Split (1)

train · 7.63k rows

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
MOTE-NAS: Multi-Objective Training-based Estimate for Efficient Neural Architecture Search	Accept (poster)	Summary: This paper proposes Multi-Objective Training-based Estimate (MOTE) for efficient NAS, leveraging landscape view and convergence speed to estimate the performance of neural architectures. It also introduces two reduction strategies for speeding up MOTE generation. Compared to other training-free NAS methods, MO...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback and positive evaluation of our work. Answers to Questions: A1. Yes, by adjusting the K value in MOTE-NAS to extend the search time, we can achieve better architectures. While the current architecture obtained through our method does not have...	Summary: The paper presents MOTE, a training-based estimate for Neural Network accuracy, as a proxy method to accelerate Neural Architecture Search. The intuition behind MOTE is the non-convexity and non-linearity in the training loss landscape. Consequently, the authors provide a model that characterizes the training ...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. Answers to Questions: A1. To determine whether MOTE is affected by random weight initialization, an additional experiment was conducted, as shown in TABLE II of the attached file. The results indicate that the effect of random weight initia...	Summary: The paper introduces MOTE-NAS, a novel approach for efficient Neural Architecture Search (NAS). MOTE-NAS suggests a novel proxy utilizing both macro-level loss landscape smoothness and micro-level convergence speed to predict the performance. By utilizing reduced architectures (RA) and datasets (RD), MOTE-NAS ...	Rebuttal 1: Rebuttal: Many thanks for your high appreciation of our work. We greatly value your feedback. Answers to Questions: A1. If our paper is accepted, all the typos you mentioned will be corrected and eliminated in the camera-ready version. Answers to Weaknesses: AW1. The RA design might be a...	Summary: This paper proposes a novel limited training NAS method that is able to rank the candidate architectures after training them for a limited number of epochs. The MOTE metric consists of two terms, the landscape term and the speed term. The landscape term is indicative of the loss landscape and it is the cross-e...	Rebuttal 1: Rebuttal: Thank you very much for your valuable feedback. However, we noticed that you might have misunderstood the core idea of MOTE. Specifically, the landscape term does not reduce the proportion of $\theta_{init}$ (initial model weights) or increase the proportion of $\theta$ (trained model weights) as ...	Rebuttal 1: Rebuttal: Thank you to all four reviewers for your diligent efforts and valuable suggestions. We appreciate your feedback and comments, which will help us improve the quality of our paper. In this response, we will (1) summarize our paper's contributions and main limitations, (2) address the RD issue raised...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback	Accept (poster)	Summary: This paper proposes AMOR, which is a modular framework for answering questions by reasoning over external knowledge bases. AMOR is designed as a Finite State Machine (FSM), which provides a structured way to break down the question into smaller pieces and solve complex tasks through various steps. Each state i...	Rebuttal 1: Rebuttal: > Regarding the complexity and human feedback dependency. We would like to address your concerns as follows: - Regarding the “Separately Fine-tuned LLMs”: We would like to emphasize that this argument might be inaccurate since we use the same MA-MoE model for all modules and activate differe...	Summary: This work proposes AMOR, a modular approach to building knowledge agents using open-source LLMs. AMOR decomposes tasks into reasoning logic, represented as a finite state machine (FSM) composed of sequentially chained modules. These modules include tools e.g, document retrieval and LLMs, e.g., answer extractio...	Rebuttal 1: Rebuttal: > Weakness: Regarding the measure of uncertainty. We agree that it is crucial to provide measures of uncertainty to assess the statistical significance and robustness of the results. To address this concern, we show the mean and standard deviation across three independent runs in the table below....	Summary: This work presents a modular pipeline for QA tasks. The pipeline consists of several modules such as question decomposition, document/passage retrieval, answer extraction, etc. Training data is separately constructed for each module (based on existing datasets) and models are individually fine-tuned for the re...	Rebuttal 1: Rebuttal: > Weakness 1: Regarding the scope and problem formulation - Scope. AMOR aims to develop a general framework for building adaptable modular LLM agents that can leverage external knowledge sources to tackle complex reasoning tasks. However, we appreciate the reviewer's advice that being more ex...	Summary: This paper proposed an architecture for advanced reasoning in LLMs. The architecture contains several modules dedicated to different tasks in the reasoning flow, each of which can be trained separately using related datasets constructed from public datasets. The proposed method disentangles the reasoning proce...	Rebuttal 1: Rebuttal: > Weakness 1: Regarding the difference between AMOR and prior reasoning methods Please kindly refer to "Author Rebuttal by Authors" provided at the very beginning. > Weakness 2: Regarding the technical details. We acknowledge the importance of transparency and reproducibility in scientific rese...	Rebuttal 1: Rebuttal: > Regarding the difference between AMOR and prior reasoning methods Please kindly refer to the attached pdf file for an illustration of the reasoning processes of AMOR and prior reasoning methods. The table below further elaborates the advantages and drawbacks of prior agents in terms of the foll...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI	Accept (poster)	Summary: This paper presents a diffusion model for MRI acceleration. In which, an autoregressive image diffusion (AID) model is proposed to sequentially generate MRI image conditions on a given prior image sequences. This method is evaluated on the accelerated MRI reconstruction task using the public available dataset...	Rebuttal 1: Rebuttal: ## Author Response We thank the reviewer for the insightful comments. We address the reviewer's questions and concerns below: ### Weaknesses: 1. Comparison with other methods: We have compared our method with the CSGM method from Reference [1] in the general rebuttal, using an external mode...	Summary: The paper proposes an autoregressive diffusion model, where each image in an MRI sequence is generated by a diffusion model, but the noise predictions of the diffusion model are autoregressively conditioned on previous MRI images in the sequence. Essentially, previously introduced single-image diffusion based ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and insightful questions. We address the reviewer's questions and concerns below: 1. Comparison to other MRI reconstruction approaches: We have compared our method to CSGM method from Reference [1] in the general rebuttal, using an external mo...	Summary: This paper introduces an autoregressive image diffusion (AID) model for generating image sequences and accelerating MRI reconstruction. The model combines autoregressive and diffusion approaches to leverage inter-image dependencies, aiming to improve reconstruction from undersampled k-space data in MRI. It was...	Rebuttal 1: Rebuttal: ## Answers to Questions We thank the reviewer for the detailed review and insightful questions. We address the reviewer's comments and questions below: 1. Computational Cost Comparison: We have included a section in the general rebuttal that provides a detailed comparison of the computationa...	null	null	Rebuttal 1: Rebuttal: # General Response The authors would like to thank the reviewers for their valuable feedback and insightful comments. We have carefully considered all the comments and suggestions and have addressed them in this rebuttal. One PDF file is provided that contains a table and four figures that presen...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries	Accept (poster)	Summary: This paper studies the sparse linear bandit problem without the prior knowledge of sparsity. The studied problem also considers general setup in which the context is chosen by an adaptive adversary and action set is not imposed with additional assumptions. Then, A OFUL based algorithms are proposed and regret ...	Rebuttal 1: Rebuttal: We appreciate your valuable time and effort in offering detailed feedback on our work. In the following, we address your questions one by one. --- Q1: Can authors provide another distribution selection case which can recover $dS/\Delta$ instance-dependent bound in known sparsity and adaptive adv...	Summary: This paper proposes statistically efficient linear bandit algorithms capable of handling cases where prior knowledge of the sparsity level $S$ is not given. The first algorithm, SparseLinUCB, achieves a $\tilde{O}(S \sqrt{dT})$ regret bound without any stochastic assumptions on the context vector, covering adv...	Rebuttal 1: Rebuttal: We appreciate your valuable time and effort in offering detailed feedback on our work. In the following, we address your questions one by one. ---- Q1: The SparseLinUCB algorithm does not make stochastic assumptions about the action set (context vectors), thus providing theoretical guarantees ev...	Summary: This paper studies the stochastic linear bandits when the action set can be arbitrarily chosen without some additional assumptions. And the authors propose a randomized sparsity-agnostic bandit algorithm using the model selection idea, and show that EXP3 can be used as the master algorithm to obtain a decent r...	Rebuttal 1: Rebuttal: We appreciate your valuable time and effort in offering detailed feedback on our work. In the following, we address your questions one by one. --- Q1: The used techniques are based on the existing literature (e.g. seqsew, exp3 master algorithm). It will be better to show the theory novelty of th...	Summary: This paper studies Linear bandits with adversaries when the underlying parameter $\theta$ is sparse. It combines a previous sparse linear regression algorithm named SeqSEW with LinUCB, proposing an algorithm named SparseLinUCB. It also proposes a variant of the EXP3 algorithm named AdaLinUCB. Regret bounds dep...	Rebuttal 1: Rebuttal: Thank you for your valuable time and effort in providing detailed feedback on our work. We now address your questions one by one. --- Q1: The paper misses many very related works, especially those on K-armed bandits and linear bandits. A1: It would be useful to have some concrete pointers to th...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming	Accept (poster)	Summary: On the theoretical side, the paper studies the problem of sample complexity of learning data-driven projection matrices for accelerating high-dimensional LP solving. Given n-dimensional LPs drawn from some problem distribution, the goal is to bound the number of problem instances needed to learn an $n\times k$...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for providing invaluable comments based on a deep understanding of data-driven algorithm design. We respond to each comment below: >Weaknesses: >- The i.i.d. assumption needed in theoretical results may be too strong in practice. >- The proposed methods for learnin...	Summary: This paper considers data-driven approach for learning projections for LPs. Given an LP with $m$ constraints and $n$ variables, it establishes bound on learning a projection $P\in \mathbb{R}^{n\times k}$ that reduces $n$ to $k$. The main contribution is to establish uniform convergence bound on pseudo-dimensio...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for providing insightful comments and a positive evaluation. We respond to each comment below. > Weaknesses: > > The techniques for proving the upper and lower bounds on pseudo-dimension are quite standard, authors should emphasize the technical difficulties for pr...	Summary: The paper attempts to theoretically analyze a method called Data-Driven Projections in Linear Programming. As discussed in the paper, projection methods aim to reduce the size of high-dimensional LPs. While random projection methods have improved the efficiency of LPs, data-driven projections have achieved bet...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's valuable feedback. We present our response to the comments below. > Weaknesses: > - I think the contribution of the paper is marginal. > - Although it is mentioned in the paper that the generalization is independent of the choice of the projection matrix $P$, t...	Summary: The paper proposes a data-driven approach to an accelerated solution of linear programming problems belonging to a common family. To this end, the dimensionality of the problems is reduced by a projection learned from a training set of problems. The paper first gives a theoretical generalization bound for this...	Rebuttal 1: Rebuttal: We are truly grateful for the reviewer's thoughtful and inspiring feedback. Below we present our responses to the comments. > Weaknesses: > > The most significant issue that I see is that the derived generalization bound is vacuous for the LP families experimentally studied in the paper, [...] H...	Rebuttal 1: Rebuttal: ## Global response We sincerely thank all reviewers for their efforts in reviewing our paper and providing invaluable feedback. This global response reports the results of two additional experiments. While these were primarily conducted to address the comments by Reviewer vPup, notably, Figur...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention	Accept (poster)	Summary: This paper improves upon the previous work, M3DRef-CLIP, through three key modifications: First, the authors incorporate an additional proposal probability prediction branch followed by a NMS operator to filter out low-confidence and redundant object proposals. Second, they learn camera pose residuals to dynam...	Rebuttal 1: Rebuttal: ## To Reviewer rjTU ``` Q10. Time and memory complexity comparisons ``` Thanks for the suggestion. We show the FLOPs and inference time of each proposed module and a comparison with the baseline model M3DRef-CLIP in Tab. R5. All experiments are conducted on Multi3DRefer validation set on a singl...	Summary: This paper introduces a novel two-stage approach for multi-object 3D grounding from a point cloud based on a given query phrase. The first stage of D-LISA uses a dynamic proposal module that selects a variable number of box proposals instead of a fixed maximum, addressing the issue of determining the optimal n...	Rebuttal 1: Rebuttal: ## To Reviewer WVQA ``` Q8. The dynamic vision module only removes the low-probability boxes and uses NMS to filter overlapping boxes, which is not novel. ``` We believe the reviewer is referring to the dynamic box proposal module. For the dynamic box proposal module, we do not claim NMS to be t...	Summary: This paper proposes D-LISA, a two-stage framework for multi-object 3D grounding. D-LISA consists of three novel components that make the method effective, namely a dynamic box proposal module, a dynamic multi-view renderer and a language informed spatial fusion module. Comprehensive Experiments are done on Mul...	Rebuttal 1: Rebuttal: ## To Reviewer W21s ``` Q4. Comparisons with SOTA methods on Nr3D benchmark in Tab. A2. ``` As is mentioned in L415-416, the Nr3D benchmark assumes perfect object proposals, which is not the most realistic setting. Hence, we follow M3DRef-CLIP to consider the setting where object proposals n...	Summary: The paper introduces D-LISA, a two-stage approach for multi-object 3D grounding that incorporates three innovative modules. First, a dynamic vision module generates variable and learnable box proposals. Second, a dynamic multi-view renderer extracts features from optimized viewing angles. Third, a language-inf...	Rebuttal 1: Rebuttal: ## To Reviewer su8L ``` Q1. Core issues targeted for multi-object 3D grounding ``` In L26-33, we summarized the targeted issues and the proposed solutions. Concretely, we identified that: - object proposals are selected based on a fixed maximum number, - the feature extractions from the prop...	Rebuttal 1: Rebuttal: We thank all the reviewers and the AC for the thorough reviews. We are happy to see the reviewers' supportive comments and feedback. Reviewers #su8L and #W21s commend the paper for its clear and well-structured writing. Reviewers #W21s and #rjTU appreciate the comprehensive experim...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images	Accept (poster)	Summary: The authors introduce a novel method named MV2Cyl, which reconstructs technical 3D objects withing the sketch-extrude paradigm by utilizing a 2D prior model and a learnable radiance field derived from multi-view images. To accomplish this, the 2D prior model is trained on a labeled dataset to predict semantic...	Rebuttal 1: Rebuttal: - Adapting methods to multi-view images (Image encoder + CAD decoder). Thank you for the suggestion. First, we want to note that for Point2Cyl and SECAD-Net, it is not feasible to directly replace the point cloud encoder with an image encoder since the encoder does not simply output a latent c...	Summary: This paper proposes a new method to reconstruct extrusion cylinders from multiview images. The key idea is to train a CNN to predict the binary mask for each surface and the sketch of each surface. Then, these predictions are used in learning 3D neural fields for each surface and sketches by the volume renderi...	Rebuttal 1: Rebuttal: - Surface label association. Yes, you are right that the predicted segmentation maps of the multi-view images are instance segmentation labels. Hence we use Hungarian matching (see Ln 209-215 main paper) to align the labels between the segmentation maps of the training images with the segmenta...	Summary: MV2Cyl is a method that proposes to solve the 3D reverse engineering of CAD models. The network takes as input multi-view images and outputs extrusion cylinders. The method extends Point2Cyl [58] that proposed to predict extrusion cylinder from point clouds. It is argued that multi-view images can easily be ob...	Rebuttal 1: Rebuttal: - Binary operations. The binary operations can be recovered in a simple/straightforward approach such as an exhaustive search against all possible primitives-operations combinations (2^K possibilities for a model with K primitives). We take the best combination as the output configuration, whi...	Summary: This paper proposes a method to predict extrusion cylinders from images of a CAD part. Specifically, it takes a set of masked multi-view images as input; these are then processed independently by instance segmentors trained to find extrusion curves and surfaces. Neural fields are then fitted, that reconstruct ...	Rebuttal 1: Rebuttal: - Work focuses on clean synthetic data; lacking real data experiments. We note that it is difficult to obtain real data with ground truth CAD (sketch-extrude) parameters. In fact, to the best of our knowledge, such a dataset does not currently exist. Hence we opt to use synthetic data to train...	Rebuttal 1: Rebuttal: We appreciate the invaluable feedback from all the reviewers on MV2Cyl. The thorough and insightful comments have significantly contributed to the improvement of our work. We have carefully considered each question and suggestion and have provided detailed responses to the comments individually. ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: ## Summary of the Paper: Problem Statement: Given multi-view images (RGB) of a 3D shape, the paper aims at recovering a set of extrusion cylinders to represent the underlying 3D shape. Motivation: Existing works that address 3D shape reconstruction problem through sketch-extrude take raw ...	Rebuttal 1: Rebuttal: - 2D curve segmentation network ablation. See Table A2 and Figure A4 in the supplementary for an ablation on the necessity of the 2D curve segmentation network; referred to as the “Surface only” approach. We see that without the curve segmentation module, occlusions between the extrusion cyli...	null	null	null	null	null	null
Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification	Accept (poster)	Summary: This paper proposes a time-series classification method which exploits temporal consistency (implemented by contextual information). Also, the proposed method can handle noisy class boundaries. Strengths: S1. This paper presents a novel problem formulation, time-series classification with noisy class boundari...	Rebuttal 1: Rebuttal: W1. We apologize for any confusion caused. Fig. 1 is intended to emphasize that, despite the similarity in seizure patterns, different annotators can still provide inconsistent labels for the same type of seizure and across different recordings from the same patient. For simplicity, we only illus...	Summary: This paper addresses the challenge of segmented time series classification (TSC) for Multiple classes with Varying Duration (MVD) data. The authors propose Con4m, a consistency learning framework that leverages contextual information with a focus on inconsistent boundary labels. The method incorporates continu...	Rebuttal 1: Rebuttal: W1&Q1. We consider $N_l$ and $E_g$ as auxiliary hyperparameters that work together rather than core hyperparameters of the model. When selecting these values, we focus on ensuring that the model’s loss approaches convergence for the newly added level within $E_g$ epochs. In contrast, $E_\eta$ has ...	Summary: The paper proposes Con4m, a novel framework for time-series classification and temporal action segmentation that leverages contextual information. The framework is designed to improve the prediction accuracy by incorporating context from surrounding data segments. The proposed method combines time-series class...	Rebuttal 1: Rebuttal: W1&2. 1. Our setup differs significantly from segmentation models (FLOSS[1], ESPRESSO[2], ClaSP[3]) in that they are able to identify change points but are unable to determine the specific classes before and after these points, particularly in multi-class tasks. 2. Our public datasets are multiv...	Summary: The authors proposed a learning framework called $\textit{Con}4\textit{n}$ that leverages contextual prior of Multiple classes with Varying Duration (MVD) to enhance the discriminative power of consecutive time series segments while harmonizing inconsistent labels associate to these later. The authors stated a...	Rebuttal 1: Rebuttal: Thank you very much for recognizing our work. W1. We will integrate Figures 2 and 3 into a single illustration to improve clarity. --- W2. We will explicitly clarify the meanings of `p^` and `p~` in the main text: `p^` represents the model's independent prediction for a sample, while `p~` denot...	Rebuttal 1: Rebuttal: G1 (Results for a new dataset). Based on the suggestion from reviewer uhXL, we conducted a search of the WOODS dataset and found that the Human Activity Recognition (HHAR) subset aligns well with our scenario and setup. Consequently, we include the HHAR dataset in our experiments. Due to time con...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness	Accept (poster)	Summary: This paper introduces a dual-critic prompting framework, INDICT, to consider both helpfulness and security during code generation. Specifically, the author introduces two critics, one for helpfulness and the other for security, to provide suggestions on improving the initially generated code. Besides, the auth...	Rebuttal 1: Rebuttal: Thank you for your reviews! Please refer to our responses below. ### Q1: ..It is generally believed and widely proven effective of adopting a multi-agent collaborative system during content generation Even though multi-agent collaborative systems have been proposed for content generation, it is...	Summary: The paper describes a method to improve the helpfulness and safety of LLMs for code generation tasks. It uses two critics - one for safety and one for helpfulness that communicate with each other to iteratively provide feedback to the actor model or the actual agent that is tasked with the code completion task...	Rebuttal 1: Rebuttal: Thank you for your comments. Please refer to our responses below. ### Q1: For the HarmBench evaluation - how have the red-teaming methods been applied with the critics in place? …Was the evaluation also done using completions?... For the HarmBench benchmark, we followed the original evaluation s...	Summary: This paper proposes a framework for generating both safe and helpful code. It integrates an internal dialogues of critiques against the given task and the corresponding generated response. It queries external knowledge through relevant code snippets and tools like web search and code interpreter. INDICT is eva...	Rebuttal 1: Rebuttal: ### Q1: …how your method differs from existing actor-critic architectures?... We provided a systematic and comprehensive comparison between INDICT and related actor-critic approaches such as CodeRL in our global response#1 above. Compared to existing actor-critic methods, INDICT is different in t...	Summary: This paper presents a new framework called INDICT that employs two complementary critic agents to improve both the safety and helpfulness of LLM-generated code. Each critic agent is obtained by prompting an LLM with task-specific instructions and knowledge obtained from external tools such as web search and Wi...	Rebuttal 1: Rebuttal: Thank you for your comments! Please refer to our responses below to your questions. ### Q1: …In the evaluation, INDICT is only compared with vanilla LLMs… Following your recommendations, we selected 7 strong baselines from different research lines (see our global response #2 above). We also incl...	Rebuttal 1: Rebuttal: We thank the reviewers for providing insightful comments on our paper. Please refer to this global response for our high-level answers to the common concerns. For more detailed explanations and analysis, please refer to the corresponding threads of individual reviewers. ### 1. Comparison with re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Query-Based Adversarial Prompt Generation	Accept (poster)	Summary: This paper proposes a query-based adversarial prompt generation method. It eliminates the prior attack's dependence on adversarial transferability and local surrogate models. The attack can evade the OpenAI and Llama Guard safety classifiers with a near 100% success rate. Strengths: ## Originality * The propo...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for carefully evaluating our work. We apologize for any confusion we may have caused, and will thoroughly review the presentation of our paper to avoid confusing future readers. ## Originality Following your suggestion, we will carefully revise the introductio...	Summary: This paper modifies GCG, an attack on LLMs to elicit harmful responses, to create a query-based black-box attack with two primary goals: 1) Enable targeted attacks that are not possible with simple transfer-based attacks and 2) Enable attacks to still occur when no feasible surrogate model exists. To modify GC...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for carefully assessing our work. We hope the following will address all outstanding concerns and we are happy to further discuss during the discussion period. ## Comparisons against other black-box attacks Unfortunately, there is a dearth of works to compare ...	Summary: The paper presents a novel query-based attack method designed to generate adversarial examples that induce harmful outputs in aligned language models. Building on the GCG attack by Zou et al. (2023), this method employs a query-based strategy that eliminates the need for transferability, resulting in a signifi...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their helpful comments! We hope that the following will address any residual concerns and we are happy to further discuss during the discussion period. ## Performance of AutoDAN The reason that AutoDAN performs so poorly is because it is being evaluated in...	Summary: This paper delves into the topic of adversarial examples and prompt injection attacks on aligned language models. A new strategy (GCQ) for black-box adversarial attacks is proposed which does not need access to a surrogate model, but only uses black-box access to the target model. This strategy is an extension...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for carefully reading our work and providing useful and constructive feedback. We hope that the following will address the points raised and are happy to further discuss during the discussion period. ## Comparison to other attacks Unfortunately, there is a lac...	Rebuttal 1: Rebuttal: We would like to thank all of our reviewers for their insightful comments. In the general rebuttal we would like to elaborate a bit on the harmful string attack. ## Harmful string attacks In our language modeling results, we focus on the harmful string attack. This attack objective is to get th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Instructor-inspired Machine Learning for Robust Molecular Property Prediction	Accept (poster)	Summary: This paper proposes a framework InstructMol for utilizing unlabeled data to help molecular property prediction on out-of-distribution (OOD) domains. The framework combines (1) a molecular model f that predicts (pseudo)labels with (2) a binary classifier g as instructor that evaluates the probability of lab...	Rebuttal 1: Rebuttal: Dear Reviewer UbNR, Thank you for your comprehensive and insightful review of our paper. We appreciate your recognition of the strengths and contributions of our work, as well as your constructive feedback on areas for improvement. We are pleased that you found our framework effective in util...	Summary: The paper introduces InstructMol, an innovative learning framework designed to address the challenge of data sparsity in chemical and biological sciences by leveraging large-scale unlabeled data through reliable pseudo-labeling. Unlike traditional methods that rely on transferring knowledge between domains, In...	Rebuttal 1: Rebuttal: Dear Reviewer pmns, Thank you for your detailed review and insightful feedback on our paper, "InstructMol." We appreciate your recognition of the strengths, particularly in addressing data scarcity in biochemical research and the clarity of our presentation. We are glad that you found the pap...	Summary: This paper targets the problem of label-scarcity in the domain of molecular property prediction. It can be seen as an improved version of proxy labeling. It utilizes a separate model that measures pseudo-labels’ reliability and helps the target model leverage large-scale unlabeled data. This method applies to ...	Rebuttal 1: Rebuttal: Dear Reviewer nboj, Thank you for your thoughtful review and detailed feedback on our paper. We appreciate your recognition of the significance of addressing label scarcity in molecular property prediction and the contributions of our proposed method. We are glad that you found our work timel...	Summary: The authors develop a method, called InstructMol, for adding pseudo-labels to any training task by including an "instructor" that is trained to discriminate real labels from pseudo-labels, and whose uncertainty is used to modulate the training loss for the primary predictors. The authors show that adding this ...	Rebuttal 1: Rebuttal: Dear Reviewer dbD2, Thank you for your detailed review and thoughtful comments on our paper. We appreciate your recognition of the strengths, particularly the novelty of our pseudo-labeling method, the innovative loss function for utilizing all pseudo-labels, and the strong performance shown ...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors present "InstructMol" which does not require transferring knowledge between multiple domains, which avoids the potential gap between the pretraining and fine-tuning stages. and demonstrate it on real-world molecular datasets and out-of-distribution (OOD) benchmarks. Strengths: Instructive Learning...	Rebuttal 1: Rebuttal: Dear Reviewer 73Xa, Thank you for your detailed feedback on our InstructMol. We appreciate your insights and suggestions, which are invaluable for refining our work. We are pleased to hear that you found our Instructive Learning Framework effective for improving generalization in OOD molecula...	null	null	null	null	null	null
Agent Planning with World Knowledge Model	Accept (poster)	Summary: This paper presents a parametric world knowledge model designed to enhance agent planning. The model synthesizes knowledge from expert and sampled trajectories for training purposes. It incorporates prior task knowledge for global planning and dynamic state knowledge for local planning. The implementation show...	Rebuttal 1: Rebuttal: We are deeply grateful for your valuable time and insightful feedback. Below are our detailed responses to your concerns. Q1: Clarity on hyperparameters and settings We sincerely apologize for any confusion caused by the details not clarified in the paper. (1) As shown in Eqn 8-10, our ...	Summary: This work is concerned with LLMs planning abilities in agent datasets. Instead of only fine-tuning the agent model on expert trajectories, they add “task knowledge” information. This information is created by comparing reject trajectories and expert trajectories, following previous work (NAT; Wang et al., 2024...	Rebuttal 1: Rebuttal: We are deeply grateful for your valuable time and insightful feedback. Below are our detailed responses to your concerns. Q1: When removing the state knowledge (figure 3), it seems that this approach does not outperform NAT, which uses SFT on the same trajectory preference data. We greatly a...	Summary: The paper presents a parametric World Knowledge Model (WKM) to enhance agent planning by providing both global prior task knowledge and local dynamic state knowledge. Traditional LLMs often perform trial-and-error actions and generate hallucinatory actions due to their limited understanding of the physical wo...	Rebuttal 1: Rebuttal: We are deeply grateful for your valuable time and insightful feedback. Below are our detailed responses to your concerns. Q1: The approach heavily relies on expert trajectories to synthesize both task and state knowledge. In fact, most mainstream agent planning methods currently either rely ...	Summary: This paper introduces a parametric World Knowledge Model (WKM) to enhance agent planning by integrating both global task knowledge and dynamic state knowledge. The authors claim that their approach can mitigate issues like blind trial-and-error and hallucinated actions in large language model (LLM) agents. The...	Rebuttal 1: Rebuttal: We are deeply grateful for your valuable time and insightful feedback. Below are our detailed responses to your concerns. Q1: The motivation for rejecting trajectories is not sufficiently justified. In fact, regarding your concern about the rejected trajectories, **we have explained in lines...	Rebuttal 1: Rebuttal: Dear all reviewers, Thank you for your thoughtful reviews! We appreciate all of your positive comments highlighting the strengths of our work for a summary: ## Our Strengths Summarized by Reviewers - Reasonable motivation: - "The motivation for the methodology is clear and logica...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD	Accept (poster)	Summary: This paper provides an asymptotic analysis of Unified Distributed SGD (UD-SGD) under heterogeneous agent dynamics and a large family of communication topology. It shows that under certain assumptions: i) regularity of the gradient, ii) Ergodicity of Markovian sampling, iii) decreasing step size and interva...	Rebuttal 1: Rebuttal: > Q1: In line 26, the authors claim that $\\mathcal{L}$ represents the collection of local minima of objective function $f$. In line 246, the authors claim that $\\theta^\\in\\mathcal{L}$. Since when $f$ is non-convex, $\\mathcal{L}$ do not have a single element and $\\theta^$ could have a lot o...	Summary: This paper studies the asymptotic convergence behavior of federated learning under the UD-SGD framework with Markovian data. The authors establish a new central limit theorem that considers the strategy of every agent, which goes beyond the existing bounds that only focus on the worst-performing agent. Their t...	Rebuttal 1: Rebuttal: > Q1: What is the main technical challenge when utilizing Poisson equation to prove Theorem 3.3? Specifically, what is new compared with the analysis in reference [23] and [30]? The main technical challenge in utilizing the Poisson equation to prove Theorem 3.3 lies in addressing the consensus er...	Summary: This paper conducts an asymptotic analysis of Unified Distributed SGD (UD-SGD), which has a generalized communication patterns (modelled with a doubly stochastic communication matrix). The paper investigates several different sampling strategies, such as i.i.d. sampling, shuffling, and Markovian sampling Stre...	Rebuttal 1: Rebuttal: > Q1: Are all of the results obtained using the same communication matrix (referring to line 982 in appendix G1)? Since the theory is generic for all double stochastic W maybe the authors could present the average result over 5 random W? We thank the reviewer for the question and the suggestion. ...	null	null	Rebuttal 1: Rebuttal: We thank all three reviewers for their comments and for the time and effort they put into reading, understanding and evaluating our paper. In particular, we appreciate the question to make our technical contribution much clearer (Reviewer WSiF), and we should conduct more experiments to support ou...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities	Accept (poster)	Summary: The authors propose a novel Kernel Language Entropy (KLE) method for uncertainty estimation in white- and black-box LLMs. KLE defines positive semidefinite unit trace kernels to encode the semantic similarities of LLM outputs and quantifies uncertainty using the von Neumann entropy. It considers pairwise seman...	Rebuttal 1: Rebuttal: Dear Reviewer tEe6, Thank you for the positive assessment of the novelty, practical effectiveness, and empirical comparison of our work. We would like to address the concerns and questions you raised below: Weaknesses >The motivation is clear in Fig 1, but why choose the kernel to solve th...	Summary: The paper introduces the method "Kernel Language Entropy", capturing semantic similarities of output sequences via semantic kernels and subsequently estimating uncertainty using the von Neumann entropy. Strengths: - The paper proposes a novel approach to estimate uncertainty in LLMs. - It presents a solid the...	Rebuttal 1: Rebuttal: Dear Reviewer DkpB, Thank you for your positive assessment of novelty and theoretical motivation of our work. Please, let us address your questions and pointed weaknesses: Weaknesses >Section 3 is inconsistent in its use of subsections and subheadings. It begins with a motivating example (su...	Summary: In this work, the authors propose Kernel Language Entropy, which shares a similar concept to semantic uncertainty but additionally considers semantic similarity. Based on this proposed theory, they design graph kernels and weight functions to estimate LLM uncertainty. Furthermore, they demonstrate that their m...	Rebuttal 1: Rebuttal: Dear reviewer Raee, Thank you for your positive evaluation of our work and for highlighting its originality, quality, clarity, and significance. We hope to address your concerns and questions in our response below. Weaknesses >KLE requires iterative sampling from the LLM, which is compu...	Summary: This paper is highly motivated by and heavily draws from Kuhn et al. (ICLR, 2023) “Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.” In Kuhn’s original paper they propose an unsupervised way to calculate the semantic uncertainty of LLMs by (1) generating a...	Rebuttal 1: Rebuttal: Dear reviewer S5q9, Thank you for a thoughtful and constructive review. We are pleased to hear that you found the experimental setting and the research problem in our work interesting. We hope to address your concerns in our reply below. Weaknesses > […] the empirical results (Table 1) are ...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful reviews, valuable suggestions, and for taking the time to read our paper! We particularly appreciate the positive recognition of many aspects of our work, including its novelty (tEe6, DkpB, Raee), significance (tEe6, Raee), empirical comparison and expe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation	Accept (poster)	Summary: This paper addresses the human pose estimation task with signals of 6 IMUs on the body. Because there are spatial correspondence in multiple IMUs across the body and temporal correspondence in the signal, this paper proposes to model the spatial relation of the 6 devices and the temporal relation in the signal...	Rebuttal 1: Rebuttal: Q1: Why does the baseline already outperform SOTA? Thank you for your comments! We hope to clarify that our baseline is a "strong" one that applies the powerful spatial-temporal framework [1-5] to inertial pose estimation task, thus outperforming SOTA. Specifically, as elaborated in [1-5], th...	Summary: In this paper, the authors study the inertial pose estimation problem and propose to add Sequence Structure Modules (SSM) to the spatial-temporal transformer architecture. The proposed SSM carries prior structural information of both spatial and temporal domain, and is shown to outperform multiple baselines an...	Rebuttal 1: Rebuttal: Q1: Why does SSM structure work && The difference between attention and SSM. We believe there is a misunderstanding. In short, our SSM is significantly different from attention modules as its values is independent from input tokens; and it can be used with or without heuristics, i.e.,...	Summary: Existing transformers have shown great promise in modeling temporal data. However, in the field of inertial pose estimation from inertial measurement units (IMUs), the lengths of time series are often fixed. This is a property that transformers can take advantage of. Thus, this paper proposes a Sequence Struct...	Rebuttal 1: Rebuttal: Q1: The generalization of the module under more sensors Thank you for your suggestion! Although we focused on the more challenging "sparse" settings (6 IMUs) in our paper, showing additional results of applying our modules to more sensors could further demonstrate their generalization ability...	Summary: This paper proposes a novel sequence structure learning and modulation approach that endows Transformers with the ability to model and utilize such fixed-sequence structural properties for improved performance on inertial pose estimation tasks. Specifically, this paper introduces a Sequence Structure Module (S...	Rebuttal 1: Rebuttal: Q1: Clarifications on Innovations We believe there is a misunderstanding and hope to clarify that: our technical innovations and contributions lie not in the integration of spatio-temporal information, but in addressing an inherent shortcoming of the native transformer architecture. S...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for providing detailed and constructive comments that have helped to improve the quality of our manuscript. - We have provided rebuttals to the comments of each reviewer. - We have also attached a pdf file containing figures that were requested by the revie...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalizable Person Re-identification via Balancing Alignment and Uniformity	Accept (poster)	Summary: This paper investigates the side effects of data augmentation in domain generalizable person ReID problem and proposes a framework for mitigating the negative effects. It is found that the data augmentation enhances the performance of a ReID model on its training domain, while degrading the performance of it o...	Rebuttal 1: Rebuttal: Dear Reviewer 4LRb, We sincerely appreciate your thorough review and are grateful for your positive remarks on the motivation and insights behind our paper. We have addressed your main concerns below. ### Regarding the polarized effects across different backbones, losses and augmentations **...	Summary: This paper investigates the polar effects of data augmentation in the domain of generalizable person re-identification. To address the problem of augmented data degrading out-of-distribution performance, this paper proposes a Balanced Alignment and Uniformity (BAU) framework, which normalizes the representatio...	Rebuttal 1: Rebuttal: Dear Reviewer fzqs, Thank you for constructive reviews and your time and effort in evaluating our work. Below, we address your concerns and questions. ### Clarification of alignment and uniformity We apologize for not sufficiently explaining these concepts in the context of ReID. For ReID, ...	Summary: Although data augmentation can improve in-distribution performance, it may lead to a sparse representation space, thereby reducing out-of-distribution performance. To address this issue, the authors proposed a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which effectively regulariz...	Rebuttal 1: Rebuttal: Dear Reviewer Ywj9, Thank you for your insightful comments and positive remarks on our paper's structure and experimental evidence. We appreciate your feedback and have addressed your main concerns below. ### Regarding whether polarized effects can be resolved by contrastive loss To addres...	Summary: The authors investigate the polarized effects of data augmentations in DG re-ID and reveal that they can lead to sparse representation spaces, which are detrimental to generalization. To address it, they propose a novel BAU framework that mitigates the polarized effects of data augmentations by balancing align...	Rebuttal 1: Rebuttal: Dear Reviewer L6wV, Thank you for your thorough review and constructive feedback. We greatly appreciate your time and effort in evaluating our work. Below, we address your concerns and questions. ### Regarding the effects of different alignment strategies We report the impact of various alig...	Rebuttal 1: Rebuttal: Dear reviewers and chairs, We sincerely appreciate all reviewers for their time and efforts for reviewing our work. We are glad that the reviewers found our work "clear motivation and idea"(Ywj9, 4LRb), "simple and effective"(L6wV, 4LRb), "well presented"(Ywj9, 4LRb), and "sufficient experimenta...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Public-data Assisted Private Stochastic Optimization: Power and Limitations	Accept (poster)	Summary: The paper presents some new lower bounds for public-data assisted private SCO, when some public examples are available, either with or without labels. In the unlabeled case, a simple algorithm for GLM assisted with unlabeled public examples is presented and analyzed (and it is shown that this removes a dimensi...	Rebuttal 1: Rebuttal: * Presentation: We will add a table to our updated version as you suggest and clarify tightness of the rates. Please see the global response PDF for this table. * Missing conditions on $n_\text{pub}$ : Theorems 3, 6, and 7 claim the rate is achievable given access to some number of publi...	Summary: The paper investigates a public-data-assisted differential privacy problem. Firstly, for labeled public data, the author introduces a novel mean estimation lower bound of $\tilde{\Omega}\left(\min \left{\frac{1}{\sqrt{n_{\mathrm{pub}}}}, \frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n \epsilon}\right}\right)$. Secondly, ...	Rebuttal 1: Rebuttal: * Response to Questions: Differentially private subroutines often incur some penalty proportional to the problem dimension, $d$. Thus, at a high level, the reason for performing the projection before applying the DP subroutine is to ensure that the penalty does not scale with $d$. Indeed, we sh...	Summary: This paper studies the effectiveness of using public data to assist private convex optimization. Private convex optimization gives the solver query access to a convex function $f:W\times X \to \mathbb{R}$ and samples $S$ drawn i.i.d. from some unknown distribution $D$. The solver is asked to find some point $w...	Rebuttal 1: Rebuttal: * Response to Weakness 1: We completely agree exact constants are important here, and we will make a note to better emphasize this. However, we do not believe this devalues the importance of characterizing the asymptotic rates, as we do in our paper. Most of the existing understanding of (pri...	null	null	Rebuttal 1: Rebuttal: See the attached pdf for the lower bound table that will be added the revision. Pdf: /pdf/7553660fd9ab4f5dd36e7a113513fc739be4f551.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards	Accept (poster)	Summary: The authors present a theoretical framework for learning $\omega$-regular properties in unknown MDPs through average reward optimization (via an "optimality preserving" approach). Compared to previous work, the approach allows for multi-chain MDP. The idea is to formalize the property through a deterministic...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. We will take their suggestions into account when revising the paper. In particular, we will use the additional page for the camera-ready version to elaborate on practical aspects and the construction in Sec. 5. Next, we address the reviewer’s qu...	Summary: The paper studies the link between reinforcement learning with $\omega$-regular objectives to reinforcement learning with limit-average reward. It is shown that one cannot reduce RL with $omega$-regular objectives to RL with limit-average objectives by only replacing the reward function (Proposition 4), but th...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. We agree with the reviewer that investigating the possibility of a polynomial translation for the general case (where the transitions with positive probability are not known) is a very interesting future direction. --- Rebuttal Comment 1.1: ...	Summary: This paper tackle several open problems in the field of specification driven learning using Reinforcement Learning (RL) algorithms. It proves that $\omega$-regular objectives can be translated in an optimality preserving manner to limit-average reward MDPs. A PAC-MDP convergence proof for limit-average reward ...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and we will take their suggestions into account when revising the paper. We proceed to address the questions raised by the reviewer. # Choice of DRAs vs. LDBAs > How does the choice between Limit Deterministic Büchi Automata (LDBAs) and DRAs t...	Summary: This paper considers the problem of reducing temporal logic objectives to limit-average reward problems which enable using reinforcement learning to compute policies. The key contributions are an explicit construction and analysis for the setting where the MDPs transition support is known, followed by a relaxa...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. We will take their suggestions into account when revising the paper. In particular, we will clarify the relation to the additional references as detailed below. # Translation to Eventual Discounted Rewards [1] > In particular, this work's goal...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures	Accept (poster)	Summary: This work presents a method for performing a shared component analysis (SCA) in the case of multi-modal unpaired data drawn from a linear mixture. This problem (and method) will be referred to as Unaligned SCA. Unaligned SCA is tackled by matching the probability distributions of the embedded (features) multi-...	Rebuttal 1: Rebuttal: [Empirical Results, CLIP, and Recent Baselines] (i) “Only Practical Result is in Fig 4.” We believe that there might have been some misunderstanding. Fig. 4 is used to validate Theorem 3. The blue dot markers suggest that conditions (a-b) in Theorem 1 might not be satisfied. To clarify, ...	Summary: The paper considers the identifiability of shared components from a linear mixture. The theory requires multiple domains. However, compared to previous works, the required domains do not need to be aligned in this work. A practical estimation model has been proposed according to the theory. Strengths: 1. The ...	Rebuttal 1: Rebuttal: [Regarding "Many Shared Component Learning Methods Exist"] We would like to note that the existing identifiability research on shared component learning (including nonlinear mixture based ones) from unaligned multi-domain data is in fact rather limited (although empirical studies are a...	Summary: This work considers a problem similar to classical Canonical Correlation Analysis (CCS), which assumes a linear generative model for data $(x_1, x_2)$: $x_1=W_1z$, $x_2=W_2z$ and aims to identify the underlying components. This problem has been extended previously to include "private information": $x_1=W_1z_...	Rebuttal 1: Rebuttal: [Linear mixture models (LMMs) are Ill-posed] In general, LMMs are not identifiable. Because for any $\bf{y}=\bf{A}\bf{x}$, where both $\bf{A}$ and $\bf{x}$ are unknown, one can find an infinite number of invertible $\bf{Q}$ such that $\bf{y}=\bf{AQQ}^{-1}\bf{x}$. Then, both $(\bf{A},\bf{x})$ ...	Summary: This work considers the identifiability of linear latent representations that are shared (i.e., identical) across data modalities, in the special case that they are unaligned/unpaired. The approach leverages GAN-style training to achieve divergence minimization between the latent distribution of each modality....	Rebuttal 1: Rebuttal: [Code Clarity] We will clean the code and change the variable names according to the notation used in the paper. We found that Fig. 5 sometimes could not be replicated due to occasional failure of GAN convergence. We fixed the issue by increasing the regularization parameter $\beta$ from 0.00...	Rebuttal 1: Rebuttal: ### [ Overall Response ] We sincerely thank all the reviewers for their effort in reviewing our manuscript. Our responses are summarized as follows: Reviewer DxMx suggested improving code clarity and observing the effect of sample size with a new evaluation metric. Following the comment...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Universal Growth Rate for Learning with Smooth Surrogate Losses	Accept (poster)	Summary: This paper analyzes the growth rate of the H-consistency bounds (which subsume excess risk bounds) for various smooth surrogate losses commonly used in binary and multiclass classification. Specifically, for binary classification, the work establishes a tight square-root growth rate near zero (under mild condi...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work. Please find our detailed responses below. Questions: Based on this work, do you have any concrete suggestions for practitioners to choose surrogate losses (assuming they do not know about H-consistency at all)? Response: We have discussed some...	Summary: Since optimizing zero-one loss is intractable and it does not have properties such as differentiability, a common approach in learning theory is to replace it with a surrogate loss function. H-consistency bounds relate the excess error for surrogate loss to zero-one loss. This paper establishes a square-root g...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work. Please find our detailed responses below. Weaknesses: The paper studies only smooth surrogate losses, not piecewise linear ones such as hinge loss. However, using smooth surrogate loss functions is very common in machine learning applications. **R...	Summary: This paper presents a comprehensive analysis of the growth rate of H-consistency bounds (and excess error bounds) for various surrogate losses for some intractable loss used in classification. The authors prove a square-root growth rate near zero for smooth margin-based surrogate losses in binary classificatio...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work and for your suggestions to improve its readability. Please find our detailed responses below. **Weaknesses: The paper should provide a more intuitive statement on the motivation and implication of these error bounds and provide more insights about the ...	Summary: The paper provides an analysis of the growth rate of H-consistency bound for surrogate losses in binary and multi-class classification. The authors prove square root growth rate near zero for smooth margin-based surrogate losses for binary classification as well as for smooth comp-sum and constrained losses f...	Rebuttal 1: Rebuttal: Thank you for your encouraging review. We have carefully addressed all the questions raised. Please find our detailed responses below and let us know if there is any other question. **1. I understand that H consistency-based bound helps convert a surrogate-based bound to a bound that we require. ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
COSMIC: Compress Satellite Image Efficiently via Diffusion Compensation	Accept (poster)	Summary: The authors propose COSMIC, a simplified and efficient compression method for satellite earth observation images. Due to the increasing number of satellites and the volume of image data, existing compression schemes are difficult to deploy with the limited computing power and energy available on satellites. CO...	Rebuttal 1: Rebuttal: Thank you for your careful review and valuable feedback. ### Q1. More performance comparison tests of various models need to be included. Please see our general response 2 above. ### Q2. The process of handling metadata is not clearly explained. Sorry for the confusion in writing. First, we nor...	Summary: This paper presents a novel method to address the challenge of transmitting the increasing volume of satellite images to ground stations. The core innovation lies in designing a lightweight encoder that reduces computational complexity on satellites, coupled with a diffusion-based compensation model on the gro...	Rebuttal 1: Rebuttal: ## Q1. Encoder Degradation and Training Specificity. These two questions are just repeating our last section, i.e. Limitations & Future work. Moreover, we are frustrated to find two detectors (GPT-zero and Scribbr) have 100% confidence of AI-generation on your review. It's clearly dou...	Summary: The authors propose a novel method to compress satellite images using a learned algorithm that relies on a diffusion model on the ground to decode the compressed image. The proposed method is designed for deployment on satellites. Strengths: A novel method to compress satellite images by using a diffusion mod...	Rebuttal 1: Rebuttal: Thanks for the very detailed review and suggestions. Fig.S2 can be found in global response PDF. ### Q1. Please rewrite the text and the figures to clarify the distinction between training and testing stages. Sorry for the confusion in writing. The training is divided into two stages. In the fi...	Summary: This paper presents COSMIC, a coding scheme designed for satellite-to-ground image transmission. It addresses the disparity in computing performance between the satellite and ground station. COSMIC features a lightweight encoder on the satellite, reducing FLOPs by 2.6 to 5 times, to achieve a high image compre...	Rebuttal 1: Rebuttal: We thank the reviewer for this thoughtful review and we are glad to see their positive assessment. Note that Fig.S2 can be found in the PDF attached to the global response. ### Q1. More in-depth discussion on power challenges. Please see our general response 1 above. ### Q2. Issues in the writi...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful comments and acknowledging that the paper is well-written and with clear logical flow (AGki), the method is interesting and novel (gvA5/GEfY/AGki), highly feasible (gvA5/upSj/AGki) and potential to be extended to other edge devices (GEfY), and the evalu...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation	Accept (poster)	Summary: Sequential recommendations is well studied problem with impact across industries which try to personalise a users experience based on their past interactions. Recent popularity of LLMs has led to a lot of research into their use for this task through various generation methodologies. One of those methods is th...	Rebuttal 1: Rebuttal: # Response to Reviewer wwLq Comment: We gratefully thank you for your valuable comments! Here we meticulously give point-by-point responses to your comments, and further revise our manuscript following your suggestions. We sincerely appreciate the depth of your insights, which have undoubted...	Summary: The author focus on extend sequential recommendation task with the help of large language models. The author proposed instance-wise LoRA and integrate with mixture of experts framework to capture specific interests of user preferences. Experiments results on two benchmark datasets demonstrate the effectiveness...	Rebuttal 1: Rebuttal: We gratefully thank you for your valuable comments! Here we meticulously give point-by-point responses to your comments, and further revise our manuscript following your suggestions. Hope that our responses can address all your concerns. --- > **C1: Questions about experimental details and moti...	Summary: The paper introduces iLoRA, which combines LoRA with user representation-guided mixture of expert architecture. The motivation is clear and the writing is good. Extensive experiments are conducted on three public datasets, demonstrating the performance of the proposed method. Strengths: 1. Timely study on lar...	Rebuttal 1: Rebuttal: We sincerely thank you for your time and valuable comments. Your main suggestions about the evaluation setting help us substantiate wide applicability of our proposed iLoRA. To address your concerns, we have detailed our responses point-to-point below. --- > **C1: Fragile Setting of Sampled Eva...	Summary: The paper addresses the challenge of personalizing language models for sequential recommendation tasks, where user behaviors exhibit significant individual variability. The proposed solution, Instance-wise LoRA (iLoRA), adapts the mixture of experts concept to tailor LLMsfor this variability. Strengths: The i...	Rebuttal 1: Rebuttal: We appreciate your comments, which greatly improve our paper. Below we provide the point-to-point responses to address your concerns and clarify the misunderstandings of our proposed method. --- > C1: More Refined by Integrating Experts with Clustering Thanks. Based on your suggestions, we ...	Rebuttal 1: Rebuttal: # Summary of strengths acknowledged by the reviewers and the responses to address their concerns Comment: Dear ACs/SACs/PCs, We would like to summarize the strengths of this work acknowledged by the reviewers, and the responses we have made to address all the reviewers’ concerns. ------ *...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences	Accept (poster)	Summary: The paper shows that place cells can emerge in networks that autoencode temporally continuous sensory episodes based on spatially smoothed Gaussian random fields. The obtained place fields reproduce the disputed idea of remapping, the established fact that such spatial representations are uncorrelated, and a s...	Rebuttal 1: Rebuttal: We thank reviewer kdgt for their time and suggestions. >Can we really say that CA3 is an autoencoder or that it is its function to represent place fields? The idea that CA3 functions like an AE directly corresponds to the autoassociative model of CA3 [1-3]. The 'autoencoding' property of the net...	Summary: The paper explores the emergence of place cells in neural networks by simulating the hippocampal area CA3, specifically when trained to recall and reconstruct temporally continuous sensory experiences encountered during navigation in simulated environments. The authors model this area as a recurrent autoencode...	Rebuttal 1: Rebuttal: We thank reviewer KNH2 for their time and suggestions. We will resolve the reviewer’s comments in turns: >The paper is written in a somewhat unusual style with the results following right after the introduction without a separate methods section making it difficult to follow the approach and und...	Summary: This paper presents a novel approach to understanding the emergence of place fields in the hippocampus. The authors propose that place cells can emerge from networks trained to remember temporally continuous sensory episodes, without explicit spatial input. They model the hippocampal CA3 region as a recurrent ...	Rebuttal 1: Rebuttal: We thank reviewer 6VUu for their time and suggestions. >Limited quantitative comparison with actual neural data from rodent studies. We thank the reviewer for their suggestions. We addressed this in the global response. >Reliance on simplifying assumptions about sensory input structure. >WSM s...	Summary: This study demonstrates that place cells can develop in networks trained to remember temporally continuous sensory episodes. The model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The autoe...	Rebuttal 1: Rebuttal: We thank reviewer hM85 for their time and suggestions, and are happy that they liked the paper. We will address the reviewer’s comments one-by-one: >A more in-depth visualization in Figure 3 would be nice. I like this framing of the problem in the text. Maybe show each example (suboptimal encodi...	Rebuttal 1: Rebuttal: We thank all reviewers for your time and valuable comments. We have addressed the common suggestions below and will resolve additional comments in each individual reply. >How might the emergence of place fields (PFs) change in response to different parameter settings? We thank the reviewers’ for...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization	Accept (poster)	Summary: The paper proposes a novel conditional generative modeling approach using diffusion models for offline model-based optimization (MBO). The method constructs synthetic trajectories toward high-scoring regions, trains a conditional diffusion model, and samples multiple trajectories to explore and select high-fid...	Rebuttal 1: Rebuttal: Thank you for the positive feedback and further suggestions that could enhance our manuscript. > (Weakness 1) The authors have adopted the Diffusion model for offline model-based optimization, which is a current trending approach. It is recommended that the authors emphasize their unique contribu...	Summary: The paper introduces Guided Trajectory Generation (GTG) for offline model-based optimization (MBO). GTG creates synthetic trajectories from offline data, using locality bias to ensure consistent improvement. A conditional diffusion model generates these trajectories based on their scores. The method then uses ...	Rebuttal 1: Rebuttal: Thank you for the critical reviews, insightful feedback, and further suggestions to enhance our manuscript. > (Weakness 1) More varied discrete benchmarks and high variance in TFBind10 results We excluded NAS as it takes too long to evaluate. Instead, to verify the effectiveness of our method on...	Summary: The paper consider the problem of offline optimization where the goal is to find the optima of a black-box function in a zero-shot manner without online evaluations. The key idea is to generate trajectories with a locality biased heuristic and employ a conditional diffusion model to learn the distribution of t...	Rebuttal 1: Rebuttal: Thank you for the valuable review and positive feedback!! As only two questions have been raised by the reviewer, we will address it in this response. If you have any additional questions, please do not hesitate to let us know! > (Weakness 1) Unreliable results of superconductor task. We acknowl...	Summary: This paper introduces Guided Trajectory Generation (GTG), a novel conditional generative modeling approach to solve the MBO problem. GTG consists of three parts, including trajectories construction, model training and trajectories sampling and filtering. Experimental results on various tasks, including a toy 2...	Rebuttal 1: Rebuttal: Thank you for the insightful review and positive feedback! > (Weakness 1 & Question 1) Clarification on the difference between the proposed method and Decision Diffuser. We would like to highlight that our method aims to find a design that maximizes the target black-box function while Decision Di...	Rebuttal 1: Rebuttal: We sincerely thank the review committee for their detailed feedback. We appreciate the recognition of our paper's strengths, highlighted by the reviewers: well-written (gJu1, nEhc), novel (nEhc, FGy7), and extensive experiments and ablations (gJu1, MZpe, nEhc). In response to the revie...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling	Accept (poster)	Summary: The paper proposes a physics-AI hybrid modeling framework for fine-grained weather forecast. They propose to adaptively tune a PDE kernel together with a neural network as the encoder. Following the Euler time stepping, the PDE kernel can perform a fine-grained temporal forecast, which act as a physics-guided ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your thoughtful review! We are pleased that you appreciate the innovative combination of AI and physics. We will address your remaining questions below. > Q1: The experimental details are not sufficient. Thank you for your suggestions. The table below outlines the e...	Summary: In this paper, a hybrid model (a model that combines machine learning with physics) is demonstrated for nowcasting and medium-range weather forecasting. WeatherGFT uses machine-learned weightings that combine two successful methods for weather forecasting (machine learning and the traditional numerical, PDE-ba...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your insightful review and detailed feedback! We are glad for your recognition of the significance of our hybrid modeling of physics and AI. We appreciate your feedback on specific claims in our paper, and will refine specific statements and add citations of relevan...	Summary: The paper proposes WeatherGFT, a physics-AI hybrid model designed to generalize weather forecasts to finer temporal scales beyond the training dataset. By integrating PDE kernels for physical simulation and neural networks for adaptive bias correction, the model aims to provide accurate 30-minute forecasts usi...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your thoughtful review and detailed feedback! We are delighted that you value the innovative fusion of AI and physics in our research. We have revised the paper according to your suggestions and will now respond to the remaining queries you have. > Q1: The paper is w...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers, We thank all reviewers for their efforts in reviewing our submission and their recognition of our work, e.g., 'fuse AI and PDE is innovative' and 'demonstrate generalization capabilities' from Reviewer CYo5, *'the paper does a good job address both of these existing proble...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
In-Context Learning with Representations: Contextual Generalization of Trained Transformers	Accept (poster)	Summary: This paper studies the training dynamics of multi-head Transformers by gradient descent on non-linear regression tasks via ICL. This work shows the linear convergence to the global minimum of the training and the in-context inference. An impressive contribution is that transformers are proven to learn contextu...	Rebuttal 1: Rebuttal: ## Response to Reviewer vG3D Thank you for your insightful review. We've added experiments to validate our theory in the supplementary pdf and our global response. Below we address your concerns. If our responses resolve your questions, we'd highly appreciate your consideration in raising the...	Summary: This paper tackles the problem of task learning with representations using transformers. It presents a theoretical proof for convergence of training of a single-layer multi-head softmax attention transformer to the global minimum of the loss. Specifically, given $N$ example tokens and their corresponding task ...	Rebuttal 1: Rebuttal: ## Response to Reviewer MzSs Thank you for your time in reviewing our paper. Below We address your points. If our responses adequately address your concerns, we would be grateful if you could consider increasing your current score. And we are happy to answer your additional questions. > **(W1) T...	Summary: The paper investigates the theoretical understanding of in-context learning, focusing on whether transformers can generalize to unseen examples within a prompt by acquiring contextual knowledge. The paper analyzes the training dynamics of transformers using non-linear regression tasks, demonstrating that multi...	Rebuttal 1: Rebuttal: ## Response to Reviewer TyrV Thank you for your valuable feedback. We agree that empirical validation is crucial and have included experimental results in the supplementary PDF and the global response. Note that doing in-context training on real datasets is highly demanding and usually requir...	Summary: This paper presents a rigorous theoretical analysis of the training dynamics and generalization capabilities of a one-layer transformer with multi-head softmax attention for in-context learning (ICL) of non-linear regression tasks. The authors consider a more challenging and realistic setting where prompts con...	Rebuttal 1: Rebuttal: ## Response to Reviewer dubr Thank you for your review and positive comments. We've included some experiments to verify our theoretical findings in the supplementary pdf and the global response. Below we address your other points. If our responses resolve your concerns, we'd highly appreciate...	Rebuttal 1: Rebuttal: We conducted the following experiments to validate our theoretical findings. The attached file includes the experimental plots. We will add these results as an experiment section in our revision. Set up. We conduct experiments using synthetic dataset (which is standard practice of this line o...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Variational Distillation of Diffusion Policies into Mixture of Experts	Accept (poster)	Summary: This paper introduces a theoretical method for extracting an MoE policy from a pretrained diffusion policy. The advantage of MoE policy is that it allows faster sampling speed compared with the diffusion policy more stable training and better performance compared with methods that train MoE from scratch. Str...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for taking the time to review our work and the many helpful comments and suggestions. We hope the following replies sufficiently address the raised questions and concerns. We will update the paper accordingly. --- > One main advantage of MoE policy against de...	Summary: This study introduces Variational Diffusion Distillation (VDD), a novel method that distills pre-trained diffusion models into Mixture of Experts (MoE) frameworks. VDD addresses diffusion models' drawbacks of intractable likelihoods and long inference times, while leveraging their ability to represent complex ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for taking the time to review our work and the many helpful comments and suggestions. We hope the following replies sufficiently address the raised questions and concerns. We will update the paper accordingly. > The challenges posed, although relevant to diffu...	Summary: This paper presents a variational inference method for distilling denoising diffusion policies into Mixture-of-Experts (MoE) policies. The primary motivation is to combine the strengths of both worlds - the ability to learn complex, multi-modal distributions of diffusion models - and the efficiency of MoEs off...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for taking the time to review our work and the many helpful comments and suggestions. We hope the following replies sufficiently address the raised questions and concerns. We will update the paper accordingly. --- > While EM is arguably be able to handle some...	Summary: This paper presents Variational Diffusion Distillation (VDD), a method that distills denoising diffusion policies into Mixtures of Experts (MoE) using variational inference. Diffusion Models excel in learning complex distributions for behavior learning but have drawbacks like slow inference times. MoEs address...	Rebuttal 1: Rebuttal: We thank the reviewer for the very positive comments and are grateful for the valuable suggestions and comments. We hope the following replies sufficiently address the raised questions and concerns. We will update the paper accordingly. --- > Authors may find it interesting to visit another pe...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable feedback. We would like to reiterate our main action points in response to the reviewers’ suggestions and concerns. New results can be found in the PDF file that accompanies the rebuttal. - Open-Sourced Code-Base: We provided a link to an anonymous G...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studies the knowledge distillation problem in diffusion models by distilling denoising diffusion policies into Mixtures of Experts (MoE) using variational inference. The goal is to combine the advantages of diffusion models, with the fast inference capabilities of MoE models. The authors construct a...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for taking the time to review our work and the many helpful comments and suggestions. We hope the following replies sufficiently address the raised questions and concerns. We will update the paper accordingly. > […] the paper claims that "VDD is not straightfo...	null	null	null	null	null	null
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts	Accept (spotlight)	Summary: The paper aims to deal with the task of AD patient classification with missing modalities, and proposes a framework, Flex-MoE. Flex-MoE also utilizes a learnable missing modality combination bank to complete the missing embeddings of missing modalities. The training strategy and the gating mechanism of Flex-Mo...	Rebuttal 1: Rebuttal: We thank reviewer 6yyS for mentioning our work as `practical`, `addressing missing modalities is worth attention to the AD community` and `rationale and decent techniques`. For the concerns, we provide responses below: --- [W 1, 3: Expression from Line 159 - 167, 199] The main idea of...	Summary: The paper presents a multimodal learning framework, Flex-MoE (Flexible Mixture-of-Experts), designed to integrate diverse modalities in Alzheimer's Disease (AD) research using a Sparse Mixture-of-Experts design. Flex-MoE sorts samples based on the number of available modalities and processes them through modal...	Rebuttal 1: Rebuttal: We thank reviewer qdnn for the comprehensive review of our paper. Specifically, we appreciate the reviewer's recognition of our key contribution in addressing missing modalities in AD as `crucial` and `highly relevant`. For the concerns raised, we provide the following responses: --- **[W 1 ...	Summary: This paper introduces Flex-MoE, a novel multimodal learning framework for Alzheimer's Disease that handles missing modalities using a Sparse Mixture-of-Experts design and demonstrates its efficacy on the ADNI dataset. Strengths: 1. The idea of Flex-MoE is clear and straightforward, effectively addressing the ...	Rebuttal 1: Rebuttal: We thank reviewer BwQM for their careful review and for mentioning that Flex-MoE is `straightforward`, `effectively addresses missing modalities in AD`, and `easily understandable`. For the remaining concerns, we provide details below: --- [W 1: Computational efficiency and scalability] ...	null	null	Rebuttal 1: Rebuttal: In this study, we propose Flex-MoE, a novel framework designed to address the issue of missing modalities in the AD domain, where existing studies often (1) rely on single modality and complete data, and (2) overlook modality combinations. As a remedy, Flex-MoE includes a **missing mod...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT	Accept (poster)	Summary: Lumina-Next is an improved version of Lumina-T2X, featuring a core architecture that employs a Flow-based Large Diffusion Transformer (Flag-DiT). Through empirical experiments and analysis, Lumina-Next introduces an enhanced Next-Dit architecture and develops a fast sampling algorithm, which boosts the model'...	Rebuttal 1: Rebuttal: ### Q1: Whether identifiers such as [nextline] and [nextframe] are unnecessary when using 3D RoPE is not sufficiently discussed in the paper. Good point! Lumina-T2X adopted learnable [nextline] and [nextframe] tokens to achieve flexible modeling of 2D/3D signals with 1D RoPE. However, we found th...	Summary: The paper introduces Lumina-Next, an enhanced version of the Lumina-T2X model, which is a Flow-based Large Diffusion Transformer (Flag-DiT) aimed at transforming noise into various modalities like images and videos based on text instructions. Compared with Lumina-T2X, Lumina-Next introduces the following impro...	Rebuttal 1: Rebuttal: ### Q1: The work does not show comparisons with these state-of-the-art methods in terms of text-image alignment and image quality. Thank you for the suggestion. We have further supplemented the quantitative comparison experiments. Due to time constraints, we only compared Lumina-Next with represe...	Summary: The paper introduces a new multi-modal generation model, Lumina-Next, which extends the previous Lumina-T2X approach by several innovations: i) 3D rotary position embedding (RoPE) ii) extra normalization to stablize training iii) frequency- and time- aware scaled RoPE for training free resolution extrapolatio...	Rebuttal 1: Rebuttal: ### Q1: Quantitative evaluations on all generation tasks. Thank you for the suggestion. We have further supplemented the quantitative comparison experiments. Due to time constraints, we only compared Lumina-Next with representative SoTA T2I models, SD3 and PixArt. To better illustrate the aesthet...	Summary: This paper introduces the next generation of Lumina-T2X, Lumina-Next, which offers improved architecture, a scaled dataset, optimized sampling techniques, and a more efficient context extrapolation strategy. The improved architecture shows faster convergence rates, while the optimized sampling technique enable...	Rebuttal 1: Rebuttal: ### Q1: There is a lack of quantitative experiment comparison results on some Text2Image benchmarks to demonstrate the superiority and effectiveness of the proposed method. Thank you for the suggestion. We have further supplemented the quantitative comparison experiments. Due to time constraint...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: Lumina-T2X encounters challenges including training instability, slow inference, and extrapolation artifacts. This paper introduces Lumina-Next, which improves Lumina-T2X with improved architecture, scaled dataset, optimized sampling techniques, and better context extrapolation strategy. On the architecture s...	Rebuttal 1: Rebuttal: ### Q1: Incremental Changes. Good point! We agree that Lumina-Next is largely based on Lumina-T2X with several improvements. However, we would like to highlight that these changes are made after comprehensive examinations and are essential for scaling this flow-based diffusion transformer or appl...	null	null	null	null	null	null
DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection	Accept (poster)	Summary: This paper proposes a deep-learning-based method for performing "knockoff"-based feature selection. The main contribution is the proposal of a new optimization problem, which incorporates a four part loss. The method is evaluated on synthetic, semi-synthetic, and real-world data, showing comparable and, at ti...	Rebuttal 1: Rebuttal: Thank you for your comments. Please refer to the following for our response. Comment: it's not clear why (4) is fundamentally "good" … - Response: Thank you for the comments and questions. We will add more descriptions on the motivation for the two terms in the revised manuscript. Essentially, t...	Summary: This paper considers the construction of knockoff features in the variable selection framework of model-X knockoffs. The authors propose a deep learning-based method for generating knockoff features. Extensive numerical simulation and real-data examples show that the proposed method has advantageous perform...	Rebuttal 1: Rebuttal: Thank you for your comments and advice. Please checkout our responses below. Question: The introduction of the dependency regularized perturbation is for power boosting, and yet in the ablation study, this feature seems to help more with FDR control as opposed to power-boosting. Is there a reason...	Summary: The work introduces DeepDRK, a new algorithm for improving FDR in the model-X knockoff framework. In this framework, a knockoff covariate is generated for each existing covariate where then knockoff covariates have to satisfy the swap property. Given a knockoff statistic that satisfies the flip-sign property, ...	Rebuttal 1: Rebuttal: Thank you for your comments and advice. Please checkout our responses below. Comment: It seems necessary to see 4.1 results for various beta scales used for generating the response variable to compare different methods in various levels of difficulties to make sure the value of 15 has not been se...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers, We have completed more experiments, as suggested in your comments. Please refer to the PDF file for details. Specifically, Figure 1 refers to additional experiments on the change of $\beta$ coefficient scales. We include three scales, 5, 10, and 20, in addition to the default 15....	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Theoretical guarantees in KL for Diffusion Flow Matching	Accept (poster)	Summary: This paper proposes theoretical guarantees for diffusion flow matching, which is of the most popular generative models. Specifically, it provides an upper bound of the KL divergence between the target distribution and the one learned by the DFM. This work promotes the theoretical understanding of the discretiz...	Rebuttal 1: Rebuttal: Questions: _If you in addition assume the velocity field is Lipschitz, will you be able to relax assumption H1 to have finite second moments?_ We thank the reviewer for highlighting this issue. Indeed, for a Lipschitz velocity field, assumption H1 can be relaxed to only require finite second mom...	Summary: This paper gives theoretical guarantees for Diffusion Flow Matching similar in spirit to existing results for Denoising Diffusion Probabilistic Models and Probability Flow ODE. This work requires weaker assumptions than prior work, replacing the Lipschitzness of the score function with a relative Fisher inform...	Rebuttal 1: Rebuttal: Questions: _The authors claim to tackle "all sources of error" in the abstract, though I would argue the statistical convergence rates remain open._ In our defense, we thought we have already acknowledged the lack of analysis on statistical convergence rates. In the conclusion, we suggest that “...	Summary: This work provides theoretical guarantees for diffusion flow matching (DFM) models, which are a recent class of generative models similar to score-based generative models (SGM). Extensive background is given in sections 1 and 2, and section 3 contains the results, namely, bounds on the KL divergence from the t...	Rebuttal 1: Rebuttal: Questions: _Are SGMs a sub-case of DFMs, for a well-chosen coupling and bridge, and perhaps up to a change of time variable?_ We thank the reviewer for raising this point. As suggested by the reviewer, after an appropriate time transformation ($t=\exp(-\tau)$), the Ornstein–Uhlenbeck (OU) proces...	Summary: The paper derives theoretical guarantees for a flow matching procedure which constructs a diffusion-like process which allows for samples obtained from a source distribution $\mu$ to a target distribution $\nu^*$. The goal is to build a suitable simple sampling procedure where a source sample is updated throug...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the thorough review and valuable comments. We greatly appreciate the reviewer’s recognition of the novelty and significance of our work, as well as the potential of our ideas for future theoretical and practical advancements in flow models. --- Rebuttal Commen...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their positive evaluation of our work and their valuable feedback. Their constructive comments will undoubtedly help enhance our original work, and we are committed to incorporating the suggested modifications. The reviewers acknowledge that our work advances t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics	Accept (spotlight)	Summary: This paper works on learning multi-human cooperative object manipulation, specifically the collaborative carrying of objects. This paper proposes a two-stage method to learn collaborative object carrying. In the first stage, the agent learns single-person object carrying from motion capture data and heuristic-...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and feedback. We hope the following clarifications address your concerns. > W1: This paper simulates an oversimplified humanoid without hand modeling considering the fact that it works on object manipulation problems, for which the hands play essential roles. ...	Summary: This work address the problem of multi-character collaboration for object transporting tasks. Different from previous works approaching the multi-character HOI task with tracking-based method, this work learns a physics-based multi-agent policy with reinforcement learning. Instead of directly training a multi-...	Rebuttal 1: Rebuttal: Thank you for your insightful comments. Many of the weaknesses you pointed out are exactly the areas we plan to address in future work, as we aim to make our method more generalizable. > Q1: This paper builds a framework for multi-agent cooperation policy learning with AMP for single-agent policy...	Summary: In this paper, the authors introduce a novel framework, Cooperative Human-Object Interaction (CooHOI), aimed at tackling the problem of multi-agent object transportation. The framework consists of two phases: initially, a single agent learns to perform tasks, followed by multiple agents learning to collaborate...	Rebuttal 1: Rebuttal: Thank you for your valuable comments and feedback. > W1.1 : It appears that the shapes and categories of manipulated objects are not very diverse, potentially constraining the method's ability to generalize to novel objects. We sampled nearly 40 common everyday objects for training, which fall i...	Summary: This paper proposes a framework for multi-agent cooperative manipulation in the context of humanoids carrying and transporting large furniture. This task is decomposed into several steps. First, they train a single humanoid to learn how to hold and carry relatively small objects. The agent is trained with grou...	Rebuttal 1: Rebuttal: Thank you for your valuable time and insightful comments. We hope the following clarifications address your concerns. > W1: I’m curious about the robustness of the proposed system. If I understand correctly, the bounding box information is given as ground-truth parameters. However, in reality, th...	Rebuttal 1: Rebuttal: We thank the reviewers for their detailed, valuable, and insightful feedback. We are pleased that the reviewers recognize our dedication to addressing the under-studied and important application of physics-based multi-agent cooperation HOI (Reviewer kufk). We appreciate the acknowledgment that...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Soft-Label Integration for Robust Toxicity Classification	Accept (poster)	Summary: The paper proposes a bi-level optimization framework for toxicity classification that integrates crowdsourced annotations with soft-labeling techniques. It aims to enhance robustness against spurious correlations by optimizing soft-label weights through GroupDRO. The method alternates between minimizing empiri...	Rebuttal 1: Rebuttal: We thank Reviewer 8fSW for the constructive and insightful comments. Please see our response to each of your questions below. 1. Explanation of Mathematical Notations in Eqn. (3) Thank you for your valuable feedback. Regarding the definition of $R$ in Eqn. (3), we would like to do further ex...	Summary: The paper presents a novel approach to toxicity classification by integrating crowdsourced annotations through soft-labeling and employing a bi-level optimization framework. The method aims to address the limitations of traditional toxicity classifiers that rely on single annotator labels and are prone to spur...	Rebuttal 1: Rebuttal: We thank Reviewer jUAS for the constructive and insightful comments. Please see our response to each of your questions below. 1. Computational overhead induced by bi-level optimization compared with single-loop optimization We add new experiments to compare the computational overhead induce...	Summary: The authors propose a two- layer optimization framework that integrates crowdsourced annotation and soft labeling techniques to optimize the soft label weights to improve the robustness of textual content toxicity classification. The method uses Group Distributionally Robust Optimization (GroupDRO) to optimize...	Rebuttal 1: Rebuttal: We thank Reviewer ZkTs for your insightful comments. Please see our response to each of your questions below. 1. Experiments on other public datasets We add experiments on a public HateXplain dataset [1]. It contains three classes -- "hatespeech", "offensive", "normal". We consider both hat...	Summary: This paper presents a bi-level optimization framework to integrate crowdsourced annotations with the soft-labeling technique and optimize the soft-label weights by GroupDRO to avoid the OOD risk. Strengths: * This paper introduces a novel approach to learn the soft label of (potentially) toxic content based o...	Rebuttal 1: Rebuttal: We thank Reviewer KPSa for the constructive and insightful comments. Please see our response to each of your questions below. 1. Discussion about pros and cons compared with SOTA commercial LLMs Thank you for your questions about the pros and cons of our method against SOTA commercial llms. ...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to express our gratitude for your constructive feedback on our submission and we appreciate the time and effort you have dedicated to reviewing our paper. In response to your valuable recommendations, we have incorporated additional experiments that align with your ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks	Accept (poster)	Summary: To address the issue where certain operators (e.g., spiking self-attention, SSA) in existing spiking Transformers cannot be executed on asynchronous neuromorphic chips, this work designs the Spiking Token Mixer (STMixer) architecture, which consists exclusively of operations supported by asynchronous scenarios...	Rebuttal 1: Rebuttal: Comment 1: The technical contribution is limited... Response to comment 1: I appreciate the reviewer's feedback but respectfully disagree that the technical contribution of this paper is limited. This paper does not merely propose a structure (implant ANN former structure into SNN) to achieve t...	Summary: Most of the spiking neural network architectures can not truely show the superiority on the neuromporphic hardware, since in event-driven scenarios, the spike arrival times are not precise and could result in significant differences in the output, like there is a max pooling layer. This paper propose the Spik...	Rebuttal 1: Rebuttal: Comment 1: Does the proposed method increase the energy consumption? The authors could provided detailed explanations. Response to comment 1: TheSML method does not increase the energy consumption during the inference stage. After the training phase end, the SML method eliminates the added bloc...	Summary: This article examines problems with the SSA module in Spikformer in asynchronous scenarios and suggest a new module, the Spiking Token Mixing (STM) module, which consists solely of network components that cater to asynchronous environments. Besides, This article proposed the information protection spiking patc...	Rebuttal 1: Rebuttal: Comment 1: From your code and Table 2 (SML->SDT). The performance improvement mainly comes from several SML blocks, which consist of several ANN layers. When considering adding residual connection and several SML blocks, the STMixer can even be seen as an ANN model (floats input is connected to t...	null	null	Rebuttal 1: Rebuttal: General Response We appreciate all of the reviewers' comments and reviews. Here, we would like to provide a general response to reemphasize the motivation of this paper and its contribution to the SNN field. The main goal of this work is to design a well-performing SNN model that is friendl...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling	Accept (poster)	Summary: The paper introduces a framework named TREAT (Time-Reversal Symmetry ODE) aimed at improving dynamical system modeling through a physics-informed approach. It incorporates Time-Reversal Symmetry (TRS) as a regularization term to enhance model precision. This approach is shown to preserve energy in conservative...	Rebuttal 1: Rebuttal: We sincerely thank you for your valuable comments and suggestions for improving our paper! We would like to make the following claims and hope they can address your concerns. ### W1. About the use case for Vanilla neural ODE Thank you for the valuable suggestion! Our TRS loss can be couple...	Summary: The paper proposes a novel Time-Reversal Symmetry (TRS) graph neural ODE, where the TRS is introduced as a soft regularisation term. Strengths: The paper presents a novel method to combat numerical errors for graph neural ODEs. Weaknesses: The paper does not introduce early enough that the time-reversal symm...	Rebuttal 1: Rebuttal: We're grateful for your support and helpful feedback! Please kindly find our response below to the questions raised in the review and let us know if you have any further questions. ### W1: Reference about TRS-ODEN. Thank you for your advice! We would like to discuss more about TRS-ODEN in t...	Summary: This paper proposes a regularization term to enforce Time-Reversal Symmetry (TRS) for modeling dynamical systems. The method helps preserve energies for conservative systems while serving as a strong inductive bias for non-conservative reversible systems. They also prove that TRS loss can universally improve m...	Rebuttal 1: Rebuttal: We thank you for your insightful comments on improving our paper. However, we believe there’s some misunderstanding and would like to make the following claims to address your concerns. ### W1: The difference to the final implementations. We would like to mention that both methods are approxi...	Summary: This paper proposes a method to enhance neural ordinary differential equations (ODEs) by enforcing approximate Time-Reversal Symmetry. A self-supervised regularization term is introduced to align forward and backward trajectories predicted by a neural network, promoting energy conservation and stability in the...	Rebuttal 1: Rebuttal: We sincerely appreciate your insightful comments and valuable advice on improving our paper. We highly appreciate your recognition of the novelty and significance of our work. Regarding your questions, we detailed our responses below. ### **W1: Empirical results on additional real-world examples....	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Induced Model Matching: Restricted Models Help Train Full-Featured Models	Accept (spotlight)	Summary: This paper proposes a method to train models which have access to all of the features by making their marginal distribution match a known weaker model which only uses a subset of those features to predict. The authors relate this to knowledge distillation and noising, and come up with an approximate objective ...	Rebuttal 1: Rebuttal: Thank you for your deep reading of our paper and for being open-minded about revisiting your assessment! Regarding the key points you raise: + Empirical results dated — You are correct that the motivation for comparison with LSTMs was because of the noising baselines existing there. We used ...	Summary: The paper considers the learning problem when in addition to the training set an additional _restricted_ model is available. The restricted model is trained on a different dataset, potentially containing a only subset of features. It is proposed to augment the training loss with the special induced model match...	Rebuttal 1: Rebuttal: Thank you for your very constructive review! We address some of the points raised: + Notation — Thank you for your suggestions on improving the notation. We have been working on a few potential alternatives to further clarify our presentation. Here's what we suggest to make things more readab...	Summary: The authors introduce the problem of “Induced Model Matching” where there exists a small and restricted model that only takes into account some of the features and is able to predict relatively well the label given these features. The key question of this paper is how one can leverage such a small model when t...	Rebuttal 1: Rebuttal: Thank you very much for deeply understanding and appreciating our paper. We address some of the points raised: + Computational cost — Our general attitude is: given a fixed amount of data and a feature-restricted model, how can we do most with it? We are thus mostly concerned with statistical...	Summary: - Algorithm: This paper proposes a framework for how a good but restricted feature model, e.g. $\bar{P}(y \mid x_1)$, can be used as guidance when training a full-feature model, e.g. $Q(y \mid x_1, x_2, x_3...)$. \ Instead of the knowledge distillation objective from weak teachers, which directly adds a regul...	Rebuttal 1: Rebuttal: Thank you very much for deeply understanding and appreciating our paper. We address some of the points raised: + Scale of the experiments — The computational overhead of the model and the fact that we didn't have the infrastructure for large models, meant that we dedicated ourselves to simple...	Rebuttal 1: Rebuttal: We thank you all for your insightful and positive reviews. We are encouraged by your appreciation of our work and for your constructive criticism. We are lucky to have received such high quality feedback. We have individually addressed all the points that you've raised. There is, however, one com...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Stochastic Concept Bottleneck Models	Accept (poster)	Summary: Focusing on concept bottleneck models, this paper extracts concept dependencies with a multivariate normal distribution and derives an intervention strategy based on the confidence region of the normal distribution that incorporates concept correlations for better interventional effectiveness. Strengths: 1. T...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and questions! Below is our point-by-point response. > Intervention by setting values can be difficult in some case, the example given by the paper: "intervene on the CBM by setting its value to 1" is an extreme case. Humans are not good at estimating probab...	Summary: This paper introduces a novel concept dependency modelling scheme via an explicit distributional parameterization based on multivariate Gaussian distributions. This allows for capturing the dependencies between different concepts, while giving rise to an effective intervention strategy. The experimental resu...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. In our response below, we address the remaining open points. > What is the complexity compared to a standard diagonal approach? As the reviewer rightly points out, modeling dependencies comes at a complexity overhead cost. In terms of memory complexity S...	Summary: This paper presents a method of performing interventions on Concept Bottleneck Models. The method parametrizes the space of concepts with a generative model of Bernoulli distribution and concept logits with a normal distribution, whose mean and variance depends on the input data distribution. The method also c...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your comments and positive feedback. Please find below our answers to the open points. > While the overall writing of the paper is clear, Section 3.3 was a bit challenging to follow, especially the part about the confidence region. It is not entirely clear to me wha...	null	null	Rebuttal 1: Rebuttal: Dear reviewers, We would like to thank all of you for your thorough reviews and constructive feedback! Below, we summarise our responses to your main concerns, additional results, and changes to be implemented upon acceptance in the revised manuscript. * We have included experiments on a new lar...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learn To be Efficient: Build Structured Sparsity in Large Language Models	Accept (spotlight)	Summary: This paper proposes a method to increase the structured sparsity of models through training, called Learning-to-be-efficient (LTE). It introduces a new training loss that guides the model to activate fewer neurons while maintaining original performance. Simultaneously, LTE employs a threshold-based sigmoid rou...	Rebuttal 1: Rebuttal: We are glad that the reviewer found that our work has extensive evaluation and shows strong empirical performance. We thank the reviewer for the constructive feedback and appreciate the opportunity to address the points you have raised. --- > Q1: Due to the mainstream dense models having an...	Summary: The paper presents a new approach (LTE) aimed at improving the inference efficiency of large language models by developing structured activation sparsity. The method trains LLMs to activate fewer neurons in FFN layers while attempting to maintain task performance. The approach works by grouping neurons into ex...	Rebuttal 1: Rebuttal: We are glad that the reviewer found that our work has extensive evaluation and our custom kernel increases the applicability. We thank the reviewer for the constructive feedback and appreciate the opportunity to address the points you have raised. --- > Q1: The two-step training increases th...	Summary: This article introduces a novel training algorithm, LTE, designed to train large language models (LLMs) to achieve more structured activation sparsity during inference. Thus, it enhances their efficiency without compromising performance until a very high sparsity. Strengths: 1. LTE performs excellently across...	Rebuttal 1: Rebuttal: We are glad that the reviewer found that our work has excellent performance. We thank the reviewer for the constructive feedback and appreciate the opportunity to address the points you have raised. --- > Q1: Stage 1 of LTE will train all the model's parameters. How do you implement Dejavu? ...	Summary: This work aims to introduce structured sparsity to large language models (LLMs) to improve their execution efficiency. To achieve this, it enhances previous MoEfication methods by employing a sigmoid-based non-competitive routing function and a threshold-based expert selection, allowing for adaptive expert num...	Rebuttal 1: Rebuttal: We are glad that the reviewer found that our work is well-motivated and sound. We thank the reviewer for the constructive feedback and appreciate the opportunity to address the points you have raised. --- > Q1: Novelty concerns: A sigmoid router was proposed in previous MoE works like [1][2]...	Rebuttal 1: Rebuttal: ## General Response Dear reviewers, We thank all the reviewers for their constructive reviews towards improving our work. We are pleased that reviewers found our paper’s advantages: “LTE constantly achieves better performance-sparsity trade-off across multiple models, datasets, and task types....	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning	Accept (poster)	Summary: SubgDiff is introduced to improve molecular representation learning by integrating substructural information into the diffusion model framework. It offers three key technical contributions (subgraph prediction, expectation state, and k-step same subgraph diffusion) to enhance the network's understanding of mo...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive suggestions and useful feedback! > W1: The denoising process needs better explanations. AW1: Thanks for the suggestive advice. We will further describe the details of the denoising process in the paper. The only difference from the traditional diffu...	Summary: The paper proposed SubgDiff which is a diffusion model used in self-supervised learning setup to enhance the molecular representation learning. It introduces motif enhancement during the diffusion process to force the model to learn more structure information. Strengths: 1. The idea of enhancing motif informa...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper and sharing these important points. These comments highlight several areas where we can improve the clarity and thoroughness of our paper. We acknowledge the issues raised and would like to address each point. > W1: The authors directly use the baseline results f...	Summary: The paper presents a new denoising diffusion probabilistic model (DDPM) named SubgDiff, designed to enhance molecular representation learning by incorporating substructural information into the diffusion process. SubgDiff introduces a mask operation that selects subgraphs for diffusion, aiming to better captur...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments and thoughtful feedback on our work. [W1. Highly specialized datasets] Thanks for highlighting this important concern. The subgraph prediction model shares the molecular encoder with the denoising network and has an additional classification hea...	Summary: The paper proposes a diffusion-based pretraining method using subgraphs to learn enhanced molecular representations. Unlike previous methods which normally add noise to every atom, this paper proposes adding noise based on subgraphs. The method is evaluated on various downstream tasks to demonstrate its effect...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments and positive feedback! [Weakness 1. Chemical intuition] Thank you for your insightful comment! We agree that chemical intuition and domain knowledge are crucial in molecular representation learning, and we'd like to address how our method incorp...	Rebuttal 1: Rebuttal: ## General Response Dear reviewers, Thanks to all the reviewers for your time and effort during the review process and the constructive advice. In addition to the response to each reviewer individually, we conduct the ablation study and sensitivity analysis of the k-step same subgraph and diffus...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation	Reject	Summary: This paper proposes a new weakly supervised semantic segmentation task. This task uses pixel-level categorical distribution as the label in the training stage. KL divergence is used as the training loss. Experiments on three public segmentation datasets show the effectiveness of the proposed method. Strengths...	Rebuttal 1: Rebuttal: Comment 1: Labeling effort on complex images. Images from PASCAL VOC (like Figure 1) are easy to annotate. It contains few classes and the background is generally clean. The density of target objects is low, and hence it’s also suitable for the proposed grid-based size target annotation way. ...	Summary: The paper titled "Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation" proposes a novel method of semantic segmentation that leverages approximate size targets instead of full pixel-level supervision. The method involves using categorical distributions to represent the expected average p...	Rebuttal 1: Rebuttal: Comment 1: Simplicity of Method: While the proposed method is innovative, it seems relatively simple. There might be opportunities to enhance its contributions with further development or by integrating additional techniques. \ Response: We appreciate the recognition of the simplicity of o...	Summary: This paper introduces a novel image-level supervision method for semantic segmentation using approximate segment size targets. It utilizes categorical distributions for expected average predictions, reducing annotation cost and complexity. The authors propose a zero-avoiding KL divergence as a training loss, c...	Rebuttal 1: Rebuttal: Comment 1: The paper claims robustness to size target errors but provides limited detailed analysis on this aspect. Including more experiments to quantify and analyze how different levels of size target errors impact performance would provide a clearer understanding of the method's robustness....	Summary: The paper introduces a novel image-level supervision method for semantic segmentation, utilizing approximate targets for the relative sizes of segments in training images. These targets, represented as categorical distributions for the expected average prediction over pixels, are integrated using a zero-avoidi...	Rebuttal 1: Rebuttal: Comment 1: The title of the paper is misleading. It claims that approximate size targets are sufficient, but the work also uses image labels for supervision. \ Response: We would like to reassure the reviewer that there was no intention to mislead the readers about the size-target supervis...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful and positive feedback on our size-target approach for image-level semantic segmentation. We are encouraged by their recognition of the novelty (GY7b, FTCz, AEBs, thaL), simplicity (GY7b, AEBs), and practicality (FTCz, AEBs, thaL) of our approach. We are g...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
UDON: Universal Dynamic Online distillatioN for generic image representations	Accept (poster)	Summary: The paper introduces a novel method for enhancing universal image embeddings through a multi-teacher knowledge distillation approach. The method, named UDON, employs a dynamic sampling technique and a shared backbone across multiple domain-specific teachers and a universal student model to efficiently distill ...	Rebuttal 1: Rebuttal: > __This paper misses a series of very important works for embedding learning [1,2]. The [1] proposed the Matryoshka representation learning (MRL) that the OpenAI's recent embedding model adopts. MRL would unfold the embedding model into multiple dimensions such as 64, 128, 512 or more. This paper...	Summary: The paper proposes Universal Dynamic Online distillatioN (UDON), which is a multi-teacher distillation method designed for universal image representations. UDON adopts a knowledge distillation strategy by distilling information from multiple teacher model trained for different domains to a student model to lea...	Rebuttal 1: Rebuttal: We thank the reviewer for the comment about how the UDON method works compared to the USC baseline. We will provide some clarifications and explain our interpretation of the key components that allow UDON to produce a better universal embedding than previous work, based on the findings from the ex...	Summary: The paper tackles the problem of multi-domain fine-grained instance recognition/retrieval. The authors propose to train a unified backbone for all modalities with online distillation with domain-specific teachers, improving the performance compared to naive single backbone baselines and being competitive with ...	Rebuttal 1: Rebuttal: > __[important] The paper tackles generalization for instance recognition/retrieval systems, but the scaling axis of this question has not been studied. This is lacking since in the past few years we have witnessed scalable pre-training in terms of data and parameters being a very effective soluti...	Summary: The paper proposes an online distillation approach in a multi-teacher setup w/ weight sharing for efficiency. A strategic dynamic batch sampling process has been proposed to help domains w/ slower learning during training. Strengths: The unified backbone and batch sampling strategy is novel and powerful. The ...	Rebuttal 1: Rebuttal: > __Can you elaborate more on impact of the throughput drop in real-world industry applications?__ The 20% throughput drop only impacts the model training stage. The inference time of our model is exactly the same as previous state-of-the-art models from [44], which makes our inference-time compa...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback. We are encouraged that they recognize our contributions as novel (Mmnc, uq4A), interesting (CCei), and simple/elegant (xwWg). Reviewers also highlight the value of our experimental validation/ablations (Mmnc, CCei, **x...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection	Accept (poster)	Summary: This paper proposes 3DET-Mamba, the first attempt to exploit the State Space Model for end-to-end 3D object detection. It introduces a local-to-global scanning technique, including an Inner Mamba block to capture local geometry and Dual Mamba blocks to extract scene features in a global view. Furthermore, it p...	Rebuttal 1: Rebuttal: Q1: Computational costs and scaling up performance. A1: - To further verify the effectiveness of 3DET-Mamba, we report latency and memory costs in the following table. It can be seen that our model achieves better results with less computational cost and lower latency compared to 3DETR. ...	Summary: The paper proposes an end-to-end 3D detector named 3DET-Mamba that fully takes advantage of Mamba. 3DET-Mamba can model long-range global information (Dual Mamba) while exploiting local information (Inner Mamba). Experiments conducted on the ScanNet and SUN RGB-D datasets validate the effectiveness of the pro...	Rebuttal 1: Rebuttal: Q1: Difference between Algorithm 2 and Figure 3. A1: Thanks for reviewing our article carefully. The algorithm 2 shows the detailed operation steps of our model. The figure 3 is a schematic figure and may omit some details. According to the suggestion of the reviewer, we will modify Figur...	Summary: This paper proposes leveraging Mamba blocks for 3D point cloud modeling in the form of 3DET-Mamba, an application of SSM for 3D object detection. Similar to the prior 3DETR model, this approach partitions the point cloud into "patch" using Mamba blocks to capture local information, complemented by global model...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. Q1: Comparison of time and space complexity between 3DET-Mamba and other methods and the effects of changing resolution. A1: To demonstrate the superior performance of 3DET-Mamba, we compare the FLOPs and latency of our model with the pervious transform...	null	null	Rebuttal 1: Rebuttal: Dear AC and reviewers, We thank all reviewers (Reviewer m4H4-R1, Reviewer 2ZE6-R2, Reviewer eAYh-R3) for approving our contributions, including our exploration of mamba for end-to-end indoor 3D object detection for the first time (R3). The experimental results are convincing (R3), demonst...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch	Accept (poster)	Summary: This paper studies the process of reward design and as a result reward misspecification when a human designer has potentially misspecified beliefs about the robot's operating domain or generally trouble with designing a reward leading to the robot generating desired behavior in its own domain. The paper formal...	Rebuttal 1: Rebuttal: We thank the reviewer for all the constructive feedback and for catching the typos. We will incorporate them into the final draft. Below, we have provided responses to specific questions and comments raised. IRD: We will make sure to provide a more clear description of IRD in the evaluation secti...	Summary: The paper addresses the problem of reward misspecification. It introduces a framework (EAL) to capture how humans go from setting expectations about a problem to specifying the reward for it. The problem is modeled as a single Human-Robot interaction. After introducing the formalism, the authors propose an al...	Rebuttal 1: Rebuttal: We thank the reviewer for all the comments. We will make sure to incorporate them into the paper. Below, we have provided responses to some specific concerns and questions raised in the review. Formal Proof: We will be more than happy to include detailed formal proof for all the propositions and ...	Summary: The paper tackles the problem of reward misspecification in settings where humans have potentially incorrect beliefs about the environment. Instead of treating a true human reward function as the fundamental object, they introduce expectation sets, which specify the states that the human does or doesn't expe...	Rebuttal 1: Rebuttal: We thank the reviewer for all the feedback. We will make sure to incorporate them into the paper. Below, we have provided responses to some specific concerns and questions raised in the review. Transference: We want to thank the reviewer for all their constructive comments. In regards to the ques...	null	null	Rebuttal 1: Rebuttal: We thank the reviewer for all the comments and feedback. We are extremely happy that the reviewers found our paper well-written, novel, interesting, and useful. We will make sure to incorporate all suggestions, recommendations, and corrections. We have tried to provide specific answers to each rev...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis	Accept (poster)	Summary: This paper tackles the generalizable NVS problem from sparse-view inputs. The main claim is that relying on epipolar geometry cues harms the performance, and, thus, it removes these geometric priors. It adopts the pre-training and supervised reconstruction/NVS training pipeline as in Dust3r. The authors verify...	Rebuttal 1: Rebuttal: ### Weaknesses 1. Overclaim Issue We would like to clarify any confusion regarding our use of the term "epipolar-free." This term specifically indicates our method's avoidance of epipolar sampling and cost volume techniques, which are commonly used in generalizable novel view synthesis (GNVS)...	Summary: This paper addresses the task of 2-view generalizable novel view synthesis. It introduces a cross-view completion model as prior assistance and incorporates cross-view Gaussian alignment after predicting Gaussian attributes to enhance cross-view consistency. Strengths: 1. The introduction of pretraining the c...	Rebuttal 1: Rebuttal: ### Q1. The most influential module in Table 3 The results in Table 3 show that the absence of the epipolar-free cross-view mutual perception results in the most significant performance decline. This highlights the crucial role of the pretraining and network structure of Croco in providing 3D ...	Summary: This paper introduces a robust pipeline for generalizable 3D Gaussian novel view synthesis, utilizing a cross-attention model trained on large-scale datasets. This approach enables the model to generalize effectively to new scenes without depending on epipolar geometry, which is commonly used in traditional me...	Rebuttal 1: Rebuttal: ### Weaknesses 1. Missing experiments in a sparse-view setting and comparison with other sparse-view methods We thank you for identifying the novelty of our idea. Nevertheless, maybe we did not illustrate the difference between GNVS and sparse-view NVS clear enough and make you miss the focus ...	Summary: This work presents eFreeSplat, a model to address generalizable novel view synthesis without relying on epipolar prior. Specifically, it extracts the cross-view mutual perception by leveraging a pre-trained CroCo model. It then improves the alignment of multi-view Gaussians by using an iterative updating strat...	Rebuttal 1: Rebuttal: ### Weaknesses 1. Performances regarding cross-dataset generation Thank you for your valuable suggestions on our work. We fully agree with your view on the importance of cross-dataset testing and, following your advice, have added cross-dataset test results on the DTU dataset. For specific exp...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful feedback! We are encouraged that they found our motivation for using a pre-trained model to address non-overlapping and occluded regions interesting and well-grounded (w4rj, MtQr, 1Kz3). They also praised our paper's well-written presentation (w4rj) and ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Fast and Memory-Efficient Video Diffusion Using Streamlined Inference	Accept (poster)	Summary: In this paper, the authors focus on reducing the computational requirements and high peak memory usage for video diffusion models. Specifically, a train-free framework is proposed, which consists of three parts: Feature Slicer, Operator Grouping, and Step Rehash. Those three steps result in significant memory ...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below. --- #### W1. Generalization on other variants and backbones. We agree that a more comprehensive evaluation could help demo...	Summary: This paper proposes a training-free video diffusion inference acceleration method, which includes three processes: Feature Slicer, Operator Grouping, and Step Rehash. Compared to the baseline, the proposed method shows significant improvements in memory usage and inference speed. Strengths: - The method descr...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below. --- #### W1. The experiments need to be improved. Please refer to Global rebuttal A3 and A2 for more detailed resul...	Summary: This paper presents a framework for reducing the computational demands of text-to-video diffusion models. The main idea involves dividing input features into subfeatures and processing them parallelly, thereby reducing peak memory usage during sampling. To address the increase in overall compute time caused by...	Rebuttal 1: Rebuttal: We sincerely appreciate the feedback from the reviewer. First, we would like to kindly clarify some misunderstandings here: 1. We do provide the definition of the “full computation step” in the “line 265, 266”. 2. Our method can be applied to DiT backbones without any design changes. 3. Our work ...	Summary: The paper introduces a novel, training-free framework to optimize video diffusion models. This framework, consisting of Feature Slicer, Operator Grouping, and Step Rehash, significantly reduces peak memory usage and computational overhead while maintaining video quality. Extensive experiments demonstrate that ...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below. --- #### W1. Concern about maintaining video quality. Please refer to Global rebuttal A1 and A4. As discussed in Gl...	Rebuttal 1: Rebuttal: We thank the reviewers for acknowledging that our work importance and broad applicability (Reviewer mL5N, M5Qw), our method is novel, and high-performing (Reviewer mL5N, M5Qw, 5EX5), our experiments are comprehensive (Reviewer mL5N), and our paper is well-written (Reviewer 2uzE, 5EX5, M5Qw). --- ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Your contrastive learning problem is secretly a distribution alignment problem	Accept (poster)	Summary: The paper presents a novel perspective on contrastive learning (CL) by framing it as a distribution alignment problem using entropic optimal transport (OT). It trains an encoder network $f_\theta$ by iteratively updating encoder parameter $\theta$ and corresponding transport plan $P$ among encoded augmentation...	Rebuttal 1: Rebuttal: Thank you for your feedback. We appreciate your time and suggestions. Our specific responses to your questions are provided below. 1. “ the specific criteria for convergence are not clearly defined. [...] concerns about the algorithm's efficiency in large-scale applications. The experiments lack...	Summary: The paper recasts several self-supervised learning (SSL) paradigms, such as SimCLR, in an optimal transport framework. In many SSL variants each batch contains two views of the same data sample. This means that the embeddings of a batch can be viewed as the union of two sets, each one containing only one of th...	Rebuttal 1: Rebuttal: We sincerely thank you for your thorough evaluation and insightful feedback on our manuscript. Based upon your suggestions, we plan to make major revisions to this paper to improve the quality of the presentation. Below we provide replies to your other questions and concerns. 1. Suggestions on re...	Summary: This paper introduces a framework called Generalized Contrastive Alignment (GCA) that connects contrastive learning to distribution alignment using optimal transport. The key contributions include: 1. Establishing a novel class of losses and algorithms for representation learning through GCA, showing how diff...	Rebuttal 1: Rebuttal: Thanks so much for your detailed feedback! We will now provide a point-by-point response to your questions. 1. “Scaling to larger datasets would be beneficial.” Reply: Thanks for your suggestion. Please see that we have added the ImageNet100 and SVHN evaluation results to Table R1 in the ge...	Summary: This paper proposed to view contrastive learning (CL), a popular framework for learning data representation in machine learning. Specifically, the work builds on some recent works that view CL as an alignment problem with optimal transport, showing some previous popular CL frameworks are the special form of th...	Rebuttal 1: Rebuttal: We sincerely thank you for your comments and detailed feedback. We appreciate the opportunity to discuss the contributions of our work in connection to Shi et al. and also expand our comparisons based upon your suggestion. 1. Discussion of overlap with Shi et al. Reply: Thanks for your comm...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback. We appreciate that the reviewers thought that our work “provides an interesting new perspective on what SSL does” (Reviewer TBTc) and “the methodology and theory introduced is sound” (Reviewer 96Mt), provides “ a solid theoretical foundation” (R...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification	Accept (poster)	Summary: The paper is well-written and proposes a new modular mechanism that can (selectively) combine local and global interactions in a model. The authors discuss the theory behind their proposed smoothness operator and pooling mechanism and provide detailed proof for their claims. They have also evaluated their mode...	Rebuttal 1: Rebuttal: We thank Rev. uFQt for their valuable insights. Next we address their concerns. The choice of encoder. We apologize for not providing a rationale for the chosen encoder in each dataset. When we started our experiments, we fixed ResNet18 w/ Imagenet weights as the default one. This is the one ...	Summary: The paper proposed a multi-instance learning approach. The basic idea is to make use of the spatial dependency between training samples. A smoothing operator was proposed to regularize the attention matrix with respect to inter-sample similarity (to my understanding), which the authors claimed to improved both...	Rebuttal 1: Rebuttal: Thanks for your feedback. First we provide a general clarification. Then we address the rest of points. --- GENERAL CLARIFICATION It is unfortunate that the concepts of classification and localization used in the manuscript are not found clear. These concepts are taken from the MIL literatur...	Summary: The authors propose a technique to improve the localization capabilities of the current MIL, especially for CAD models that perform CT and WSI analysis. The method is based on seeing the attention attributed to each patch as a graph and minimizing its Dirichlet energy, promoting smooth transitions on the atten...	Rebuttal 1: Rebuttal: We thank Rev. jUey for their positive and valuable feedback. Next we address their concerns. Visual comparison between ABMIL and SmAP. As pointed out by Rev. jUey, the fact that the Dirichlet energy decreases when using the proposed operator (Table 3 in the paper) does not imply that the inst...	null	null	Rebuttal 1: Rebuttal: ## GLOBAL RESPONSE We thank the three reviewers for taking their time to read our work, providing constructive and valuable feedback. We appreciate it. We are happy that the feedback is in overall positive: "well-developed and well written", "minor adjustments and additional informati...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Are Self-Attentions Effective for Time Series Forecasting?	Accept (poster)	Summary: This work proposed a cross-attention only transformer model for time series forecasting Strengths: Interesting work. This study thoroughly examines the effectiveness of cross-attention layers in transformers and proposes several useful techniques to build a high-performance model. Weaknesses: 1. It is intrig...	Rebuttal 1: Rebuttal: We appreciate the reviewer's insightful assessment of our paper. First of all, we would like to summarize the key points of our rebuttal before detailed responses to each of your concerns. 1) Distinct Role of Self-Attention and Cross-Attention In transformer architectures, self-attention and...	Summary: The paper titled "Are Self-Attentions Effective for Time Series Forecasting?" introduces a novel time series forecasting architecture named Cross-Attention-only Time Series transformer (CATS). The central hypothesis of the paper is that self-attention mechanisms, a key component of the Transformer models, may ...	Rebuttal 1: Rebuttal: We appreciate the reviewer's assessment of our paper. We especially thank the reviewer for pointing out areas such as the readability of Section 3.2, and we will ensure that our paper is revised thoroughly. However, we would first like to address two critical points. **1) Novelty of the Proposed ...	Summary: The paper presents a novel architecture, the Cross-Attention-only Time Series transformer (CATS), which challenges the conventional use of self-attention mechanisms in Transformers for time series forecasting. The authors propose a model that leverages cross-attention instead, aiming to enhance long-term forec...	Rebuttal 1: Rebuttal: We appreciate your detailed feedback on our paper. Your insights have been invaluable in helping us refine our work. We acknowledge the concerns raised regarding our experimental setup and the scope of our evaluations, and we are grateful for the opportunity to address these points. Below, we pro...	null	null	Rebuttal 1: Rebuttal: Dear all, We would like to thank the editor and the reviewers for their careful comments and suggestions. We summarize the reviews according to our own perspective. Strengths. We are glad that Reviewers sgNU, Q7Js, and ztsM found that our results "introduce an innovative approach in time se...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Improved Algorithms for Contextual Dynamic Pricing	Accept (poster)	Summary: This paper expands the line of work on contextual dynamic pricing where the buyer's valuation may be noisy. The authors give a simple way to obtain an unbiased estimate for the true valuation function, and propose an exploration - exploitation type algorithm that learn simultaneously the true valuation functio...	Rebuttal 1: Rebuttal: We thank the reviewer for the comment. ### Weaknesses i.i.d noise: This assumption is standard in the literature investigating the problem of dynamic pricing. For instance, all the papers mentioned in Table 1 share such noise structure. Nevertheless, as we mentioned in the conclusion, it cou...	Summary: The paper studies the contextual dynamic pricing problem with binary demands and an unknown noise distribution. It presents a general framework for a pricing algorithm and proves its regret bounds for two specific types of valuation functions: linear and non-parametric. In the linear case, its results improve ...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. ### Weaknesses: Regarding the statement made in line 186 and the differences with the work in [1] we kindly refer the reviewer to the second question in the response to reviewer Beqc, where we provide more explanation regarding what we meant by this stat...	Summary: The paper addresses the problem of dynamic pricing using contextual information, aiming to maximize a seller's revenue by setting prices based on the context and buyer valuations. Buyers purchase products if the prices are lower than their valuations. The authors propose an algorithm called VALUATION APPROXIMA...	Rebuttal 1: Rebuttal: We thank the reviewer for the comment. ### Weaknesses Experiments: We agree with the reviewer that it would be beneficial to further demonstrate the advantages of our approach over previously suggested algorithms through empirical evaluation. We started implementing our algorithm and the var...	Summary: This paper studies the problem of online contextual pricing under a linear noisy valuation model, with the noise distribution unknown to the seller. This work proposes an "exploration-then-elimination" algorithm that achieves $O(T^{2/3})$ optimal regret under the assumptions of (1) stochastic feature s...	Rebuttal 1: Rebuttal: We thank the reviewer for the careful analysis. ### Weaknesses 1/ We thank the reviewer for providing us with this relevant reference. As noted by the reviewer, both our work and that of [1] use the idea of sharing knowledge across contexts to improve the estimation of the c.d.f. $F$. However, o...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation	Accept (poster)	Summary: This paper proposes a generative method for dichotomous image segmentation. In detail, non-rigid and rigid editing techniques are used to generate high-quality synthetic masks. Those masks are leveraged for segmentation model training, which typically requires expensive dichotomous image labeling. Strengths: ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their meticulous review and helpful suggestions. We address each point as follows: ### Q1: Typos and Errors We apologize for these oversights and will correct all identified issues in the revised version: a. We will change (V,E) to (V, E_s) in line 140 and en...	Summary: This paper introduces a new approach for generating diverse and precise datasets. The authors first introduce a mask editing method that combines rigid and non-rigid editing techniques to generate high-quality synthetic masks leveraging geometric priors from diffusion models and adversarial training and self-a...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's comprehensive examination of our work. Our responses to each point are as follows: ### Q1: Novelty of the method and combination of existing approaches While our approach does integrate existing methods, its novelty lies in: 1. **Task-specific adaptations fo...	Summary: ⁤The paper introduces MaskFactory, a method for producing high-quality synthetic datasets for Dichotomous Image Segmentation (DIS) tasks. ⁤⁤The method includes a two-stage process: mask editing (combining rigid and non-rigid transformations) and image generation using multi-conditional control methods. ⁤⁤The s...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's perceptive observation. We provide a more in-depth analysis below: Q: The paper acknowledges issues with unnatural images in complex scenarios, but could delve deeper into how these might impact practical applications. A: We address this concern by focusing...	Summary: This paper introduces MaskFactory, a novel approach aimed at addressing the challenges of generating high-quality synthetic datasets for Dichotomous Image Segmentation (DIS) tasks. The authors tackle the challenges by leveraging a blend of rigid and non-rigid editing techniques to generate accurate synthetic ...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' thorough evaluation and constructive feedback. We are grateful for the opportunity to clarify and expand on our work. ### Q1: Can you elaborate on the methodology, especially regarding mask editing and unique designs? We will expand the methodology section ...	Rebuttal 1: Rebuttal: We appreciate the reviewers' thoughtful comments and suggestions. To address some of the concerns raised and provide additional support for our claims, we have prepared a global PDF with supplementary visual evidence. This document contains three key figures that demonstrate the effectiveness and ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints	Accept (poster)	Summary: This article tackles the problem of multi-armed bandits with general constraints: at each time step, the learner receives a reward and $m$ costs, corresponding to $m$ independent constraints, and its goal is to maximize the reward while maintaning the cumulative cost corresponding to each constraint at a sub-l...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments about the paper. We agree that a more detailed discussion on this point is necessary and will include an extended version of our response in an additional paragraph. However, we would like to emphasize that the primary contribution of our paper is demonst...	Summary: This paper studies the problem of multi-armed bandits under long-term constraints. They give an algorithm with regret and violation guarantees simultaneously for both stochastic and adversarial constraints (i.e. best-of-both-worlds guarantees). Specifically, they guarantee $\tilde{O}(\sqrt{T})$ regret and viol...	Rebuttal 1: Rebuttal: Thank you for the positive feedback on our work. ### On related works: We will gladly include the related works highlighted by the reviewer in the final version of the paper. However, we do not consider these works to be technically very related to ours. We outline the main reasons below. Firs...	Summary: This work studies the bandit with constraints problem where the authors consider two possible settings for the constraints -- one where the constraints are stochastic, sampled i.i.d. from some unknown distribution, and one where the constraints are adversarial. The rewards are always assumed to be generated by...	Rebuttal 1: Rebuttal: Thanks for the positive comments about our paper. On the OGD interpretation of the estimator updates: the main benefit of viewing the update as a variant of gradient descent is that it allows us to simplify the analysis in the proofs. We will add a remark in the main paper to clarify this poi...	Summary: This paper studies multi-armed bandits with cumulative cost connstraints, with the focus of designing a 'best of both worlds' algorithm that has small constraint violations in both the stochastic and adverasarial settings (where the constraints as well as rewards can vary with time arbitrarily), and attain eit...	Rebuttal 1: Rebuttal: Thanks for the positive comments about our work and for pointing out the line of research on safe bandits. We will acknowledge it in the final version of the paper. On the constants: we didn’t try to optimize the constants as we were mainly interested in achieving asymptotically optimal regret r...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation	Accept (poster)	Summary: This work introduces a novel training-free paradigm which uses the retrieval augmentation to expand the knowledge boundaries of existing large restoration models by incorporating reference images as external knowledge to facilitate the restoration of high-fidelity details. The authors propose the nearest neigh...	Rebuttal 1: Rebuttal: ### [Q1: Variation of the outputs from different LRMs] > In Table 1, why there is a variation of the performance improvement when applying the proposed method to different LRMs? For example, there is a 0.38 dB PSNR improvement in SeeSR but only a 0.03 dB improvement in SUPIR. Additionally, why do...	Summary: The paper introduces ReFIR, a novel method designed to enhance the capabilities of Large Restoration Models by incorporating external knowledge through the retrieval of high-quality, content-relevant images. The main contribution is to (1) Gives both quantitative and qualitative results on how existing LRM wo...	Rebuttal 1: Rebuttal: ### [Q1: Difference from other works] > Difference form other methods. The proposed method seems relevant to some of works in image editing tasks, such as MasaCrtl and Prompt2prompt, which also use training-free technique to modify the behavior of the diffusion model. A detailed explanation about...	Summary: This paper presents a plug-and-play approach to enhance the quality of Diffusion-based super-resolution models by leveraging reference images. The authors exploit CLIP to effectively filter high-definition images with similar semantics from a pre-trained dataset. These reference images are then employed to rep...	Rebuttal 1: Rebuttal: ### [Q1: Reference image presentation] Thanks for your advice, we will add the retrieved image in Fig.5 and Fig.6 in the revision to improve the presentation quality. ### [Q2: Improving ReFIR's ability in real-world scenarios] > Tables 1 and 2 highlight a significant performance ... detailed d...	Summary: The paper titled "ReFIR: Grounding Large Restoration Models with Retrieval Augmentation" introduces a novel framework called Retrieval-augmented Framework for Image Restoration (ReFIR). This method addresses a significant issue in diffusion-based Large Restoration Models (LRMs) — the tendency to generate hallu...	Rebuttal 1: Rebuttal: ### [Q1: Reliance on the image quality and relevance] > The effectiveness of ReFIR is significantly dependent on the quality and relevance of the images retrieved from external databases. This reliance could limit the framework's effectiveness in scenarios where highly relevant and high-quality ...	Rebuttal 1: Rebuttal: ## Global Author Rebuttal ### [1. Remarks by authors] We would like to express our sincere gratitude to all the reviewers for taking their time reviewing our work and providing fruitful reviews that have definitely improved the paper. We are encouraged that they find our method - "offers a sca...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks	Accept (poster)	Summary: The paper concerns using the proposed "Dynamical Systems Framework" (DSF) to better understand the differences between linear SSMs, RNNs, linear attention and Softmax attention. The DSF shows a way to write each of these models as a linear time-varying recurrence. The paper aims to use this to answer questions...	Rebuttal 1: Rebuttal: Thank you very much for the helpful feedback and pointers! Changes or clarifications (as deemed appropriate) for every single point raised will be incorporated in the final version of the paper. In what follows we provide a brief discussion on the points that were raised in the review: **Prior st...	Summary: This paper shows that many sequence models (including attention, SSMs, and recurrent models) can be viewed as linear time-varying dynamical systems. This is helpful for answering some questions about the differences and similarities between these architectures. Strengths: Importance of unifying framework....	Rebuttal 1: Rebuttal: Thank you very much for the helpful feedback and pointers! Changes or clarifications (as deemed appropriate) for every single point raised will be incorporated in the final version of the paper. In what follows we provide a brief discussion on the points that were raised in your review: **High-le...	Summary: This paper provides a dynamical system based framework for principled comparisons between various existing recurrent architectures (linear attention, SSMs, etc.). There are focuses on the formulation and the role of the hidden state dimension. Some experimental results on the multi-query associative recall (MQ...	Rebuttal 1: Rebuttal: Thank you very much for the helpful feedback and pointers! Changes or clarifications for every single point raised will be incorporated in the final version of the paper. We provide a brief discussion on the points that were raised in the review: Benchmark: Our main goal was to show-case some...	Summary: This paper introduces the Dynamical Systems Framework (DSF), a theoretical approach for analyzing and comparing various foundation models in AI. The DSF reformulates attention-based models, State Space Models (SSMs), and Recurrent Neural Networks (RNNs) into a common dynamical systems representation. This allo...	Rebuttal 1: Rebuttal: Thank you very much for the helpful feedback and pointers! Changes or clarifications (as deemed appropriate) for every single point raised will be incorporated in the final version of the paper. In what follows we provide a brief discussion on the points that were raised in the review: **Empirica...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their time and effort evaluating our paper. We believe the insightful reviews helped us to greatly improve the paper. The main contents of the rebuttal are several added insights we gained from the DSF, extended experiments including the LRA benchmark, ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Causal Effect Identification in a Sub-Population with Latent Variables	Accept (poster)	Summary: This paper addresses the S-ID (Sub-population Identification) problem in causal inference, extending it to scenarios with latent variables. The S-ID problem seeks to determine if a causal effect in a specific sub-population can be uniquely computed from observational data pertaining to that sub-population. The...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments. We first answer the two questions and then discuss the reviewer's concerns mentioned in the weakness section. ------ > Q1: Is there any example or situation that it returns a false negative? We have not found any examples where the algorithm produc...	Summary: This paper extends the sub-population causal effect identifiability (S-ID) problem to include latent variables by adapting classical graphical definitions such as connected-components and Hedges. It proposes a sound algorithm to compute causal effects in sub-populations with latent variables. Strengths: 1. Th...	Rebuttal 1: Rebuttal: We appreciate the reviewer's suggestions and positive feedback. --- > Example 1 could be a better one because socioeconomic status can cause cardiovascular disease. We acknowledge that the causal graph might not be completely accurate - the primary goal of this example is to demonstrate the dif...	Summary: This paper extends the S-ID problem, which asks if a causal effect within a specific sub-population can be identified using only observational data from that group. The authors consider the scenarios where some variables are latent. They provide a sufficient graphical condition to determine whether a causal e...	Rebuttal 1: Rebuttal: We thank the reviewer and appreciate the positive feedback on the importance of the problem and the clarity of the presentation. Below we answer the questions. --- > Although the work provides rigorous theoretical contributions, it would have been nice to evaluate how it would also work empirica...	Summary: The paper presents a sound algorithm for checking the s-identifiability of causal effects under sub-populations. This work complements earlier work on s-ID by generalizing the causal graphs to allow hidden confounders (no causal sufficiency). Specifically, the paper introduces the notions of s-components and s...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed feedback and appreciate the positive comments on the contributions and presentation of the paper. ----- > Are there any insights on the difference between having $P^s(V)$ vs. having $P(V)$ for identifiabilty? For example, would more variables become depende...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery	Accept (poster)	Summary: The paper investigates the limitations of popular UED approaches, such as PLR and ACCEL, demonstrating that they do not improve upon the Domain Randomisation baselines, where levels are randomly sampled. The author's main claim is that learnability does not correlate with the scoring functions MaxMC and PVL us...	Rebuttal 1: Rebuttal: We appreciate your thorough review and useful comments, particularly highlighting that we are focusing on an "important problem", and that our algorithm is "simple, and easy to understand". Please find our responses below. # Weaknesses ## Different Definitions of Learnability Intuitively, we jus...	Summary: This experimental paper proposes a UED method (SFL) for JaxNav, a continuous single- and multi-robot navigation task in a partially observable setting. The authors document the shortcomings of UED methods, (Domain Randomisation, Prioritised Level Replay, and ACCEL) on this partially observed, continuous actio...	Rebuttal 1: Rebuttal: Thank you for your positive review, particularly highlighting that our paper is "technically sound" and that the learnability score is "original". Please find our responses to your comments below. > Lines 15-17. Suggest to delete spurious statements such as “We had tried our best to make current...	Summary: This work proposes a new Unsupervised Environment Design (UED) method, called Sampling for Learnability (SFL), developed for navigation tasks. SFL upsamples environment configurations with high learnability scores, i.e., p*(1-p), where p is the success rate in a specific configuration. The paper highlights the...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, especially for highlighting our "simple yet novel UED approach" and that our paper is "well-written". Below, we address the issues raised in the review: # Correlation analysis for SFL >Section 4.1 analyses MaxMC and PVL, popular UED score functions, in t...	Summary: The paper introduces a new metric based on solve rates for evaluating the learning potential of tasks in a multi-task RL environment. It also presents a new evaluation protocol based on worst-case performance to better characterize the robustness of different methods. The work uses this protocol to compare the...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your thorough review and helpful suggestions! We also appreciate you mentioning that our paper "includes many contributions" and "could help to move further ACL research toward more promising directions." Please find our responses and changes below. # Hyperparameters ...	Rebuttal 1: Rebuttal: Dear reviewers, we appreciate your detailed reviews and concrete suggestions for improvement. We are especially grateful for reviewers mentioning that our paper is "well-written", "technically sound", and "includes many contributions", including an algorithm that is "novel, simple, and easy to und...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
$C^2M^3$: Cycle-Consistent Multi-Model Merging	Accept (poster)	Summary: The paper proposes a new, cycle-consistent method of merging more than two neural networks in weight space by simultaneously solving multiple permutation-based merge process. The key innovation is to ensure cycle consistency when merging $n>2$ models. The authors showed that the method, together with technique...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and spot-on questions. We now proceed to address the raised concerns and answer the questions to the best of our capabilities. - W1: model merging foundations based on prior work. We agree with the reviewer that our work builds on top of exist...	Summary: This paper further put forward a kind of cycle consistent Multi-Model Merging to merge models after permute simultaneously. It addresses the limitations in previous approaches that only handled pairwise merging. It uses a "universe" space to factorize permutations between models, optimizing all layer permutati...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and spot-on questions. We will now do our best to address each and every critique and question. Sensitivity to hyperparameters We thank the reviewer for raising this point. We would like, in fact, to clarify this aspect: **when, in the limita...	Summary: The paper introduces Cycle-Consistent Multi-Model Merging for merging neural networks by optimizing neuron permutations globally across all layers, ensuring cycle consistency when merging multiple models. Utilizing the Frank-Wolfe algorithm, this approach addresses inter-layer dependencies and guarantees that ...	Rebuttal 1: Rebuttal: We thank the reviewer for their efforts in providing insightful comments and questions. Method’s scalability to large models or datasets. \| Paper \| Conference \| Datasets \| Architectures \| \| --- \| --- \| --- \| --- \| \| Git Re-Basin [1] \| ICLR22 \| MNIST, CIFAR10, CIFAR100, ImageNet \| MLP, VGG...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments and questions. We are happy to see the contribution of our work be appreciated, with reviewers finding the work innovative, empirically advantageous, and addressing a limitation in existing approaches (djfE). The concept of a universe space ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization	Accept (poster)	Summary: This paper is concerned with the following natural generalization of polynomial optizimation: Given a distribution $\nu(\omega)$ and a polynomial $f(x,\omega)$, find the tightest (in expectation) function $c(\omega)$ that lower bounds $f(x,\omega)$ everywhere. Given the computational hardness of the problem...	Rebuttal 1: Rebuttal: We thank Reviewer iKyk for their detailed comments. We believe that the reviewer’s primary concerns are addressed by our global response: - Limitations of trigonometric polynomial assumption and the possibility of extending to more general polynomial families - Expanded cluster basis hierarchy di...	Summary: The manuscript considers variants of stochastic-SOS hierarchy for parametric polynomial optimization (POP), which had been previously considered using similar (joint+marginal) methods (https://arxiv.org/abs/0905.2497). Unfortunately the (joint+marginal) methods did not work very well, perhaps because parametri...	Rebuttal 1: Rebuttal: We thank reviewer Dyr8 for their detailed comments. In particular, we appreciate all the external citations they flagged. The reviewer highlights that the sensor network localization (SNL) problem can be reduced to trivial sizes citing work solving instances of 20k-100k sensors, while our numeric...	Summary: This work proposes a new sum of squares (SOS) based relaxation for stochastic polynomial optimization,called Stochastic SOS (S-SOS). For a class of problems where the cost function $f$ can depend on variables $x$ as well as randomly drawn parameters $\omega$, the authors propose a hierarchy of relaxations whos...	Rebuttal 1: Rebuttal: We thank Reviewer Cxce for their detailed comments. With regards to the typos identified, we appreciate the flagging of these and will correct them in the next version. The reviewer is indeed correct in that Equation (6) should have the plus sign. We thank the reviewer for flagging additional ap...	Summary: In their study, the authors investigate parametric polynomial optimization where the function to be minimized is $ f(x, \omega) $. Here, $ x $ represents the decision variable, and $ \omega $ signifies a noise parameter. The primary goal is to approximate the best lower bound, \( c^*(\omega) = \inf_x f(x...	Rebuttal 1: Rebuttal: We thank Reviewer pMRv for their detailed comments. Concerning our work’s novelty and relevance, we believe that our proposal is one of specific interest to the ML/AI community here. Applied mathematicians have studied SDPs and convex optimization for some time, while ML/AI scientists are more i...	Rebuttal 1: Rebuttal: We saw the following points come up in multiple reviews so we thought it would make sense to address it as a global response. The reviewers chiefly seemed to have concerns about the following: - The limitations of our theoretical assumptions (particularly the trigonometric polynomial one) and the...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The Sum of Squares (SOS) technique is well-known in polynomial optimization studies. This paper studies its variant, in which the function to be lower bounded has random parameters drawn from some probability distribution. The main contribution is a cluster-based SDP hierarchy for the Stochastic-SOS (S-SOS) me...	Rebuttal 1: Rebuttal: We thank Reviewer MyRu for their detailed comments. The reviewer brings up a good point about presenting the assumptions in a subsection. We will set aside a section in the appendix for this purpose. As for novelty in our proof techniques or lack thereof, it was not obvious to us that one could ...	null	null	null	null	null	null
ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing	Accept (poster)	Summary: This paper presents a task decomposition method that achieves more robust 3D scene editings. The main contribution is the concept of decomposition of the desired task (represented by a prompt) and the adaptive 3D Gaussian Splatting training process. The edited appearance and geometry using decomposed tasks are...	Rebuttal 1: Rebuttal: ### W1. The semantic meaning of decomposition and evaluation of subtasks - In our method, the semantic meaning can be interpreted as "how IP2P acts with such interpolated embedding." Though we cannot write down the text instructions corresponding to the interpolated embedding, we can still visuali...	Summary: This paper presents ProgressEditor, which decomposes the 3D scene editing task into multiple subtasks and progressively modifies the scene which is represented by 3D Gaussians. The subtask decomposition is defined as the linear interpolation of the encoding of the editing prompt. Given the editing instruction,...	Rebuttal 1: Rebuttal: ### W1. Quantitative assessment - Please refer to the "Quantitative Evaluation" in the global author rebuttal. Thank you. ### W2/Q2.2. Additional subtasks $r_0$ and $r_n$ - Our framework is designed in a setting, where the input and output format of the scene can be in any scene representat...	Summary: This work focuses on instruction-based 3D scene editing. It proposes a progressive editing framework by decomposing the complex editing task into different subtasks based on the difficulties. In this way, it could ensure multi-view consistency in each easy subtask and finally obtain consistent editing for the ...	Rebuttal 1: Rebuttal: ### W1/Q1. About the editing difficulty w.r.t. the weight $r$. - We provide a visualization of per-view edited results (i.e., each image is edited individually with IP2P) w.r.t. different $r$'s as FigPDF.A. The multi-view inconsistency situations are as follows: - $r_0$: All the views are ...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive and insightful comments: - We propose an "interesting, novel, and reasonable" idea (Mz9K, 49ym, PcMD) to solve the instruction-guided 3D editing task, in a well-presented and illustrated way with clearly stated motivations (Mz9K). - The proposed met...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Similarity-Navigated Conformal Prediction for Graph Neural Networks	Accept (poster)	Summary: This paper addresses the problem of the lack of reliable uncertainty estimates in semi-supervised node classification tasks using Graph Neural Networks. This paper shows that nodes with the same label as the ego node play a critical role in the non-conformity scores of the ego node. The authors propose a met...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the insightful and detailed comments. Please find our response below: > 1. Scalability / Computation cost [W1] Thank you for your valuable feedback. The time complexity of SNAPS is primarily determined by the computation of corrected scores. In this work, we us...	Summary: This paper introduces a novel algorithm, SNAPS, which enhances conformal predictions by aggregating non-conformity scores based on feature similarity and structural connections. Extensive experiments validate SNAPS' effectiveness, demonstrating its ability to produce more compact prediction sets with higher si...	Rebuttal 1: Rebuttal: We appreciate the reviewer's insightful feedback. Please find our response below: > 1. Sampling method for large-scale dataset [W1] To reduce the computation burden, we utilize a random subset from the original nodes (80,000 / 2449029, OGBN Products). Despite its simplicity and limited acces...	Summary: The authors propose a new score function for conformal predictions on graphs. Given any baseline score the new score is aggregated based on the neighbors in the given graph; and the neighbors from a secondary kNN graph constructed based on the similarity between input features. The approach is motivated by not...	Rebuttal 1: Rebuttal: We deeply appreciate the valuable comments and will incorporate these suggestions into the final version. We are certain they will substantially improve the presentation of our work. Please find our response below: > 1. Calibration set size [W1 & Q3] Here, we provide effect analysis of vario...	Summary: The authors apply conformal prediction to graph neural networks by aggregating the non-conformity scores based on both one-hop neighbors and feature similarity. The framework is verified through various experiments on graph ML benchmark datasets, where it's shown to generate smaller prediction sets and higher ...	Rebuttal 1: Rebuttal: We appreciate the reviewer's recognition and valuable suggestions. Please find our response below: > 1. Discussion regarding the assumption in Proposition 2 [W1] Thank you for the great suggestion. Here is our discussion regarding the assumption in Proposition 2. Given a data pair $(\boldsy...	Rebuttal 1: Rebuttal: We thank all the reviewers for their time, insightful suggestions, and valuable comments. We are certain that they will make our work more complete. We are glad and encouraged that reviewers find the method is well-motivated (aWFN, xCn2) and theoretical (3cJc, xCn2), our method is **simple...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scaling Retrieval-Based Language Models with a Trillion-Token Datastore	Accept (poster)	Summary: The paper introduces a substantial datastore named MASSIVEDS for retrieval-in-context language models (RIC-LM). It details a comprehensive construction pipeline, with a notable deviation from the traditional datastore construction sequence. Specifically, it places indexing and retrieval at the initial stage, f...	Rebuttal 1: Rebuttal: We thank the reviewer for acknowledging that our work makes valuable contributions to the advancement of these systems! Weakness 1. It remains uncertain if the findings would hold with a single data source, whether they apply to specific tasks beyond general ones like QA, and how interaction...	Summary: This paper studies the effect of scaling datastores for retrieval-based language models. A trillion-token datastore, MassiveDS, is constructed and then filtered to remove contaminated and duplicate documents. A distributed pipeline is proposed to index and retrieve from MassiveDS with a modest compute budget. ...	Rebuttal 1: Rebuttal: We thank the reviewers for acknowledging the importance of our research question and the value of our findings and open-sourced resources! We would like to address the reviewer’s concerns below: Weakness 1. The claim of proposing the 'largest' datastore could be reconsidered as there are othe...	Summary: This paper studies the impact of scaling the datastore (retrieval dataset) on retrieval-based language models. The contributions are: - MASSIVEDS a 1.4 trillion-token datastore for retrieval-based LMs that will be made open-source. - A pipeline to study the impact of the datastore scaling on the language mode...	Rebuttal 1: Rebuttal: We thank the reviewer for highlighting our open-source contribution and acknowledge the potential impact on the community! We would like to address the reviewer’s concerns below: Weakness 1. This work does not study the impact of the scaling of the datastore on inference time. A1. Our ma...	Summary: The paper introduces MASSIVEDS, the largest and most diverse open-sourced datastore for retrieval-based language models, containing 1.4 trillion tokens. The authors design a MASSIVEDS pipeline to efficiently explore the impact of different datastore features by reordering the datastore construction operations ...	Rebuttal 1: Rebuttal: We thank the reviewer for acknowledging the importance of our study and the solidness of our work! We would like to address the only concern by the reviewer on the choice of retriever below. Q1. The retriever and reranker used in this paper may be somewhat outdated. Recent models on the MTEB ...	Rebuttal 1: Rebuttal: We appreciate the reviewers' strong support for the contributions of the paper and their insightful comments. This general response outlines how we have responded to their concerns and provides the requested supplementary results. Summary of common concerns and our corresponding response. * R...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper "Scaling Retrieval-Based Language Models with a Trillion-Token Datastore" explores a new dimension of scaling language models (LMs) by considering the amount of data used during inference. The study focuses on retrieval-based LMs, which access a large external datastore during inference, and examines...	Rebuttal 1: Rebuttal: We thank the reviewer for acknowledging the novelty and solidness of our work! We would like to address the concerns and questions below: Weakness 1. The evaluation is primarily focused on QA and knowledge-intensive tasks where one can expect the retrieval based LMs to work well. Inclusion of...	Summary: The paper studies the effects of scaling retrieval datastore in retrieval augmented language models. The authors present the impacts of various design choices such as data size and data selection. A testbed dataset MassiveDS is also introduced. Strengths: - The authors present a substantially larger retrieval...	Rebuttal 1: Rebuttal: We thank the reviewer for pointing out the comprehensiveness of our work! We would like to address the concerns and questions below, and we will edite the paper accordingly. Weakness 1. Large-scale embedding-based search is not a new topic but has been studied for years. Arguably it is a matu...	null	null	null	null
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation	Accept (poster)	Summary: This work introduces a ControlNet-based conditioning method to enable pre-trained diffusion models to be layout-conditioned. The conditioning model processes each object in parallel and fuses them as residual features for the pre-trained diffusion model. In addition to the commonly used COCO dataset for evalua...	Rebuttal 1: Rebuttal: We thank you for your affirmation and constructive comments.We address each of your comments below, additional figures and experimental results can be found in the Author Rebuttal material PDF. ## Weakness 1. We have detailed the construction pipeline of the custom dataset HiCo-7K in Fig.3 of Re...	Summary: This paper studies layout-to-image generation. It proposes HiCo, a diffusion model that supports a complicated, hierarchical set of bounding boxes as the layout condition. The authors also constructed the HiCo-7K benchmark to provide challenging tasks for evaluations. The experiment results show that the propo...	Rebuttal 1: Rebuttal: Thanks for your wonderful review and detailed comments. Here we address each point of your comments, additional figures can be found in the Author Rebuttal material PDF. ## Weakness 1 We are very grateful to the reviewer for presenting constructive ideas. Currently, HiCo only supports axis-aligne...	Summary: This paper propose HiCo (Hierarchical Controllable) Diffusion Model for layout-to-image generation. HiCo Net is a multi-branch structure that is introduced to hierarchically generate the global background and foreground instances for different layout regions. The author further evaluate the performance of mult...	Rebuttal 1: Rebuttal: We really appreciate your detailed review and valuable suggestions. We address each of your comments below, additional figures can be found in the Author Rebuttal material PDF. ## Question 1 & Weakness 2&3. HiCo achieves hierarchical generation by decoupling each object’s position and appearance...	null	null	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their thoughtful, overall positive feedback and encouraging feedback. We are particularly pleased that the reviewers believe that our method achieves spatial disentanglement by separating each object, making it seamlessly compatible with SD community pl...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors	Accept (poster)	Summary: The authors propose a transformer-like NN by unrolling iterative optimization algorithms that minimize graph smoothness, which is used for imaging tasks. Strengths: 1. The method is well-illustrated, and the theoretical details are convinced. 2. Experiments in imaging tasks show superior experimental results ...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed comments. 1. We thank the reviewer for the two suggested references. The first reference [1] indeed provides unrolling techniques based on proximal gradient descent (Section 2 in [1]) and IRLS (Section 3). While the objective in equation (1) in [1] employs a...	Summary: This paper proposes a novel approach to build interpretable and lightweight transformer-like networks by unrolling graph-based algorithms. The authors propose unrolling iterative graph-based algorithms for signal restoration with graph smoothness priors (minimizing roughness) into interpretable neural network ...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed comments, which we respond point-by-point below. 1. Interpretability in the context of "algorithm unrolling" (see [14, 15, 16]) means that each neural layer corresponds to an iteration in an iterative algorithm minimizing a formulated optimization objective....	Summary: The authors build a "white-box" Transformer-like neural network through algorithm unrolling and graph signal processing. This network utilizes convolutions to learn low-dimensional features per node, constructs sparse similarity graphs, and employs low-pass filtering at each layer, significantly reducing the p...	Rebuttal 1: Rebuttal: 1. The reviewer raised a good point that the affinity notion in conventional self-attention mechanism in equation (23) using query and key matrices, Q and K, is asymmetric, while the feature distance in graph learning in equation (25) is symmetric, leading to an undirected graph. However, one key ...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful and detailed comments. We responded to each reviewer individually in separate rebuttals below. We also attached a PDF containing extra experimental results requested by two reviewers. Pdf: /pdf/4fcec15dcf9a6bae7730cd682945a7cf5e68ed04.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
$\textit{Read-ME}$: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design	Accept (poster)	Summary: This paper proposes to perform data-specific pruning from a regular LLM and, with a small amount of continual training, builds a set of smaller LLMs. Then they are used as experts, and a router network is trained to route requests to them. Note that the routing decision is one per query and not per token or pe...	Rebuttal 1: Rebuttal: Thanks for your time and effort for the review. We answered your questions as follows. [W1 - Effectiveness of Read-ME] Thank you for your question. - We would like to first clarify that our method significantly outperforms Sheared-Llama. For example, Sheared-Llama nearly performs at random...	Summary: The paper proposes a novel framework to enhance the efficiency of pre-trained LLMs by transforming them into post-hoc Mixture-of-Experts (MoE) models. The key innovation lies in decoupling the router from the MoE backbone, which facilitates pre-gating and lookahead scheduling, thereby improving memory manageme...	Rebuttal 1: Rebuttal: [W1 - Additional experimental results] Thanks for the suggestion! To validate that our method remains effective in other scenarios, we use the Mistral model as the pre-trained dense model, and convert it to the MoE structure, with the proposed method. The task is challenging because we do not ...	Summary: This paper proposes Read-ME, a novel framework for pruning large LLMs into smaller MoEs with minimal training cost. Read-ME separates the gating routers from the critical paths of the inference process and trains an individual expert subnetwork to perform offline pre-gating. With this refactorization of MoEs, ...	Rebuttal 1: Rebuttal: Thanks for all the interesting questions. Please see below. [W1 - MoEs Motivation] We validated that MoE achieves better cost-accuracy trade-off than small dense models acquired by pruning, and provided the results in Appendix C.1. We mentioned that prior compression efforts focus on conver...	Summary: The paper proposes an inference-aware method to convert a pretrained dense model into a Mixture-of-Experts architecture, where each expert is smaller than the original dense model. To extract the different experts, they use a dataset from given subdomain to identify the top activated channels. To route among t...	Rebuttal 1: Rebuttal: [W1 - Comparison with layer-by-layer loaded Llama ] Thanks for the question. We compared the latency of the layer-by-layer loaded llama and Read-ME model in the following table. Table A. Latency comparison \|Method\| Latency [ms] \| \|---------\|--------------\| \| Layer-by-layer Llama-7b \| 11...	Rebuttal 1: Rebuttal: We thank all reviewers [R1(wHVe), R2(ZdPD), R3(EGvf), R4(AX77)] for their thoughtful and constructive feedback. We are grateful that the reviewers found our approach interesting and effective [R1,R2,R3], the paper well-written and well-organized [R2], and the experimental results thorough and exte...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions	Accept (poster)	Summary: The authors present SkiLD, an unsupervised RL method that learns skills by augmenting DIAYN reward with a graph-state dependency reward to induce meaningful changes in object interactions. Strengths: Experiments: Experiments are performed on a reasonably comprehensive set of 10 downstream tasks in 2 domai...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable and constructive feedback. We are particularly excited that the reviewer finds our idea of learning skills to induce diverse interactions between state factors novel and our evaluation sound. Please find below our responses to address your con...	Summary: In the presented paper the authors propose a novel skill discovery method called Skill Discovery from Local Dependencies (SkiLD). The method utilizes the concept of local dependencies to incorporate the interaction of factors in a factorized state space. A novel intrinsic reward signal is introduced to guide t...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable and constructive feedback. We are particularly excited that the reviewer finds our method well-motivated and our presentation clear. Please find below our responses to address your concerns regarding our work: >Regarding the use of overexagge...	Summary: The paper introduces SkiLD (Skill Discovery from Local Dependencies), an unsupervised skill discovery method. Unlike existing methods that focus on state diversity, SkiLD leverages state factorization to guide skill learning by inducing diverse interactions between state factors. This method is designed to be...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable and constructive feedback. We are particularly excited that the reviewer finds our presentation clear and our evaluation sound. Please find below our responses to address your concerns regarding our work: >The method's reliance on accurately ...	Summary: The paper introduces SkiLD, a novel method leveraging state factorization to guide skill learning in unsupervised reinforcement learning. SkiLD emphasizes learning skills that induce diverse interactions between state factors, which are crucial for solving downstream tasks. The authors demonstrate that SkiLD o...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the valuable and constructive feedback. We are particularly excited that the reviewer finds our idea of learning skills to induce diverse interactions between state factors novel and our evaluation sound. Please find below our responses to address your con...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful feedback and suggestions. We are particularly excited that the reviewers find our idea of learning skills to induce diverse interactions between state factors well motivated (R3), novel (R1, R2, and R4), effective (R1, R2), and sound (R1, R2, R4). We als...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
How to Use Diffusion Priors under Sparse Views?	Accept (poster)	Summary: This paper mainly investigates the behavior of SDS in sparse-view 3D reconstruction, pointing out that SDS may unexpectedly harm the 3D reconstruction quality in this case. Compared to SDS in text-to-3D, sparse-view reconstruction requires leveraging visual cues encoded in input images (named "inline prior"), ...	Rebuttal 1: Rebuttal: Thanks for the careful review. We appreciate for concerns and valuable suggestions and questions. Here are our corresponding responses. * Analysis of the motivation. With detailed discussion about mode-seeking in previous works [A, B], here we provide a theoretical analysis of our motivation....	Summary: This paper introduces a novel approach for synthesizing novel views from sparse view inputs using diffusion priors. The authors conduct a thorough analysis of SD optimization under sparse views and propose an inline prior guided score matching algorithm to rectify the distribution of rendered images. The 3DG...	Rebuttal 1: Rebuttal: Thanks for the efforts and patience of the careful reviewing. We appreciate the suggestions and questions for this paper. Here we provide detailed responses. * Qualitative results for SDS. As shown in Fig.1 (a) of the attachment, the guidance of SDS will produce the imaginary reconstructi...	Summary: This paper deals with the problem of novel view synthesis from a sparse set of input views. While this problem has been tackled with depth or semantic regularization in the past, the authors approach the problem by introducing priors from a pre-trained diffusion model following a few recent works like ReconFus...	Rebuttal 1: Rebuttal: Thanks for your efforts and patience in reviewing this paper. We appreciate the positive comments, valuable concerns, and suggestions on our work. Here are our responses to the mentioned weaknesses and questions. * Additional ablation study. To supplement more complete experimental results, w...	null	null	Rebuttal 1: Rebuttal: We thank all ACs and reviewers for their efforts in reviewing, valuable comments and suggestions for this paper. We addressed the reviewer's comments and questions in individual responses to each reviewer and provided supplementary figures and tables in the one-page pdf attachment.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Causal language modeling can elicit search and reasoning capabilities on logic puzzles	Accept (poster)	Summary: This work attempts to solve a filtered list of Sudoku puzzles by training a transformer model with data derived from solutions produced by a mechanistic 'simple solver' (rather than a sophisticated recursive planner). They show that the training regime transformer model can be engineered to enable the model t...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and constructive feedback. We will make a pass over the paper and fix the typos pointed out and any other errors we find. We address your comments below. - Selecting puzzles solvable by a solver: We emphasize that we did this filtering on both our train an...	Summary: This paper applies causal language models (Transformers) to solve Sudoku puzzles, reporting a 94.21% accuracy rate in solving the puzzles completely. The authors claim to demonstrate that these models can develop human-like reasoning capabilities by employing insights from CoT prompting through carefully struc...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and constructive feedback. We address your comments below. - Lacks theoretical or methodological advancements: We reiterate that our main contribution is not proposing a new theory or a new method to train LLMs. Rather it is an advance in our understanding...	Summary: * This work presents a study of solving sudoku puzzles via causal language modeling. * Given the sequence of filled places and their values in sudoku, the model must output the series of empty cell positions and the values that correspond to them. * They study how the model performs with various input represe...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and constructive feedback. We will make a pass over the paper and fix the typos pointed out and any other errors we find. We address your specific comments and questions below. - No SoTA models are evaluated on the sudoku puzzle data: We reiterate that our ...	Summary: This paper assesses causal language models', particularly transformer decoders', abilities to solve Sudoku puzzles. The authors encode Sudoku puzzles into sequences, representing each cell as a (row, column, value) triple, and train a model from scratch on 1.8M puzzles. They then evaluate the trained model on ...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and constructive feedback. We will make a pass over the paper and fix the typos pointed out and any other errors we find. We address your comments below. - Clarifying our probing methodology: We agree that the more common way to perform a probing analysis ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their careful reviews and constructive feedback. We first address comments raised by multiple reviewers. - Comparison to traditional solvers and AI approaches: We reiterate that our main focus is not to propose a new approach to solve the sudoku puzzle. We study t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Predict Structural Vibrations	Accept (poster)	Summary: The paper suggests a method of using operator network theory in order to form predictions of the resultant steady state vibrational patterns which appear on a beaded flat plate under several levels of external excitation. They firstly use numerical structural engineering software (rooted in FEMs) to generate a...	Rebuttal 1: Rebuttal: Dear reviewer, thanks for taking the time to write your detailed and helpful review and for recognizing the contribution of our benchmark, given that there are scarcely any publically available in the mechanics domain. General Comments: > The necessity of the FQO-UNet model having to inter...	Summary: This work presents a benchmark dataset of 12,000 rectangular plate geometries with different beading patterns and their corresponding vibrational responses. The authors suggest that the dataset can be used to construct surrogate models to aid in the design and optimization of plate structures for noise reducti...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for taking the time to write your thoughtful review. We appreciate that you recognize our dataset and neural network architecture as novel, as the area of engineering and more specifically vibration prediction is not well established in the ML community. Please also take ...	Summary: This paper proposes a new surrogate deep learning model, the Frequency-Query Operator (FQO), designed to study structural vibrations in excited plates by mapping these plates as well as specific excitation frequencies to the resulting vibrations patterns. It introduces a new benchmark featuring 12000 plate geo...	Rebuttal 1: Rebuttal: Dear reviewer, thanks for taking the time to write your informative and helpful review. Your recognition of the contributions of our work as well as finding it well written and organized is highly appreciated! Please also take a look at our general answer, where we detail some additional resul...	Summary: The paper report development of a surrogate model for prediction of the structural vibrations. The paper reports outperformance of the their method to physics informed architectures such as DeepOnet. Strengths: The authors tackles and interesting and important problem in engineering domain which is predictio...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for taking the time to write your thoughtful review. We are pleased that you also consider structural vibrations to be an interesting and important problem in the engineering domain. We discuss generalization and domain shift in more detail in Section 2 of our general ans...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you for many valuable and thoughtful comments. We are pleased that the reviewers recognize the value of our novel benchmark (ifbc, 1y6b, YeHT) and method (YeHT, ifbc) for the important problem (1y6b, ifbc) of structural vibration prediction. as well as rating th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable	Accept (poster)	Summary: This paper studies reconstruction attacks on machine unlearning. The authors propose a reconstruction attack that can accurately recovers the deleted sample given the pair of linear models before and after sample deletion. This is made possible by leveraging the closed-form single-sample training algorithm for...	Rebuttal 1: Rebuttal: [Response to Weaknesses 1] The assumption of having access to the training data distribution is standard throughout the membership inference literature. Because we are trying to compare the risks of data deletion to the known risks absent data deletion, we adopt the same model. In general we vi...	Summary: This paper presents reconstruction attacks against Machine Unlearning in the following sense: the attacker is assumed to have access to a model's parameters before and after the removal of a single data point; they then produce a guess for this point, which is evaluated in terms of its cosine similarity to the...	Rebuttal 1: Rebuttal: [Response to Weaknesses] We take the position that security assurances should be based on minimal assumptions. Here, we view the assumption that an attacker who has API access to the model does -not- have access to model parameters to be dangerously strong. Consider for example a d dimensional ...	Summary: The authors propose an attack that can accurately recover unlearned samples through reconstruction attacks on linear regression models. They extend this work to include linear models with fixed embeddings and generalize it to more generic loss functions and model architectures by employing Newton’s method for ...	Rebuttal 1: Rebuttal: [Response to Weaknesses] Our study is explicitly focused on exposing the risk present in even very simple models, such as linear regression, logistic regression, SVMs, and feature augmentation using random Fourier features. For such models, full retraining is feasible, and would be the expecte...	Summary: This work focuses on investigating privacy issues in machine unlearning. Specifically, assuming the availability of model parameters before and after unlearning, as well as the ability to sample data from the original data distribution, the proposed reconstruction attack aims to recover deleted samples. By ana...	Rebuttal 1: Rebuttal: [Response to Weaknesses 1] We take the position that security assurances should be based on minimal assumptions. Here, we view the assumption that an attacker who has API access to the model does -not- have access to model parameters to be dangerously strong. Consider for example a d dimensiona...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Pre-trained Large Language Models Use Fourier Features to Compute Addition	Accept (poster)	Summary: The authors utilize discrete Fourier transform to determine which Fourier components play the most important role in computing the addition of relatively small numbers (< 520) in Large Language Models, such as the GPT-2 family, and, _likely_, Phi-2, GPT-J, and others. They found out that MLP modules of GPT-2 u...	Rebuttal 1: Rebuttal: Thank you for the supportive feedback and comments. *Q1: Why not repeat the experiments from Table 1 for other models.* The goal of this paper is to understand how pre-trained LLMs solve addition tasks. We conduct most of the experiments, such as Figures 1-5, on GPT-2-XL to provide a deep a...	Summary: This paper focuses on understanding the mechanisms the LLMs employ to carry out mathematical operations, in particular sum of two numbers. It demonstrates: a) Models utilize Fourier features, with different parts of the model utilizing different frequency ranges — attention mechanism uses high frequency compo...	Rebuttal 1: Rebuttal: Thank you for the supportive feedback and thoughtful comments. ***Q1: With the use of tools / functions in conjunction with the LLMs, mathematical operations are often carried out using a calculator or similar tool. Thus attempting to improve LLMs' arithmetic abilities may not be very fruitful. H...	Summary: This paper analyzes how language models perform addition, showing that they use Fourier features. Most of the analyses are done on fine-tuned models. First, periodicity is shown with a logit-lens technique on different layers. Fourier analysis shows this nicely in the frequency space, and shows differences bet...	Rebuttal 1: Rebuttal: Thank you for the supportive feedback and thoughtful comments. *Q1: Why 'memorization and recombination' cannot be implemented by the full model, with this gradual refinement process as an implementation of memorization.* Thank you for your feedback. Our assertion is based on prior research ...	Summary: This paper analyzes how language models conduct the task of mathematical addition. Specifically, the paper shows that when performing the mathematical addition task, the internal states of language models exhibit periodical patterns, referred to as "Fourier Features" in the paper. The paper then conducts Fouri...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s supportive comments and constructive suggestions. Q1: The term "modular addition" should be defined before its first use. We have added a definition of "modular addition" in Section 3.1 to the revised version of our paper for clarity: "Modular addition is an ari...	Rebuttal 1: Rebuttal: Response to All Reviewers: We thank reviewers [R1(YRn2), R2(XwiY), R3(wYe3), R4(mHEL)] for their thoughtful and highly supportive feedback! We are glad that the reviewers found the analysis method interesting and [R1, R3], the observations about the application of Fourier features in languag...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Set-based Neural Network Encoding Without Weight Tying	Accept (poster)	Summary: This paper proposes SNE, or Set-based Network Encoding, a method of encoding the weights of arbitrary neural networks in order to predict properties such as performance, generalization gap, training loss, number of epochs, etc. Specifically, using a chunking mechanism as well as layer-wise and block-wise posi...	Rebuttal 1: Rebuttal: We thank the Reviewer for taking the time to offer constructive feedback for improving the paper. We respond to the questions raised below. Provide examples of the application of this research other than asserting that they must exist. We add the following to elaborate on the potential appli...	Summary: This work introduces a set-based neural network encoding that processes each chunk of an input model independently to predict network properties. The proposed model, SNE, is trained using Logit Invariance instead of weight tying to maintain generalizability to unseen architectures. Unlike previous approaches, ...	Rebuttal 1: Rebuttal: We thank the Reviewer for taking the time to offer constructive feedback for improving the paper. We respond to the questions raised below. **The writing is generally good but could be improved in certain areas. For example, the abstract contains a redundant sentence: “by utilizing a layer-wise e...	Summary: This work tackles an original and interesting challenge: predicting neural networks properties from their trained weight values. To do so, the authors propose to leverage set to set and set to vector transformations in order to encode weight values. Furthermore, they propose to account for the deep neural netw...	Rebuttal 1: Rebuttal: We thank the Reviewer for taking the time to offer constructive feedback for improving the paper. We respond to the questions raised below. could the authors provide results with a predictor trained on CNNs from arch1 and predict the performance of transformers? We provide the requested resu...	Summary: The study introduces Set-based Neural Network Encoding (SNE) without weight tying, allowing for encoding network information into a compact form. SNE utilizes Logit Invariance Regularization to ensure the correct functional equivalence of network permutations, departing from traditional weight tying methods. T...	Rebuttal 1: Rebuttal: We thank the Reviewer for taking the time to offer constructive feedback for improving the paper. We respond to the questions raised below. **The proposed method did not ensure weight-space equivariance by design but use a way that is more like an augmentation plus a consistency loss. The theor...	Rebuttal 1: Rebuttal: General Response We thank all Reviewers for taking the time to offer constructive feedback for improving the paper. Based on the suggestions of Reviewers nYWS, we provide the following additional results. Evaluation on Transformers We generate a model zoo of transformer classifiers ba...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Belief-State Query Policies for User-Aligned POMDPs	Accept (poster)	Summary: This paper proposes a method for computing optimal policy in a given POMDP and a specific policy class (the class of BSQ policies). Strengths: In this work, to ensure compliance with user's preference, the policies are directly parameterized by the preferences (formalized by boolean formulas). This approach i...	Rebuttal 1: Rebuttal: Thank you for your feedback. Weakness 2: We assume that a policy must comply with a BSQ preference to align with the user’s intentions. Therefore, we can prove that all aligning policies are included in the policy class. Weakness 3: The baseline RCompliant is there to (1) show the BSQ p...	Summary: This paper provides a method for computing policies with user preferences called belief state queries (BSQ). A BSQ consists of a condition and a desired action and can be used for expressions like: "Given a high likelihood of a, do b." To express this, the condition of a BSQ compares the probability of a first...	Rebuttal 1: Rebuttal: Thank you for the feedback and questions. Q1: Maximizing the probability of reaching the goal does not factor in the time required for goal completion. Therefore, the optimal strategy would be to use the maximum allowed time for gathering information before choosing to reach the goal. By min...	Summary: This paper introduces a method for partially observable policy optimization under belief-state query (BSQ) policy constraints (or in the author's terminology, preferences). A BSQ preference, as defined by the authors, is a class of policies parameterized by probability thresholds $\theta$ on beliefs about cert...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and for appreciating the strength of technical contributions in the paper. We agree that the BSQ framework deserves a much-needed deeper analysis, and the framework developed here takes key steps towards it. We believe that presenting this new analytical para...	Summary: The paper presents a new way of encoding preferences using so-called belief state queries in goal-oriented POMDPs. The modelling allows for optimising agent behaviour while complying to those preferences. In a formal analysis, the paper shows that the expected value function, although non-convex (abstract) / n...	Rebuttal 1: Rebuttal: Thank you for your detailed feedback and suggestions. We will incorporate these changes in the final version of the paper. We also plan to use the extra page to update the figures and add a portion of the limitations into the conclusion section. Our work is the first to formally define a BSQ fra...	Rebuttal 1: Rebuttal: We thank the reviewers for their detailed reviews and comments. We answer the questions posed by the reviewers separately. Please find them in the response below the reviews.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders	Accept (spotlight)	Summary: This paper makes a novel observation (at least to me) that for mask-based autoencoder paradigm for point cloud self-supervised pretraining, the centers of patches are important and the reconstruction objective does not necessarily rely on representations of the encoder. This is different from the 2-D case on m...	Rebuttal 1: Rebuttal: Thank you for your time, detailed comments, and valuable suggestions. We are delighted that you recognize the clear motivation, high efficiency, and novelty of our PCP-MAE. Here are our responses to the questions you raised: > Q1. The limitation of our approach. We have discussed the limitations...	Summary: The paper proposes a novel self-supervised learning method called PCP-MAE for point cloud understanding. The key innovation of PCP-MAE is that it guides the model to learn to predict the positional embeddings of the centers of masked regions, rather than directly providing the coordinates. This approach encour...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. There may be some misunderstandings on your part regarding certain aspects of our work. We hope that our response will address your concerns. Here is our clarification. > Q1.1. Comparisons with previous methods are unfair. It seems that the main i...	Summary: The paper studies the point cloud pretraining under the self-supervised learning paradigm. Authors experimentally found that the position embedding in the decoder may decrease the learning ability of the encoder and propose a new method to overcome this issue. Strengths: 1. The paper is clearly written and ea...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper and for providing your detailed feedback. Below, we answer your questions in detail: > Q1. The importance of SSL for 3D. Upon revisiting our statement, we find it more appropriate to remove "collect" in line 27 of the original paper, which we wil...	Summary: This work examines representation learning of 3D point clouds using masked autoencoding. A known issue with such an approach is that the coordinates of the patch centers leak significant information about the geometry and semantics of the shape being reconstructed, which degrades the representations learned by...	Rebuttal 1: Rebuttal: Thank you for the time, thorough comments, and nice suggestions. Your endorsement of our method and experiments gives us significant encouragement. Here are our clarifications. > Q1.1. Improvements over previous MAE-based methods, such as Point-FEMAE, are quite marginal, and perhaps not statistic...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their detailed and valuable suggestions and are grateful for the encouraging comments: 1. The paper is clearly written and easy to follow. (DT8p, nJYR, rD3v) 2. The core observation and motivation remain novel and clear. This paper is well-motivated and provid...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper proposes a masked autoencoding based self-supervised approach for 3D point clouds. The approach which terms as PCP-MAE, learns to predict centers for Point Masked AutoEncoders. The paper investigate that the coordinates of the centers are essential in the point cloud field and the decoder can even re...	Rebuttal 1: Rebuttal: Thank you for the time, thorough comments, and nice suggestions. We are pleased to clarify your questions one-by-one. > Q1. The paper writing should be improved. Thank you for your feedback. We will improve the manuscript's language and structure for better clarity and readability, and these upda...	null	null	null	null	null	null
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts	Accept (poster)	Summary: This paper is motivated by the fact that traditional scaling approaches are computationally expensive and overlook the significance of efficiently improving model capabilities from the vision side. The authors introduce CuMo, a method to enhance MLLMs by incorporating sparsely-gated Mixture-of-Experts (MoE) bl...	Rebuttal 1: Rebuttal: Q1: Why MoE in the vision encoder and connector enhances the model's capabilities A1: MoE has been widely used in LLMs to improve the capacity [1] of text generation as it improves the model size during training while keeping inference costs lower at inference. In our work, we apply MoE in th...	Summary: The paper introduces CuMo, a novel approach to enhancing multimodal large language models (LLMs) by integrating sparsely-gated Mixture-of-Experts (MoE) blocks into both the MLP connector and vision encoder. CuMo addresses the challenge of scaling multimodal LLMs effectively by leveraging MoE's efficiency in pa...	Rebuttal 1: Rebuttal: Q1: Computational efficiency and training time of MoE. \| CuMo \| CLIP \| MLP \| LLM \| Total \| Time \| \|--------------------------\|-------\|-------\|-------\|--------\|------------\| \| Mistral-7B \| 0.30B \| 0.025B\| 7.25B \| 7.58B \| ~16h \| \| + Top 2-in-...	Summary: The paper presents upcycling for large multimodal models (LMMs). It specifically looks at how to enable upcycling for the different components of an auto-regressive based multimodal model (e.g., LLaVA). It shows how the MLP and vision-encoder (in this case, CLIP) are the two modules that should be upcycled and...	Rebuttal 1: Rebuttal: Q1: Limit on the gains from upcycling and when training MoE-based ViTs or MoE connectors from scratch will be much better / potentially start outperforming the dense-only case? A1: The conclusion of using 20% additional capacity to catch up the upcycled model in original sparse upcycling and ...	null	null	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their thoughtful comments. We feel encouraged that the reviewers find that: - The innovation of our method integrates MoE design into the vision side of current multimodal LLMs (NGUV, rTFY, 5mox), and the implementation is simple and easy to follow (5mox)....	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unlocking the Potential of Global Human Expertise	Accept (poster)	Summary: This paper designs a framework called Realizing Human Expertise through AI (RHEA) to combine and distill solutions from a diverse set of models or solutions provided by experts. The steps are: Define Problem -> Gather Solutions -> Distill Model -> Evolve Solution. Examples were presented to demonstrate the fra...	Rebuttal 1: Rebuttal: _Response to question on details of the Distill process:_ We will move the core details of Distill from the Appendix to the main text and ensure that there is enough in the main text to have a clear idea of the process. _Response to suggestions to add more specific details to the Contributions a...	Summary: The authors introduce a new evolutionary framework for combining expert policy predictions called RHEA and evaluate performance in both a synthetic domain that is highly interpretable and in a predictor from the XPRIZE Pandemic Response Challenge. The solutions are analyzed by qualitatively and quantitatively ...	Rebuttal 1: Rebuttal: > I did not get the sense that the “policy intervention problem” is something that is well studied. How is the problem statement here different from a contextual bandit or similar formalisms (knapsack problems?) Are there other baselines for this problem besides lesioned versions of the current fr...	Summary: This paper introduces RHEA (Realizing Human Expertise through AI), a framework for combining diverse human expertise to solve complex problems using artificial intelligence. The key contributions are: * Recognizing the challenge of integrating diverse human expertise to solve global problems like those in publ...	Rebuttal 1: Rebuttal: Our responses to remaining comments are grouped by topic: _NeurIPS vs. general science venue:_ We agree that this approach could be well-appreciated by broad audiences. Indeed, NeurIPS encourages application papers that have broad appeal, and we believe the introduction of this framework to the ...	null	null	Rebuttal 1: Rebuttal: Main Response: Thank you to the reviewers for the feedback. We were glad the reviewers agreed that the work was valuable, and suggested how it could be strengthened further by addressing some outstanding questions. This main response focuses on three main points brought up by multiple reviewers: ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Constant Acceleration Flow	Accept (poster)	Summary: The paper presents an extension to rectified flow by Liu et al., 2023, a method based on optimal transport that can match samples from two distributions. This class of methods may have different uses but is often employed for the efficient generation of new samples and for morphing one sample to another. As su...	Rebuttal 1: Rebuttal: We highly appreciate the reviewer's effort for the detailed feedback and for spotting the typos. We will make corrections in the final version. Q.1 [Significance of work & real-world application] Response to Q.1 In response to the concern regarding the significance of our method, we woul...	Summary: This work proposes Constant Acceleration Flow (CAF), which, instead of learning a velocity-based flow model like in Flow Matching, jointly trains an initial velocity model together with a constant time-dependent acceleration model. This framework aims to learn straighter paths and thus enable better results fo...	Rebuttal 1: Rebuttal: We truly appreciate the reviewer for the constructive feedback. We would like to clarify the reasoning behind our approach and address the concerns raised. Q.1 [Why use EDM for reflow?] Response to Q.1 In response to the reviewer’s concern regarding the reflow procedure, we would lik...	Summary: This paper develops a new flow based generative model whose vector field is constructed similar to rectified flows (straight path between source and target samples), but instead of using a path with a constant speed, uses a path with constant acceleration. This requires learning a neural network to parametriz...	Rebuttal 1: Rebuttal: Q.1 [Missing discussion with AugBM] The related work should include references to building diffusion models with non-markov path measures (see De Bortoli et al., 2023 and its related work). Response to Q.1 We appreciate the reviewer for pointing out the related works that we have missed...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate all reviewers' detailed feedback. Here, we present additional quantitative results to address their concerns. --- 1. [ImageNet 64x64 Results] In response to the concerns about the limited evaluation of our method, we provide additional quantitative results (FID, Incep...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
RGFN: Synthesizable Molecular Generation Using GFlowNets	Accept (poster)	Summary: This study modifies prior GFlowNet-based frameworks to produce molecules satisfying synthesizability. While previous GFlowNet-based molecular generation completes molecules by adding fragments or atoms, the proposed method repeats following stems to generate molecules: (1) selects reaction templates, (2) selec...	Rebuttal 1: Rebuttal: # Weakness 1 Response: Thank you for this comment, we agree that the preliminary section present in the original manuscript was insufficient for understanding the method. We revised it to include the following information, which hopefully will address Reviewer’s remark: > Another way to rephrase t...	Summary: This paper presents a model for molecule design called Reaction-GFlowNet (RGFN). RGFN is an extension of the GFlowNet framework [4] (which has previously been used to generate molecules by building them out of small fragments) to generate molecules through virtual chemical reactions, ensuring that the molecule...	Rebuttal 1: Rebuttal: # Significance We thank the Reviewer for pointing out additional references that were included in the revised version. While the idea of extending GFNs to operate in the reaction space might seem straightforward, significant work was needed to implement the approach, not emphasized enough in the o...	Summary: This work proposes a GFlowNet-based framework for synthesizable molecule design, which comprises of building block selection stages and reaction selection stages. The GFlowNet-based model uses chemical oracle functions as reward and the goal is to generate synthesizable molecules with the desired property quan...	Rebuttal 1: Rebuttal: # Weakness 1 Response: Regarding limited library size, please refer to the Overall Response. We agree with the oracles being a limiting factor, but as the Reviewer states, this is not a unique problem to RGFN. # Question 1 Response: The reviewer raises a good point. We agree that the SA scores are...	Summary: This work proposes a workflow to synthesize molecules with reaction templates starting from some building blocks. Through this pipeline, the synthesizability of the generated molecules can be improved based on some experimental evidence of molecular drug design tasks. Strengths: (1) This reviewer agrees with ...	Rebuttal 1: Rebuttal: # Weakness 1 Response: Thank you for pointing out the omission of the Qiang et al., 2023 manuscript (“Bridging the gap..”) and the work by Nguyen et al. (“A generative model … reaction trees”) , we added the references to the revised version. In the related work section we cited a review by Meyers...	Rebuttal 1: Rebuttal: Thank you very much for your detailed feedback on our manuscript. We understand and have carefully considered your concerns with our work, and aim to provide a summarized response to the most common points here. # Small Fragment Library We agree with the Reviewers that the search space is indeed...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals	Accept (poster)	Summary: This work concerns stimuli that come from an underlying low-dimensional latent space. It studies a path-integrating problem on this space, in which a network is shown two images, has to infer a displacement signal between these images, and use it to traverse the space in a few different ways specified by a ser...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed feedback and for raising excellent questions. We will try to address all questions one-by-one. > Relative to CSCG, TEM and models like them, I think this work is simply answering a different part of the cognitive mapping question Thank ...	Summary: This paper develops a new dimensionality reduction approach based on velocity in latent space. It is inspired by, and tries to emulate, aspects of entorhinal grid cell activity. The critical ingredient is a "loop-closure" constraint that drives the model to build a metric map of the latent space. The results d...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed feedback and for raising excellent questions. We will try to address all questions one-by-one. > State-based SR doesn't assume action inputs Thank you for this note. We will clarify this in our text and distinguish between state-based SR...	Summary: This work proposes a novel neural circuit model that explains how grid cells in the medial entorhinal cortex can map both spatial and abstract cognitive spaces. The model extracts low-dimensional velocity signals from high-dimensional abstract domains using self-supervised learning, leveraging grid cells’ spat...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed feedback and for raising excellent questions. We will try to address all questions one-by-one. > There is little discussion about grid cells, and the description of how the continuous attractor network of grid cells works is insufficient....	Summary: This work explores how the brain could theoretically generalize its representations of velocity signals used to map spatial domains into abstract velocity signals that map abstract cognitive domains. To do so, proposes a self-supervised ANN algorithm and architecture to learn a low-dimensional latent space th...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed feedback and for raising excellent questions. We will try to address all questions one-by-one. > Figure 4 var/axes and Figure 3 isotropy equation Thank you for the suggestions! We will make the necessary edits to the figure. > **Dimen...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful and detailed review of our paper and their overwhelmingly positive feedback. In this general response, we address some common themes across the reviews. We will additionally provide detailed answers to each reviewer in subsequent comments. To go with this r...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Convergence of $\text{log}(1/\epsilon)$ for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis	Accept (poster)	Summary: The paper provides smoothed analysis results four gradient-based algorithms for two-player zeros-sum games: OGDA, OMWU, EGDA and IterSmooth. The considered setting assumes that the payoff matrix is injected by noise where each element of the noise matrix is i.i.d. Gaussian $\mathcal{N}(0, \sigma^2)$. In this s...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and service. In the presentation of section 3.2, the matrix $Q$ is defined based on a particular pair of indexes $(i,j) \in B \times N$. Are all subsequent claims hold for all $(i,j)$? Matrix $Q$ is indeed defined with respect to a certain pair $(i,j) \in B...	Summary: The paper is concerned with studying the convergence of some state-of-the-art gradient-based algorithms for solving zero-sum games. For these algorithms, it is known that in the worst case, the number of their iterations grows polynomially in 1/e, where e is the error bound. The paper shows that for many of t...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and service.	Summary: This paper performs a smoothed analysis for zero-sum games. Existing convergence rate guaratees of gradient-based algorithms often depend on condition number-like quantities which can be exponential in dimension. This paper shows that for the average case or smoothed case (as opposed to worst-case) the error c...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and service. The paper lacks adequate comparison with existing literature. There has been considerable research on smoothed analysis of optimization problems (see [1] for example). We will make sure to cite and discuss [1] (and some of the references therei...	Summary: This paper studies smoothed analysis of gradient-based algorithms for computing equilibria in zero-sum games. In general, regret minimization can be used to compute an $\epsilon$-equilibrium in a number of iterations polynomial in $1/\epsilon$. If inverse polynomial or better precision in $\epsilon$ is desired...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and service. We also thank the reviewer for pointing out the issues with clarity. We will make sure to incorporate the suggestions in the revision. *Could you clarify precisely how gradient based methods in the smoothed setting improve over, say interior poin...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors apply the smoothed analysis framework predominantly studied by (Spielman and Teng'04) to some common sequential algorithms for learning the Nash equilibria in zero-sum bimatrix games. To this end, they look at EGDA, OGDA, OMWU and IterSmooth. They look at these algorithms that have known last-itera...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and service. Can the authors comment if the smoothed analysis could give insights going beyond bimatrix games, such as the performance of the algorithms studied in this paper but for convex-concave settings or for low-rank bimatrix non-zero sum games? This ...	null	null	null	null	null	null
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models	Accept (poster)	Summary: This paper proposes a retrieval-augmented generation (RAG) method that is inspired by the hippocampal indexing theory of human memory to enable longer knowledge storage and efficient knowledge integration over new experiences. Strengths: 1. This paper's idea is interesting and shows impressive performances. ...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort spent reviewing our paper. We are glad they found our method interesting and enjoyed the `Node Specificity` portion of our methodology. We also appreciate their suggestions and long-term memory references, which we will definitely include in our update...	Summary: This paper presents Hippo-RAG, which enables knowledge integration across retrieval results and supports long-term memory with a mechanism that resembles the hippocampal memory indexing theory. Hippo-RAG includes two steps: offline indexing to extract, encode, and index the passages to KG, and online retrieval...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for the time and effort they dedicated to reviewing our paper as well as for their comments and questions. - **W1: The mechanism is great, but the generalization of the method is not good enough. Intuitively, the mentioned mechanism should work for several sc...	Summary: The paper introduces HippoRAG, a retrieval framework inspired by hippocampal indexing theory to enhance large language models (LLMs) in integrating new information. The algorithm is a combination of LLMs, knowledge graphs (KGs), and the Personalized PageRank algorithm. HippoRAG outperforms existing retrieval-a...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort spent on reviewing our paper. We appreciate their helpful suggestions and the related work they brought to our attention. - **W1: Some baselines from KG-LLM for multi-hop QA literature are missing. These could be beneficial to replace Page Rank for a...	null	null	Rebuttal 1: Rebuttal: We thank all of the reviewers for the time and effort they dedicated to reviewing our work, we believe our work will be significantly enhanced from incorporating their suggestions. We are delighted to know that reviewers found the parallels between our methodology and hippocampal memory indexing ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection	Accept (poster)	Summary: The paper presents a Domain-Aware Adapter (DA-Ada) for DAOD based on VLMs, which aims to improve the model performance when applied to an unlabeled target domain. DA-Ada incorporates two types of adapters, including a Domain-Invariant Adapter (DIA) to learn domain-invariant knowledge and a Domain-Specific Ada...	Rebuttal 1: Rebuttal: Comments: We sincerely thank you for the valuable comments. We are encouraged to see that our work is recognized as moderately interesting and effective. We will explain your concerns point by point. Q1: **In Line 139, ...low-dimensional features have less information redundancy and are more su...	Summary: This paper presents a method to tackle the domain adaptive object detection (DAOD) task within the framework of visual-language models (VLM). The authors propose a Domain-Aware Adapter (DA-Ada) to enhance the visual encoder's ability to learn both domain-invariant and domain-specific features. DA-Ada consists ...	Rebuttal 1: Rebuttal: Comment: We sincerely thank you for your comprehensive comments and constructive advice. We are pleased to see our work being regarded as effective, and the motivation as clear and straightforward. We will explain your concerns point by point. Q1: **The performance of the source-only baseline i...	Summary: This work focuses on domain adaptive object detection (DAOD) with the vision-language models. The core idea behind this paper is the frozen visual encoder with a domain-agnostic adapter only captures domain-invariant knowledge for DAOD. To this end, this paper proposes a novel Domain-Aware Adapter (DA-Ada) to ...	Rebuttal 1: Rebuttal: Comment: We sincerely thank you for your comprehensive comments and constructive advice. We are pleased to see our work being regarded as reasonable and effective. We will explain your concerns point by point. Q1: **Although the proposed method is reasonable...the domain-invariant and domain-s...	Summary: This article proposes a Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. The DA-Ada framework consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowl...	Rebuttal 1: Rebuttal: Comment: We sincerely thank you for your comprehensive comments and constructive advice. We are pleased to see our work being regarded as achieving significant improvement. We will explain your concerns point by point. Q1: **The DIA module is a sequence of operations...explanation behind this ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find that: 1. The idea of learning domain-aware adapter is moderately interesting (Reviewer N5kf), reasonable (Reviewer nqrX) and the motivation is clear and straightforward (Re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Multi-Agent Domain Calibration with a Handful of Offline Data	Accept (poster)	Summary: The paper addresses the challenge of performance degradation when RL policies trained in one domain are deployed in another with different dynamics. The proposed solution, Madoc (Multi-agent domain calibration), uses a small amount of offline data from the target domain to adjust the source domain's physics pa...	Rebuttal 1: Rebuttal: ### Q1: The objective in Sec. 4.1. Eq. 4 is obtained through reasonable approximations. The explanations for each derivation step are as follows: - Eq. 2 is derived from Eq. 1 based on the definitions of trajectory distribution and KL divergence, transforming the objective into the expectatio...	Summary: This paper introduces Madoc, a domain transfer method that calibrates a source domain using small amount of offline data from the target domain via multi-agent reinforcement learning (MARL). More concretely, MARL is used to tune physics parameters governing dynamics in the source domain to more closely match t...	Rebuttal 1: Rebuttal: ### Q1: Weak empirical results. Our method Madoc has achieved a trade-off between high mean and low variance in return performance. - On the one hand, Madoc requires online interaction with the source domain to search for the domain parameters that best match the offline dataset. Consequently,...	Summary: The authors, introduce Madoc, a novel framework for domain calibration. By leveraging offline data from the target domain, it dynamically adjusts physics parameters, enabling direct policy deployment. To tackle the challenge posed by a large domain parameter space, the authors propose modeling domain calibrati...	Rebuttal 1: Rebuttal: ### Q1: Method Complexity and Comparisons with Offline RL. Madoc adds more modules compared to CQL, but the model complexity and GPU memory cost remain acceptable. We conducted experiments on the hfctah-med-rep task of the D4RL benchmark to evaluate the model complexity of different methods....	null	null	Rebuttal 1: Rebuttal: We appreciate valuable comments from all reviewers. We have carefully clarified the ambiguous parts and supplemented our work with additional experiments to address the raised issues. Our revisions can be briefly summarized as follows: - Method. - We have further elaborated on the motivation ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics	Accept (poster)	Summary: The paper proposes a multi-variable OT-based framework for solving Single-Cell related problems. The main idea behind the approach is to use the solutions for different variants of discrete entropy-regularize OT problems to learn a continuous parametric plan (measure) given by its conditional distributions, wh...	Rebuttal 1: Title: Additional Answer to Reviewer GBWj Comment: > *What is the “conditional mean” regime of the GENOT model?* ➤ This refers to mapping a point $\mathbf{x}$ to the target domain via the conditional mean, i.e., by averaging multiple samples from the conditional distribution $\pi^\star_\varepsilon(\cd...	Summary: The paper proposes a framework for realigning cells in single-cell genomics which lies in the field of neural Optimal Transport (OT) solvers. The method utilizes a generative flow-based model for computing entropic OT couplings and tackles several practical challenges, e.g., it allows for using arbitrary cost ...	Rebuttal 1: Comment: > *The situation with quadratic setup is the most tricky here, since it is known that GW problem admits multiple solutions and, thus, the optimization problem may differ at each step of training depending on the calculated discrete entropic GW plans.* ➤ We agree that the quadratic setup is the...	Summary: The authors present Generative Entropic Neural Optimal Transport (GENOT), a flexible method for learning entropic couplings for linear and quadratic entropic optimal transport (EOT), unbalanced OT, and OT across incomparable spaces. At its core, their method uses conditional flow matching (CFM) to solve for th...	Rebuttal 1: Rebuttal: > *The authors present promising and comprehensive set of empirical results [...] and addresses important and challenging problems in single-cell biology.* ➤ We thank the reviewer for the positive feedback. > ***It seems to me that a key novelty of this work is the use of quadratic OT for le...	null	null	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their encouraging feedback, constructive criticism, and thoughtful comments, as well as for pointing out typos. In response to questions about the uniqueness of quadratic OT solutions raised by reviewer NoBU, we included an analysis of the stability of dis...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers	Accept (poster)	Summary: The authors present a novel method for interpretable image classification by incorporating a vision transformer (ViT) into the prototypical neural network framework which provides case-based reasoning to neural network based image classifiers. They claim that most existing prototypical methods are convolutiona...	Rebuttal 1: Rebuttal: Thank you for your thorough review and comments. We are happy that you find our method sound and our experiments ample. We address your comments below: > The lack of qualitative comparison with other ViT methods.. We attempted to compare our visualizations to those from ProtoPFormer [1], but are...	Summary: The authors introduce ProtoViT, a model that leverages the Visual Transformer (ViT) architecture and integrates prototypical parts for case-based reasoning. This method is self-explainable, adhering to the rule "this looks like that." A novel aspect of ProtoViT is the use of prototypical parts of varying sizes...	Rebuttal 1: Rebuttal: Thank you for your thorough review and comments. We are happy that you find our matching algorithm engaging and a significant contribution and our experiments thorough and comprehensive. We address your comments below: > The primary concern lies in ensuring that the ViT backbone can maintain prot...	Summary: This paper presents a novel strategy to learn interpretable visual prototypes for visual transformers, with a good property of offering spatially deformed prototypes. The method also introduce an slot mechanism which can learn an adaptive number of prototypical parts. The proposed are wisely designed for visua...	Rebuttal 1: Rebuttal: Thank you for your thorough review and comments. We are happy that you find our method novel and well-designed for transformers. We address your comments below: > The first concerns is about the use of deformable prototypes that containing K sub-prototypes, which means the proposed method will hav...	Summary: The paper presents ProtoViT, a method for interpretable image classification. ProtoViT incorporates ViT backbone with deformed prototypes that explains its predictions. ProtoViT consists of three components: (1) a feature encoding layer with a pre-trained ViT backbone, which computes a latent representation ...	Rebuttal 1: Rebuttal: Thank you for your comments. We are glad that you find our paper interesting, well-written and nicely presented. We address your points below: > ... Is it possible to highlight the examples where the method could not predict the correct class? It would be then interesting to understand the reasons...	Rebuttal 1: Rebuttal: We thank all the reviewers for their comments. In addition to addressing all of your concerns individually, we believe the following clarifications and additional experiments may be of interest to all of you. These additional experiments further reinforce the strength of our method and will be add...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Disentangled Generative Graph Representation Learning	Reject	Summary: The paper introduces DiGGR (Disentangled Generative Graph Representation Learning), a self-supervised learning framework that aims to guide graph mask modeling through disentangled latent factors to enhance the disentanglement of learned representations. Extensive experiments across 11 public datasets for node...	Rebuttal 1: Rebuttal: ## Q1: Computation Comleaxity Grateful for your comments. We will expand the discussion on complexity and scalability in our revisions, focusing on the following three aspects: 1. Complexity Analysis: We discussed the network's complexity in Section 3.4 and will later compare the time and space ...	Summary: The paper introduces a framework called DiGGR, aimed at improving the robustness and explainability of generative graph models by addressing the issue of entangled graph representations. Strengths: 1. The paper tells the story in an easy-to-read way, and the whole paper is quite easy to follow. 2. The problem...	Rebuttal 1: Rebuttal: ### W1: Novelty Thank you for the time and effort you have dedicated to our paper. It is true that DiGGR and [1-4] use some common techniques in disentangled learning. However, we sitll believe our approach offers novelties in the following aspects: (i)The main goal of our paper is to use disenta...	Summary: The work proposes a disentangled generative self-supervised learning method for graphs. The authors introduce a latent factor learning module to capture the heterogeneous factors in the nodes. The proposed method factorizes the graph into factor-specific subgraphs, and jointly trains a disentangled Graph MAE a...	Rebuttal 1: Rebuttal: ## Q1: Computation Complexity We genuinely appreciate the time and effort you dedicated to a thorough reading of our paper. Based on your suggestions, we conducted a training time comparison experiment. We found it to be very helpful for readers to understand the actual model complexity, and we w...	Summary: The paper proposes a self-supervised learning framework DiGGR, aimed at enhancing the disentanglement of learned graph representations. The authors argue that existing generative graph models tend to overlook the entanglement of learned representations, leading to non-robust and non-explainable models. DiGGR a...	Rebuttal 1: Rebuttal: We genuinely appreciate the time and effort you dedicated to thoroughly reading our paper. Our code is uploaded in the Supplementary Material, with optimal hyperparameters in the config file. Set "use_best_cfg = True" to reproduce our results. Specific training hyperparameters and dataset deta...	Rebuttal 1: Rebuttal: ## Global Response We thank all the reviewers for their valuable suggestions on our paper. We will first address a common issue: Differences with Previous Work. We have carefully reviewed the paper [1-5] you provided, and while it is true that both our method and them utilize KL divergence...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings	Accept (poster)	Summary: The authors designed a federated learning algorithm that leverages a Bayesian network to enhance the model's robustness, and uses data distillation methods to reduce overhead and adapt to scenarios with different client model structures. The authors theoretically discussed the differential privacy characterist...	Rebuttal 1: Rebuttal: We appreciate reviewer's insightful comments and suggestions. We are encouraged by reviewer's positive remarks about the formal privacy guarantee and the experiments on the alignment dataset. We address reviewer's specific concerns below - -- Clarification on the contribution We agree with th...	Summary: The authors propose FedBNN, a novel personalized federated learning (FL) framework that leverages Bayesian principles to address challenges posed by heterogeneous data and computational resources among clients. FedBNN uses Bayesian neural networks to enable robust local training on small datasets by quantifyin...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s detailed feedback. We are encouraged to see that the reviewer acknowledges the strength of our work, stating that "*Overall, the extensive experimental evaluation, coupled with the theoretical foundations, provides strong evidence for the effectiveness and significance...	Summary: This work integrates Bayesian neural networks, knowledge distillation, and differential privacy into the federated learning framework. The resulting framework can maintain privacy during training and provide uncertainty estimates for its predictions. Empirical results demonstrate that the proposed method outpe...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s valuable feedback. We are pleased to see that the reviewer finds the work well-written, noting that "The authors have clearly explained the motivation, methodology, and experimental settings" and recognizing that "*This work addresses data and system heterogeneity an...	Summary: The paper proposes FedBNN, a Bayesian approach for personalized federated learning. The approach relies on the availability of an auxiliary small public unlabelled dataset, called Alignment Dataset, that can be used as a mean to distill knowledge across clients. In FedBNN, clients maintain an estimate of a po...	Rebuttal 1: Rebuttal: Dear Reviewer rtXt, We are thankful to the reviewer for the thorough feedback on our work. It is encouraging for us to see that the reviewer found the paper easy to read and follow, appreciated the rigorous "experimental evaluation that considers a wide range of settings and competitors", an...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Precipitation Downscaling with Spatiotemporal Video Diffusion	Accept (poster)	Summary: This paper presents a novel framework for spatio-temporal precipitation downscaling, comprising two modules: a deterministic downscaling module and a diffusion module. The model is able to outperform the SOTA models, especially in extreme events and in mountainous areas. Strengths: 1. Multiple losses, such as...	Rebuttal 1: Rebuttal: _Thank you for taking the time to read our work. We are happy you find novelty in the realism-distortion tradeoff and appreciate the PE and EMD metrics. We address your concerns as follows:_ - ___“...the effectiveness of the sharing features across the modules is not proven by ablation experiment...	Summary: The method proposes a diffusion model to statistical downscale precipitation. The model requires a combination of high resolution and low resolution video data for training (a common scenario in weather and climate modeling). The diffusion model takes in a video of low resolution atmospheric variables and outp...	Rebuttal 1: Rebuttal: _Thank you for taking the time to read our manuscript. We are happy you see the utility of our work in climate modeling and appreciate the ablation study. We address your concerns as follows:_ - ___Missing related work:___ We appreciate the reviewer's suggestion, and will include these refere...	Summary: This paper extends video diffusion model to precipitation super-resolution, where a deterministic downscaler is used to produce initial results and a temporally-conditioned diffusion model is utilized to refine previous coarse results. By combing deterministic and statistical downscaling models, "mode averagin...	Rebuttal 1: Rebuttal: _Thank you for taking the time to read our work. We are pleased that you find the paper easy to read, appreciate the residual nature of our model, and find the experiments sufficient. We address your concerns as follows:_ - ___“...any design that guarantees that the output high-resolution frames ...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for taking the time to review our work and appreciate the detailed feedback and constructive comments. In this response, we address a common point about a claimed limitation of the paper. All other questions are addressed in reviewer-specific responses. Some reviewers point...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions	Accept (poster)	Summary: The authors combines - the notion of M-matrix from linear algebra - the notion of positive semidefinite kernel from kernel learning theory to create the notion of M-kernel. The M-kernel can be used for solving non-negative constrainted least squares in an RKHS. Strengths: - The idea is novel, to my knowledg...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the highly positive comments, by which we are strongly encouraged! We feel sorry for the grammatical flaws in this paper. Despite this, we appreciate your thorough understanding of our work's content and value. We promise to improve the grammatical quality o...	Summary: The paper considers learning in RKHS with non-negativity constraints $f(x) \geq 0$, which turns up with surprising regularity in areas of machine learning. The proposed approach uses an elegant "trick" involving restricting the kernel selection the so-called inverse $M$-kernels that allows the constraint to b...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for the highly positive and constructive comments. It is incredibly encouraging to know that a reviewer with practical experience in the estimation of non-negative functions finds value in our work! Below, we provide a detailed response to each of the comments. ...	Summary: The paper proposes a new kernel family that can be used to fit non-negative functions tractably, which has theoretical novelty. The universal approximation properties are analysed, and a connection is made to permanental processes. The method is applied to univariate regression, density estimation and intensit...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for carefully reading our paper and giving valuable comments. While the reviewer has positive evaluations of the core part of this paper (Soundness: good, Presentation: good, Contribution: good), we understand that the critical concern of the reviewer lies in th...	null	null	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their valuable comments. We responded in as much detail as possible. We believe that we can entirely dispel the concerns raised by the reviewers. Please see the added figure (Figure C1) in response to Reviewer WaA4's suggestion. Pdf: /pdf/9636818fa26c0c6815...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Policy-shaped prediction: avoiding distractions in model-based reinforcement learning	Accept (poster)	Summary: The paper addresses the problem of distractions in model-based RL by proposing a policy-shaped prediction (PSP) method, which combines segmentation and adversarial learning to accurately identify and prioritize policy learning on crucial parts of dynamics by incorporating saliency maps from image-based environ...	Rebuttal 1: Rebuttal: We thank the reviewer for their excellent comments. Evaluation of additional segmentation models: Thank you for this great suggestion. We now include evaluation the 'tiny' variant of the recently released SAM2 segmentation model. This increases the speed of the segmentation step by over 2x (...	Summary: This paper introduces Policy-Shaped Prediction (PSP), a method in model-based reinforcement learning (MBRL) designed to focus on significant aspects of an environment by reducing the influence of distracting information. PSP incorporates a pre-trained segmentation model, a task-aware reconstruction loss, and a...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable comments. High variance in Hopper Stand: Thank you for this observation. Hopper Stand is a challenging environment with fairly sparse reward. Indeed none of the other MBRL methods were ever able to achieve success on this task in the Reafferent enviro...	Summary: This paper presents a novel model-based reinforcement learning (MBRL) method that focuses on important parts of image-based environments with distractions, aiming to improve policy learning. The proposed method introduces gradient-based weighting, segmentation-based aggregation, and adversarial action predicti...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and helpful comments. 1. We are grateful to the reviewer for catching this typo. In Figure 1, we should be using t as the subscript, not i. We will update the figure and text accordingly. We will additionally make clear that while v_i and a_i are...	Summary: ### Review Summary This paper presents a novel approach to improving model-based reinforcement learning (MBRL) by identifying that detailed but irrelevant aspects of the world can exhaust the capacity of the world model, thus hindering the learning of important environment dynamics. The proposed method, Polic...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments. Effectiveness of Policy Gradient Information: We do see some evidence of the chicken-and-egg problem. Importantly, though, our interpolated weighting term (see Eq. 3 in the manuscript) provides a route for escaping this problem by allowing...	Rebuttal 1: Rebuttal: ### Overview for all reviewers We thank the reviewers for their excellent thoughtful and constructive comments. We agree with the reviewers' assessment that Policy-Shaped Prediction (PSP) is a novel and effective method to address a well-motivated and relevant problem: reducing the influence of di...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Happy: A Debiased Learning Framework for Continual Generalized Category Discovery	Accept (poster)	Summary: The paper presents a novel approach to the task of Continual Generalized Category Discovery (C-GCD). The proposed framework, named Happy, aims to address the challenges of continuously discovering new classes from unlabeled data while preventing the forgetting of previously learned classes. The authors identif...	Rebuttal 1: Rebuttal: Thank you for your insightful advice and valuable questions, we will respond to your concerns point by point. > W1: Complexity of the Framework. * Effect of each component. Considering that C-GCD is a challenging task, each component is essential and addresses specific issues. (1) Learning n...	Summary: The paper proposes a novel method for the Continual Generalized Category Discovery task, addressing the challenges of discovering new classes and preventing forgetting. The approach introduces Clustering-guided Initialization and Group-wise Soft Entropy Regularization for class discovery, as well as Hardness-a...	Rebuttal 1: Rebuttal: Thank you for your insightful advice and valuable questions, we will respond to your concerns point by point. > W1: Why not use these old class data directly instead of hardness-aware prototype sampling? * Good question. At each continual stage of C-GCD, all training data are unlabeled, i.e., **...	Summary: The paper points out two-bias issues in CGCD: prediction bias in probability space and hardness bias in feature space. To tackle those two issues, they propose cluster-guided initialization and soft entropy regularization to mitigate prediction bias, and they propose hardness-aware prototype sampling to mitiga...	Rebuttal 1: Rebuttal: Thank you for your insightful advice and valuable questions, we will respond to your concerns point by point. > W1: Overclaim the CGCD setup. * Differences from [16,18]. We consider (1) more continual stages (5>3 and 10>3 in table 4) with more novel classes to discover (50%>30% of total cla...	Summary: The article presents a method for Continual Generalized category discovery. A de-biasing learning framework for the Category Discovery (C-GCD) task is designed to address the challenge of continuously discovering new concepts in an ever-changing environment while maintaining recognition of known categories. Tr...	Rebuttal 1: Rebuttal: Thank you for your insightful advice and valuable questions, we will respond to your concerns point by point. > W1: Lack of a comprehensive social impact analysis. * We summarize the negative impacts: (1) Bias/error accumulation. Given the stringent unlabeled conditions of C-GCD, the initia...	Rebuttal 1: Rebuttal: We thank all reviewers for their dedication and insightful comments, and we believe these comments are significant for improving the overall quality of this paper. We are pleased that the reviewers appreciate our paper from various aspects, including the novelty of the method [TDQJ,jUDg,cVL6], cl...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LORA-MOO: Learning Ordinal Relations and Angles for Expensive Many-Objective Optimization	Reject	Summary: This paper proposed the LORA-MOO framework, a surrogate-assisted MOO algorithm that learns surrogates from spherical coordinates. This includes an ordinal-regression-based surrogate for 10 convergence and M −1 regression-based surrogates for diversity. Strengths: The considered problem is pretty important. W...	Rebuttal 1: Rebuttal: Weakness 1: Line 113-116, a bit too repetitive. Response: Thanks for your comment. We explain the connection between SAEAs and BO in lines 113 - 116 since some less-skilled readers may not be knowledgeable enough about this. Actually, we met many reviewers who do not know the connection between ...	Summary: This paper proposes a surrogate-assisted evolutionary many-objective optimization algorithm, named LORA-MOO. LORA-MOO is composed of a surrogate for ordinal modeling, which focuses on convergence, and m-1 surrogates for distribution modeling, which focus on diversity. Empirical study demonstrates the effective...	Rebuttal 1: Rebuttal: Weakness: Response: Thanks for your comments. As we listed in our contributions (Section 1), the main differences are: 1) Our ordinal-regression-based model which trained on dominance relations and artificial ordinal relations. 2) The idea of modeling surrogates in spherical coordinates and use ...	Summary: This paper proposes a novel surrogate-assisted evolutionary algorithm named LORA-MOO, the core contribution is the introduce of ordinal-regression-based model spherical coordinates approximation to SAEA and LORA-MOO can find a good trade-off between optimization efficiency and optimization results. Strengths...	Rebuttal 1: Rebuttal: Weakness: Response: Thanks for your comment. We were confused that why the reviewer thought our contributions were limited. Our work proposed a novel model and a novel optimization method. In addition, we noticed that the reviewer was the only reviewer who thought our presentation was not good. ...	Summary: This paper introduces a surrogate assisted method for multi-objective optimization. The approach learns a surrogate function with the ordinal values as the regression labels. The ordinal values are generated using a iterative algorithm with the most dominated solutions having the highest ordinal values. The or...	Rebuttal 1: Rebuttal: Weakness 1: The approach presented in the paper is not sufficiently novel. Ordinal regression for multi-objective optimization has been studied before [1]. The differences with related prior work have not been discussed in detail. Response: Thanks for your comment. We would like to add the follo...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Long-range Meta-path Search on Large-scale Heterogeneous Graphs	Accept (poster)	Summary: This paper proposes an efficient meta-path search method on large-scale heterogeneous graphs. The proposed progressive sampling strategy and sampling evaluation strategy is effective for reducing the memory and time overhead, especially when the maximum hop is large. Experimental results show the effectiveness...	Rebuttal 1: Rebuttal: Thanks for your constructive comments. In the following, we respond to your concerns point by point. --- ### W1: This paper has limited novelty because the proposed method is simple and straightforward. R1: Although the other three reviewers enjoyed the novelty, we appreciate that the ...	Summary: The paper proposes a novel framework LMSPS, aimed at efficiently utilizing long-range dependencies in large-scale HIN. The framework addresses two primary challenges: reducing computational costs while maximizing information utilization and overcoming the over-smoothing problem common in GNNs. LMSPS employs a ...	Rebuttal 1: Rebuttal: Thanks for your positive comments that greatly encourage us. In the following, we respond to your concerns point by point. --- ### **W1: I still have some doubts about the necessity of modeling long-range dependencies in heterogeneous graphs. Could the authors provide an example to illustrate sp...	Summary: The paper proposes a new framework called Long-range Meta-path Search through Progressive Sampling (LMSPS), which differs from traditional meta-path-based GNN training methods on heterogeneous graphs. LMSPS introduces a strategy for building a search space that includes all meta-paths related to the target nod...	Rebuttal 1: Rebuttal: Thanks for your positive comments that greatly encourage us. In the following, we respond to your concerns point by point. --- ### **W1: What does robust generalization to other classes imply or reveal about the process of long-range meta-path GNN training? Will the sampling search impact the m...	Summary: This paper presents an empirical study demonstrating that not all meta-paths are useful; some even negatively impact performance. Selecting the most meaningful meta-paths is crucial. The authors propose LMSPS, a super-net-based method to select beneficial meta-paths effectively. Strengths: S1. The presentatio...	Rebuttal 1: Rebuttal: Thanks for your constructive comments. In the following, we respond to your concerns point by point. --- ### W1: The paper's title is misleading. I think the major idea is to select effective meta-paths efficiently to overcome the issue of the exponential increase in meta-paths. R1: Th...	Rebuttal 1: Rebuttal: We are very grateful to the reviewers for carefully reviewing our paper and providing constructive comments and suggestions that have helped improve our submission. We especially thank the reviews for recognizing that our paper has: 1. good originality on method ((Reviewers djZo and 71Wo) an...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Piecewise-Stationary Bandits with Knapsacks	Accept (poster)	Summary: The paper studies Bandits with Knapsack (BwK) under a piecewise stationary environment. For the online matching problem where the true reward is fully known in each time period, the paper obtains a $\Omega(1/\ln(\eta_{\max}/\eta_{\min}))$ where $\eta_{\min}$ and $\eta_{\max}$ are such that all rewards and reso...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's time and effort in evaluating our manuscript. We highly value your feedback and would like to address your concerns and questions point by point. 1. “The ratio of $\ln (\eta_{\max}/\eta_{\min})$ seems to be weak. Imagine if $r_t=r$ and $c_t=c$ for all $t$, but $...	Summary: The paper studies the bandits with knapsacks problem in a piecewise-stationary environment. The paper proposes an algorithm guaranteering a near-optimal competitive ratio for the problem. The guarantees hold wrt a dynamic benchmark which is stronger than the standard stationary benchmark employed in adversaria...	Rebuttal 1: Rebuttal: We are genuinely grateful to the reviewer for dedicating time and effort to evaluating our paper. We are glad to address your question. In fact, the computational load of our algorithm is noticeably lighter than Immorlica et al. (2019). We solve $\text{LP}(r^{(l)}, c^{(l)},\eta_{\min} \cdot \alph...	Summary: This paper studies the bandits with knapsacks problem in a piecewise-stationary environment and designs an algorithm that achieves a provably near-optimal competitive ratio. Instead of using a static benchmark, the performance guarantee is present based on a dynamic benchmark. Strengths: - The problem setup, ...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s time and effort in evaluating our manuscript. Your feedback is highly valued, and we would like to address your concerns and questions point by point. Regading "Weaknesses": “It is hard for readers not having expertise in BwK to get the intuition of why the...	Summary: This paper addresses the challenge of piecewise non-stationary stochastic bandits with knapsacks. In bandit with knapsacks, at each round a learner is asked to choose an action and receives both a reward and a budget cost. The goal of the learner is to maximize its cumulative reward while satisfying some cumul...	Rebuttal 1: Rebuttal: We appreciate your time and effort in reviewing our paper. We address your concerns and questions one by one below: 1. “The main part of the paper is too technical with many different notations, inline mathematical formulas, and not easy to follow.” Response: We apologize for the dense notation...	Rebuttal 1: Rebuttal: Dear Review Team, we are grateful for your careful reading and thoughtful comments. They are highly relevant and very insightful. On top of the point-to-point responses to individual reviewers, we would like to summarize and clarify critical concerns. 1. Are the problem and the algorithm sufficie...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper studies Bandits with Knapsacks in a piecewise-stationary environment, where the underlying reward can change over time. The authors provide provably near-optimal competitive ratio for this setting, which achieves a dynamic benchmark and obtains stronger results compared to existing adversarial Bwk w...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's time and effort in evaluating our manuscript. We highly value your feedback and would like to address your concerns and questions in what follows. Regarding "Weaknesses": 1. Clarification on Algorithm Details (Full-Feedback Deterministic Outcome Setting): We w...	null	null	null	null	null	null
GraphVis: Boosting LLMs with Visual Knowledge Graph Integration	Accept (poster)	Summary: - This paper proposes an instruction tuning method with visual knowledge graph to enhance the large vision language models with external knowledge and perform better on QA tasks. Strengths: - It is a new idea that organize the external knowledge as an image to enhance the LVLMs. Weaknesses: - Only LLaVA-v1.6...	Rebuttal 1: Rebuttal: We're grateful for your support and helpful feedback. Please find our response below for the questions raised in the review and additional experiments. --- Q1. More experiments should be conducted on diverse LVLMs to demonstrate the effectiveness of GraphVis. A1. Thank you for your su...	Summary: The paper presents a method to improve large vision language models (LVLMs) by integrating knowledge graphs (KGs) visually. The approach, GraphVis, uses LVLMs to understand KGs through image visualizations, enhancing comprehension and reasoning. It employs a curriculum fine-tuning strategy, starting with simpl...	Rebuttal 1: Rebuttal: Thank you for your support and constructive feedback. Please find our detailed response below for the questions raised in the review. --- Q1. Further research would be needed to confirm its effectiveness across various KGs. A1. Thanks for raising this important aspect. As we have also...	Summary: GraphVis introduces a novel method for integrating knowledge graphs (KGs) into large language models (LLMs) by preserving the graph structure through the visual modality. Utilizing Large Vision Language Models (LVLMs) and a curriculum fine-tuning scheme, GraphVis enhances both textual QA and VQA performance, d...	Rebuttal 1: Rebuttal: We appreciate your support and suggestions, for which we have included additional experiments accordingly. We hope our explanations below answer your questions and provide more clarity. --- Q1. How the properties of the generated graph images, such as size and resolution, affect the model’s ...	Summary: The paper introduces GraphVis, a novel approach that enables Large Vision Language Models (LVLMs) to reason about visual knowledge graphs for QA tasks. Unlike previous methods that either input knowledge graph (KG) triplets directly to LLMs or use graphical neural networks to capture structured representation...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback. We appreciate your recognition of the originality and significance of our work, and grateful for the positive feedback on the clarity and quality of our paper. Regarding the raised questions, please find our detailed response below with additional experime...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their insightful and encouraging feedback. We are grateful for the recognition of the novelty and significance of our work (Reviewer GUVR, 2Am7,omgG,Tf8h), extensive experiments and superior performance (Reviewer GUVR, 2Am7,omgG), clear writing flow (Review...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Vision-Language Models are Strong Noisy Label Detectors	Accept (poster)	Summary: This paper proposes a novel method for learning with noisy labels leveraging pre-trained foundation models. The method is motivated by new findings that prompt learning is more robust to noisy labels when fine-tuning CLIP. The paper then designs a simple detector by learning both positive and negative prompts ...	Rebuttal 1: Rebuttal: Dear Reviewer mmEa, We sincerely appreciate the reviewer for the thoughtful feedback. We are encouraged by comments like The proposed method is well-motivated and This paper makes an important contribution. We address your concerns one by one. > W1. It is unclear why positive and negative pr...	Summary: This paper focuses on improving the finetuning performance of pretrained vision models by removing the noisy labeled data. Specifically the authors propose a two-stage method: the first stage is to learn a noise detector by prompt learning of the text encoder in CLIP. The second stage is to full-finetune the p...	Rebuttal 1: Rebuttal: Dear Reviewer FyAK, We sincerely appreciate the reviewer for the thoughtful feedback. We are encouraged by comments like The analysis of the relationship between noisy data and finetune methods is very insightful and is well-written and easy to follow. We address your concerns one by one. > ...	Summary: The paper proposes a method for detecting noisy samples using vision-language models (CLIP). The main idea is to efficiently adapt (via prompt tunning) the clip model on noisy data and use this adapted model to select clean data. In the second stage, the clean data can be used to fully fine-tune a backbone mod...	Rebuttal 1: Rebuttal: Dear Reviewer 6CXy, We sincerely appreciate the reviewer for the thoughtful feedback. We are encouraged by comments like The general setting is sound and Good results on multiple noisy datasets. We address your concerns one by one. > W1~W2.2. There needs to be more fair comparisons with base...	Summary: The paper introduces a Denoising Fine-Tuning (DEFT) framework to address the challenge of noisy labels in vision-language models, particularly focusing on models like CLIP. The DEFT framework leverages the robust alignment of textual and visual features pre-trained on extensive image-text pairs to filter out n...	Rebuttal 1: Rebuttal: Dear Reviewer g1PL, We sincerely appreciate the reviewer for the thoughtful feedback. We are encouraged by comments like a versatile solution and strong evidence of the method's effectiveness. We address your concerns one by one. > W1. The heavy reliance on pre-trained models may limit the f...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling	Accept (poster)	Summary: This paper studies aligning samples from LLMs with human preferences using best-of-$n$ (BoN) sampling methods. While BoN methods can yield more desirable outputs without changing off-target behavior, they are computationally expensive since they require $n$ samples from the LLM for every prompt. To address th...	Rebuttal 1: Rebuttal: Thank you for your review! - (A1): Since the expectation also accounts for $x$, the left side and the right side are not equivalent. For different prompts, the distribution of rewards varies. This is fundamental: some prompts are easy for the base model (e.g., “what is 2+2?”) and the optimal alig...	Summary: The paper describes theoretical results about the Best-of-n sampling procedure in LLM inference. To reduce the computational cost of the procedure, authors develop a novel finetuning method called BoNBoN. Experiments on dialog generation and text summarization show that BoNBoN achieves a higher win-rate for th...	Rebuttal 1: Rebuttal: Thank you for your review! First, thanks for all your suggestions about Figure 3! To make it more informative, we will add the theoretical optimal line in the figure and more detailed information in the caption. For the question in the second point, the $n$ in Fig 3 is 8 (we mentioned in Section ...	Summary: They claim that best-of-n is an optimal policy with respect to the tradeoff between win rate and the KL divergence. Based on the analysis they propose a strategy to train a model so that it gets to policy similar to the BoN policy. Strengths: The research question is interesting. BoN and the other learning-ba...	Rebuttal 1: Rebuttal: Thank you for your review! - With respect to prior work: could you expand on the set of references you have in mind? E.g., with links or paper titles. It is not clear to us what papers you’re envisioning, or what connections you have in mind. - For RLHF (and DPO), it is standard procedure to first...	Summary: This paper addresses aligning samples from large language models (LLMs) with human preferences using best-of-$n$ sampling, which involves drawing $n$ samples, ranking them, and selecting the best one. It tackles two main problems. First, it explores the relationship between best-of-$n$ sampling and Reinforceme...	Rebuttal 1: Rebuttal: Thank you for your review! With respect to multiple aspects: we agree this is a fundamental challenge with preference modeling, and a very interesting subject for future research. We note, however, that this problem is essentially fundamental to all post-training procedures. E.g., even explicit r...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful comments and constructive submissions. Where appropriate, we have incorporated these into the main text (details in reviewer-specific replies), and we believe this has strengthened the paper. The reviewers agree that the paper addresses an important and...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective	Accept (poster)	Summary: This paper proposes an offline reinforcement learning method called A2PO, which aims to solve the problem of constraint conflicts in mixed-quality datasets collected from multiple behavior policies. A2PO optimizes offline learning by explicitly constructing advantage-aware policy constraints, especially in the...	Rebuttal 1: Rebuttal: Thanks for your support on the idea, writing, and experiment. And we will solve your doubts with text description and experimental support. **[Q1: Although this paper presents a new approach, the comparison with existing techniques may not be in depth enough and lacks an analysis of why the alg...	Summary: The authors propose an advantage aware offline RL algorithm for datasets consisting of data from multiple behavior policies. The algorithm consists of two steps, (1) behavior policy disentangling: Wherein a CVAE is trained to output actions conditioned on normalized advantage and state. (2) Policy optimization...	Rebuttal 1: Rebuttal: Thanks for your positive comments on the experiment's completeness and the writing's quality. We will address your concerns in this section. **[Q1.1: The Agent policy optimization is unclear: Why is a behavior regularization term (2nd term in (8)) needed when you have a generative model trained o...	Summary: The paper proposes using CVAE to train an agent and using a decoder, which conditions on state and advantage value, to generate actions. The Q function and V function are trained simultaneously, and the policy is trained in a TD3-BC style. Experiments show that the method can outperform baselines in most tasks...	Rebuttal 1: Rebuttal: Thank you for affirming the idea's novelty and the experiment's comprehensiveness. In this section, we aim to address your concerns. [Q1: In line 169, what prior distribution of $p(z)$ did you choose?] Sorry for the confusion. As stated in line 167 of the original paper, the prior distributi...	Summary: This paper presents A2PO. A2PO is an offline RL method for learning with datasets that was collected by a diverse set of poliices. The aim of the method is to disentangle the data being collected by each policy by using advantage calculation and then using this information to better learn policies from the dat...	Rebuttal 1: Rebuttal: We sincerely appreciate your positive support of our work. In the future, we will make further improvements to the A2PO to contribute to the offline rl community.	Rebuttal 1: Rebuttal: Please refer to the attachment for the figures of all results during rebuttal. Pdf: /pdf/d414d0661363431c5e577f33debd8e73b81baf26.pdf	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces A2PO, a novel approach to offline reinforcement learning that addresses the constraint conflict issue in mixed-quality datasets by using a Conditional Variational Auto-Encoder to disentangle action distributions and optimize policies towards high advantage values. A2PO demonstrates superio...	Rebuttal 1: Rebuttal: Thanks for your supportive review of our method, experiments, and writing skills. We will address your doubts here. [Q1. since we can directly select the good trajectories according to the rewards, is there any ablation study to show that CVAE can do better than this simple method?] Thank...	null	null	null	null	null	null
Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning	Accept (poster)	Summary: This work studied the ODD detection in mathematical reasoning, which presents a new measurement, TV score, based on the observed pattern collapse property and early stabilization of GLMs. It seems the first discussion on the OOD detection in mathematical reasoning. Strengths: This appears to be the first disc...	Rebuttal 1: Rebuttal: Thanks for your constructive comments! We will respond to the weaknesses and questions you raised in the following areas: --- > More Metrics (W1 & Q3) Thank you for suggesting richer evaluation metrics about AUPR and F1. The results are below: \| Method \| Llama2-7B \| \| \| \| GPT2-XL \| \| \| \| ...	Summary: This paper presents a trajectory-based method for OOD detection in the mathematical reasoning setting. OOD detection is extensively studied in the text setting and image setting. The main motivation of this work is claimed to that mathematical reasoning poses significant challenges to embedding-based methods d...	Rebuttal 1: Rebuttal: Thanks for your constructive comments! We will respond to your concerns one by one. --- > W1: The motivation is not strong and convincing. Figure 1 ... ### 1. Input Space As for the phenomenon that embedding varies less in different domains of input space: We have discussed it in Section 5...	Summary: This paper studies the OOD problem in GLMs under mathematical reasoning and found that the the patter collapse phenomena in the output space. The trajectory violation score is proposed to distinguish the ID and OOD samples. A thorough evaluation shows that the proposal can outperform traditional algorithms und...	Rebuttal 1: Rebuttal: Thanks for your constructive comments. We will respond to your concerns one by one. --- > W1: The input data for the empirical study in Figure 3 is not introduced. Thus, whether it can reflect the scenario of mathematical reasoning is not clear. Thanks for pointing this out, and sorry for t...	Summary: This work discusses a novel method for out-of-distribution (OOD) detection in generative language models (GLMs), particularly in the context of mathematical reasoning tasks. The key insights are: 1) The high-density output space in mathematical reasoning leads to a "pattern collapse" that causes larger discrep...	Rebuttal 1: Rebuttal: Thanks for your constructive comments! We will respond to your concerns one by one. --- > W1 & Q1: The mechanism of TV score ... more analysis and visualization of trajectory ... intuition behind the choice of trajectory volatility ... ### 1. Mechanism of TV score and its relationship to "p...	Rebuttal 1: Rebuttal: General Rebuttal: Motivation line from "pattern collapse" to "early stabilization" and TV score --- To summarize, our motivation line is: (Section 1, Figure 1): We find "pattern collapse" in the output space **-> (Section 2.1, Theoretical Intuition and Proving): The "pattern collapse" lea...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL	Accept (poster)	Summary: The paper explores interactive decision-making scenarios encompassing bandits, contextual bandits, and reinforcement learning, focusing on the concept of regret minimization. It highlights the Graves-Lai constant, where its zero value is crucial for achieving bounded regret in interactive decision-making. This...	Rebuttal 1: Comment: References to the official Rebuttal below: \[1\]: Foster, Dylan J., et al. "The statistical complexity of interactive decision making." arXiv preprint arXiv:2112.13487 (2021). \[2\]: Dong, K., & Ma, T. (2023). Asymptotic instance-optimal algorithms for interactive decision making. The Eleventh In...	Summary: This paper studies anonymous delay (i.e. when it is unknown which trial the delayed reward came from) in interactive decision making. It gives a strongly negative result that if the reward delay distribution is not exactly known and the “Grave-Lai constant” is non-zero then no algorithm has sub-polynomial regr...	Rebuttal 1: Rebuttal: ### Answer to comments in "Weaknesses" Comment 1. > "Whilst Theorem 4.1 rules out (unless the Grave-Lai constant is zero) sub-polynomial regret with unknown reward distributions, it does not rule out algorithms with very small polynomial regret. A lower bound on a polynomial exponent wou...	Summary: This paper studies the relationship between bounded regret and delay robustness in interactive decision-making, which captures bandits, contextual bandits, and reinforcement learning. The authors show that the Graves-Lai constant being zero is necessary for achieving delay model robustness when reward delays a...	Rebuttal 1: Comment: References to the official Rebuttal below: \[1\]: Hao, B., Lattimore, T., & Szepesvari, C. Adaptive exploration in linear contextual bandit. In International Conference on Artificial Intelligence and Statistics (pp. 3536-3545). PMLR. (2020) \[2\]: Kang, H., & Kumar, P. R. (2023). Recommender sys...	Summary: The paper investigates interactive decision-making in bandits, contextual bandits, and reinforcement learning, focusing on the Graves-Lai constant's role in achieving bounded regret. It establishes that a zero Graves-Lai constant is necessary and sufficient for bounded regret, but questions its practical utili...	Rebuttal 1: Comment: References to the official Rebuttal below: \[1\]: Foster, Dylan J., et al. "The statistical complexity of interactive decision making." arXiv preprint arXiv:2112.13487 (2021). \[2\]: Dong, K., & Ma, T. (2023). Asymptotic instance-optimal algorithms for interactive decision making. The Eleventh In...	Rebuttal 1: Rebuttal: ## 1. Overall comments and thank you response We first want to thank all reviewers for putting enormous efforts in reviewing this paper. \ We are happy to hear that there was no major issue found by the reviewers, while we were told by all 5 reviewers that this paper * makes significant contributi...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper considers the problem of regret minimization under delayed rewards. The paper considers the setting where the Decision-making with Structured Observations (DMSO) setting and asks when the learner can achieve logarithmic regret when the reward signal is delayed and we only have an estimate of the del...	Rebuttal 1: Rebuttal: ### Answer to comments in "Weaknesses" Comment 1. > It was not clear to me for a long time whether the agent also received the time step for which the reward corresponds to, when it received the delayed reward at a later time step. Author response to Comment 1:\ Thank you for pointi...	null	null	null	null	null	null
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization	Accept (poster)	Summary: The paper introduces CoMERA, a novel training method for large AI models that focuses on optimizing both computing and memory efficiency through rank-adaptive tensor compression. CoMERA aims to reduce training costs and environmental impact by achieving high compression ratios and maintaining accuracy. Key con...	Rebuttal 1: Rebuttal: ## Responses to Weaknesses: *Weakness 1: Scalability to larger models.* We are conducting larger experiments. The preliminary result is in Figure 1 in the attached PDF and details are in main author rebuttal. We pre-train CodeBERT-Large with 357 million parameters on CodeSearchNe...	Summary: The manuscript presents techniques for efficient training from scratch based on tensor decompositions. The authors propose several modifications to the basic training approach to improve accuracy as well as several optimizations for tensor-compressed training, achieving training speedup. In the experiments, th...	Rebuttal 1: Rebuttal: ## Responses to Weaknesses: *Weakness 1: Baseline time in Figures 5 and 8.* Response: Thanks a lot! We will include baseline time in the figures. For your convenience, we attach results in following Table 1 and Table 2. Table 1 shows time and memory cost for embedding lookup forward-...	Summary: This work proposes using low-rank tensor train decomposition to accelerate deep learning model training and save memory usage. In the algorithm, both embedding tables in recommendation systems and large linear weights are written as a tensor train, and rank-adaptive optimization is used to adaptively reduce th...	Rebuttal 1: Rebuttal: ## Responses to weaknesses: *Weakness 1: presentation: section 4.2 is hard to understand. For contraction path optimization, it would be good to visualize the process using tensor diagrams.* Response: Thanks a lot for the suggestion! We prepared the tensor diagrams to visualize the contracti...	Summary: This paper leverages the tensor decomposition concept in model training and tries to address two questions: the first is how to enable rank-adaptive, and the second one is how to obtain real efficiency. This paper achieves a 2 − 3× speedup per training epoch compared with standard training with on-par accuracy...	Rebuttal 1: Rebuttal: *Weakness 1: “The problem formulation in 3.1 and how to solve the l0 norm is not quite new.”* Response: Thanks a lot for the comment! * Our novelty is to formulate the problem of balancing performance and size as a multi-objective problem. The linear scalarization for gently pruning ...	Rebuttal 1: Rebuttal: ## Common Concerns We would like to thank the reviewers for their fruitful suggestions and comments. We have addressed ALL review comments (see the response to each reviewer). Here we summarize some concerns arised in the review process. ## **Scalability of tensor-compressed training to l...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning	Accept (poster)	Summary: This work introduces a new (fast) SDE-based sampling technique to derive actions from a diffusion based policy. Strengths: - Compares favorably against most relevant benchmark (Diff-QL) - Good ablation studies; this was helpful to understand the importance of each component. - Strong connection to standard to...	Rebuttal 1: Rebuttal: We appreciate your constructive feedback and the opportunity to improve our work. Your insights are invaluable, and we look forward to incorporating these revisions to strengthen our submission. Weakness 1. *Why do you introduce a discount factor but in all equations use a finite time horizo...	Summary: The paper presents Entropy-Regularized Diffusion Policy with Q-Ensembles for offline reinforcement learning. This method addresses Q-value overestimation on out-of-distribution (OOD) data by using a mean-reverting stochastic differential equation (SDE) to transform action distributions into a Gaussian form, co...	Rebuttal 1: Rebuttal: Thank you for your detailed review and for recognizing the strengths of our work. We appreciate your positive feedback on the novelty and theoretical robustness of our proposed method. 1. Computational cost. We acknowledge that diffusion policies require longer training and inference time d...	Summary: The paper proposes to use reverse-time SDE as the policy in an actor-critic algorithm. To make it work, entropy regularization is added, for which an entropy approximation scheme is suggested. Furthermore, to improve stability, an ensemble of Q-networks is employed, and the pessimistic lower-confidence bound (...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and for recognizing the novelty and potential impact of our work. We appreciate your positive feedback and are happy to provide our point-to-point response below: 1. Influence of multi-modality of the policy...SAC with Q-ensemble and LCB In offline RL, t...	Summary: The paper proposes that adding entropy regularization to offline RL is beneficial, and using pessimistic Q-value estimation through ensemble methods can provide a better estimate of the Q-value. Figure 1 explicitly shows the benefit of the ensemble Q method. The methods show impressive performance in D4RL. St...	Rebuttal 1: Rebuttal: Thank you for your thorough review and insightful feedback. Your comments have been invaluable in guiding our efforts to refine and clarify our work. Below, we address your main concerns in detail: 1. Mean-reverting SDE and VP SDE. Our mean-reverting SDE is derived from the famous Ornstein-...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their detailed reviews and constructive comments. We have conducted additional experiments to address the raised concerns and further validate our approach. All figures can be found in the attached PDF file. Below, we summarize the key results and discussio...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.