebuttal: We thank Reviewer s86g for reviewing our paper, and for the positive feedback on the novelty of our approach, the quality of our methodology and solution, the clarity of our writing and significance of our work. We wish to provide the following clarifications. The requested experimental results (...	Summary: This paper addresses the problem of data distribution valuation in data markets, where buyers need to evaluate the quality of data distributions to make informed purchasing decisions. The authors formulate the problem and identify three technical challenges: heterogeneity modeling, defining the value of a samp...	Rebuttal 1: Rebuttal: We thank Reviewer YGYg for reviewing our paper, and for appreciating the novelty of our approach and acknowledging our theoretical and empirical results. We would like to respond to the feedback and comments as follows, > Based on Theorem 1 the valuation of $D$ boils down to the samples availabl...	null	null	Rebuttal 1: Rebuttal: We wish to thank the reviewers for reviewing our paper and providing the detailed feedback. We especially appreciate the positive feedback on the __novelty of our work__ (all reviewers), the __clarity of presentation__ (Reviewers `s86g`, `Sj3P`), and the __quality of our results__ (all reviewers)....	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SegVol: Universal and Interactive Volumetric Medical Image Segmentation	Accept (spotlight)	Summary: In this paper, the authors proposed SegVol, a 3D foundation segmentation model for medical images. This model supports universal and interactive volumetric medical image segmentation of more than 200 anatomical categories. This model is also well-designed with spatial and semantic prompts. In the inference st...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and kind words. We answer your questions as follows. > Q1: Figure 2, the violin plots seem confused and it is not cited in the paper. Figure 2 is cited on Page 7 Line 205: *‘We visualize the Dice score distributions of all methods in all the tasks as viol...	Summary: This paper proposes a 3D foundation segmentation model, named SegVol, supporting universal and interactive volumetric medical image segmentation. By scaling up training data to 90K unlabeled Computed Tomography (CT) volumes and 6K labeled CT volumes, this foundation model supports the segmentation of over 200...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. Below we answer the specific questions. > Q1: As shown in Appendix Table 5, the number of train-set volumes is very unbalanced, which will affect the performance of the proposed method. It is supposed that the unbalanced training set may cause poor ...	Summary: The paper describes the utilization of SegVol, a deep learning model, to segment any organ/tumor/lesion on 3D CT data. objectives Create a universal segmentation model that can segment with : - any type of labeling - good performances on complex tasks - low computational cost (i.e., no sliding window) ...	Rebuttal 1: Rebuttal: Thank you for your high recognition of our paper and the detailed feedback. We answer your questions as follows. > Q1: Clarity of the training algorithm. Describing the steps with a paragraph or diagram. Thank you for the suggestion. We will provide the detailed text description of the Train...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers’ constructive comments and valuable suggestions. We are glad to see that our paper received high praise from all reviewers. Especially, Reviewer 8irr finds that ‘*the results are impactful for the community of medical image segmentation, and could have pl...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AdaNovo: Towards Robust \emph{De Novo} Peptide Sequencing in Proteomics against Data Biases	Accept (poster)	Summary: The authors introduce a novel approach for the robust de novo sequencing of peptides from mass spectrometry experiments. The novelty on the application side is the focus on post-translationally modified peptides that are commonly ignored in pure de novo sequencing approaches, however, biologically of highest r...	Rebuttal 1: Title: Response to Reviewer DL85 (1/2) Comment: > Missing PTMs' the effect on the peptide sequencing overall has to be ignorable. Can you indicate absolute numbers for peptides with/without PTMs in the 9 species benchmark? Thanks for your insightful and to-the-point reviews! We provide the absolute numbe...	Summary: The paper proposed a new method in protein sequencing for tandem mass spectra, especially in solving post-translational modifications. Specifically, two (say conditional and unconditional) decoders are designed for protein sequences generation, from which conditional mutual information is calculated and furt...	Rebuttal 1: Rebuttal: > Q1: Is "AdaNovo w/o decoder #2 and any reweighting" actually Casanovo? Thanks for your helpful reviews! Yes, "AdaNovo w/o decoder #2 and any reweighting" is Casanovo indeed. Therefore, we have performed the ablition study in the original version (Table 2-5). > *Q2: Is 72% accuracy on AA...	Summary: In the field of proteomics, tandem mass spectrometry has been crucial for analyzing protein composition in biological tissues. However, existing methods struggle to identify amino acids with Post-Translational Modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids,...	Rebuttal 1: Rebuttal: > Q1: Have the authors tried beam search in AdaNovo? Thanks for your helpful reviews! Following your valuable advice, we apply the beam search (beam size = 5) in AdaNovo and observe consistent improvements over greedy search in Table Re3 (peptide-level) and Table Re4 (amino acid-level)....	Summary: This work introduces AdaNovo, a framework for improving peptide sequencing by addressing biases in training data. It calculates conditional mutual information (CMI) between mass spectra and amino acids/peptides, enhancing robustness against noise and improving PTM identification. Besides, the model consists of...	Rebuttal 1: Rebuttal: > Q1: The extra cost of memory compared to Casanovo is significant, which could limit the max length of predicted peptide sequences. Thanks for your insightful reciews! **In mass spectrometry, proteins are enzymatically broken down into peptides for analysis, with peptide lengths typically rang...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper presents AdaNovo, a novel framework designed to address the biases in training data used for de novo peptide sequencing in proteomics. The main contribution is the calculation of Conditional Mutual Information (CMI) between mass spectra and amino acids, enabling robust training that mitigates the neg...	Rebuttal 1: Rebuttal: > Q1: For Figure 4, can you supply the dataset size and the performance? How does the amount of PTMs impact the performance of Casanovo and AdaNovo? Thanks for your helpful comments! We provide the dataset size and performance in Table Re1. We would add these results in Figure 4 of the revi...	Summary: The paper introduces AdaNovo, a novel framework for de novo peptide sequencing that significantly improves the identification of post-translational modifications (PTMs) and enhances robustness against data biases in proteomics. AdaNovo utilizes Conditional Mutual Information (CMI) to reweight training losses b...	Rebuttal 1: Rebuttal: > Q1: In eq.3 use MI (X , Z; Yj \| Y<j ) but in model it is CMI(X , Z; Yj \| Y<j ), and should not distinguish between italic rv and bold rv. Thanks for your careful review! **Kindly note that the conditioned mutual information in model is formulated as CMI(X , Z; Yj) or MI (X , Z; Yj \| Y<j ) rat...	null	null	null	null
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents	Accept (poster)	Summary: The paper design a generative simulation platform for strategic interactions and cooperative decision-making in LLM agents. Three similar economics scenarios where all agents exploit a common pool resource with sustaining it for the future are tested. They design a LLM-based agent architecture and test them on...	Rebuttal 1: Rebuttal: Thank you for your positive feedback on our work commenting that it is “well-written, easy to understand”, “propose a new multi-agent platform”, and “evaluations are comprehensive and insightful.” Below we address your one comment raised in the review: > Are these three scenarios too similar? th...	Summary: The paper introduces a generative simulation platform to investigate the dynamics of resource sharing among multiple large language model (LLM) agents. Specifically the authors construct a common pool resource problem where the classic social science problem, tragedy of the commons, can be demonstrated. Author...	Rebuttal 1: Rebuttal: Thank you for your positive feedback on our work, especially your comments acknowledging that our GovSim “provides a novel platform,” “bridges the development of LLM-agents with classic social science theories,” has “a unique perspective,” “offers thorough analyses and a variety of experiments”, a...	Summary: This paper proposes GOVSIM, a simulation platform for studying cooperative decision-making in Large Language Model (LLM) agents. The authors test various LLMs in three resource-sharing scenarios, finding that only a few instances (2 out of 45) achieve sustainable outcomes. They demonstrate that communication b...	Rebuttal 1: Rebuttal: Thank you for your positive feedback on our work. ## Addressing Weaknesses > I regard the primary flaw of this article as it does not test the performance of GPT-4 Although some studies suggested enhanced performance of GPT-4, whose latest version was released on 13 Jun 2023, this is no longer...	Summary: This paper presents GOVernance of the Commons SIMulation (GOVSIM), a generative simulation platform to study strategic interactions and cooperative decision-making among large language model (LLM) agents. The authors investigate sustainable resource sharing in a society of AI agents using different LLMs to det...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review and your recognition of its four strengths in terms of novelty, comprehensive analysis, open-source contribution, and ethical considerations. We aim to address your concerns and demonstrate the robustness and impact of this research. ## Addressing Weaknesses ...	Rebuttal 1: Rebuttal: Firstly, we would like to thank all reviewers for the valuable feedback. Three out of four reviewers recommended acceptance (with ratings of 7, 6, and 6) and we believe we have addressed the key concerns of Reviewer 2 directly. We are very encouraged by the large number and diversity of positive c...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mixture of Tokens: Continuous MoE through Cross-Example Aggregation	Accept (poster)	Summary: This paper proposes a new MoE architecture called Mixture of Tokens (MoT). The motivation for this architecture is twofold: first it is the training instabilities incurred (among other things) by low precision training in standard sparse MoEs; secondly, it is the discontinuous nature of sparse MoEs that makes ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their questions and suggestions and appreciate the recognition of our paper's strengths. Below, we address the weaknesses and questions mentioned in the review. If our answers address the reviewer's concerns, we would like to kindly ask for the reconsiderati...	Summary: In this paper, the authors propose a novel method called Mixture of Tokens (MOT), an expert-based architecture that addresses the drawbacks of existing Mixture of Experts (MoE) approaches, such as reduced training efficiency, instability, and the necessity of using load balancing losses and elaborate training ...	Rebuttal 1: Rebuttal: We want to thank the reviewer for their comments and questions. We also appreciate the mention of the simplicity of integrating our method into existing approaches. If the reviewer's concerns have been addressed, we would like to kindly ask for the reconsideration of the rating. **Regarding batch...	Summary: This paper proposes a new routing algorithms for MoEs: the mixture of tokens. The context is the following: routing in MoEs is tricky because one token gets usually assigned to one or a few experts and so the gradient feedback to update the router is not great. Therefore, several recent papers proposed to eith...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their feedback and questions. We also appreciate the recognition of the value of the research problem and the presentation of our work. We hope that the answers below adequately answered the reviewer's questions. If that is the case, we kindly ask for a reco...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for assessing our paper. We appreciate all the positive comments and will consider all the feedback to improve our work. We address each review's comments and questions in individual responses. In this general reply, we want to address just the shared concerns about co...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models	Accept (poster)	Summary: This paper proposes BAdam, a novel optimization method for memory-efficient full parameter finetuning of large language models. BAdam leverages the block coordinate descent framework with Adam as the inner solver. It partitions the model parameters into blocks and updates one block at a time using Adam steps. ...	Rebuttal 1: Rebuttal: Thank you very much for the constructive and helpful comments. We address the reviewer's concerns in a point-by-point manner below. All additional experiment results (figures and tables) are put in the one page supplementary PDF of the global rebuttal. **A. Convergence result under stochastic s...	Summary: The paper introduces BAdam, a memory-efficient optimization method for fine-tuning large language models (LLMs) by leveraging block coordinate descent (BCD) with Adam as the inner solver. BAdam aims to reduce memory consumption while maintaining or improving performance. The paper presents theoretical converge...	Rebuttal 1: Rebuttal: Thank you very much for the constructive and helpful comments. We address the reviewer's concerns in a point-by-point manner below. All additional experiment results (figures and tables) are put in the one page supplementary PDF of the global rebuttal. **A. More quantitative results on MMLU and m...	Summary: The paper introduces memory-efficient optimizer BAdam, which combines the concepts of block coordinate descent (BCD) and Adam's update rule. BAdam demonstrates that, with moderate memory consumption—more than LOMO—it can surpass LoRA and significantly outperform LOMO in fine-tuning Llama 2-7B and Llama 3-8B. A...	Rebuttal 1: Rebuttal: Thank you very much for the constructive and helpful comments. We address the reviewer's concerns in a point-by-point manner below. All additional experiment results (figures and tables) are put in the one page supplementary PDF of the global rebuttal. **A. Ablation study & effectiveness of BCD...	Summary: The authors proposed fine-tuning of LLMs with a block coordinate descent based Adam optimizer. They presented results on convergence analysis, memory and run time profiling, and the quality of resulting fine-tuned models. Strengths: + The proposed idea is clearly stated. + The memory usage analysis is compr...	Rebuttal 1: Rebuttal: Thank you very much for the constructive and helpful comments. We address the reviewer's concerns in a point-by-point manner below. All additional experiment results (figures and tables) are put in the one page supplementary PDF of the global rebuttal. **A. Ablation study on BCD, Adam, and SGD....	Rebuttal 1: Rebuttal: Dear ACs and Reviewers, This global response contains our one-page supplementary PDF of the rebuttal. All additional figures and tables are included in this file. Best regards, Authors. Pdf: /pdf/ce03c044f3f088e07245f917a6c364e116e94680.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Maximizing utility in multi-agent environments by anticipating the behavior of other learners	Accept (poster)	Summary: - The authors consider the problem of optimally exploiting a learning agent in zero-sum games and general-sum games. - For zero-sum games, they look at a specific continuous time learner, replicator dynamics, and at an analogous discrete time learner, multiplicative weights updates. They provide an explicit ex...	Rebuttal 1: Rebuttal: We thank the reviewer for the useful comments. We answer the main questions below, and move a few minor questions to an official comment, for space considerations. ``It would have been interesting to see some example numerical computations of the optimal strategy in a game and resulting simulated...	Summary: The authors present a model where an optimizer plays with a learning agent and aims to extract better rewards from the sequential decision-making game by anticipating what the learner will do selecting a strategy that outperforms the value of a game. They study two settings: a zero-sum game, where they show a ...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. We answer below the main concerns, and we answer additional questions in an official comment, due to the space limitations. ``My primary concern with this paper is in the organization and clarity of presentation...'': We thank the author for their comme...	Summary: The paper studies a control problem where an optimizer plays with a mean-based learner in a zero-sum or general-sum game. The problem follows a previous line of work that aims to understand how to play optimally with no-regret learners in repeated games. The paper shows several new results. For zero-sum games,...	Rebuttal 1: Rebuttal: We thank the reviewer and respond to the points raised by the review below: ``The authors show that this algorithm, when extended to the discrete-time setting, results in an algorithm that guarantees the optimizer the value of the corresponding one-shot game.'' This is not exactly true. We show ...	Summary: This paper outlines conditions under which, in repeated two-player zero-sum and general-sum games between a learner with an online learning strategy and an optimizer that knows the learner's strategy and utility function, the optimizer can learn a policy to achieve a higher average utility than the value of th...	Rebuttal 1: Rebuttal: We thank the reviewer and respond to the points raised by the review below: ``In section 1.2 Related Work, authors point out prior work in contracts and auction design, which could also be broadly categorized as mechanism design. Some recent related papers (eg. [1],[2] ) have proposed experiment...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization	Accept (poster)	Summary: The research introduces UA-4DGS, a novel approach designed to reconstruct dynamic scenes from monocular videos. UA-4DGS compensates for information loss due to large motion and self-occlusion by incorporating diffusion priors with pixel-wise uncertainty scores. It proposes an uncertainty-aware diffusion-based ...	Rebuttal 1: Rebuttal: Thank you for your constructive comments on improving the writing and analysis of the proposed method. We answered your two concerns on evidence of our assumptions, and lack of experiments as below. We promise to revise our paper by considering your comments including writing quality. ## W2. Lac...	Summary: This paper proposes an uncertainty-aware regularization technique that uses diffusion priors to improve the reconstruction quality of underfitted areas. and a dynamic region densification technique to address the missing initialization problem on dynamic regions. Experiments verify the proposed techniques. St...	Rebuttal 1: Rebuttal: Thank you for complimenting our novel training schemes, uncertainty-aware regularization, and densification technique for monocular video reconstruction, especially having complex object motions. The proposed uncertainty-aware regularization is a generic method, which is effective on both dynamic ...	Summary: This paper tackles the problem of modeling dynamic 3D scenes from monocular videos. To tackle the more challenging dynamic regions in 4D Gaussians, the authors propose to measure the uncertainty and guide those regions with diffusion priors, while keeping certain regions unchanged. In addition, the authors pro...	Rebuttal 1: Rebuttal: Thank you for acknowledging the strengths of our proposed method, particularly by highlighting the significantly improved performance in both the in-the-wild 4DGS task and the static few-shot task. ## W1. Low visualization quality One of our main contributions addresses the overlooked issue of ex...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable comments on improving our work. Before answering your questions and concerns, we would like to highlight our contributions. We present a novel view synthesis algorithm for dynamic scenes captured by a monocular camera. Our target task is fairly new, and we in...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration	Accept (poster)	Summary: This paper proposes FMNet, an end-to-end deep learning approach for multi-instance point cloud registration. The key novelty is an attention-weighted feature matching module that can adaptively focus on reliable point correspondences during matching. Unlike traditional two-step methods, FMNet jointly learns fe...	Rebuttal 1: Rebuttal:   We appreciate your diligent review and valuable feedback to help improve our paper.   Q1: The contribution and innovation of the method. A1: Our contributions lie in three aspects: (1) Our primary contribution does not lie in the network architecture but rather in...	Summary: This paper introduces a novel focusing-and-matching technique for addressing the multi-instance point cloud registration challenge. Instead of fitting multiple models from a set of incorrect correspondences, this method initially detects potential instance regions and subsequently performs standard pairwise po...	Rebuttal 1: Rebuttal:   We thank the reviewer for the diligent comments to improve the paper.   Q1: How does the pair-wise registration model manage these falsely detected objects? A1: Our method is a two-stage approach, so we analysis the falsely detected objects in both two stage. (1) ...	Summary: For multi-instance point cloud registration, the authors proposed a 3D focusing-and-matching network by learning multiple pair-wise point cloud registration. Specifically, a 3D multi-object focusing module is proposed to locate the center of each object and generate object proposals. In addition, a 3D dual-mas...	Rebuttal 1: Rebuttal:   We thank the reviewer for the detailed comments to improve the paper.   Q1: Detailed analysis of why the inference speed is slightly lower than MIRETR. A1: In the main paper, we have measured the total inference time of our method (0.540s) and MIRETR (0.400s) per sc...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Score-Optimal Diffusion Schedules	Accept (poster)	Summary: This paper proposes a novel algorithm for adaptively selecting an optimal discretisation schedule in the training of diffusion model. The proposed method does not require hyper-parameter tuning and adapts to the dynamics and geometry of the diffusion path. This method also achieves competitive FID scores on im...	Rebuttal 1: Rebuttal: We thank the reviewer for their kind comments on the strengths of our paper. We believe that our schedule tuning algorithm could serve as a strong default for diffusion models and might be applicable more broadly, though a more extensive exploration is difficult to undertake in an initial paper. W...	Summary: This paper proposes a method to optimize for the discretization schedule of the diffusion sampling process. The main idea is to minimize a surrogate for the total length of the diffusion sampling path between two consecutive time steps, where the length is defined in terms of the Fisher divergence between two ...	Rebuttal 1: Rebuttal: We are grateful for the reviewers for their constructive feedback. We will address each point raised individually below: - _The whole setup of predictor/corrector seems unnecessary to me._ See general rebuttal response. - _Section 2.3 and Theorem 2.1 do not seem to be related to the actual al...	Summary: A popular way to sample a target distribution is to run a "predictor-corrector" SDE, which additively combines two processes whose stationary distribution is the target: a Langevin (or "corrector") process and a reverse-diffusion (or "predictor") process. How to discretize this SDE without deterioriating the...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. We agree that more visual comparisons of the different schedules would be illustrative. To this end, we will include standard diffusion progression plots, comparing the image diffusion path for CIFAR and CelebA on different schedules. In a potential camera...	Summary: The paper proposes an improved discretization schedules based on a novel cost measure that they proposes. The proposed method can update a given schedule to achieve greater sample quality as is demonstrated with solid experiments. Strengths: Strengths * The proposed method is novel and can adapt to most given...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and suggested improvements. We agree that providing a simple error plot for cases where the true density is known would be informative. In our diffusion model, we do not have the ability to query the density directly; however, we do predict the score. In Figu...	Rebuttal 1: Rebuttal: We thank all the reviewers for taking the time to read our paper and for their kind comments on the strengths of the presentation and methodology. We also thank the reviewers for providing constructive feedback to improve the paper for the potential camera-ready version. We will address each point...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Accelerating Matroid Optimization through Fast Imprecise Oracles	Accept (poster)	Summary: The paper studies the problem of finding a maximum-weight basis of a matroid in a learning-augmented setting, where there are two different oracles that the algorithm can query to check whether a set is independent: An exact oracle that always gives correct answers (but may be slow), and a dirty oracle that ma...	Rebuttal 1: Rebuttal: ### Dependence on n-r Indeed, our results on independence oracles cannot avoid the dependence on n-r, though this can be mitigated by using a stronger oracle type, such as a rank oracle. Nevertheless, improvements over the greedy guarantee are possible, e.g. for graphic matroids of sparse graphs....	Summary: This paper aims to solve fundamental matroid optimization problems, specifically, computing a maximum-weight basis of a matroid, a complex combinatorial optimization problem, To this end, the author proposes a two-oracle model, which uses fast but dirty oracle to reduce the time to call clean oracles. Then, th...	Rebuttal 1: Rebuttal: ### Organization of the Paper and Figures We regret that space constraints prevent us from providing a more gentle introduction to the well-established field of matroid optimization. In a full version, we will include more figures and examples for our algorithms to better illustrate our results. ...	Summary: The paper mainly studies the problem of finding a maximum weight basis in a matroid $\mathcal{M}=(E,\mathcal{I})$ using two types of independence oracles "clean" and "dirty". The clean oracle determines whether a set $S \subseteq E$ is an independent set in $\mathcal{M}$, and the dirty oracle determines indepe...	Rebuttal 1: Rebuttal: ### Question Regarding Worst-case Guarantee (claimed n log n bound) Our wording in lines 71-73 might have been misleading and we will change it. Our robustness guarantee, and thus the overall guarantee of the algorithm, is at most 2n (when k=1, which is the smallest value of k for which the state...	Summary: The paper explores the concept of 2-oracle algorithms for matroid optimisation problems. The underlying idea is to equip algorithms with a second, somewhat "weaker" oracle. The second oracle is also permits the algorithm to query matroid information (similar to the first oracle) but only gets imprecise answer ...	Rebuttal 1: Rebuttal: ### Justification for Conference Fit Among the major topics of NeurIPS, our paper fits very well under "Optimization" and "Machine Learning". While we do not develop new ML methods, we instead design potential applications for ML-learned information. We theoretically analyze their potential for c...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Confidence Regulation Neurons in Language Models	Accept (poster)	Summary: This paper studies how large language models (LLMs) regulate uncertainty in next-token predictions through specific components: entropy neurons and token frequency neurons. Entropy neurons, identified by their high weight norm and minimal direct impact on logits, influence model confidence by operating within ...	Rebuttal 1: Rebuttal: Thank you for recognizing that our work provides a “good mechanistic explanation of how entropy neurons operate [...], improving our understanding of their indirect impact on model predictions.” We are encouraged by your recognition of our findings as "very valuable" and appreciate that you enjoye...	Summary: The paper investigates specific neurons in LLMs (termed "confidence regulation neurons") that modulate the uncertainty of the next token prediction by modulating the output distribution. First, entropy neurons modulate the overall entropy of the output distribution by writing to an effective null space of th...	Rebuttal 1: Rebuttal: Thank you for recognizing that our work “provides deeper insight into the role of entropy neurons” and that our paper is “clearly written and well-organized, making complex concepts accessible”. > Novelty: The novelty of the analysis is not clear-cut. Gurnee et al. identified "entropy neurons...	Summary: This paper investigates two kinds of neurons by which transformer language models calibrate their predictions. These are (1) “entropy neurons”, which can affect logit values, but do not promote specific tokens, and (2) “token frequency neurons”, which influence a model’s likelihood of outputting bigram word st...	Rebuttal 1: Rebuttal: Thank you for recognizing that our work addresses “an important area of study for trustworthy deployment of ML systems”, providing “a clearer understanding of how these previously discovered neurons can influence model behavior.” > Weakness 1: Do these two kinds of neurons interact (if so, ho...	Summary: This work studies a small but potentially important subset of neurons in the final layer for a trained transformer model that appear to regulate the confidence of a model (proxied by the variation in entropy of the model's output). Specifically, the paper builds upon entropy neurons found in previous work, ext...	Rebuttal 1: Rebuttal: Thank you for recognizing that our work “focuses on an important problem”, with experiments that are “convincing, and improve upon the findings of the previous works on which this paper is built upon.” We appreciate the many insightful questions you pose in your review. We address the weaknesses a...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their thorough feedback. We are glad they found our work well-motivated, clearly presented, and insightful. We address each reviewer’s points in the respective rebuttal sections. Additionally, we attach a PDF file with the additional results referenced in t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ProtGO: Function-Guided Protein Modeling for Unified Representation Learning	Accept (poster)	Summary: The authors distill the knowledge from function annotation. ProtGO acquires performance improvement through knowledge distillation. Compared with other methods only rely on structure and sequence, ProtGO outperforms these baselines. Strengths: 1. ProtGO introduces a novel method that can utilize function info...	Rebuttal 1: Rebuttal: Dear Reviewer qMp2, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 Protst[1] also utilizes function information of proteins, should also be listed a...	Summary: This paper proposes to learn hybrid embeddings for the protein and the GO terms. By further applying the teacher-student training schedule, during inference, the additional input of GO terms is not necessary. Experimentally, the authors demonstrates that the model achieves better results on severls tasks, e.g....	Rebuttal 1: Rebuttal: Dear Reviewer oeK7, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 The proposed method only demonstrate results on several small benchmarks. More re...	Summary: In the paper entitled "ProtGO: Function-Guided Protein Modeling for Unified Representation Learning", the authors proposed a KD-based framework to incorporate GO knowledge to learn an unified, multi-modal embedding for a given protein. The cross-domain knowledge make the embeddings performs good in various dow...	Rebuttal 1: Rebuttal: Dear Reviewer kUCf, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 In table 1 and table 2, the authors may consider add ESM-1b/ESM-2/ESM-3 as sequen...	null	null	Rebuttal 1: Rebuttal: First and foremost, we would like to express our sincere gratitude for the insightful and constructive feedback provided by the reviewers on our manuscript. We greatly appreciate their positive reception of ProtGO's potential and its timely relevance in the field of protein research. We are parti...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DarkSAM: Fooling Segment Anything Model to Segment Nothing	Accept (poster)	Summary: This paper introduces DarkSAM, a prompt-free universal attack framework against the Segment Anything Model (SAM) in a quasi-black-box setting. The framework consists of a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. While SAM uses geometric prompt inputs to guide se...	Rebuttal 1: Rebuttal: # Responses to Reviewer pM4H # ------ Q1: SAM is a milestone work, and a series of follow-up studies have been proposed recently. However, this paper does not provide an up-to-date review in Section 2.1 and lacks comparison in Section 4, weakening its significance. A1: Thanks for your ...	Summary: This work investigates adversarial attacks against Segment Anything Models (SAMs) and presents DarkSAM, the first universal adversarial attack designed for these models. DarkSAM leverages a single perturbation to effectively undermine SAM’s object segmentation capabilities across a variety of images and prompt...	Rebuttal 1: Rebuttal: # Responses to Reviewer dgw7 # ------ Q1: It is recommended that the authors further supplement the experimental section with relevant analyses, such as explaining why the spatial domain attack is more critical than the frequency domain attack within the proposed framework. A1: Thanks ...	Summary: This paper introduces DarkSAM, a universal adversarial attack against the Segment Anything Model and its variants. DarkSAM aims to prevent these models from successfully segmenting objects within images. The experimental results demonstrate the effectiveness and transferability of the proposed method. I have...	Rebuttal 1: Rebuttal: # Responses to Reviewer YLRY # ------ Q1: The related work can be improved. This paper could benefit from an expanded discussion on adversarial attacks targeted at traditional segmentation models. A1: Thank you for the constructive feedback! We will include a more comprehensive discuss...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling	Accept (poster)	Summary: This paper conducts a systematic study on the expressive power and mechanisms of Transformers for sequence modeling. It explores various properties of Transformers, including the number of layers, attention heads, width, and the use of dot production. The theoretical results are supported by experimental evide...	Rebuttal 1: Rebuttal: We appreciate the reviewer's recognition of our work and helpful comments. Below, we provide detailed responses to the reviewer’s questions. - W1. The motivation of this paper appears weak. Response: Thank the reviewer for this inquiry. While Transformer shows remarkable capabiliti...	Summary: The paper investigates the approximation capabilities of transformers with relative positional encodings and derives approximation rates for three types of sequence modeling tasks. Each task corresponds to a particular choice of target function class. Namely, a first class of mappings are fixed, long but spars...	Rebuttal 1: Rebuttal: We thank the reviewer for the appreciation of our work and insightful comments. We answer the reviewer's questions in the following. - W1. Never consider transformers with all the bells and whistles. Response: The reviewer is correct. Simplifying models appropriately in different setti...	Summary: The paper provides a thorough analysis of the expressive power of Transformers. It does so by investigating the relative importance of different architectural components, such as the mixing layer (dot product attention), the feed-forwad block and positional embeddings. More specifically, the paper studies thre...	Rebuttal 1: Rebuttal: We appreciate the reviewer's recognition of our work and helpful comments. Below, we offer detailed responses to the reviewer’s questions: - W1. Some concepts could be better introduced: for example, it was not clear to the reviewer what the authors meant by "memories" in the introduction of ...	null	null	Rebuttal 1: Rebuttal: ### Global Response to All Reviewers. - First, we sincerely thank all the reviewers for their appreciation of our results, i.e., theoretical analysis of the expressive power of Transformer for sequence modeling. Our analysis provides valuable insights (also supported by experiments) into the ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Private and Personalized Frequency Estimation in a Federated Setting	Accept (poster)	Summary: This paper studies the problem of personalized frequency estimation in federated learning. The authors propose both private and non-private algorithms based on clustering and Good-Turing estimators and demonstrate promising empirical results on real-world datasets. Strengths: 1. The problem of data heterogene...	Rebuttal 1: Rebuttal: Thank you for the feedback. To address concerns, we add new experiments with different values of $\varepsilon$, number of local points $m$, and plots showing the sparsity of the estimated cluster centers in practice. We also point to our ablations on $K$ in our paper, and clarify concerns with Thm...	Summary: This work introduces a private and personalized histogram estimation approach based on Good-Turing estimates, when the clients are clustered. Theoretically, they provide performance guarantees for different settings when cluster center is known and cluster assignments are known. In practice, they note that nei...	Rebuttal 1: Rebuttal: Thank you for the feedback and a positive assessment of our work. To address your concerns, we present new ablations of our algorithms under different values of per-client training data points $m$, local only training baselines, highlight existing ablations in our paper on the number of clusters, ...	Summary: The paper presents both non-private and private per-user histogram estimation approaches that can be applied to problems such as next word predictions. The approach relies on iteratively clustering among different users to find similar user subgroups and then fine-tuning within each user to get user-specific e...	Rebuttal 1: Rebuttal: Thank you for the feedback! To address concerns we provide an algorithm’s box for the end-to-end algorithm, clarify that assumptions in Sec 4 are only for theoretical motivation, and results in Sec 6 are on real datasets where they needn't hold. We clarify our DP definition is not relaxed at all, ...	Summary: The paper proposes a federated learning approach for frequency histogram estimation in a distributed setting. The proposed approach relies on first clustering users that have similar subpopulation distributions before performing the estimation in a privacy-preserving manner. Finally, the performance of the app...	Rebuttal 1: Rebuttal: Thank you for the feedback and a positive assessment of our work. To address concerns, we refer to plots in the paper that ablate $K$, clarify guarantees in Thm. 5.2 are in terms of the typical $(\varepsilon, \delta)$ approx. DP, and discuss the choice of the stricter user-level DP definition. We ...	Rebuttal 1: Rebuttal: We thank all reviewers for their feedback and list some of the new experiments we add to the 1-page PDF which we will incorporate using the extra page in the final version. We also address a common concern on the choice of number of clusters $K$ for our Algorithm 3. ___ ## **List of new experimen...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Tight Bounds for Learning RUMs from Small Slates	Accept (poster)	Summary: This paper studies the learning of Random Utility Models (RUMs). A RUM is a probability distribution $P$ over the set of permutations over $[n]$. Fix a permutation $\pi$ and consider any subset $T \subseteq [n]$, known as a slate. The winner of the slate (corresponding to $\pi$) is the highest ranked element...	Rebuttal 1: Rebuttal: We thank the reviewer for the suggestions and feedback. In the revision, we will provide more story-building and motivation around the problem. Regarding related works on RUMs, in [Chierichetti et al., ICML 2021] they take in input a RUM $R$ and their goal is to output a new RUM $R’$ whose supp...	Summary: This paper studies the problem of learning a Random Utility Model with limited information. A RUM is a distribution on the symmetric group on $n$ letters and a slate is a nonempty subset of the universe of letters. The paper studies the problem of learning the RUM given access to the probabilities that a giv...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and suggestions. Exponential time: please see the general comment. On a high level, the main technical contributions are: (i) relating the approximation degree of the AND function to the slate size to learn a RUM (Theorem 9 and Theorem 12), (ii) relating th...	Summary: A random utility model is defined by a distribution over permutations of $1,2,\dots,n$. Given a non-empty subset $S \subseteq \{1,2,\dots,n\}$, an oracle stochastically returns which one in $S$ is the highest according to this distribution. This paper considers the problem of estimating a random utility model ...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. Regarding the computational complexity of the algorithms, please see the general comment. Also note that, while querying all the slates of size $O(\sqrt{n})$ is required to learn the complete RUM, if we are interested only in some target slates, our algorith...	Summary: The paper considers the Random Utility Model (RUM) problem and gives upper and lower bound on plate size. RUM is a classic economic model that is used to understand user behavior by modeling choices from subsets of available items. In the RUM problem, there is a set of element $[n]$ and there is a probability...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. 1. Please see the general comment. 2. We were motivated to consider this model by the observation that modern user interfaces work hard to present small and easily scanned slates for the user to consider; typical examples of such interactions are the 10 b...	Rebuttal 1: Rebuttal: We thank the reviewers for their insights, and the time and efforts spent reviewing our work. We consider each comment carefully below, but let us begin by addressing a common concern regarding the computational complexity. While the asymptotic complexity of the algorithms is high, there are some ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model	Accept (poster)	Summary: This paper proposes a weight quantization method to quantize the UNet of SDv1.5 to 1.99 bits while maintaining model performance comparable to the floating-point model. The approach includes a series of techniques such as bit-width allocation for mixed-precision quantization, low-bit diffusion model initializa...	Rebuttal 1: Rebuttal: Q1. Comparison with QAT-based approaches such as Q-DM and TDQ. A1. Thanks for the suggestion. We have compared the QAT-based approach LSQ and EfficientDM in Table 3 of the main paper. Here, we provide more results for Q-DM and TDQ on PartiPrompts with a CFG scale of 7.5. Compared to Q-DM with...	Summary: The paper presents BitsFusion, a novel method for weight quantization of diffusion models, specifically applied to the UNet architecture in Stable Diffusion v1.5. The approach quantizes weights to 1.99 bits, achieving a model size reduction of 7.9 times while enhancing or maintaining image generation quality. ...	Rebuttal 1: Rebuttal: Q1. About the motivation of this paper. A1. The overall motivation for performing quantization on the diffusion model is to reduce significant burdens for transferring and storing the model due to its large size. To this end, we propose several methods with corresponding motivations for model...	Summary: This paper demonstrates a quantized diffusion model called BitFusion, which successfully quantize the Stable Diffusion (SD) v1.5 to 1.99 bits with 7.9x smaller size. They first analyse the SD model in a layer perspective and assign the optimal bit based on the analysis. Then they propose a training pipeline t...	Rebuttal 1: Rebuttal: Q1. Comparison with adaround \[1\], which is used in Diffusion Model quantization research \[2\]. A1. Thank the reviewer for suggesting the adaround, which is a relevant method \[1\]. However, we would like to kindly emphasize that adaround \[1\] focuses on post-training quantization by pro...	Summary: This paper proposes a novel weight quantization method called "BitsFusion" for compressing the Stable Diffusion v1.5 model. The primary goal is to address the issue of large model sizes, which hinder the deployment of diffusion models on resource-constrained devices. The BitsFusion framework quantizes the UNet...	Rebuttal 1: Rebuttal: Q1. About training computation. A1. Thanks for the suggestions. For the stage-I training, we use 8 NVIDIA A100 GPUs with a total batch size of 256 to train the quantized model for 20K iterations. The training time is within 40 hours. For the stage-II training, we use 32 NVIDIA A100 GPUs wit...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable suggestions and feedback. We appreciate the reviewers acknowledge the strengths of this paper, including: - addressing the challenges for low-bits quantization of Stable Diffusion (Reviewer W2xs); - thorough, comprehensive, and valuable analysis a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Accelerating Non-Maximum Suppression: A Graph Theory Perspective	Accept (poster)	Summary: The article "Accelerating Non-Maximum Suppression: A Graph Theory Perspective" explores a novel way to optimize Non-Maximum Suppression (NMS) in object detection using graph theory. The authors present two new methods, QSI-NMS and BOE-NMS, which greatly enhance the efficiency of NMS while maintaining mean Aver...	Rebuttal 1: Rebuttal: We sincerely appreciate Reviewer HR1f's thoughtful review of our work and the insightful questions you have raised. Your feedback is invaluable in helping us improve and refine our research. We will address each of your concerns in detail. --- > Q1: The connection between the proposed method...	Summary: The paper, "Accelerating Non-Maximum Suppression: A Graph Theory Perspective," presents a novel approach to enhancing the efficiency of the Non-Maximum Suppression (NMS) algorithm used in object detection. By analyzing NMS through graph theory, it introduces two new optimization methods: Quicksort Induced NMS ...	Rebuttal 1: Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate your recognition of our innovative approach, comprehensive evaluation, and practical impact. Below, we will address each of your concerns one by one. --- > Q1: Details on Graph Construction (Line 99-104): The paper mentio...	Summary: This paper presents a method from a new perspective to enhance the efficiency of the Non-Maximum Suppression (NMS) algorithm with affordable accuracy decrease. The authors introduce a novel perspective by analyzing NMS through the lens of graph theory, revealing its intrinsic structure as a directed acyclic ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback and for acknowledging the value of our work. We will address your questions and concerns in the following responses. --- > Q1: A case study that illustrates the overly suppressed samples is welcomed. A1: This question is fundamental and others are als...	Summary: This work focuses on improving the latency of Non-Maximum Suppression (NMS), a crucial step for nearly all object detectors. The work analyzes NMS, as a directed acyclic graph (DAG) treating bounding boxes as nodes, and suppression relationships as arcs allowing NMS solutions based on dynamic programming. Bas...	Rebuttal 1: Rebuttal: Thank you for your feedback and for recognizing the value of our new graph theory perspective on non-maximum suppression. We will address your concerns in the following responses. --- > Q1: The worst case complexity of the proposed approaches is still $\mathcal{O}(n \log n)$, while other appr...	Rebuttal 1: Rebuttal: We appreciate the reviewers' thoughtful feedback on our work. We are grateful for the reviewers' recognition of the following strengths: 1. Innovative Application of Graph Theory: - Reviewer pEVt highlighted that our paper introduces a new graph theory perspective for non-maximum suppressi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models	Accept (poster)	Summary: The paper introduces ALPS, an optimization-based framework for one-shot pruning of large language models (LLMs). ALPS leverages an ADMM-based algorithm with operator splitting and preconditioned conjugate gradient methods to achieve improvements in sparsity and perplexity over state-of-the-art methods, particu...	Rebuttal 1: Rebuttal: Reply to W1 and W2: Thanks for these references—we will include them in our revised paper. ALPS differs significantly from the mentioned works as follows: + Comparison with "Progressive weight pruning of deep neural networks using ADMM": This paper applies ADMM to the original loss function, ...	Summary: This work presents an LLM pruning framework that formulates the problem as finding a sparse weight matrix to reconstruct the layer-wise activations. This work incorporates the operator splitting technique and preconditioned conjugate gradient methods to solve the pruning problem. Experiments demonstrate that t...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their thoughtful feedback. Below, we provide some answers/clarifications. **Experiments could be more solid since many experiments are run on OPT, which is somewhat out-of-date. It would also be better to consider more challenging benchmarks, such as GSM8K ...	Summary: This paper introduces ALPS, a novel optimization-based framework for one-shot unstructured pruning of LLMs. The key contributions are: - Formulating LLM pruning as an l0-constrained optimization problem solved using operator splitting (ADMM). - A penalty parameter update scheme to accelerate convergence. - A ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their thoughtful feedback. Below, we provide some answers/clarifications. **It would be beneficial to include evaluations on more recent and extremely large models to demonstrate the method's applicability. The current evaluation tasks are relatively limite...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful comments. In response to their comments, we have conducted additional numerical comparisons and included additional discussions in relation to the existing work. We present a summary below: + Comparison with existing methods & Novelty [see Ref 1PwE]: We...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms	Accept (poster)	Summary: The paper investigates distributionally robust Markov decision processes (RMDPs) in the discounted infinite-horizon setting. The authors consider the case where the transition kernel can be arbitrarily chosen from a prescribed (given) uncertainty set centred around a reference kernel, where the uncertainty set...	Rebuttal 1: Rebuttal: Answer to reviewer QVRk We appreciate the reviewer for careful review and insightful feedback. It is rewarding to know that the reviewer recognizes the significance of our contributions. In what follows, we provide our response to the reviewer's comments. > Overall, I found the main pape...	Summary: This paper dives into the theoretical understanding of learning robust MDPs with a generative model. The robust set is modeled as a distribution ball induced by the general $L_p$-norm centered around the nominal model. The sample complexity is provided for both $\mathcal{S}\times\mathcal{A}$-rectangular and $...	Rebuttal 1: Rebuttal: Aswer to revierwer Tr2g We appreciate the reviewer's comprehensive feedback and recognition of the significance of our contributions. > Q1. One of the key message is that learning general $L_p$-norm robust MDPs is easier than learning standard MDPs. So I think it is less discussed how the ex...	Summary: The paper presents an analysis of the sample complexity of solving distributionally robust Markov decision processes (RMDPs) with general Lp norms as the distance metric for the uncertainty set. The authors consider both the sa-rectangular and s-rectangular settings and provide near-optimal upper and lower bou...	Rebuttal 1: Rebuttal: Answer to reviewer 5t5a We appreciate the reviewer's careful review and insightful feedback. It is rewarding to know that the reviewer recognizes the significance of our contributions. In what follows, we provide our response to the reviewer's comments. > Q1) The primary issue is that this p...	Summary: This paper proposes tighter than prior art sample complexity bounds for Robust Markov Decision Processes. In sa-rectangularity and s-rectangularity conditions with non-zero uncertainty measured using $L_p$ around a nominal transition kernel, the upper bound is $\frac{SA}{(1-\gamma)^3\\epsilon^2}$. The setup a...	Rebuttal 1: Rebuttal: Answer to Reviewer 6HyR We appreciate the reviewer for recognizing our contributions, and for providing constructive suggestions. Here some comments on the different questions raised : > Q1: NSA been not use in Eq. 17, 18,19,20,21. Although the intention of the authors is clear, but that wou...	Rebuttal 1: Rebuttal: # General response: First, We would like thank the reviewers for their careful reading of the paper and their insightful and valuable feedback. ### Highlight of our new challenges and technical contributions We would like to highlight the technical challenge to answer reviewers 6HyR and 5t5a a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?	Accept (poster)	Summary: The paper studies the optimality of neural collapse in intermediate layers of a deep neural network on classification tasks with more than 2 classes. 1. The paper shows that the NC2 property (formation of a simplex ETF/orthogonal frame) in the intermediate layer features is not optimal, and constructs a stron...	Rebuttal 1: Rebuttal: We thank the reviewer for a detailed review. The typos pointed out by the reviewer will be corrected in the revision, and we thank the reviewer for spotting those. We address all the other concerns below (including the technical issue about the Sherman-Morrison formula), and we would like to kindl...	Summary: Deep Neural Collapse (DNC) refers the the neural collapse phenomenon that has been observed on intermediate layers of a deep network (nb. whereas neural collapse (NC) focuses only on the penultimate layer). Similar to Unconstrained Feature Model (UFM) for analyzing NC, Deep UFM (DUFM) is the comparable framewo...	Rebuttal 1: Rebuttal: Thank you for the comments and the positive evaluation of our paper. We address all questions below. line 209 (clarification) about K=2, L=2 Our statement was only meant to say that the cases $K=2$ and $L=2$ were already treated in prior work [51, 48]. We will clarify this in the manuscript....	Summary: This paper theoretically explores the deep neural collapse (DNC) phenomenon within non-linear deep models for multi-class classification. Neural collapse (NC) is a phenomenon in deep overparameterized networks where, the last layer's feature vectors align with class means and their corresponding classifier vec...	Rebuttal 1: Rebuttal: Thank you for the comments and the positive evaluation of our paper. We address your concerns and questions below: While the SRG solution has low-rank, it is not clear whether a globally optimal solution also has one. This is a great point. We agree that we do not rigorously show this in our...	Summary: Papyan et. al that showed that at the terminal phase of training there are four phenomena called neural collapse on the last layer of a deep neural network architecture. The authors extended Papyan's work by considering the earlier layers of nonlinear deep neural networks. They proved the first neural collapse...	Rebuttal 1: Rebuttal: Thank you for your positive review. If any questions come up during the discussion phase, we’ll be happy to address them. --- Rebuttal Comment 1.1: Title: Area Chair to Authors Comment: Authors: Unfortunately this review will not be considered as part of the decision, unless the reviewer updates...	Rebuttal 1: Rebuttal: We thank all the reviewers for their reviews. Here, as part of an answer to reviewer hQr6's question, we upload a PDF representing the gram matrices of class-means in all 5 layers of the SRG solution corresponding to the lower row (right) of Figure 4. As can be seen, all gram matrices (up to small...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models	Accept (poster)	Summary: This submission presents a framework that leverages a large language model (LLM) as the backbone architecture for human-mobility-related tasks, including user identification, next location prediction, and arrival time prediction. The authors introduce a POI (Point of Interest) Point-wise Embedding Layer and a ...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and constructive comments. ### [W1&W2&Q1] We design the VIMN module to reprogram the check-in sequences. After passing through the VIMN module, the check-in sequences are aligned to the natural language semantic space, producing the resulting $\mathbf{h}_i$. T...	Summary: The paper proposes a novel architecture, MobilityLLM, for utilizing pre-trained LLMs with various specific embedding modules for various mobility tasks. Specifically, the authors proposed to use any pretrained LLM with adding four unique components: PPEL (POI location embedding), VIMN (timestamp embedding with...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and constructive comments. ### [W1] Thank you for pointing out these errors. We have made the corrections and checked other parts of the paper for similar issues. ### [W2] Compared to trajectory data (such as vehicle trajectories), check-in data are record...	Summary: This paper combines large language models (LLMs) to better analyze check-in sequences and understand human mobility behaviors, and proposes a visiting intention memory network(VIMN) and a shared pool of human travel preference prompts (HTPP) to capture the semantics of human visiting intentions . The experime...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and constructive comments. ### [W1] The user embedding $U_i$ is an index-fetch embedding module implemented using the nn.Embedding module in PyTorch. It finds the corresponding embedding vector for a given user index. ### [W2] Sorry for the confusion. We w...	Summary: This paper aims to leverage large language models (LLMs) to predict human mobility behavior recorded in social media check-ins. The authors argue existing models fail short to model the visiting intentions and travel preferences embedded in check-in sequences, which could be addressed by the semantic understan...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and constructive comments. ### [W1&Q1] In the paper, we ran each set of experiments 5 times and reported their mean values. Therefore, the reported results are not single accidental one. Below, we also report the variances of metrics for the proposed model and t...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure	Accept (poster)	Summary: This paper considers the problem of learning numerical protein representations using both sequential and structural data, and presents CoupleNet, a method that uses two graph types, one based on the amino acid sequence and the other based on the protein's tertiary structure, to extract protein representations ...	Rebuttal 1: Rebuttal: Dear Reviewer JwZm, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 Comparisons with state-of-the-art protein language models and their structure-awa...	Summary: This work proposes the CoupleNet, a novel framework designed to interlink protein sequences and structures to derive informative protein representations. It integrates multiple levels and scales of features, constructing a dynamic graph to capture both local and global structural geometries. Experimental res...	Rebuttal 1: Rebuttal: Dear Reviewer ANTD, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 The experiment results do not include error bars and related analysis about multi...	Summary: The authors tackle the limitation of modeling inter-dependencies between protein sequences and structures. To solve this limitation, this work proposes CoupleNet which dynamically couples protein sequences and structures. Specifically, the authors propose to construct two-type dynamic graphs (sequential graph ...	Rebuttal 1: Rebuttal: Dear Reviewer fLEX, We are grateful for your thorough review. Your comments are highly valued, and we would like to express our heartfelt gratitude. We do our utmost to address the questions you have raised: Q1 I suggest the authors concisely compare those works and elaborate on the advantag...	null	null	Rebuttal 1: Rebuttal: First and foremost, we would like to express our sincere gratitude for the insightful and constructive feedback provided by the reviewers on our manuscript. We are particularly thankful for the Reviewers' recognition of the method of our study; it is important to combine sequences and structures...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DeNetDM: Debiasing by Network Depth Modulation	Accept (poster)	Summary: This paper presents a useful theoretical framework that shows that samples that exhibit spurious correlations lie on a lower rank manifold and that the depth of a network acts as an implicit regularizer for the rank of the attribute subspace. Building upon this, the paper proposes a method DeNetDM that creat...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. W1. Empirical Evaluation: Including newer more challenging datasets could further improve this paper. A. In response to the reviewers' suggestions, we have evaluated the effectiveness of our approach on the CelebA dataset, where blonde ha...	Summary: This paper proposes the unsupervised debiasing strategy via modulating network layers depth. It proves that the network with deep layers exploits bias attributes more than that with shallower layers, and shows that training on such less-biased network with shallow layers exhibit debiased learning. The proposed...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and suggestions. W1. Empirical analysis on networks' depth to learning of different ranks (bias-aligned / bias-conflicting) is limited to CMNIST. Additional experiments on other benchmark datasets, e.g., C-CIFAR10, BAR, and BFFHQ, are required f...	Summary: The submission proposes that deeper networks are more likely to use biased features than shallower ones. Using this idea, they develop a training algorithm to encourage reliance upon non-spurious attributes. This is done by training a deep and shallow network as a product-of-experts, then distilling the shallo...	Rebuttal 1: Rebuttal: We appreciate the reviewer's insightful suggestions and comments. Below, we provide detailed responses to each of the questions and concerns raised. W1/Q1. One key weakness in approaches of this flavour is the reliance upon use of a “debiased’ validation set to pick crucial hyper-parameters. ...	Summary: This work makes several contributions in relation to network depth and dataset bias. It shows that bias-aligned samples lie on a lower rank manifold compared to bias-conflicting samples. This is linked with network depth by showing how deeper networks tend to prefer spurious correlations, which is demonstrated...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments and questions. W1/Q1. Why not build a shallower biased branch? A. TL;DR: Both core and spurious features are indeed available at shallower depths. However, since we do not use explicit bias annotations, we have no a priori way of telling them apart....	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their valuable feedback and thoughtful comments. We appreciate the opportunity to address the concerns raised and provide clarifications. We offer detailed responses to each reviewer, aiming to address the issues and enhance the clarity of our work. We utilize ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Non-parametric classification via expand-and-sparsify representation	Accept (poster)	Summary: This paper addresses how to use expand-and-sparsify techniques to non-parametric classification. More specifically, it uses expand-and-sparsify on the test data point $x$ and then use the response regions of $x$, which are the sets of training data sharing same activation coordinates as $x$, to serve as the ne...	Rebuttal 1: Rebuttal: We thank the reviewer for providing constructive feedback and pointing out the typos (weakness 1). We will fix those. Regarding weakness 2, it is not ``necessary" to use expand-and-sparsify representation in non-parametric classification. Note that there different types of non-paranetric classifi...	Summary: In this paper, the authors studied the problem of binary classification when the feature vectors are on the unit sphere in $\mathbb{R}^d$. The paper proposed a non-parametric algorithm based on a method called EaS (Expand and Sparsification). They proved that this method is universally consistent and that its ...	Rebuttal 1: Rebuttal: We thank the reviewer for providing constructive feedback. Regarding weakness 1, while our work is inspired by the work of Dasgupta and Tosh [2020], we have clearly articulated how our work is different and advances the result of Dasgupta and Tosh [2020] in lines 96-111. Regarding weakness 3, i...	Summary: The paper studies expand and sparsify the approach to non-parametric classification. The high-level idea is to first lift the example $x \in \mathcal{X} = \mathcal{S}^{d-1}$ to a $\{0,1\}^m$, a $m$-dimensional Boolean cube with exactly $k$ ones. The lifting is done by a linear mapping $x \mapsto \Theta x$ foll...	Rebuttal 1: Rebuttal: We thank the reviewer for providing encouraging feedback. We are in fact working on using this expand-and-sparsify representation idea to solve other ML problems. The question you have is an important one. The conditions for consistency in Theorem 3.3 and the quantitative rates in Theorem 3.12 ar...	Summary: The paper introduces two algorithms for non-parametric binary classification utilizing the expand-and-sparsify (EaS) representation. The first algorithm employs a winners-take-all approach for sparsification. It demonstrates consistency and achieves a minimax-optimal convergence rate that depends on the data d...	Rebuttal 1: Rebuttal: We thank the reviewer for providing thoughtful and encouraging feedback. The reviewer's suggestion of including consistency and convergence rates of various non-parametric classification is an excellent one and if accepted we will definitely include a discussion on consistency and convergence rate...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation	Accept (poster)	Summary: This paper introduces a new algorithm for source estimation, the task of estimating a model's parameters probability distributions consistent with observations. In opposition to previous algorithms, the proposed method is sampling-based and can hence be used when the model is a simulator, implicitly defining t...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback of our work. W/Q1: "Is there something that prevents you from comparing against NEB in Figure 4?" We thank the reviewer for their suggestion, and have now compared Neural Empirical Bayes (NEB) with Sourcerer on the high-dimensional simulators (S...	Summary: This paper deals with the problem of identifying the distribution of a source variable $s$ that generates observations $x$. ## The problem The authors propose to minimize a classical objective consisting of two terms: 1. A reconstruction term, that encourages the recovered source distribution to induce a d...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. We address the reviewer's concerns below. W1: "The computational constraints on the model are heavy": The reviewer raises concerns about computational constraints on the model, which we clarify here for the two terms in the cost function: First, the ...	Summary: This paper proposes an approach to estimating a maximum-entropy source distribution (akin to a prior distribution over simulator parameters) for a given set of observations and simulation model. Their method assumes a differentiable simulator that may be deterministic or stochastic, and it uses neural samplers...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and careful reading. We address the main concerns and questions raised by the reviewer below. Thank you for identifying typos, we will correct them. **W1: "My main concern is that the work presented looks like it is not a large or significant contribution ...	Summary: The authors propose to use the maximum entropy principle (possibly tempered with a prior) in order to reduce the ambiguity in solving the problem of source distribution estimation. They propose to use a sample-based technique that optimizes for the Sliced-Wasserstein distance measuring the discrepancy between ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive assessment of our work. We address the reviewer’s questions below. W1: "It would be interesting to see a more thorough study for models with even higher dimensionality": We thank the reviewer for their suggestion to investigate the performance of our a...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their detailed engagement and constructive and positive feedback. Our paper introduced Sourcerer, a method for estimating maximum-entropy source distributions with sample-based distances. Reviewers found our approach to be “well-motivated” (J2pY) and “simpl...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces “Sourcerer,” a new method for source distribution estimation, focusing on maximum entropy distributions to effectively handle ill-posed problems common in simulating scientific phenomena. This approach leverages the Sliced-Wasserstein distance for sample-based evaluation, offering a signif...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough feedback. The reviewer raises some questions and concerns about our approach, which we address below. W1: "The proposed method only applies to differential simulators": In the case where the simulator is neither differentiable nor provides explicit lik...	null	null	null	null	null	null
Generalization of Hamiltonian algorithms	Accept (poster)	Summary: This work provides a novel approach to bound the generalization error (high probability bounds) of the Gibbs algorithm as an important example of Hamiltonian algorithms. The authors also applied their method to a different example of the Hamiltonian algorithm. Strengths: Strengths: 1- A novel approach is pro...	Rebuttal 1: Rebuttal: Please also see the general rebuttal for planned improvements to the paper. 1 - The improvements for the Gibbs algorithm go beyond constants. (4) is smaller than the inequality in the next display by a factor of $1/\sqrt{\ln \left( 1/\delta \right) }$. If the confidence parameter $\delta $ goes t...	Summary: Summary ------- The papers presents a method to bound the `generalization gap', i.e the difference between the expected and empirical losses for a hypothesis h drawn from a probability class returned by the stochastic learning algorithm. The authors present a general-purpose method to bound the exponentiate...	Rebuttal 1: Rebuttal: Please also see the general rebuttal for planned improvements to the paper. Q1: $\mathcal{H}$ is a loss-class and $h\in \mathcal{H}$ is a hypothesis composed with a fixed loss function. Loss-classes are a notational simplification frequently used in the analysis of generalization. Q2: The bounds...	Summary: This submission is a purely theoretical work, whose main goal is to bound $\Delta(h,\mathbf{X})=\mathbb{E}[h(X)]-\frac{1}{n}\sum^n_{i=1}h(X_i)$, i.e. equation (1). After some preliminary results, the results that show this bound under different conditions are Theorem 3.4, Theorem 3.5 and Theorem 3.8. Some poss...	Rebuttal 1: Rebuttal: Special thanks to you for the encouraging words and the careful reading, which uncovered several typos and inaccuracies. Please also see the general rebuttal for planned improvements to the paper. Question 1: L89. Yes, it should be "for all $h\in \mathcal{H}$ and $\mathbf{x% }\in \mathcal{X}^{n}...	Summary: This paper introduces a general method to bound the logarithm of the expecattion of the exponential of the generalization gap for stochastic learning algorithms. This method is applicable when the distribution of the algorithm concentrates exponentially around its mean, extending to cases where the Hamiltonian...	Rebuttal 1: Rebuttal: Please also see the general rebuttal for planned improvements to the paper. Experiments to demonstrate the theoretical results are planned for future work. For real data the costly part is the repeated sampling from the Gibbs distribution, because one has to await the mixing time between Monte-Ca...	Rebuttal 1: Rebuttal: Many thanks to the reviewers, who provided many useful comments. Major planned improvements are: 1. The revision will contain a section on future directions and limitations. It will mention potential applications to iterated algorithms and weakly dependent data. The limitations part will point to...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning	Accept (poster)	Summary: This paper introduces the Eye-gaze Guided Multi-modal Alignment (EGMA) framework, which leverages radiologists' eye-gaze data to enhance the alignment of medical visual and textual features. By using synchronously collected eye-gaze data during diagnostic evaluations, EGMA improves generalization and achieves ...	Rebuttal 1: Rebuttal: W: "The reliance on eye-gaze data from...where the collection of eye-gaze data is not practical." We thank the reviewer for this great comment. In fact, when designing experiments for zero-shot classification and zero-shot image-text retrieval, EGMA took this issue into account. Experimental ...	Summary: The paper proposes EGMA, a novel framework for medical multi-modal alignment integrating eye-gaze data into vision-language pre-training. EGMA outperforms existing methods in image classification and image-text retrieval tasks, demonstrating significant advancements and improved feature representation with eve...	Rebuttal 1: Rebuttal: W1: We greatly appreciate the reviewer's suggestion. Supervision information used for localization and segmentation tasks, such as bounding boxes and masks, is stronger than the labels used for classification tasks. Additionally, the amount of refined manual annotation required for localizatio...	Summary: This paper proposes a cross-modal alignment method that can optionally learn from eye tracking data that is collected together with the speech of radiologists. The proposed Eye-gaze Guided Multi-modal Alignment (EGMA) system consists of losses that optionally incorporate alignment objectives between sentences ...	Rebuttal 1: Rebuttal: Q1\&W1: "Please clarify how the Mean(Max())..." "Despite the authors’ best efforts, Fig. 2C and Fig. 2D...allows for gaze-free supervision)" We thank the reviewer for this great question and apologize for any confusion caused by casual description in our paper. First, to clarify the role of f...	Summary: This paper proposes utilizing eye-gaze information to aid in learning representations from paired images and texts. The method was validated on four medical datasets, demonstrating the feasibility of integrating gaze information for multi-modal alignment. Strengths: The idea of using additional information b...	Rebuttal 1: Rebuttal: W1\&Q1: We thank the reviewer for these great questions and apologize for any confusion caused by the lack of discussion in our paper. Indeed, noise and errors in eye-gaze data are as common as noise in images, and these can all affect the model's final performance. In this work, the errors in...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their careful reading, valuable comments, and recognition of the contributions of our work. We have provided itemized responses to the questions and suggestions from the reviewers. We are also pleased to receive the positive feedbacks from reviewers, partic...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions	Accept (poster)	Summary: This paper proposes using a second-step Riemannian flow matching (RFM) model, separately trained, to improve the quality of crystal structures generated by a pre-trained large language model (LLM), closely following Gruver et al. [1]. Specifically, the authors first follow Gruver et al. to fine-tune an LLM to...	Rebuttal 1: Rebuttal: ## > “Why not just use MLFFs like CHGNet to increase the stability of generated crystals from LLMs?”, “Why only compare with methods without this kind of second-step refinement module?” We appreciate the reviewer’s thorough knowledge on the matter of refinement, but believe a clarification is nec...	Summary: The paper proposed the FlowLLM, a hybrid approach for material generation that combines LLMs and RFMs, effectively leveraging their complementary strengths. Namely, this method generates samples from the parametric distribution using a two-step procedure, using 1) LLM and 2) RFM. By adopting this hybrid approa...	Rebuttal 1: Rebuttal: ## Generation Time Considering the overall generation “cost” is a good point. Could you please clarify the following? > I would appreciate a comparison of the generation times between using 1) RFM with a simple base distribution and 2) FlowLLM without prior sampling from the LLM. We interpret thi...	Summary: This paper presents a generative model for crystals that uses a LLM as a base distribution and a flow matching model to refine the 3D structure. Both the LLM and flow matching parts are mostly borrowed from prior studies. However, the authors demonstrate a significant improvement in the stability of generated ...	Rebuttal 1: Rebuttal: ## Novelty Addressed in the global rebuttal. ## Rejection Rate of the LLM Addressed in the global rebuttal. ## Novel and Unique Rates The reviewer's suggestion to report the novel and unique rates separately is well-taken. For our best model, 48% of the generated structures are stable and novel, o...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their detailed reviews and insightful comments. We are proud to say that all reviewers are recommending acceptance at this time. However, we aim to address any remaining critiques to improve the paper even further! We summarize the perspective of the reviewers as follow...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting	Accept (poster)	Summary: The paper introduces novel loss functions - the Fourier Amplitude Loss (FAL), Fourier Correlation Loss (FCL) and a Regional Histogram Divergence (RHD) - to improve the realism of the predictions of precipitation nowcasting models without the use of generative models. The loss is applied to established precipit...	Rebuttal 1: Rebuttal: We would like to thank the reviewer’s feedback and suggestion. Here is our response to each of the reviewer bJuK's concern. --- > The authors sensibly use MSE loss as a baseline when evaluating their model... Among the papers the reviewer suggested, BMSE [1] performs linear scaling at different...	Summary: This paper propose a new Fourier Amplitude and Correlation Loss (FACL) to replace the traditional L_2 losses in precipitation nowcasting task. They evaluated the FACL on one synthetic dataset and three radar echo datasets, which demonstrates their method improve perceptual metrics and meteorology skill scores....	Rebuttal 1: Rebuttal: We would like to thank the reviewer’s feedback and suggestions. Here is our response to each of the reviewer 7mGa's concerns. --- > The evaluation is incomplete and unconvincing. The csi-m of EarthFormer is much lower than the results in [1] (0.3982/0.44). We adopted the official Earthfomer mo...	Summary: This paper proposes the FACL loss function and provides theoretical and empiral proofs on how it boosts clarity and structure for images. The paper also shows how the loss behaves with generative setups and additionally proposes a new metric that is tolerant to deformations. Strengths: - Clearly demonstrates ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer’s feedback and suggestion. Here is our response to each of the reviewer w5PB's concern. --- > Instead of the stochastic modification to Moving-MNIST, [1] already introduced a chaotic yet deterministic N-Body MNIST to mimic the complexity of Earth system intera...	null	null	Rebuttal 1: Rebuttal: To AC and reviewers, We sincerely appreciate all the constructive reviews from the reviewers. We have summarized the weaknesses the reviewers were concerned the most with, as well as our responses. --- ### Inclusion of more datasets, more previous losses, and more generative models. Reviewer ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Graph Neural Networks and Arithmetic Circuits	Accept (poster)	Summary: The paper establishes a series of expressiveness/expressive efficiency equivalence between graph neural networks (GNNs) whose aggregation/combine operations can be computed by arithmetic circuits, and various sub-classes of constant-depth arithmetic circuits. The main challenge consists of showing expressivene...	Rebuttal 1: Rebuttal: ## Weaknesses 1\., 2\. & 3\. We will carefully revise our introduction to eliminate repetition and to ensure that all concepts are sufficiently treated. Currently our introduction starts with a subsection "Background and Related Work", we will split this section into two parts and collect relevan...	Summary: The paper investigates the computational power of GNNs by demonstrating that the expressiveness of GNNs with different activation functions is equivalent to the capabilities of arithmetic circuits over real numbers. The authors introduce a new GNN variant called C-GNNs, which are equipped with constant-depth a...	Rebuttal 1: Rebuttal: Regarding the mentioned weaknesses: Thank you for pointing out these issues, we will definitely keep these in mind when revising the paper. We strife to appeal to the general NeurIPS audience, and for this purpose focussed also to presenting a thorough general introduction to the paper. This i...	Summary: In this article, the authors present new contributions on the understanding on the computational framework provided by GNNs. In particular, they draw a connection between Arithmetic Circuits and GNNs. Based on a new definition of GNN using arithmetic circuits, they show that the function computed by a GNN on...	Rebuttal 1: Rebuttal: ## Regarding the mentioned weaknesses: The nature of the relationship between AC-GNNs and C-GNNs lies in the definitions. In Remark 3.4 we assume the aggregation functions to be computable by $\text{FAC}^0_{R^k}$-circuit families, which the max function is not. So to be able to express an AC-GNN t...	Summary: This paper provides a new lens through which the expressive power of GNNs is analyzed via arithmetic circuits and derives expressiveness limits for general GNNs. Strengths: - this paper is indeed modular, well-written, and cleanly organized - it introduces an interesting perspective for analyzing GNN expressi...	Rebuttal 1: Rebuttal: I think this paper would benefit from more detailed discussion on the relationship with WL tests. The Weisfeiler Lehman tests (and its variants) usually refer to the problem of graph isomorphism, and give insight to the question whether two graphs can be distinguished by some GNN model. Since ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study	Accept (poster)	Summary: The core question raised in the paper is whether LLMs can also learn through teaching (LbT). The authors demonstrate that the idea of LbT can be easily integrated into existing LLM training/prompting processes and propose three methods, each mimicking three levels of human LbT: observing feedback, learning fro...	Rebuttal 1: Rebuttal: Q1: The idea presented in the article is a well-known idea. Thank you for suggesting relevant references from the machine teaching literature. We will cite and discuss them in our revised version. While these papers have similarities in terms of how the teacher should organize teaching materi...	Summary: In this paper the authors investigate whether the principles of 'Learning by Teaching' (LbT) in humans can be applied and used in LLMs. To investigate this they propose 3 techniques and map them different to LbT levels. The first technique M1 aims at improving answer quality by developing a scoring function ...	Rebuttal 1: Rebuttal: Q1: The LbT score seems to be reliant on having the final answer being verifiable. ... More extensive evaluation on diverse tasks may be needed to assess the generalizability of LbT. Thanks for this valuable question. LbT can indeed be extended to open-ended problems, such as dialogue, writin...	Summary: This paper the use of learning by teaching methods in the context of LLMs. Strengths: The paper is well written and methodologically sound. Weaknesses: The concise results should be briefly and systematically stated in the final Conclusion chapter, which is missing. Technical Quality: 3 Clarity: 4 Questio...	Rebuttal 1: Rebuttal: Q1: The concise results should be briefly and systematically stated in the final Conclusion chapter, which is missing. Thank you for the suggestion. We will add a conclusion section and summarize the concise numbers together with the general conclusion there.	Summary: This paper "Can LLMs Learn by Teaching? A Preliminary Study" presents a novel approach towards LLM learning by teaching with three methods: observing student feedback, learning from student feedback, and learning iteratively. The contribute two key findings: teaching student models are an effective way to impr...	Rebuttal 1: Rebuttal: Q1: Figure 4, Table 2, Table 3, and Table 4 must include at least standard error or ideally 95% CI to prove statistical significance. Thanks for this valuable suggestion. Due to the high cost of running experiments with LLMs, we reported the standard errors using the "bootstrapping" method [1...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their valuable time and effort in reviewing our paper. We are encouraged that the reviewers recognize our paper as novel and interesting (UmKq, mNjM); see its potential impact on the LLM community (UmKq, mNjM); note the abundance of experiments included (Um...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Road Less Scheduled	Accept (oral)	Summary: This paper proposes a new optimization procedure for deep learning that yields good any-time performance instead of requiring learning rate schedules that decay to zero. The method can be performed on top of standard optimizers, making it easy to apply to existing networks. The paper theoretically describes th...	Rebuttal 1: Rebuttal: Thank you for providing line-by-line comments we will update the camera ready and arxiv to reflect all of your suggestions. We also address them below L20: Specify whether g_t is evaluated at x or z - Good point, we will update. L29: Maybe clarify that this “T in advance” applies to many popula...	Summary: The paper proposes a scheduler free method for training, analyzes it theoretically to show that it matches the theoretical benefits of Polyak averaging while recovering the performance of standard cosine decay used in practice. Their approach can be interpreted as being an interpolation of Polyak averaging and...	Rebuttal 1: Rebuttal: # Weaknesses 1. This is a really good point. It is possible to get Polyak averaging to converge by using a smaller LR value. For the IWSLT14 illustrative plot we did not include a full LR sweep (as we did with all experiments in the experiments section) when we should have. We have ran this LR s...	Summary: The authors propose a method for training neural nets without needing to know the total training time T in advance. This contrasts with a standard training setup where one chooses a learning rate schedule in advance, and the schedule must include an a priori chosen stopping time T (e.g. cosine schedule or line...	Rebuttal 1: Rebuttal: # Strengths We are glad that you find our work interesting! We think that this approach has wide applicability and we are doing our best to spur adoption by doing an open source release in both PyTorch and Jax. An entry using our method was entered into the AlgoPerf competition earlier this year, ...	Summary: This paper proposed an optimization style for stochastic optimization, termed schedule-free optimization, which is free of manually selected/tuned learning rate schedulers. The proposed method enjoys both the worst-case optimal last-iterate convergence and promising empirical performance. The authors also intr...	Rebuttal 1: Title: Reviewer 1 Rebuttal Comment: Thank you for the detailed review, we appreciate it. We are very glad you see the potential of our method. We would like to start by addressing each of your concerns separately: Weaknesses: 1) additional forward passes This is a good point! In our PyTorch and Jax imp...	Rebuttal 1: Rebuttal: Several reviewers requested additional plots to examine the hyper-parameter sensitivity of our method. We have ran as many experiments as time allowed in the rebuttal period, covering the sensitivity to learning rate, momentum and the duration of training. Pdf: /pdf/e6bdfecf9b532a9f137faf26ab1e428...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning-Augmented Algorithms with Explicit Predictors	Accept (poster)	Summary: The paper introduces a new framework for learning-augmented online algorithms. Learning-augmented algorithms (aka algorithms with predictions) are a very active subfield of beyond worst-case algorithm analysis. For online algorithms, which cope with uncertainty on their input, it gives an algorithm an addition...	Rebuttal 1: Rebuttal: We thank the reviewer for highlighting the positive aspects of our work and pointing out a typo. > As far as I understand it one can model the traditional black-box setting via the agnostic setting by using a singleton hypothesis class and the 'traditional' prediction error as loss. Is this true?...	Summary: This paper proposes a new framework to use machine learned predictions to improve online algorithms. Recent work that has done this (in a slightly different framework) goes under the name learning-augmented algorithms (LA algorithms) or algorithms with predictions. The main difference in this work compared to ...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Most (if not all) works on learning augmented algorithms solve some learning problem in an ad-hoc manner by assuming a black-box access to some predicitions. In our framework, we "open the box" and define the learning problem explicitly. This allows for a ...	Summary: This paper considers a new formulation for learning-augmented algorithms/algorithms with predictions and applies it to the fundamental problems of online caching, online load balancing, and non-clairvoyant scheduling. In the new formulation, the predictions/predictor is made more explicit and is a part of the...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive words and for their useful suggestions to improve our manuscript. We respond the questions of the reviewer explicitly. > To what extent can these techniques be generalized? E.g., what can be said about -server? Our framework can be extended to $k$-server,...	Summary: The paper studies three online algorithms in the learning augmented model: caching, load balancing, and non-clairvoyant scheduling. The goal seems to be to 'online learning' flavored learning-augmented algorithms. Rather than having a single predictor that synthesizes or predict something about the online inpu...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. However, their claim about our performance bound for non-clairvoyant scheduling being meaningless is incorrect. We study non-clairvoyant scheduling with the classical objective of minimizing the sum of completion times which has a different magnitude tha...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ScaleKD: Strong Vision Transformers Could Be Excellent Teachers	Accept (poster)	Summary: This paper introduces a novel knowledge distillation method called ScaleKD. The method aims to leverage well pre-trained vision transformer models as teacher models for a variety of student model architectures.the authors first adopt a cross attention projector to align student features with the teacher's. The...	Rebuttal 1: Rebuttal: Thank you for the recognition of our work and the constructive comments. 1. To your comments regarding strengthening the motivation of our work, >Our responses: (1) Yes, in previous KD works like [1] using CNNs as the teachers, a stronger model is not always a better teacher. Following ...	Summary: This paper presents a new knowledge distillation method, named ScaleKD. Previous works mostly use CNNs to distill vision transformers. However, how to use vision transformers to distill CNNs is less explored. This paper shows that pretrained vision transformers are good teachers for other types of student mode...	Rebuttal 1: Rebuttal: Thank you for the recognition of our work and the constructive comments. 1. To your concern regarding the presentation of the Introduction section, >Our response: (1) Indeed, paragraphs in the Introduction section of our original manuscript are not short. Although the key messages are or...	Summary: This paper focuses on whether the pre-trained vision transformer models could be used as teachers to distilling knowledges to heterogeneous neural network architectures. The proposed ScaleKD aims to solve three problems including 1) feature computing paradigm different, 2) model scale differences, and 3) knowl...	Rebuttal 1: Rebuttal: Thank you so much for the recognition of our work and the constructive comments. 1. To your comments about discussing more related works [1-4], >Our responses: (1) Thanks for pointing out these four existing works which address KD between two transformers [1] or between the transformer a...	Summary: This paper concentrates on the distillation of knowledge from a large-scale, pre-trained, ViT-based teacher model to heterogeneous architectures. It incorporates three distinct designs: a) a Cross Attention Projector (CAP), which serves as the fundamental design that bridges the structural disparity between a ...	Rebuttal 1: Rebuttal: Thank you so much for the recognition of our work and the constructive comments. 1. To your comments about the presentation of DFM and TPP, > Our responses: (1) The basic motivation of our DFM and TPP components is to align model scale and knowledge density differences in a joint but not...	Rebuttal 1: Rebuttal: Dear Reviewers, Area Chairs, Senior Area Chairs and Program Chairs, We sincerely thank all four reviewers for their thorough and constructive comments. We are glad that the novelty, method component designs, validation pipeline and performance of our work have been mostly recognized by all four r...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null

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