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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Neglected Hessian component explains mysteries in sharpness regularization | Accept (spotlight) | Summary: It is known that SAM can improve generalization, while weight noises and gradient penalties often fail. This work reported that the structure of the Hessian can explain the inconsistency by identifying the key role of NME. This work first studied gradient penalties and show that methods using second order info... | Rebuttal 1:
Rebuttal: We thank the reviewer for their questions, and answer some here.
_The empirical results only include ResNets. May the empirical conclusion also depend on model architectures? How about the results of very simple models (FCN/LR) and very complex models (Transformers)? Note that they have very diff... | Summary: The authors examine the performance of training methods for neural
networks, which utilize (approximate) second order information. They
note that often only the curvature of the loss function is taken into
account (rather than that of the loss functions) and demonstrate that
this approach leads to drawback in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and will fix the errors noted. We address some selected concerns below.
_In terms of the use of second derivatives the manuscript sends a bit of a mixed message._
The overall point about second derivatives is a bit subtle; our work suggests that second d... | Summary: This paper investigates the importance of considering second order information, specifically the structure of the Hessian of the loss, in deep learning. It decomposes the Hessian into the Gauss-Newton matrix and the Nonlinear Modeling Error (NME) matrix, with focus on the often-overlooked NME. Through empirica... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review. We address the reviewer's questions and suggestions below.
_How do you solve SAM? Have you also neglected the second-order term in your empirical analysis? What would be the effect if this term were not neglected in your situation?_... | Summary: This paper studies the influence of the second-order component of the Hessian in sharpness-aware minimization and optimization methods that involve gradient penalties. First, they show that the Hessian decomposes into the component of the Hessian that people usually consider [GN] and a term that includes the s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We address the reviewer's questions and suggestions below.
_In section 2 when deriving the NME, can you include an explicit example where the NME is large? Either analytically or a plot demonstrating the evolution of, e.g., its Frobenius norm over ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How many classifiers do we need? | Accept (poster) | Summary: The authors address the setting of ensembling, where the predictions of multiple models are combined to improve accuracy and form more robust conclusions. The authors define a quantity η (polarity among agents within a dataset), and show both empirically and theoretically that this quantity is nearly constant ... | Rebuttal 1:
Rebuttal: Thank you for reviewing this paper and we really appreciate your comments and questions.
[Weakness 1] Agreed. That was the reason why we narrowed down the scope to interpolating models in Conjecture 1 although Theorem 1 holds for any ensemble. We think it would be an interesting direction to see... | Summary: This paper analyzes the majority vote error of an ensemble of classifiers. A new quantity called polarity is introduced. The polarity of neural networks is analyzed empirically and theoretically, and stronger bounds on the majority vote error are derived based on the polarity. Finally, the previously derived b... | Rebuttal 1:
Rebuttal: Thank you for reviewing this paper; we really appreciate your comments and questions.
[Weakness 1] We agree that further empirical evidence would strengthen our claim. It is difficult to obtain an ensemble of well-trained interpolating models in practice, so we are limited in our selection of mo... | Summary: This paper focuses on quantifying the impact of number of classifiers on the error rate of majority vote decision strategy for ensemble classifiers. They define the notion of “polarity” of an ensemble and use this notion to characterize the relationship between majority vote error rate and disagreement between... | Rebuttal 1:
Rebuttal: Thank you for your positive appraisal of our work!
[Weakness 1] Please see the general response. Interpolators are models that are trained to perfectly fit the train data, and appear prominently in the double descent literature, as well as prior explorations into the benefits of ensembling.
[We... | Summary: The authors introduce polarity $\eta$ to characterize an ensemble of classifiers. Polarity is the probability that over half the models in an ensemble make an incorrect prediction divided by the expected square fraction of models that make an incorrect prediction. This quantity is bound by a concentration ineq... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper; we greatly appreciate your comments and questions.
[Weakness 1] Please see the general response: we hope that the newly presented form of Theorem 1 addresses your concerns. In the original wording, we state that an $\eta$ greater than the stated l... | Rebuttal 1:
Rebuttal: As there are overlapping comments regarding Theorem 1 and Conjecture 1, we provide clarification and a rephrased version of the theorem and conjecture here. Firstly, for Theorem 1, we recognize that the current wording may be misleading, so we reword the theorem in the following way for clarity:
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Label Delay in Online Continual Learning | Accept (poster) | Summary: This paper outlines a new continual learning framework to handle label delays in data streams, a situation where labels lag behind data collection. Extensive testing revealed that neither increased computational resources nor advanced techniques like Self-Supervised Learning significantly overcome the challeng... | Rebuttal 1:
Rebuttal: We thank the reviewer for their efforts reviewing our paper and that the reviewer finds our method to be simple and that they agree on the setup of delayed labels.
**Q1: the challenge of matching input data at time step 𝑡 with labels at time step 𝑡+𝑑 perfectly may prove more difficult than ac... | Summary: This paper introduces the problem of label delay in Online Continual Learning (OCL), where a model is trained continually on an unlabelled stream where labels are revealed with a fixed delay. The problem becomes learning from semi-supervised data with the objective of quickly adapting to the unlabeled distribu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their recognition of our presentation, the problem we study and the solution we proposed, and appreciate the reviewer for the valuable suggestions. Here are our responses to the reviewer's concerns:
## Q1: Evaluation metric
Using the Online Accuracy metric, in online cont... | Summary: This manuscript delineates a novel method, termed as IWMS, which is devised to address the problem of label delay in online continual learning, where new data may not be labeled due to slow and costly annotation processes. The IWMS exhibits an innovative usage of fine-grained Gaussian Mixture prototypes, alon... | Rebuttal 1:
Rebuttal: We thank the reviewer's recognition for our extensive experiments and the simplicity of our proposed method and the value suggestions. The following are our responses to the reviewer's concerns:
**Q1: Need further explanation regarding the implementation and each module within the IWMS method and... | Summary: The paper addresses the problem of label delay in online continual learning. The proposed framework explicitly models this delay, revealing unlabeled data from the current time step and labels delayed by a specific number of steps. Extensive experiments demonstrate that increasing computational resources alone... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' positive comments on our method, experiments, and paper presentation. We address the above concerns here.
**Q1: The discussion of related work on the label delay problem is not comprehensive.**
We thank the reviewers for pointing out the missing references. We will i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems | Accept (poster) | Summary: LLM inference systems often generate multiple answers (function calls) to a query and then aggregate the answers with rules like vote, filter-vote. This paper investigates how the number of function calls influence the influence of performance of the compound system. More concretely, this paper found that ther... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We answer the questions as follows.
***The results are not surprising***: We appreciate your comment but we would like to respectfully argue that our results are interesting. First, many practitioners and researchers believe that they can consistently impro... | Summary: This paper evaluates the LM calls and the task performance based on two compound system designs: Vote and Filter-Vote, for performing multiple-choice selection tasks. The authors conduct theoretical analysis for the system designs and the scaling behavior by proposing formal notion of query difficulty and mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and support of our paper! Please see below for our response.
***present the overall results of all datasets in the experiment section if the page limit permits***: Thanks for your suggestion! We will move more empirical results to the experiment sect... | Summary: In this paper, the authors studied the scaling properties of compound inference systems. Theoretically and empirically, the authors answered the properties of multiple LM calls.
Strengths: 1. Answered several important questions in multiple LM calls, which may benefit the compound inference systems.
2. Heur... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and support of our paper! Please see below for our response.
***Simple System***: We indeed focus on simple systems. There are two reasons. First, they both represent real-world systems. For example, the Cot@32 approach by Google Gemini is indeed a ... | Summary: Recent state-of-the-art results in language tasks have been achieved using compound systems that make multiple calls to Language Models (LMs) and aggregate their responses. However, there is limited understanding of how the number of LM calls affects the performance of these compound systems. This paper studie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and support of our paper! We have addressed the questions as follows.
***Are there methods beyond Vote and Vote-Filter that can be applied here? Can this approach generalize if difficulties are probabilistic to begin with?***: Thanks for bringing up ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback! Please find our answers and clarifications in the individual responses. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Practical Bayesian Algorithm Execution via Posterior Sampling | Accept (poster) | Summary: This paper proposes a new method within the Bayesian algorithm execution framework, which enables finding a target set of points in a highly efficient manner.
Strengths: The problem tackled in this paper is highly important. The proposed method is sound and shows strong improvements vs. relevant benchmarks, i... | Rebuttal 1:
Rebuttal: Dear Reviewer BTqx,
We sincerely appreciate your feedback and positive evaluation of our work. We have addressed your comments below and are ready to discuss any new questions or concerns that may arise during the discussion period. Additionally, given your positive assessment, we would deeply ap... | Summary: This paper introduces a scalable posterior sampling algorithm (aka PS-BAX) in the framework of Bayesian algorithm execution (BAX). Its fundamental basis is built on a key observation that the property of interest for many tasks is a target set of points, which for many scenarios such as standard bayesian optim... | Rebuttal 1:
Rebuttal: Dear Reviewer Ta95,
We thank you for your feedback and questions. We are glad that you found our paper "well-written" and our empirical evaluation "extensive." We hope to address your concerns in the following clarifications.
**Q1.** *"This paper is an incremental work on INFO-BAX… it lacks rigo... | Summary: This paper utilizes the idea of Bayesian sampling in the Bayesian algorithm execution (BAX) framework. As claimed by the authors, this is the first extension of posterior sampling beyond the optimization setting. The idea is very simple and natural and the performance seems reasonable. While I am not familiar ... | Rebuttal 1:
Rebuttal: Dear Reviewer FDTf,
We sincerely thank you for your comments and questions. We are glad that you found our paper "well structured and well written." You expressed concerns related to the notation and clarity of our theoretical results. We hope our response below addresses these concerns. Since no... | Summary: This work proposes a posterior sampling algorithm for Bayesian algorithm execution, where the goal is to infer the output of an algorithm $O$ applied to an unknown function $f$. The algorithm is simple to implement and computationally more efficient than previous works based on mutual information maximization... | Rebuttal 1:
Rebuttal: Dear Reviewer qVHf,
We sincerely appreciate your feedback and questions. We are glad that you found the problem of study "interesting and practically relevant," our method providing "a robust baseline for the problem," and our paper "well-written." Your major concern relates to the correctness of... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely thank you for your thoughtful comments and questions. We are pleased that you found our paper well-written (qVHf, FDTf, Ta95, BTqx), addressing an interesting and practically relevant problem (qVHf, BTqx), and proposing a sound algorithm (qVHf, FDTf, BTqx) with clear ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Graph Mating | Accept (poster) | Summary: In this paper, the authors present a method for learning-free and label-free model reuse for graph neural networks. They dub the task Deep Graph Mating (Grama). Without relying on costly fine-tuning or re-training, Grama aims to generate a child model by reusing and fusing knowledge from pre-trained parent mod... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer tRzs**
We appreciate the reviewer for the positive support and constructive comments.
`W1. & Q1.` **Statistics recomputation details**
>"The authors miss the discussions on the Grama case where pre-trained models already contained normalization layers, which is commo... | Summary: This paper proposes Deep Graph Mating, a novel task for model reuse in non-Euclidean domains, specifically focusing on GNNs. The goal is to create a child GNN that combines knowledge from pre-trained parent GNNs without requiring re-training, fine-tuning, or ground-truth labels. The process firstly identifies ... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer Uv5e (Part 1/2)**
We truly appreciate the reviewer's insightful comments, and would like to address them as follows. Due to character limitations, we have to split our response into two parts. The second part will be provided as a comment following our initial response.... | Summary: The paper tackles pre-trained model fusion for graph-centric tasks. The pre-trained models share the same architecture, but differ in the graph datasets they are trained on. The pipeline involves two core approaches. The first one matches parameters in pre-trained parent models by aligning the aggregated messa... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer 856w**
We would like to thank the reviewer for the very helpful feedback. We will address each of the reviewer's comments in detail as follows.
`W1.` **Inadequate literature review**
>"Discussions on existing model fusion approaches for CNNs are not adequate. The pres... | Summary: The paper is interested in re-using Graph Neural Networks trained from one task to another task (e.g., transfer learning). This motivation is nice. We've seen such trends in computer vision (e.g., network trained on ImageNet used for CIFAR), or more recently, everyone is using LLMs for a variety of tasks they ... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer rPnJ (Part 1/2)**
We appreciate the reviewer's constructive comments and thoughtful suggestions, and we sincerely apologise for any confusion or ambiguity caused by our use of notations.
We are committed to addressing each of the reviewer's concerns as outlined below.... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Practical Shuffle Coding | Accept (poster) | Summary: The design of compression mechanisms for compression of unordered objects is considered. The scenario models various applications of interest such as compression of unlabeled graphs and multisets. A novel recursive solution, called recursive shuffle, is introduced. An advantage of this compression mechanism, c... | Rebuttal 1:
Rebuttal: We thank reviewer tkqf for taking the time to review our paper.
It will be helpful to respond to part of your second question first:
> In Table 4, sometimes WL$_1$ and sometimes WL$_2$ hashing yields better results. I wonder if there is an intuitive explanation and a way to determine what type o... | Summary: This paper proposes recursive shuffle coding, a general method for optimal compression of unordered objects using bits-back coding. And the paper further presents present incomplete shuffle coding, allowing near-optimal compression of large unordered objects with intractable automorphism groups. When combined,... | Rebuttal 1:
Rebuttal: We thank reviewer Htst for their review, comments, and questions.
## Weaknesses
> The two methods seem to address different issues and do not clarify their connection.
Your observation is correct, the two methods address different issues. They appear together in this paper since it is convenient... | Summary: This paper proposed an entropy coding method of large unordered data structures. The newly proposed method allows one-shot compression and achieves competitive speed. The experimental results demonstrate the advantages.
Strengths: This paper proposed a new entropy coding method for large unordered data sets... | Rebuttal 1:
Rebuttal: We thank reviewer NPhs for taking the time to review and comment on our paper.
### Questions
> What are the applications of one-shot compression?
Example applications include storing/transmitting large social, web, network, or compute graphs, JSON files (nested multisets), machine learning datas... | Summary: Coding of unordered structures is considered. This paper addresses the two main limitations of the entropy coding method proposed by Kunze et al. (2024): high cost of automorphism group calculation and poor compression of single unordered objects. To solve the first problem, it is suggested to approximate the ... | Rebuttal 1:
Rebuttal: We thank reviewer AUZQ for their thoughtful, thorough review.
## Weaknesses
> It seems that the method can be abstracted from the ordered/unordered objects and permutations, and can be described in terms of equivalence relations solely. This approach should unify the "plain" and "approximate" met... | Rebuttal 1:
Rebuttal: We are delighted that all reviewers agreed on the good soundness of the paper, with multiple pointing out its mathematical rigor, and most reviewers appreciate its presentation.
Based on reviewers’ requests, we are excited to report striking new experimental results on our supplemental page, desc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scanning Trojaned Models Using Out-of-Distribution Samples | Accept (poster) | Summary: The paper addresses the problem of detecting trojaned models. The paper proposes a trojaned model scanning method using out-of-distribution (OOD) samples. Specifically, it is observed that trojaned classifiers can erroneously identify adversarially attacked OOD samples as in-distribution (ID) samples. Therefo... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments. Responses to specific points are provided below:
> **W1:**
* We apologize for the referencing error. We intended to refer to Figure5 in SectionE.
* We mentioned that near-OODs are those that share semanti/stylistic features with the IDs making them harder to... | Summary: The authors propose a trojan scanning technique that leverages the sensitivity of the network's confidence when near-OOD samples undergo an adversarial attack. The authors argue that the greater variation in confidence can be used to discriminate whether a network has been backdoored, and present extensive exp... | Rebuttal 1:
Rebuttal: Thank you for your useful comments. Please find our responses below:
>**W0.1&W0.2:**
We appreciate your suggestions and will implement them to improve clarity and logical flow in the final manuscript based on your recommendations.
----
>**W1:**
We conducted additional experiments to establis... | Summary: This paper proposes a general strategy for distinguishing between trojaned and clean models. The generality of the approach lies in its applicability to various types of trojan attacks, different label-mapping strategies, and its ability to work with and without clean training data. The authors claim that dist... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback on our paper. We greatly appreciate your insights and suggestions.
> **W1:**
We acknowledge that extracting hyperparameters may be considered a limitation of our method, as previously discussed in our limitations section. However, it is important to... | Summary: The paper proposes TRODO, a method for identifying trojaned classifiers, which relies on the intuition that in presence of a backdoor it should be easier than for clean classifiers to make the model classify an out-of-distribution input as in-distribution by adding an adversarial perturbation. In practice, a P... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have provided the following response:
>**W1.1:**
We believe the epsilon value of the attack plays a key role compare to steps in our setup. If epsilon is large, as you mentioned, our signature for both clean and Trojan classifiers would be the same. This ... | Rebuttal 1:
Rebuttal: One of the common concerns raised by the reviewers was TRODO's performance against adaptive attacks. To address this concern, we conducted additional experiments and present the results here. Also there were concerns on the details of hard transformations, which we have also addressed in the follo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper points out a limitation of existing backdoor model scanning methods:
They fail to detect backdoored models trained with adversarial training. It
propose a new backdoor model scanning method by utilizing adversarial shifts in
Out-of-distribution samples. Experiments on MNIST, CIFAR-10, GTSRB, CIFAR-1... | Rebuttal 1:
Rebuttal: We appreciate your insightful review. Here is our detailed response:
>**W1:**
We sincerely apologize for this oversight. There were some issues with the command that removed these tables from our submitted paper. Here, we have provided those tables and assure you that they will be included in th... | Summary: The paper introduces a novel trojan scanning method named TRODO (TROjan scanning by Detection of adversarial shifts in Out-of-distribution samples). TRODO leverages the concept of "blind spots," where trojaned classifiers mistakenly identify out-of-distribution (OOD) samples as in-distribution (ID). The method... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Our responses to each point are provided below:
>**Q1&Undefined Threat Model**
Sorry for the confusion. We have briefly stated our threat model in lines 212-226 of the paper. We further present our threat model more clear in depth here. We assure the revie... | null | null | null | null |
Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure | Accept (oral) | Summary: This paper presents the first generalization analysis of the learning algorithm driving the two-stage recommender systems. Specifically, it considers a representative two-stage recommender system with a tree structure, which consists of an efficient tree-based retriever and a more precise yet time-consuming ra... | Rebuttal 1:
Rebuttal: We greatly appreciate your high recognition of our work, and are eager to share our thoughts with you.
**Response to Questions 1:**
> **Questions 1:** Can the authors comment on the potential limitations of the derived generalization upper bounds?
Thank you for your question. While the derived ... | Summary: This paper studies two-stage recommender systems. The authors focus on analyzing the generalization error of the retriever and ranker components within a two-stage recommendation model, specifically examining the Rademacher complexity. The findings are supported by both theoretical analysis and empirical studi... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and providing the valuable suggestions and constructive comments.
**Response to Weakness 1:**
> **Weakness 1:** Although this paper is generally well-written, I suggest the authors create a separate "Experiments" section and include a list of notations ... | Summary: This paper theoretically analyzes the generalization bounds of two-stage recommender systems using Rademacher complexity. It examines the generalization bounds of the tree-structured retriever and the subsequent ranker, respectively. The paper concludes that the more branches a tree-structured retriever has, t... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and providing the constructive comments. We will try our best to address your concerns with planned revisions based on your valuable feedback.
**Response to Weakness 1:**
Thank you for your detailed feedback. Our analysis of tree-structured models indee... | Summary: This paper analyzes the generalization error of two-stage recommender systems with a tree structure, which consist of an efficient tree-based retriever and a more precise but time-consuming ranker. The authors use Rademacher complexity to establish generalization error upper bounds for various tree-based retri... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work and the opportunity to address the concerns raised in your review. We value your insightful feedback and would like to share our thoughts in response.
**Response to Questions 1:**
> **Questions 1:** The analysis of tree-based retriever models is compre... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent | Accept (poster) | Summary: This paper presents a new approach to enforce constraints on the output of neural networks. Termed **G**LinSAT, this paper extends the application scope of LinSAT from positive linear constraints to general linear constraints. The authors also adopt ideas from OptNet/Cvxpylayers to derive implicit gradients in... | Rebuttal 1:
Rebuttal: Thanks for the thorough review and valuable comments. We are encouraged with your acknowledgment of our motivation, methodology, empirical results, and contribution. Below we respond to your specific comments.
**W1: Inference time of GLinSAT is sometimes longer than LinSAT with its default 100 it... | Summary: The paper introduces "GLinSAT," a novel neural network layer designed to enforce general linear and bounded constraints on neural network outputs using a differentiable approach. The method leverages entropy-regularized linear programming, transforming it into an unconstrained convex optimization problem that ... | Rebuttal 1:
Rebuttal: Thanks for the thorough review and valuable comments. We are encouraged with your acknowledgment of our methodology, empirical results, and contribution. Below we respond to your specific comments.
**W1: Some related works need to be reviewed**
Thanks for sharing these works other than different... | Summary: This paper proposes a new architecture named GLinSAT that projects the output of a neural network to the feasible region of bounded and general linear constraints. The approach is based on a gradient-based approach for solving an entropy-regularized linear program and can be implemented with backpropagation/di... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, constructive advice, and recognition of our idea and empirical results. Below we respond to your specific comments.
**W1: How to make it possible for integer constraint satisfaction?**
Thanks for the comment. As shown in Section A.8, A.9, A.11 in Appendix, when ... | Summary: The main contribution of this paper is a method for enforcing arbitrary linear constraints on the outputs of a neural network. This is achieved in a differentiable way so that the entire pipeline can be trained end to end to solve constrained optimization problems. This is achieved by viewing the problem of pr... | Rebuttal 1:
Rebuttal: Thanks for the thorough review and valuable comments. We are encouraged with your acknowledgement to our idea, experiments and contribution. Below we respond to your specific comments.
**W1: Is the contribution compared with previous works incremental?**
Although existing satisfiability layers, ... | Rebuttal 1:
Rebuttal: Dear Chairs and Reviewers,
We greatly appreciate the reviewers' time, valuable comments, and constructive suggestions. We are delighted that all the reviews have expressed a positive inclination towards accepting our submission. Overall, the reviewers acknowledge our methodology as "interesting" ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties | Accept (poster) | Summary: The paper addresses the anomaly explanation task by repairing the normal appearances of input inputs. Specifically, this paper designs four properties to guide the repair process that works in both image and time series domains. To demonstrate the effectiveness of the proposed method, this paper conducts exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful suggestions on how to improve our experiments and presentation. We will incorporate these changes into our manuscript, and we believe that they will help us greatly improve the quality of our work. We have included some results of our work-in-progress experim... | Summary: The paper proposes an anomaly repair technique. Based on proposed four
properties, it trains a generative model that can fix anomaly data to a benign
one. The proposed properties include similar to the data, etc., which are
globally applicable to any dataset. The evaluation is performed on VisA dataset
and the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on problem motivation and potential risks. We will revise our manuscript to address these concerns. Below are our responses to the comments and questions.
* **Motivation for Fixing Anomalous Inputs.**
Anomaly repair is useful when the input data is noisy an... | Summary: Paper proposes a method for anomaly repair that goes one step beyond an anomaly detection and/or an anomaly localization method. While anomaly detection focuses on identifying which objects (images, time series, etc.) are anomalous, and anomaly localization focuses on identifying regions within the object (ima... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review. Their feedback will help us greatly improve the clarity throughout the manuscript. We address the reviewer's comments and questions below.
* **Scope of the assumptions.**
Although we use reconstruction-based anomaly detection as a motivating exampl... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and feedback. Their comments and suggestions will allow us to greatly improve our manuscript in exposition narrative, technical details, and experiment results. We are in the process of running additional experiments involving newer models like EfficientAD [1]... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Should We Really Edit Language Models? On the Evaluation of Edited Language Models | Accept (poster) | Summary: The paper explores the general abilities of post-edited language models. Concretely, the paper performs a comprehensive empirical evaluation no various model editing methods and language models. The paper summarizes key findings based on the number of edits, the scale of the language model, the type of tuning,... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> Q1: Though the experiments are comprehensive and organized, the findings are more about empirical observations rather than systematic analysis. The paper d... | Summary: The work evaluates the impact of various editing methods on LLMs. Specifically, it investigates how different editing techniques affect the general abilities of models, considering factors such as the number of edits, model scale, safety, and different aspects of model capabilities. Interesting findings and co... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> Q1: The outcome of the empirical study is somehow .... would add more value to the results.
**Reply:** Thanks for your suggestion. we have re-organized th... | Summary: [Post rebuttal update] The authors have addressed my main concern about providing an overarching framework for their evaluation, using the Elasticity - Plasticity tradeoff. I will raise my score from 5 to 6.
This paper evaluates the influence of several model editing methods on the models' general capabiliti... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> Q1: The main weakness ... stratified view is missing.
**Reply:** Thank you for your valuable feedback. We address your questions and concerns regarding th... | null | null | Rebuttal 1:
Rebuttal: ## **General Response to All of Reviewers**
We appreciate all the reviewers for their thoughtful comments and suggestions on our paper.
We are very glad to see that the reviewers find our focused problem is important and useful (R1, R2, R3) ,the insights are valueable and reliable (R1, R2) and t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Do LLMs Build World Representations? Probing Through the Lens of State Abstraction | Accept (poster) | Summary: This work investigates what kind of abstractions LLMs use to encode the world, distinguishing goal-oriented abstractions (discarding world dynamics that are not necessary for achieving the goal) from world-general abstractions (including dynamics irrelevant for the goal). The authors note that prior work looki... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive feedback. We address all of your concerns below.
### **Weakness 1: Results are unsurprising**
> **(unsurprising finding)** LLMs fine-tuned for a task start forming more useful goal-oriented state abstraction representations… is unsurprising …
* It's i... | Summary: This paper proposes a new framework for studying world state abstractions from LLM representations. The framework, based on state abstraction theory, focuses not on assessing whether a model has a single world representation but assessing different levels of possible abstractions. These are each roughly functi... | Rebuttal 1:
Rebuttal: Thank you for your time and detailed review. We address all of your concerns below.
### **Major Weakness: Running more experiments with more datasets/LLMs**
> REPLACE … planning task based on **a limited set of containers and objects**.
* We acknowledge that RePlace has predefined domains of co... | Summary: This paper investigates whether different levels of world abstractions can be decoded from LLM representations. The study is performed using a synthetic dataset of simple planning problems involving moving objects between containers. The study takes inspiration from RL to define different levels of abstraction... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging feedback. We address all of your concerns below.
### **Major Weakness 1: Findings are expected**
> **(Findings unsurprising)** If full problems and solutions were presented during adaptation, then it seems expected that the model would produce repres... | Summary: This work makes the observation that prior work in probing LLM for planning tasks comes to different conclusions on whether there are internal state abstractions in LLM hidden layers. This work hypothesizes that the disagreement comes from that these works are probing LLM with different tasks, which may necess... | Rebuttal 1:
Rebuttal: Thank you for your time and review. We appreciate the opportunity to clarify two critical aspects that may have been misinterpreted: our claims/hypothesis and the concept of goal-oriented abstractions.
### **Weakness**
> This work hypothesizes that the **disagreement** comes from that these wor... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and feedback. We are encouraged that all aspects of our work are widely recognized. The reviewers found our addressed problem to be new and important (R1, R2, R3), our framework novel and neat (R2, R4, R5), our task/datasets thoughtfully designed and thus usef... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this work, the authors attempt to examine whether large language models (LLMs) possess representations that can work as the world model. To this end, they target different state abstraction levels, world-irrelevant abstraction, $Q^*$-irrelevant abstraction, and $pi^*$-irrelevant abstraction, which are based... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive feedback. We address all of your concerns below.
### **Weakness 1: finding not entirely surprising**
>(**Unsurprising findings**) The finding that the fine-tuned language models do not preserve the features needed for recovering the world dynamics may not... | null | null | null | null | null | null |
Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection | Accept (poster) | Summary: The paper proposes an Unified Domain Generalization and Adaptation(UDGA) scheme for multi-view 3D object detection. The main components of the proposed method are (1) depth inconsistency-based constraints from multi-view and (2) an efficient domain adaptation scheme (LEDA). The multi-view depth inconsistency c... | Rebuttal 1:
Rebuttal: ### **(1) Discussion with RGB feature-based methods**
Thank you for your constructive suggestion. As reported by DETR3D [4], CVT [5] and BEVFormer [6], RGB feature-based methods are considerably robust against calibration noise. **However, these methodologies experience significant performance de... | Summary: This paper is about Unified Domain Generalization and Adaptation for outdoor 3D object detection. To address the geometric misalignment between the source and target domains, Multi-view Overlap Depth Constraint that leveraging the strong association between multi-views and Label-Efficient Domain Adaptation are... | Rebuttal 1:
Rebuttal: ### **(1) Explanation of Table 1**
Thank you for your insightful review. We understand that there was some confusion regarding the misaligned results in Table 1 due to insufficient explanation. Since previous methods did not adopt consistent experimental methods, a fair comparison is only possible... | Summary: The paper presents an adaptation of 3D object detectors to varying target environments using two major strategies. The proposed multi-view overlap depth constraint leverages associations across views to learn view-invariant features. Additionally, a LORA-like structure is designed for parameter-efficient adapt... | Rebuttal 1:
Rebuttal: ### **(1) More details of LEDA.**
**Disconnected strategies**
We hope that further explanations of LEDA in global response will enhance your understanding. We empirically observe that direct fine-tuning approaches (with a small fraction of data) often fail to align between source and target, ma... | Summary: This paper focuses on multi-view 3D object detection. The authors proposed a unified domain generalization and adaptation-based detection method. To enhance the detection model for unseen datasets and address the geometric misalignment problem, the authors proposed a multi-view overlap depth constraint module ... | Rebuttal 1:
Rebuttal: ### **Code release**
We plan to release the source code upon acceptance.
Additionally, in this rebuttal, we provide further explanations, analyses, and results (please refer to **global response** and **Rebuttal PDF**). We hope that these materials will be helpful to you.
*If you have any quest... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort to review our work. We have carefully read and considered all of the comments and suggestions provided by the reviewers. To assist with your understanding, we provide detailed analyses and additional experiments of Label-Efficient Domain Adaptation(LEDA) in the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Predictive Attractor Models | Accept (poster) | Summary: The paper deals with training generative sequence models under a few constraints to make it biologically plausible.
Specifically, states consist of a sparse binary representation (i.e. neurons that are on/off and sequence training and generation are performed using operations that are described as "local and ... | Rebuttal 1:
Rebuttal: Thank you for the review and the great remarks on PAM’s strengths and the clarity of the presentation, supplementary material, and video. We appreciate the positive rating!
**Weaknesses**
* Biological plausibility is typically referred to as imposing constraints such as local learning rules [1] ... | Summary: The paper presents a novel sequence memory architecture focused on multistability, ie: generating multiple possible futures from every present context. They propose to train this architecture in a biologically plausible way via predictive coding under the Free Energy Principle, in the online learning setting w... | Rebuttal 1:
Rebuttal: Thank you for the positive remarks. Yes, to our knowledge, we are the first to model multiple possibilities as a GMM in a state space model and prove that maximizing the likelihood of a query observation under the mixture model is equivalent to a Hopfield recall function (Theorem 1).
**Weaknesses... | Summary: This paper examines a biologically plausible model for sequential memory which overcomes various issues (capacity, forgetting, size of context window) in previous models. The proposed model is essentially an extension of Hierarchical Temporal Memory which allows the model to generate predictions.
Strengths: *... | Rebuttal 1:
Rebuttal: Thank you for your positive remarks on the simplicity of the model and the importance of the challenge being solved by PAM.
**Weaknesses**
* PAM extends both HTM and Hopfield Network (HN). It allows HTM to autoregressively generate sequences (enabling HTM to do more than just anomaly detection) ... | Summary: The paper presents Predictive Attractor Models (PAM), designed to improve sequence memory by resolving issues like catastrophic forgetting. PAM processes inputs in real-time, remembering each just once, using lateral inhibition in cortical minicolumns to preserve old memories. By generating future predictions ... | Rebuttal 1:
Rebuttal: Thank you for highlighting the strengths of PAM in retaining old memories, continual learning, and generating multiple possibilities.
**Weaknesses**
1) PAM extends HTM by providing a mechanism for filtering noisy inputs and generating sequences in an autoregressive manner.
* The current impl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and useful suggestions. We think that these comments have helped us better refine the paper.
We provide additional supplementary results to help address some of the concerns on scaling, connection sparsity and comparing to transformer architecture. We ref... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Predictive Attractor Models (PAM), a novel biologically-inspired approach for sequential memory and prediction. PAM combines a predictive model based on sparse distributed representations (SDRs) with an attractor model for generating future predictions. Key contributions include an online... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive remarks on novelty, theoretical grounding, and comprehensive evaluation of key challenges in sequential memory. We have provided additional figures to address your concerns.
**Weaknesses**
* Similar to other papers on biologically-plausible sequent... | null | null | null | null | null | null |
Search for Efficient Large Language Models | Accept (poster) | Summary: This paper introduces a neural architecture search method for Large Language Models (LLMs) comprising three steps: inheriting the most salient weights from the original model to form the initial sub-network, using an evolutionary algorithm to search for the optimal sub-network, and reconstructing the original ... | Rebuttal 1:
Rebuttal: Thanks for the suggestions from the reviewer.
### Weakness 1. Potentially biased evaluation
To mitigate the potential biased evaluation problem, we do not use the data from specific downstream tasks (QA datasets such as MMLU or ARC). Instead, for our candidate evaluation and reformation, we use... | Summary: This paper introduces an architecture search method based on mask mutation and candidate evaluation to find a subnet with better performance in the LLM. An evolution-based algorithm is applied to globally search the subset with a special initialization from the evaluation of parameter importance. After the sea... | Rebuttal 1:
Rebuttal: Thanks for the suggestions from the reviewer.
### Weakness 1. Search cost
As we discussed in the global rebuttal, we can reduce the search epoch number from 50 to 20 for LLaMA-7B, which costs 2 hours and can still achieve better performance than other methods. For LLaMA-65B model, we only adopt ... | Summary: The paper introduces a new model architecture search method for LLMs that does not require additional training. The proposed architectures have structural sparsity and reach better performance than SOTA pruning baselines.
Strengths: - The proposed method does not require additional training.
- The output mo... | Rebuttal 1:
Rebuttal: Thanks for the suggestions from the reviewer.
### Weakness & Question: Comparison with SparseGPT
The pruning or search granularity of our method and SparseGPT are different, thus they should not be directly compared. Our method searches smaller compact models, which is more like the structural p... | Summary: This paper proposes a technique for searching for efficient LLM subnets for fast inference, while still attaining strong performance. The proposed technique involve a training-free pruning stage based on a genetic algorithm, followed by a ``weight rectification'' stage that improves the resulting subnet. Empir... | Rebuttal 1:
Rebuttal: Thanks for the suggestions from the reviewer.
### Weakness 1. Difference from network pruning
Our proposed search method significantly differs from standard neural network pruning techniques. Specifically, our method includes a search initialization process to identify the effective initial arch... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging that our work overcomes prior research limitations and benefits the community (Reviewer Xurt, BSTA), our method is novel, general, and high-performing (Reviewer n31G, Qp2U, Xurt, BSTA), our experiments are comprehensive (Reviewer Xurt), and our paper is wel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound | Accept (poster) | Summary: The authors suggest an approach for controlling errors of various types (simultaneously) within a PAC-Bayes framework. They derive a high probability bound on the KL divergence between the empirical and distribution risk vectors. This generalizes earlier work in the binary case by Maurer, 2004 and Begin et al ... | Rebuttal 1:
Rebuttal: Thank you so much for your thorough review and many insightful comments! We are especially glad you find our results creative and the paper well-written.
You mention the paper would be better motivated by an explicit example showing the utility of our method above existing ones. We did not initia... | Summary: The notion of a set of "error-types", introduced by this work, is a user-defined partition of the product space of predictions and responses, generalizing well-known summaries of the erring behavior of predictors. This work presents a PAC-Bayes bound on the divergence between and empirical and true distributio... | Rebuttal 1:
Rebuttal: Thank you for your kind review, especially for noting that our approach is much more flexible than that of Morvant et al. (2012), as one is not limited to the confusion matrix and can instead consider arbitrary user-specified error types.
As for your question on previous results, we have now run ... | Summary: This paper introduces a novel PAC-Bayes bounds that extend classical kl-based bounds to vector-valued losses that can control several error-types simultaneously. The bound is converted into a differentiable minimization objective and details for the practical implementation of the bound are provided in the App... | Rebuttal 1:
Rebuttal: Thank you kindly for taking the time to carefully review our paper. Thank you especially for pointing out the unfortunate omission of Wu, Y. S., & Seldin, Y. (2022) from our related work section! The paper is indeed very related and we have now incorporated a discussion of it into our paper.
As f... | Summary: In the standard statistical learning theory control of generalization
error typically means studying deviations of the empirical risk from
the risk (for instance, with high probability over the sample). In
the language of statistics such control doesn't make a difference
between type I & II errors. This pape... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for taking the time to give constructive feedback on our work! We are especially grateful for your insight that it is difficult to compare our bound against the standard union bound since we have bounded a different quantity - the vector rather than scalar ... | Rebuttal 1:
Rebuttal: We genuinely thank all four reviewers for their many insightful comments, constructive feedback, and astute observations. We have responded to all four reviewers individually. We hope they let us know in the discussion phase if there is anything we should clarify, or any further results they would... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unifying Generation and Prediction on Graphs with Latent Graph Diffusion | Accept (poster) | Summary: This paper proposes a Latent Graph Diffusion model that unifies multiple tasks such as classification, regression, and generation in a generative task. Additionally, this model achieves feature generation in multiple categories, including node-level, edge-level, and graph-level.
Strengths: 1. This paper attem... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful comments. We now address your concerns as follows.
1. Experiments (weakness 1).
* Originally, LGD is designed to be a single framework that can do both generation and regression/classification, where we put our experimental focus on demonstrating that latent... | Summary: The paper proposes Latent Graph Diffusion to generate node, edge, and graph-level features to meet the need of different tasks under the unified framework. Extensive experiments show the competitive performance across various graph-based tasks.
Strengths: 1. It is good to reformulate regression and classifica... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and acknowledgement.
1. Writing (weakness 1). We will refine the writing of the paper and provide as much important information as possible in the main text.
2. Novelty (weakness 2). We believe that our unified formulation is novel and beneficial. Regardin... | Summary: This paper proposes a model framework, LGD, which addresses multiple types of graph tasks and can simultaneously handle both generative and predictive tasks. Specifically, the LGD framework employs a method similar to stable diffusion, using a pretrained graph encoder to obtain latent representations and then ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the constructive comments and suggestions. We address your concerns as follows.
1. Clarification on the introduction section (weakness 1). The introduction section mainly states the motivation to build a graph foundation model, but we do not claim that we have... | Summary: This paper presents Latent Graph Diffusion (LGD), a graph generation framework adept at handling a variety of tasks including generation, regression, and classification at different objects—node, edge, and graph levels. LGD approaches regression and classification tasks as conditional generation challenges and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful comments and suggestions. As presented in the global response, we have conducted extensive new experiments to improve the paper. Here we address your concerns as follows.
1. General graph generation (weakness 1). As explained in our paper, LGD is capable of ge... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful comments and suggestions. To address the concerns, we conduct new experiments as follows.
* For QM9 generation task, we (i) add more persuasive evaluation metrics including FCD and NSPDK to better evaluate the generation quality; (ii) take rec... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MotionCraft: Physics-Based Zero-Shot Video Generation | Accept (poster) | Summary: In this work the authors propose MotionCraft, a new zero-shot video generator to craft physics-based and realistic videos. MotionCraft is able to warp the noise latent space of an image diffusion model, such as Stable Diffusion, by applying an optical flow derived from a physics simulation. The authors show th... | Rebuttal 1:
Rebuttal: > The main concern is about the experiments. This paper only have 5 video results in total, which is not sufficient. I am worrying that the method is highly unstable and not robust, thus the author cannot present more video results. If this is the case, I think this manuscript is not suitable for ... | Summary: The paper presents MOTIONCRAFT, a novel zero-shot video generation method that leverages physical simulations to create realistic and physically plausible videos. Unlike traditional video diffusion models that require extensive training and large datasets, MOTIONCRAFT uses a pre-trained image diffusion model, ... | Rebuttal 1:
Rebuttal: > There are now many approaches to zero-shot video generation, such as https://openreview.net/forum?id=zOjW6yVYkE. The authors only compared their method with T2V0 (a relatively earlier method), which may make the experimental results insufficient. It would be better to include a more comprehensiv... | Summary: The paper works on the zero-shot video generation task and proposes, MotionCraft. It uses physics simulations to generate optical flow that follows physical dynamics. Then, optical flow is applied to warp the noise in the latent space with the stable diffusion model. This approach ensures coherent motion appl... | Rebuttal 1:
Rebuttal: > More quantitative comparison with baselines. Table 1 only reports the comparison with T2V0 on the generated videos. However, it is not clear which benchmark it is. Is it possible to compare with other baselines on more benchmarks, like MUG, MHAD?
We appreciate the reviewer’s suggestion to inclu... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
We appreciate the valuable feedback and suggestions provided by the reviewers. We have carefully addressed the concerns raised and integrated additional experiments and clarifications to expand the original material in the suggested ways. Please find below a detail... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals | Accept (poster) | Summary: The paper presents EEGPT, a 10-million-parameter pretrained transformer model designed for universal EEG feature extraction. The model employs a dual self-supervised learning method for efficient feature extraction and demonstrates state-of-the-art performance on various downstream tasks with linear probing.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. We have uploaded a revision with the changes marked as blue. Our detailed responses are as follows:
---
#### **W1: The function of the adaptive spatial filter is unclear. Why is this module not included during pretraining? An ablation study is nee... | Summary: The authors propose a novel pretraining strategy, EEGPT, that is essentially a multi-task self-supervision loss consisting of a masked autoencoder-style reconstruction objective, and an alignment loss that is reminiscent of knowledge distillation approaches such as data2vec. EEGPT is applied to representation ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your appreciation and constructive comments. We have uploaded a revision and used blue to mark the new changes.
---
#### **W1&Q8**
See Revision Section 3.3 for test results of model variants with comparable number of parameters.
**[Table: Results of experiments on TUA... | Summary: The paper presents a new pretrained model called EEGPT (EEG pretrained transformer), designed to improve the analysis of EEG (electroencephalography) signals. EEGPT is a model with over 10 million parameters (up to 100M for the largest model) that aims to solve common problems in EEG analysis, where the challe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We have uploaded a revision and used blue to mark the new changes. Our detailed responses are as follows.
#### **W1**
We added more test results for more datasets in the ablation experiment and scale law experiment, see Revision Section 3.4 & A... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for your time and constructive feedback. During the rebuttal, we have prepared a revision and used blue to mark the new changes. Below are the results of the added experiments as the response.
**[Table 1: Ablation study for pretraining methods (Appendix A.2)]**
In the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation | Accept (poster) | Summary: This paper addresses internal biases in LLMs that cause prompt brittleness. Unlike previous external adjustment methods, it examines the roles of MLPs and attention heads in creating these biases. The proposed UniBias method identifies and masks these biased components, thereby enhancing the model's in-context... | Rebuttal 1:
Rebuttal: Dear Reviewer CnNd,
We deeply appreciate your thorough review and valuable feedback. Below is a summary of our answers (**A**) to the weaknesses (**W**) you raised.
---
**[W1]**: There seems to be some disconnect between the problem it aims to solve and two of the objectives of the preliminary ... | Summary: This paper aims to address prompt brittleness in in-context learning methods introduced by seemingly inconsequential changes such as vanilla label bias, recency bias, and selection bias. They use the logit lens technique, where the linear function is applied from the final layer of a decoder-only transformer ... | Rebuttal 1:
Rebuttal: Dear Reviewer 8E5c,
We deeply appreciate your thorough review and valuable feedback. Below is a summary of our answers (**A**) to the weaknesses (**W**) and questions (**Q**) you raised.
---
**[W1]**: Show shared biased components across tasks.
**[A1]**: We greatly value your suggestion! Inspir... | Summary: This paper seek to address prediction bias in ICL through intervening feedforward vectors and attention heads.
Strengths: * This paper studies a critical problem of in ICL.
* The proposed method is well-motivated.
Weaknesses: The major problem with the proposed method is that it requires approximately 20 lab... | Rebuttal 1:
Rebuttal: Dear reviewer LH5i,
We deeply appreciate your thorough review and valuable feedback. We appreciate your acknowledgment of the significance of the problem we investigated and motivation behind our approach. Below is a summary of our answers (**A**) to the weaknesses (**W**) you raised.
---
**[W1]... | Summary: The paper explores the influence of FFNs and attention heads in LLMs on biases, resulting in model predictions that exhibit favoritism towards specific labels. The authors propose UniBias, an inference-only technique designed to detect and mitigate biased components within LLMs by analyzing and manipulating FF... | Rebuttal 1:
Rebuttal: Dear Reviewer BhZE,
We deeply appreciate your thorough review and valuable feedback. Below is a summary of our answers (**A**) to the weaknesses (**W**) and questions (**Q**) you raised in the review.
---
**[W1]**: Assess how well the findings generalize across different types of LLMs and datase... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our gratitude for the time and effort you've dedicated to reviewing our paper.
We are deeply grateful for your recognition of our work:
* The proposed UniBias method is novel (*Reviewer BhZE, 8E5c*), well motivated (*Reviewer LH5i*), straightforward and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Intrinsics Emerge from Training to Relight | Accept (spotlight) | Summary: This paper proposes a fully data-driven relighting method applicable to images and real scenes. The approach requires only paired images of the same scene under different illumination as inputs. The trained model can also produce albedo-like maps even though it is not trained without such supervision. The expe... | Rebuttal 1:
Rebuttal: We appreciate Reviewer n7pv's recognition of the novelty and usefulness of our work, particularly in training models for relighting without supervision on ground-truth intrinsic properties. We are also grateful for the positive feedback on the clarity and straightforwardness of our presentation, a... | Summary: This paper proposes a fully data driven method to perform scene relighting that does not require any groundtruth lighting supervision. As such, it allows for scene relighting by training only on real paired images of the same scene under different illuminations rather than requiring synthetic data that contain... | Rebuttal 1:
Rebuttal: We appreciate Reviewer t1Cn's feedback and are grateful for their recognition of the simplicity and effectiveness of our proposed method, the thoroughness of our experiments, and our approach to unsupervised training. We are pleased to see the acknowledgment of our design choices, the clarity and ... | Summary: The paper proposes a 2D relighting pipeline based on latent space manipulation, which is purely data-driven without explicit intrinsic representations such as geometry and materials. Given a single image input, the proposed model recovers latent variables representing scene intrinsic properties and latent vari... | Rebuttal 1:
Rebuttal: We appreciate Reviewer VUQQ's feedback and are grateful for their recognition of the novelty of our proposed method, our approach to unsupervised training, and the quality of our results and paper writing. We now address the specific concerns and questions raised by the reviewer.
**Concern: Lack... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and constructive feedback on our paper. Reviewers consistently highlighted the "novelty" [VUQQ, n7pv] of our approach, particularly appreciating the method's ability to perform relighting using latent intrinsic properties without explicit representations, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration | Accept (poster) | Summary: When using the Bellman update with incomplete data, the estimation error is hard to eliminate.
To solve this problem, the authors develop a novel framework EMIT, which can be used to enhance existing RL algorithms by iteratively solving a current empirical MDP for stable finite-time performance, and can progre... | Rebuttal 1:
Rebuttal: - Can EMIT improve the performances of the recently proposed RL methods such as TD7 and CrossQ?
We instantiate EMIT with TD7, and results are shown in Fig.1(b) in the rebuttal pdf. We find that EMIT can enhance the performance of TD7 on Ant and get similar performance on HalfCheetah. It may becau... | Summary: The paper introduces the Empirical MDP Iteration (EMIT) framework, which enhances online reinforcement learning by regularizing algorithms with a sequence of empirical MDPs derived from replay memory data. By focusing on in-sample bootstrapping, EMIT ensures stable and unique convergence of Q-functions, leadin... | Rebuttal 1:
Rebuttal: - Can you plot to see how far is Q from the empirical Q?
We plot the difference in Fig.4 in the rebuttal pdf. We find $\Delta(Q,\widehat Q)$ is similar to $\Delta(Q,\widehat Q^*)$ in the later stage of training since $\widehat Q$ will converge to $\widehat Q^*$.
- when you state that "Q neither ... | Summary: This paper introduces a novel framework called Empirical MDP Iteration (EMIT) to improve the stability and performance of reinforcement learning algorithms. Traditional reinforcement learning algorithms optimize a Markov Decision Process (MDP) using the Bellman equation, which can lead to unstable optimization... | Rebuttal 1:
Rebuttal: - Limited Novelty in Core Ideas. How does EMIT fundamentally differentiate itself from existing methods like Implicit Q-Learning and In-Sample Actor-Critic?
The main contributions shown in our work is that in online RL, iteratively solving a sequence of empirical MDPs is better than just solving ... | Summary: The authors transfer insights from IQL to online RL, demonstrating the performance of online RL can be enhanced by leveraging a Q-function that performs a max only over actions in the replay buffer when updating the Q-network. They use this network to:
* encourage exploration by driving the agent towards state... | Rebuttal 1:
Rebuttal: - Why integrate into TD3 instead of SAC when as far as I know SAC seems to outperform TD3?
We integrate EMIT with DQN and TD3 mainly because they are directly built upon optimizing value-based Bellman equations, which aligns with our theoretical analysis and is exactly we aim to improve with EMIT... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their comments, suggestions for improvement, and interest in the paper. Besides the detailed responses to each reviewer's comments, we submit a rebuttal pdf to supplement our responses. This pdf includes four figures to address the reviewers' concerns. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CODA: A Correlation-Oriented Disentanglement and Augmentation Modeling Scheme for Better Resisting Subpopulation Shifts | Accept (poster) | Summary: To accommodate the presence of spurious correlations and group imbalance, the paper introduces a method CODA to reliably recognize the same object across a range of spurious attributes. The paper disentangled invariant and causal features concerning the object identification from spurious features. The perform... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and positive assessment.
**Response to W1:**
Thank you for this interesting comment, which inspires us to think of CODA under transfer learning. To achieve disentanglement, the variance encoder is regularized by an auxiliary label prediction task, which is ... | Summary: This paper proposes a novel framework for addressing subpopulation shifts. The framework contains two key components. The first one is Correlation-Oriented Disentanglement, which separates the spurious information from class information. Then, with disentangled spurious and class embeddings, the framework can ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and encouraging evaluation.
**Response to W1:**
Thank you for your observation regarding the concern about the gender consistency on CelebA presented in Figure 3. The reconstruction results suggest that causal and label-irrelevant features such as backgroun... | Summary: This paper proposes CODA (Correlation-Oriented Disentanglement and Augmentation), a novel framework for addressing subpopulation shifts caused by spurious correlations and group imbalance (SC-GI). The key contributions are:
1. A correlation-oriented disentanglement (COD) method that learns to separate variant... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and positive evaluation.
**Response to W1:** Thank you for this insightful comment. Labeling spurious attributes (SA) indeed require extra efforts. Yet, we would like to justify its necessity:
1. Robust methods without using SA in training would suffer sev... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ShowMaker: Creating High-Fidelity 2D Human Video via Fine-Grained Diffusion Modeling | Accept (poster) | Summary: This paper proposed a pipeline for 2D human motion retargeting, based on 2D key points and face encoding via fine-grained diffusion modeling.
Strengths: The paper is well-written, and the demo presents satisfying hand and face modeling results.
Weaknesses: 1. Missing recent baselines in the experimental comp... | Rebuttal 1:
Rebuttal: # Response to Reviewer xzB4
Thanks for such a detailed review. Here are my responses.
According to the provided reviews, we believe there are a few misunderstandings on the task setting that need to be clarified.
__Task \& Setting Issues__
  (1) Unlike the approaches concentrating on ho... | Summary: This paper proposes a novel conversational human video generation framework named showmaker based on the fine-grained diffusion model. This paper proposes a novel Key Point-based Fine-grained Hand Modeling module and construct a key point-based codebook to handle the challenging hand generation. Meanwhile, thi... | Rebuttal 1:
Rebuttal: # Response to Reviewer R54L
Thanks very much for your valuable suggestions and for pointing out the typos in the manuscript. Here are my responses.
- Q1. About the comparison with talking head synthesis.
A1. Thanks for your suggestion, we will add the talking head comparison in the demo to t... | Summary: This paper proposes a 2D human video generation framework called ShowMaker, which can generate half-body conversational videos based on 2D keypoints as motion conditions. ShowMaker includes a texture enhancement module for the face and hands, and demonstrates some effectiveness.
Strengths: 1. ShowMaker achiev... | Rebuttal 1:
Rebuttal: # Response to Reviewer HRx2
Thanks very much for taking time out of your busy schedule to review our manuscript. We reply as follows.
- Q1. The experimental settings. (Response to weakness 1 and question 2)
A1. Our goal is to extend the talking head task to generate an expressive half-body co... | Summary: This paper represents an early attempt to advance the field of 2D digital human synthesis in real-world scenarios, extending the traditional talking head task to include more complex body movements, such as hand gestures. The authors present a novel framework utilizing dual-stream diffusion models for full-bod... | Rebuttal 1:
Rebuttal: # Response to Reviewer jdWr
Thanks very much for your careful review comments. We'll answer your questions one by one.
- Q1: About Fig 1 (d).
A1. Thanks for pointing out this mistake. We will remove the “Reference Pose” from Figure 1 (d) and refine the whole figure in the revised manuscript.
... | Rebuttal 1:
Rebuttal: # To all reviewers and ACs
Thanks very much for all the reviewers' efforts and suggestions. We appreciate the positive comments on the following:
1. The generation framework holds significant practical value, particularly in conversational scenarios like TV shows.
2. The paper is well-written and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack | Accept (poster) | Summary: This paper introduces Vaccine, a technique that improves the security of Large Language Models (LLMs) by incorporating perturbation-aware alignment during fine-tuning. Vaccine integrates specifically crafted perturbations in the alignment phase to produce invariant hidden embeddings that withstand harmful pert... | Rebuttal 1:
Rebuttal: We thank the reviewer for the generally positive review. As below, we show extra results and discussion to address the concerns.
**W1: Computational overhead scales with model size**.
**Step time**. Because Vaccine requires double forward-backward pass in each optimization step, the training ti... | Summary: This paper proposes a novel alignment technique called "Vaccine", which addresses the security risks of large language models (LLMs) during user fine-tuning. It is found that even a small amount of harmful data can destroy the alignment effect of a model, leading to the "alignment destruction effect".The Vacci... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive review comments. As belows, we try to address the separate comment.
**W1: Double training time**
Indeed, Vaccine requires double training time, because in every step we need to first search for the perturbation, and then apply the perturbation... | Summary: This paper presents a novel approach to enhance the security of finetuning-as-a-service for Large Language Models (LLMs). The proposed method, Vaccine, introduces a perturbation-aware alignment technique to mitigate the risk of harmful data introduced during user finetuning. The paper demonstrates that Vaccine... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work. Below we try to address the concern on **resource overhead**.
**GPU memory**. Because in the second forward-backward pass we need to register the perturbation to the hidden embedding, Vaccine requires slightly more GPU memory usage. ... | Summary: This paper introduces a novel phenomenon called harmful embedding drift, which occurs when a few harmful data points uploaded by users cause misalignment in the fine-tuned LLM. To combat this, this paper proposes a technique called Vaccine, which uses perturbation-aware alignment to produce invariant hidden em... | Rebuttal 1:
Rebuttal: **W1+Q1: Reource ovehead**
**GPU memory**. Because in the second forward-backward pass we need to register the perturbation to the hidden embedding, Vaccine requires slightly more GPU memory usage. We in the following show comparison results with the normal finetune (SFT) and a recent alignme... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers and the AC for their efforts in reviewing our Vaccine paper! Per the initial review, all the reviewers hold a positive view of our paper. The compliments are too many to be counted, e.g., "(Vaccine) focuses on an interesting problem and have diverse evaluation... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Black-Box Forgetting | Accept (poster) | Summary: This paper explores a selective memorization problem of classification models under a black-box setup. The proposed method selectively applies different learning objectives, cross-entropy minimization, and entropy maximization for classes to be memorized and classes to be unmemorized, respectively; they become... | Rebuttal 1:
Rebuttal: ### Q1. Lack of novelty.
Let us recap the main novelties of this paper.
1. We proposed a novel task called Black-Box Forgetting, which aims to achieve selective class forgetting under the assumption that the parameters and gradients of the model are inaccessible.
2. Aiming at improving derivativ... | Summary: This paper studies the black-box forgetting problem. To this end, the authors optimize the input prompts with a proposed latent context sharing scheme by CMA-ES optimization. The authors achieve the selective forgetting goal by minimizing the cross-entropy loss on memorized classes and maximizing entropy loss ... | Rebuttal 1:
Rebuttal: ### Q1. Why must we forget some classes?
We assume that the reviewer already acknowledged the benefit of preventing information leakage through forgetting. In addition to this, we here would like to emphasize the potential benefits of exploring selective forgetting.
1. Toward addressing the "Right... | Summary: This paper addresses the problem of selective forgetting of specified classes, which involves tuning a pre-trained model to reduce the classification accuracy for only the specified classes without affecting the accuracy for the others. The proposed model introduces a novel method for Black-Box Forgetting base... | Rebuttal 1:
Rebuttal: ### Q1. Could the same result not be achieved using just the class name?
Good suggestion! We tried to tune the latent contexts by only using the class names (i.e., class embeddings). Specifically, let $z_c$ and $z$ denote the class embeddings before and after prompt tuning for the class to be forg... | Summary: The authors propose to apply selective forgetting to black-box pretrained models, instead of the usual white-box settings. Since it is a black-box method, there is no parameter update and instead the prompts are the ones being optimized to decrease the performance on the target class to be forgotten. This is d... | Rebuttal 1:
Rebuttal: ### Q1. CMA-ES is covered quite shortly.
We will expand the description of CMA-ES in the first paragraph of Line 105 in Sec. 3 as follows:
> We employ CMA-ES, a widely used evolutionary algorithm for black-box optimization in continuous, because a textual prompt to be optimized is a continuous va... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful review and constructive feedback. We are happy to see that the reviewers acknowledged the major contributions of this paper. Namely,
1. We proposed a novel task called Black-Box Forgetting, which aims to achieve selective class forgetting under the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models | Accept (poster) | Summary: This paper focuses on the parameter efficient fine-tuning (PEFT) for foundation models in the few-shot learning settings. The authors reveal that the adapted models lack accurate fine-grained uncertainty quantification capabilities. Specifically, the few-shot tuned models perform remarkable accuracy but with l... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your constructive comments and suggestions. Here are the responses to the clarifying questions:
**Q1: LLM Clarification**
The proposed ideas in this work could potentially be extended to PEFT of LLM models for some downstream tas... | Summary: This submission presented a lightweight Bayesian Parameter Efficient Fine-Tuning (Bayesian-PEFT) framework for large transformer-based foundation models. Experiments across diverse datasets demonstrated the improved calibration performance by Bayesian-PEFT on multiple other PEFT techniques.
Strengths: * The p... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your constructive comments and suggestions. Here are the responses to the clarifying questions:
**Q1: The dependence on Evidential Deep Learning is quite significant. To some extent, the proposed method extends EDL, rather than be... | Summary: This paper studies the problem of parameter efficient fine-tuning. The authors pointed out mis-calibration issues caused by parameter efficient fine-tuning, which is, more specifically, under-confident estimation when fine-tuning data is limited. To solve this issue, the author proposed Bayesian PEFT, a Bayesi... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your constructive comments and suggestions. Here are the responses to the clarifying questions:
**Q1: It would be interesting to see more in depth discussion and empirical analysis on the observation of uncertainty of PEFT, partic... | Summary: This paper advances the Bayesian Parameter Efficient Fine-Tuning (Bayesian-PEFT) approach: a strategy to fine-tune large pre-trained foundation models with well calibrated classifier and ability to gracefully deal with out-of-distribution (OOD) data thanks to uncertainty quantification from ensembling.
First t... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your constructive comments, thorough review, and suggestions. Here are the responses to the clarifying questions:
**Q1: Framing of few-shot learning problem**
In this work, we consider few-shot classification problem, i.e., the $N... | Rebuttal 1:
Rebuttal: ## Overall Response
We thank all reviewers for their valuable feedback and constructive suggestions. We identify some important questions raised by multiple reviewers and answer them together in our general response below.
**Q1: Under-confidence behavior of PEFT methods w.r.t. number of classes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Models With Learned Adaptive Noise | Accept (spotlight) | Summary: This paper introduces the Multivariate Learned Adaptive Noise (MULAN) model, a novel diffusion process that adapts the noise schedule to different regions of an image. The authors claim significant improvements in log-likelihood estimation and training efficiency, challenging the conventional assumption that t... | Rebuttal 1:
Rebuttal: # Response to uSJa
We thank the reviewer for their detailed and thorough feedback. We address their concerns below. In the appendix, we have provided numerous experiments trying to explore how the noise schedule relates to different aspects of an image namely:
1. Frequency distribution.
2. Inte... | Summary: This paper proposes to extend diffusion models by learning a per-pixel noise schedule for the forward noising process, that can be conditional on a context or on an auxiliary variable. This leads to faster convergence and SOTA results on density estimation on the simple benchmark datasets CIFAR-10 and ImageNet... | Rebuttal 1:
Rebuttal: ## Response to 22Fq
We thank the reviewer for their detailed and thorough feedback. We address their concerns below.
## Concern 1: Evaluating on larger scale experiments (ImageNet-64)
Unfortunately, it is not feasible for us to train an ImageNet-64 model within our academic research group. Usi... | Summary: This paper introduces an enhanced framework for variational diffusion models (VDM). Rather than using a uniform noise scheduler for all pixels, the new approach assigns different schedulers to individual pixels, adapting to the data distributions. The authors highlight the novelty of this extension, noting tha... | Rebuttal 1:
Title: Response to reviewer QQVN
Comment: ## **Concern 1:** Intuition behind ELBO being dependent on the diffusion trajectory
Imagine you’re piloting a plane across a region where cyclones and strong winds are present. If you plot a straight line course directly through these adverse weather conditions, th... | Summary: The authors propose MuLAN, a method to learn a multivariate (pixel-wise for images) noise injection schedule for diffusion models, leading to improved likelihood estimates compared to prior work. They provide extensive experimental results and an ablation study to demonstrate the efficiency of their method.
S... | Rebuttal 1:
Title: Response to reviewer 1B1k (1/3)
Comment: We want to thank the reviewer for their constructive feedback. We address each concern below.
## Concern 1: Demonstrating the utility of density estimation on downstream tasks
Optimizing for the likelihood is directly motivated by applied problems such as da... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models | Accept (poster) | Summary: This paper introduces DIFFUSIONHOI, a novel Human-Object Interaction (HOI) detector that utilizes text-to-image diffusion models for HOI detection. It efficiently focuses on complex relationships between objects, providing a strong basis for HOI modeling. The relation-driven approach enhances image generation ... | Rebuttal 1:
Rebuttal: **Q1: Concerns about unfair comparisons as DIFFUSIONHOI has a larger total parameter count.**
**A1:** Though DIFFUSIONHOI contains a larger count of parameters, this does not incur heavy consumption in computation resources compared to existing work.
First, the training time ($\textit{i.e.}$, 5.7... | Summary: This paper introduces DIFFUSIONHOI, a new HOI detector leveraging text-to-image diffusion models. Unlike previous one-stage or two-stage models, diffusion models excel at discerning mid/low-level visual concepts as generative models and possess strong compositionality to handle novel concepts expressed in text... | Rebuttal 1:
Rebuttal: **Q1.1: Number of parameters in the VQGAN.**
**A1:** We only utilize the encoder of VQGAN as the backbone which contains 39.2M parameters and is smaller than that of ResNet-101 (42.8M). To address your concern about unfair comparison with models using ResNet-50 as the backbone, we provide a detai... | Summary: This paper tackles the human-object interaction (HOI) detection task.
It aims to utilize the feature of generative models like diffusion models to help human-object interaction classification.
More specifically, it utilize the inversion process in the diffusion model to learn the embedding for human-object i... | Rebuttal 1:
Rebuttal: **Q1: Analysis on the training cost.**
**A1:** For relation-centric inversion, unlike the original textual inversion technology that learns text embeddings within the image space, we optimize relation embeddings within the latent space by reconstructing interaction features. This results in reduc... | null | null | Rebuttal 1:
Rebuttal: **To all reviewers**
We express our sincere gratitude to all reviewers for their valuable time and thorough assessment of our manuscript. In response, we have carefully addressed each concern raised, and provided point-to-point clarifications which shall be integrated into the new version of our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models | Accept (poster) | Summary: This paper proposes an approach called LM-WEATHER that utilizes pre-trained language models (PLMs) as foundation models for on-device modeling of heterogeneous meteorological variables. LM-WEATHER enhances PLMs-equipped devices with local weather knowledge through a lightweight personalized adapter. Additional... | Rebuttal 1:
Rebuttal: Thank you for providing us with your valuable feedback. We have carefully considered your questions and would like to address them as below:
---
**Response to Weaknesses**
**W1**. Further explain the significance of using the averaging operation.
We appreciate your concern and would like to cla... | Summary: This paper proposes LM-WEATHER, which builds upon previous work to explore further the powerful capabilities of PLMs in modelling meteorological variables. By learning sequence modelling capabilities from natural databases and applying them to on-device heterogeneity meteorological variable modelling, a lightw... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments. We've carefully considered and addressed your concerns as follows ***(additional results are in Global Rebuttal PDF file)***:
---
**Response to Weaknesses**
**W1. Some confusion about Figure 1.**
We will revise Fig. 1 to clarify the explanation of... | Summary: The paper aims to develop weather foundation models by leveraging the on-device data on many distributed sensors. Specifically, a federated learning and low-rank adaption mechanism have been applied to a time-series-based foundation model training on Meteorology data.
Strengths: 1) The paper proposes a new wa... | Rebuttal 1:
Rebuttal: Thank you for providing us with your valuable feedback. We have carefully considered your questions and would like to address them as below:
-------
**Response to Weaknesses**
**W1. Justification for using PLM and clarity on weather dataset.**
Although our dataset consists solely of time series... | Summary: This paper introduces LM-WEATHER, a framework leveraging pre-trained language models (PLMs) for on-device meteorological variable modeling. The framework integrates personalized adapters into PLMs, enhancing their ability to handle heterogeneous weather data efficiently. Key contributions include superior perf... | Rebuttal 1:
Rebuttal: Thank you for providing us with your valuable feedback. We have carefully considered your questions and would like to address them as below ***(additional results are in Global Rebuttal PDF file)***:
---
**Response to Weaknesses and Question (& means joint response)**
**W1. Details about the tra... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. ***Additional experiments are included in the attached PDF file***, indexed as follows:
* *Reviewer Jjnz:* Table 1, Table 2
* *Reviewer mBbn:* No additional experiments supplements.
* *Reviewer tT2F:* Table 3 - Table 9
* *Reviewer C54F:* Addition... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compressing Large Language Models using Low Rank and Low Precision Decomposition | Accept (poster) | Summary: This paper proposes a framework that combines quantization and low-rank approximation. Given a neural net weight matrix $W\in \mathbb{R}^{n\times d}$, it considers the following decomposition: $W=Q+LR$, where $Q$ is a quantized sketch of $W$ with very few bits (2 or 4 in their experiments), and $L\in \mathbb{R... | Rebuttal 1:
Rebuttal: > If we allow $\mathbf{L}$ and $\mathbf{R}$ to be computed to full bit-precision, could the statement/result of Theorem 4.1 be simplified?
Yes, in Thm. $4.1$, the error from quantizing the low-rank factors is captured in the additive $\epsilon$ term. When $\mathrm{B_L} \to \infty, \mathrm{B_R} \... | Summary: This paper introduces CALDERA, a new post-training compression algorithm for large language models (LLMs). CALDERA uses the inherent low-rank structure of LLM weight matrices by approximating them via a low-rank, low-precision decomposition $W \approx Q + LR$, where $L$ and $R$ are low-rank factors with quanti... | Rebuttal 1:
Rebuttal: > wider range of LLMs
We have performed quantization experiments on the Mistral 7B model, which can be found in Table 2 of the global response PDF. The PPLs obtained using CALDERA are consistently lower than QuIP# (no RHT fine-tuning) for comparable average bits. For future work, with additional... | Summary: This paper introduces CALDERA, a novel post-training method that combines quantization and low-rank decomposition techniques for compressing large language models. The primary contribution lies in the design of a combined pipeline and the application of low-rank decomposition. Experimental results demonstrate ... | Rebuttal 1:
Rebuttal: > Novelty
We propose an idea which is simple in its execution; that does not mean it lacks novelty. CALDERA is the first work that combines quantization with a low-rank decomposition -- both of which are usually treated independently in existing works on LLM compression. Additionally, a significa... | Summary: The article introduces CALDERA, a post-training compression algorithm for large language models (LLMs) that leverages the low-rank structure of weight matrices to achieve significant compression. CALDERA approximates a weight matrix $W$ using a low-rank, low-precision decomposition $W \approx Q + LR$, where $Q... | Rebuttal 1:
Rebuttal: > Quantization Artifacts
We agree that compression introduces artifacts. However, PPLs on language modeling datasets like Wikitext and C4 are generally considered to be reasonably good indicators of the performance of an LLM, as can be seen in the existing literature. A comprehensive evaluation o... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We are very grateful for the valuable time you spent in reading our paper and sharing your concerns, and greatly appreciate the voluntary nature of the review process. In this global response, we have have summarized the major points from our individual responses.
**Additional ex... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a new decomposition procedure to achieve low-precision and low-rank compression of large language model (LLM) weight matrices. Such a new method could capture high singular components accurately while compressing less significant ones. An efficient algorithm is proposed to optimize the quant... | Rebuttal 1:
Rebuttal: > experimental comparison of model size
We have performed ablations over multiple sizes of the Llama-2 family. Please refer to Table 2 of the global response. The PPLs obtained using CALDERA are consistently better than QuIP# (without fine-tuning) for comparable average bits.
> evaluating the co... | null | null | null | null | null | null |
NeCGS: Neural Compression for 3D Geometry Sets | Reject | Summary: The manuscript introduces a neural compression paradigm for effectively compressing diverse sets of 3D geometry models. The authors propose a two-stage framework that first converts irregular mesh models into a regular 4D TSDF-Def volume representation and then employs a quantization-aware auto-decoder network... | Rebuttal 1:
Rebuttal: ### **Comment 1.** *The manuscript ... more practical.*
**Response:**
* Thanks for the valuable suggestion. First, we clarify the optimization process of converting 3D models into TSDF-Def 4D volumes is efficient, as shown in the table below, while 15 hours refers to the time consumed by the whole... | Summary: This paper proposes a neural compression algorithm, NeCGS to significantly compress geometry datasets. The algorithm mainly consists of 2 components, 1) regular geometry representation: This is an optimization algorithm to optimize the TSDF field such that the error between the original geometries and the geom... | Rebuttal 1:
Rebuttal: ### **Comment 1.** *The reconstruction results ... baseline methods?*
**Response:** * Actually, when **zooming in** Fig. 5 of the manuscript, the decompressed shapes by our NeCGS exhibit superior quality to those by GPCC, showcasing significantly smoother shapes decompressed by NeCGS.
* We refer... | Summary: This paper proposes a method to compress 3D geometry of diverse categories of objects. In the first step, the paper proposes a method to first convert an irregular mesh to a regular representation like a 4D TSDF-Def volume that implicitly describes the geometry. After this, an auto-decoder is trained that lear... | Rebuttal 1:
Rebuttal: ### **Comment 1.** *The intuition behind preferring TSDF-Def 4D volume over TSDF 3D volume is unclear, ... The quantitative results in Table 2 only show marginal improvements. An brief intuitive explanation of the design choice is helpful.*
**Response:**
* For a geometry dataset usually containing... | Summary: this paper looks at the problem of compressing 3d shapes (esp geometry). this paper proposes a two stage approach. the first stage is regular geometry representation. the second stage is compact neural compression. results show some improvements.
Strengths: 1. compressing 3d shapes is important to many applic... | Rebuttal 1:
Rebuttal: ### **Comment 1.** *this paper over claims what it does. in L1-3, it says that they made the first attempt to tackle the problem of compressing 3D geometry sets containing diverse categories. this isn't true. there are at least two papers doing geometry compression of 3D geometry [a], [b].*
**Res... | Rebuttal 1:
Rebuttal: We thank the reviewers for the time and effort in reviewing our work, as well as your recognition of the novelty of our work.
We are grateful to the reviewers for acknowledging our NeCGS algorithm: 1. Reviewers qNSe, jc1U, and 18DA have all noted the significant compression performance of our NeC... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training | Accept (poster) | Summary: This paper presents FewViewGS to regularize sparse view 3DGS from unseen viewpoints without relying on pre-trained depth estimators. The main contribution is to reproject the pixels to an unseen view and calculate the losses at corresponding pixels found by image matching. Experiments on LLFF, DTU, and Blender... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review. Below, we address the main concerns raised in this review.
***Q: The main contribution of this paper shares the same insight with SPARF.***
***A:*** Although both our method and SPARF target solving the overfitting issue, there are key difference... | Summary: This paper introduces a novel few-shot Gaussian Splatting method for synthesizing novel views. Unlike conventional approaches that rely on pre-trained monocular depth estimation or diffusion methods, the proposed method leverages the matches of available training views to generate novel sample views between th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and thorough review. We will integrate the additional results and analysis in the next version. In the following, we address the main concerns raised in this review.
***Q: How do the training and inference times of the proposed method compare to those of ... | Summary: This paper proposes a new method for sparse-view novel view synthesis. It proposes a multi-stage training scheme including pre-training, intermediate, and tuning stages. It introduces pre-trained dense matching models to find pixel correspondences between different-view images and encourage consistency. A Loca... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and thorough review. We will integrate the additional results and analysis in the revised version. In the following, we address the main concerns raised in this review.
***Q: It lacks comparisons on more input views, such as the 6-view and 9-view settings... | Summary: This paper tackles the problem of few view (or sparse view) 3DGS multistage training with correspondence-driven losses that enforce projected colors, depths, and semantic features (extracted by a pre-trained VGG) are consistent. Contributions are straightforward and geometrically inspired.
In addition, the au... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful and thorough review. We will further integrate the explanation in the revised version. Below, we address the main concerns raised in this review.
***Q: Intuitions behind a 3-stage training strategy are not well established. If intermediate training already... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback and positive comments from the reviewers. We are encouraged that the reviewers found that:
- Our paper is well-organized (Reviewer 8CAh).
- Our method is effective and convincing (Reviewer Eqzk, Reviewer ZPEG).
- Our method achieves SOTA performance, with s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow | Accept (poster) | Summary: This paper proposes a new framework for Maximum Entropy Reinforcement Learning using Energy-Based Normalizing Flows (EBFlow). The authors argue that this framework offers three main advantages: Firstly, the soft value function can be obtained without estimation. Second, the framework combines policy evaluation... | Rebuttal 1:
Rebuttal: We thank the reviewer’s time and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
---
**Comments**
**C1.** Despite the fact that the proposed method is the first to apply EBFlow to reinforcement learning (RL), the novelty of the algorithm may appear ... | Summary: This paper proposes to use the energy-based normalizing flow (EBFlow) to represent the policy and value functions in maximum entropy (MaxEnt) RL. Specifically, the paper builds the connection between EBFlow and MaxEnt RL by linking the conditional unnormalized density that depends on the input to the action va... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review and would like to respond to the reviewer’s questions as follows.
---
**Comments**
**C1.** **(1)** There is a potential concern about the empirical investigation: The proposed method is tuned over the target smoothing... | Summary: The paper presents a new framework for Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) using Energy-Based Normalizing Flows (EBFlow). Traditional MaxEnt RL methods, particularly for continuous action spaces, typically utilize actor-critic frameworks and alternate between policy evaluation and policy impro... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review, and would like to respond to the reviewer’s questions as follows.
---
**Comments**
**C1.** There are some unreasonable aspects in the organization of the content. For example, Section 3.2 appears somewhat abrupt; it ... | Summary: The paper introduces a new MaxEnt RL framework called Meow based on Energy-based Normalizing Flow, which integrates the policy evaluation steps and the policy improvement steps and results in a single objective training process. Besides Meow enables the calculation of the soft value function used in the policy... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review and would like to respond to the reviewer’s questions as follows.
---
**Comments**
**C1.** **(1)** The calculation of the determinant is usually time-consuming. **(2)** So the reviewer wonders how the training time of... | Rebuttal 1:
Rebuttal: This global comment includes additional experimental results and extended discussions addressing the questions raised by reviewers UfJ6 and S3sW.
---
### **Additional Results**
The attached PDF file contains five figures, denoted as **Figs. 1-5**, which encompass the following content:
- **Fig... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a new method for MaxEnt RL based on the recently proposed energy-based normalizing flows (EBFlow). The adoption of EBFlow allows us to overcome two major issues with training MaxEnt RL algorithms: (I) sampling from an energy-based model (of the policy) and (ii) approximating the soft value f... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback and effort spent on the review and would like to respond to the reviewer’s questions as follows.
---
**Questions**
**Q1.** Typo at line 149, "Jocobian".
**Response:** Thank you for pointing this out. We will correct the typo in the final revision.... | null | null | null | null | null | null |
Clustering with Non-adaptive Subset Queries | Accept (poster) | Summary: **[Setting]**
This paper studies the problem of clustering $n$ items into $k$ clusters using an oracle that can tell how many ground-truth clusters are represented in any given subset $S$ of items. The goal is to develop non-adaptive algorithms (where all queries are chosen before the oracle answers anything) ... | Rebuttal 1:
Rebuttal: Thanks for this constructive evaluation. Please see the global response for the question related to motivation. The rest of the responses are provided below. We will also add some high level ideas from the appendix to the main paper as suggested.
**Can the authors elaborate on what they mean by ... | Summary: The problem of cluster recovery via membership queries asks to assign each point in a dataset to its $k$ ground-truth cluster by using as few queries as possible to an oracle. A well-studied oracle is the same-cluster oracle that answers, given two points, whether the points are in the same or in different clu... | Rebuttal 1:
Rebuttal: Thank you for the very thoughtful review.
**Unbounded subset size**
We agree that unbounded subset size is a strong assumption and we would like to better understand the query complexity for bounded size in follow-up work. We have shown that $O(\frac{n^2}{s^2} k\log n)$ is possible non-adaptivel... | Summary: The paper studies the clustering problem with non-adaptive subset queries. The problem formulation is as follows. Suppose we are given an oracle $q: \mathcal{V}\rightarrow \mathbb{R}$ such that for any query on a subset $S\subseteq V$ of vertices, the oracle returns how many clusters are in $S$ in the optimal ... | Rebuttal 1:
Rebuttal: Thanks for this very encouraging message. Please see the responses to your evaluation below.
**It might be good to further clarify what ‘non-adaptive’ means in your paper**
Thank you, that is a good point. Our notion of non-adaptivity is only that the queries are made in one round. I.e. queries ... | Summary: The paper gives results on clustering a dataset using "subset queries." This is a generalization of "same-cluster queries." The same cluster query asks whether two given elements belong to the same optimal cluster. The subset query asks how many different clusters the elements of a given subset span. There has... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful evaluation of the paper. Please see the global response for the motivation of the model.
**Subset queries may not be easy to answer accurately**
This is a fantastic point. Clustering under noisy subset queries is a direction we're interested in exploring in follow-up wo... | Rebuttal 1:
Rebuttal: We sincerely thank all of the reviewers for their very thoughtful reviews and comments.
**Motivation for Subset Queries.**
As reviewer SHzL has pointed out, clustering with subset queries is a natural extension of the well-studied same-cluster queries. While with same-cluster queries we would r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness | Accept (spotlight) | Summary: This study examines the impact of softmax attention on ICL regression, advancing beyond the typical linear treatment of the topic. In particular, the authors find that 1) softmax attention leads the model to adapt to the target function's Lipschitz constant and 2) enables the model to recover low dimensional s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find the response below, and a global response above.
1. **Do trained models have the same decomposition for $\mathbf{W}_K^\top \mathbf{W}_Q$ as in
Equation (4)?** We indeed observe this decomposition empirically, please see the atta... | Summary: This paper analyzes how softmax attention learns to perform in-context learning (ICL) through pretraining. The authors show that softmax attention adapts its "attention window" based on the Lipschitzness and noise characteristics of the pretraining tasks. They provide theoretical analysis for affine and ReLU-b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find the response below, and a global response above.
1. **No ``close" examples, simple function classes.** Please see the global rebuttal (Points 1 and 2). To summarize, it is possible that the intermediate layers of the model can learn t... | Summary: This paper explores how softmax attention in transformer models enables in-context learning (ICL), where a model can adapt to solve new tasks using only a few input examples without additional training. The authors focus specifically on regression tasks, where the model must predict a continuous value given so... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find the response below, and a global response above.
1. **Simple function class.** Please see Point 1 of the global rebuttal.
2. **Extensions** Please see Point 3 of the global rebuttal.
3. **Emphasis on Theorem 4.4.** We thank the rev... | Summary: The paper studies in-context learning (ICL) of one-layer attention-only transformers in a regression task. The paper argues that the product of query and key projection matrix is associated with the Liptchitzness of input data. The notion of attention windows is introduced based on this. The paper shows that a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Please find the response below, and a global response above.
1. **Closeness of the setting to reality.**
The data model we consider in the paper is widely studied (Ahn et al. 2023, Akyürek et al. 2023, Mahankali et al. 2023, Garg et al. 2022, von... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thoughtful and detailed feedback. Some reviewers raised common concerns and questions; we will address these next here.
1. **Restricted function class (Reviewers BJxR, ftLf).** We expect that our results will hold for quite general function classes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning | Accept (poster) | Summary: This work addresses a very practical challenge against successful FL deployments, which is of unlabeled data at FL clients. Furthermore, the problem is set in the regime of low count of labeled samples at server. The proposed solution to train a model in semi-supervised manner includes having an adaptive confi... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful comments and feedback. In our revised manuscript, we intend to address these points as follows:
### Clarification of methodology
About Eq10
- At each communication round, the selected clients individually calculate their adaptive thresholds based on their ow... | Summary: This paper focuses on the federated semi-supervised learning (FSSL) scenario, which is a more challenging problem in FL. There are two different scenarios in FL, labels-at-server and labels-at-clients and this paper tackles the former issues. The author found the gap between SSL and FSSL is confirmation bias. ... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful comments and feedback. In our revised manuscript, we intend to address these points as follows:
### Effect of $(FL)^2$ on confirmation bias
Thank you for your feedback. Since the wrong pseudo-labels usually lead to confirmation bias[1], we evaluated pseudo-... | Summary: This paper studies the federated semi-supervised learning (FSSL) problem. A significant gap between the centralized semi-supervised learning and FSSL is found due to the confirmation bias. To address this issue, the current paper proposes a new FSSL algorithm, by incorporating three new ideas, namely client-sp... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful comments and feedback. In our revised manuscript, we intend to address these points as follows:
### Theoretical justification
Thank you for your feedback. We added more experiments to show that our method is robust in different settings. We plan to prove our... | Summary: The paper proposes a new method for federated semi-supervised learning tasks where only the server has a small amount of labeled data. The paper combines 3 different methods to tackle the problem and claims to reduce the confirmation bias issue with the method proposed.
Strengths: The paper is well-written an... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful comments and feedback. In our revised manuscript, we intend to address these points as follows:
### Novelty of $(FL)^2$
We appreciate your question regarding the difference between $(FL)^2$ and related works (FreeMatch and FlatMatch). We would like to clarify... | Rebuttal 1:
Rebuttal: ## More experiments
We conducted additional experiments using the CIFAR-100, Fashion-MNIST, and AGNews datasets, and introduced a non-iid-0.1 setting for the SVHN and CIFAR-10 datasets. Additionally, we performed an ablation study to determine the optimal rho value for Adaptive Sharpness-Aware Mi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization | Accept (poster) | Summary: This work propose shuffling gradient-based methods for nonconvex-linear and nonconvex-strongly convex minimax optimization and obtain complexity results.
Strengths: The presentation is clear. Based on my knowledge on both shuffling and minimax optimization, I found that the algorithms and complexity results a... | Rebuttal 1:
Rebuttal: We thank you for your appreciation of our strengths and your positive evaluations. Please see our general responses along with the individual reply to you below.
**The contribution ... below**
We thank you for your comments. While we agree that some individual techniques in our paper are not ne... | Summary: This paper focuses on nonconvex-concave, finite-sum (stochastic) minimax problems with possibly nonsmooth regularization. Aiming to find $\epsilon$-stationary points, the paper proposes shuffling-based proximal gradient descent-ascent algorithms and verifies gradient computation complexity upper bounds via a b... | Rebuttal 1:
Rebuttal: We thank you for your appreciation of our contributions and your detailed reviews. Please see our general responses along with the individual reply to you below.
**Comparisons of complexity**
Thank you. As you mentioned above, we found the three references [1] [2] and (Emmanouilidis et. al., 20... | Summary: This paper proposes shuffling gradient-based methods for addressing two classes of minimax optimization problems: nonconvex-linear and nonconvex-strongly concave settings. The first algorithm focuses on the nonconvex-linear minimax setting and the second algorithm, consisting of semi-shuffling and full-shuffli... | Rebuttal 1:
Rebuttal: We thank you for your appreciation of our strengths and soundness. Please see our responses to your concerns and questions below:
**The proposed ... expansive**
We can choose a simple $b$ in (9) so that $u^*_{\gamma} ( \cdot )$ in Step 9 of Algorithm 1 has a **closed-form solution**. For instan... | Summary: The paper presents new shuffling gradient-based methods for solving two classes of nonconvex-concave minimax optimization problems: nonconvex-linear and nonconvex-strongly concave settings.
The first algorithm is designed for the nonconvex-linear setting and achieves state-of-the-art oracle complexity, emplo... | Rebuttal 1:
Rebuttal: We thank you for your appreciation of our strengths and your positive evaluations. We addressed your comments in the general response. We repeat it below for your convenience:
**1. Comparison of complexity with other shuffling methods**
Thank you for your suggestions. Among the stochastic shuff... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you all so much for reviewing our submission. We valued your comments and appreciation of our strengths and contributions.
We have addressed each comment individually for each reviewer. In this general response, we highlight some common responses to all the reviewers.
**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OPEL: Optimal Transport Guided ProcedurE Learning | Accept (poster) | Summary: The paper presents OPEL, a novel framework for procedure learning from videos that leverages optimal transport (OT) to align key steps across different video instances. OPEL treats video frames as samples from an unknown distribution and formulates the distance calculation between them as an optimal transport ... | Rebuttal 1:
Rebuttal: Thanks for your encouraging and insightful feedback. Please find the answers to your specific comments:
**Comparison with AS methods:** Procedure learning (PL) and action segmentation (AS) are related but not the same. PL, when applied to a set of instructional videos depicting the same task, inv... | Summary: The authors propose a novel approach for procedure learning leveraging optimal transport, OPEL. OPEL integrates optimality and temporal priors, and incorporates a novel inter-video contrastive loss. OPEL achieves significant improvements on egocentric and third-person benchmarks.
Strengths: 1. An interesting ... | Rebuttal 1:
Rebuttal: Thanks for your encouraging feedback. Please find answers to your specific comments:
**Presentation:** We agree that the equations are too dense in Section 3, and the formulations can be simplified. We will update accordingly as per the reviewer’s suggestion during revision.
The V1 to V4 in Fig... | Summary: The paper proposes an unsupervised method for procedure learning that identifies the key steps and their orders in several videos of the same task.
The paper formulates the distribution of video frames as an optimal transport (OT) problem to compute the distances between the key steps.
To handle the variation... | Rebuttal 1:
Rebuttal: Thanks for your encouraging feedback. Please find the answers to your specific comments:
**Explanation of priors:** The concept of the **Optimality Prior** is crucial when dealing with video alignment, especially in challenging scenarios. When two videos are perfectly aligned (case 1 of Fig. 2B)... | Summary: OPEL is a novel technique for Procedure Learning. Procedure learning is the task of finding key steps in an action (such as cooking brownie) and aligning the videos based on these key steps.
OPEL proposes to use the optimal transport distance between the two videos as the similarity metric rather than direct ... | Rebuttal 1:
Rebuttal: Thanks for your encouraging and insightful feedback. Please find the answers to your specific comments. Also please refer to our overall rebuttal response to all reviewers and the corresponding pdf containing additional figures and tables to support our claims.
**Method factors:** The reviewer co... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and feedback. We are encouraged that the reviewers like the soundness and novelty of our approach for procedure learning along with comprehensive evaluation (Reviewer XDhK), and enhancement of results over current state-of-the-art (SOTA) wor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge | Accept (poster) | Summary: The paper presents UDKAG, a novel framework designed to enhance the capabilities of Large Vision-Language Models (LVLMs) by integrating up-to-date knowledge retrieved from the internet during the inference phase. The authors have developed a hierarchical filtering model to identify pertinent content from searc... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper and for your recommended acceptance.
We sincerely appreciate your valuable comments and feedback.
---
> **Question: I'm curious about the generalization ability after integrating up-to-date knowledge. Will the model forget some important ... | Summary: This paper investigates a novel approach to augment large vision-language models with up-to-date knowledge from the internet. The author proposes to extensively leverage existing search engines (e.g., Bing and Google) and foundation models like ChatGPT for web information searching and parsing. A hierarchical ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the significance of my work and for your constructive comments, which will help enhance the quality of my paper.
---
> **Weakness1: The primary concern is the cost of leveraging the search engine and LLM like ChatGPT. The author should elaborate more on this aspect to s... | Summary: The paper proposes a framework that enhances large vision-language models (LVLMs) to handle visual question answering (VQA) tasks involving up-to-date knowledge. The system utilizes a search engine to retrieve relevant websites and parses their content for more comprehensive information. To manage the large vo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We value your suggestions.
Here, we would like to mention that our work focuses on multimodal Internet-Augmented Generation (IAG), a type of method to enhance LVLMs in handling multimodal prompts based on Internet knowledge.
IAG is an emerging top... | null | null | Rebuttal 1:
Rebuttal: ### **General response**
We would like to thank the Area Chairs and the Reviewers for carefully reading our paper and providing valuable comments.
We are pleased to hear that the majority of the reviewers found our paper "well-written " (jo4p) and "strongly motivated" (qhJo, jo4p).
We also appre... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective | Accept (poster) | Summary: This paper proposed a dataset (PVCP) followed by a solution (PPSENeT), which is the first one that focuses on the 3D pre-collision pose of pedestrians. The dataset is collected by dashcam that contains various poses with both static and dynamic backgrounds. Various types of annotations are provided, including ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and valuable comments on our dataset and paper writing.
Thank you very much for your careful review, we will reply to your opinions on Questions and Limitations respectively.
------
**Questions Reply:**
**Reply1.** We fully understand your conce... | Summary: The paper conducts the first Pedestrian-Vehicle Collision Pose dataset(PVCP) including the pedestrian-vehicle collision pose from the dashcam perspective and pose annotations. Moreover, the paper presents the method(PPSENet) to estimate the collision pose and shape based on PVCP.
Strengths: 1. The first Pedes... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work, especially the PVCP dataset and annotation tools.
Thank you very much for your careful review.
------
Indeed, our annotation tool has already been made accessible to the public, but due to anonymity requirements, we have withheld the full ... | Summary: This paper collects a new dataset that includes pedestrian-vehicle collision poses from the dashcam perspective and presents a two-stage framework for estimating human pose and shape parameters. The dataset has the potential to benefit the study of pedestrian injuries. Given the challenge of obtaining ground t... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work in pedestrian injury research.
Thank you very much for your careful review, and we will reply to your opinions on Questions and Limitations respectively.
------
##### **Questions Reply:**
**Reply1.** Unlike regular and periodic postures, p... | Summary: This work focuses on the pre-collision posture of pedestrians in real traffic scenarios. The authors semi-automatically constructed the first pedestrian-vehicle collision pose dataset PVCP by collecting pedestrian-vehicle collision poses from the perspective of dashcams, which includes more than 40,000 acciden... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work and especially the significance of the PVCP dataset.
And thank you very much for your review. We will address your comments point by point.
------
**Reply1.** Our PPSENet employs a two-stage strategy. In the first stage (ITP), we train usi... | Rebuttal 1:
Rebuttal: We appreciate the thorough review and insightful comments from AC and all reviewers. **The reviewer's comprehensive feedback is as follows：**
- **Reviewer SmGM** acknowledged the importance and quality of our PVCP dataset for autonomous driving research and sought further opinions on the innovati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift | Accept (poster) | Summary: It is crucial to assess and quantify uncertainties associated with distribution shifts in the context of unsupervised domain shift. The paper propose methodologies for aggregating prediction intervals, which capture the range of likely outcomes for a given prediction. The authors support these methodologies th... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments.
1. **Lack of intuitive understanding**: Thank you for your concern. To achieve a minimal-width prediction interval, it is crucial to accurately capture the shape of the interval. However, a single predictor may fail to do so due... | Summary: The authors study the problem of how to construct prediction intervals on a target domain under both covariate shift and domain shift assumptions (i.e., the source and target domains are related either via a bounded density ratio, or a measure-preserving transformation), designed to ensure adequate coverage wh... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments.
1. **Extension of empirical evaluation**: Thank you for your comment, please see our global response for the details of the additional experiments.
(i). We now have two more real-data experiments on our first method, Algorithm ... | Summary: Building on the work of Fan et al. (2023), this paper addresses the challenge of computing prediction intervals in an unsupervised domain adaptation setting, where labeled samples are available from a related source domain, and unlabeled covariates are available for the target domain. The primary objective is ... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments.
1. **The paper assumes familiarity with Fan et al. (2023)** Thank you for raising this concern! We will carefully proofread the revised version of the manuscript according to your suggestions.
2. **Mention optimal aggregation**... | Summary: This work proposes a method to construct prediction intervals in distribution shift problems, where unlabeled data from the target domain is available. The method is inspired by Fan et al (2023) in the i.i.d. setting and assumes we can estimate a transform (defined by reweighting or a transport map) that maps ... | Rebuttal 1:
Rebuttal: Thanks a lot for reading our paper and for your insightful comments. We answer your questions and concerns in the following.
1. **Novelty compared to Fan et al. (2023)**: Please see our global response.
2. **Assumptions on the covariate shift**: Covariate shift is a common assumption, even fo... | Rebuttal 1:
Rebuttal: # Global response
We thank all the reviewers for their insightful comments. We address a few concerns that were raised by multiple reviewers.
1. **Comparison with Fan et al. (2023):** The key contribution of our paper lies in adapting the methodology from Fan (2023) (which only addresses no-shift... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Coherent 3D Scene Diffusion From a Single RGB Image | Accept (poster) | Summary: The task is to perform 3D scene reconstruction from a single RGB views, such that they can output both scene object poses and geometries. Prior work cannot jointly output both poses and geometries, one design choice that is validated by this work to be effective. Method-wise, extending on SALAD, this work intr... | Rebuttal 1:
Rebuttal: **1. Reconstructing background objects**
Indeed, structural elements can be considered part of holistic scene reconstruction.
Including structural elements in the scene prior can certainly be beneficial and can improve the coherence of the scene.
We will extend our formulation with the structura... | Summary: This paper proposes a novel framework for 3D scene reconstruction from single images based on diffusion models. This framework jointly predicts 3D object poses and shapes with seperate diffusion models and captures global scene context with a standard mutli-head attention module. To train this model, the paper... | Rebuttal 1:
Rebuttal: **1. Clarification of model architecture**
Thank you for the feedback regarding the architecture description of our model in Section 3.7. We will improve the clarity of this section in the final paper. In the rebuttal PDF, we have included an architecture figure of the shape model (Fig. 4).
In ... | Summary: In this work, the authors propose a diffusion-based method for generating coherent 3D scenes from a single input image. They use a diffusion model to jointly denoise the 3D poses and geometries of all objects. To address the incomplete ground truth of existing datasets, they also introduce a surface alignment ... | Rebuttal 1:
Rebuttal: **1. Clarification of shape learning**
To support our latent diffusion approach for modeling shapes, we represent 3D shapes following the disentangled formulation of SPAGHETTI [1]. SPAGHETTI learns the disentanglement into 16 Gaussians and per-Gaussian “intrinsics” in a self-supervised way throug... | Summary: This paper proposes an approach to reconstruct the 3D surfaces of multiple objects from a single RGB image. Given an instance-segmented RGB image as input, the poses and shapes of objects are jointly predicted and denoised using a diffusion model conditioned on the input image. A one-sided chamfer distance (fr... | Rebuttal 1:
Rebuttal: **1. Generalization to unseen/uncommon object categories**
Our shape prior is learned across all categories and in our experiments, we have seen that the shape Gaussians are indeed quite stable and align well with geometric parts (Figs. 1 & 2). However, we do rely on strong priors for seen catego... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive and valuable feedback. We are pleased that *all* reviewers recognized the soundness of our approach for the challenging problem of single-image 3D reconstruction.
Our method was highlighted as solid (scCV) and efficient (W8nX) by approp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Universal Mesh Movement Networks | Accept (spotlight) | Summary: The authors propose a method for general mesh movement. They design a network that takes the original mesh and the monitor values as input to predict the mapping for the adapted mesh. This makes it able to handle any mesh without the need of training PDE-type-specific models.
Strengths: 1. The proposed method... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments that precisely recognize the strengths of our work! We are also very happy the Reviewer Jzyt checked our supplementary video and found it nice and useful. Below, we provide our point-to-point responses to the comments, denoted by **[W]** for weakness... | Summary: This paper introduces a learnable model for mesh movement - which is a method for improving efficiency of a PDE solver by moving mesh nodes, while keeping the topology fixed. The proposed model itself is a two-stage neural architecture comprising a transformer encoder - taking a representation of the input mes... | Rebuttal 1:
Rebuttal: We thank Reviewer jEN5 for acknowledging the contributions, soundness, and presentation quality of our paper. And greatly appreciate Reviewer jEN5 for proposing these insightful questions. Below, we provide our point-to-point responses to the comments, denoted by **[W]** for weaknesses and **[Q]**... | Summary: The paper introduces the Universal Mesh Movement Network (UM2N), a deep-learning model that enhances solving Partial Differential Equations (PDEs) through adaptive mesh movement. UM2N, with a Graph Transformer encoder and a Graph Attention Network (GAT) based decoder, is trained on a PDE-independent dataset, a... | Rebuttal 1:
Rebuttal: We thank Reviewer yaVX providing a detailed summary of our strengths and valuable suggestions. Below, we present our detailed responses to the comments, indicating [W] for weaknesses and [Q] for questions.
**Response to W1:**
As the reviewer correctly identifies we do compare our method with exis... | Summary: In the present work, authors tackle the challenging task of accelerating PDE-solving process with deep learning, focusing on mesh movement problem. They suggest a new architecture that combines graph transformer encoder and graph attention decoder to significantly accelerate the PDE-solving. Authors propose a ... | Rebuttal 1:
Rebuttal: We like to thank the reviewer for their kind words on the presentation and level of contribution of our paper. Below, we present our detailed responses to the comments, indicating **[W]** for weaknesses and **[Q]** for questions.
We appreciate the feedback regarding the amount of explanation we w... | Rebuttal 1:
Rebuttal: We are glad to receive valuable and constructive comments from all the reviewers. We have made a substantial effort to clarify reviewers' doubts and enrich our experiments in the rebuttal phase. In our responses, Tab. Rxx or Fig. Rxx
refers to the new Rebuttal results in the attached PDF. Below is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records | Accept (poster) | Summary: The paper introduces AutoDP, a novel AutoML framework that performs combined task grouping and architecture search with a novel surrogate model. The proposed model achieved higher performance in multiple tasks on MIMIC VI dataset compared to common single-task, multi-task, and AutoML approaches.
Strengths: - ... | Rebuttal 1:
Rebuttal: Thank you very much for providing such positive feedback to our work. For the runtime concern, we include the specific GPU hours in Table 4. MTG+DARTS has the same runtime as our method. We maintain this for a fair comparison with the baselines. We have shown that the computational cost is feasibl... | Summary: The paper proposes an automated multi-task learning framework, AutoDP, for disease prediction using EHR data. It optimizes task grouping and model architecture to enhance prediction performance. AutoDP efficiently searches a vast space of task combinations and architectures by employing a surrogate model-based... | Rebuttal 1:
Rebuttal: Weaknesses:
1. We focus on the disease prediction problem in this paper. Our contention is that this problem is a very important problem with potential for high impact such that the method should be publicized for practitioners and users of ML in the field. However, we believe but have not shown t... | Summary: N/A
Strengths: N/A
Weaknesses: The authors' identity can be easily inferred by googling the linux username ("sxc6192") found in the .idea/deployment.xml file in the supplementary material.
Technical Quality: 1
Clarity: 1
Questions for Authors: N/A
Confidence: 5
Soundness: 1
Presentation: 1
Contributio... | null | Summary: The paper discusses an automated approach for multi-task learning on electronic health records, called AutoDP. This approach aims to improve the design of task grouping and model architectures by reducing human intervention. Specifically, AutoDP searches for the optimal configuration of task grouping and archi... | Rebuttal 1:
Rebuttal: 1. Architecture search space: We introduce our search space at Appendix A. We will try to move parts of it into the main paper to make the full paper clearer. We define the candidate operation set as {Identity, Zero, FFN, RNN , Attention}, which includes widely used operations for processing EHR t... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The research work presents an exploration into the potential of multi-task learning (MTL) within the context of electronic health record (EHR) data analysis and clinical prediction tasks. This study innovatively addresses the critical challenges of task grouping and model architecture design, which are essenti... | Rebuttal 1:
Rebuttal: Q1: Thank you for pointing this out. We will further improve Figure 1 in the camera-ready version for better clarity and comprehensibility.
Q2: We have included Averaged Precision for considering the class imbalance. Averaged Precision and AUPRC are essentially the same thing in this setting. P... | null | null | null | null | null | null |
VISA: Variational Inference with Sequential Sample-Average Approximations | Accept (poster) | Summary: This paper presents a method for approximate inference in expensive to evaluate models where the gradients may not be available. The presented approach proceeds in a trust-region-optimization fashion where a series of deterministic surrogates objectives are optimized. Each objective is optimized until a trust ... | Rebuttal 1:
Rebuttal: **Motivation for ESS as trust-region criteria.**
See general response.
**Choice of ESS threshold.**
See general response.
**IWFVI vs Reweighted Wake-Sleep (RWS).**
RWS is used in the context of amortized variational inference and optimizes both the parameters of the model and the parameters o... | Summary: This paper introduces a new method for variational inference called VISA, which stands for Variational Inference Using Sequential Sample-Average Approximations. VISA is based on importance-weighted forward-KL variational inference and allows for reusing model evaluations across multiple gradient steps. This ma... | Rebuttal 1:
Rebuttal: **Motivation for ESS as trust-region criteria.**
See general response.
**Robustness to optimization methods.**
We indeed use Adam (optax implementation) for all experiments and will clarify this in the final version of the manuscript. Based on your suggestion, we conducted additional experiments ... | Summary: The paper proposes to use a sample-average approximation for variational inference. In contrast to previous works, the method uses the forward-KL which does not require differentiability of the joint likelihood. In order to sample from an approximate posterior instead of the exact posterior, a sequential trust... | Rebuttal 1:
Rebuttal: **Sensitivity to the learning rate.**
See general response.
**No real-world experiments.**
See general response.
**Failure cases of the Forward KL-divergence.**
While we consider the mode-covering behavior of the forward KL-divergence as a positive, there can be instances where a mode of the var... | Summary: The authors propose to reduce the number of model evaluations during the optimization of the variational lower bound. To this end, they integrate sample avarage approximations (SAAs) into IWFVI framework, which allows updates of variational parameters while keeping approximate samples generated from a variatio... | Rebuttal 1:
Rebuttal: **Clarification regarding our methodology.**
VISA does not optimize the reverse KL-divergence or corresponding variational lower bound but a forward KL-divergence or corresponding variational upper bound. This is a crucial difference to previous related work, which studies SAAs in the context of t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their detailed reviews! We are delighted to see the clarity of our manuscript and rigor of our experiments listed among the strengths of our work. We address the point raised by multiple reviewers below and respond to the individual concerns and questions i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cascade Speculative Drafting for Even Faster LLM Inference | Accept (poster) | Summary: This paper concentrates on the inference efficiency of LLMs and thinks that the autoregressive generation contained in drafting process of speculative decoding leads to the suboptimal performance of speculative decoding. It introduces a novel speculative execution algorithm, Cascade Speculative Drafting (CS ... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the advantages of our method. We appreciate the insightful and detailed feedback, and would like to address each of your concerns.
> W1: The model employs a heuristic approach, involving a large number of hyperparameters, especially the K-matrix, whose quant... | Summary: The paper proposes to use multiple draft model for speculative decoding. Specifically, the smallest model could be the statistic language model which has negligible latency therefore reducing the cost of autoregressive regression. Experiments show the proposed method works better than baselines.
Strengths: (1... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the idea, analysis, and experiments of our paper. We appreciate the feedback provided and would like to address the weaknesses mentioned.
> Q1: Algorithm 1 is heavy and should be simplified for the reader.
While we made numerous attempts to simplify the alg... | Summary: This study introduces a novel method to accelerate large language model (LLM) decoding by integrating speculative decoding with two types of model cascades: vertical and horizontal. The horizontal cascade utilizes larger draft models for generating initial tokens, while smaller models assist in producing subse... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the significance of our contribution. We appreciate the insightful feedback provided and would like to address each of your questions.
> Q1: Algorithmic Complexity
To make an additional smaller draft model meaningful in the CS Drafting algorithm, it needs to... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Learning in Medical Image Registration: Magic or Mirage? | Accept (poster) | Summary: The paper delves into the comparison between classical optimization and learning-based medical image registration methods. Some valuable insights are proposed and the authors propose a general recipe to choose the best paradigm for a given registration problem.
Strengths: 1. The motivation behind the work is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and helpful feedback, we are glad to find they found the paper having a clear motivation, detailed observations, clear illustrations and easy-to-read paper. We believe we have addressed all the remaining concerns and hope the reviewer increases their score ... | Summary: The work benchmarks the traditional methods and deep learning-based methods for medical image registration and gives a general recipe to choose registration methods.
Strengths: Comprehensive experiments: The authors implement several classical variational and deep learning-based registration models on four pu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and helpful feedback, which has significantly contributed to improving the quality of our paper. We believe we have addressed all the remaining concerns and hope the reviewer will consider increasing their score and advocating for the acceptance of our pa... | Summary: This paper discussed about an explicit correspondence between mutual information of the distribution and performance of classification registration methods. The authors argued that this correlation will not be affected by the learning-based methods. They validated this hypothesis on both classical and learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for their highly detailed and insightful feedback, this has immensely helped in improving the quality of our work. We believe we have addressed all major concerns and incorporated changes in the paper.
**“I would argue that this is typically not always true considering the r... | Summary: The manuscript investigates the characteristics of two types of registration approach based on traditional variational optimization and deep learning. Experiments revealed a correlation between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical regist... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful feedback, and are pleased to note the recognition of the originality of our work, the well-motivated design of our experiments, and the challenge posed to existing claims in the DLIR literature. We address some of their concerns below, and hope t... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback and for taking time to improve the quality of our work. We are glad that reviewers found our work reflecting original thinking and drawing useful insights [tuv2], well motivated problem [Aeaf], consideration of both classical and DLIR methods [2... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EMR-Merging: Tuning-Free High-Performance Model Merging | Accept (spotlight) | Summary: The paper presents a method called EMR-MERGING (ELECT, MASK & RESCALE-MERGING) for merging models finetuned on different tasks into a single model with multi-task capabilities without the need for additional tuning or training. This method addresses the limitations of existing model merging techniques, which o... | Rebuttal 1:
Rebuttal: Thank you for your hard work and constructive comments.
---
## Weakness 1: Additional Computational Overhead.
**Ans**: The unified task vector, masks and rescalers are computed during the merging process, which is before the inference process. During the inference process, before we evaluate the... | Summary: The authors identify two issues in current model merging methods: significant performance degradation from the multi-task counterpart and requirement of additional data and training. To tackle those issues, they propose EMR merging, which first creates a unified task vector, then selects masks based on each ta... | Rebuttal 1:
Rebuttal: Thank you for your hard work and helpful comments.
---
## Weakness 1: limited novelty. The three steps are a combination of TIES and DARE.
**Ans**: The proposed method is totally different from DARE+TIES-Merging.
+ The motivation for EMR-Merging is different from existing methods.
We first d... | Summary: Model merging directly fuses multiple independently trained models at the weight level to obtain a single comprehensive model, which is a current research hotspot. This paper proposes a new model merging method EMR-MERGING to reduce the performance gap between the merged model and the independent models.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your hard work and kind comments.
---
## Weakness 1: parallel ability.
**Ans**: 1) Most multi-task model merging methods cannot handle the situation where multi-task samples are included in one inference because only one classification head can be applied during one inference. All t... | Summary: This paper aims to improve the performance of model merging in the multi-task learning domain. By electing a unified model, identifying a mask and a rescaling factor for each task, the proposed method EMR-Merging is able to significantly improve the merging performance over the previous state-of-the-art, such ... | Rebuttal 1:
Rebuttal: Thank you for your hard work and admitting the value of our contribution.
---
## Weakness 1: why the proposed way works, what if the average of the task vector is close to zero, and the intuition of the method.
**Ans**: When we elect the sign vector, we first element-wisely add the task vectors ... | Rebuttal 1:
Rebuttal: # General Response:
We thank all the reviwers for their time and constructive comments. We appreciate the reviewers' praise of the strengths of our paper including:
- the idea that decoupling model merging into unified parts and task-specific parts to avoid conflicts (reviewer Lqs2).
- extensive... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond | Accept (poster) | Summary: The paper studies the in-context learning capabilities of linear attention (ATT) and linear state space layers (SSM) on a linear regression task. It shows that for both ATT and SSM there exists a parametrization such that they perform as well as one step of preconditioned gradient descent (PGD) with optimal pr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the relevance of our work to contemporary ML research and AI applications. Indeed, our focus is developing a theoretical understanding of in-context learning (ICL) that is insightful for practical settings. Below, we respond to the concerns raise... | Summary: The authors examine the capabilities of Transformers with linear attention in performing in-context learning (ICL) by implementing a linear estimator through gradient descent. The existing studies mostly consider IID task and feature vectors and fully parameterized attention weights. This work expands on these... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and the recognition of the credibility of our findings. Below, we address the questions and concerns raised by the reviewer.
**W1:** While our study focuses on single-layer architectures similar to previous work, it nonetheless presents novel and... | Summary: This paper studies how transformers can use ICL to solve linear regression problems. It is shown that state space models, transformers are both capable of performing linear regression as well as gradient descent (which implements the least squares solution). There are results about LORA and RAG.
Strengths: Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and questions on our submission.
**W1: Novelty of contribution and prior art.** Ahn et al. analyzes the loss landscape of linear transformers. Their results only apply to special IID data models (see their Table 1) but they also characterize critica... | null | null | Rebuttal 1:
Rebuttal: ## Common response to all the reviewers
We thank the reviewers for their constructive comments and insightful questions. We are glad that Reviewer HGXo acknowledges the credibility of our conclusions and Reviewer EE6j notes the high relevance of our study to contemporary machine learning research ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Full-Distance Evasion of Pedestrian Detectors in the Physical World | Accept (poster) | Summary: This paper is dedicated to improving the field of physical adversarial attacks on pedestrian detection, focusing on the robustness of attack performance over distance. To bridge the appearance gap caused by distance between the digital and physical spaces, the authors propose a distant image converter (DIC). T... | Rebuttal 1:
Rebuttal: Question: From the videos provided in the supplementary materials of this paper, the performance of the attack does not seem to be as good as claimed in the paper at 8m and 14m.
Answer: We have extracted all frames in the 8-meter and 14-meter demonstration videos and evaluated the ASRs by countin... | Summary: To achieve full-distance attacks, the authors summarize three factors that distort the performance including atmospheric, camera hardware, and effect filters. Then, the authors simulate these factors in the digital world via DIC. To overcome the conflict of different distances requiring different low-frequency... | Rebuttal 1:
Rebuttal: Question: Why does this paper not adopt the TC-EGA approach as a baseline and generate an Expandable FDA?
Answer: Unlike the adversarial texture paper [1] which used the YOLOV2 and V3 models as the target models, we used YOLOV5 as our main target model. Table S3 of the NAPGuard Appendix [4] shows ... | Summary: The presents an adversarial attack that works in real world at different distances and fools object detectors. The method utilizes several advanced techniques like atmospheric perspective, camera and filter simulations, multi-frequency optimization to bring the method closer to real-world scenarios. The method... | Rebuttal 1:
Rebuttal: Question: The adversarial patches/clothes are really massive.
Answer:
We used relatively large patches and full-body clothing in our experiments for two reasons. First, it is now a common practice to employ large adversarial patterns such as full-body clothing [5][6] or fully-covered car paint ... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewers have put into this manuscript, which will help us improve the quality of the revised paper. It is encouraging that the reviewers find our work to be “important” [vr4j, ouf1], our design to be “reasonable” and “convincing” [vr4j, ouf1], our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Chaos to Clarity: 3DGS in the Dark | Accept (poster) | Summary: This paper introduces a new framework for 3D reconstruction and denoising from raw images. Specifically, a noise extractor and a noise-robust reconstruction loss are proposed to deal with the overfitting issue of 3DGS to the noises heavily distributed in raw images. Experiments on the rawnerf dataset and ablat... | Rebuttal 1:
Rebuttal: We appreciate your valuable suggestions and comments. Here are our detailed responses to your feedback:
1. **Novelty Limited Due to the Need for Noise-Clean Paired Data:**
We want to clarify that the pretrained model aims to provide a good initialization and **does not have strict restriction... | Summary: The paper tries to use raw images with high dynamic range (HDR) for training 3D Gaussian Splatting (3dgs). It first analyzes how noise from raw image affects the optimization of 3dgs especially when the number of training views is small. To address this issue, first it uses a lens distortion correction network... | Rebuttal 1:
Rebuttal: We appreciate your valuable suggestions and comments. Here are our detailed responses to your feedback:
1. **Lambertian Surface Assumption:**
**We use the Lambertian surface assumption to simplify our analysis.** However, our method can also be applied to non-Lambertian surfaces, such as mirr... | Summary: This paper proposes a novel self-supervised learning framework to reconstruct HDR 3D Gaussian Splatting (3DGS) from noisy raw images. This addresses the issue of noise degrading reconstruction quality and inference speed in 3DGS, especially in scenarios with limited views. The proposed method demonstrates supe... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough review and valuable suggestions. Here are our detailed responses to your comments:
1. **Comparison with NeRF Baselines:**
We acknowledge the importance of comparing our method with NeRF-based methods. We have conducted a comparison between NeRF and HDR... | Summary: This paper investigates the issue of 3DGS overfitting noises in input images, and proposes a self-supervised learning framework as the solution. The paper integrates a noise model as prior to relax the constraints in the 3DGS optimization framework.
Strengths: 1. The paper provides a detailed analysis of how ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and valuable suggestions on our paper. In response to your suggestion regarding comparisons on more sparse-view settings, we have conducted additional quantitative comparisons using the LLFF dataset with simulated noisy raw images. Below are the details of ou... | Rebuttal 1:
Rebuttal: # Thanks to All the Reviewers for the Insightful Comments
We would like to thank the reviewers for their efforts and insightful comments. We appreciate the reviewers’ acknowledgment of the **novelty**, **performance**, and **presentation** of our proposed method. For example:
- Reviewer JuHQ not... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a self-supervised learning framework for reconstructing HDR 3DGS from noisy raw images. The proposed method integrates a noise extractor and a noise-robust reconstruction loss to mitigate the effects of noise in raw images. By leveraging a noise distribution prior, the framework improves bo... | Rebuttal 1:
Rebuttal: We deeply appreciate your thorough review and valuable feedback on our submission. Here are our detailed responses:
1. **More Quantitative and Qualitative Results:**
Given the limitations of the RawNeRF dataset, we conducted additional quantitative comparisons using the LLFF dataset with simu... | null | null | null | null | null | null |
Accelerated Regularized Learning in Finite N-Person Games | Accept (poster) | Summary: The paper studies the extension of Nesterov’s accelerated gradient algorithm to the solution of N-player games, named "follow the accelerated leader" (FTXL). The method is studied in both continuous and discrete time, and under various information oracles. The convergence of the algorithm is super-linear when ... | Rebuttal 1:
Rebuttal: Dear Reviewer sdz4,
Thank you for your positive evaluation and constructive comments! We reply to your questions below:
> The convergence of FTXL in all settings requires sufficiently close initialization or has to do with the neighborhood. The exact definition of "sufficiently close" or this ne... | Summary: The submission studies the convergence of finite N-person games. Combining the idea of (NAG) and (FTRL), the submission proposes a continuous-time dynamic (FTXL-D) and its discrete-time scheme (FTXL). The novelty of integrating momentum and regularization into an algorithm allows the construction of quadratic ... | Rebuttal 1:
Rebuttal: Dear Reviewer 3QUL,
Thank you for your strong positive evaluation and constructive comments! We reply to your questions below:
> A smoother transition toward (FTXL-D) in lines 182–186 is desired. [...] What is the correspondence of $x$ and $y$ of a game in (HBVF)? [...] These analogs, with the a... | Summary: The paper proposes a momentum-based follow-the-regularized-leader (FTRL) type algorithm for finding the Nash equilibrium in finite games. The paper first investigates the continuous second order ODE and a concrete game, and then devises the appropriate FTRL scheme. The convergence rate for the proposed algorit... | Rebuttal 1:
Rebuttal: Dear Reviewer qtGD,
Thank you for your overwhelmingly positive evaluation and constructive comments. We reply to your questions below:
> Are lower bounds of learning a finite game with the three kinds feedbacks known? If so, it would be great to provide a survey.
The closest lower bounds that w... | Summary: This paper primarily focuses on introducing a Nesterov's accelerated gradient (NAG) algorithm for online learning in games.
Initially, the author shows that a continuous-time version of NAG converges to a strict Nash equilibrium at a rate that is quadratic.
This rate of convergence is notably faster than that ... | Rebuttal 1:
Rebuttal: Dear Reviewer yttq,
Thank you for your input. For your convenience (and that of the committee), we reproduce and reply to your comments below one-by-one.
[**Note:** all bibliography reference numbers are as in our paper]
> Results are only applicable to games with a strict Nash equilibrium. [... | Rebuttal 1:
Rebuttal: Dear reviewers, dear AC,
We are sincerely grateful for your time, comments, and positive evaluation!
To streamline the discussion phase, we replied to each of your questions and comments in a separate rebuttal below, and we will of course integrate all applicable points in the next revision ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning | Accept (spotlight) | Summary: The main contributions
- Using Bayesian Optimization for hyperparameter search on LoRA’s for full-model fine-tuning
- Using EVHI to learn coefficients to fuse models from within a checkpoint
Other contributions
- Finding discrepancy between metric and loss
Strengths: - Paper is well-written and easy to f... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback. We have answered your questions and concerns in this response. Please let us know if you have any follow-up questions.
**[W1, Q3, Q4] Empirical observations of discrepancy between metric and loss landscape, and hyperparameter alignment are performed on th... | Summary: The paper presents a novel approach to model fusion through Bayesian Optimization for fine-tuning pre-trained language models on downstream tasks. The authors address the challenges associated with hyperparameter selection and the discrepancy between loss and metric landscapes during the fine-tuning process. T... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback. We have answered your questions and concerns in this response. Please let us know if you have any follow-up questions.
**[W1] There are no discussions on computational efficiency: More detailed complexity analysis or comparisons could help readers underst... | Summary: This paper proposes "Bayesian Optimization Model Fusion" (BOMF), which is a method to fuse model weights using Bayesian Optimization over a set of metrics for a given target task. The authors motivate the necessity of BOMF for model fusion in large language model fine-tuning by providing evidence that there ex... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback. We have answered your questions and concerns in this response. Please let us know if you have any follow-up questions.
**[W1, W2] It feels as though there are two distinct contributions that are not necessarily related. Because of this disconnect, one of ... | Summary: This paper introduces Bayesian Optimization Model Fusion (BOMF), a method for improving fine-tuning of pre-trained language models. BOMF addresses the challenge of selecting optimal models and hyperparameters by utilizing multi-objective Bayesian optimization to consider both loss and desired metrics during mo... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback. We have answered your questions and concerns in this response. Please let us know if you have any follow-up questions.
**[W1] The observed improvements from the proposed BOMF method are relatively small**
The improvement of BOMF is not negligible compare... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable and constructive comments.
The reviewers acknowledge that the paper is well-written, easy to follow, and recognize the originality of using Bayesian optimization for model fusion in BOMF (R-ut18, R-csBg, R-WU7w, R-JjKU). Reviewers also recognize the paper... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Listenable Maps for Zero-Shot Audio Classifiers | Accept (poster) | Summary: This paper describes an extension of the LMAC method for explaining decisions made by audio classifiers. The novelty in the present article is to extend from fixed-vocabulary settings to zero-shot / open text description settings. To accomplish this, the authors propose a training objective that aims to pres... | Rebuttal 1:
Rebuttal: We thank you for your comments. Our replies are below:
- Regarding your comment on the lack of ablation study regarding different terms of the loss function:
We have conducted an ablation study (both quantitative and qualitative) to showcase the relevance of the unimodal diversity loss intro... | Summary: The paper introduces a post-hoc interpretation method for zero-shot audio classifiers, named LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context). It addresses the challenge of interpreting predictions from zero-shot audio classifiers that define audio classes based on textual prompts, wher... | Rebuttal 1:
Rebuttal: We thank you for your constructive and positive comments. Our replies are below:
- Regarding your comment as to the discrepancy between the L-MAC-ZS Full and L-MAC-ZS (CT) in some cases:
This is potentially due to optimization, and choice of hyperparameters (note also that this is mainly obse... | Summary: In this paper, the authors focus on interpreting the decisions of zero-shot audio classifiers, particularly ones based on the Contrastive Language-Audio Pretraining (CLAP) model. To achieve this, the authors propose to learn a decoder that predicts a "listenable" audio saliency map (a mask on the input spectro... | Rebuttal 1:
Rebuttal: We thank you for your comments. Our replies are below:
- Regarding your comments on the organization of the paper, and the fact that the paper contains sections pertaining to L-MAC and CLAP:
Our aim was to make this submission as self-contained as possible, and to provide the reader with ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their comments. We provide a reply to each reviewer in the corresponding rebuttal. Here, we report the additional quantitative results obtained during the rebuttal period to address the reviewer's concerns. The reviewer replies refer to the table numbers listed below... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport | Reject | Summary: This paper proposes the affinity score, which measures the non-linearity of an activation function $\sigma(X)$ given the distribution of $X$.
The affinity score is defined based on how well the 2-Wasserstein distance $W_2(X, Y)$, where $Y=\sigma(X)$, is approximated by $W_2(N_X, N_Y)$, where $N_X$ and $N_Y$ ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for their comments.
> I would like to see how the distribution in Fig. 3(C) changes when other metrics such as R^2 are used instead of the proposed $\rho_{aff}$.
We thank the reviewer for this suggestion. We've added histograms for... | Summary: This study proposes empirical statistics about different DNN architectures in the hope to shed some light into why some architectures are better than others for some computer vision tasks. To do so, the study leverages common optimal transport results on DNN's internal representations, under some strong assump... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for their comments.
> I do not agree with the following statement `Without non-linear activation functions, most of DNNs, no matter how deep, reduce to a linear function unable to learn complex patterns.`
We believe that there is a... | Summary: This paper introduces a novel method for quantifying the non-linearity of activation functions in neural networks, termed the "non-linearity signature." Using an affinity score derived from optimal transport theory, it measures the non-linearity of individual activation functions. It defines the non-linearity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments about our work.
> The authors should discuss more activation functions
We thank the reviewer for this suggestion. We kindly note that the individual behavior of 9 different activation functions (Sigmoid, ReLU, GeLU, ReLU6, LeakyReLU, tanh, hardta... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. We are glad to know that they found our work **novel** (Reviewer 2p6h), **insightful** (Reviewer 8vYn), our experiments **solid** (Reviewer 2p6h), and the writing **clear** (Reviewer 2p6h). Below, we summarize the additions that we present in the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting | Accept (poster) | Summary: This paper proposes a novel self-speculative decoding framework Kangaroo with a double early exiting strategy for accelerating LLM inference. It addresses the challenge of inference latency and shows effectiveness through extensive experiments, achieving significant speedups and outperforming the competitors w... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and valuable suggestions. We answer each of these comments below.
### **More explanation of the extension to tree decoding for Kangaroo**
Thank you for your valuable suggestions. We will provide a more detailed formal expression and description of... | Summary: The authors introduce "Kangaroo" a novel self-speculative decoding framework designed to accelerate LLMs using a double early exiting strategy. This approach leverages the shallow sub-network and LM Head of the target LLM to construct a self-drafting model and employs a dynamic early exiting mechanism to enhan... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and valuable suggestions. We answer these comments below.
### **How did you determine the architecture and its suitability across different model architectures.**
The iterative process of designing the adapter network in Kangaroo is reflected in T... | Summary: Authors proposed a new method to speed up large language model inference called self-speculative decoding. Instead of training a separate, costly draft model to maintain token acceptance rates, Kangaroo uses a shallow sub-network of the large model itself as the draft model. A lightweight adapter module is tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and answer each of these comments below.
### **Evaluate more LLM families other than Vicuna**
To validate the robustness of Kangaroo across different architectures, we additionally trained adapter networks on Llama2-13B-Chat and Llama-3-8B-Instruct... | Summary: The paper presents Kangaroo, a novel self-speculative decoding framework designed to accelerate the inference of LLMs while maintaining an identical sampling distribution: a new speculative decoding method: self-speculative decoding method using the model's own subnetwork as the speculative small model. The dr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and answer each of these comments below.
### **The main difference between Kangaroo and Draft & Verify**
Both Draft & Verify and Kangaroo are self-speculative decoding algorithms that construct a small model by reusing the parameters of the origina... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and valuable feedback. We are encouraged by the positive reception of Kangaroo, as reflected in comments like ``The proposed method is well-motivated and the proposed token-level early exiting mechanism is interesting`` from Reviewer NQcw, `... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a new self-speculative decoding framework, Kangaroo, for accelerating large language models (LLMs) by leveraging a double early exiting strategy. Kangaroo is able to enhance inference efficiency without the need for a separate draft model. It utilizes the shallow sub-network and the LM Head... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and answer each of these comments below.
### **1. Insufficient explanation and comparison to existing early-exiting methods.**
We highlight the **fundamental differences** between Kangaroo and existing early-exiting methods in the following table:... | null | null | null | null | null | null |
D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning | Accept (poster) | Summary: This paper proposes a novel approach named Diffusion-based Representation with Random Distance matching (D2R2) for tabular few-shot learning. It leverages the powerful expression ability of diffusion models to extract essential semantic knowledge crucial for the denoising process. During the training process o... | Rebuttal 1:
Rebuttal: Thanks for your comments!
**Q1.**
We plan to add more explanations about shortcomings of existing methods as follows.
Firstly, tabular data comprises heterologous features, which underscores the importance of simultaneously modeling continuous and categorical features. The current SOTA method ... | Summary: The paper proposed a new diffusion-based representation learning method for tabular data, namely D2R2, specifically for few-shot learning. It is the first paper to use the diffusion model for tabular data representation learning. The method trains a conditional diffusion model with a combined loss of vanilla d... | Rebuttal 1:
Rebuttal: Thanks for your comments!
**Q1. What's the relationship and key novelty in the method compared to existing methods?**
Existing papers about representation learnings are all designed for image data. Paper [1] introduces a "latent Denoising Autoencoder" (LDAE) architecture where the learned represe... | Summary: The paper introduces a novel method, Diffusion-based Representation and Random Distance Matching (D2R2), to address the challenge of few-shot learning on tabular data. This approach leverages the robust representational capacity of diffusion models to extract essential semantic knowledge from tabular data, the... | Rebuttal 1:
Rebuttal: Thanks for your comments!
**Q1. The paper lacks a clear description of the input format for tabular data.**
In our study, "tabular data" refers to the dataset organized in tables, which is a structured format that presents information in rows and columns. It is defined as $D = \{\boldsymbol{x} _... | Summary: The paper proposed a few-shot learning framework for tabular data by designing a diffusion based semantic knowledge encoder and introducing a random distance matching mechanism to preserve distance information in the embeddings. During classification, an instance-wise iterative prototype scheme is utilized to ... | Rebuttal 1:
Rebuttal: Thanks for your comments!
**Q1. Does the proposed method scale with larger datasets or those with higher feature dimensions?**
In our experiments, we utilized seven datasets that are widely used in tabular data research, as referenced in popular papers [28], [2], and [54] in our paper. Among the... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Order-Independence Without Fine Tuning | Accept (poster) | Summary: State-of-the-art language models (LMs) are now used to perform tasks (such as, e.g., question answering) with no fine-tuning; these models are fed the question and target options in their context, and their next-token distribution is then used to select an answer from this set of options.
LM outputs, however, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reading our paper and providing some useful questions.
# Relevancy
Order dependency is an important open problem in the LLM NLP literature [1,2,3,4]. Known motivations include increasing model robustness and reducing ordering biases. To these we add appl... | Summary: The paper addresses the problem of order dependency in Large Language Models (LLMs), which causes inconsistency in outputs when the order of semantically identical sub-sequences is changed. The authors propose a technique that eliminates order dependency in transformer-based LLMs. The method is both theoretica... | Rebuttal 1:
Rebuttal: Thank you for the kind review and we are very glad to see other people are excited by the directions this work suggests. As the other reviewers posed many questions, we have some more results to show in the general rebuttal document.
# Question Responses
## Impact of Enumeration
We didn’t test ... | Summary: The paper aims to address an issue within large language models (LLMs): their sensitivity to the order of input sequences, known as order dependency. This problem causes LLMs to produce inconsistent outputs when the order of semantically identical inputs is changed.
### Key Contributions:
1. **Set-Based Prom... | Rebuttal 1:
Rebuttal: Thank you for taking the time to go over the paper and provide some useful feedback.
# Relevancy
Order dependency is an important open problem in the LLM NLP literature [1,2,3,4]. Known motivations include increasing model robustness [4] and reducing ordering biases [2]. To these we add applic... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and are glad that everyone appreciates that we have obtained the first rigorously proven solution to an open problem – achieving order-independence – in NLP. Our solution is conceptually intuitive, and while the proof requires careful attention,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Selective $G$-Bispectrum and its Inversion: Applications to $G$-Invariant Networks | Accept (poster) | Summary: The paper proposes a novel invariant layer for group-convolution networks (GCNNs) based on the "selective bi-spectrum".
With respect to the expensive bispectrum which has $O(|G|^2)$ coefficients, the selective bispectrum only contains a subset of $O(|G|)$ coefficients which still ensure the completeness of the... | Rebuttal 1:
Rebuttal: Dear Reviewer Wppf,
We thank you very much for your review and for your time. We address your comments and questions below.
### Experiments on larger datasets
We address this concern in our global response to all reviewers.
### Accuracy versus computational cost
We agree that showing accuracy... | Summary: The paper addresses the problem of achieving invariance to nuisance factors in signal processing and deep learning, particularly those describable by group actions (e.g., rotations, translations). The authors propose the selective G-Bispectrum, a computationally efficient variant of the G-Bispectrum, which red... | Rebuttal 1:
Rebuttal: Dear Reviewer ZVp5,
We thank you for your time and thoughtful review. We address your comments and suggestions below.
### Experiments on larger datasets
Thank you for raising this point. We comment on it in our global response to all reviewers.
### Showing accuracy versus computational cost
... | Summary: The work focuses on the problem of achieving invariance to extraneous nuisance variables in signal processing and deep learning by utilizing group actions such as rotations and translations.
The focus is on the G-Bispectrum, which is a tool used to extract signal characteristics that are invariant to such act... | Rebuttal 1:
Rebuttal: Dear Reviewer UtSC,
We thank you for your review and insightful comments and suggestions. We address your points below.
## Continuous Groups
We thank you for asking the very interesting question of how to generalize to continuous groups.
The formulas defining the full and selective G-Bispectra... | Summary: The paper proposes a new invariant layer for group convolutional neural networks (G-CNN). The proposed layer computes the spectral coefficients of the input data, that is based on higher-order spectral analysis: the bispectrum, which is the Fourier transform of the triple correlation (G-TC). While there can be... | Rebuttal 1:
Rebuttal: Dear reviewer P7yz, we wish to thank you again for your time. In what follows, we address the points you made in your review.
# Issues with ill-defined notions:
## The term "Selective G-Bispectrum"
You are right to mention that the "Selective G-bispectrum" admits degrees of freedom, that is, it... | Rebuttal 1:
Rebuttal: We thank the four reviewers for their time and attention. All reviewers found our manuscript very well-written and easy to follow. We find this very encouraging especially given its substantial theoretical components in group theory. The reviewers also appreciated the novelty and depth of our theo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SPEAR: Exact Gradient Inversion of Batches in Federated Learning | Accept (poster) | Summary: This paper presents a novel method to recover batched input data from gradients in fully-connected networks. Based on a detailed analysis of the rank of gradients, it utilizes matrix decomposition to first figure out the batch size and then recover the input tensor through sparsity. Good comparisons with previ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very positive review. We are particularly happy to read that the reviewer assesses our contribution to be of great value and finds the comparison to related work convincing. We will include all proposed writing suggestions. We now address all questions of the review... | Summary: This paper studied the gradient inversion problem in federated learning. In particular, an honest-but-curious server follows the federated training protocol but aims to infer private information from gradients collected from clients. The paper proposed a novel approach, SPEAR, that exploits the low-rank and sp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review. We are happy to read that they find our contribution insightful and inspiring future work and credit our significantly better performance and theoretical analysis. Further, we are glad the reviewer deems the paper nicely structured.
**Q1: Can the ... | Summary: This paper introduces a novel approach to input reconstruction for neural networks, focusing specifically on linear layers followed by ReLU activations.
The key aspects of this method are:
Low-rank decomposition: The authors leverage low-rank matrix decomposition techniques to simplify the representation of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and are glad to read they found our contribution really interesting, the proofs clean and precise and the reconstruction quality really good.
**Q1: Can SPEAR scale to batch sizes greater than 64?**
Yes, we address this in the general response (Q1... | Summary: The paper presents SPEAR, a novel algorithm for exact gradient inversion of batches in federated learning. Unlike previous methods, SPEAR achieves exact reconstruction for larger batches by leveraging low-rank structure and ReLU-induced sparsity in gradients. The authors provide a theoretical foundation and an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive review and are excited that the reviewer finds that SPEAR marks a significant advancement for gradient inversion. We are also happy to read that the reviewer attests that our method builds on a strong theoretical basis and credits our soundness, presenta... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and helpful feedback. We are encouraged they consider our work to mark a significant advancement (Brdw), provide valuable insight (LPF2, Xnvh), our theory to be well founded (Brdw, 38cL), our presentation to be good (Brdw, LPF2, Xnvh) and our reconstructio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contextual Linear Optimization with Bandit Feedback | Accept (poster) | Summary: This paper studies Contextual Linear Optimization (CLO) with bandit feedback and presents a class of algorithms termed Induced Empirical Risk Minimization (IERM). The authors also derive the regret upper bounds. The regret analysis accounts for the misspecification of the policy class and incorporates a margin... | Rebuttal 1:
Rebuttal: Thank you for your comments. We agree that providing more background information on the CLO problem would be helpful. Here, we clarify the unknown quantities and the interaction between the learner and the environment.
In classic CLO problems, the learner aims to solve the optimization problem $\... | Summary: This work addresses the partial bandit feedback setup for contextual linear optimization. The authors propose an algorithm based on Induced Empirical Risk Minimization (IERM), which incorporates doubly-robust estimation to handle the reward model misspecification. They provide a regret bound for the partial ba... | Rebuttal 1:
Rebuttal: Thanks for your comments. We now address each point individually.
**Comparison to partial monitoring.** It is interesting to compare our problem with partial monitoring. There are at least two major differences. One is that partial monitoring usually considers an online setting where the learner... | Summary: The paper aims at the following problem. The learner observes side information $x$ and needs to output $z$. The Nature will then generate $Y$ (which depends on $x$, but may be randomised) and the learner will suffer loss $Y'z$. The key feature of the setup is that $Y$ is not directly observable. The paper desc... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We now further clarify our problem and explain the data, estimates, random variables, and nuisances involved.
First, let's consider a linear programming problem $\min_{z\in \mathcal{Z}} y^\top z$ where $z$ is the decision variable, $\mathcal{Z}$ is the constraint set,... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your thoughtful comments and constructive suggestions. We are encouraged to hear that you find our theoretical results solid, our numerical experiments demonstrate the theoretical results well, and our approach useful and practical.
We appreciate your suggestions f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition | Accept (poster) | Summary: The paper considers a minimization problem, where the optimizing variable is composed of $d$ probability distributions. The notion of quasar convexity is generalized by allowing for different quasar-convexity parameters $\gamma_i, i=1,...,d$ for the $d$ distributions. Instead of using the gradient function for... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We will respond to each of your comments individually in the following.
## Algorithms Descriptions:
Thanks for your suggestion. We will use equations and sentences to introduce the algorithm. One randomly picks $t$ following probability means we output... | Summary: The authors study a typical optimization model where the optimization variable is composed of multiple probability distributions. For this optimization problem, they propose a new structural condition/landscape description named generalized quasar-convexity (GQC) beyond the realms of convexity.
Strengths: It ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We will respond to each of your comments individually in the following.
## Motivation:
In many non-convex optimization problems, the objective function often admits ''convexity-like'' properties, such as matrix completion, phase retrieval, and neural ... | Summary: This paper introduces a novel optimization model for addressing problems involving multiple probability distributions, a common scenario in fields such as policy optimization and reinforcement learning. The authors present a new structural condition called Generalized Quasar-Convexity (GQC), which extends the ... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions. We will respond to each of your comments individually in the following.
## Discuss the applicability of these assumptions in broader contexts:
Thanks for your suggestion. We will add more discussions on the applicability of the assumption and our framewo... | Summary: The paper studies the optimization of generalized quasar convex (GQC) functions, a new global
structure introduced in this paper. This definition relaxes the quasar convexity (QC)
condition, in a way that different components of the optimization variable satisfy
the QC like condition with different parameter... | Rebuttal 1:
Rebuttal: We thank you for dedicating your time and effort to reviewing our manuscript. We appreciate that you acknowledge our paper. We will also carefully revise the paper to enhance its readability. | Rebuttal 1:
Rebuttal: ## Experiments:
We add a simple simulation to validate our proposed algorithm. We do experiments on learning one single neuron network over
a simplex, which has been discussed in Example 3.4 in Section 3.2. The objective function is written as
\begin{align}
f(p,P)=\frac{1}{2}E_{x,y}(\sum_{i=1}^m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PaCE: Parsimonious Concept Engineering for Large Language Models | Accept (poster) | Summary: This paper introduces Parsimonious Concept Engineering (PaCE), a novel framework for aligning LLMs by modifying their activation space. This framework constructs a large-scale concept dictionary in the activation space and partitions concepts into benign or undesirable categories. During inference, PaCE decomp... | Rebuttal 1:
Rebuttal: Dear Reviewer rWAy,
Thank you for your insightful reviews and kind support for acceptance. It is our pleasure to reply to your comments.
**Time efficiency**
Thank you for discussing PaCE's time efficiency. We would like to emphasize that the main contributions of the paper are (1) a new data... | Summary: This paper introduces an activation engineering framework for LLM alignment. PaCE addresses the challenge of reducing undesirable outputs while retaining benign concepts through a two-stage process: Concept Construction and Partition, and Activation Decomposition and Intervention. It constructs a large-scale c... | Rebuttal 1:
Rebuttal: Thank you for your time on the paper. We are happy to address your comments below.
**Quality of the concept dictionary**
Excellent question! The efforts are made in two-fold: **the concept words** (indexes) and **the context stimuli** (contents) that define the semantics of each word.
* Fo... | Summary: This work proposes an activation engineering framework for alignment, i.e., PaCE, consisting of a task-driven concept dictionary and linear decomposition algorithm. Thus, the toxic or harmful input can be decomposed into a new activation vector combination, that removes the harmful activation. This is a genera... | Rebuttal 1:
Rebuttal: ### Response to Reviewer s54k (1/2)
Dear Reviewer s54k,
Thank you for the thoughtful feedback and constructive questions. We address the concerns and provide additional evaluations to validate our work. We add the suggested improvements to enhance the quality of our work.
**Preparatory Ph... | Summary: This paper presents a framework for aligning LLMs by using sparse coding techniques on a comprehensive concept dictionary. PaCE effectively controls and modifies neural activations of LLMs to achieve alignment goals such as response detoxification, faithfulness enhancement, and sentiment revising. The proposed... | Rebuttal 1:
Rebuttal: Dear Reviewer Cdr5,
We greatly appreciate your constructive feedback and kind acknowledgment of the novelty and advantages of our approach. It is our pleasure to address your comments and provide clarification below.
**Computational Efficiency**
Thank you for acknowledging the task (e.g., det... | Rebuttal 1:
Rebuttal: ### Global Response
We thank all reviewers for their valuable time and intellectual input on the paper. We take the opportunity to provide a global summary here and communicate with the reviewers in the individual rebuttals.
**Presentation Quality**
As per reviewers’ comments, the paper ha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Thought of Search: Planning with Language Models Through The Lens of Efficiency | Accept (poster) | Summary: The paper analyst the use of LLM in planning, and propose to write the successor function and goal test in code instead of directly solving the problem. The paper showed experiments that using the code can get a higher accuracy and lower number of calls to the LLM compared to LLM-based solutions.
Strengths: T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We hope that our clarification regarding the statistics on when feedback was needed alleviate the reviewer’s concerns and can allow to raise their rating.
## Answer to the Question:
We have provided the average number of interactions with the model (req... | Summary: The authors propose a position paper that argues current works for LLMs for planning waste significant compute, on top of having poor algorithmic and empirical performance. The authors also propose ideas on how to use LLMs more efficiently and effectively by using them to preprocess search algorithms instead o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and support. We hope that the reviewer could advocate for the paper.
Your understanding regarding the %States is correct, we will clarify the text in the final version.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for their response and clarifi... | Summary: Existing LLM-based planning approaches usually involve searching and multiple passes of the model, which leads to significant inefficiency and cost while failing to guarantee the correctness of the generated plans. Motivated by this issue, this paper first analyzes the soundness, completeness, and complexity o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and hope that we can somewhat alleviate their concerns and get their strong support for this work.
Existing literature on code generation e.g., [1,2], as well as the literature on generalized planning with LLMs e.g., [3] shows evidence that automated feed... | Summary: The authors propose a Thought of Search: thinking before searching strategy to solve Automated Planning problems using LLMs. They use the GPT-4 LLM to generate a Python code for generating successor states and goal test functions which are the crucial parts of any search. They argue that this method is sound a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
## Answers to the Questions:
1. Our purpose was to show the feasibility of the approach rather than comparing which models are better at generating successor functions / goal test for the four search problems. For that purpose one language model is su... | Rebuttal 1:
Rebuttal: The main objective of our work is to fill the gap in the current literature on planning with LLMs with regard to the computational complexity and properties of the proposed algorithms. Our main contribution is precisely this investigation. We show that the current literature proposes inefficient m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient and Private Marginal Reconstruction with Local Non-Negativity | Accept (poster) | Summary: This paper introduces residuals-to-marginals (ReM), a novel approach to constructing marginals from input data residuals while maintaining differential privacy. ReM operates by first estimating true residuals from noisy ones, then transforming these estimates into target marginals. The authors propose two vari... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback on our paper and suggesting improvements to clarify the presentation. Below, we address various concerns raised in the review.
Since ReM is a reconstruction method, the appropriate comparison is not with full end-to-end synthetic data or query answerin... | Summary: The paper studies the problem of reconstructing a data distribution from noisy measurements of a set of carefully selected marginal queries and using the reconstructed data distribution to answer a set of workload queries. The difficulty in the problem lies in the high dimensionality of the data distribution, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback on our paper and suggesting improvements to clarify the presentation. Below, we address various concerns raised in the review.
Regarding time complexity, please see the global response for specific running time bounds, which we will include in the revi... | Summary: Matrix mechanisms are a foundational class of methods in differential privacy for efficiently answering large sets of marginal queries on tabular data. However, matrix mechanisms infamously suffer in high-dimensional data domains, where memory and compute explode. Significant efforts have been made to overcome... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback on our paper and suggesting improvements to clarify the presentation.
In the revision, we will do our best to improve readability of the paper and better motivate residual queries in Section 2.1.
Regarding why there might be multiple measurements of... | Summary: The paper introduces ReM method for efficiently reconstructing answers to marginal queries with differential privacy. This aim is to minimize the error and allow scalability to high-dimensional datasets. As an extension, this paper also proposes ReM-LNN which ensures that the reconstructed marginals are non-ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing feedback on our paper and suggesting improvements to clarify the presentation. Below, we address various concerns raised in the review.
Private query answering is valuable in its own right outside of its application for synthetic data generation. Much work in d... | Rebuttal 1:
Rebuttal: Below, we address two concerns raised in the reviews: time complexity and natural cases where multiple residuals are measured.
Regarding time complexity, let $\gamma, \tau$ be tuples of attributes and suppose each attribute has domain size $m$. Then an answer to marginal $M_\gamma$ has size $m^{|... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis | Accept (poster) | Summary: The paper studies cognitive diagnosis based on graph learning. The proposed method utilizes multiple graphs to assist the representation learning. Specifically, the authors further disentangle another two graphs from the most comprehensive student-exercise-concept interaction graph by removing certain node and... | Rebuttal 1:
Rebuttal: ## Response to Q.1
The related work on disentangling graph representation learning is as follows, and we will add this to the revised paper:
1. Learning potential representations of disentangling in complex graph networks to achieve model robustness and interpretability has been a hot topic in r... | Summary: In this paper, the authors introduce a meta multigraph assisted disentangled graph cognitive diagnosis model. Its main contribution is to propose a disentangled graph framework, which disentangles the student-exercise-concept dependency graph into an exercise-concept interaction graph and a concept-concept rel... | Rebuttal 1:
Rebuttal: ## Response to Q.1
As you suggested, we added KaNCD [R1] to be compared with the proposed DisenGCD, and the comparison results are summarized in **Table I:Upper** in ***global.pdf***, where only the Math dataset is used because there is no enough time to validate on other two larger datasets.
... | Summary: This paper introduces DisenGCD, a new framework for cognitive diagnosis in educational contexts. The authors make several contributions:
1) They propose a disentangled graph learning approach, separating the typically unified graph into three distinct graphs: student-exercise-concept interactions, exercise-con... | Rebuttal 1:
Rebuttal: ## Response to Q.1
We must admit that the design of the proposed DisenGCD did not consider the scenery of dynamic changes in knowledge structures. However, the proposed DisenGCD and RCD are trained in an inductive manner in graph learning. Therefore, it is feasible for DisenGCD and RCD to handle t... | Summary: The paper presents DisenGCD, a novel cognitive diagnosis model designed to enhance the robustness and accuracy of student, exercise, and concept representations by leveraging a meta multigraph-assisted disentangled graph learning framework. DisenGCD constructs and disentangles three specific graphs for interac... | Rebuttal 1:
Rebuttal: ## Response to Weak.1 & Q.1
We admit that the proposed DisenGCD seems complex and needs more resources for its implementation because more graphs need to be handled. In future work, we would like to design a more efficient paradigm to learn robust representation in a unified graph.
As for **Qu... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback provided by all the reviewers. We have carefully addressed their questions and concerns in our response, aiming to provide satisfactory answers.
Here we uploaded a file named "global.pdf" to show some necessary results and comparisons, which contains ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Role of Attention Masks and LayerNorm in Transformers | Accept (poster) | Summary: - This paper investigates the role of attention masks and layer normalization (LayerNorm) in mitigating the rank collapse issue in transformer models, and gets following conclusions:
- A long as there is a token which all other tokens in the sequence can directly or indirectly attend to over a fixed number of... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and providing constructive feedback. In line with the reviewer’s suggestion, we have rerun all the numerical experiments, validating our theoretical findings on 3000 examples. The results are provided in the rebuttal pdf file. In what f... | Summary: This paper theoretically studies the role of attention masks and layer norm in the convergence to the rank collapse degeneracy in Transformers, which are two architectural components that have previously been overlooked in studying the rank collapse phenomenon. The authors first define the problem through the ... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive assessment and insightful comments, which have helped strengthen our work. Below, we provide individual responses the comments you raised.
**Weaknesses**
**W1** We agree with the reviewer that one should be aware of the effect of skip connections when analyzin... | Summary: In this work, the authors investigate the issue of rank collapse in Transformers, providing insights into how attention masks and layer normalization can mitigate this problem. The paper includes extensive analysis, addressing two important questions and offering valuable contributions to the field.
Strengths... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and positive assessment of our work. After carefully reviewing your feedback, below we provide answers to the comments you raised.
> Q1: It seems obvious that causal masking and local attention would help mitigate the issue of rank collapse in Transformer/A... | null | null | Rebuttal 1:
Rebuttal: ## Response to all reviewers
We would like to thank the reviewers for carefully reading our paper and giving insightful comments and constructive feedback.
We are glad that our work was recognized as “interesting”, “rigorous” (Reviewer ivrv) and “offering valuable contributions to the field” (R... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction | Accept (poster) | Summary: This paper is motivated by the observation that BC is well-known to be vulnerable to the covariate shift resulting from the mismatch between the state distributions induced by the learned policy and the data collection policy. To solve this problem, the authors formulate a robust BC training objective and empl... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback.
**1. Addressing Concerns on Marginal Performance Improvements Compared to Baselines**
In response, we have updated our main experiments to include the DRO baseline DR-BC [18] and variation of $f$-divergence for our approach accordingly: inspired by the cho... | Summary: This paper studies imitation learning when the offline dataset does not come from the stationary expert distribution. To address this problem, the authors introduce the objective of distributionally robust optimization into behavioral cloning. To avoid overly pessimistic solutions, the authors further incorpor... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
**1. Experimental Design and Real-World Applicability Concerns**
Our experimental setup, though based on simulated data, is intended to rigorously assess the adaptability and robustness of our approach under realistic conditions. This strategy also ensures r... | Summary: This paper devices distribution correction ratio estimation (DICE)-based optimization to mitigate the covariate shift issue in the Behavior cloning algorithm. They test their heuristic loss on Mujoco benchmarks.
Strengths: The design of drildice in Section 3.2 and its evaluation on Mujoco benchmark are the st... | Rebuttal 1:
Rebuttal: First and foremost, we sincerely appreciate your insightful feedback.
**1. Justification on Alternative Optimization**
To clarify, our initial statement suggested that alternating optimization provides a practical approach to approximating the solution of the problem as outlined in Equation (7)... | null | null | Rebuttal 1:
Rebuttal: # General Response
We are grateful for the insightful and detailed feedback provided by all reviewers. Below, we summarize our response to the main concerns raised. Should any points require further clarification or detailed discussion, we are fully prepared to engage in discussions during the au... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unitary Convolutions for Learning on Graphs and Groups | Accept (spotlight) | Summary: The paper introduces two unitary graph convolution operators (UniConv and Lie UniConv), and studies their performance and ability to avoid over-smoothing even in deep Graph Neural Networks. UniConv (short for separable unitary convolution) takes the form f_{Uconv} = exp(iAt)XU (with A the adjacency matrix, i t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for their insightful questions. We have conducted additional experiments to answer the reviewers' main questions and will also amend the manuscript accordingly. We respond to individual points below.
___
> While I generally appreciated the pape... | Summary: The paper proposes to use so-called "unitary group convolutions" in graph neural networks, with main motivation to do steps to overcome gradient collapse or explosion and oversmoothing effects in graph neural networks under equivariance constraints. This fits within a more general line of work in which unitary... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions. As an overall point, the reviewer made many comments about normalized convolution which we were unsure of the specific message. We kindly ask the reviewer to clarify.
___
> The originality of this approach is a bit limited, since normalizing ... | Summary: In this paper the authors propose a mathematically consistent treatment of unitary convolutions in the context of neural networks defined on graphs and groups. In the graph neural network context, the main idea is to transform a standard linear convolutional layer $f_\text{conv} = \mathbf{AXW}$ with $\mathbf{A... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and going through the paper in detail. There are many insightful questions and points of feedback that we will incorporate into the draft.
___
> There aren't many experiments on Lie UniConv... it would have been interesting to see comparison betw... | Summary: This paper introduces convolutional layers for GNNs that are based on group-convolutions. Some of the proposed layers have interesting theoretical properties such as avoiding oversmoothing or vanishing gradients. Experimentally, these layers are able to aggregate information across long distances and achieve s... | Rebuttal 1:
Rebuttal: We thank the reviewer for praising the novelty and theoretical results in our work. We have read their concerns about the format and writing of the paper, and hope to address them below.
___
> In Section 3.1 (Lie) UniConv layers are introduced... it was not clear that this is the central definiti... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments, recommendations, and feedback.
There were some common themes in the reviews which we want to address in an overall response here. We have also included a one page pdf containing plots and tables which help address reviewers' conce... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposed a new convolution operator UniConv on graphs and general tasks. Both graph convolution and general form convolution form are provided based on the group theory. With the toy experiment, it shows the advantages of the proposed method clearly. Based on both theoretical and experimental analys... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and feedback. We respond to their questions and comments below.
___
> Though the general form of convolution is provided. It is not evaluated in experiment. I will suggest putting these content in the appendix in this case. Otherwise, it is... | null | null | null | null | null | null |
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity | Accept (poster) | Summary: This paper aims to address the fact that the current parameter efficient fine-tuning methods cannot simultaneously achieve high-quality, efficient training or scalable LLM services. Therefore, a series of structured sparse fine-tuning methods for LLM are proposed, which simultaneously achieve state-of-the-art ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We will carefully revise our paper based on your comments. Our responses to your questions are detailed below. We would greatly appreciate your input on whether our revisions address your concerns.
**Q1**: The symbols in the proof process of t... | Summary: The paper introduces a structured pruning method for LLMs. The main idea is to permute the rows and columns of the weight matrices and select a submatrix during the fine-tuning process. The authors show that the proposed technique outperform previous techniques in terms of accuracy and efficiency.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. We respond to your questions below and would appreciate it if you could let us know if our response addresses your concerns.
**Q1**: Novelty in the pruning technique itself.
**A1**: Thank you for your comment. We acknowledge that structur... | Summary: Current PEFT methods for LLMs fail to achieve high quality, efficient training, and scalable serving simultaneously. To overcome this, the authors developed Structured Sparse Fine-Tuning (S²FT), which excels in all three areas. S²FT improves generalization by selecting a few heads in the Multi-Head Attention (... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for your valuable feedback. Below, we address your concerns point by point and we will revise our paper according to your suggestions. We would appreciate it if you could let us know whether your concerns are addressed by our response.
**Q1**: Empirical Resul... | Summary: This paper introduces a new family of methods called Structured Sparse Fine-Tuning (S$^2$FT) for large language models (LLMs). S$^2$FT aims to achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability simultaneously. The method selects a few heads in the multi-head attent... | Rebuttal 1:
Rebuttal: Thank you so much for the insightful and valuable comments! They are very helpful for further improving the clarity and quality of our paper. We'll revise our manuscript in the camera-ready version to address all of your concerns.
**Q1**: How can we verify the model adaptation ability in real-wor... | Rebuttal 1:
Rebuttal: We thank reviewers [R1(CFWk), R2(8C1L), R3(d9g6), R4(Cfys)] for their thoughtful and highly supportive feedback! We were glad that the reviewers found the problem significant and interesting [R2], the observations and theoretical analysis insightful and highly valuable [R3, R4], the methods novel,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lucy: Think and Reason to Solve Text-to-SQL | Reject | Summary: This paper addresses the challenge of developing effective LLM-based assistants for querying SQL databases. In this context, users pose questions to a relational database in natural language, and the goal is to generate a SQL query that correctly answers the user's question when executed. The authors focus on ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions!
We will clarify the following points in the paper:
> Efficiency solving an NP-complete problem (CSP)
Solving CSP takes less than a second in all experiments we tried. As the reviewer pointed out, solving CSP is an NP-complete problem. However, moder... | Summary: In this paper the authors introduce LUCY, a new LLM based framework for converting text-to-SQL to query databases. Primarily this framework focusses on addressing user queries to databases that contain a large number of tables with complex relations between them.
The core idea of this approach is to decompose ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions!
> For the new benchmark introduced based on the Cloud Resources dataset, it is shown that LUCY significantly outperforms the state-of-the-art. What are the queries used in this benchmark? What sets them apart from the standard benchmarks where the exec... | Summary: The author proposes a new method, Lucy, designed to handle large databases with complex relationships between objects. Lucy operates through three steps: MatchTables, GenerateView, and QueryView. It first identifies relevant tables and attributes using LLMs, constructs a combined view with an automated reasone... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions!
> Zero-shot vs multi-shot
In large industrial databases with complex relationships, using multi-shot will not improve accuracy with respect to database constraints. There are two reasons for that:
* First, the structure of the database i... | Summary: The paper introduces Lucy, a framework for solving Text2SQL by LLMs, particularly for complex enterprise databases. Lucy leverages LLMs' understanding and reasoning capabilities to handle intricate database relationships and constraints. The framework operates in three phases: identifying relevant tables and a... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions!
> What is the key difference between the proposed framework and competitors (e.g., MCS-SQL, MAC-SQL, Chat2Query)?
The key difference is that all text-to-SQL methods rely on LLMs to reason about database constraints. These constraints are hard constra... | Rebuttal 1:
Rebuttal: We thank you for your comments for their comments! We would like to clarify the following important points:
[Main contribution] Our primary contribution is the **elimination of the weakest point in LLM-based text-to-SQL methods: reasoning about database constraints**. We propose utilizing powerfu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quadratic Quantum Variational Monte Carlo | Accept (poster) | Summary: This work introduces a new gradient formulation for Variational Quantum Monte Carlo based on the discretized imaginary time evolution of the Schrödinger equation. In their empirical evaluation, their Q²VMC algorithm consistently converges faster and to lower energies than the traditional VMC objective at no ad... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. We appreciate the reviewer’s detailed feedback and suggestions for improving our paper. We acknowledge the challenge to balance the introduction of quantum background concepts for machine learning audiences, and the strict page limit. For the camera-ready version, we have provided... | Summary: This paper proposed a new QMC method that utilizes the imaginary time evolution of the Schrodinger equation. Unlike Diffusion Monte Carlo (DMC) which uses Langevin dynamics to simulate the dynamic of the imaginary time evolution, this paper suggests a way to perform the update in discrete steps and then projec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions.
## Weaknesses:
1. We appreciate the reviewer's insight regarding the relation of our work with the widely known Diffusion Monte Carlo (DMC) method. In summary, our method is closely related to DMC in terms of using the Imaginary ... | Summary: In this paper, the authors propose the Quadratic Quantum Variational Monte Carlo (Q$^2$VMC) algorithm to enhance the optimization process of Quantum Variational Monte Carlo (QVMC). Unlike the standard QVMC, Q$^2$VMC employs an improved projection method to guide the update direction of the ansatz. The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions.
## Weaknesses
1. Regarding the improvement in speed and convergence provided by our method, our primary goal was to highlight that researchers can easily integrate the Q^2VMC method into their implementations without hyperparamet... | Summary: This paper centers on quantum chemistry, specifically targeting the ground state of molecular systems. Unlike previous methods that apply approximate natural gradient techniques to the wavefunction, Q2VMC executes natural gradient optimization on the distribution. Experimental results demonstrate that Q2VMC si... | Rebuttal 1:
Rebuttal: ## Weaknesses and Questions
1. We thank the reviewer for pointing out the typographical error. The typo "wavefunctios" has been corrected to "wavefunctions," and the entire paper has been carefully proofread to address any other typos.
2. Regarding the integration of our proposed methods with Wa... | Rebuttal 1:
Rebuttal: ## Global Rebuttal
We thank the reviewers for their thorough and insightful comments. We have carefully considered all feedback and made substantial revisions to address the concerns raised. This global rebuttal is intended to introduce the additional data provided in the accompanying one-page PD... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices | Accept (poster) | Summary: • This paper proposes ECP-ViT, which optimizes the ViT model by introducing the core-periphery (CP) principle.
• The Core-Periphery Principle Guided self-attention mechanisms successfully reduce memory bandwidth by eliminating data transformation.
• By applying pruning and removing data transformation, the opt... | Rebuttal 1:
Rebuttal: ### Q1 (Table 1, Table 9) Are there experimental results on latency for layout transformation and computation in other frameworks such as TFLite and Pytorch-Mobile?
**Response:**
| Model | Implicit Transformation (ms) | Explicit Transformation (ms) | Computation (ms) | Latency ... | Summary: This paper introduces ECP-ViT, a framework that accelerates Vision Transformers on mobile devices using a core-peripheral guided self-attention mechanism. This approach reduces computational demands and achieves up to 16.1x speedup on a OnePlus 11 GPU, enabling efficient real-time deployment of ViT models whil... | Rebuttal 1:
Rebuttal: ### Q1 More comparison between other lightweight ViT methods.
[1] Elasticvit: Conflict-aware supernet training for deploying fast vision transformer on diverse mobile devices, ICCV 2023
[2] Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet ... | Summary: This paper presents ECP-ViT, a real-time framework for deploying Vision Transformers (ViTs) on mobile devices. Inspired by the brain's core-periphery principle, this method guides self-attention in ViTs to reduce computational demands and eliminate data transformation operations. ECP-ViT integrates algorithm-s... | Rebuttal 1:
Rebuttal: ### Q1 Confusions in line 67 of the introduction.
**Response:**
Thank you for the suggestion. We will revise the introduction to provide a brief context about pruning before mentioning our support for it. Specifically, we will add a paragraph to explain the concept and relevance of pruning in th... | Summary: This paper introduces ECP-ViT, a framework designed to improve the performance of vision transformers (ViTs) on mobile devices. The authors observed the intensive and irregular memory access involved in the data transformation for self-attention layers, which significantly slows down transformers compared to t... | Rebuttal 1:
Rebuttal: ### Q1 Need to provide a detailed comparison of memory behavior before and after applying the ECP technique.
**Response:**
Thank you for your question regarding the memory access pattern differences before and after applying ECP-ViT. Our pruning technique ensures that computations remain dense,... | Rebuttal 1:
Rebuttal: ### Q1 (Figure 4) How are core nodes determined?
**Response:**
In our ECP-ViT, we consider the image patches as nodes and predefine a series of core ratios to divide nodes into cores and peripheries. During training, we employ Grad-CAM to identify important regions of the images and assign the c... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a ViT accelerating framework, ECP-ViT, to deploy ViT models on smartphones. This framework consists of two parts: 1) Core-Periphery Guided Self-Attention (reducing the computational and bandwidth cost of ViT) and 2) Data Layout Selection based on compiler optimizations (removing the time-con... | Rebuttal 1:
Rebuttal: ### Q1 Explain dimension reduction heuristic
**Response:**
Thanks for pointing this out. Below is the detailed pseudo code for the heuristic. The score is collected by running mini benchmarks, and the different layouts are defined by the frameworks. The process begins by identifying key nodes in... | null | null | null | null | null | null |
John Ellipsoids via Lazy Updates | Accept (poster) | Summary: The paper proposes provably faster algorithm for computing John ellipsoids. The first idea is to approximate Leverage score with an existing faster algorithm given by Theorem 1.3. The second idea is to use fast matrix multiplication and lazy updates to reduce polylog suboptimality.
Strengths: - Provably faste... | Rebuttal 1:
Rebuttal: > despite that the main result does not rely on Theorem 1.3, there is no proof or reference for it.
> Could you please refer to the proof of Theorem 1.3?
The proof of Theorem 1.3 is sketched in the paragraphs immediately below it.
We first summarize the result of [DMMW12]. It is known that we ... | Summary: The authors give a leverage score approximation algorithm that runs in nearly linear time for dense matrices. Specifically, to find leverage scores for a matrix $A \in \mathbb{R}^{n \times d}$, they give an algorithm based on fast matrix multiplication that runs in time $\widetilde{O}(nd)$. This, combined with... | Rebuttal 1:
Rebuttal: > I wish more applications had been discussed. ...
We note that $(1+\epsilon)$-approximate John ellipsoids have many applications to statistics, machine learning, and computational geometry, as is discussed in our introduction as well as in the works of [CCLY19, SYYZ22]. Some notable applications... | Summary: This paper considers the computing of an approximate John ellipsoid. They improve the algorithm by lazy update and fast matrix multiplication. They also give low-space streaming algorithms using similar ideas.
Strengths: This paper improves John ellipsoid algorithm via lazy update and fast matrix multiplicati... | Rebuttal 1:
Rebuttal: > The authors do not give justifications in the checklist.
We believe we have included a justification for the paper checklist whenever the list item warrants additional justifications. In particular, we give an in-depth discussion of the limitations of our work in Section 3. We are happy to give... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Replicable Uniformity Testing | Accept (poster) | Summary: In this paper the authors study the problem of replicable uniformity testing. The non-replicable version of the problem can be stated as follows: given some $\varepsilon > 0$ and sample access to some distribution $p$ on $[n]$ what is the minimum number of samples $m$ to distinguish whether $p$ is the uniform ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments! In the proof sketch we have focused on the sub-linear regime ($m \leq n$). The dependence $1/(\varepsilon^{2} \rho^{2})$ is incurred to handle the super-learning regime ($m \geq n/\varepsilon^2$). We redirect the reviewer to the proof of Lemma 3... | Summary: This work studies uniformity testing in the context of replicable algorithms. Knon uniformity testing algorithms are non-replicable in the following sense: a) If the unknown distribution equals the uniform, then they output 1 whp, b) if they are epsilon-away from uniform, output 0 whp c) when the distance is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments. Below we provide some more succinct explanation of this surprising linear dependency on $\rho$. First we note that this sample complexity is perhaps not so surprising if one focuses on designing testers for the specific hard instance we construct... | Summary: The concept of "reproducible" learning was introduced in STOC 22 paper and the concept is a very relevant to modern day research.
They also showed how algorithms based on statistical estimations can be easily converted to reproducible algorithm with a little overhead.
In this paper the authors have tried to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. While we have reviewed the extensive line of work on uniformity testing to the best of our abilities, we admit that our survey might not be complete. Thus, we would greatly appreciate any pointers to missing citations.
Indeed, there is an extensive litera... | Summary: This paper studies uniformity testing under the replicability constraint. Given samples from an unknown distribution P, we need to decide whether P is uniform or eps far from uniform with high probability. Additionally, the algorithm needs to report the same answer in two different random input samples with hi... | Rebuttal 1:
Rebuttal: While our lower bound holds only against symmetric algorithms, we remark that all known uniformity testers in prior works are indeed symmetric. Moreover, in our opinion, symmetric algorithms are natural for the problem of uniformity testing as the property itself is invariant under domain relabeli... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RMLR: Extending Multinomial Logistic Regression into General Geometries | Accept (poster) | Summary: - Instead of adopting complex approaches for extending MLR to Riemannian manifolds via general geometry extensions such as gyro structures and generalized SINE rules, this study generalizes to Riemannian manifolds using a simple approach based on the logarithm map.
- The authors show the several experimental r... | Rebuttal 1:
Rebuttal: We thank Reviewer $\textcolor{brown}{jvDJ}$ for the constructive suggestions and insightful comments! In the following, we respond to the concerns in detail. 😄
***
**1. Parallel transport is not the only option for determining $\tilde{A} _k$ in the RMLR (Eq. (11)).**
- **The ways to determine $... | Summary: The authors extend multinomial logistic regression into spaces where they only require a logarithmic map. They do so in order to accomplish tasks such as classification
Strengths: The paper is well organized and written. There is a good balance of theoretical results and practical applications. It is nice to ... | Rebuttal 1:
Rebuttal: We thank Reviewer $\textcolor{green}{5QCn}$ for the careful review and the suggestive comments. Below, we address the comments in detail. 😄
***
**1. Linearization by a single fixed tangent space or coordinate system fails to capture global geometry; Our RMLR adopts *distinct dynamic* tangent spa... | Summary: This paper extends the multiclass logistic regression into general Riemannian spaces, contributing to the field of Riemannian deep learning. Starting from the concept of Riemannian hyperplanes, the present work constructs the distance from Riemannian points to Riemannian hyperplanes and derives Riemannian Mult... | Rebuttal 1:
Rebuttal: We thank Reviewer $\textcolor{blue}{dBzm}$ for encouraging feedback and valuable comments. Below, we address the comments in detail. 😄
***
**1. Details on the parameters learning of the MLR.**
Due to page limits, the optimization details are discussed in Apps. G.1.3 and G.2.3. Generally, our RM... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models | Accept (poster) | Summary: This paper presents TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model designed to enhance the efficiency and speed of drug discovery. It addresses the slow processing speeds of 3D-SBDD generative models by offering up to 30 times faster inference speed while maintaining or improving on key... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for the great insights for authors to consider. As stated in your limitations, we acknowledged that comparison with de-novo generative molecules were important. However, since direct comparison would be unfair, we had to build repurposed versions of de-novo models whic... | Summary: This paper proposed a pocket-conditioned 3D molecular scaffold hopping model based on the well-established consistency models. The framework is superior in terms of inference speed. Besides, the authors also proposed a corresponding RL method to fine-tune the model towards generating molecules with desirable p... | Rebuttal 1:
Rebuttal: We first thank the reviewer for the constructive feedbacks regarding additional baselines/metrics for the authors to consider.
Please refer to global rebuttal as well as PDF attached!
**Q1. Other scaffold-hopping baselines (Table 3,4)**
**A1**.
Thank you for adding valuable information regardin... | Summary: Given a protein pocket and a reference ligand, the authors suggest a method to generate different scaffolds to be able to eventually come up with new ligands with similar or even improved properties. Precisely, the authors learn a consistency function which maps noise to a scaffold (created as a 3D-conformatio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive feedback on our model. Thank you for highlighting the typos and mathematical notations to fix; we will ensure they are corrected in the final draft.
**Q1. Concerns regarding reward hacking (Table 2)**
**A1.**
As the reviewer mentioned, authors ag... | null | null | Rebuttal 1:
Rebuttal: We appreciate all the valuable feedbacks from the reviewers. Here we answer a question asked in common about comparing our model with de-novo molecule generative diffusion models.
**Q. Comparison with other SBDD diffusion models (Table 3)**
**A:** Although there exists a plethora of de-novo 3D-S... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization | Accept (poster) | Summary: This paper proposes the Quadruple Multimodal Contrastive Learning Framework (QUEST) to capture shared and unique task-relevant information in the process of contrastive learning, enabling models to capture more unique information in downstream tasks to achieve better performance. Specifically, this paper intro... | Rebuttal 1:
Rebuttal: Dear Reviewer zJ9C,
We sincerely thank you for taking the time to review our paper. Our responses to your comments are provided here:
---
**W1: "In the shared Decoder of Equation 1."**
**A1:** Thank you for your valuable review comments. There is indeed a typo in Equation 1. For shared informa... | Summary: The paper focuses on developing a new multi-modal representation learning approach where the extraction and integration of both shared and unique information across multimodal data is the focus. The method aims to pull shared representations closer while aligning the unique representations with the shared repr... | Rebuttal 1:
Rebuttal: Dear Reviewer 9n5E,
We sincerely thank you for taking the time to review our paper. Our responses to your comments are provided here:
---
**W1 “Theoretical justification that the encoder information can be disentangled to shared and unique representations”**:
**A1:** To the best of our knowled... | Summary: A new Multimodal Contrastive Learning method named QUEST is proposed to deal with the fine-grained alignment problem between different modal. Both quaternion contrastive objectives and orthogonal constraints are proposed to extract sufficient unique information. The quaternion vector spaces are designed to sim... | Rebuttal 1:
Rebuttal: Dear Reviewer KzFg,
We sincerely appreciate your thorough review and insightful comments. Please find our responses below.
------
**W1-3: ”Presentations/Grammar/Typos”**
**A1:** We apologize for the some typos in this paper and we have fixed it. We have re-examined Equation 5 and refined them... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers, we would like to sincerely thank all the reviewers for taking the time to read our paper and provide valuable feedback. We are delighted that reviewer KzFg (zJ9C) acknowledged the superior performance, innovation (9n5E, zJ9C), and reasonable motivation (9n5E, zJ9C) of our approach.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Conditional Distribution Calibration in Survival Prediction | Accept (poster) | Summary: This paper proposes a new postprocessing method, CSD-iPOT, for survival analysis based on conformal prediction.
Strengths: The proposed method tries to achieve the conditional calibration, which is known to be hard.
Weaknesses: This paper puts emphasis on achieving conditional calibration (not marginal calib... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing interesting comments but are sorry that you did not agree with the other reviewers about the merit of our paper. To deal with your concern:
>W1: … Lack of extensive discussion on the hardness of conditional calibration is a serious problem of this paper.
Thank... | Summary: The authors study the problem of how to create individual survival distribution (ISD) models which are well-calibrated, both in a marginal and conditional sense, without negatively affecting the discriminative performance.
They refine the "Conformalized Survival Distribution" (CSD) approach and propose "Confo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions. To address the concerns:
>W1 & Q1: … would be helpful to have a similar visualization to illustrate the difference between CSD and CSD-iPOT.
Great suggestion! The revised version will include a side-by-side visual comparison of o... | Summary: The paper enhances the Conformalized Survival Distribution (CSD) post-processing framework to account conditional calibration. The proposed framework, CSD-iPOT, utilizes a conformal set to adjust survival curves vertically, aligning them with predetermined percentiles at test time. Unlike CSD, which relies on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for these wonderful comments and suggestions! Wrt your insightful concern:
>The paper highlights that CSD-iPOT lacks theoretical guarantees for preserving Harrell's concordance …
Yes, we acknowledged this limitation in the paper. However, we argue this is not a big issue for... | Summary: While previous work focuses on calibration in a marginal sense, this paper proposes a post-processing approach that also imposes conditional calibration on all individual features. Therefore, the proposed method can guarantee equal calibration for different groups, ensuring fairness.
Contributions:
1. The pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions. To address the concerns:
> W1: Missing related work in conformal prediction: the paper claims the method is based on conformal prediction but it is not mentioned and illustrated clearly in the main content …
The current method (de... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and thoughtful feedback! In particular, we are grateful for the largely positive reception of our work. To mention a few key points from the reviewers:
>The paper is well-written and easy to follow. The visual plot offers great intuitive illustration... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space | Accept (poster) | Summary: This paper proposes a Mamba-based framework, namely MambaLLIE, for low-light image enhancement. Specifically, the authors claim that they have two technical contributions:
(i) A global-then-local state space block that integrates a local-enhanced state space module and an implicit Retinex-aware selective kern... | Rebuttal 1:
Rebuttal: **Q1: Why use Mamba for low-light image enhancement?**
**A1:** Our **Motivation** aims to take into account both global and local image restore. As we know, the low-light enhancement task faces challenges of global color degeneration and local noise disturbance. Global color degeneration suffer f... | Summary: This paper presents MambaLLIE, an implicit Retinex-awre low light image enhancement framework with modified state space blocks. Specifically, a global-then-local state space block (GLSSB) is designed, which incorporates a local-enhanced state space module (LESSM) and an implicit Retinex-aware selective kernel ... | Rebuttal 1:
Rebuttal: **Q1: About novelty and the advantages of GLSSB.**
**A1:** We argue that the GLSSB is novel in terms of _*the new exploration of the vision state space model*_ and _*technical improvements for the Retinex-aware low light enhancement task*_.
Our **Motivation** aims to take into account both glob... | Summary: The authors proposed a Mamba-inspired method for LLIE, which is designed to address some challenges of the existing method, MambaIR. By integrating GLSSB consisting of LESSM and IRSK, the authors could make the model capture a large local receptive field while preserving global understanding natures. The overa... | Rebuttal 1:
Rebuttal: **Q1: Technical contributions over MambaIR.**
**A1:** We want to emphasize that our target and contribution are distinct from MambaIR. 1). Our MambaLLIE is specifically designed for the low-light enhancement (LLIE) task, whereas MambaIR is proposed for image restoration, including image super-res... | Summary: This paper introduces a low-light image enhancement module (MambaLLIE). This module has a U-shaped structure and each GLSSB block follows the Transformer-based design. LESSM is proposed to capture the spatial long-term dependency. IRSK is proposed to introduce large and selective kernels for enhancing feature-... | Rebuttal 1:
Rebuttal: **Q1: The difference between LESSM and VSSM.**
**A1:** Thanks for your comment, we would like to rebut that the **MAIN** innovation presented in LESSM is the integration of local 2D dependencies into VSSM and formulate a simple yet efficient local-enhanced state space module (LESSM) to aggregate ... | Rebuttal 1:
Rebuttal: We thank all reviewers and chairs for your time, constructive comments, and recognition of our work. We appreciate the positive comments on our idea, contributions, and state-of-the-art performance, such as *"novel and interesting,"* *"well illustrated,"* *"attracts attention from the community,"*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint | Accept (poster) | Summary: The paper studies the problem of approximately maximizing a submodular function under a constraint that the sets can be of size at most $k$. By carefully combining algorithms from previous work and developing them further, the authors obtain an algorithm that guarantees a $0.385$-approximation with $O_\epsilon... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work. We have thoroughly addressed each of the reviewer’s raised concerns and are eager to engage in further discussion to ensure all remaining issues are resolved.
1. **In terms of the running time, i.e., not the number of queries, how d... | Summary: The paper introduces a new algorithm, FAST-LOCAL-SEARCH, for maximizing non-monotone submodular functions, achieving a 0.385-approximation with low query complexity. It combines initial solution search, accelerated local search, and stochastic greedy improvement steps to outperform existing algorithms in machi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed review. We hope that our response below addresses all their concerns about the paper. If further clarification is needed, we will be happy to provide it.
1. **The novelty of the paper is limited. The idea of this paper is derived from existin... | Summary: This work addresses the problem of maximizing a non-monotone submodular function subject to a cardinality constraint. In submodular maximization, we have a set of elements (ground set) and a function that assigns a value to any subset of elements. A function is submodular if adding an element to a smaller set ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s positive evaluation of our work. In what follows, we address the concerns raised by the reviewer in detail. We will be happy to engage to address any lingering concerns.
1. **Comparing the number of query calls in addition to the output value is essential, as ... | Summary: The paper presents a practical $0.385$-approximation algorithm using $O(n+k^2)$ queries for non-monotone submodular maximization under a cardinality (size) constraint, where $n$ is the number of elements in the ground set and $k$ is the maximum size of a feasible solution. As a comparison, the state-of-the-art... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's meticulous evaluation of our paper. Thank you for your invaluable feedback. In what follows, we address the reviewer’s questions/comments. We will be happy to engage with you to address any lingering concerns
1. **There are many typos in the proofs (see Ques... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their constructive feedback and insightful questions about our research. Your dedication and expertise in helping us enhance our work is invaluable. We are grateful for your observations and thank you for your contributions.
Finally, attached here is our P... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning | Accept (poster) | Summary: This paper analyses the problem of the transferability of the reward functions learned from expert demonstrations in regularized MDPs. After having formalized the problem, the authors introduce some assumptions that permit to connect the distance between equivalence classes of reward functions to the suboptima... | Rebuttal 1:
Rebuttal: Thank you for your feedback, valuable comments, and helpful suggestions. While we addressed some of your concerns in the response to all reviewers, we would like to respond in detail to your comments below.
## Our comments:
- __(C)__ *"Regularized MDPs do not represent a realistic model for exper... | Summary: The paper establishes bounds on the reward transferability for inverse reinforcement learning in discrete MDPs with known transition matrix. It is well known that the reward function learned in a single environment is in general not transferable as the reward shaping potential can not be identified and therefo... | Rebuttal 1:
Rebuttal: Thank you for your feedback, valuable comments, and helpful suggestions. While we addressed some of your concerns in the response to all reviewers, we would like to respond in detail to your comments below.
## Our comments:
- __(C)__ *"While I can imagine that concepts could be transferred to con... | Summary: This paper introduces the logical framework regarding rewards identification (up to potential shaping transformations, which can be achieved under entropy regularization) and rewards transferability (up to a constant, more difficult to achieve). The paper considers a practical scenario where only expert demons... | Rebuttal 1:
Rebuttal: Thank you for your feedback, valuable comments, and helpful suggestions. While we addressed some of your concerns in the response to all reviewers, we would like to respond in detail to your comments below.
## Our comments:
- __(C)__ *"(...) I think experimental validation on real-world applicati... | Summary: In the context of inverse reinforcement learning (IRL) -- i.e. the task of recovering a reward based on demonstrations from experts acting (approximately) optimally with respect to that reward -- this paper studies the question of transferability to new environment dynamics: if we learn a reward function from ... | Rebuttal 1:
Rebuttal: Thank you for your feedback, valuable comments, and helpful suggestions. While we addressed most of your concerns in the response to all reviewers, we would like to respond in detail to some of your comments below.
## Our comments:
- __(C)__ *"I think the paper is not optimized to be read by a bro... | Rebuttal 1:
Rebuttal: Thank you for the valuable reviews. In the following, we propose changes and answer to questions of general interest.
## Intuition building
Reviewers hiXS and fi4j suggested adding more intuition on key concepts of the paper. We will address this by adding clarifying sentences in the background se... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parameter Competition Balancing for Model Merging | Accept (poster) | Summary: They propose a PCB for merging which computes parameter importance using 3 steps for intra-balancing and inter-balancing, which outperforms previous methods.
Strengths: - Paper well structured and easy to follow
- Method outperforms baselines
Weaknesses: - Lack of novelty - similar to TIES with some modifie... | Rebuttal 1:
Rebuttal: **Reply to Reviewer Zv3Q** \
Thank you for your valuable comments. We will explain your concerns point by point.
**Weaknesses 1：About the novelty.** \
**Reply**: Please refer to the second point in our general response document.
**Weaknesses 2：About the theoretical motivation.** \
**Reply*... | Summary: This paper introduces an innovative technique named PCB-MERGING (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging.
Strengths: 1. This paper re-examines existing model merging methods, highlighting the critic... | Rebuttal 1:
Rebuttal: **Reply to Reviewer s8Ho** \
Thank you for your valuable comments. We will explain your concerns point by point.
**Weaknesses 1：More details in Figure 1 and 2** \
**Reply**: The term 'magnitude' refers to the magnitude of the task vector in Fig. 1. We will clarify this in the introduction of o... | Summary: The authors propose an improved method to merge task vectors, called Parameter Competition Balancing (PCB-merging). The proposed method is simple, efficient, and attains superior performance in evaluations.
Strengths: 1. In Figure 1, the authors show a very interesting phenomena, where scaling the top percent... | Rebuttal 1:
Rebuttal: **Reply to Reviewer 28gt** \
Thank you for your valuable comments. We will explain your concerns point by point.
**Weaknesses 1：More details in Figure 1.** \
**Reply**: The term 'magnitude' refers to the magnitude of the task vector in Fig. 1. We will clarify this in the introduction of our fi... | Summary: This paper focuses on the model merging problem. The authors propose PSC-MERGING to adjust the coefficients of each parameter for effective model merging. Specifically, PSC-MERGING uses intra-balancing to weight the importance of parameters within tasks and inter-balancing to assess parameter similarities acro... | Rebuttal 1:
Rebuttal: **Reply to Reviewer 6PSY** \
Thank you for your valuable comments. We will explain your concerns point by point.
**Weaknesses 1：The setting of the hyperparameter $r$.** \
**Reply**: The reviewer has some confusion and misunderstandings regarding our experiment settings.
Firstly, our hyperp... | Rebuttal 1:
Rebuttal: **General Response**
We appreciate your consideration in taking the time to review our comments. We have received feedback from four reviewers, all of whom have provided thoughtful insights. Almost all reviewers found our paper to be well-organized, motivated, practical, and easy to follow. Additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Sample Complexity for Multiclass PAC Learning | Accept (poster) | Summary: This paper presents improved sample complexity upper and lower bounds for multiclass classification, shaving previous upper bounds to within a factor of $\log(1/\epsilon)$ from the conjectured optimal dependence on $\epsilon$, and adding a dependence of $\log(1/\delta)$ to previous best lower bounds. To do so,... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below, $d$ denotes DS dimension and $d_k$ denotes $k$-DS dimension.
## List learning
* The advantage of reducing to list learning is that it reduces the original problem to an easier problem: list learning is easier than multiclass learning. Indeed, a multiclas... | Summary: For the problem of analyzing the optimal PAC sample complexity for multi-class learning, two possible routes to induce the improved sample complexity and error rate are proposed. On the one hand, benefiting from the reduction from multi-class learning to list learning and the boosting technique, the dependence... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate your positive feedback.
First, we need to emphasize that in this paper, we study multiclass PAC sample complexity for general concept classes which can have **infinite** number of labels ($|\mathcal{Y}|=\infty$). Our results hold for general concep... | Summary: This work is focused on improving sample complexity upper and lower bounds on multiclass classifcation for a general hypothesis class. Their bounds are given in terms of the so-called DS-dimension of the hypothesis class, which can be viewed as a generalization of the VC dimension to the multiclass setting. Th... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate your positive feedback.
We provided a proof sketch for Theorem 2.7 in line 229-241. We will elaborate more on it and include the proof intuition for other major theorems in the revision. We believe that the extra space can be accounted for by the a... | Summary: The paper considers the problem of analyzing the sample complexity of Multiclass PAC Learning. The key contributions of this paper are two-fold: (1) Give an improved upper bound for the sample complexity by a poly-log factor, and (2) give the first (formal) lower bound for the Multiclass PAC Learning problem, ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate your positive feedback.
We will include the proof sketches and high-level intuitions of the results presented in the Appendix in the revised version of the paper. | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper gives better bounds on the complexity of multiclass learning using DS-dimension and provides a lower bound. The improvement is relatively minor.
Strengths: The main strength of this paper is that it addresses one of the most fundamental problems in learning theory -- the complexity of multiclass le... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below, $d$ denotes DS dimension and $d_G$ denotes graph dimension.
## Graph dimension
* The DS dimension is the right quantity to look at here.
The optimal multiclass PAC sample complexity is described by DS dimension and **not** by graph dimension.
As is st... | null | null | null | null | null | null |
Diffusion Actor-Critic with Entropy Regulator | Accept (poster) | Summary: The paper proposes DACER — an actor-critic that uses the inverse diffusion process as policy. Additionally, some noise is added to actions to increase the entropy (similar motivation as in SAC but implemented differently). Empirical evaluations and ablations show that the method is quite capable.
Strengths: O... | Rebuttal 1:
Rebuttal: We appreciate you for the careful reading of our paper and detailed discussions.
### **> Weakness 1**
As you mentioned, it is meaningful to compare with other algorithms that have multimodal characteristics. We chose to compare the performance with two online RL with Diffusion model algorithms me... | Summary: The paper presents a novel method using diffusion models as a policy parameterization for online reinforcement learning. The method works by learning a Q function and backpropagating through the reverse diffusion process in order to update the diffusion policy weights, similarly with Diffusion-QL. In order to ... | Rebuttal 1:
Rebuttal: Thank you for insightful review. We report below the changes we are currently making to address your comments.
# Q1
Our work emphasizes proposing the reverse diffusion process as a novel policy expression, combinable with existing online RL algorithms. Section 2.2 highlights the method's portabili... | Summary: This paper proposes using the reverse diffusion process as the policy for actor-critic-based online reinforcement learning. An EM-based mixture model is fitted to estimate current diffusion policies' entropy to balance exploration and exploitation. The proposed method, DACER, demonstrated on-par or improved pe... | Rebuttal 1:
Rebuttal: We thank you for the careful reading of our paper and constructive comments in detail.
### **> Weakness 1**
1. As you pointed out, the entropy of GMM does not have a closed-form solution. Eq.15 provides **an upper bound for the entropy of GMM [1]**, which can be used for approximate estimation. W... | Summary: The authors propose DACER (Diffusion Actor-Critic with Entropy Regulator), an online RL algorithm that uses the reverse diffusion process as a policy in order to capture multimodal behaviours. To balance exploration and exploitation, the authors propose carrying out entropy regularization by estimating the ent... | Rebuttal 1:
Rebuttal: We thank you for the careful reading of our paper and constructive comments in detail.
### **> Weakness 1**
Thank you for your important information. We have added a paragraph to introduce image generation with online RL algorithms.
"Diffusion models are widely applied in the field of image gen... | Rebuttal 1:
Rebuttal: Overall author rebuttal:
We thank all reviewers for their thoughtful comments. We greatly appreciate all the reviewers' acknowledgment that our method **is very novel and is the cutting-edge work combining diffusion model and online RL. In addition, our method achieved SOTA in MuJoCo tasks and is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bandits with Abstention under Expert Advice | Accept (poster) | Summary: This paper considers the problem of prediction with expert advice in the bandit feedback setting with the possibility of abstention. The game is sequential: where in each round the each of $E$ experts gives a distribution over $K+1$ actions/arms. One of these actions represents abstention, yielding a reward of... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper.
> The results obtained are not easily comparable to existing ones in the literature.
Our results can be, for example, compared to EXP4 (see reply below) and SpecialistEXP (see e.g., our reply to reviewer Edre). In both cases we achieve a... | Summary: This paper investigates the problem of prediction with expert advice in contextual adversarial bandits, introducing the CBA (Confidence-rated Bandits with Abstentions) algorithm, they apply CBA to adversarial contextual bandits and achieve near-optimal upper regret bound.
Strengths: The CBA in adversarial con... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper.
>Weakness 1: This algorithm is not as efficient as Exp4.
Given that the numerical precision of our machine is bounded, our algorithm has exactly the same $\mathcal{O}(EK)$ efficiency as Exp4. See the additional result presented in the ge... | Summary: The paper considers the problem of prediction advice under the bandit framework.
There are $K$ arms plus a special $(K+1)$0-th arm, which always incurs the gain of 0; this arm may be interpreted as the action of abstaining. The learner outputs a distribution over $K+1$ handles and earns the scalar product gai... | Rebuttal 1:
Rebuttal: Thanks for the very encouraging feedback!
---
Rebuttal Comment 1.1:
Comment: Thank you. This is to acknowledge the response. | Summary: This paper first considers the problem of bandits with expert advice, allowing the learner to abstain in any round. It introduces an algorithm, CBA, based on mirror descent, which can achieve a better cumulative regret bound in comparison with EXP4.
The algorithm is then applied to adversarial contextual band... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper.
>[...] $\log|B|$ gap and $|B|$ can be exponential in the number of contexts $N$.
While the comment is true, $|B|$ can indeed be as large as $2^N$ in principle, such a large basis set would defy its purpose (as an inductive prior on the ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback.
We are happy to provide an additional result that was prompted by a question from Reviewer 3cAo. The reviewer asked how the regret bound and efficiency are affected if $\lambda$ is not exactly determined but only approximated (we previously assumed boun... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LP-3DGS: Learning to Prune 3D Gaussian Splatting | Accept (poster) | Summary: The paper proposed a pruning method for Gaussian Splatting training, which employ a learnable mask to find optimal pruning rate.
Strengths: The paper is well-written and the proposed method is easy to understand.
Weaknesses: * The proposed method use a regularization loss to encourage the model to prune, so ... | Rebuttal 1:
Rebuttal: ### Response to weakness 1:
We would like to emphasize that the main contribution of our work is not merely the use of a regularization loss to encourage the model to prune, but rather the development of a learning framework that can automatically learn a pruning ratio embedding during the traini... | Summary: This work introduces LP-3DGS, aiming to compress 3DGS by replacing the previously manually set threshold with a learning-to-prune scheme. In particular, LP-3DGS learns a binary mask to automatically prune 3DGS, where such a mask is regularized with the Gumbel-Sigmoid technique. Experiments on three benchmarks... | Rebuttal 1:
Rebuttal: ### Response to weakness 1:
Please refer to the Common Question about the term "contribution" for more details.
### Response to weakness 2:
Please refer to the Common Question about the term "optimal" pruning for more details.
### Response to weakness 3:
We have comprehensively compared our m... | Summary: This paper proposes a method to optimally prune Gaussians that do not participate in the rendering or optimizing process of the 3D Gaussian Splatting 3D reconstruction algorithm. The key idea in this paper is to use a Gumbel-sigmoid function instead of the standard Sigmoid function to optimize a binary mask ba... | Rebuttal 1:
Rebuttal: ### Response to weakness 1\&3:
Please refer to the Common Question about the clarification and summary of our technical contributions.
We would like to highlight that the main contribution of our work is not merely adopting the Gumbel-Sigmoid to prune 3DGS models but rather developing a learning... | Summary: Whereas other works set a fixed pruning ratio or threshold, this paper introduces a method to learn how to prune Gaussians from a scene while retaining high image fidelity. Different scenes have a drop in fidelity at different pruning percentages and the goal of this work is to remove the need to train the mod... | Rebuttal 1:
Rebuttal: ### Response to W1:
Thank you for the comment. Please refer to the Common Question about the term ``optimal" pruning.
### Response to W2:
For Radsplat/Mini-Splatting, they calculate their defined importance score for each Gaussian. If the target is to prune 60% of the entire model, the Gaussia... | Rebuttal 1:
Rebuttal: ## Common question about the term "optimal" pruning (Reviewer o7SR and Reviewer 1UP2 )
We agree with Reviewer o7SR that the term "optimal" may be misleading. We will change it to "learned pruning ratio" and provide a more detailed definition in the updated manuscript. In the domain of 3DGS model ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values | Accept (poster) | Summary: In this paper the authors consider the problem of $\min_x f(x)$ with an order oracle that returns $sign (f(x)-f(y)+\delta)$ for some bounded noise $\delta$. The method is based on line search integrated with existing randomized coordinate update algorithms. Convergence rates on non-convex, convex, strongly con... | Rebuttal 1:
Rebuttal: Dear **Reviewer ErqJ**,
We thank you for your feedback on our work.
> **I don't think the analysis result of...**
We provide detailed answers to all questions below, including concerns about the significance and novelty of our results.
> **Line 77 is not finished. Line 78...**
Thank you, we w... | Summary: This paper addresses challenges in black-box optimization by introducing the concept of an Order Oracle. This oracle compares function values without assuming access to their actual values, allowing for the creation of new optimization algorithms. The paper proposes both deterministic and stochastic approaches... | Rebuttal 1:
Rebuttal: Dear **Reviewer WKzP**,
We thank you for your feedback on our work. We provide detailed answers to the comments and questions raised in the review below.
> **Elaboration on Motivation.**
Despite the potential motivation given in the introduction, as well as the mention of Appendix A, which deta... | Summary: this paper focuses on solving the black-box optimization problem with order oracle method. specifically, the author provided a non-accelerated deterministic optimization algorithm, which relies on weighted sampling of the coordinate and the GRM method to resolve a linear search subproblem. the author provided ... | Rebuttal 1:
Rebuttal: Dear **Reviewer uSy5**,
We thank you for your interest in our work. We attach below detailed responses to the comments and questions raised in the review.
> **i have some concerns on the novelty of this paper...**
We tried to prepare the text of the paper so that it would be easily accessible t... | Summary: The paper considers the “Order Oracle” for optimization, which does not require the function values or gradient information, but only the relative ordering of the function values at different points. This can also be done with some small noise. This model captures the challenges encountered in real-world black... | Rebuttal 1:
Rebuttal: Dear **Reviewer K16N**,
We thank you for your positive evaluation of our work! Below we provide detailed answers to all comments and questions that arose in the review.
> **The authors have motivated the use of this oracle, but it would be useful to have some concrete examples on how is it being... | Rebuttal 1:
Rebuttal: Dear **Reviewers**, we thank you for taking the time to prepare reviews of our work. We have prepared detailed answers to the comments and questions that arose in the reviews. Our responses will be found under the official review. However, we would like to emphasize the highlights from your review... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper explores the use of a zero-order oracle called the Order Oracle to solve optimization problems where exact objective function values are unknown. This oracle focuses on comparing the order of objective function values rather than requiring exact values. The authors propose new non-accelerated algor... | Rebuttal 1:
Rebuttal: Dear **Reviewer XqbR**,
We would like to thank you for your time for preparing the review.
> **The numerical experiments show the evaluation of OrderRCD and OrderACDM in standard quadratic functions. Experiments on real-world problems would strengthen the validation. The paper can include speci... | null | null | null | null | null | null |
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks | Accept (poster) | Summary: This paper proposes an approach to decrease the number of parameters in LoRA by introducing a collection (aka "vector bank") of sub-vectors and composing Lora matrices across modules/layers using this collection. Each sub-vector within a given LoRA module/layer is then formed as a linear interpolation of sub-v... | Rebuttal 1:
Rebuttal: Dear Reviewer o6ri,
**1. Weakness #1 – GPU acceleration and computation overhead**
VB-LoRA’s implementation is straightforward, and the proposed factorization approach is also simple to implement in modern deep learning frameworks such as PyTorch, allowing us to fully leverage GPU acceleration. ... | Summary: The authors propose an extremely parameter-efficient methods, VB-LoRA, for finetuning an LLM. Specifically, VB-LoRA has a shared vector bank (similar to a codebook), the adapter parameters (A and B) of the linear layers in an LLM are constructed from this bank by selecting the most effective bank vectors. Due ... | Rebuttal 1:
Rebuttal: Dear Reviewer eNJC,
**1. Weakness #2 – why MNLI and QQP are discarded?**
We adhered to the experimental settings established by our baseline, VeRA, which did not include MNLI and QQP. Despite having access to computational resources, it is a shared resource in university. We chose to focus on Na... | Summary: This paper explores parameter-efficient fine-tuning (PEFT) methods in the context of further reducing the number of fine-tunable parameters, even to the extreme. The main idea is to reduce the number of parameter within LoRA modules as much as possible while maintaining or even improving fine-tuning performanc... | Rebuttal 1:
Rebuttal: Dear Reviewer wW2k,
**1. Weakness #1 – evaluations on instruction tuning**
Thank you for your suggestion. In response, we fine-tuned the Mistral-7B and Gemma-7B models on the MetaMathQA dataset and evaluated them on GSM8K and MATH datasets, and compared them with the suggested work LoRA-XS. Our... | Summary: The authors present a modified version of LoRA called VB-LoRA which is a highly parameter efficient fine-tuning method. It uses a vector bank to represent the model parameters as a composition of vectors. This vector bank is then used to select top-k vectors using the top-k softmax function which are thereby p... | Rebuttal 1:
Rebuttal: Dear Reviewer e7Nd,
**1. Weakness #1, Question #2 – the parameter efficiency when rank is high**
First, it’s important to note that rank may not be directly comparable across different methods. In many approaches, rank dictates the number of independent trainable parameters. However, in our meth... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable opinions and comments. In response to the reviewers' requests, we have added additional mathematical reasoning experiments for the Mistral-7B and Gemma-7B models.
### Mathematical Reasoning Experiments
We fine-tuned the Mistral-7B-v0.1 and Gemma-7B mode... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BMRS: Bayesian Model Reduction for Structured Pruning | Accept (spotlight) | Summary: The paper introduces Bayesian model reduction for structured pruning (BMRS), an efficient method for structured pruning of neural networks. It improves Bayesian structured pruning with multiplicative noise by combining it with Bayesian model reduction, enabling a principled pruning strategy based on efficient ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful review! We are happy that they noted that **the paper tackles and important problem**, it is **well-written** and **clearly and comprehensively addresses a gap in the literature**, and that **the experiments are thoughtfully designed**. We addres... | Summary: This paper proposes a new probabilistic approach to structured pruning of neural networks. Inspired by variational dropout, the authors derive a method to learn a multiplicative noise distribution, which is encoded in a multiplicative noise layer. Pruning algorithms are derived based on assumed priors over the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and engagement with the paper. We are happy that they found the **approach interesting and well motivated**, **the writing clear**, **the math sound**, the **connection to floating point format interesting**, and **that we derive a variety of pruning algorithms... | Summary: This paper works on structured pruning using Bayesian models. They try both post-training pruning and continuous pruning for MNIST and CIFAR-10 datasets.
Strengths: The writing is easy to read and follow.
Weaknesses: 1. The novelty is quite limited:
The BMRS basically in my opinion is a naive extension as p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing the paper. We would like to address the points they make in their review:
**Novelty**
We respectfully disagree with the characterization of our method as a naive extension of variational dropout. We derive novel pruning criteria for a class of struc... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and their reviews. We are glad that they generally found the **approach interesting**, the **math sound**, and the **problem important**. We are also happy that they all found the writing clear and the paper presented well. To contextualize the reviews, we wou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Open-set Noise in Learning with Noisy Labels | Reject | Summary: The paper extends the problem setting of learning with noisy labels (LNL) to include open-set noise, where noisy labels may come from unknown categories, in contrast to the traditional focus on closed-set noise. The authors theoretically compare the impacts of open-set and closed-set noise and analyze detectio... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review. We sincerely appreciate your time and effort in reading our paper, as well as the insightful and constructive feedback.
>*Q1: Some technical details …needs to be polished.*
A1: We will make further revisions to the manuscript to improve clarity. We wo... | Summary: This paper introduces an approach to address the challenge of open-set noise in the context of learning from noisy labels. The authors propose a method that differentiates between 'easy' and 'hard' types of open-set noise, which is critical for improving the robustness and performance of learning models faced ... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review. We sincerely appreciate your time and effort in reading our paper, as well as the insightful and positive feedback.
> *Q1: Dependency on Specific Methods: The reliance on entropy-based techniques for noise distinction may not generalize across all sce... | Summary: This paper focuses on open-set label noise problem. Authors first formally extending closed-set transition matrix to open-set transition matrix and define two noise ratios for open-set and closed-set separately.
Then authors define error inflation rate as a measurement for noisy label impact and measure for t... | Rebuttal 1:
Rebuttal: Thanks very much for the careful review. We sincerely appreciate your time and effort in reading our paper, as well as the insightful and constructive feedback.
>*Q1: The experiment parts lack of baselines. ..., previous baselines on easy open-set noise ... set up the benchmark.*
A1: Thanks ver... | Summary: The paper refines the problem of learning with noisy labels (LNL) by addressing the often overlooked issue of open-set noise. It provides a comprehensive theoretical analysis comparing the impacts of open-set and closed-set noise, introduces novel datasets for empirical validation, and explores the effectivene... | Rebuttal 1:
Rebuttal: Thanks very much for the careful review. We sincerely appreciate your time and effort in reading our paper, as well as the insightful and positive feedback.
> *Q1: The author summarizes two types of open-set noise, i.e., the easy and the hard noise, which is very similar to the symmetric and asy... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. We are encouraged that they find our work comprehensive and well-structured, with thorough theoretical analysis and interesting findings. Specifically, we appreciate the recognition of our method's novelty and its empirical validation through n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tell What You Hear From What You See - Video to Audio Generation Through Text | Accept (poster) | Summary: This paper proposes a multimodal video-to-audio generative model called **VATT** that can generate audio and audio captions given input silent videos and optional text prompts. It is capable of performing controllable text-guide video-to-audio generation and video-to-audio captioning. The framework consists of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and valuable feedback. We are glad to see that the reviewer acknowledges the presentation of our work, the technical highlights and thorough experiments. We address the feedback below.
**W1.** Indeed, one major contribution of our work is the V2A t... | Summary: The paper introduces a two-stage model for video-to-audio generation controlled by text. In the first stage, a large language model (LLM) generates audio captions based on the video content. In the second stage, the model uses video frames and the generated audio captions to predict masked audio tokens, thereb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback. We are glad that the reviewer is satisfied with the quality of provided samples and generation capability of our model, and that our model is capable of performing both video-to-audio captioning and video-to-audio generation tasks. We address the... | Summary: In this paper, the authors propose VATT, a multi-modal generative framework for text-guided video-to-audio generation. VATT consists of two key modules VATT Converter and VATT Audio. The former component maps video features to LLM vector space with a projection layer. The latter one generates audio tokens with... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful feedback and acknowledging the quality of generated samples from VATT. We address the feedback and concerns below.
**W1:** The quality of generated captions do affect the generation quality of the model in the V+T -> A stage. We conduct an additional ablation ... | Summary: - The paper proposes a model for video-to-audio generation (main task) and video-to-audio captioning (auxilliary task).
- The text is optionally used to control audio generation for ambiguous video cases.
- They use two step approach i.e. video-to-caption stage and video+text-to-audio stage
- Stage 1: video-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and valuable feedback. We appreciate the reviewer feedback that our proposed methods include novelties and technical contributions. We address the raised concerns below.
**W1 & Q1:** We manually verified the validity of LTU-generated captions prio... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Flow Matching: Learning Straight Trajectories in Just One Step | Accept (poster) | Summary: This paper aims to solve the optimal transport problem between two distributions within a flow matching framework.
This can be achieved by finding the best velocity model $u_t$ in a class of optimal vector fields, i.e., $u_t$ be implicitly induced by defining the $x_1=\nabla \Phi(x_0)$ where $\Phi$ is a convex... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your feedback and kind words. Please find below the answers to your questions.
**(1) Convergence time. Plots of convergence.**
Following your request, we provide the plots of convergence (in $L^2$-UVP) depending on training time in the experiment on the W2 benchmark ... | Summary: This work proposes Optimal Flow Matching (OFM), which restricts standard Flow Matching to specific vector fields that yield straight trajectories by design. This is implemented by considering vector fields such that a convex function exists whose gradient pushes the initial to the final point.
The authors pro... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your detailed feedback. Please find below the answers to your questions.
**(1) The reduction of our OFM to sampling of $t = 1$ only.**
Our method requires considering all times $t\in [0,1]$ by its design. Considering $t=1$ *breaks the theory and does not work*. Inde... | Summary: The paper goal is to learn the optimal transport map between any two distributions. The approach derives a flow matching loss which is minimized by the velocity field of the optimal transport map.
Strengths: The paper investigate the interesting subject of the optimal transport problem.
Weaknesses: The pape... | Rebuttal 1:
Rebuttal: Dear reviewer, we thank you for your feedback. Please find below the answers to your questions.
**(1) In essence, does the author claim that the loss presented in equation (16) is minimized by the velocity field of the optimal transport map for any plan ? As stated by the author, it seems this l... | Summary: This paper introduces the Optimal Flow Matching (OFM) algorithm, which improves upon Rectified Flow and OT-CFM by generating exact straight trajectories and recovering the optimal transport map in one iteration. OFM optimizes Flow Matching loss using vector fields and a convex function. The algorithm is implem... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your feedback. Please find the answers to your questions and comments below.
**(1) Pretrained autoencoders. Does OFM still work in the pixel space or a higher-resolution dataset?**
In theory, OFM can work directly in pixel space and has no limitation for dimensionali... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and time. We appreciate that reviewers acknowledge: importance and soundness of the chosen problem (Bhuh, YPMs), our theoretical contribution connecting FM and OT methods (Bhuh, nvT5, qt8g), and experimental verification of the theoretical part (qt8g, Bhuh... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligner: Efficient Alignment by Learning to Correct | Accept (oral) | Summary: This paper proposes a lightweight and model-agnostic alignment method, Aligh, which learns the correctional residuals between preferred and dispreferred answers using a seperate model. Extensive experiments are conducted to demonstrate its effectiveness across 11 different LLMs, focusing on helpfulness, harmle... | Rebuttal 1:
Title: Official Reply to Reviewer tcrV
Comment: Dear Reviewer tcrV:
Thank you very much for taking the time to review Aligner and for your valuable feedback. In your initial comments, you highlighted concerns regarding Aligner's experiments in the OOD setting and anomalies in the data points in Table 1. To ... | Summary: The authors proposed a novel alignment paradigm, fine-tuning a pre-trained LLM as an 'aligner' module to map the target LLM's misaligned zero-shot responses to corrected aligned responses.
Advantages of this technique over alignment-via-tuning strategies like RLHF/DPO is that it is agnostic to model size/arc... | Rebuttal 1:
Title: Official Reply to Reviewer 3sZj
Comment: Dear Reviewer 3sZj,
We greatly appreciate your time and effort in reviewing our work on Aligner. We are grateful for your support. In your initial feedback, your main concerns were regarding Aligner's experiments in the OOD setting and the OOD reward model co... | Summary: The paper has the following contributions:
1. Resource Efficiency: Aligner is notably smaller, requiring far fewer parameters compared to traditional models like DPO and RLHF1.
2. Plug and Play: It can be easily integrated with various large language models (LLMs) without needing to adjust parameters, ideal f... | Rebuttal 1:
Title: Official Reply to Reviewer 7osp
Comment: Dear Reviewer 7osp:
Thank you very much for your time and valuable feedback on the Aligner review. In your initial feedback, you expressed concerns about the more expensive data annotation required for Aligner and the model's self-critic and self-correct cap... | Summary: This work introduces Aligner, a small model designed for post-processing the outputs of Large Language Models (LLMs) to improve their results. Despite incorporating concepts like Interpretability and Residual Correction, the fundamental issue of lacking novelty remains unchanged. Furthermore, the experimental ... | Rebuttal 1:
Title: Official Reply to Reviewer JeaM
Comment: Dear Reviewer JeaM,
Thank you very much for taking the time to review Aligner and for your valuable feedback. In your initial comments, you noted that Aligner's experiments lacked certain baselines such as `BoN` and `results on mainstream subjective and objec... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Functional Gradient Flows for Constrained Sampling | Accept (poster) | Summary: The paper proposes to adapt particle based variational infence to the context in which the distribution of interest is supported on a subset of R^d. It is supposed that the subset on which the distribution is supported is characterized as a lower level set of a function, g, and that this function has a gradien... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: I did find (...) improve the manuscript.
A1: Thanks for your suggestion! We will modify the notations accordingly in our revision.
`W2`: The examples all (...) large... | Summary: The paper proposed a particle based method (using neural networks) to sample probability densities which are supported on subdomain of $\mathbb{R}^d$. This is done in the spirit of Stein variational gradient descent but using neural network instead of kernels and by incorporating the constraint into the func... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: Relative weakness is that the proposed method does not seem to outperform existing algorithms. Maybe a more detailed comparison with existing method in terms of costs w... | Summary: The authors develop a solution to constrained sampling by introducing a boundary condition for the gradient flow which would confine the particles within the specific domain. This gives a new functional gradient ParVI method for constrained sampling, called constrained functional gradient flow (CFG), with prov... | Rebuttal 1:
Rebuttal: Thank you for your careful review! We address your comments as below.
### Weaknesses
`W1`: Experiments on a real-world application could strengthen the paper.
A1: Thanks for the suggestion! In our experiments, the monotonic BNN is applied to real-world problems. The setting is originally derive... | Summary: This paper addresses the challenging problem of constrained sampling from an unnormalized distribution. Building on the principles of Stein variational gradient descent (SVGD), which merges the strengths of variational inference and Markov Chain Monte Carlo (MCMC), this work innovates by applying SVGD in a con... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: In Bayesian neural network experiments, the paper overlooks numerous baselines of existing Bayesian Neural Networks (BNNs) using variational inference, MCMC, and Laplac... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and will modify our paper accordingly in our revision. Here we address some of the common issues raised by the reviewers.
**Geometric generalization of our method**
Some of the reviewers are interested whether our proposed method can genera... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors study functional Wasserstein gradient descent methods with constraints in support of target distribution. They study the variational problem of vector fields using the least square formula between the current step and the gradient of score functions. To maintain the constrained set, the authors res... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: The authors construct (10) as the projected vector field. The choices of lambda seem to be lacking.
A1: Thanks for the suggestion! We choose lambda to be 1 in toy expe... | Summary: The paper introduces an extension of the ParVI method for sampling on measures supported on constrained subset $\Omega$ of $\mathbb{R}^d$, where $\Omega$ is the sublevel set of a phase-field function $g$. The paper presents a construction in terms of a discontinuous particle velocity field, with the discon... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`:
My main concern is what happens when the step-size is very small. In this case, I can easily see scenarios where:
(1) The particles reach (...) non-trivial.
A1: Th... | null | null | null | null |
Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering | Accept (poster) | Summary: The authors propose a novel appearance modeling technique for an chor-based 3D Gaussian Splatting representation based on normal information and an incident lighting parametrization with MLPs. In addition to representation several regularization techniques are used to stabilize normal and incident light optimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for helpful comments and suggestions. We are glad to address the issues raised in the review.
**Q1: Comparison to NeRF-based methods**
We would like to include the comparisons to NeRF-based methods here and in the final version. It is important to highlight that 3DGS-based ... | Summary: This paper proposes a novel appearance modeling of 3D Gaussian Splatting (3DGS) for both accurate appearance representation and geometry reconstruction. Existing 3DGS methods suffer from the trade-off of the accuracy of appearance and geometry due to the disconnection between surface geometry and rendering. To... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and providing constructive comments.
**Q1: The illumination of diffuse and specular components is independent of each other. This is physically implausible.**
We agree that diffuse and specular components cannot be independent physically. However, ... | Summary: Normal-GS successfully combines color estimation with surface normal optimization, achieving remarkable surface normal prediction without sacrificing view synthesis quality compared to previous methods. In calculating diffuse color, the traditional method that extracts the surface normal components from the in... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and making constructive comments.
**Q1: Clearly explain the network output, and how components like opacity and diffuse albedo are calculated.**
We would like to clarify it here and in the final version. The whole procedure about how to calculate I... | Summary: This paper addresses the challenge of achieving high rendering quality and accurate geometry in computer vision and graphics. While recent advancements in 3D Gaussian Splatting (3DGS) have enabled real-time high-fidelity novel view synthesis, the discrete and noisy nature of 3D Gaussian primitives hinders accu... | Rebuttal 1:
Rebuttal: Thank you very much for your review and valuable feedback. Below we address the concerns raised in the review.
**Q1: L102-103, the authors claim that PBR-Based 3DGS methods show lower rendering quality than original 3DGS, which is not correct, please refer to the tables in Gaussian Shader [7].**
... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their constructive and insightful comments, especially for recognizing our work by
- **Clear presentation.** "easy to follow and understand the technical details" (5i8r), "well-documented" (cnbL), "excellent presentation" (68er), "easy to read and all comp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Group-wise oracle-efficient algorithms for online multi-group learning | Accept (poster) | Summary: Multi-group learning has been attracted attention and solutions for small-sized groups are already available. This paper further studies the online learning case when the group $\mathcal{G}$ is large or even possibly infinite. In addition to designing an algorithm, the paper also provides extensive theoretical... | Rebuttal 1:
Rebuttal: Thank you for your insight and helpful review. Your comments will be very helpful in improving the presentation of the final paper.
To your first point about the multi-class case, we actually do address this in Appendix C. In the main body, we only included a Remark on line 248, but Appendix C in... | Summary: This paper studies the problem of online multi-group learning where the algorithm should achieve sublinear regret with respect to all groups. They propose a group-wise oracle-efficient algorithm that avoids enumerating all groups by accessing the group oracle. An $\tilde{O}(\sqrt{dT/\sigma})$ regret bound is d... | Rebuttal 1:
Rebuttal: Thank you for your insight and helpful review. Your comments will be very helpful in improving the presentation of the final paper. In particular, we appreciate your feedback on the presentation of our results and related work, and we believe that addressing these issues will greatly improve clari... | Summary: The paper provides oracle efficient algorithms for online multi-group learning. The goal is to obtain sublinear regret for each group subsequence $g \in G$ simultaneously.
[BL20] showed that $o(T_g)$ regret is possible with finite $H$ and $G$ but must enumerate both $H$ and $G$. [Ach+23] showed $o(T_g)$ regre... | Rebuttal 1:
Rebuttal: Thank you for your insight and helpful review. Your comments will be very helpful in improving the presentation of the final paper.
We definitely agree that a main open question in our work is the nature of the $(\mathcal{G}, \mathcal{H})$-optimization oracle. To our knowledge, [GKR22] is the onl... | Summary: This paper studies the online multi-group learning problem, and considers it in both smoothed context and general ($\gamma$-approximatable) settings. Unlike the traditional online learning setting, this paper considers achieve low regrets for all groups (subsequences). It utilizes the adversary moves first (AM... | Rebuttal 1:
Rebuttal: Thank you for your insight and helpful review. Your comments will be very helpful in improving the presentation of the final paper.
We will take care in the camera-ready version to make revisions to Section 1 that improve its clarity. In particular, the additional space of a page should allow us ... | Rebuttal 1:
Rebuttal: We have included a draft of the table that will appear in the Introduction section of the camera-ready version of our work to address Reviewers K3De and sHAP. This table includes a summary of existing results in comparison to ours.
Pdf: /pdf/0a161b055cf97bcbddcc915a8e22e2b8787ab01b.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Scalability of GNNs for Molecular Graphs | Accept (poster) | Summary: This paper investigates the scaling of GNNS across various settings including width, depth, number of molecules, number of labels, and diversity in pretraining across 12 datasets. Different conclusions were drawn from experiments conducted during both pretraining and fine-tuning stages. Finally, the authors in... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on the paper which is of utmost value to our work. Below we address your concerns point-by-point. We also kindly invite the reviewer to refer to the general rebuttal where we share further information and a summary of the feedback we received.
... | Summary: This paper investigates the scaling of GNNs on molecular tasks. In their setting, they pre-train a GNN on multiple molecule dataset and then fine-tune these GNNs on different datasets (“down-stream tasks”). They investigate how the performance of different GNNs changes with parameter count, depth, training sam... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on the paper which is of utmost value to our work. Below we address your concerns point-by-point. We also kindly invite the reviewer to refer to the general rebuttal where we share further information and a summary of the feedback we received.
... | Summary: The paper examines the scaling behavior of GNNs in the context of molecular graph representation and prediction. The authors analyze various GNN architectures, including message-passing networks, graph Transformers, and hybrid models, on a large dataset of 2D molecular graphs. They find that increasing the sca... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback on the paper which is of utmost value to our work. Below we address your concerns point-by-point. We also kindly invite the reviewer to refer to the general rebuttal where we share further information and a summary of the feedback we received.
>**... | Summary: The paper investigates the scalability of GNNs in molecular graph applications. It highlights the relationship between model size, dataset size, and performance upon message-passing networks, graph Transformers, and hybrid architectures using a large dataset of 2D molecular graphs. It shows that supervised pre... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on the paper which is of utmost value to our work. Below we address your concerns point-by-point. We also kindly invite the reviewer to refer to the general rebuttal where we share further information and a summary of the feedback we received.
... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing detailed feedback on the paper and appreciating its presentation (YrHv, kaW1, FQ72, Lxfs), organization (kaW1, FQ72) and scientific contribution (FQ72, Lxfst). Overall, we have updated the paper and responded to the reviewer’s concerns in the individual rebutta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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