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title string	paper_decision string	review_1 string	rebuttals_1 string	review_2 string	rebuttals_2 string	review_3 string	rebuttals_3 string	review_4 string	rebuttals_4 string	global_rebuttals string	dataset_source string	conference_year int64	review_5 string	rebuttals_5 string	review_6 string	rebuttals_6 string	review_7 string	rebuttals_7 string	review_8 string	rebuttals_8 string
Scalable Kernel Inverse Optimization	Accept (poster)	Summary: The paper presents an innovative approach to inverse optimization using kernel methods. Inverse optimization (IO) aims to learn the unknown objective function of an expert decision-maker from past data by determining the optimization goal given the optimal solution, which is the reverse of traditional optimiz...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments and questions. Please find our response below. The nonlinear and high-dimensional nature of kernel methods makes the models less interpretable. A clear understanding and interpretation of the optimization model may be required. We agree with the reviewe...	Summary: This paper extend the hypothesis class of Inverse Optimization functions to RKHSs, there by the feature mappings could lie in a infinite dimensional space. This paper also discuss about the scalability issue and then proposed a SSO algorithm to the proposed KIO model. Strengths: Clarity: This paper is well-wr...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments. Please find our response below. The choice of kernels is usually problem-dependent. It will be better to discuss some different types of kernels. Besides, the hyperparameter tuning of kernels should be reported. Please refer to the global rebuttal for...	Summary: This paper concerns the topic of Inverse Optimization (IO) which deals with learning the objective function of the decision maker given previous data from an expert decision maker. By kernelizing the objective function, they extend the framework to features from a potentially infinite-dimensional space. The ma...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments and questions. Please find our response below. In Section 4.2, how does one concatenate the optimal solutions of the n solved small problems to form an initial guess? Have you evaluated different possible ways to form an initial guess? In our approach,...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for their time and insightful feedback. We have addressed the reviewer's questions by providing additional information and simulation results. In summary: ## Kernel comparison table Based on feedback from all reviewers, we have extended our numerical results to evalua...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LG-CAV: Train Any Concept Activation Vector with Language Guidance	Accept (poster)	Summary: This paper proposed a LG-CAV model that leverage the pretrained vision language model to train CAV without label. This includes a concept ensemble model that employ data augmentation on concept text, a DSR module that optmize the selection of probe image and a model to align prediction of class to concepts cal...	Rebuttal 1: Rebuttal: Thank you for dedicating your time and effort to providing valuable suggestions for this paper! We will rigorously revise the paper based on your review! > Weakness 1. Since LG-CAV leverage pretrained model, I am wondering whether this framework can handle unseen class besides just supervised...	Summary: This paper proposes LG-CAV, a method to train Concept Activation Vectors (CAVs) for any concept without labeled image data, leveraging knowledge from pre-trained vision-language models like CLIP. The authors introduce several techniques to improve CAV quality, including Gaussian alignment, concept ensemble, ...	Rebuttal 1: Rebuttal: Thank you for investing your time and effort in offering valuable suggestions for this paper! We will rigorously revise the paper based on your review! > Weakness 1. Some of the proposed modules (e.g., Gaussian alignment) seem heuristic. It would be better to give some theoretical justificati...	Summary: The paper introduces Language-Guided Concept Activation Vectors (LG-CAV), a method to train Concept Activation Vectors (CAVs) without labeled data by leveraging pre-trained vision-language models such as CLIP. LG-CAV uses concept descriptions to guide the training of CAVs by aligning the activation values of c...	Rebuttal 1: Rebuttal: Thank you for dedicating your time and effort to provide valuable suggestions for this paper! We will make rigorous revisions to the paper based on the review! > Weakness 1. Why it successfully addresses the data scarcity issue. Response: Previous methods can only train the CAV for the t...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching	Accept (poster)	Summary: Transformer-based Diffusion Probabilistic Models (DPMs) have shown great potential in image generation tasks but are often hindered by extensive computational requirements. This paper introduces the Efficient Diffusion Transformer (EDT) framework to address these computational challenges. The EDT framework fea...	Rebuttal 1: Rebuttal: Thanks for your positive feedback and insightful comments. We will answer your questions in the following: --- *1. The usage of pre-trained VAE.* * In the field of Latent Diffusion Models, pre-trained VAE is a commonly used model. When training diffusion models or generating images, the...	Summary: This paper introduces the Efficient Diffusion Transformer (EDT) framework to address the high computational requirements of transformer-based Diffusion Probabilistic Models (DPMs). The EDT framework features a lightweight diffusion model architecture, a training-free Attention Modulation Matrix inspired by hum...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback and suggestions. We will answer your questions in the following: --- *1. The experiment under optimal CFG settings.* * According to DiT and MDTv2, their optimal CFG settings are 1.5 and 3.8, respectively. Based on our experimental exploratio...	Summary: The Efficient Diffusion Transformer (EDT) framework is developed, featuring a lightweight architecture designed based on thorough computational analysis. Inspired by human sketching, EDT alternates between global attention and location attention. Additionally, the Attention Modulation Matrix enhances the detai...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and suggestions. We will address your concerns in the following answers: --- *1. Experiments on extra dataset CelebA-HQ.* * We conducted a new experiment on CelebA-HQ 256×256 for the unconditional image synthesis task. We train EDT-S, DiT-S, and MDTv2-S ...	Summary: The paper introduces a new efficient diffusion-based model, namely EDT. First, they revisit the masking strategy proposed in MDT and provide some insights regarding the discrepancy in the training objective. To this end, EDT uses a more efficient masking mechanism that focuses on the main generation task inste...	Rebuttal 1: Rebuttal: We appreciate the professional and insightful comments. We address each comment as follows: --- *1. Performance improvement by AMM.* - We demonstrate that AMM is both effective and efficient through several experiments detailed in Tables 9, 10, and 11 in the Appendix. Tables 9 and 10 ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their constructive comments and insightful suggestions. We carefully add experiments and figures according to the comments of all the reviewers. --- We are encouraged that the reviewers pointed out our work "is novel", "tackling an important application" (**R...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On the Worst Prompt Performance of Large Language Models	Accept (poster)	Summary: The authors propose a new benchmark to study the robustness of LLMs to prompt variations. Different from previous work, this paper mainly focuses on semantically equivalent prompts rather than taks-level instructions. The experiments demonstrate that many popular LLMs are sentitive to the form of prompts. More...	Rebuttal 1: Rebuttal: Thank you for the positive feedback. We appreciate that you acknowledge the thorough evaluations and insightful guidelines in our paper. We provide point-to-point responses to address your concerns as follows: [W1]: potential remedies or strategies to enhance prompt robustness. [A1]: Tha...	Summary: This paper introduces a new benchmark, ROBUSTALPACAEVAL, that contains semantically equivalent queries of diverse real-world tasks, and uses it to conduct extensive experiments on ChatGPT and six open-source LLMs. It highlights variability in model performance and difficulties in predicting the worst prompt. ...	Rebuttal 1: Rebuttal: Thank you for your thorough review and for highlighting the strengths of our work. We appreciate your constructive feedback requiring further clarifications. Below, we address each of your points in detail. **[W1]: The methodology utilizes gpt4_turbo as the evaluator and the reference model for o...	Summary: In this paper, the author(s) propose a benchmark for prompt performance of large language models. In particular, the author(s) leverage GPT4 to generate variants of prompts and hence constitute a dataset. Afterwards, the author(s) evaluate the performance of these generated prompts, and explore the identificat...	Rebuttal 1: Rebuttal: Thank you for your valuable insights of our work. We appreciate your thoughtful feedback, and we would like to address your concerns as follows: [W1]: The methodology and result analysis of this study can be elaborated. [A1]: We appreciate the reviewer's feedback on the need for a more d...	Summary: This paper proposes a new benchmark, RobustAlpacaEval, a benchmark with semantically equivalent queries. The authors evaluate performance as the worst performance across all the prompts and show that many prompt consistency methods have a limited improvement on this benchmark. The paper also shows that it is d...	Rebuttal 1: Rebuttal: We appreciate your in-depth review and the recognition of our novelty, the importance, and the empirical results. We understand your concerns and address them with the following clarifications. [W1]: relation to prior work & future impact of our work. [A1]: Thank you for your insightful...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper studies the worst performance an LLM can have on input queries by testing on paraphrases of each query and report the original, worst, best, and average performance. It has been found that there is a large gap between the best and worst performance. It is then found that there is no particular "wors...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and highlighting our work's strengths. We appreciate your constructive feedback on areas needing further elaboration. We address each of your points below. [W1 & Q1]: definition of the "worst" or "best" performance & the number of paraphrases [A1]: Th...	null	null	null	null	null	null
Universality in Transfer Learning for Linear Models	Accept (poster)	Summary: The paper derives a new universality result, which can be leveraged to solve a mirror descent optimization problem for a large family of data distributions by relating the solution to a Gaussian distribution with matching first and second-order statistics. This result is then applied to analyze transfer learni...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and suggestions to help us improve the quality of our work. Below we address the questions and other points raised in the review one by one. In assumption 2.2, why should the covariance depend on the mean $\mu$ We apologize for the conf...	Summary: The paper investigates the application of transfer learning within the framework of linear models. The authors focus on a model-based approach, where a model pre-trained on a source distribution is fine-tuned on a few samples from a target distribution. Authors extend the concept of universality, traditionally...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and suggestions to help us improve the quality of our work. Below we address the questions and other points raised in the review one by one. **1. The empirical validation, while convincing, is somewhat limited in scope. The experiments are c...	Summary: The paper studies model-based transfer learning. In this setting, a model is pre-trained on the source data, and the learner aims to fine-tune it on the target data by running SGD initialized at the pre-trained model. The paper focuses on linear regression and classification, aiming to generalize the assumptio...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and suggestions to help us improve the quality of our work. Below we address the questions and other points raised in the review one by one. **The paper attempts to generalize the assumption of Gaussianity but is limited to the linear model,...	Summary: This work considers transfer learning (fine-tuning) for linear models in the over-parameterized regime in the proportional regime $k / n \rightarrow \infty$. In this setting, gradient descent converges to the solution a convex optimization problem with linear constraints. This work builds on this result to sh...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and suggestions to help us improve the quality of our work. Below we address the questions and other points raised in the review one by one. **My main concern is that the setting is not conducive to studying transfer learning. Transfer learn...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness	Accept (poster)	Summary: This paper first makes two observations that neurons exhibiting significant weight changes during clean unlearning also tend to play crucial roles in poison unlearning, and neurons in the backdoored model are always more active compared to those in the clean model. The authors showcase on commonly used backdoo...	Rebuttal 1: Rebuttal: Dear Reviewer kmL5, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our well-formulated analysis and effective method. We hope the following responses could help clarify the potential misunderstanding and alle...	Summary: The authors propose a novel two-stage backdoor defense method TSBD. The proposed method is based on two key observations 1) the weight changes of neurons during clean and poison unlearning are correlated, 2) the backdoored neurons exhibit a larger gradient norm during unlearning. Respectively, the proposed def...	Rebuttal 1: Rebuttal: Dear Reviewer obuU, thank you very much for your positive appraisal and great interest in our paper. We are encouraged by your positive comments on our good paper presentation, insightful observations, and convincing evaluations. We hope the following responses can help answer your que...	Summary: The paper addresses the security threat posed by backdoor attacks in deep neural networks (DNNs). The authors explore model unlearning from the perspective of weight changes and gradient norms, making two key observations: weight changes between poison and clean unlearning are positively correlated, and neuron...	Rebuttal 1: Rebuttal: Dear Reviewer P1hB, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our novel and insightful method, good paper presentation, and rigorous evaluation. We hope the following responses can help alleviate you...	Summary: The paper introduces two key observations in backdoored models, presenting a two-stage backdoor attack defense method, where the two observations are the following: a strong positive correlation between weight changes in poison and clean unlearning, and the stronger neuron activation in backdoored models compa...	Rebuttal 1: Rebuttal: Dear Reviewer Vmzu, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our novel and insightful method, superior performance, thorough experimental setup, and good paper presentation. We hope the followin...	Rebuttal 1: Rebuttal: ## General Response We sincerely thank all reviewers for their valuable time and constructive comments. ___ Q1: Systematic comparison with RNP [1]. R1: We aim to address the concerns regarding the differences between our work and RNP. More precisely, we emphasize their differences from ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Robust group and simultaneous inferences for high-dimensional single index model	Accept (poster)	Summary: This paper studies the high-dimensional single index model (SIM), which takes the form $Y=g(X^T\beta, \epsilon)$ with $\epsilon$ and $X$ being orthogonal. Although this model has flexibility and interpretability, its efficiency is adversely affected by outlying observations and heavy-tailed distributions. The ...	Rebuttal 1: Rebuttal: Thanks very much for your valuable comments. ## Weaknesses The group inference is helpful to decide whether a group of predictors are important or not for the response. If we find a group of predictors are important, we would like to know which specific predictors in the group are significant. Fo...	Summary: This paper proposes a robust group hypothesis testing procedure for a high-dimensional single index model based on a data-driven transformation of the response variable. The key observation is that under the the linearity condition (LC) of the predictors, a wide class of single index models can be equivalently...	Rebuttal 1: Rebuttal: Thanks very much for your valuable comments. ## Weaknesses We have added more simulations to illustrate our procedures and compare them with other methods. The simulation settings are summarized in global response, and the simulated results are displayed in the attached pdf file. ## Question 1 As ...	Summary: The paper proposes an algorithm for group inference in single-index models with an unknown link function, that is robust to heavy-tailed noise in the responses. The central idea of the approach is based on the property of elliptical distributions that the linear input-output correlations remain along a fixed d...	Rebuttal 1: Rebuttal: Thanks very much for your valuable comments. ## Weakness 1 As noticed by you, in our paper, we say our procedure is robust since our methods do not need any moment condition for the error term in the single-index model. For your concern, we further discuss the robustness of our procedure based on ...	null	null	Rebuttal 1: Rebuttal: Dear Program Chairs, Senior Area Chairs, Area Chairs and Reviewers, Thank all of you for your insightful comments and valuable suggestions, which have significantly enhanced the quality of this work. In this response, all the comments have been carefully addressed and accommodated. A summary of r...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Modular Conditional Diffusion Framework for Image Reconstruction	Accept (poster)	Summary: This paper proposes a modular conditional diffusion framework for image reconstruction. Specifically, a small module is trained and combined with pretrained IR networks and DPMs. Experiments show that this method is effective. Strengths: 1. According to the quantitative and qualitative results, the method sho...	Rebuttal 1: Rebuttal: > Q18 The figures in this paper are of poor quality. The authors should revise the figures to make the formulas clear and professional. In our submitted manuscript we made sure that both Figures 1 and 2 are vector images and that the formulas inside these figures follow the same LaTeX style a...	Summary: This paper proposes a new approach to improving the efficiency and applicability of Diffusion Probabilistic Models (DPMs) for various image restoration (IR) tasks. A new modular diffusion probabilistic image restoration (DP-IR) framework combines pre-trained state-of-the-art IR networks with generative DPMs. T...	Rebuttal 1: Rebuttal: > Q14 Computational cost analysis: A more detailed analysis of the computational cost, including memory usage and inference time, would be helpful. While we agree with the reviewer that the inclusion of such information has practical value, we would also like to highlight existing problems th...	Summary: This manuscript proposed a modular conditional diffusion model for image reconstruction, consisting of three components: a pre-trained image restoration network, a denoising network, and a fusion network. Strengths: The model reduces computational load by minimizing the number of network modules that need to ...	Rebuttal 1: Rebuttal: > Q8 The standard and rationale for selecting the baseline models have not been detailed. For example, why were specific image restoration networks, denoising networks, and fusion networks chosen? Were these choices based on certain performance metrics, relevant literature, or experimental res...	Summary: This paper proposes a modular diffusion probabilistic IR framework to combine the performance benefits of existing pre-trained state-of-the-art IR networks and generative DPMs with a light-weight fusion network. Experimental results on burst JDD-SR, dynamic scene deblurring, and super-resolution demonstrate it...	Rebuttal 1: Rebuttal: > Q1 The contribution of accelerated sampling seems trivial. As the author said, references [13],[52] already proposed a similar idea. We agree with the reviewer that our acceleration strategy indeed bears certain similarities to the strategies presented in the referred papers. However, as we...	Rebuttal 1: Rebuttal: # General response We would like to thank all reviewers for the insightful questions and valuable suggestions. In this response we would like to address common questions that were asked by more than one reviewer. ## Limitations and further perspectives In the manuscript (lines 356-363) we have ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Covariate Shift Corrected Conditional Randomization Test	Accept (poster)	Summary: The paper introduces a novel approach to addressing covariate shift in conditional independence tests. By leveraging importance weights and the control variates method,the paper proposes Covariate Shift Corrected Pearson Chi-squared Conditional Randomization (csPCR) test, which maintains asymptotic Type-I erro...	Rebuttal 1: Rebuttal: We extend our sincere thanks for your reviewing work and insightful comments. Please see our responses to your comments and questions as below. $\textbf{Weaknesses 1}$: In the college admission example, why don't the economists just collect the college admission results from the target populatio...	Summary: This paper proposes a new variation of CRT to be applied in the presence of covariate shifts. The paper presents a method and one extension for each, with higher power. Then, the authors present some needed theoretical results and finish with experiments. Strengths: - The paper proposes a theoretically correc...	Rebuttal 1: Rebuttal: We extend our sincere thanks for your reviewing work and insightful comments. Please see our responses to your comments and questions as below. $\textbf{Weakness 1}$: It would be interesting if the authors could estimate the full density ratio in their simulations (possibly in a high-d scenario)...	Summary: This paper introduces the Covariate Shift Corrected Pearson Chi-squared Conditional Randomization (csPCR) test, designed to address covariate shift in conditional independence testing. The csPCR method incorporates importance weights and employs the control variates method to enhance test power and reduce vari...	Rebuttal 1: Rebuttal: $\textbf{Weakness}$ 1: Dependence on Accurate Estimations of Density Ratio \& $\textbf{Question 3}$: Theoretical Limitations? Response: We believe our method's limitation lies mainly when the model-X assumption fails, i.e., when the distribution of covariates cannot be accurately learned. In such...	Summary: This paper addresses the issue of conditional independence under covariate shift. The authors' goal is to test the conditional independence for causal inference in the target data. The authors can use the source data whose distribution is potentially different from that of the target data. For this problem, th...	Rebuttal 1: Rebuttal: We extend our sincere thanks for your reviewing work and important comments. We believe your main confusion lies in the existence of V and the fact that $P(V \mid X, Z)$ can be different between the source and target. This can possibly cause the situation that $H_0: X \perp Y \mid Z$ does not hold...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers and chairs for their feedback. We have addressed each of the reviewers' points individually and have included a PDF with additional experiments. The first of the experiments demonstrated that our proposed csPCR method has more stable Type I error rate control and h...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features	Accept (poster)	Summary: The paper proposes differentially private scalable kernel ERM and KME algorithms that are more general than the prior works. The authors provide privacy and convergence proofs for their proposed approaches, and experimentally compare their approach with prior works. Strengths: - Thorough discussion on backgro...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer's positive feedback.	Summary: The paper studies scalable kernel learning algorithms under differential privacy. First, the authors propose an algorithm for DP K-means Nyström approximation to obtain an orthonormal basis and their corresponding random feature map. Then, the authors use the basis and feature map for kernel ERM algorithm and ...	Rebuttal 1: Rebuttal: W1 Contributions While it is true that our algorithms utilize existing private learning schemes, we emphasize two novel contributions and one practically critical point: First, while the Nyström-based method is a known approach, applying it under privacy constraints is a novel and significan...	Summary: This paper considers the problem of differentially private kernel learning. The main idea of proposed algorithm is to approximate the kernel matrix using the Nystrom kernel embeddings. The landmark points on which the approximation is built are chosen as the centroids given by k-means algorithm on data based-o...	Rebuttal 1: Rebuttal: W1. The utility of the versatile method We acknowledge that the utility of learning from privatized data for versatile DP kernel ERM may be inferior compared to a DP kernel ERM algorithm dedicated to a specific task. For instance, when the RKHS of a given kernel has a finite dimension $d$, ke...	null	null	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their constructive feedback. We have addressed all comments to the best of our ability. Detailed point-by-point responses to the reviewers' comments are provided below. Additionally, please see the attached one-page PDF for further experimental results. Pdf...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Latent Feature Mining with Large Language Models	Reject	Summary: This paper proposes a framework to augment latent features from observed features, with the help of LLM. They frame the problem as a text-to-text reasoning problem. The method can be adapted to different domains easily. The method is also validated with a real world dataset. Strengths: Overall, the presenta...	Rebuttal 1: Rebuttal: Thank you for your comments and feedback. Here are our responses to your questions: --- - Question 1: Suggestion on adding experiments about social bias and fairness of using LLMs for inference. - Response: We appreciate your concern about the bias and fairness of using LLMs for in...	Summary: The authors propose a unique form of LLM data-augmentation that attempts to generate informative latent variables to improve downstream tasks. They do this by transforming the latent feature mining task into a text-to-text propositional reasoning task. Validation is performed with a case study in the criminal ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedbacks and comments, here are our response to your questions: - Question1: Concern about potential biases from LLMs in latent variable finding. - Response: Thank you for highlighting this concern. **Our framework aims to minimize biases by leveraging dom...	Summary: The paper presents a framework that uses LLMs to improve predictive modeling by augmenting observed features with inferred latent features. This approach transforms the latent feature mining task into a text-to-text propositional reasoning task, enabling LLMs to infer unobserved yet crucial factors from availa...	Rebuttal 1: Rebuttal: Thank you for your valuable feedbacks! - Question 1: How to measure the impact of errors? How to ensure the approach doesn't amplify potential errors? - Response: As discussed in Section 4, we incorporate human-in-the-loop interventions to identify and remove erroneous synthetic re...	Summary: This paper used large language models to infer latent variables that are important for downstream prediction tasks to augment the existing models. In particular, the author demonstrated the use of the proposal on a criminal justice system use case, in which the LLM-mined-latent features significantly boost the...	Rebuttal 1: Rebuttal: Thank you for your comments and feedbacks. Here are our responses to your questions: --- Question 1: Suggestion on adding experiments on other domains/dataset to prove generalizability. Response: We have conducted additional experiments in the healthcare domain with the MIMIC-IV d...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning	Accept (poster)	Summary: This work investigates the tail risk minimization in meta-learning from theoretical and practical perspectives. Overall, this work is well-written, novel and theoretically enriches TR-MAML[1]/DR-MAML[2]. In the realm of large models, meta-learning plays a crucial role due to the pressing concern of distribut...	Rebuttal 1: Rebuttal: We sincerely thank # Reviewer 8vBx for these helpful comments. The remainders focus on questions to answer. --- 1. Advantages and disadvantages between group DRO and tail risk minimization in meta-learning Thanks for this comment. The group DRO method employs risk reweighted algorithm t...	Summary: This paper proposes an enhancement to the previous work termed DR-MAML by reformulating it as a Stackelberg game. Theoretical investigations regarding its solution concept, convergence rate, and generalization bound are provided. Numerical experiments demonstrate the improved robustness of the proposed method....	Rebuttal 1: Rebuttal: We sincerely thank # Reviewer 6Ej4 for these insightful comments. The remainder mainly focuses on concerns to address. --- 1. Application scopes and contribution clarifications Thanks for the comment. Sorry for confusing you in contributions, and we further summarize: (1) Regarding the...	Summary: The paper provides theoretical investigations for better understanding of an existing method in literature that focuses on minimizing expected tail risk. Equivalence of the algorithm is shown to a Stackerlberg game, which allows to study its convergence rate and asymptotic bounds on performance and generalizat...	Rebuttal 1: Rebuttal: We sincerely thank # Reviewer ci3Y for these insightful comments. The remainders focus on concerns and questions to address. --- 1. Additional explanations on the expected tail risk minimization Thanks for this advice. We'll include more explanations in Line 115 as follows: > Wang e...	null	null	Rebuttal 1: Rebuttal: We sincerely thank all reviewers and area chairs for their work. This global response summarizes reviews, addresses concerns, answers questions, and reports changes in the manuscript. --- ### I. Review Summary We thank all reviewers for their comments: 1. a *well-constructed paper easy t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Quantile Activation: Departing from single point estimation for better generalization across distortions	Reject	Summary: The paper introduces a novel activation function called Quantile Activation (QACT), which aims to improve the robustness of neural networks against various data distortions. The authors propose an end-to-end framework that combines QACT with modified loss functions and quantile classifiers, evaluating their ap...	Rebuttal 1: Rebuttal: Overfitting: Interestingly, while we do add a lot of operations, we do not actually increase the number of trainable parameters. Algorithm 1 in the article is a fixed function which depends on the entire batch of inputs and has no parameters. Hence, we do not expect overfitting any more than t...	Summary: The paper introduces Quantile Activation (QACT) to enhance classification model robustness against distributional shifts. Unlike traditional classifiers, QACT outputs the relative quantile of a sample in its context distribution, allowing for context-dependent classification. Validated on datasets like CIFAR10...	Rebuttal 1: Rebuttal: Discussion on Context Distribution and Batch Dependency: Note that we obtain the context distribution from the other samples in the batch. This is discussed in lines 166-175 as well. We reiterate this in conclusion. Transformers also considers context? Note that transformers process one ...	Summary: The authors propose a new activation function called quantile activation (QACT) which outputs the relative quantile of the sample in the context distribution. Furthermore, the paper validates the proposed activation across several experimental settings, and compare it with conventional techniques. They test ro...	Rebuttal 1: Rebuttal: We thank the reviewer for the comments and interesting questions. 1. Generative Models vs. Quantile Activation: We do agree with the reviewer that generative models are indeed one approach to estimating the density. As a simple case, one can estimate the mean/stdev for each neuron and use ...	null	null	Rebuttal 1: Rebuttal: We would like to take this opportunity to present a broad outline of our article and its contributions. Broad Claim: The central hypothesis of this article is that incorporating input samples from the context distribution when obtaining representations enhances robustness to distortions. Howe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DiffPhyCon: A Generative Approach to Control Complex Physical Systems	Accept (poster)	Summary: This paper proposes an algorithm for controlling complex physical systems, particularly in long-term settings. The method is based on diffusion models and energy methods, utilizing data generated by traditional finite difference methods. Strengths: * The problem studied in this paper is important and well-mot...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive and detailed comments. We are glad that the reviewer finds our work clear and well-motivated with detailed results. Below, we address the reviewer’s questions one by one. > Comment 1: ...The current paper does not address these problems convincingly ...	Summary: This paper introduces Diffusion Physical Systems Control (DiffPhyCon), where diffusion models are used to generate a near-optimal controller for a system described by a partial differential equation (PDE). In this generative approach, a learned generative energy function and control objective is minimized. Add...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s valuable feedback and helpful suggestions. We are pleased to hear that the reviewer finds our paper well-written, well-motivated, and extensive in its results. Below, we address the reviewer’s questions one by one. >Comment 1: ... Thus, I suggest to revise the in...	Summary: The model proposed a variant of diffusion models to control complex dynamical systems. Its contributions are threefold. First, the proposed model could optimize the trajectory and control sequence simultaneously. Second, it proposed a reweighting prior technique to generate a superior control sequence to the t...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments. We are glad that the reviewer recognizes the novelty, clarity, results, and significance of our work. Below, we address the reviewer’s questions one by one. >Comment 1. How is the offline dataset generated? Does it include the optimal control...	Summary: The method learns the energy function, $\epsilon_\theta$, which is used in the energy optimization target to generate the control sequences and system trajectory. A denoising network is trained to approximate the gradient of $\epsilon_\theta$. The network and optimization framework takes a global state of u an...	Rebuttal 1: Rebuttal: We thank the reviewer for the helpful comments. We are glad that the reviewer appreciates our jellyfish results and dataset contribution. Below, we address the reviewer’s questions one by one. >Comment 1: The experiments are relatively limited - only include one 1D example and one 2D example. ...	Rebuttal 1: Rebuttal: # General Response We thank the reviewers for their thorough and constructive comments, as well as AC's organization in reviewing our paper. We are glad that the reviewers think that our paper is well-written (DFgi, n6Yf, Mp9v) and well-motivated (n6Yf, Mp9v). Reviewers 8Er5 and DFgi reco...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Online Composite Optimization Between Stochastic and Adversarial Environments	Accept (poster)	Summary: This paper studies the problem of online composite optimization under the Stochastically Extended Adversarial (SEA) model, where the environments select loss functions in a manner that interpolates between fully stochastic and fully adversarial scenarios. Specifically, the authors establish regret bounds for t...	Rebuttal 1: Rebuttal: Many thanks for the constructive reviews! --- Q1: I believe that some experimental studies could further strengthen the theoretical results. A1: Thanks for the helpful suggestion! We have conducted empirical studies to verify our theoretical findings. Detailed descriptions can be fou...	Summary: This paper investigates online composite optimization in the regime between stochastic and adversarial environments, establishing theoretical guarantees for three types of time-varying loss functions: smooth and general convex, smooth and strongly convex, and smooth and exp-concave. These bounds not only exten...	Rebuttal 1: Rebuttal: Many thanks for the constructive reviews! --- Q1: Although the theoretical results are substantial, the paper lacks experimental validation. A1: Thanks for the helpful suggestion! We have conducted empirical studies to verify our theoretical findings. Detailed descriptions can be fou...	Summary: This paper investigates online composite optimization within the SEA model. Specifically, it demonstrates that by appropriately adjusting the predictor and step size of the algorithm known as OptCMD, similar regret bounds to those in online optimization within the SEA model can be achieved for general convex f...	Rebuttal 1: Rebuttal: Many thanks for the constructive reviews! --- Q1: The techniques proposed in this paper do not offer significant novelty. A1: We acknowledge that our research is partially inspired by existing studies, and some techniques employed may not be entirely novel. However, we would like to ...	Summary: The authors study the online composite optimization under the Stochastically Extended Adversarial (SEA) model proposed by Sachs et al., 2022. They show that optimistic composite mirror descent (OptCMD), a variant of OMD by [Scroccaro et al., 2023], can achieve regret guarantees that match existing bounds for t...	Rebuttal 1: Rebuttal: Many thanks for the constructive reviews! --- Q1: What technical challenges does the new setting throw beyond a different selection of algorithm's parameters? Do these challenges require the authors to come up with any technical innovation in their proofs? A1: Thanks for the insightf...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive comments and appreciations of our work. Since both Reviewers tdoW and wJwu suggest conducting experiments to validate our theoretical results, we present the following general response regarding the empirical studies. --- Setup. In our paper, we ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models	Accept (poster)	Summary: The paper proposes an algorithm to learn a (low-rank) variational posterior distribution over (a subset of) LLM weights during fine-tuning by combining Low-Rank Adaptation (LoRA) and Bayes by Backprop (BBB). To make the algorithm work in practice, several nontrivial modifications are introduced, such as certai...	Rebuttal 1: Rebuttal: Thank you for the constructive and encouraging comments as well as the insightful questions. We are glad that you find our method ``"justified"`` by ``"theoretical and empirical arguments"``, our paper ``"well written"``/``"easy for the reader to follow"``, and the empirical evaluation showing our...	Summary: This paper proposes a method called Blob, which uses mean-field variational inference on LoRA parameters to obtain Bayesian estimation on LLM. This will result in a richer posterior structure on the weight than diagonal, while maintaining the same computational cost as diagonal covariance. During training, a n...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and insightful questions. We are glad that you like our idea and find our method ``"promising"`` and its performance ``"good"``/``"competitive"``. Below we address your major questions (W2, W3, Q3, and Q5) in detail. The remaining questions will be answered...	Summary: Proposes to have a Bayesian version of LoRA by placing priors over the low rank matrices. This evolves into placing prior over one of the low rank matrices. Inference is via variational inference. Results are mixed, though it shows the method is competitive with others. Strengths: - The eventual simplicity of...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and insightful questions. We are glad that you find our proposed methodology ``"competitive"`` with ``"simplicity"``, ``"efficiency"``, and ``"good contribution"``. Below we will address your questions in detail. **W1. The flow of the paper is rather poor,...	Summary: This work introduces a Bayesian Deep Learning framework for finetuning LLMs with probabilistic LoRA. Unlike existing work by Yang et al. [1] which uses a Laplace approximation, the variational distribution is parameterized using diagonal Gaussians as done in [2] (”Bayes by Backprop”). Specifically, the autho...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and insightful questions. We are glad that you find the problem we address ``"important"``, our empirical evaluation of BDL on LoRA ``"make sense"``, and our paper ``"clear"``. Below we will address your questions in detail. **W1. Novelty is limited compar...	Rebuttal 1: Rebuttal: # General Response We thank all the reviewers for their valuable and constructive comments. We are glad that they like our idea (Adn3) and find the problem we address ``"important"`` (Lm4V), our paper ``"clear"``/``"well written"/"introduced in an adequate pace and order"`` (Lm4V, AGd6), our me...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Active Classification with Few Queries under Misspecification	Accept (spotlight)	Summary: This paper considers active learning of halfspaces with noise that is both computationally efficient and query efficient without distributional assumption on X. Since it is known such problem is "hard" in the standard label query paradigm, it considers a new query model called "threshold statistical queries" (...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work. Improving the cubic term in the label complexity is definitely an interesting open question. We will add a conclusion section in the future version of the work to introduce some potential future directions. --- Rebuttal Comment 1.1: Comment: Thank...	Summary: This paper focused on the problem of active learning from enriched queries. In order to be abale to learn halfspaces without restrictive distribution assumptions, authors propose Threshold Statistical Query (TSQ) which genarizes the region query and the classic statistical learning model query. Using the propo...	Rebuttal 1: Rebuttal: We appreciate the constructive feedback and thank the reviewer for carefully reviewing our manuscript. We will improve the presentation of the paper based on the suggestions and include a conclusion section with further discussion. Below is our response regarding the question on the noise format. ...	Summary: This paper extends the work on pool-based active learning from the realizable case to non-realizable settings: where the observed labels (or answers to active learning queries) can have noise. The paper focuses on learning half-spaces, a fundamental learning theory problem. Existing works on pool-based activ...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work and providing many constructive suggestions. We will improve the presentation in the future version of the manuscript. We think exploring the power of TSQ for other hypothesis classes such as the intersection of halfspaces or high dimensional boxes c...	Summary: This paper is concerned with active learning of halfspaces under persistent Massart noise, i.e., active learning under data $(X,Y)$ for which $\exists w^* \in \mathbb{R}^d, \eta \in [0,1/2)$ such that $P(Y = \mathrm{sign}(\langle w^*, X\rangle)) \ge 1-\eta$. The paper proposes a new query language for this t...	Rebuttal 1: Rebuttal: We thank the reviewer for carefully reviewing our work and for the constructive feedbacks. Below is our response about the practicality of TSQs. > While I appreciate the clear contextualiation of the investigation of query structures in active learning, I think the practicality of TSQs is not ver...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments, and that all the reviewers appreciated the paper for the following (i) Novelty and interesting result (VrwT,vqip,WAF8,DNzP), (ii) technically clean and interesting (**VrwT, WAF8,...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On Sparse Canonical Correlation Analysis	Accept (poster)	Summary: The paper presents an efficient MISDP algorithm for sparse CCA. Strengths: Sparse CCA is an important and interesting problem. The analysis is useful for this area. Weaknesses: - Typos: optimiality -> optimality line 167 - Limitations are not fully addressed - No separate evaluation of sparsity in the experi...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and feedback. In the following, we provide detailed responses to each of the issues raised by the reviewer. 1. [Q1: Typo] We will fix the typo in the revision. []() 2. [Q2: Limitations] The limitations mainly arise from two aspects: Algorithm ...	Summary: The paper proposes several algorithms for the Sparse Canonical Correlation Analysis (SCCA) problem. First, the authors present an exact semidefinite programming representation of the classic CCA problem. These results are then used to derive an equivalent combinatorial formulation of the Sparse CCA problem, w...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their careful evaluation and valuable suggestions. We have addressed the reviewer’s comments below. 1. [Q1: Experimental comparison] We have compared the proposed local search algorithm with the SCCA methods of [10, 32, 41, 37] in correlation value, sparsity, a...	Summary: To enhance the interpretability of CCA, the authors explore sparse CCA. It is interesting to note that sparse CCA generalizes sparse PCA, sparse SVD, and sparse regression. The authors derive efficient algorithms to solve sparse CCA and perform the theoretical analysis for sparse CCA. The effectiveness of the ...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their careful evaluation and valuable suggestions. We have addressed the reviewer’s comments below. 1. [Q1: Experimental comparison] We have compared the proposed local search algorithm with the SCCA methods of [10, 32, 41, 37] in correlation value, sparsity, a...	Summary: This paper presented analyses on sparse canonical correlation analysis (SCCA) with optimization tools, including an SDP relaxation formulation. The authors discussed the theoretical properties and demonstrated numerical experiments. Strengths: The paper presented detailed theoretical developments supported by...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments and feedback. In the following, we provide detailed responses to each of the issues raised by the reviewer. 1. [Q1: Understand the real datasets] We have applied the local search algorithm to evaluate the performance of SCCA against different sp...	Rebuttal 1: Rebuttal: We thank the review team for their insightful comments and feedback, which significantly improve the quality of our paper. In the following, we provide detailed responses to the key issues raised by the reviewers. 1. [Experimental comparison] We have compared the proposed local search algorithm ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DEPrune: Depth-wise Separable Convolution Pruning for Maximizing GPU Parallelism	Accept (poster)	Summary: This paper proposed a pruning method target at the previously neglected Depth-wise Convolution layers which are widely used in vision models. This work is based on the Diagonal-wise Refactorization (DR) computation strategy used in GPU, and pruning one weight point in depth-wise convolution means convert the c...	Rebuttal 1: Rebuttal: Thank you for your support of our paper and the valuable feedback. We respond to your reviews as follows.[m-#] means manuscript's reference. **Weakness 1) [The overhead of pruning method is not analyzed. How much time does it need to decide pruning points, loading balance and the parameters in HS...	Summary: This paper presents the Depth-wise Separable Convolution Pruning (DEPrune). DEPrune prunes point-wise convolution and depth-wise convolution (DW-conv). And it is optimized by considering and analyzing the computation of Depth-wise Separable Convolution on GPU. Experimental results validate the speedup of DSPru...	Rebuttal 1: Rebuttal: Thank you for the time you have taken to review our work and for the constructive feedback. If the reviewer allows us to revise this paper, we will try our best to improve the quality of this paper. Weakness 1) [Some references are incomplete, for example, [23][39].] We greatly appreciate y...	Summary: This paper presents DEPrune, which prunes depthwise separable convolution models into a structured sparsity pattern that is friendly to depthwise refactorization. Strengths: 1. Much of the motivation for conducting CNN pruning is to pursue the most lightweight — often time edge-deployed — model run under much...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s time and effort in providing constructive feedback on this manuscript. We will incorporate all of the suggestions into the final version of the paper. [m-\#] means manuscript's reference. Weakness 1) As the reviewer pointed out, it is important to com...	Summary: The paper addresses an important topic of pruning depth-wise separable convolutions (DSConv) called DEPrune. While structural model pruning methods like channel pruning can achieve significant speed-up for regular convolutions, they cannot secure notable speed-up on DSConv layers as they mainly prune the point...	Rebuttal 1: Rebuttal: We would like to express our gratitude to the reviewer for their constructive and insightful feedback. Below, we have provided our responses to the comments. If the paper is accepted, we will incorporate these helpful suggestions into the camera-ready version. **Weakness 1) [I hope that the autho...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' time and effort in providing constructive and valuable feedback on this manuscript. In response to the reviewers' questions, we have conducted evaluations: 1. Reviewer CSWm's Question 4: We provided peak memory usage data presented in Author-Rebuttal-Table ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes	Accept (poster)	Summary: The paper investigates topological reasoning for lane graph prediction for autonomous driving. The paper focuses on improving the prediction of the topological structure of the lane graph. Therefore, they propose two new mechanisms, the first is a geometric approach that estimates connectivity based on the dis...	Rebuttal 1: Rebuttal: Thanks for your careful reading of our paper. We hope our response and clarifiction can ease some of your concerns and you could reconsider your rating. Q1: Why the paper does not compare with TopoMLP? A1: Thank you for your insightful question. I will respond to it in detail. Altho...	Summary: Reasoning about lane topology is becoming more and more important for the autonomous driving community. Current methods mostly focus on improving the perception performance and ignore the reasoning part of the task. The authors raise the importance of the relationship between lanes, namely the lane distance an...	Rebuttal 1: Rebuttal: Thanks for your careful reading of our paper. We hope our response and clarifiction can ease some of your concerns and you could reconsider your rating. Q1: Lane segment is not discussed in the methodology session. A1: Thanks for your attention to this detail, which is indeed omitted...	Summary: The authors claim previous topology reasoning methods typically boost reasoning performance by enhancing the perception of lanes and directly adopt MLP to learn lane topology from lane query. In this work, the authors propose to make full use of lane geometric distance and lane query similarity for topology r...	Rebuttal 1: Rebuttal: Thanks for your careful reading of our paper. We hope our response and clarifiction can ease some of your concerns and you could reconsider your rating. Q1: Like an incremental work based on TopoNet. The contribution is limited. The contributed part is only a similarity module for mapping...	Summary: This paper proposes an interpretable method for lane topology reasoning based on lane geometric distance and lane query similarity. The authors reveal that the lane topology is easily disturbed by the endpoint shifts. Based on this, the proposed post-processing module improves the robustness and performance of...	Rebuttal 1: Rebuttal: Thanks for your careful reading of our paper. We hope our response and clarifiction can ease some of your concerns. Q1: The proposed method is relatively simple and involves only post-processing modules, which at the same time leads to limited contributions and incremental improvements. ...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their valuable time and comments. In order to better response for the questions, we have attached a Rebuttal PDF that includes some figures related to Reviewer 8GTK's questions. We hope that following responses could address reviewers’ concerns. Pdf: /pdf/093ac...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining	Accept (poster)	Summary: In this work, the authors introduce SLTrain, a novel method for pre-training large language models (LLMs) that combines sparse and low-rank matrix structures to enhance parameter and memory efficiency. The low-rank component is learned via matrix factorization, while the sparse component is achieved by uniform...	Rebuttal 1: Rebuttal: Thank you for acknowledging the strengths of our work and providing many constructive feedback. **1. (W1) As the model size scales up, the perplexity score gap between SLTrain and Full-Rank increases, whereas GaLore maintains a more consistent performance at scale. Consequently, the reduction in...	Summary: The submission proposes an approach to reduce memory and computational overhead in training large neural networks. It combines sparse training with low-rank adaptations to achieve efficient training without significant performance degradation. The paper includes an evaluation of SLTrain compared to full-rank, ...	Rebuttal 1: Rebuttal: We sincerely thank you for your feedback. We would like to take this opportunity to address your concerns and questions individually. We hope our clarifications would lead to your re-evaluation of the contribution of this work. **1. (W1) (1) $BA + S$ requires full matrix to be stored in memory (...	Summary: The authors propose SLTrain that performs a low-rank factorization of the weights as well as a sparse matrix of factors that represents which parameters to update. The authors show that their method can achieve significant memory savings compared to GaLore while retaining performance. Strengths: - the paper i...	Rebuttal 1: Rebuttal: Thank you for the positive and constructive comments on our work. 1. (W1) Lack of theoretical justification. We motivate the low-rank plus sparse modelling from the empirical observations (Figure 2 in the main text and Figure 2 in the one-page supplementary PDF) that the pretrained weights ...	null	null	Rebuttal 1: Rebuttal: Dear Reviewers and ACs We sincerely appreciate the time and effort you have invested in managing our submitted paper. We are especially grateful for your constructive and thoughtful feedback. In response to your comments, we have provided a formal justification and we have also included a *one-pa...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization	Accept (poster)	Summary: In this work, the authors propose finetuning the noise that a one-step diffusion model predicts a clean image from, with respect to an ensemble of preference constraints. Because only a one-step diffusion model is used, optimizing the noise is fast in terms of number of steps (and therefore wall clock time). ...	Rebuttal 1: Rebuttal: We would like to clarify the contributions of ReNO to ensure the problems ReNO is aiming to solve are understood. We tackle the question of whether we can generally enhance T2I models without any fine-tuning at test time. We propose to tackle this through the Noise Optimization framework by ...	Summary: The paper introduces Reward-based Noise Optimization (ReNO), a novel approach to enhance Text-to-Image (T2I) models at inference by optimizing the initial noise based on human preference reward models. ReNO significantly improves model performance within a computational budget of 20-50 seconds, outperforming a...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed comments and are especially glad they emphasize the significant enhancement achieved by ReNO. Below, we address the concerns raised in the review. > ***Therefore, optimizing time is a crucial factor that requires attention. The paper points out that the o...	Summary: The paper presents a novel approach called Reward-based Noise Optimization (ReNO) to enhance the performance of one-step Text-to-Image (T2I) models. ReNO optimizes the initial noise of T2I models using a human preference reward model, addressing the limitations of current T2I models in capturing complex detail...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed comments. We are especially glad that they appreciated the simplicity and effectiveness of ReNO. Below, we address the concerns raised in the review. > ***ReNO is essentially a runtime optimization approach that leverages advanced reward models to achieve...	Summary: The paper introduces Reward-based Noise Optimization (ReNO), a novel method to improve Text-to-Image (T2I) models by optimizing the initial noise during inference using human preference signals. This approach addresses the limitations of current fine-tuning methods, which often lead to "reward hacking" and poo...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed comments. We are especially glad that they enjoyed the paper. Below, we address the concerns raised in the review. > *Limited comparison to related optimization approaches* We address the comparison to DOODL in the global response. For D-Flow, the p...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their time and their detailed and insightful comments. We appreciate their recognition of the ReNO's significant enhancement in general performance (UuFQ,8y7L,mwA2), and its novelty (UuFQ, mwA2), clarity (UuFQ, 8y7L), and practicality (UuFQ,...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SAND: Smooth imputation of sparse and noisy functional data with Transformer networks	Accept (poster)	Summary: The paper addresses the limitations of ordinary transformers for doing imputation of functional data over irregularly longitudinal data. The authors propose a novel new variant of transformer that takes derivatives into account, called SAND. Theoretically, it's shown that SAND with a certain number of hidden n...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable and positive feedback. --- Rebuttal Comment 1.1: Title: Thank you too Comment: We thank the authors for their thanks.	Summary: This paper studies the problem of how to perform imputation of the underlying function from noisy or sparse observations with functional data. In particular, the authors present "SAND" (Self-Attention on Derivatives), a variant of the transformer architecture, by introducing $\mathrm{diff}(\cdot)$ (derivative...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable and positive feedback, which we have used to strengthen our paper. Please see our responses below. ### [Weakness 1] Answer: Thank you for the comment. We acknowledge that most of our theorems focus on SAND. However, our theorem does reveal one reason th...	Summary: This paper proposes a new class of transformers for sparse and noisy functional data. In particular, a new module, namely self-attention on derivatives (SAND), is incorporated vanilla transformers to model the sub-derivative of the imputed curve, thereby promoting smoothness. The authors also theoretically pro...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable and positive feedback, which we have used to strengthen our paper. Please see our responses to the weakness below. Our responses to the questions are listed in the global author response. ### [Weakness 1] Answer: Thank you for highlighting this aspect....	null	null	Rebuttal 1: Rebuttal: We are grateful for the detailed feedback from all reviewers, which has significantly contributed to refining our manuscript. We would like to address a specific concern raised by two reviewers (92dE and pcLe) regarding the use of attention in SAND's $\rm{diff}(\cdot)$ operator and the des...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning	Accept (poster)	Summary: The paper explores the use of structured matrices instead of low-rank matrices for approximating the finetuning updates for Transformer models. The sturcture imposed on the approximation matrices determines the number of trainable parameters. The paper explores use of two structured matrices: Circulant and Toe...	Rebuttal 1: Rebuttal: We would like to sincerely thank the Reviewer for their very valuable feedback and comments. > Studies on larger tasks: One of the key things that we showed in our experiments was the improved performance in the low data regime. Specifically across a plethora of low-resource image experiments (V...	Summary: The paper introduces a new technique called SURMs which aim to use structured matrices for PEFT. The technique is tested against many different adapter variants in Vision Adaptation and Natural Language. The unique structure of the matrices allow for efficient computation of the products Strengths: - The idea...	Rebuttal 1: Rebuttal: We would like to sincerely thank the reviewer for their valuable feedback and comments. > Structured matrices for low rank adaptation has previously, as noted in the paper Knoecker products have been in previous literature, while Circulant and Toeplitz structures are, to my knowledge, have not. N...	Summary: The paper proposed a general framework for parameter-efficient fine-tuning, based on structured unrestricted-rank matrices (SURMs) to substitute LoRA or other parameter-efficient finetuning methods. Three variants of SURMs are included, named Kronecker, Toeplitz, and Circulant, based on the matrix type. The pr...	Rebuttal 1: Rebuttal: We would like to sincerely thank the Reviewer for their very valuable feedback and comments. > NLP experiments: We'd like to emphasize the breadth and depth of experimental evidence we have provided for SURMs. First, we compared SURMs on 6 image classification datasets and compared our methods a...	Summary: This paper explores structured unrestricted-rank matrices (SURM) for parameter-efficient fine-tuning (PEFT) of large-scale Transformer models. This method (SURM) was the first to apply low displacement rank matrices (LDRM) which could support fast matrix-vector multiplication and showed flexibility in finding ...	Rebuttal 1: Rebuttal: We sincerely thank the Reviewer for their valuable feedback and comments. > Any reasons why the proposed version w/ the Kronecker product outperforms the previous works, such as Kadaptation? Kadaptation approximates the gradient update using: $\Delta W = \sum_{i=1}^n A_i \bigotimes B_i$ where $B...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Marrying Causal Representation Learning with Dynamical Systems for Science	Accept (poster)	Summary: This paper aims to connect causal representation learning with parameter identification in dynamical systems. By doing so, existing causal representation learning approaches can be used for estimating parameter in dynamical systems with identification guarantees. On the other side, it also demonstrates the app...	Rebuttal 1: Rebuttal: ### Reply to weaknesses (same order as given by the reviewer) 1. The [global rebuttal](https://openreview.net/forum?id=MWHRxKz4mq&noteId=n59QJ5gxJF) clarifies our paper’s novelty and contribution. Additionally, we successfully demonstrated parameter identification in various existing systems....	Summary: The authors draw a connection between assumptions in the (neural) ODE and causal representation learning (more precisely, latent variable identifiability, but I will follow the authors and refer to it as CRL) literatures, by framing ODE inverse problems as latent variable problems. A particular focus is given ...	Rebuttal 1: Rebuttal: ### Reply to weaknesses We appreciate the reviewer’s thoughtful comments. We concur that identifying parameters from an unknown system is highly challenging and can be difficult to interpret. We also acknowledge that the CRL-identifiability provided in `Corollary 3.2` is limited, ...	Summary: This paper proposes a theory and methodology for representation learning from dynamical systems. In particular, it proposes a model in which latent causal variables deterministically generate an observed time-series trajectory through an ODE, with the task of identifying the latent variables from observed time...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and will address the concerns individually.   ### Reply to weaknesses 1. Please refer to our general clarification on the novelty and contribution in [global rebuttal](https://openreview.net/forum?id=MWHRxKz4mq&noteId=n59QJ5gxJF). 2. ...	Summary: This paper bridges causal representation learning (CRL) with dynamical system learning. It introduces partially identifiable and practical models by merging methodologies from both CRL and dynamic systems. The authors develop models capable of handling out-of-distribution classification tasks and treatment...	Rebuttal 1: Rebuttal: ### Reply to weaknesses Thank you for the positive feedback and for providing this interesting related work (listed as [`17`] under our references). Although our paper may superficially resemble [`17`], there are important differences between the two. Please allow us to make a quick clari...	Rebuttal 1: Rebuttal: We are extremely grateful to the reviewers and AC for their time and valuable feedback. We very much appreciate that they found the problem we are tackling is “very interesting and potentially impactful”(`bh3V`), “of great importance for e.g. science”(`3NaZ`), “incredibly important”(`aFgN`),...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DePLM: Denoising Protein Language Models for Property Optimization	Accept (poster)	Summary: This paper proposes a new method, DePLM, for supervised fine-tuning of protein language models (PLMs) for fitness prediction tasks. DePLM uses a denoising framework with a rank correlation objective to iteratively denoise PLM likelihoods and only retain the component of the likelihood that corresponds to the f...	Rebuttal 1: Rebuttal: Many thanks for the confirmation of our methodological novelty, and the constructive and valuable comments on the experiments. We have conducted addtional experiments to make the results and conclusion stronger. Here, we provide details on the comments below. >Experiments > 1. More baselines and ...	Summary: In this work authors tackle the problem of optimizing protein sequences towards a given property. They outline limitations of existing methods using Protein Language Models within an optimization loop to optimize property as those pLMs are not tailored towards a given property. They introduce a rank-based diff...	Rebuttal 1: Rebuttal: We appreciate Reviewer ZGbQ's constructive comments, which have significantly improved our paper. Below, we address each comment in detail. > Marginal improvement from structural information The marginal improvement observed using the structural information can be attributed to the dataset selec...	Summary: In this work, the authors propose Denoising Protein Language Models (DePLM) to enhance protein optimization by refining evolutionary information (EI) in protein language models (PLMs). Since traditional methods struggle with considering multiple functional properties simultaneously and lack generalizability to...	Rebuttal 1: Rebuttal: We sincerely thank Reviewer 5NpU for your insightful feedback. We have addressed your concerns below and hope our responses provide clarity: > Computational Cost > 1. The framework relies on a sorting algorithm to define the forward diffusion process. What is the computational cost? Will it be an...	null	null	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' thorough and constructive feedback on our manuscript. In response, we have conducted a series of additional experiments and analyses to address your concerns and strengthen our paper. Below, we provide an overview of the new results included in the uploaded P...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Aligning Diffusion Models by Optimizing Human Utility	Accept (poster)	Summary: This paper introduces Diffusion-KTO, a novel approach for aligning text-to-image diffusion models with human preferences using per-image binary feedback (like likes/dislikes) rather than pairwise preference data. The key contributions include: 1. Extending the human utility maximization framework used to alig...	Rebuttal 1: Rebuttal: Thank you for the positive review! We are excited to hear that you found our method innovative and significant to improving text-to-image models. We are also happy to hear that you appreciated our comprehensive experiments and overall presentation. Please find our responses below. **A more thoro...	Summary: This paper (DKTO) combines D3PO and KTO. D3PO let's us apply DPO to Diffusion models using pairwise preferences, and KTO is a way to align generative models (specifically autoregressive LLMs) using pointwise preference. For example, this gives us a way to tune text-conditioned, image generative models from thu...	Rebuttal 1: Rebuttal: Thank you for your review! We are glad that you appreciated the contributions and comprehensive experiments presented in this paper. Regarding the concerns on specific formulations, we have provided a detailed review of our formulation in the main rebuttal that should address these concerns. The...	Summary: This paper presents Diffusion-KTO, a novel preference learning algorithm for diffusion models. The proposed preference learning algorithm is based on Kahneman & Tversky Optimization (KTO). Diffusion-KTO enables aligning a diffusion model using only binary pointwise feedbacks, improving data efficiency and robu...	Rebuttal 1: Rebuttal: Thank you for your review! We are happy to hear that you find our problem area important and that you appreciate the presentation of our work and our experimental results. Please see our responses below. The proposed algorithm is almost a simple application of KTO to diffusion models. Pleas...	Summary: This paper proposes Diffusion-KTO, which extends the KTO theory to develop a preference optimization algorithm for Strengths: * The derivations are clean, direct, and seem reasonably principled to me. * The experimental results are good and show a clear improvement over preexisting work. However, I would inc...	Rebuttal 1: Rebuttal: Thank you for your positive review! We are excited to hear that you appreciate the importance of our problem statement, the strength of our experimental results, and the presentation of our method. Please find our responses below. The novelty is somewhat limited Please see the “Novel Contri...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback. We are glad to hear that the reviewers recognize our strong experimental results (Reviewers MiLc, 7SUT, s9JR, YybC), the importance of learning from binary preference data (Reviewers MiLc, 7SUT, YybC), and the novelty of extending the utility...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Analysis of Corrected Graph Convolutions	Accept (poster)	Summary: This paper studies the effects of removing the top eigenvector of the adjacency matrix used for aggregation in graph convolutions. For the contextual stochastic block model (CSBM), this is theoretically shown to be beneficial. The authors provide several theoretical statements describing misclassification rati...	Rebuttal 1: Rebuttal: We answer the concerns below. > The structure of the paper is confusing. Two Theorems are provided without proofs. Proofs are also not in the Appendix. We prove both our theorems rigorously in the appendix. For the proof of Theorem 4.1, we refer to Appendix C (Title: Proofs in Section 6). Please...	Summary: This paper studies the concept of oversmoothing via a CSBM modeling of a GNN structure, and views the behaviors of vectors after repeated multiplications of a graph. Importantly, they consider a scheme where a dominant eigenvector is "left out", so that it does not dominate the behavior of the evolution so muc...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful questions and comments along with the encouraging review. We answer the questions below. > Comment/question 1: It's not clear to me how informative / predictive the various statistical results are to oversmoothing in practice. Can there be some f...	Summary: This paper studies over-smoothing from $k$ rounds of graph convolutions in the Contextual Stochastic Block Model by considering vanilla graph convolutions and a corrected one where the principal eigenvector is removed. Using spectral analysis, the authors derive the partial and exact recovery for both cases an...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for their thought-provoking questions and comments, and are grateful for the encouraging review. We answer the questions below. > Question 1: One aspect of the result is the dependence on the density of the graph as well as good SNR ratio. Can the authors comment o...	Summary: In this paper, the authors present a comprehensive theoretical analysis using the contextual stochastic block model (CSBM) to evaluate the performance of vanilla graph convolution after removing the principal eigenvector to prevent over-smoothing. They conduct a spectral analysis for k rounds of corrected grap...	Rebuttal 1: Rebuttal: We are grateful for the thorough feedback on our work. It certainly helped us improve our manuscript. > The multi-class case could be more complicated. It is mentioned that the authors would like to analyze the multi-class CSBM using more sophisticated architectures and I look forward to further ...	Rebuttal 1: Rebuttal: $$ \def\norm#1{{\\|#1\\|}} \def\E{{\mathbb{E}}} $$ ## Multi-class Analysis We define the L-Block CSBM with parameters $p,q,L,n,m$. We have $n$ nodes and $L$ classes, $\mathcal{C}_1,...\mathcal{C}_L$, of size $n/L$. For each node $i$, we have a feature vector $x_i\in \mathbb{R}^m$ with distrib...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization	Accept (poster)	Summary: This paper explores the method for No-Reference Point Cloud Quality Assessment. The key idea is to involve the disentangled representation learning to minimize mutual information between representations of point cloud content and distortion. The authors conduct experimental performance comparisons on three pub...	Rebuttal 1: Rebuttal: `R4-Weakness 1`: Thank you for your precious comments about presentation. We will provide more detailed explanations and make the paper easier to understand. The explanations of some terms are as follow: * The tight upper bound of mutual information (MI) means an upper boundary that is alw...	Summary: This paper proposes a novel no-reference quality assessment model tailored for point-cloud data. A disentangled representation learning strategy is leveraged to account for both content-aware information and distortion-aware information. Comprehensive experiments are conducted and the effectiveness of this pro...	Rebuttal 1: Rebuttal: We thank Reviewer #3 (hUdG) for the constructive comments. Our responses are as follows: `R3-Weakness 1`: Thank you for your insightful comments. We first review the related IQA/VQA papers and then analyze the key difference with our DisPA. The paper review will be added to our main paper. A...	Summary: This paper proposes a novel disentangled representation learning framework called DisPA to decouple the representation learning process of point cloud content and distortion. To sufficiently disentangle these two representations, the DisPA uses two branches to learn them and adopt different training philosophi...	Rebuttal 1: Rebuttal: We thank Reviewer #2 (oeD7) for the insightful comments. Our responses are as follows: `R2-Weakness 1`: Thanks for your constructive comment. As you said, the DisPA can be used for IQA without changing the architecture and training pipeline. We use projected images instead of 3D native point ...	Summary: This article's motivation is interesting. It combines content-aware and distortion-aware characteristics to train a 3D quality assessment network. Additionally, it employs a MAE-based method to train a content-aware encoder, uses patches to focus the network on learning distortion, and applies an MI module to ...	Rebuttal 1: Rebuttal: We thank Reviewer #1 (2ATd) for the insightful comments. Our responses are as follows: `R1-Weakness 1`: Thank you for your in-depth comments. The gap between the partially masked image and the complete image is addressed by fine-tuning, as well as the main training objective (see Equation...	Rebuttal 1: Rebuttal: Dear Reviewers and ACs: We sincerely appreciate all the reviews. They give insightful and high-quality comments on our paper. We would like to emphasize that our work proposes a novel and effective disentangled representation learning framework for point cloud quality assessment called DisPA. We ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems	Accept (poster)	Summary: The authors develop RL with Adaptive Control Regularization (RL-ACR) to enable safe RL exploration. This solution uses two agents: a safety regularizer to enforce safety constraints, and an adaptive agent to perform exploration. They demonstrate their method on four critical applications against leading benchm...	Rebuttal 1: Rebuttal: [Weakness 1]: The reviewer asked about the convergence result of RL-ACR. Response: For state $s$, the regulation on RL policy converges to 0 ($\beta(s)$ converges to 0) with the assumption that RL learns a better policy than the regularizer. This is a reasonable assumption since the reg...	Summary: The paper proposed a safe RL algorithm using adaptive regularization from model predictive controller. The regularization is implemented via weighted sum of a MPC controller and model-free RL policies. The weights is adaptive and learnable through a “focus network”. The focus network is updated through optimiz...	Rebuttal 1: Rebuttal: [Weakness 1]: The reviewer stated that assuming the existence of a safe MPC policy is too strict, not realistic, and not mentioned in the manuscript. Response: we believe that the reviewer may have missed a few points. First, this assumption is explicitly mentioned and discussed in the p...	Summary: In this article, the authors address the problem of safe reinforcement learning in single life setting. The agent must learn an optimal policy in one single episode without being unsafe throughout the learning. The proposed method relies on mixing a prior safe policy (the safety regularizer) with a reinforceme...	Rebuttal 1: Rebuttal: [Weakness 1]: The reviewer pointed out that it is useful to quantify the deviation of the combined policy from the regularizer policy. Response: We thank the reviewer for the interesting suggestion. We analytically quantified that for state $s$, the upper bound on the return deviation o...	Summary: The paper proposed RL-ACR algorithm to solve safe RL exploration problem where the RL policy must be safe during and after training. It achieves this by learning a "focus network" which mixes the action from MPC-based safety regularizer and conventional RL module. The algorithm is claimed to address "single-li...	Rebuttal 1: Rebuttal: [Weakness 1]: The reviewer stated that some components of the proposed method have been previously explored. Response: While our work contains components of the suggested references, the resulting algorithm has a completely new approach to safety-critical control; in general, we believe ...	Rebuttal 1: Rebuttal: ## Global Rebuttal We thank the reviewers for reviewing the paper and providing valuable feedback. We have provided point-by-point responses to the weaknesses and questions listed by the reviewers. Here, we list a few important notes on some of the comments (some leading to additional data/experi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance	Accept (poster)	Summary: this paper proposes an exploration-enhanced equivariant graph neural networks. The effective utilization of samples has been achieved in the experimental results. and It is an improvement on the function approximation for MARL and thus is compatible with most MARL actor-critic methods. Strengths: The algorit...	Rebuttal 1: Rebuttal: Thank you for your response and praise in strengths! We added some additional plots in the global response. Let us know if you have any questions regarding those additional results.	Summary: The paper applies Equivariant Graph Neural Networks (EGNN) to address the issue of low sample efficiency in multi-agent reinforcement learning (MARL). The authors demonstrate that in certain scenarios, EGNN outperforms Multi-Layer Perceptrons (MLP) and Graph Neural Networks (GNN). Key contributions include sho...	Rebuttal 1: Rebuttal: Thank you for your response. We have updated our charts, and discuss the novelty further below. We hope you will consider increasing the score given the updates and content. 1. See the common rebuttal for an additional baseline. [3] is not a MARL algorithm. we attempted to replicate [2], but fail...	Summary: This paper studies an intricate issue, called “early exploration bias”, when applying EGNN, an GNN preserving Euclidean invariance/equivariance, to cooperative MARL that exhibits Euclidean symmetries. The paper reveals that, with randomly initialized weights, the output of the EGNN layers is not centered aroun...	Rebuttal 1: Rebuttal: Thank you for your thorough, in depth response! Our replies are below: Weaknesses 1. Note that we added a new comparison with [20] demonstrating ours outperformed the results from [20]. Regarding SEGNN, we initially decided to use EGNN specifically because SEGNN and related networks (ie those us...	Summary: This paper studies the setting of multi-agent reinforcement learning (MARL). The work tries to tackle the challenges of generalization and sample efficiency using inductive biases. In this case, the work proposes to use equivariant graph neural networks to model the policy and value function of a multi-agent a...	Rebuttal 1: Rebuttal: Thank you for your thorough review. We appreciate the time you took to dive into the details of our work and provide specific advice for improving these results. We tried to take your feedback into account in our updated results. We increased the number of seeds up to 10, and we added a new bas...	Rebuttal 1: Rebuttal: We would like to thank all of their reviewers for their thoughts, comments and advice for improving this research work. We know how busy you all are, and we are grateful you have taken time out of your schedule to provide a thorough and fair review. We are encouraged they found appreciated our in...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper demonstrates for the first time the successful application of Equivariant Graph Neural Networks (EGNNs) to standard MARL benchmarks with complex action spaces. To address the Early Exploration Bias, the paper provides proof of the biases in standard EGNNs and the unbiased nature of E2GN2. In practic...	Rebuttal 1: Rebuttal: Thank you for your review and comments. 1. There is no MLP on the output of the GNN, EGNN, or E2GN2. Adding an MLP to the output would cause us to lose the guarantee of equivariance as well as the ability to add more agents without retraining. The output is similar to what is shown in the diag...	null	null	null	null	null	null
Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters	Accept (poster)	Summary: The authors present an algorithm for adversarially robust $L_p$-estimation estimation in the turnstile streaming model, which improves on the space-complexity of existing algorithms in the regime $p \in (1, 2)$. They achieve this improvement via an alteration of the dense-sparse tradeoff technique of the exist...	Rebuttal 1: Rebuttal: > The actual improvement over the previous work seems very minimal, i.e. a tiny reduction in the space complexity on a very small portion of possible choices of p. However, the relation to heavy-hitters seems like an interesting and non-trivial insight used to achieve this improvement, and the alg...	Summary: In adversarially robust streaming, one wants to design streaming algorithms that work well even in the interactive setting, in which the stream is not fully fixed in advance, but is constructed element by element by an adversary who can see the current solution provided by the streaming algorithm. If the algor...	Rebuttal 1: Rebuttal: > The moments/norms for which the paper improves the space requirements are not the most important ones, which are I think are $p=0$ and $p=2$. In general, a common goal is to find the heavy-hitters above a desired threshold that may be arbitrary. In this case, we want $\|x_i\|>\tau$ for some thres...	Summary: This paper focuses on the L_p estimation (of the frequencies of items in a stream) in the adversarially robust streaming setting. The previous work by Ben-Eliezer, Eden and Onak achieved an $\tilde{O}(m^{p/2p+1})$ space, which is slightly better than the $P(\sqrt{m})$ space bound due to the flip number techniq...	Rebuttal 1: Rebuttal: > The only obvious weakness is that the result is a very slight improvement, sometimes in the third decimal place. While the insight is nice, the techniques are a linear combination of different algorithms for different cases. While I did not find the techniques very exciting, I think this passes ...	Summary: The work develops new algorithms in the adversarially robust streaming model. In this model, the algorithm observes updates to some vector arrive in the form of a data stream and maintain estimates to some property of the vector as it changes. At each time-step i, the algorithm receives the update of the vecto...	Rebuttal 1: Rebuttal: > Other than the $L_1$-heavy hitter estimation, many of the improvements are extremely incremental, for instance the aforementioned improvement of the dependence on the length $m$ of the stream for $L_{1.5}$-norm estimation to $m^{0.373}$ from $m^{0.375}$. > Other than the results for L_1-heavy-h...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful comments and valuable insight. We also appreciate the positive feedback, such as: - The adversarially robust streaming framework is natural and well-motivated. (Reviewer KENe) - For the L_1-heavy-hitter problem the work gives qualitative improvement on t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering	Accept (poster)	Summary: This paper introduces G-Retriever, which combines LLMs, GNNs and RAG for graph question-answering tasks. The authors first develop a more comprehensive benchmark named GraphQA. Then they present G-Retriever, which has four main steps including indexing, retrieval, subgraph construction and answer generation. S...	Rebuttal 1: Rebuttal: We thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions. > Reviewer: In Section 5.1 (Indexing), the authors use a pre-trained LM to encode the text attributes of nodes and edges into representations...	Summary: This paper proposes a retrieval-augmented method G-Retriever for graphs with textual attributes. It introduces a graph question answering (GraphQA) benchmark by converting existing graph datasets into uniform format. Subsequently, it proposes a G-Retriever method to answer questions related to the textual grap...	Rebuttal 1: Rebuttal: We wish to thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions. > Reviewer (W1 & Q1): Explanation on “chat with their graph” Authors: By “chat with their graph,” we mean that users can interac...	Summary: This paper proposed a Graph Question Answering (GraphQA) benchmark with data collected from different tasks including ExplaGraphs, SceneGraphs, and WebQSP. Then, they proposed G-Retriever method, introducing the first retrieval-augmented generation (RAG) approach for general textual graphs, which can be fine-t...	Rebuttal 1: Rebuttal: We would like thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions. > Reviewer: I think several baselines are missing: (1) Simply feed the retrieved top-k nodes/edges (plus its neighbors) to the LLM...	Summary: The work builds a new GraphQA benchmark for real-world graph question answering and presents G-Retriever, an architecture adept at complex and creative queries. Given a query and a graph, G-Retriever retrieves a connected subgraph from the original graph according to the query. The subgraph, a textualized gra...	Rebuttal 1: Rebuttal: We thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions. > Reviewer: The framework seems to mainly work for larger graphs. For tasks with smaller graphs, such as the ExplaGraphs dataset with an aver...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their time and effort in evaluating our paper. In this global response, we aim to clarify the contributions of the GraphQA benchmark (G1), elaborate on our PCST-based retrieval method (G2), and present new experiments conducted in response to the reviewers' fe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach	Accept (poster)	Summary: This paper presents a method to assess the extent to which social biases are encoded in LLMs. The motivating example throughout the paper is assessing the degree of gender bias in the context of job applications in the labor market. The method (PRISM) is designed as follows: given text (presumably output by th...	Rebuttal 1: Rebuttal: Thanks for the detailed and thorough comments. Below please find our responses to specific comments: --- W1: We acknowledge and face challenges in evaluating social bias in text: 1. The evaluation benchmarks in this field are scarce and underdeveloped so we can only turn to human labeling. This...	Summary: This research investigates the social biases present in ChatGPT-generated job applications using a novel experimental design that responds to real job advertisements. By simulating the job application creation process, the study examines the language patterns and biases that surface when the model is prompted ...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude to the reviewer for the time and effort in providing the comments and suggestions. Below, we address each of your points in detail. --- W1: Thank you for your question, which we address in three aspects: 1. Use Case: In our settings, we used pre...	Summary: This paper investigates the impact of social biases in the application of LLMs within the labor market. The authors examine biases in job applications generated by ChatGPT based on given job posts. They introduce a new bias evaluation method called PRISM (Probability Ranking bIas Score via Masked language mode...	Rebuttal 1: Rebuttal: We are very grateful for the thoughtful comments and heartwarming acknowledgment, as well as the time and effort devoted to the reviewing process. Below please see our response to the specific point in the reviewing comment. --- W1: Our experiments were specifically conducted using ChatGPT due to...	Summary: This paper presents a novel bias detection algorithm, PRISM, and uses it to evaluate biases in job applications generated from prompts that include job postings. The technique uses a masked language model (MLM) to find the probability of “masculine” and “feminine” words replacing the masked word in the text. T...	Rebuttal 1: Rebuttal: Thanks for the detailed and thorough comments. Below please find our responses to specific comments: ---- W1: the use of LLMs like ChatGPT for generating job applications is indeed increasingly common among job seekers. This trend has been highlighted in discussions and concerns raised by employ...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you so much for your time and effort in reviewing our paper. We want to highlight some key aspects of our paper: 1. Our method represents the first research on unsupervised bias evaluation in text using contextual information. 2. Our word lists are based on established word ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization	Accept (poster)	Summary: The paper presents a novel method using consistency models as a policy parameterization to address online reinforcement learning. The paper first uses the dormant ratio metric to study properties of the consistency-AC method and determine that it is suitable for online RL. Then, it presents the CP3ER method, w...	Rebuttal 1: Rebuttal: Many thanks for the careful review and constructive feedback. Please note our global response with the attached PDF. In the modified revision, we expound on the correlation between the dormant ratio and the performance of the policy network in Section 3.3 as shown in the global response. > These t...	Summary: This paper proposes a novel method that employs a consistency model for policy parameterization in online visual reinforcement learning settings. It initially identifies the issues of prior consistency actor-critic methods in high-dimensional observation online RL settings through the lens of dormant ratio. Su...	Rebuttal 1: Rebuttal: Thank you for your detailed review and helpful suggestions. Please note the global response and the attached PDF. > The proposed modification is straightforward compared with existing algorithms. The key challenge lies in addressing the instability during the training of consistency policy. By an...	Summary: The paper addresses challenges in visual RL with high-dimensional state spaces, specifically focusing on improving sample efficiency and training stability. It introduces a novel method called CP3ER, which incorporates sample-based entropy regularization and prioritized proximal experience regularization to st...	Rebuttal 1: Rebuttal: Thank you very much for your recognition of our work. > Why using eq. (8) for sampling weight? Can other functions that shares a similar look demonstrated in Fig 4(b) also work? In fact, Equation (8) is an empirical formula inspired by the Sigmoid function, and it has been improved to meet the...	Summary: The paper analyzes the problems faced by extending consistency policy to visual RL under the actor-critic framework and discovers the phenomenon of the collapse of consistency policy during training under the actor-critic framework by analyzing the dormant rate of the neural networks. The authors propose a con...	Rebuttal 1: Rebuttal: Thank you for your detailed review and valuable comments. Please note the global response with the attached PDF. > The method is not directly applicable to domains with discrete action spaces. Indeed, our method has not yet been extended to discrete action spaces because we use consistency mo...	Rebuttal 1: Rebuttal: Please see the attached one-page PDF for additional experimental results. We would like to express our sincere gratitude for the efforts and valuable feedback provided by all the reviewers. We are very pleased that our work has been recognized by the reviewers, and we have also noted some poi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation	Accept (poster)	Summary: The paper proposes SaSPA, which uses several large, pre-trained generative, language, and vision-language models to produce high-quality synthetic images. Specifically they focus on images for FGVC, where the high inter-class similarities make it challenging to synthesize data that is not only diverse (capturi...	Rebuttal 1: Rebuttal: Thank you for your detailed review and insightful feedback. We appreciate your recognition of SaSPA's well-motivated and carefully crafted pipeline, as well as your positive comments on the presentation and thoroughness of our experiments. Your constructive points will help us improve our work fur...	Summary: The paper proposes a data-augmentation technique tailored to fine-grained image classification. The goal is to increase class fidelity while maintaining high variance in the images, something current diffusion-based data augmentation techniques are struggling with, especially in the fine-grained domain. The ...	Rebuttal 1: Rebuttal: We appreciate your recognition of our method's novelty and effectiveness in addressing training with limited data. We're glad you found the paper well-written and the evaluations helpful. Our code has already been released for reproducibility. **“The baseline classifier architecture is a standard...	Summary: This paper presents SaSPA, a generative augmentation method specifically designed for FGVC. This method generates diverse, class-consistent synthetic images through conditioning on edge maps and subject representation. They use ControlNet condoned on edge maps and uses blip diffusion for its ability to zero-sh...	Rebuttal 1: Rebuttal: Thank you for your feedback. We appreciate your recognition of the novelty of SaSPA. We're glad you found our strategy and analysis impressive and valuable. **“Datasets are of small scale on which the experiments have been done. I would expect this method to perform well on fine-grained evaluati...	null	null	Rebuttal 1: Rebuttal: We appreciate the reviewers' time, thoughtful comments, valuable suggestions, and their recognition of the potential positive impact of our method. Below, we address their common questions and concerns, in addition to the individual response per-review. In response to the feedback received, we hav...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models	Accept (poster)	Summary: This paper studies efficient parameter estimation from generalized linear models with adversarial noise (a.k.a. agnostic learning), assuming a known link function. The goal is to find a parameter vector that best explains the data. The authors show that $d^{\lceil k^*/2 \rceil}$ samples suffice for this prob...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper, as well as the positive evaluation of our work. We respond to the reviewer's comments below. #### Response to Questions ##### Question 1: (Q1) We agree that care is needed when comparing to [DPVLB24]. As the reviewer remarks,...	Summary: This paper studies the problem of _agnostic learning_ of single-index models, which consists of trying to learn a distribution $\mathcal D$ on $\mathbb R^d \times \mathbb R$ with an estimator of the form $y = \sigma(\langle w, x \rangle)$. Since the problem is not necessarily well-posed, the goal is to reach a...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort in reviewing our paper and the positive assessment. Below, we provide specific responses to the points and questions raised by the reviewer. >(Question 1): Can such a measure be defined in your setting, instead of considering the worst-case scenario...	Summary: The paper provides a polynomial-time algorithm reaching the optimal CSQ sample complexity (upto sub-leading factors) for a general class of single-index models under the setup of adversarial noise. Similar to existing works, the algorithm utilizes the empirical estimate of the k_th Hermite tensor of the target...	Rebuttal 1: Rebuttal: General Response to Reviewer 9oLo We thank the reviewer for the provided feedback. Before responding to specific questions and comments, we address two main points, which we believe are the main sources of the reviewer's somewhat negative view of our work. We hope that upon clarifying the con...	null	null	Rebuttal 1: Rebuttal: Top-Level Response We thank the reviewers for their time and effort invested in evaluating our paper. We are encouraged by reviewers finding our results technically novel (9olo), conceptually interesting (wBuC), and rating the presentation of our work as excellent (**z...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences	Accept (spotlight)	Summary: The paper introduces Adaptive Randomized Smoothing (ARS), an innovative theoretical extension of Randomized Smoothing (RS) based on f-Differential Privacy (f-DP) to enhance the robustness certification of machine learning models. Empirical results demonstrate that ARS surpasses both standard RS and its variant...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We are happy to answer the question about alternative norms. > Does the theoretical framework extend to alternative norm types, such as L1 or L2 norms? While the ARS theory (f-DP based RS and composition) applies to other norms such as L1 or L2, our specific ...	Summary: This work presents the first sound composition of randomized smoothing. Based on the novel theoretical results, this work presents the first sound way to compose a mask generator with the Gaussian sampling required for randomized smoothing, thereby reducing the effective dimension and improves the certificatio...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We address the two main requests listed under weaknesses: > the main practice in the field is to report results on {cifar-10, ImageNet} times {𝜎=0.25,0.5,1.0} > “I request the authors to present the full benchmark for comparison.” Thank you for the suggesti...	Summary: This paper proposes a framework to derive robustness certificates for smoothed classifiers based on f-differential privacy (f-DP). The framework achieves the same tight certificate for Gaussian smoothed classifiers as Cohen et al. (2019), while enabling analysis of adaptive multi-step smoothing mechanisms usin...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We start by answering the main questions in the review and then address further comments. > Q1. “[...] can a certified radius be derived for a composition of heterogeneous f-DP mechanisms [...]?” This is a great question! As noted in the review, the theory di...	Summary: The paper uses tools from differential privacy (f-DP relating privacy to hypothesis testing, and also compositional rule of DP) to design a differentially private 2-step mechanism. The first step is interpreted here as creating a mask, and the second step is the "standard randomized smoothing" on the masked i...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We will add a background section on f-DP in the appendix and forward reference it before Prop 2.1 to provide more context. We will unify the citations to prefer the published edition (2022) over the arXiv edition (2019). Thank you for catching our increase/decr...	Rebuttal 1: Rebuttal: We thank all of the reviewers for their constructive reviews. We provide answers to questions in our review-wise responses, and we have completed experiments following the suggestions and requests that we include in the pdf attached to this global response. Specifically: - We provide CelebA expe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
RETR: Multi-View Radar Detection Transformer for Indoor Perception	Accept (poster)	Summary: This work introduces a novel method RETR for indoor object detection and segmentation based on multi-view radar heatmaps. RETR extends the popular DETR framework and incorporates modifications specific to multi-view radar perception, such as depth-prioritized feature similarity via TPE, a trip-plane loss, and ...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable feedback! We provide our point-to-point responses below. Due to space constraints, we have shortened your comments for brevity. *Most commercial radars have 2D virtual antenna arrays, e.g., 168, instead of only a pair of 1D antenna arrays. Is the...	Summary: The primary content of this paper is an introduction to a multi-view radar detection transformer algorithm (RETR) for indoor perception. The algorithm achieves effective object detection in indoor environments by utilizing multi-view radar data and combining self-attention mechanisms and cross-attention mechan...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable feedback! We provide our point-to-point responses below. **Radar perception datasets primarily utilize point clouds as the data form. Methods based on heatmaps for indoor multi-view radar perception are not yet sufficient. The author mainly compar...	Summary: In this paper, the authors propose Radar dEtection TRansformer (RETR), an extension of the popular DETR architecture, tailored for multi-view radar perception. RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. Stren...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable feedback! We provide our point-to-point responses below. **For indoor perception with radar, multi-path is expected. It would be good to add a paragraph to discuss this issue and how to mitigate multi-path or its impact to the perception/segmenta...	Summary: The paper introduces Multi-View Radar Detection Transformer for indoor object detection. Inspired from DETR, the authors propose and end-to-end RETR to detect objects from the radar inputs. To improve the feature association across the two radar views, the authors introduce a new Tunable Positional Embedding. ...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable feedback! We provide our point-to-point responses below. **I am wondering how the Top-K Feature selection impacts the performance of the network. How can we choose the number of K? It seems that there is no experiment to validate the choice of K. I...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful comments, suggestions and questions. In the rebuttal form to each reviewer, we provide detailed point-to-point responses. In these point-to-point responses, we often refer to the attached PDF for additional results in the form of tables and figures. Pd...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Swift Sampler: Efficient Learning of Sampler by 10 Parameters	Accept (poster)	Summary: The paper introduces Swift Sampler (SS), an efficient algorithm for the automatic learning of data samplers in deep learning model training. SS addresses the challenges of high-dimensionality, sharpness, and costly evaluation in sample-based methods by mapping samplers to a low-dimensional space of hyper-param...	Rebuttal 1: Rebuttal: ### W1: The effects of different components or hyperparameters of the method. Thank you for your valuable feedback. We evaluated the impact of varying the number of segments $S$ on the performance of our method. The experiments were conducted on the CIFAR10 dataset with a noise rate of 20%. W...	Summary: The paper focused on designing a learnable training data sampler to improve the model performance. A method named Swift Sampler (SS) is proposed, which is formulated as a function mapping data feature to sampling probabilities, represented by a small number of parameters. In addition, the SS Smooths the object...	Rebuttal 1: Rebuttal: ### W1: Can the author explain the effectiveness of components separately? Thank you for your valuable feedback. Each component of the SS method addresses a specific challenge in optimizing the sampling probabilities: 1. Low-Dimensional Representation: Reduces the search space, making op...	Summary: The purpose of this paper is to create a sampler that can assign appropriate sampling probabilities to training data in order to improve performance. Unlike previous approaches that relied on heuristic rules or expensive learning methods, this paper proposes an automatic and efficient sampler search algorithm ...	Rebuttal 1: Rebuttal: ### W1: Are the final results in experiments also reported on the validation set? Thank you for your insightful feedback. In the manuscript, we utilize two distinct validation sets: 1. Outer Loop Validation Set: Used within the Bayesian Optimization process to evaluate and search for the...	Summary: The paper introduces a method for automatically learning optimal data sampling strategies using BO based sampling. The proposed method formulates the problem as a bilevel optimization, using a low-dimensional representation of samplers (10 parameters) and BO to search this space. Key novel points include techn...	Rebuttal 1: Rebuttal: ### W1: Theoretical analysis Thank you for your insightful comments. We will expand our manuscript to include a theoretical analysis section focusing on the bounds of the representation error of the SS algorithm. Due to the valuable feedback you provided, we have included the entire theoreti...	Rebuttal 1: Rebuttal: Dear Reviewers and Area Chair, Thank you for your thoughtful and constructive feedback on our submission. We greatly appreciate the time and effort you have taken to review our work. We have carefully considered each of your comments and would like to address the all of points raised by the revie...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The main problem tackled by the paper is to obtain the optimal dataset sampler for training a deep neural network given a fixed dataset and a model. The search space of the sampler is defined as sampling probability functions over the items in the training dataset. The method involves a two-level optimization ...	Rebuttal 1: Rebuttal: ### W1: How this method can be beneficial in more practical scenarios? We appreciate the reviewer's insightful comments. To address the concerns raised, we have conducted additional experiments on large-scale datasets and tasks with limited data. 1. **Foundation Model Training on Large-Scale...	null	null	null	null	null	null
How to Boost Any Loss Function	Accept (poster)	Summary: The paper presents a framework for boosting any (bounded) loss function given access to a weak learner. The authors present how recent developments in boosting have involved turning the boosting problem into an optimization problem, where different combinations of assumptions on the loss such as convexity, lip...	Rebuttal 1: Rebuttal: We thank the reviewer for highlighting that our " [...] main is very well written [...] " and a long strength section that indeed summarizes well some of our contributions. We hope this rebuttal answers the questions to strenghten further the review's polarity. > What does $R_$ and $N_$ denote ...	Summary: The paper investigates the theoretical aspect of boosting algorithms in machine learning. The authors propose a new algorithm, SECBOOST, which aims to optimize any loss function using zeroth-order information. This approach dis different from traditional boosting methods that require first-order information su...	Rebuttal 1: Rebuttal: We thank the reviewer for the whole content of the strength section, which summarizes the key strengths of our approach. > I would connect more to real-world applications. Even when we deliberately formatted our paper for a theory report, we understand the reviewer's standpoint. We in fact ran e...	Summary: - This paper discusses an alternative boost algorithm by using the zeroth-order optimization technique. The key benefit by using such technique is that it does not require the loss function to be convex, differentiable or Lipschitz. They provide theoretical results and validate them with experiments. Strength...	Rebuttal 1: Rebuttal: We thank the reviewer for writing that our contribution is "[...] potentially very important [...]" and hope to give here the arguments sought by the reviewer to enforce further this claim. > First, Assumption 5.4, is this easy to verify for some given learners? [...] [RA5.4E] This assumption is...	Summary: Boosting can be regarded as a general optimization problem, and most of the currently popular Boosting techniques tend to do so, and do it by using $1^\text{st}$ order (gradient) information to minimise a loss function. This work proposes an algorithm to minimise an arbitrary loss function using only $0^\text{...	Rebuttal 1: Rebuttal: We thank reviewer bfG5 for a passionate review. We particularly appreciate that reviewer bfG5 put the key strength of our paper in bold faces. This is rarely seen in reviews in general and in our case for example, none of our 6-6-7 other reviews use bold faces to describe the strengths of our ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training	Accept (poster)	Summary: This paper presents a sparse training method, $S^2$-SAM, which applies sharpness-aware minimization to sparse training. The authors demonstrate that sparsity during training leads to a sharper (to use the authors' words, "more chaotic") loss surface, something that can be mitigated by a variant of sharpness-aw...	Rebuttal 1: Rebuttal: Dear Reviewer UgUE, Thank you for your review and thoughtful suggestions on our paper. Regarding the questions you raised, we believe they are important points that merit further attention. **W1: No result on sparsities higher than 90 on ImageNet; how much we lose by using S$^2$SAM compared to ...	Summary: The authors of this paper posit that sparse training is difficult due to a chaotic loss landscape as opposed to standard training of a dense network. In order to address this problem, they propose to perform sparse training with a Sharpness Aware Minimization approach. In order to do so efficiently, they lever...	Rebuttal 1: Rebuttal: Dear Reviewer Z2wG, W1: Some methods are able to train sparse networks that outperform their dense counterparts, do these findings suggest that sparse networks can also be found without performing S$^2$SAM? Is S$^2$SAM necessary? It would be beneficial to compare with LRR. Thank you for your...	Summary: This article introduces S2-SAM (Single-step Sharpness-Aware Minimization), an innovative sharpness-aware optimization method tailored specifically for sparse training with zero extra computational cost. Strengths: 1. The method improves the generalization ability of sparse neural networks, which is a signific...	Rebuttal 1: Rebuttal: Dear Reviewer uQD3, We appreciate your review of our paper. The issues you have raised are very important and deserve further discussion. Below are our responses to your comments. **W1: S2-SAM seems to be a zero-extra-cost variety of SAM that is implemented in sparse cases. However, when the sp...	Summary: This paper studies the challenges of training sparse neural networks directly and identifies one of the contributing factors, i.e., the chaotic loss surface. Consequently, it proposes a new method, i.e., Single-step Sharpness-Aware Minimization (S2-SAM), tailored specially to train sparse networks. S2-SAM is b...	Rebuttal 1: Rebuttal: Dear Reviewer ZjXc, We sincerely appreciate your thoughtful comments. All source code will be released after the paper is accepted. Q1: Systematic study to quantify and illustrate the proposed method? Thank you for the question. We want to stress that our paper is focusing on providing a u...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Generalization bounds for mixing processes via delayed online-to-PAC conversions	Reject	Summary: The authors establish generalization error bounds for non-iid data based on the online-to-PAC conversion. In particular, the authors extend the online-to-PAC conversion techinique to non-iid settings utilizing the online learning with delayed feedback. In the paper, the authors illustrate 1. a method of non-i...	Rebuttal 1: Rebuttal: Q1) Notable logical gap: Lemma 3 only gives a regret bound independent of $P^$, but it seems Corollaries 3 and 4 need $P^$-dependent regret bound. I believe this is fixable, but still, need some fix. Thanks for pointing this out! This typo is indeed a bit confusing: the right hand side is of co...	Summary: This paper studies the generalization error of statistical learning in a non-i.i.d setting, where the training data distribution could have temporal dependency. They develop a framework that reduces the generalization error in this case into the regret of an online learning problem with delayed feedback. Then,...	Rebuttal 1: Rebuttal: Regarding the technical novelty of our method, please see our general response. Regarding instantiating our results for some specific settings: We omitted these instantiations due to space limitations, otherwise they can easily be derived by analogy with results of Lugosi and Neu (2023). We will ...	Summary: This paper extends the Lugosi-Neu(2023) framework for upper bounding the generalization error of statistical learning algorithms to the non-i.i.d. setting, by considering that the training samples are drawn from a suitably mixing stochastic process. They show that the existence of a delayed online learner with...	Rebuttal 1: Rebuttal: Q1) Can the authors comment on the delayed learning setup vs the normal online setup of Lugosi-Neu (2003)? See our general response to all reviewers regarding the necessity / usefulness of delays in this setting. The setting of online learning with delays is well-studied, and the results we borr...	Summary: The paper focuses on learning from non-i.i.d data. Specifically, the authors develop a framework that derives generalization guarantees through a reduction to an online learning game with delays, where achieving low regret translates to low generalization error. They present specific bounds when using EWA and ...	Rebuttal 1: Rebuttal: Q1) Could the authors provide their perspective on potential future directions and limitations of their framework? We believe this framework and its flexibility should motivate the investigation of generalization bounds in more general non-i.i.d. settings. There are still many questions not cove...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and constructive feedback on our submission, which we will incorporate to improve our paper. We are glad to see that all reviewers have appreciated our contribution, and in particular the simplicity and generality of our framework. Some reviewers have asked a...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper provides a framework for proving generalization bounds for non-i.i.d. data sequences, building upon a recent framework introduced by Lugosi and Neu (2023) that reduces PAC to online learning. This technique recovers some known PAC Bayesian bounds for non-i.i.d. scenarios and various other implicatio...	Rebuttal 1: Rebuttal: Q1) besides extending the online game to accommodate delays and using ideas from the online learning literature, what are the technical challenges/contributions in this paper? See our general response to all reviews above. Q2) Do you know if the paper by Lugosi and Neu (2023) was already publish...	null	null	null	null	null	null
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization	Accept (poster)	Summary: This paper studies scalable bi-level optimization problems and points out the limitations of most traditional methods. Among all, to mitigate the high memory cost issue of the GU method, it proposes (FG)2U such that the space consumption is reduced to $\mathcal{O}(M)$ from $\mathcal{O}(MN)$. Convergence analys...	Rebuttal 1: Rebuttal: We thank the reviewer for the overall positive evaluation and will try to address the concerns raised point by point. Regarding the complexity We direct the reviewer to the general response for computational cost analysis and discussions regarding the computation in practice, including the e...	Summary: The paper introduces a method called Forward Gradient Unrolling with Forward Gradient, abbreviated as (FG)²U, which is designed to address the large memory requirements of forward method in bi-level optimization in large-scale machine learning model Strengths: 1. The method significantly reduces memory overhe...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We will try to address the concerns point by point. Regarding the computational complexity We direct the reviewer to our general response. We place a computational cost analysis in A.1 and a more detailed discussion on practical computation in A.2. **...	Summary: This paper presents a novel gradient unrolling algorithm for bi-level optimization. The authors highlight that existing methods for calculating meta gradients in the literature are not memory efficient. They propose a sampling-based method, (FG)^2U, to estimate the meta gradient. This approach approximates the...	Rebuttal 1: Rebuttal: We thank the reviewer for the overall positive evaluation and will try to address the concerns raised point by point. Regarding the complexity The reviewer's understanding of Theorem 3.4 is generally correct. We would like to highlight the following points: Firstly, the convergence rate in ...	null	null	Rebuttal 1: Rebuttal: ## (A) General Response We thank all reviewers for their constructive feedback. In this response, we will address some common concerns raised by the reviewers. We will revise the manuscript covering the following discussions. All reviewers raised concerns about the computation. More specifical...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Estimating Ego-Body Pose from Doubly Sparse Egocentric Video Data	Accept (poster)	Summary: This paper presents a framework to estimate full-body human poses from egocentric head mounted display. The input of the system mainly contains 2 parts: head tracking signal given by HMD, and sparse hand pose signal estimated from egocentric video. The algorithm, DSPoser, is composed of two stages: temporal co...	Rebuttal 1: Rebuttal: We appreciate the valuable comments aimed at improving our paper. We will revise the draft according to the reviewer's suggestions. > Q1: Limited novelty Our novelty lies in our approach to solving the body pose estimation problem given doubly sparse data, specifically in how we address the unde...	Summary: This paper proposes a system to estimate full-body pose from forward-facing egocentric videos. Dubbed “doubly sparse video data,” such data streams have the distinct characteristic that only the headset pose is persistent, while the hand pose estimation is only occasionally available. The proposed method first...	Rebuttal 1: Rebuttal: We appreicate the acknowledgement of our motivation and the novelty of our method. We will improve the draft based on the valuable comments of the reviewer. > Q1: Qualitative comparisons against state-of-the-art approaches and video visualizations of the results. We appreciate your comment on th...	Summary: The paper introduces the task of full-body pose estimation from temporally and spatially sparse tracking inputs. It differs from the prior work in assuming only the partial availability of hand tracking, which is a common scenario for head-mounted displays (HMD) without hand controllers. To address this proble...	Rebuttal 1: Rebuttal: We appreciate the reviewers insightful feedback on our paper. We will improve the draft based on the comments. > Q1: Stronger baselines & Reorganization of Table 1 and 2 Due to limited time and resources, we are currently only able to provide baseline results on the AMASS dataset. We are current...	Summary: This paper presents a new method for ego-body pose estimation from egocentric videos. Compared to previous methods that assume hand tracking signals are always available, this paper focused on the case that hand poses captured intermittently from egocentric videos. To solve this, this paper proposes a two-stag...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewers' insightful comments for our paper. > Q1: FoV modeling result of EgoPoser (ECCV 2024). \begin{array}{c\|cc\|cc\|cc}\hline \textbf{Strategies}&\textbf{MPJPE (180°)}&\textbf{MPJVE (180°)}&\textbf{MPJPE (120°)}&\textbf{MPJVE (120°)}&\textbf{MPJPE (90°)}&\textbf{MP...	Rebuttal 1: Rebuttal: We thank all the reviewers for their time and effort in helping us improve the paper. We appreciate your acknowledgment of the novelty and valuable suggestions to improve our method. In this rebuttal, we want to clarify a few common questions raised by reviewers. Please note that experiments a...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper introduces an approach for estimating full-body pose from egocentric videos combined with sparse head and hand positions. The key contribution lies in utilizing sparse temporal annotations of hand positions to achieve a complete representation. The method is evaluated on the publicly available Ego-Ex...	Rebuttal 1: Rebuttal: We appreciate your acknowledgment of our paper's novelty and your valuable suggestions for improving our method. We fully recognize the importance of open-source code in advancing research, as this paper is also built upon the publicly available code of other researchers. > Q1: Key contributions...	null	null	null	null	null	null
Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering	Accept (poster)	Summary: This work develops efficient algorithms to approximate the Pareto-optimal set for different bi-objective clustering problems with solid theoretical guarantees. The problem can have different clustering objectives (e.g., k-separation, k-center, k-diameter, k-median, k-means, k-MSR) and/or different metrics (e.g...	Rebuttal 1: Rebuttal: 1. Centers Chosen from the Point Set We decided to restrict to this case in the theory part. In 2.1 we could switch to choosing centers from a larger space, e.g., the infinite metric space $R^d$ because this algorithm is still a 2-approximation even if this case. It would make even more sense t...	Summary: This paper presents a novel framework for clustering pareto optimization. This paper studies the approximately pareto-optimal solutions for bi-objective k-clustering problems. The authors focus on the computationally very efficient single linkage / must-link constraints and the computation of pareto-optimal so...	Rebuttal 1: Rebuttal: W1: This paper is not very motivated. The authors seem not explain the motivation for studying the pareto-optimal algorithm for the clustering problem. Moreover, the authors did not give the motivation why the combination of these clustering objectives is studied. We chose these objectives sinc...	Summary: The paper introduces novel algorithms to approximate the Pareto-optimal solutions for bi-objective k-clustering problems. The authors focus on combinations of clustering objectives such as k-center and k-means, or k-center with two different metrics. Usually, these objectives are conflicting and cannot be opti...	Rebuttal 1: Rebuttal: *Weakness 1: It is not entirely clear the main novelty aspect of the work. In the setting of sec 2.1 where k-sep is combined with other objectives, the main takeaway seems to be that the authors were able to integrate existing state-of-the-art guarantees into their framework. Similarly for other s...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for considering our work and providing detailed reviews. Replies to remarks and questions can be found in the individual rebuttals for each review.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Robust Offline Active Learning on Graphs	Accept (poster)	Summary: This paper proposes an offline active learning method that selects nodes to query by explicitly incorporating information from both the network structure and node covariates. This paper establishs a theoretical relationship between generalization error and the number of nodes selected by the proposed method. ...	Rebuttal 1: Rebuttal: Thank you for acknowledging the theoretical analysis of our algorithm and for your insightful suggestions! We have carefully considered your concerns regarding the insufficient experimental results, and have worked diligently to address them. >**Weakness: Recent work on graph active learning[1] h...	Summary: The paper proposes a strategy for collecting labeled data for a semi-supervised learning algorithm focused specifically on learning on graphs. The paper provides a theoretical analysis of the proposed method, capturing both the quality of the samples that are selected for labeling as well as the the prediction...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive feedback, which greatly improves our paper! We address the reviewer's comments point by point below. >Weakness 1: empirical analysis does not very convincingly Thank you for the comment! In the global response PDF, we included additional experiments...	Summary: The paper addresses the challenge of active learning on graphs where labeling node responses is costly. The authors propose an offline active learning method that selects nodes by incorporating both network structure and node covariates. The method leverages graph signal recovery theories and random spectral s...	Rebuttal 1: Rebuttal: Thank you for acknowledging our algorithm's novelty and providing insightful feedback! We address your concerns point by point below. ## Weaknesses: >(A) Performance on networks with varying levels of homophily and heterophily Thanks for raising an excellent point about the generalizability o...	Summary: The work proposes an offline/batch active learning method for querying labels for nodes of a graph. The setting assumes access to noisy responses for the subset of nodes queried by the active learner. On the theoretical side, gains are shown over random selection, and bounds are shown on the generalization err...	Rebuttal 1: Rebuttal: W.1 We appreciate the reviewer highlighting relevant literature. Both our method and [1] derive relation between the performance of graph-based active learning and sample complexity. [1] quantifies the complexity on the graph domain of network. In contrast, our method examines the complexity ...	Rebuttal 1: Rebuttal: We are grateful to all reviewers for their time and insightful feedback. We are encouraged that the reviewers found our work: 1. contributes on significant area with umerous applications (4PW9, hH2h) 2. novel and theoretically solid (4PW9, emFb, hH2h, 7FJA) 3. introduces the trade-off between inf...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning	Accept (poster)	Summary: This paper introduces a type of universal natural language constraint to model diverse real-world safety requirements. Also, to avoid using the specific human-designed cost function, this paper introduces a Unified Trajectory-Level Textual Constraints Translator (U3T) for aligning text with trajectories and as...	Rebuttal 1: Rebuttal: Thanks for the constructive comments. We are grateful to the the reviewer's conductive comments. We will answer the questions below. We believe we addressed all the concerns and are glad to follow up in the discussion phase. ---- Q1: Inference speed Answer: We perform the trajectory le...	Summary: * The paper proposes U3T, a new system for more robust safe RL under general text constraints. * The key innovation of the paper is to generalize text constraints such that the constraints don’t refer to a specific entity/state and addressing cost sparsity where constraints are only violated at the final time ...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. And we are very pleased that you appreciate the effectiveness and direction of our research. We would like to address your concerns below. --- W1.1: About text encoder Answer: We used pre-trained bert-base-uncased [1] as a text encoder. During U3T ...	Summary: - This work proposes an approach to training RL agents with constraints. The proposed approach learns language embeddings of constraints and embeddings of trajectories. During training, a similarity score is used to align the space of constraint embeddings and the space of trajectory embeddings. - Moreover, th...	Rebuttal 1: Rebuttal: We thank the reviewer for carefully reviewing our submission. We address each point of your concerns separately below. ----- W1: Relation to prior works Answer: We appreciate the reviewer’s constructive question. See the overall response about the relationship to previous work. We ...	null	null	Rebuttal 1: Rebuttal: We thank all reviewers for their time spent reviewing our paper and are grateful for the endorsement on novelty ("the approach is novel in that the technique of credit assignment is applied to the constraint" - cY35, "the paper solves an interesting and intuitive problem" - z2Bi, "through a no...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
From Similarity to Superiority: Channel Clustering for Time Series Forecasting	Accept (poster)	Summary: The paper introduces the Channel Clustering Module (CCM), a novel approach to enhance time series forecasting models. CCM addresses the limitations of traditional Channel-Independent (CI) and Channel-Dependent (CD) strategies by dynamically clustering channels based on their intrinsic similarities. This approa...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback and positive comments. We address the potential concerns as follows. >The computational efficiency of CCM, especially in large-scale applications, is not extensively discussed. As discussed in Sec.4.3, the computational complexity of CCM scales linearly with th...	Summary: Time series forecasting has been a topic of interest, with previous studies exploring different strategies. The Channel-Independent (CI) strategy treats channels individually, improving forecasting performance but lacking generalization and ignoring channel interactions. On the other hand, the Channel-Dependen...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback and positive comments. We address the potential concerns as follows. >Some LLM-based models were not selected as baselines. After careful consideration, we've decided not to include LLM-based time series models in this manuscript for several reasons. Firstly, m...	Summary: The paper presents a new Channel Clustering Module (CCM) for time series forecasting, which dynamically groups channels based on intrinsic similarities to balance the strengths of Channel-Independent (CI) and Channel-Dependent (CD) strategies. CCM improves forecasting accuracy by enhancing model generalization...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback, which significantly improves the quality of this work. We also address below the potential concerns. >The introduction of CCM increases the model's complexity. The scalability of CCM to extremely large datasets remains untested. What strategies to ensure the sc...	Summary: The paper proposes a new Channel Clustering Module and a corresponding Cluster Loss to group similar channels using a cross-attention mechanism. This creates a hybrid between channel independent and channel dependent approaches. Experiments in the paper compare both with and without the proposed module on a va...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback and positive comments. We address the potential concerns as follows. >Adding evaluation on synthetic dataset will help solidify the claims. While synthetic datasets serve their purpose in controlled experiments, our study's emphasis on real-world datasets is cr...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits	Accept (poster)	Summary: The paper derives a new time-independent inequality for likelihood ratios in the Generalized Linear Model. The proof is based on a PAC-Bayesian approach with a well-chosen prior. The result is applied to GLM bandit models, improving a variant of GLM-UCB. The resulting regret bound removes a exponential depen...	Rebuttal 1: Rebuttal: We thank the reviewer for valuable feedback and suggestions, and for recognizing our paper's technical soundness and contributions. Number of clumsinesses, Typos, Organization issues We apologize for the typographical errors and the organizational issues identified in the manuscript. We are ...	Summary: Confidence intervals for GLMs using a PAC-Bayes approach. Strengths: Confidence sequences and bandit algorithms for GLMs are a hot topic, and the authors contribution to that topic is solid, both because of the strength of the result itself, and because the proof itself is very simple and easy to verify---som...	Rebuttal 1: Rebuttal: We thank the reviewer’s valuable reviews and comments. W1. “Numerically tightness” of CS We intended "numerically tight" to mean numerically tighter than previously known and non-vacuous, a phrase often used in prior works on PAC-Bayes. We will clarify this sentence in the revised manuscript...	Summary: This paper proposes Likelihood ratio based confidence sequences for generalized linear inference, who's width only depends logarithmically on $S$, the bound on norm of the parameter vector. This is achieved by utilizing a pac-bayesian change of measure inequality, with the prior and posterior distributions cho...	Rebuttal 1: Rebuttal: We thank the reviewer for several enlightening questions and suggestions that will certainly improve our paper. W1. & Q3. Statistical issues with ellipsoidal relaxation Indeed, the ellipsoidal CS introduces an additional factor of $S$, as it uses the self-concordance control (Lemma A.4). For...	Summary: This work considers generalized linear models where the distribution of observations, conditionally on a context vector $x$ and an unknown parameter $\theta^\star$, are generated from a (known) exponential family of distribution. Their main contribution is a new confidence sequence for online estimates of $\th...	Rebuttal 1: Rebuttal: Thank you for your detailed review, for recognizing the significance of our technical contributions, and for providing valuable feedback. W1. Writing and paper organization + Regret Analysis Thank you for your suggestions. Indeed, Section 3.2 implicitly assumes that the reader is familiar wi...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for providing detailed and insightful reviews. We are especially encouraged to see that the reviewers recognize the simple yet effective proof ideas (q1y1, HLaD), technically solid contribution in reducing $poly(S)$ to $\log S$ in the CS width (q1y1, HLaD, aQNL...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Online Classification with Predictions	Accept (poster)	Summary: The paper studies an online classification problem that interpolates between transductive online classification and (standard) online classification. The interpolation is controlled by a "predictor" entity that tries to predict the examples to be labelled: If the predictor is good, the setting is more like tra...	Rebuttal 1: Rebuttal: We thank the reviewer for noting that we present a new and fairly natural problem in online learning. We will incorporate all suggestions and fix typos in the final camera-ready version. We address each weakness and question below. Strengths 2. This technique of constructing a few online le...	Summary: This paper studies the complexity of the online classification problem when provided with a predictor $\mathcal{P}$ that can forecast future features of data samples. Using black-box access to this predictor, the authors provide a beyond-worst-case analysis of online classification algorithms that can adapt to...	Rebuttal 1: Rebuttal: We thank the reviewer for finding the theoretical results to be solid and the presentation to be clear. We address the concerns below. - Indeed, the stream of instances $x_1, ..., x_T$ could be very unpredictable and $M_P$ can be very large. However, in this case the minimum in Theorem 3.1 is ob...	Summary: The paper belongs to the field of statistical learning. The learner is seeing pairs $(x,y)$ and needs to predict labels $y$s to compete (in terms of the expected number of errors) with any hypothesis from the given class. The main result of the paper, Theorem 3.1, establishes a connection between the offline ...	Rebuttal 1: Rebuttal: We thank the reviewer for finding that our work presents an "interesting result answering an important question." We address the concerns below. - We thank the reviewer for pointing out our use of "adversary" in Page 4 Section 2.3. We will change this to "Nature" to make it consistent with the ...	Summary: This paper studies learning-augmented online classification, where the classifier has access to a predictor that forecasts future examples. The paper proposes an algorithm that uses these predictions, and the algorithm's performance depends on the prediction error. The proposed algorithm is robust (never worse...	Rebuttal 1: Rebuttal: We thank the reviewer for noting that our main result is intuitive and elegant. We address each of the weaknesses and questions below. Weaknesses - In this paper, we consider abstract instance spaces $X$. Accordingly, we did not discuss how the predictions can be generated since there is unl...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Improving robustness to corruptions with multiplicative weight perturbations	Accept (spotlight)	Summary: To achieve robustness against corruptions, most of existing works relying on incorporating some corruptions during the training. In contrast, this paper takes a different perspective and propose a weight-based approach called DAMP to achieve model robustness against corruptions without compromising accuracy on...	Rebuttal 1: Rebuttal: 1. "There could be a related work [1] focusing on (adversarial) corruptions, where it also approaches the model robustness problem from model weights perspective...." Thank you for the suggestion. This is indeed an interesting related paper which proposes a score called CRoZe that can quickly...	Summary: This submission tackles the problem of generalization of image classifiers to corrupted images. The paper shows a link between data augmentation as well as previous methods such as Adaptive Sharpness-aware Minimization (ASAM) and multiplicative weight perturbations. Effectively, ASAM works as an adversarial we...	Rebuttal 1: Rebuttal: 1. "...It seems like DAMP is mostly an approximation of ASAM which allows faster training (and that is great) but at the same time, it is not as effective as ASAM. So effectively, this method introduces a trade-off between performance (accuracy) and training time..." There is a misunderstandi...	Summary: The paper presents Data Augmentation via Multiplicative Perturbations (DAMP), a novel method to enhance DNN robustness against image corruptions by optimizing with random multiplicative weight perturbations. This approach improves generalization on corrupted images without compromising accuracy on clean ones. ...	Rebuttal 1: Rebuttal: 1. "It would be beneficial to see how the method performs with different hyperparameter values, as the reported numbers for different metrics are close to each other." Thank you for your suggestion. We provide Table C in the PDF file attached to our global response showing the accuracy of V...	Summary: This paper works on improving robustness by multiplying a random Gaussian variable on weights during training. Strengths: The writing is easy to read and follow. Weaknesses: 1. The novelty is quite limited. This concept has been proposed and explored for at least a decade. For instance, variational dropout a...	Rebuttal 1: Rebuttal: 1. On the novelty of this work and its connections to previous works: The novel contributions are: 1. Showing the theoretical connection between perturbations in the input space and perturbations in the weight space, in particular, that one can simulate perturbations in the input space via mu...	Rebuttal 1: Rebuttal: We want to thank the reviewers for their time and for providing us with thoughtful comments which help us improve our work. In this global response, we first provide a brief summary of our paper. We then present additional experiment results, which are included in the attached PDF file. ## Paper s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Scaling Sign Language Translation	Accept (poster)	Summary: This paper proposes to improve the open-domain sign language translation by scaling pretraining data, model size, and number of translation directions. The proposed approach involves pretraining with a mixture of noisy multilingual YouTube SLT data, parallel corpora, and SLT data augmented with MT models. Expe...	Rebuttal 1: Rebuttal: Thanks for your insightful comments! Re: In Figure 7, the legend is blocked. We will fix it in the next version. Re: There is no open-source code or model While we can’t release the code and model subject to policy restriction, we believe our findings and scaling results could shed ...	Summary: This paper presents an approach to Sign Language Translation (SLT) that aims to scale the field by addressing limitations in data, model size, and the number of translation directions. The authors' key contributions are: * Data Scaling: The authors leverage diverse and large-scale datasets, including noisy mu...	Rebuttal 1: Rebuttal: Thanks for your insightful comments! Re: the missing references Thanks for pointing this out! Please note that our work is built on prior work discussed through personal correspondence with the authors before publication (hence our use of FLEURS-ASL#0 only, which was available to us before t...	Summary: This paper attempts to advance the development of sign language translation (SLT) technology by using large-scale pre-training data, expanding model size, and adding translation directions. Through extensive experiments, the authors have drawn many useful conclusions. Experiments show that this work can achiev...	Rebuttal 1: Rebuttal: Thanks for your insightful comments! Re: the sign language data and code used in this article will not be open source Please note that YouTube-SL-25 has been released on arXiv (https://www.arxiv.org/abs/2407.11144), including release of the clean subset of the data. We built on their work th...	Summary: The paper focuses on scaling sign language translation (SLT) by leveraging large-scale pretraining data, increasing model size, and expanding the number of translation directions. The study demonstrates the effectiveness of data/model scaling and cross-lingual cross-modal transfer in improving SLT performance ...	Rebuttal 1: Rebuttal: Thanks for your insightful comments! Re: how different model architectures or training strategies could affect SLT performance; how these factors could influence scalability and generalization? Thanks for this question! Firstly, it would be great if you could provide more context about the "...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Building a stable classifier with the inflated argmax	Accept (poster)	Summary: This paper studies and proposes a new theoretical framework to study algorithmic stability for multiclass classification with a focus on a stable class selection given predicted class probability. Based on the proposed theory, the proposed selection criterion based on bagging called "inflated argmax" was propo...	Rebuttal 1: Rebuttal: 1. *“Experiments were quite weak in my opinion to highlight the effectiveness of inflated argmax. For example, more reasonable baseline that has larger than 1 𝛽_size could have been provided to show that inflated argmax is quite favorable if it allows to predict more than one class. Curreent base...	Summary: An algorithm is considered stable if small perturbations in the training data do not result in significant changes to its output. Stability has been previously explored in the context of regression. This paper extends this concept to the multiclass framework, where previous work has focused on stability in ter...	Rebuttal 1: Rebuttal: 1. *“The authors point out my primary concern in their discussion section, highlighting the practicality of the proposed framework. Notably, bagging is an computationally expensive procedure, and while the authors suggest that parallelization can mitigate this issue, I agree that this does not nec...	Summary: This paper focuses on the stability of learning multiclass classfiers. It proposes a notion of selection stability for the learning algorithm as well as a modified argmax that returns a set of labels. Evaluations have been conducted using FashionMNIST and simple models. Strengths: - This paper focuses on the ...	Rebuttal 1: Rebuttal: 1. “The presentation of the proposed contribution can be improved.” a. “The core proposal of inflated argmax is introduced in section 3.2, which is too late.” Thank you for this feedback. We agree that introducing inflated argmax earlier in the paper would be better and will try t...	Summary: This submission studies the problem of making set-valued predictions, in which the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. Yet, this submission mentions one method f...	Rebuttal 1: Rebuttal: 1. “W1: I think adding a few more competitors would be useful in assessing the potential advantages of the proposed algorithms…”, “W2: Assessing the classifiers with respect to utility-discounted predictive accuracies, …” and “L2: the empirical study might need to be enlarged with closely rela...	Rebuttal 1: Rebuttal: We thank all of the reviewers for their insightful comments and effort in reviewing the paper. Below we discuss two topics that came up in multiple reviews. 1. Improvements to the experiments section. Based on feedback from several reviewers, we have expanded our experiments section by adding...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper considers the stability of classifiers in multi-class classification. In the considered framework, the classifier is allowed to return a set of candidate classes instead of only one class. The stability is defined as the frequency of a set predicted by one classifier having no union with a set predic...	Rebuttal 1: Rebuttal: 1. “The authors argue that the argmax is a hard operator… but the definition of stability used in the paper also uses the hard operator of the set union being equal to an empty set” The argmax does not satisfy $\epsilon$ compatibility. The point of the paper is to introduce an $\epsilon$ co...	null	null	null	null	null	null
Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling	Accept (poster)	Summary: This paper introduces a novel music AI system that creates multi-track accompaniments from a lead sheet by leveraging disentangled style factors to enhance context coherence, creativity, and computational efficiency. The proposed two-stage process begins with generating a piano arrangement using piano texture ...	Rebuttal 1: Rebuttal: Thank you for your review and constructive feedback! We acknowledge that some terms in our paper may not be sufficiently clear (W1). We also recognize our shortcomings in providing adequate motivation to justify our design choices (W3). Please allow us to first respond to the relevant poin...	Summary: The paper presents a style transfer-based music accompaniment generation system. It starts with the lead melody and fleshes out the accompaniment tracks based on various high-level information, such as instrument type and rhythmic structure. The main goal of the proposed model is to be able to generate coheren...	Rebuttal 1: Rebuttal: Thank you for your review and constructive feedback! Please allow us to first respond to the points raised in Limitations and Questions. We hope this could address your concerns. We will then comprehensively incorporate these clarified ideas into our manuscript to provide sufficiently clea...	Summary: The paper suggests creating multi-instrument accompaniment by using the piano reduction and the instrument note density (referred to as the 'Orchestration Function') as bootstrap representations. By effectively applying VAE and an autoregressive sequence generation framework, the paper demonstrates the high po...	Rebuttal 1: Rebuttal: Thank you for your review and thoughtful feedback! We hope the following will address your concerns: Weakness: Discussion on the impact of the piano reduction quality A: We introduce piano reduction as an intermediate representation from the input lead sheet to the final orchestra arrangem...	Summary: The authors introduce a new model for generating multi-track accompaniment given a lead sheet. While being strictly a two-stage method (first generate a piano arrangement from the lead sheet and then generate the accompaniment), the authors' contribution is focused on the 2nd stage, and they rely on existing...	Rebuttal 1: Rebuttal: Thank you for your review and constructive feedback! We value your thorough and insightful comments and will revise our manuscript based on these suggestions to improve the presentation. We also hope the following could address your existing concerns: **Weakness 1. Clarification of music concepts...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control	Accept (poster)	Summary: This paper presents a method for few-shot imitation learning for new embodiment. Specifically, it presents a method that learns a state representation that decouples embodiment-specific and task-specific knowledge and a meta-learning framework that transfers between embodiments and tasks. Results suggest that ...	Rebuttal 1: Rebuttal: > Q1. Given that state representation learning is decoupled from policy representation learning, it would be interesting to visualize the embedding learned by the state encoder and see how distinct it is between different morphologies. We provide a t-SNE visualization of embedding space of f...	Summary: This paper aims to generalize to unseen embodiments and tasks by few-shot behavior cloning. It proposes a modular framework to capture both shared knowledge across all embodiments and embodiment-specific knowledge. It utilizes a matching-based method to enhance the robustness to overfitting. It shows superior ...	Rebuttal 1: Rebuttal: > Q1. What is the learning curve for the models compared with PDT+PEFT? We provide learning curves of Meta-Controller and PDT+PEFT in Figure R.2 of the author rebuttal. As the reviewer pointed out, due to the modular nature of our structure encoder, our model not only achieves better perform...	Summary: This paper introduces a framework called Meta-Controller for few-shot behavior cloning that can generalize to unseen robot embodiments and tasks in continuous control problems. The key contributions are: 1. A joint-level input/output representation to unify state and action spaces across heterogeneous robot e...	Rebuttal 1: Rebuttal: > Q1. In the case of robot arm manipulation, what are the benefits of utilizing the proposed structure-motion encoder over the direct application of end-effector-based control? Compared to end-effector-based control, our method eliminates the need for separate low-level controllers manually t...	Summary: This paper introduces Meta-Controller, a few-shot behavior cloning framework designed for adaptation to various embodiment and control tasks. The framework includes a transformer-based structure-motion state encoder that captures knowledge across different embodiments, and a matching-based policy network that ...	Rebuttal 1: Rebuttal: > Q1. The ablation study in Table 2 is incomplete. Table R.1 of the author rebuttal completes the ablation study as requested (see row 1 and 2). Consistent with the discussion in Section 5.3 of the main text, we observe that removing the structure encoder $f_s$ significantly decreases perfo...	Rebuttal 1: Rebuttal: We appreciate all the valuable comments provided by the reviewers. We will incorporate the additional results and clarifications made during the rebuttal into the camera-ready version of our paper. We want to clarify that there was a typo regarding the details of embodiment and task. We use 30 ta...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials	Accept (poster)	Summary: Emu3D is a novel approach in text-conditioned 3D generation, producing high-quality 3D meshes and materials using Physically-Based Rendering (PBR). It uses a two-stage process: first generating images from standard viewpoints, then reconstructing the 3D shape and appearance. This approach is faster and more re...	Rebuttal 1: Rebuttal: We thank the reviewer for the review and the feeback! 1. Lack of support for material components such as index of reflection, scattering, coat, sheen etc.: Our model indeed only tackles essential PBR parameters (albedo, roughness and metallness) as these are the most important ones, and also...	Summary: The paper adopts a two-stage PBR information generation method. In the first stage, the text-to-image model is used to predict the PBR channel. In the second stage, it uses SDF to reconstruct the geometry and PBR information. Then, a cross-view attention texture reflector network is used to improve the coarse ...	Rebuttal 1: Rebuttal: Thank you for the review and the helpful comments! 1. Technical Novelty: To our knowledge, Emu3D is one of the first academic work that performs text-to-3D with PBR in a feed-forward way. We introduce several novel aspects that come together to achieve high quality and efficient text-to-3D r...	Summary: This paper introduces a two-stage 3D asset generation pipeline that outputs high-quality 3D meshes with PBR materials in approximately 30 seconds. The technical novelties include: - In text-to-image stage, generating multiple views of both shaded and unshaded images, and predicting PBR materials during the ima...	Rebuttal 1: Rebuttal: We thank the reviewer for the review and the feedback on our work! 1. Use of SDF instead of opacity fields: We do not claim that using SDF in 3D reconstruction or generation is novel per se (e.g., StyleSDF, SDFusion, GET3D, Latte3D, etc. uses it). We do note that we are among the first to u...	Summary: The paper mainly works on text-to-3D with PBR materials. It follows the diffusion-based multiview generation and LRM-based reconstruction paradigm. It is a fast feed-forward solution for PBR generation: The diffusion model predicts both shaded and albedo, and the LRM predicts PBR via differentiable rendering w...	Rebuttal 1: Rebuttal: Thank you for detailed review and the helpful comments! 1. Degraded PBR quality with only RGB shaded inputs (weakness #1): Yes, indeed the PBR quality suffers when only RGB shaded inputs are provided (upper half Table 1) since the material decomposition is ambiguous. For this reason, the com...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive and valuable feedback. We are pleased that all four reviewers found our presentation to be excellent, the soundness of our proposed approach to be good or excellent, and our contributions to be good. We are glad the reviewers found our method to be “...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention	Accept (poster)	Summary: To resolve the costly memory consumption of KV cache in transformer-based LLMs, this paper proposes Cross-Layer Attention (CLA). In contrast to multi-query attention (MQA) and grouped-query attention (GQA) which share KV cache across attention heads, CLA shares KV cache across contiguous layers. Pre-training e...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful and generous feedback. We address each point below: ## Could we include more comprehensive results tables in the paper? We thank the reviewer for pointing this out, and agree the paper could be improved by including more comprehensive results tables. F...	Summary: In this paper, the authors proposed an approach, Cross-Layer Attention (CLA), to accelerate the autoregressive generation procedure of Large Language Models. Going beyond multi-query attention, the main idea of CLA is to share the Key-Value caches among attention layers. Intuitively, this idea is straightforwa...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful and generous feedback. We address each point below: ## Could we evaluate CLA on larger models? Unfortunately, we lack the resources to train 10B- and 100B-scale models from scratch using CLA. Even training a single model on the same parameter and data s...	Summary: This paper introduces a new KV cache compression technique by sharing KV cache cross-layers called CLA. It can be integrated with most of the existing KV cache compression technique like MQA, GQA, and quantization. When applying over GQA, CLA can further reduces KV cache size by 2× while maintaining similar ac...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful and generous feedback. We address each point below: ## Could we report more quality metrics? In addition to Wikitext perplexity, we also report accuracy scores on Hellaswag, PIQA, WinoGrande, OpenBookQA, BoolQ, and ARC-E, which can be found in Table 3 a...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for their comments. We address their main points below: ## Could we report more quality metrics? In addition to Wikitext perplexity, we also report accuracy scores on Hellaswag, PIQA, WinoGrande, OpenBookQA, BoolQ, and ARC-E, which can be found in Table 3 and Table 4. ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Perceptual Fairness in Image Restoration	Accept (poster)	Summary: This paper considers fairness issue of image restoration and proposes Group Perceptual Index to measure the distance between restoration distribution and gt distribution. Experimental and theoretical results demonstrate that the superiority the proposed perceptual fairness over previous method. Strengths: 1. ...	Rebuttal 1: Rebuttal: ### Can our method be useful for general-content image restoration? This is a very interesting point that definitely deserves an explanation. While the proposed GPI is indeed suitable for evaluating fairness in natural images with complex structures, fairness issues are particularly critical when...	Summary: This work introduces a new method for assessing fairness in image restoration, called the Group Perceptual Index (GPI). This measure quantifies the statistical difference between the distribution of a group's original images and the distribution of their restored versions. The authors illustrate the effectiven...	Rebuttal 1: Rebuttal: ### Demonstration only on image super-resolution tasks We opted to illustrate our approach on 12 different super-resolution tasks (4 scale factors and 3 noise levels) simply because of the availability of many methods to compare against on these tasks. Note that this choice aligns with common prac...	Summary: This study presents a novel method to evaluate fairness in image restoration using the Group Perceptual Index (GPI). GPI quantifies the statistical disparity between a group's original images and their restored versions. Fairness is assessed by comparing GPIs across multiple groups, striving for perfect Percep...	Rebuttal 1: Rebuttal: ### Demonstration only on image super-resolution tasks We opted to illustrate our approach on 12 different super-resolution tasks (4 scale factors and 3 noise levels) simply because of the availability of many methods to compare against on these tasks. Note that this choice aligns with common prac...	Summary: This paper reveals that the conventional definition of fairness for image restoration is restrictive and often causes controversy. To address this issue, the authors introduce a new approach to measure fairness in image restoration tasks by proposing the Group Perceptual Index (GPI). Specifically, they propose...	Rebuttal 1: Rebuttal: ### Complexity of evaluating perceptual fairness Thank you for raising this important point. We acknowledge that computing the GPI of each group increases the complexity of evaluating fairness compared to previous methods, which typically compute the classification hit rates (e.g., counting the re...	Rebuttal 1: Rebuttal: # Thank you! We are deeply grateful to all the reviewers for dedicating their time to evaluate our paper. The feedback we received has been highly encouraging and has helped us improve the quality of our work.	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data	Accept (poster)	Summary: The authors propose a method to identify causal and exogenous variables in Gaussian Additive Noise Models from purely observational data. Strengths: The paper proposes a novel approach (the setting might have been largely considered elsewhere; see my comments in Weaknesses and Questions). - the proo...	Rebuttal 1: Rebuttal: Thank you for your detailed review! We appreciate that you found our proofs sound and our newly defined notion of identifiability to be interesting. We would like to address your comments below: > “What do you exactly mean by causal disentanglement?” Following this review (https://arxiv.org/...	Summary: This paper concerns itself with the problem of causal disentanglement from purely observational data. The setting is that data X is generated as X = H.Z where H is a linear matrix and Z are latent variables that follow a nonlinear additive Gaussian noise model. In this setting, it is shown that the latent vari...	Rebuttal 1: Rebuttal: Thank you for appreciating our problem setting and for recognizing the novelty of our defined notion of identifiability. We would like to address your concerns and questions below: > **“L184/L284 claims there are no structural results on the mixing function, however assumption 1 assumes that the ...	Summary: This paper studies the identifiability issue of causal disentanglement from observational data, within the setting of nonlinear causal model with additive Gaussian noise and linear mixing. An interesting result is that the causal variables can be identifiable at most up to a layer-wise transformation, based on...	Rebuttal 1: Rebuttal: Thank you for your encouraging review! We appreciate you recognizing both the importance of our work and the merit of our theoretical findings and algorithmic approach. We would like to address your additional comments and questions below: > **“…this paper assumed non-linear function with Gaussi...	Summary: This paper investigates causal disentanglement, learning latent causal factors from observational data without interventions. It identifies latent factors in nonlinear causal models with additive Gaussian noise and linear mixing, showing that causal variables can be identified up to a layer-wise transformation...	Rebuttal 1: Rebuttal: Thank you for recognizing the strength of our theoretical analysis and our proposed method! We would like to address your comments below: > **“The method proposed in this paper relaxes some assumptions, making it potentially more applicable to real-world scenarios. It would be better to provide ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FedAvP: Augment Local Data via Shared Policy in Federated Learning	Accept (poster)	Summary: This paper proposes FedAvP, a novel algorithm to perform data augmentation via shared policy in federated learning (FL). Extensive experiments verify the effectiveness of the proposed algorithm. Strengths: S1: The proposed algorithm is novel with solid theoretical analysis. S2: The experiments are comprehens...	Rebuttal 1: Rebuttal: W1. More experiments on FL benchmark on non-IID data We conduct experiments on comprehensive non-IID settings suggested by the reviewer, specifically the quantity-based label skew settings described in [1]. The table below presents the results across different datasets and partitioning strate...	Summary: This paper points out that the shared input-level and feature-level information poses potential privacy leakage. FedAvP only shares the augmentation policy which is not related to the data. This method leverages the first-order information as a replacement to reduce privacy leakage and communication costs. Com...	Rebuttal 1: Rebuttal: W1. Improvement to the explanation Thank you for your valuable suggestion to improve our paper. In response, we will provide more detailed explanations to address your points, including the effect of different heterogeneity levels $\alpha$, an additional baseline, the meaning of OOD (Out-of-d...	Summary: The paper introduces a novel federated data augmentation algorithm, FedAvP, designed for data augmentation of the client-side without the need to share client data information. Specifically, the authors propose a meta-learning method that allows multiple clients to collaboratively learn data augmentation polic...	Rebuttal 1: Rebuttal: W1. The scalability of FedAvP When using two operations, the neural network output $P_{\theta}$ in the paper has a 17x17 dimension, represented as $P_{\theta}(1), ..., P_{\theta}(17 \times 17)$. From this output, we sample $P_{\theta}^{1}, ..., P_{\theta}^{B}$ based on the batch size $B$ and ...	Summary: This paper proposes FedAvP, which performs data augmentation search by sharing policies among clients in a federated learning (FL) environment. They introduce federated meta-policy loss and utilize the first-order gradient information to further enhance privacy and reduce communication costs. The proposed algo...	Rebuttal 1: Rebuttal: W1. Comparison with other classic non-IID methods, such as FedNova and Scaffold. In Table 1 of the manuscript, we compare our model with baselines, including state-of-the-art federated learning and federated data augmentation algorithms. As the review suggested, we conducted an additional exp...	Rebuttal 1: Rebuttal: More experimental results We conducted additional experiments to answer Reviewer WeMd's W2 and Reviewer 866Z's W1, considering more non-IID settings and highly partitioned label skew settings [1], respectively. \|Dataset/heterogeneity degree α\|CIFAR100/5.0\|CIFAR100/0.1\|CIFAR10/5.0\|CIFAR10/0.1\|...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?	Accept (poster)	Summary: The present work pertains to analysing and improving Graph Neural Networks operating on geometric graphs that possess E(3) (Euclidean) symmetries, i.e. the tasks of interest are invariant/equivariant to rotations, translations and reflections. The paper challenges the assumption that it is sufficient to use E(...	Rebuttal 1: Rebuttal: Thank you for your comments! We provide the following responses to your concerns: > W1: More detailed background knowledge and conceptual explanations are needed to improve clarity. Thank you for your valuable suggestions! We will further summarize the formal definitions of steerable repres...	Summary: This paper challenges the prevailing notion that high-degree steerable representations are unnecessary in equivariant Graph Neural Networks (GNNs). The authors provide theoretical analysis showing that equivariant GNNs constrained to 1st-degree representations degenerate to zero functions when applied to symme...	Rebuttal 1: Rebuttal: Thank you for your comments! We provide the following responses to your concerns: > W1 & Q2: HEGNN should be tested on conventional dataset splits and compared with new baselines such as ClofNet. Nice suggestion! We have additionally conducted experiments on standard N-body benchmarks (trai...	Summary: This paper studies the necessity of higher-degree features in geometric graph neural networks (i.e. graph neural networks processing data embedded in three-dimensional space), focusing on the ability to recognize data with non-trivial rotational inner-symmetry such as k-folds and regular polygons, and based on...	Rebuttal 1: Rebuttal: Thank you for your comments! We provide the following responses to your concerns: > W1. Differences and connections with GWL. Thank you for raising the comparison with the GWL paper [1]. Here, we would like to further highlight the difference between [1] and our paper: 1. Different Motiva...	Summary: The paper studies the benefit of using higher order steerable features in geometric GNNs and theoretically identifies classes of symmetric geometric graphs where methods using only order-1 (or low order) features are guaranteed to fail. With this in mind, the authors propose a simple and efficient way to integ...	Rebuttal 1: Rebuttal: Thank you for your comments! We provide the following responses to your concerns: > Q1: The performance gap between scalarization-based models (e.g. HEGNN) and high-degree steerable models (e.g. TFN, SEGNN). It is true that our HEGNN exclusively passes invariant quantities (inner products o...	Rebuttal 1: Rebuttal: # General Response We sincerely thank all reviewers and ACs for their time and efforts on reviewing the paper. We are very glad that the reviewers recognized the problems we studied, the theories we proposed, and the models we built, and their comments really gave us a lot of inspiration and enli...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Estimating the Hallucination Rate of Generative AI	Accept (poster)	Summary: This paper presents a Bayesian interpretation of in-context learning. This interpretation enables us to calculate the hallucination rate. In other words, by considering in-context examples as observations, the posterior distribution can be computed and the hallucination rate is derived. Numerical experiments v...	Rebuttal 1: Rebuttal: Thank you for taking the time to read and comment on our work. We are glad you found the ideas original and the contribution excellent. Although we have some disagreements about the weaknesses, we think your concerns are important and aim to clarify them below. **W1: As discussed in L222, the pro...	Summary: This paper presents a method for predicting the hallucination rate of in-context learning with conditional generative models. Strengths: - NA Weaknesses: - Unclear how many queries would be require to validate the approach, as this will be very dependent of task, context, and LLM - this is clearly a missing ...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to review our work. It is disheartening that you did not find it appropriate to attribute strengths to our work. We hope to convince you of what we believe constitutes a significant positive contribution. We have addressed your concerns below and look forw...	Summary: The paper focuses on the in-context learning setting of generative AIs, such as large language models, and proposes a new definition for hallucination. It introduces a novel metric, PHR, along with a corresponding estimation method. Unlike traditional metrics, the proposed metric accounts for label ambiguity r...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and attention. We appreciate that you have identified several strengths, and recognize that your main criticisms are concerned with clarity and missing details. We respond to each of your comments below and have updated our manuscript accordingly. **W1.1: Ther...	Summary: The paper proposes a method to estimate the hallucination rate for in-context learning (ICL) in a conditional generative model (CGM) from a Bayesian perspective. The authors assume the CGM samples from a posterior predictive distribution over a Bayesian model of a latent parameter and data. They define Posteri...	Rebuttal 1: Rebuttal: We thank the reviewer for the effort made in reviewing our work. We appreciate that you have identified several strengths and recognize that your main criticisms concern evaluation and clarity of concepts. We offer clarifications, have run additional experiments, and have updated our manuscript ac...	Rebuttal 1: Rebuttal: We thank all reviewers for their insightful and constructive feedback. We are particularly encouraged by the recognition of several strengths across different aspects of our paper, which we summarize below. Reviewer VGJP acknowledges the significance of our work in addressing hallucination in...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation	Accept (spotlight)	Summary: Both low-rank and orthogonal adaptation techniques can effectively adapt large-scale pre-training models to downstream tasks. This work proposes an new adaptation methods based on Householder reflections(HR). It discloses the connection between low-rank and orthogonal adaptation and builds a unified adapter-ba...	Rebuttal 1: Rebuttal: Thanks for your appreciation of our work and constructive comments. Below, we try to resolve your concerns one by one. Q1: Improve the structure of the paper and highlight the motivation for building a unified adaptation framework. A1: We have detailed the pros and cons of LoRA and OFT i...	Summary: his paper proposes a simple yet efficient adaption method, namely HRA, which finetunes a pretrained model by multiplying each frozen weight matrix with an orthogonal matrix constructed by a chain of learnable Householder reflections. The authors interpret HRA as an adaptive LoRA that retains theoretical guaran...	Rebuttal 1: Rebuttal: Thanks for your appreciation of our work. We have resolved your concerns about the limitations and the computational efficiency of HRA in the above general response. For your remaining concerns, we provide our answer below. Q1: The evidence on the retention of prior knowledge. A1: Th...	Summary: The paper proposes a simple but effective adaptation method based on Householder reflection matrix. The authors show that this method is closely related to low-rank adaption. Diverse experiments demonstrated the effectiveness of the proposed method in comparison to a few baselines. Strengths: 1. The idea of u...	Rebuttal 1: Rebuttal: Thanks for your comments. Below we try to resolve your concerns one by one. Q1: The novelty of the proposed method. A1: We respectfully disagree with the comment that our work's novelty is relatively limited for the following three reasons. Firstly, to our knowledge, our work makes the f...	Summary: This paper proposed a new model fine-tuning method, called Householder reflection adaptation (HRA). The main idea of HRA is to fine-tune the model with a series of Householder reflections. By virtue of the Householder reflection, the orthogonality of the tuning matrix can be obtained, the number of tuning para...	Rebuttal 1: Rebuttal: Thanks for your appreciation of our work. We believe your concerns have been resolved in our general response, and we hope that our response can help increase your confidence score. We are willing to discuss with you in the next discussion phase if you have any other questions.	Rebuttal 1: Rebuttal: We thank all the reviewers for their appreciation of our work. Below, we provide a general response to their common concerns and a specific response to each reviewer's remaining questions. Q1: Discussions on limitations and societal impacts of this work. A1: Regarding the limitations of ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning	Accept (poster)	Summary: This paper presents a new unsupervised reinforcement learning (URL) algorithm called Pre-trained Embodiment-Aware Control (PEAC). PEAC is designed to tackle cross-embodiment tasks, explicitly considering the influence of different embodiments to facilitate exploration and skill discovery across embodiments. Ex...	Rebuttal 1: Rebuttal: Thanks a lot for your review, especially the praise for PEAC's contributions to embodied intelligence. Below we will address all your concerns. W1: About baselines utilizing embodiment information $e$. A: Thank you for recognizing that we have included various standard and SOTA baselines...	Summary: This papaer introduces a novel setting, cross-emodiment unsupervised RL, which deals with pre-training good policies in reward free environments across different embodiments in order to perform well on downstream tasks on unseen embodiments. The authors propose the algorithm PEAC for unsupervised learning in t...	Rebuttal 1: Rebuttal: Thanks a lot for the supportive review and constructive suggestions. Below we address the detailed comments. W1: About the embodiment context encoder in line 68. A: The embodiment context encoder is the embodiment discriminator in the main text of the paper, and we will polish our paper ...	Summary: This paper addresses the challenge of designing generalizable agents capable of adapting to diverse embodiments. The authors propose the CEURL setting as a novel framework for this problem and introduce the PEAC algorithm to address it. Recognizing that CEURL requires minimizing across different downstream tas...	Rebuttal 1: Rebuttal: Thank you for your supportive review and valuable suggestions. Below we address the detailed comments for all your questions. W1: About more complex tasks. A: In our experiments, tasks chosen in DMC and Isaacgym are standard and widely used in unsupervised RL evaluation [1, 2, 3]...	null	null	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments which help to further improve our paper. Here we first address the common concerns on baselines/ablation studies and more complicated cross-embodiment settings. Then, we provide a detailed response to the comments of each reviewer resp...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Paths to Equilibrium in Games	Accept (spotlight)	Summary: The authors study and affirm the possibility of constructing a satisficing path to a Nash equilibrium, from any initial strategy profile, in n-player normal-form games. Satisficing paths generalize best-response paths in that a player that is not best responding is not restricted in its strategy update. Player...	Rebuttal 1: Rebuttal: Thank you for your close reading of our paper and for sharing your concerns. We wish to primarily address the points of confusion identified in your review, but perhaps it would be helpful to first address your question on the connection between our work and the previous literature. Indeed, the ...	Summary: This paper explores the dynamics of strategy updates in MARL and game theory, focusing on sequences of strategies satisfying a pairwise constraint. This constraint requires that the agent best responding in one period does not switch its strategy in the next period but does not constrain the non-optimizing age...	Rebuttal 1: Rebuttal: Thank you for your kind words and for your questions. Efficiency and practical performance are important considerations for work on MARL. Our theoretical study here is somewhat orthogonal to efficiency, and we do not take a position on the exploratory ("lose-shift") mechanism used by any particula...	Summary: The paper answers the following question affirmatively: "For an arbitrary n-player normal-form game and an arbitrary initial strategy profile x1, is it always possible to construct a satisficing path from x1 to a Nash equilibrium of the game?". Strengths: The presentation of the paper is excellent, in particu...	Rebuttal 1: Rebuttal: Thank you for your engaged reading and review of our paper! Our aim with the original wording at the beginning of Section 3.2 was to contrast approaches based on satisficing with approaches based (for instance) on best responding, where in the latter it is possible that no path to equilibrium exi...	Summary: The paper delves into the strategic dynamics of MARL, focusing on the evolution of strategy profiles among agents. It introduces satisficing paths, a sequence of strategies where an agent that is best responding in one period does not switch strategies in the next, which allows for exploration in optimization....	Rebuttal 1: Rebuttal: 1. Our theoretical results have some practical consequences for MARL algorithms. First, they can be used to justify and analyze existing algorithms (such as those of references [19], [20], [33], and others) beyond the narrower classes of games for which they were designed. Second, our results info...	Rebuttal 1: Rebuttal: Dear AC and reviewers, We would like to thank you all for your time and effort in reading and reviewing our paper, and we would like to thank the reviewers for their thoughtful input and positive evaluations. For your convenience, we have responded to each reviewer's questions separately below. ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning 1D Causal Visual Representation with De-focus Attention Networks	Accept (poster)	Summary: This paper mainly improves the existing 1D causal models when handling visual inputs, generally 2D non-causal. The key insight of the authors mainly comes from Figure 1: the 1D causal models will over-focus to a few tokens instead of capturing the rich information from the whole image. To fix this issue, the a...	Rebuttal 1: Rebuttal: We sincerely appreciate Reviewer WAfj for the constructive suggestions. Please see our detailed response below. --- > **Q1: Transformer-based architectures like ViT in image classification and CLIP show certain generalizations to different resolutions. For example, people can train a model on 22...	Summary: This paper addresses the issue of over-focus in vision models by proposing strategies of using large and scheduled drop path rates and an auxiliary loss on globally pooled features. These strategies aim to encourage the model to focus on a wider range of tokens and improve network optimization. The paper is lo...	Rebuttal 1: Rebuttal: We thank Reviewer J7C5's feedback. It should first be clarified that the reviewer seems to have confused the concept of causal reasoning with 1D causal modeling in our paper. This may have led to some misunderstandings, such as the mistaken belief that we did not compare with other causal modeling...	Summary: The paper addresses the challenges of using 1D causal modeling for images, which traditionally require 2D non-causal modeling due to inherent modality differences between vision and language models. It identifies a significant issue in existing 1D causal vision models termed "over-focus," where the model's att...	Rebuttal 1: Rebuttal: We thank Reviewer 4dxw for the thoughtful review and the insights provided. We appreciate the opportunity to discuss the enhancements and implications of our research further. --- > **Q1: For CLIP experiments, it is helpful to also report comparisons for cross-modal retrival on MSCOCO to follow ...	Summary: The paper explores the feasibility of representing images with 1D causal modeling in unified multi-modal vision and language models, addressing the "over-focus" issue in existing models by proposing De-focus Attention Networks (DANs) with learnable bandpass filters and enhanced training strategies. Extensive e...	Rebuttal 1: Rebuttal: We appreciate Reviewer WFuU for the comments, yet we must emphasize that the weakness Reviewer WFuU has identified is not a sufficient reason for rejection. Our experiments based on Mamba have demonstrated that 1D visual causal modeling can achieve comparable performance to non-causal models. Othe...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Framework for Bilevel Optimization on Riemannian Manifolds	Accept (poster)	Summary: This paper studies Riemannian bilevel optimization, where variables of both lower and upper level problems are constrained on Riemannian manifolds. The authors propose several hypergradient based algorithms via Neumann series and automatic differentiation. Convergence analysis is provided for the proposed app...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating the contributions of our work in terms of comprehensive analysis and supportive experiments, as well as providing the constructive comments. 1. (W1) More examples to validate the importance of bilevel optimization on manifolds. Thank you for the sugg...	Summary: The paper proposes an RHGD algorithm (Algo 1) to solve the bilevel optimization problems (line 11). - Thm 1 proves the convergence to a stationary point of $F$ when using different approximations of the hypergradient. - Thm 2 shows the convergence under stochastic setting. - Thm 3 proves the convergence with ...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback on our work, particularly acknowledging that our paper is well-written with solid theoretical results. We also greatly appreciate the constructive comments. 1. (W1) Assumption 1 is strong. We would like to highlight that Assumption 1 is often...	Summary: This paper presents a framework for addressing bilevel optimization problems in which variables of both lower- and upper-level problems are constrained on Riemannian manifolds. It introduces multiple hypergradient estimation strategies on manifolds and investigates their estimation error. The paper includes co...	Rebuttal 1: Rebuttal: We thank the reviewer for the general appreciation of our work as well as constructive comments that we address below. 1. (W1) Similarities and disparities compared to [42]. We would like to emphasize that [42] is a concurrent work and not a prior work. It became available (publicly) aft...	Summary: This paper introduces a novel approach for solving bilevel optimization problems where both upper and lower-level variables are constrained on Riemannian manifolds. The authors propose four hypergradient estimation strategies (HINV, CG, NC, AD), analyze their estimation errors with convergence and complexity a...	Rebuttal 1: Rebuttal: We thank the reviewer for acknowledging that our theoretical analysis is thorough and our work has practical relevance. We also appreciate the constructive feedback. 1. (W1) Assumption such as geodesic strong convexity and Lipschitz continuity. The assumption of (geodesic) strong convexity ...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for the reviews. We are particularly encouraged by the numerous positive comments on our work, including, well-written (Reviewer Bnza, Reviewer 4rfq), comprehensive analysis (Reviewer CDbu, Reviewer kqCt, Reviewer JBnb), solid theoretical results (Rev...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper addresses bilevel optimization problems where variables of both the lower and upper-level problems are constrained on Riemannian manifolds. To solve such problems, the authors propose a hypergradient descent algorithm under the key assumption that the lower function is geodesically strongly convex a...	Rebuttal 1: Rebuttal: We thank the reviewer for positive comments on recognizing our solid theoretical analysis and extensive numerical results. We also greatly appreciate the constructive feedback and comments. **1. (W1) Clarification regarding [42]: (1) Do other estimators show better performance? (2) Provide num...	null	null	null	null	null	null
Demystify Mamba in Vision: A Linear Attention Perspective	Accept (poster)	Summary: This paper presents a thorough analysis of the key factors contributing to the success of the S6 module in Mamba model, and introduces a new linear attention vision network, MLLA, inspired by the S6’s design. Extensive experimental results show outstanding performance, validating the effectiveness of the propo...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. The analysis of single-head attention. Thanks for your insightful comment. - ***The concept "head" in our paper is the same as its origi...	Summary: The paper takes a deep dive into the workings of Mamba in vision related tasks and compares it to Linear Attention. They conclude that Mamba is a special form of Linear Attention and describe the role that certain parts in the architecture and give them a name that better represent their actual use. They come ...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. Reason for using Swin Transformer. We offer clarification on employing Swin Transformer: - Swin Transformer is a widely used architectur...	Summary: This paper explores the similarities and differences between the Mamba and linear attention Transformer models. It redefines Mamba as a variant of linear attention Transformer with six key distinctions. The paper also studies each design's impact, identifying the forget gate and block design as key to Mamba's ...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. Mamba for image synthesis. - Thanks for pointing out these works. ***We will give more credits to them and include detailed discussions i...	Summary: This paper primarily discusses the similarities and differences between the Mamba model and the Linear Attention Transformer, and conducts an in-depth analysis of the key factors contributing to Mamba's success in visual tasks. The paper elaborates on six major design differences of Mamba compared to the Linea...	Rebuttal 1: Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following: --- 1. The name of MLLA model. Thanks for your comment. *Firstly*, we name the final model Mamba-Like Linear Attention because it incorpora...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful and valuable comments. We have carefully considered the reviewers' comments and provided additional clarification to address each concern. Here, we offer general responses to all reviewers on two key issues. --- 1. The motivation of our work....	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning	Accept (poster)	Summary: This paper presents a study on pre-trained visual representations (PVRs) to improve sample efficiency of model-based reinforcement learning methods (MBRL). It studies a range of PVRs with different architectures, pre-training objectives and data modalities on two recent model-based algorithms: DreamerV3 and TD...	Rebuttal 1: Rebuttal: __Questions:__ > “Did the authors experiment with fine-tuning the PVRs during training instead of using frozen representations ?” We actively decided to omit fine-tuning experiments since the original promise of many of the used representations is to perform (near) SOTA on downstream tasks witho...	Summary: This paper contains an extensive study on the potential benefits of pre-trained visual representations (PVRs) for model-based reinforcement learning (MBRL). Given the success of PVRs for model-free RL (MFRL), there are reasons to believe that PVRs are equally beneficial for MBRL, something that has not yet bee...	Rebuttal 1: Rebuttal: __Weaknesses:__ > "From the experiments, it can be concluded that PVRs hardly benefit MBRL, but the paper does not really try to answer why there is a difference between MBRL and MFRL in this regard." We believe that multiple objective mismatches in the different training phases, which are not a...	Summary: This paper conducts a thorough set of experiments to evaluate the performance of pretrained visual representations (PVR) in model-based reinforcement learning. Empirical results show that PVR performs worse than learning from scratch, which is possibly due to the large reward prediction error. Strengths: - Th...	Rebuttal 1: Rebuttal: __Weaknesses:__ > “Why does the reward prediction accuracy outweigh the dynamic prediction accuracy?” Our Figures 6 and 7 show that there exist differences in the dynamics as well as reward prediction accuracies between the trained models. The calculated correlations indicate that more subtle di...	Summary: Pre-trained Visual Representations (PVRs) has been widely applied to many domains to improve OOD generalization and sample efficiency, including model-free reinforcement learning (RL). This paper explores the application of PVRs in model-based RL, which has not been done. Experiments on two suite of simulated ...	Rebuttal 1: Rebuttal: __Weaknesses:__ > “The number of algorithms in each property category is small, which means that the performance of one algorithm can easily affect the average performance of the category, making the comparison of different categories (Figure 5) less significant. For example, the "sequential data...	Rebuttal 1: Rebuttal: We thank the Reviewers for their thorough feedback. We appreciate the detailed suggestions and the recognition of the value that our results bring to the community. We were keen to integrate the Reviewers valuable feedback into our paper and will make the revisions to the paper accordingly. Here i...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach	Accept (poster)	Summary: The paper introduces an improved method for conducting property inference attacks on graph neural networks (GNNs), particularly focusing on reducing the computational overhead associated with traditional approaches that rely on numerous shadow models. The authors propose a model approximation technique to gene...	Rebuttal 1: Rebuttal: >Q2: Experiment using distinct network graphs for the target and auxiliary graphs. Thank you for your insightful comment. We select two social network datasets, Facebook and Pokec. We consider two cases: Facebook and Pokec as the target and auxiliary graphs respectively, and vice versa. We mainta...	Summary: This paper delivers an effective way to improve the efficiency of property inference attacks for GNNs. Instead of training numerous shadow models, the authors propose to train a few reference models and use an efficient approximation method to obtain other shadow models trained on slightly augmented shadow gra...	Rebuttal 1: Rebuttal: > W1: Limited scope and significance. Thanks for your comment. We'd like to emphasize that inefficiency is a major bottleneck in graph property inference attacks. Our contribution significantly enhances the efficiency of these attacks, enabling their practical application at scale, which is ess...	Summary: This paper proposes a more efficient property inference attack on graph neural networks (GNNs). Particularly, it uses model approximation methods to reduce the number of shadow models that needs to be trained by the graph property inference attack. Here only a limited number of shadow models are trained from s...	Rebuttal 1: Rebuttal: > W1: Experiments on larger datasets. As noted in Appendix A.5 and Table 8, we have already included experiments on a million-level dataset Pokec-100M, which contains 1,027,956 nodes and 27,718,416 edges. We found that the best baseline, PEPIA-DS, incurs a significant cost, while our method is 1...	null	null	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their time and effort in reviewing our paper. As suggested by Reviewer j8ts, the following PDF contains our revised figure to better illustrate the differences between our method and existing attacks. Pdf: /pdf/dcc2cdb43cfa1a054b95a5f1cc35c81109c73b1a.p...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections	Accept (poster)	Summary: The authors propose a fairness attack on GNN through node injection. They propose two node injection principles, the uncertainty-maximization and homophily-increase principle, to make fake node injections lead to a more significant fairness compromise. Strengths: This article is well-written and highly readab...	Rebuttal 1: Rebuttal: [Concerns about the discussion about node-injection-based attack (W1)] The main distinction between fairness-targeted and accuracy-targeted attacks is the different attack objectives. The accuracy-targeted attacks aim to undermine the model accuracy, while fairness-targeted attacks aim to det...	Summary: The authors propose a Fairness GNN Attack method called Node Injection-based Fairness Attack (NIFA). The proposed method aims to increase the bias of GNN models by injecting nodes into the graph. NIFA identifies nodes with high uncertainty to target them, then connects the injected nodes in such a way increasi...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for these insightful comments. Specifically, we aim to address the concerns of the reviewer with the following responses. ------ [Concerns about the attack scenarios (W1, W2)] Take a social graph like Twitter as an example, where each node denotes a user ...	Summary: This paper examines the vulnerability of GNN fairness under adversarial attacks. A gray-box node injection-based poisoning attack method, namely NIFA, is proposed. NIFA follows the newly designed uncertainty maximization principle and homophily-increase principle. Then, multiple novel objective functions are...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for these insightful comments. Specifically, we aim to address the concerns of the reviewer with the following responses. ------ [Question about the Structural Fairness (W1)] Thanks for your inspiring question! According to prior research, structural fair...	Summary: This paper proposes NIFA, a novel fairness attack method via node injection. In particular, the authors use the uncertainty maximization principle to select the target node to attack and randomly connect the injected nodes to the target nodes in the same sensitive group to increase the overall homophily. Final...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the constructive comments, and we aim to address the concerns with the following responses. ------ [Concerns about the utility (W1)] We will first analyze the potential reasons for utility decrease, and then provide some solutions for balancing the trade-...	Rebuttal 1: Rebuttal: ## To all reviewers: We sincerely thank all reviewers for their valuable feedback and encouraging comments on our paper. All four reviewers consistently approve of the topic: “interesting”, “well-motivated”, and “encouraging”, and the presentation of our work: "well-written", **"v...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks	Accept (poster)	Summary: This paper proposes a new method, "Chain-of-Agents" (CoA), to augment the long-context handling capabilities of large language models (LLMs). CoA is a framework designed to enhance the processing of long contexts by sequentially using multiple agents to handle different chunks of input text. In CoA, worker age...	Rebuttal 1: Rebuttal: Thank you for the valuable suggestions. We provide answers to all your weakness points and questions below. We hope these resolve your concerns. W1: Novelty and other baselines: While chunking the input into multiple segments seems intuitive, the novelty of CoA lies in the chain communicatio...	Summary: The paper "Chain of Agents: Large Language Models Collaborating on Long-Context Tasks" introduces a novel framework called Chain-of-Agents (CoA) to address the challenge of effectively processing long contexts in large language models (LLMs). The CoA framework leverages multi-agent collaboration, where multipl...	Rebuttal 1: Rebuttal: Thank you for the valuable feedback. We appreciate your time and efforts spent on this paper. With your insightful suggestions, our paper can improve significantly. Complexity in Implementation Indeed, one of the design principles behind CoA is to propose a simple yet effective multi-agent s...	Summary: The paper proposed Chain-of-Agents, a multi-agent LLM collaboration framework for solving long context tasks, where multiple worker agents sequentially comprehend and communicate to handle different segmented portions of the text, and a manager agent, at last, synthesizes these contributions into a coherent fi...	Rebuttal 1: Rebuttal: Thank you so much for your detailed feedback and for acknowledging the comprehensiveness of our experimental studies. We address the questions and concerns raised below, one by one. W1: Human motivation: To clarify this point, the motivation is that humans would not try to read the whole tex...	Summary: This paper is about addressing the issue of lengthy inputs when using language models. Predominant approach is RAG, but is hampered by retrieval performance. Window extension extends the architecture of the model to handle lengthy inputs, but doesn’t guarantee that the model is able to extract the relevant inf...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful feedback that has helped us to improve our submission! W1: Technique depth. The depth of our work lies in that we propose Chain-of-Agents, a multi-agent LLM collaboration framework for solving long context tasks. It is a training free, task/length...	Rebuttal 1: Rebuttal: We thank all the valuable feedback and comments from reviewers that have helped to improve our paper! We also thank the reviewers for appreciating the intuitive and interesting design of the CoA (Reviewer vGWD, FiRU, FfHC, KMmx), the effectiveness of CoA (Reviewer vGWD, FiRU, FfHC, KMmx), comprehe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning	Accept (poster)	Summary: This paper considers a setting in which there are observed tuples $(O_{i,t}, A_{i, t}, R_{i, t})$ with $ 1\leq i \leq N$, $1 \leq t \leq T$; Here, $O_{i,t}$ represents observable quantities, $A_{i,t}$ is an action generated by a behavioural policy, and $R_{i,t}$ is the associated reward. The goal of this pap...	Rebuttal 1: Rebuttal: - __Clarification of the DGP.__\ We agree with your points and will replace "independent trajectories" with "trajectories". "Independent" was used to indicate conditional independence between trajectories given latent two-way fixed effects, but this was confusing. Following your suggestion, we use...	Summary: This work proposes a novel technique for performing off-policy evaluation (OPE) in the presence of unobserved confounders that have been classified as "two-way" unmeasured confounders, viz., by assuming that there exist both time-invariant and trajectory-invariant confounders, but not time-and-trajectory-invar...	Rebuttal 1: Rebuttal: - __Omitting observations for avoiding biases.__\ This is an excellent comment! We have also carefully read [1] that you mentioned and will include it as well as our discussions in our paper, shall it be accepted. This paper discusses whether an additional random variable $Z$ should be included in...	Summary: This paper studies the problem of confounded OPE. The authors explore a new structural assumption of the data-generating process called the two-way confounding assumption. They also propose an algorithm called the two-way deconfounder that can deal with the new setting they consider. The authors perform theore...	Rebuttal 1: Rebuttal: - __Enhancing clarity of the problem setting in the Introduction.__\ Thank you for your valuable suggestion. We will include additional explanations about the problem setting in the introduction section of the final version. - __Clarifying confusing notations.__\ We apologize for any confusion cau...	Summary: The authors study off-policy evaluation for longitudinal data with hidden confounders, which are accounted for by assuming they are either time-invariant or state-invariant but not both. Strengths: * The idea is relatively original, important, and the contribution might be significant. Presentation is somewha...	Rebuttal 1: Rebuttal: - __Engagement with the deconfounder literature, limitations and criticisms of [1]__\ This is an excellent comment. A detailed discussion of the criticisms of the deconfounder algorithm also helps better motivate our algorithm. We plan to include the following discussion after our cryptic statemen...	Rebuttal 1: Rebuttal: We thank all referees for their valuable and insightful comments. We have addressed all your comments and will incorporate them shall our paper be accepted; please refer to our detailed responses to your review. Below, we would like to briefly clarify the notation $\mathbb{E}^{\pi}$ and our data g...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation	Accept (poster)	Summary: The authors leverage 3D hand pose and sEMG signals as inputs to facilitate hand pressure estimation. With data gloves, a multimodal hand-object interaction collection system is devised. Empirical experiment results show the efficacy of the proposed method. Furthermore, the feasibility and robustness is proven ...	Rebuttal 1: Rebuttal: (W1) Additional dataset reference As you suggest, we will cite and discuss in related work section as follows: A highly relevant work is the ActionSense dataset, which focuses on capturing multimodal data of human activities in a kitchen environment using wearable sensors. Similar to our appr...	Summary: This manuscript introduces a framework for estimating the pressure of 9 points on the hand during 22 distinct hand object interaction types including pinches, grasps and planar interactions. They use an 8 channel sEMG band placed at the forearm as well as hand pose estimated using a glove or monocular video ca...	Rebuttal 1: Rebuttal: (W1) Additional experiments for cross-user evaluation Please refer (Joint Response 2). (W2-1) Clarification on the comparison with PressureVision++ You are correct that the current comparison might not be entirely fair, and we acknowledge that our framework currently performs best when a...	Summary: This paper proposes a hand pressure estimation method based on 3D hand poses and t forearm surface electromyography (sEMG) signals. Accordingly, the paper constructs a multimodal dataset containing pressure, 3D hand poses, and sEMG signals. The paper experimentally verifies that combining 3D hand poses and sEM...	Rebuttal 1: Rebuttal: (W1) Clarifications on the novelty of the proposed framework - We acknowledge that there might be room for improvement in terms of methodological novelty. In this paper, however, our primary objective of this paper was to pioneer the use of electromyography and 3D hand posture data simultaneo...	Summary: This paper presents a novel framework for estimating hand pressure during various hand-object interactions using multimodal data. The framework integrates sEMG and 3D hand posture information to enhance the accuracy of pressure estimation. They introduce a dataset to validate their approach. The primary contri...	Rebuttal 1: Rebuttal: (W1) Support for advantages of integrating sEMG signals with 3D Hand Posture data - We would like to clarify that we performed an ablation study (Table 2 and 3) and discussed study results in the corresponding sections. These tables compare the performance of our model using: (1) sEMG data ...	Rebuttal 1: Rebuttal: We would like to thank reviewers for the constructive feedback. We have been putting our best effort to address the weaknesses and questions pointed by reviewers to strengthen the paper. Our rebuttal PDF includes new experimental results, diagrams, and tables to support our work. In our rebuttal...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Opponent Modeling based on Subgoal Inference	Accept (poster)	Summary: The paper proposes an algorithm for learning policy in a multi-agent environment that predicts subgoal intents of other agents. In this way, the authors address the non-stationarity problem in multi-agent learning. Experiments in three environments show an advantage of the approach over baselines. Strengths: ...	Rebuttal 1: Rebuttal: Thanks for your valuable comments. As follows, we address your concerns in detail. > Experiments: It's a bit disappointing that in most of the presented experiments, the confidence intervals of OMG and baselines generally overlap (except in the predator-prey environment). I understand that contro...	Summary: For cooperative games and general-sum games, this paper proposes opponent modeling by inferring an opponent’s subgoals, rather than inferring actions. They empirically verify that this leads to either similar or better scores over baselines in Foraging (discrete grid game), Predator-Pray (continuous game), and...	Rebuttal 1: Rebuttal: Thank you for acknowledging our novel contributions as well as raising valuable questions. > Improvements over scores in baselines are minor in Foraging. The main difference between the baselines appears to be in the reduction of the episode steps. It would be interesting to see if there is a red...	Summary: The paper is positioned within the ad-hoc teamwork problem in multi-agent systems, where an agent faces partners it has not seen before in training, and must learn to cooperate with them towards performing a task that benefits from cooperation. Authors deal with a specific requirement of ad-hoc teamwork that i...	Rebuttal 1: Rebuttal: ## Part Ⅰ Thank you for valuable comments. Below is a detailed response to your question, addressing each point individually. > I do not believe that goal and sub-goal inference for modelling agents is original. The paper is missing an entire line of research in their related works here, namely ...	Summary: This paper introduces a multi-agent reinforcement learning algorithm focused on opponent modelling through subgoal inference, termed Opponent Modelling based on Subgoal Inference (OMG). Unlike traditional models that predict immediate actions of opponents, OMG leverages historical trajectories to infer an oppo...	Rebuttal 1: Rebuttal: Thank you for acknowledging our novel contributions as well as raising valuable questions. > ''The subgoal prior model, denoted as $p_\psi$, is a pre-trained variational autoencoder (VAE) using the states previously collected in the environment'': What policy was used to collect these states to p...	Rebuttal 1: Rebuttal: We have added two experiments in the PDF as follows: * We have increased the number of seeds in the Predator-Prey model from 5 to 10 to reduce experimental error and enhance the reliability of our findings. * We have incorporated an ablation study on subgoal selection, where OMG-Random, OMG-1s,...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation	Accept (poster)	Summary: This paper proposes a Spherical Frustum Sparse Convolution Network to address the challenge of LiDAR point cloud semantic segmentation. Traditional approaches often project point clouds into 2D images and apply 2D convolutional neural networks (CNNs) or vision transformers, leading to quantization information ...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable suggestions for our paper. We are encouraged by your confirmation of the information preservation, efficiency, contextual information capture ability, performance improvement, network flexibility, and the "large potential in specific scenarios" of our SFCN...	Summary: This paper introduces SFCNet, a spherical frustum sparse convolution network designed for semantic segmentation of LiDAR point clouds. Traditional 2D projection methods suffer from quantized information loss when multiple points project onto the same pixel, leading to sub-optimal segmentation. To address this,...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable suggestions. We are encouraged by your confirmation of the presentation of our paper and our motivation for overcoming quantized information loss. The following are our responses to your concerns: --- ### Q1: Performance Gain Compared to Prior 2D Projection-...	Summary: The paper introduces a Spherical Frustum Sparse Convolution Network (SFCNet), a novel approach for LiDAR point cloud semantic segmentation that addresses the quantized information loss existing in 2D projection-based methods. By utilizing a spherical frustum structure, the SFCNet preserves all points within a ...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable suggestions. We are encouraged by your confirmation of our spherical frustum structure, efficiency, and performance improvement on small objects. The following are our responses to your concerns: --- ### Q1: Insights and Improvement Direction on Performance o...	Summary: This paper introduces a novel spherical frustum structure for LiDAR point cloud semantic segmentation, addressing the quantized information loss in 2D projection-based methods. By preserving all points within frustums and employing a memory-efficient hash-based representation, the Spherical Frustum sparse Conv...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable suggestions for our paper. We are encouraged by your confirmation of our data representation, memory-efficient hash-based representation, and efficient point sampling. The following are our responses to your concerns: --- ### Q1: Analysis of Memory Usage, Par...	Rebuttal 1: Rebuttal: We appreciate all reviewers' efforts and valuable suggestions. We are grateful to receive the positive comments from the reviewers, including memory efficiency (Reviewer AnTM, bPaa, BSNT), innovation in spherical frustum structure (Reviewer AnTM, bPaa), and performance "boost" (Reviewer bPaa, BSNT...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Probabilistic Graph Rewiring via Virtual Nodes	Accept (poster)	Summary: This work introduces an approach called implicitly rewired message-passing neural networks (IPR-MPNNs) to address limitations in message-passing graph neural networks (MPNNs): expressiveness and mainly over-squashing. Their method works by adding virtual nodes and learning to connect them to existing nodes in ...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and detailed review. Due to the space restrictions for the rebuttal, we address related work and novelty comments here, with the remaining concerns, additional results, and the bibliography in other official comments at the same level. - **Q: Related wor...	Summary: The paper proposes implicitly rewired message-passing neural networks (IPR-MPNNs), which integrate implicit probabilistic graph rewiring into MPNNs. This method involves introducing a small number of virtual nodes into the graph, allowing for long-distance message propagation without the quadratic complexity a...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback on our work. In the following, we will try to address the concerns raised by the reviewer: - W1: How are the unnormalized priors $\theta$ obtained? Is there any related optimization objective? - RW1: The unnormalized priors are the output of the u...	Summary: The paper introduces a method (IPR-MPNN) which connects nodes to a small number of virtual nodes in a learnable way. The proposed approach is more expressive than MPNN whilst circumventing quadratic complexity. It can reduce oversquashing and performs well on various benchmarks. Strengths: - The benefits of t...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive assessment of our work. In the following, we will respond to the raised issues: - W1: Empirical and theoretical comparison with MPNN + VN. - RW1: We thank the reviewer for the suggestion. The paper currently contains one such comparison in Table ...	Summary: This paper introduces Implicitly Rewired Message-Passing Neural Networks (IPR-MPNNs) to address the limitations of traditional MPNNs, such as under-reaching and over-squashing. By integrating implicit probabilistic graph rewiring and adding a small number of virtual nodes, IPR-MPNNs enable long-distance messag...	Rebuttal 1: Rebuttal: We thank the reviewer for their assessment and feedback. In the following, we respond to the reviewer’s questions. Please note that the tables containing new experiments are in the next official comment and the pdf file attached to the “Global Response”. - Q1, Q2: Do IPR-MPNNs increase the ri...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful comments and suggestions. We believe that our paper is much stronger after considering reviewer feedback. All of the new comparisons and experiments are included in the one-page pdf attached to the global response. --- In the following, we summarize ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following	Accept (poster)	Summary: The paper introduces an Exploratory Retrieval-Augmented Planning (ExRAP) framework, that tackles the problem of embodied planning in dynamic environments. The study focuses on continual instruction following, which is when the task consists of multiple conditional subtasks. The execution of each subtask is dep...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments and insights. W1 The scalablity of ExRAP. ExRAP uses the sentence embedding techniques such as DPR and BM25 for knowledge graph retriever and demonstration retriever for exploitation value. As shown in the table below, the average time taken for retriev...	Summary: This paper presents the Exploratory Retrieval-Augmented Planning (ExRAP) framework to address the challenge of continual instruction following in non-stationary embodied environments. ExRAP enhances the reasoning capabilities of Large Language Models (LLMs) by efficiently exploring the environment and maintain...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments and insights. W1 ExRAP's performance still somewhat depends on the underlying LLM's reasoning abilities. While the ablation study shows robustness to smaller LLMs, it's unclear how the framework would perform with much weaker language models or in domain...	Summary: The paper considers a problem of continual instruction following where an instruction contains multiple query-execution pairs to be performed. For this, the paper proposes ExRAP comprised of two components: query evaluation and exploration-integrated task planning. Query evaluation incrementally updates the te...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments and insights. W1. Should the continual instructions be explicit? The continual instructions are not always in the explicit form of query-execution pairs; they can also be implicit, such as "Always leave the stove open" in Appendix A.1. In that case, the...	null	null	Rebuttal 1: Rebuttal: General Response We thank the reviewers for their valuable feedback. We are encouraged that they found our approach is novel, well-written, easy to follow, adresses the important and challenging problem, and provides valuable contribution, as well as providing extensive evaluations which dem...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Tolerant Algorithms for Learning with Arbitrary Covariate Shift	Accept (spotlight)	Summary: This paper studies the problem of PAC learning with covariate shift. In particular, it examines two specific learning frameworks: one is PQ learning, where a learner is allowed to "absent" from some testing samples but is required to have good accuracy for the retained samples; the other is TDS learning, where...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort. Question 1. We note that, while we provide most of the technical details of our proofs in the appendix, we do provide a high-level explanation of our results in the main part, including Theorem 3.1 (see lines 189–223). In fact, the comparison w...	Summary: The authors consider the problem of efficiently learning a concept class under distribution shift, i.e. in the setting where the training data and testing data are drawn from different distributions. They study two frameworks: PQ learning and TDS learning. In the former, the learner is allowed to abstain from...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and appreciation of our work. - Lines 62-63: We will clarify this point, thank you for pointing out. - Lines 111-117: Our work is indeed the first to consider distribution-specific PQ-learning at all. Prior work involved reductions to other learning primitive...	Summary: This work proposes methods for learning under covariance shifts. In particular, it studies PQ learning and TDS learning models. It provides an algorithm based on filtering technique. Given sample access to two distributions, the algorithm produces a filter that rejects points from the second distribution which...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive feedback. We will carefully go through our proofs in the appendix to fix all typos for future revisions. Regarding the specific questions of the reviewer: Question 1. You are correct that Equation (B.2) should have the square of Frobenius norm on t...	Summary: This paper studies two fundamental learning setups, namely PQ learning and TDS learning (Testable Learning with distribution shift), both motivated by covariate shift. In PQ learning, the algorithm is given labeled samples from $\mathcal D^{\text{train}}$ over $\mathbb R^d \times \{0, 1\}$ with marginal dist...	Rebuttal 1: Rebuttal: Thank you for your time and for appreciating our work. In Definition 4.5, the distribution $\mathcal{D}$ represents some distribution over the features, which is unlabeled. We instead typically use $\mathcal{D}^{\mathrm{train}}$ to denote the labeled training distribution (see lines 136–137 and al...	null	NeurIPS_2024_submissions_huggingface	2,024	Summary: This work considers learning under distribution shift. Here, a learner receives i.i.d labeled data from $D_{train}$, along with i.i.d unlabled data from $D_{test}$. It then builds a classifier with the goal of achieving high accuracy over $D_{test}$. Under arbitrary conditions, this task is impossible, so in t...	Rebuttal 1: Rebuttal: We wish to thank the anonymous reviewer for their constructive feedback and suggestions. Gaussian assumption: The reviewer is correct that the outlier removal procedure works under weaker assumptions than Gaussianity. In particular, it will work for any tame distribution (see lines 155–158 fo...	null	null	null	null	null	null
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning CodeLLMs	Reject	Summary: The paper introduces ROBO-INSTRUCT, a novel framework designed to generate synthetic training data for fine-tuning small language models to create domain-specific robot programs. The framework features two main components: ROBOSIM, an algorithm that validates programs using angelic execution, and INSTALIGN, wh...	Rebuttal 1: Rebuttal: Thank you for your feedback. We would like to offer further clarification beyond the general response above to address the specific concerns and points raised in the review. *Weakness:* While ROBO-INSTRUCT offers significant advancements for fine-tuning language models in robot programming, t...	Summary: The paper tries to improve the performance of small open-sourced LLMs to generate code that can run successfully on a service robot simulator to solve tasks. The idea is to use another small model to generate program data using SELF-INSTRUCT and fine-tune a 7B model for the robotics domain. The authors note th...	Rebuttal 1: Rebuttal: Thank you for your feedback. We would like to offer further clarification beyond the general response above to address the specific concerns and points raised in the review. *Weakness:* While the paper is well-written for the scope it sets for itself, I am not sure if the contribution is sign...	Summary: This paper introduces a framework for generating paired instruction and robot code for further fine-tuning LLMs for robot-specific tasks. A symbolic simulator is used to check the correctness of the generated code and an LLM is prompted with chain-of-thought reasoning to align generated instruction. The result...	Rebuttal 1: Rebuttal: Thank you for your feedback. We would like to offer further clarification beyond the general response above to address the specific concerns and points raised in the review. *Weakness:* RoboSIM can only check for semantically meaningful steps of the code and may not catch lower-level error th...	Summary: This paper introduces ROBO-INSTRUCT, a novel framework designed to improve the code generation capabilities of smaller open-weight language models (LLMs) for domain-specific robotic tasks. ROBO-INSTRUCT leverages two key components: 1. ROBOSIM with DYNAMICEVAL: A task-agnostic simulator that dynamically synt...	Rebuttal 1: Rebuttal: Thank you for your feedback. We would like to provide further clarification beyond the general response above to address the specific concerns and points raised in the review. *Weakness:* Limited novelty: The idea of using a sim/emulator to verify the generated program has already been explor...	Rebuttal 1: Rebuttal: # General Response We appreciate the reviewers' careful consideration and positive feedback, as well as their constructive concerns. In this response, we address common points raised in the reviews, particularly the key contribution of RoboInstruct, as well as its applicability to other domains. T...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling	Accept (poster)	Summary: This work develops a deep state-space model (DSSM) for state inference in and system identification of non-linear dynamical systems. The objective function for learning the DSSM parameters is based on a smoothing formulation and thus the evidence lower bound (ELBO). The prior and likelihood parts are separate...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript. Below we respond to your suggestions/questions starting with the weaknesses section. > The mentioned limitations of previous work's limitations are not convincing. Fast evaluation of the loss function is hardly the problem that previous DSS...	Summary: Update post author rebuttal. Thanks for the clarifications in the common and personal replies. I will raise the score to Accept, trusting that you will make the improvements you mention. ________ The paper presents a class of non linear state-space models whose dynamics are defined as exponential family distr...	Rebuttal 1: Rebuttal: We thank the reviewer for their helpful suggestions. We apologize about the density of the paper at times and appreciate that you mentioned the possibility of moving some algorithmic discussion to the appendix in exchange for higher level intuitions and more in depth discussion pertaining to the ...	Summary: This paper introduces a method for scalable nonlinear Gaussian state-space modeling that relies on variational autoencoders and a low-rank covariance matrix assumption for efficient inference by optimizing an approximate variational lower bound. The authors describe the computational benefits of their method a...	Rebuttal 1: Rebuttal: Thank you for taking the time to review the paper and your comments that will undoubtedly increase the quality of our manuscript. > The paper is dense and notation heavy at times. We apologize that the paper could be dense at times. Motivated by this comment and others, in order to enhance the...	Summary: The paper presents a novel state space model (SSM) framework for learning dynamical systems with nonlinear latent dynamics. The proposed method borrows inspirations from structured variational autoencoders (SVAEs) and sample-based nonlinear Bayesian filtering, using low-rank corrections to the prior to capture...	Rebuttal 1: Rebuttal: Thank you for taking the time and effort and asking questions that we feel have helped make the manuscript stronger. Below we respond to them in order, > One suggestion is to keep only the core equations (such as the objective (21)(22), the low rank parameterization (24), etc.) and provide mor...	Rebuttal 1: Rebuttal: First, we would like to thank all of the reviewers for their time, effort, and helpful comments regarding the submitted manuscript – we feel many of your suggestions have led us to changes and additions that better position the paper. Many reviewers were positive about the clarity of writing, b...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
An Improved Empirical Fisher Approximation for Natural Gradient Descent	Accept (poster)	Summary: The paper proposes a modification of the empirical Fisher matrix that re-scales a datum's parameter gradient by its logit gradient. This aims to remove a bias in the empirical Fisher, dubbed 'inversely-scaled projection issue', which benefits data points that have already been learned. While the modified empir...	Rebuttal 1: Rebuttal: # Author Response to Reviewer Khnn Thank you for your review! >Explanation for better approximation of NG update...To me, it is not completely clear...why removing the empirical Fisher's bias towards learned data points results in pre-conditioned gradients with stronger resemblance to the true na...	Summary: This paper proposed a new Natural Gradient Descent algorithm called iEF. The authors conduct a theoretical convergence analysis of iEF. The proposed PSMGD method achieves comparable or even superior performance over existing gradient descent methods. Strengths: 1. The paper is fairly easy to read, and the ide...	Rebuttal 1: Rebuttal: # Author Response to Reviewer 4Ck3 Thank you for your review! > I think the reason why the authors use Eq. (7) to solve the inversely-scaled projection issue raised in Lines 135-136 is not well explained...GN algorithm is not proposed for resolving the inversely-scaled projection issue. We note ...	Summary: The paper analyses the Empirical-Fisher-preconditioned gradient and highlights how its components on the per-sample gradients are biases towards well-trained samples, thus leading to potentially unstable training trajectories. The authors propose to solve this issue by scaling the per-sample components accordi...	Rebuttal 1: Rebuttal: # Author Response to Reviewer 13ZJ Thank you for your review! >Regression or Classification? Section 3 begins saying that the paper focuses on classification...Then the visual illustration (Sec 4.2 and App B) is on regression problem...It would be more fair to have a toy 2D example where NDG and ...	Summary: Many approaches have been proposed to approximate natural gradient descent, however most of them rely on estimating the fisher matrix with empirical fisher. The estimation is known to have the inversely-scaled projection issue, where the update is inversely proportional to per-sample gradient norm, as such sam...	Rebuttal 1: Rebuttal: # Author Response to Reviewer Sh4Z Thank you for your review! >The evaluation only contains fine-tuning experiments with parameter efficient fine-tuning techniques, it would be nice to have some more train-from-scratch...small...tunable parameters...small number of training iterations. We agree ...	Rebuttal 1: Rebuttal: # Global Author Response Thank you all for your positive reviews! We have attached to this global response a pdf document, which contains information for additional experiments (*AE*) (4 figures with captions) that address concerns regarding our paper. This document is referred to as the "Glo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models	Accept (spotlight)	Summary: This paper introduces the concept of learnable semi-structured sparsity (N:M sparsity). This extends to practical N:M sparsity to pretrained LLMs and makes the masks learnable. the paper also make the transfer of masks from other sparsity techniques to work for N:M. Finally, the general N:M masks learned can b...	Rebuttal 1: Rebuttal: Thank you so much for the invaluable suggestions and the positive comments! We will polish our draft with the following new results and make our code and learned masks public for better reproducibility. > **Q1: My only suggestion for the paper is to add a more comprehensive related work section t...	Summary: This paper proposes MaskLLM, a learnable method to craft semi-structured sparsity in Large Language Models (LLMs). The approach involves modeling N:M masks with a categorical distribution, which is can be optimized through gumbel softmax. The key findings in this paper suggest that end-to-end learning of mask ...	Rebuttal 1: Rebuttal: Thanks for the invaluable comments and questions! > **Q1: One of my concerns is the use of non-public datasets and LLMs in this study. It would be beneficial for the paper to include more results using publicly available data and open-source models, such as LLaMA-3, to enhance the reproducibilit...	Summary: The paper introduces MaskLLM, a method for introducing semi-structured sparsity (N:M mask patterns) in LLMs. The authors show that existing model pruning methods such as SparseGPT result in significant loss in model quality at smaller scales (800M ~ 15B parameters) when using semi-structured methods. They form...	Rebuttal 1: Rebuttal: Thank you so much for the invaluable comments and suggestions about baselines, datasets, and hyperparameters! > **Q1: 1) The comparison between MaskLLM with SparseGPT prior and SparseGPT seems to be an unfair. The method seems to be much closer in performance to the SparseGPT method without prior...	Summary: The authors proposed a novel LLM pruning technique, by modeling the distribution of all possible masks and formulate the selection of optimal masks in a differentiable way. Strengths: - Important and relevant problem setup. - The solution is novel. - Thorough evaluation across a range of model/dataset combina...	Rebuttal 1: Rebuttal: We sincerely thank reviewer AwSn for the valuable comments. > Q1: Unclear computational cost. Comparison to sparse pretraining/finetuning. A1: This submission discusses the computational cost of mask learning in Lines 433-435 of the appendix. According to the original tech report of Lla...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their invaluable comments and suggestions. We will make every effort to provide additional results to support our response within this limited rebuttal period. To ensure better reproducibility, we also promise to release our code and learned masks in the future...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Visual-TCAV: Explainability of Image Classification through Concept-based Saliency Maps	Reject	Summary: Saliency methods are among the most popular approaches to explaining an existing black-box image classifier. However, they are limited to localizing class objects in an image. In addition, since they rely on per-pixel importance, they are unable to generalize accross multiple instance to provide a global expla...	Rebuttal 1: Rebuttal: Thank you for your review and for acknowledging the merits of our work. Below, we comment on the identified weaknesses and questions. W1: We agree that the mentioned paper is highly relevant to our work and should be referenced. Considering our results in comparison to that paper, an interesting ...	Summary: This work presents a method for combining TCAV with saliency maps to illustrate where feature-related concepts (e.g., “stripes” or “grass”) are activated in an image. The evaluation is largely qualitative, but the method seems to work well on ImageNet classifcation tasks. The method is also validated on a cont...	Rebuttal 1: Rebuttal: Thank you for your review and for acknowledging the merits of our work including the importance of validating the explanations. We agree that it is a step often overlooked in this field. Below, we comment on the identified weaknesses and questions. W1: Regarding concept extraction methods, they r...	Summary: The paper introduces a novel technique, Visual TCAV, which unifies concept-based explainability with saliency maps. Visual TCAV produces local explanations in the form of saliency maps, which highlight the pixels in the image that represent a given user-defined concept. The visualization is enriched with an at...	Rebuttal 1: Rebuttal: Thank you for the detailed review and for acknowledging the merits of our work. Below, we comment on the identified weaknesses, questions and limitations. W1: Yes, k is the index of the feature maps of a given layer. We'll mention it explicitly for better clarity. W2: You are correct, it is a ty...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for the constructive feedback and useful suggestions. In addition to the detailed responses, this general rebuttal summarizes the identified strengths and the changes we’ll make considering the identified weaknesses and the suggested improvements. We apologize that due to sp...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models	Accept (poster)	Summary: This paper proposes a method to integrate visual prompts into MLLMs without requiring additional training. The key idea is to optimize a learnable latent variable to enhance the attention response to visual tokens during inference, thereby improving the model's ability to focus on specific regions in the visua...	Rebuttal 1: Rebuttal: # Rebuttal >#### **Q1: The use of abbreviations and full terms is very inconsistent. For example, "Multimodal Large Language Models" is spelled out in full on Line 29-30, while the abbreviation "MLLMs" is used frequently earlier in the text. Additionally, "Linguistic" in Line 34 should be "textua...	Summary: The paper introduces a training-free approach to improve the referring capabilities of multimodal large language models (MLLM). In particular, the authors iteratively adjust the attention maps using a learnable latent variable, which is based on energy functions. They empirically validate the efficacy of their...	Rebuttal 1: Rebuttal: # Rebuttal >#### **Q1: The generalization ability of the proposed method has not been fully verified. (a) It's uncertain whether the method can be applied to MLLMs beyond LLaVA. As the foundational model strengthens, the method's effectiveness could potentially diminish. (b) The energy function, b...	Summary: This paper introduces a training-free approach to integrate visual prompts into MLLMs using learnable latent variables, aiming to enhance the model's interpretability and generalization. It adjusts visual tokens from MLP outputs and optimizes latent variables with an energy function to improve attention on rel...	Rebuttal 1: Rebuttal: # Rebuttal >#### Q1: Figures 3(a) and 3(b) show visualization results for different values of $\eta$. The paper mentions that in 3(a), $\eta$ is too small to effectively control the attention. However, the focus of attention map in 3(b) does not significantly differ from that in 3(a). Than...	Summary: This paper proposes a training-free ControlMLLM, which uses optimizable latent variables to inject visual prompts into multimodal large MLLMs. The core idea is to adjust the visual token outputs of the MLP during inference, to control the attention response and ensure that the text prompt tokens focus on the i...	Rebuttal 1: Rebuttal: # Rebuttal >#### ****Q1: The visual prompt shows great improvements over the base model LLaVA, however, it requires additional guidance information and more inference time, which may limit the applications of ControlMLLM. Especially in some complex scenarios where the guidance signals are una...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering	Accept (poster)	Summary: This paper investigates the knowledge boundaries of Large Language Models (LLMs) using semi-open-ended questions, revealing limitations in LLMs' understanding. The authors introduce a novel method involving an auxiliary model to discover low-probability ambiguous answers, constructing a dataset of 953 question...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and valuable questions! We have incorporated all the suggested experiments and the results meet the expectation. We are confident that we have addressed all of your concerns as outlined below. Based on our new experimental findings and explanations, we apprecia...	Summary: This paper focuses on detecting the “knowledge bounding” of the current large language models (LLMs), which would be helpful in handling the well-known hallucination problem in LLMs. In this paper, the authors explore in a new question answering setting (i.e. semi-open-ended questions). The authors employ LLM-...	Rebuttal 1: Rebuttal: Thanks for your thoughtful and encouraging feedback! We have carefully incorporated your suggestions and provided explanations as follows, aiming to enhance the quality and robustness of our research. ## Q1: How many questions in the real applications belong to "semi-open-ended questions"? Follo...	Summary: In this submission, the authors aim to explore the detection of large language models’ (LLMs) knowledge boundary, which is a borderline to tell us what can LLMs really know. The detection of knowledge boundary wound play a crucial role to help the researchers to deal with hallucination. Different from the wid...	Rebuttal 1: Rebuttal: We are immensely grateful for your insightful and positive comments. we have addressed each point with careful consideration to ensure that our findings are presented with greater precision and rigor. ## Q1: Explanation regarding the meaning of "semi-open-ended questions" Thank you for your insi...	Summary: The paper presents a study of generating answers to questions from the tail of an LLM's distribution (GPT-4). They begin by constructing a dataset by generating questions with multiple answers from GPT-4. For each question, they continue decoding multiple answers and define the first 75% of generated answers a...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and valuable questions! We have incorporated the suggested experiment and provided important clarifications. We hope that we have addressed your concerns and resolved possible misunderstandings. Based on our clarifications, we would greatly appreciate it if you...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts	Accept (spotlight)	Summary: This paper studies rule extrapolation, one OOD behavior, of autoregressive LLMs on different models to understand the effect of model's architecture on this specific ability. The paper also introduces a normative theory for OOD prompt completion, which well explains the empirical observation about the training...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their positive evaluation of our work, in particular highlighting its relevance, good structure, clarity and deeming our experiments extensive and well-designed. We agree with the reviewer that beyond the present experiments, there are various other options...	Summary: # Problem: We lack systematic understanding about the Out-Of-Distribution (OOD) behaviours of autoregressive language models (LMs), such as in-context learning with natural languages prompts, despite successful deployment of LMs in such OOD situations. Natural languages (NLs) prompts, i.e. real-world data, a...	Rebuttal 1: Rebuttal: We warmly thank the reviewer for their positive evaluation of our work, in particular highlighting its originality, clarity and interesting nature. Please find our replies to your comments and suggestions below. ## WQ1 (section 2.1 does not describe recursively enumerable languages) We added a d...	Summary: This paper studies the compositional generalization of auto-regressive large language models with respect to rule extrapolation in formal languages. Both linear and recurrent architectures, including transformers, are compared on the task of inferring the rules that define regular grammars, context-free gramma...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive evaluation of our chosen topic, proposed theory and experiments. We address your questions below: >One single architecture tested for each model, and one single language for each category Our architectures have been selected through initial manual hyperpa...	Summary: The article examines systematic generalization in neural networks using artificial grammar learning tasks. Distinctive to this work, the authors operationalize systematic generalization through studying how models extrapolate learned rules to ungrammatical (and thus OOD) seqeunces. Their examination considers ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive evaluation of our experiments and presentation. Please find below our replies to your concerns. ## RE normative theory We thank the reviewer for their feedback on our normative theory in Section 5. We agree that our theory relates loosely to the rule extra...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their constructive feedback and suggestions. We were happy to see that the reviewers found our topic important, our methodology solid, and our presentation clear. We supply separate replies to all reviewers, but summarize the common points in our joint ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework	Accept (poster)	Summary: This paper presents a novel approach for mutation effect prediction using a retrieval-augmented framework. The main contributions involved: * Structure Motif Embedding Database (SMEDB): a vector database storing ESM-IF local structure motif embeddings from experimentally determined protein structures in Prote...	Rebuttal 1: Title: Rebuttal Information Comment: Question 1:MSM-Mut Description: The paper describes the classification head as a series of MLPs but does not specify the number of layers. Including this information would help reproducibility. To predict the mutation effect, we first pass the information before and...	Summary: The paper presents a novel retrieval-augmented framework for enhancing the prediction of protein mutation effects, which is essential for analyzing protein functions and understanding genetic diseases. The authors design a system that incorporates similar structure information from known protein structures int...	Rebuttal 1: Comment: **Question 1: In the field of bioinformatics, it is crucial not only to make accurate predictions but also to understand the reasons behind certain predictions. Enhancing models with interpretability features can help explain the importance of retrieved motifs for each prediction, which could be va...	Summary: - The paper presents a novel retrieval-augmented framework to efficiently retrieve similar local structure motifs in protein sequences for mutation effect prediction - Current methods to understand coevolutionary patterns include MSA and domain-level structure clustering, which serve as a global representation...	Rebuttal 1: Rebuttal: Question 1: It is difficult from Table 2 to conclude the competitive performance of MSM-Mut as other DL-based methods are performing better? We apologize for any difficulty in concluding the competitive performance of MSM-Mut from Table 2. Our primary intention with this table was to demonstr...	null	null	Rebuttal 1: Rebuttal: Thank you for your insightful and constructive comments as well as your appreciation of our work. Below are some clarifications and answers to your questions. If our response does not fully address your concerns, please post additional questions and we will be happy to have further discussions. As...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Can LLMs Implicitly Learn Numeric Parameter Constraints in Data Science APIs?	Accept (poster)	Summary: In data science library APIs, there is a numerical parameter constraint between input data and parameters. This paper presents an empirical study on whether large language models (LLMs) can implicitly learn numerical constraints in data science library APIs. The findings indicate that LLMs demonstrate the abil...	Rebuttal 1: Rebuttal: Question-1: I believe that by incorporating API documentation into fine-tuning or including it within prompts, LLMs might better understand numerical parameter constraints, which could potentially address the issue you're exploring. Thanks for this suggestion. We conducted additional experime...	Summary: This paper investigates a problem, precisely the title, can LLMs implicitly learn numeric parameter constraints in data science APIs? To investigate this problem, this paper constructs a benchmark, DSEVAL, which contains a series of API calling based code completion tasks. Then, this paper evaluates several LL...	Rebuttal 1: Rebuttal: **Question-1: I am not sure whether this paper should be submitted to Dataset & Benchmark track, since this paper focuses on evaluating LLMs’ underlying capabilities for satisfying parameter constraints when calling data science APIs, rather than proposing any novel algorithmic advances, analysis,...	Summary: The authors systematically investigate how well current LLMs learn numeric parameter constraints of functions and deep learning operators in the Numpy and PyTorch libraries. Their main finding is that although it is widely assumed that current LLMs can solve arithmetic constraints, the performance of even stat...	Rebuttal 1: Rebuttal: Question-1: In Figure 5, why does the accuracy drop much more significantly for Linear than for the other functions/constructors? Is this only true for DeepSeekCoder-33B? Thanks for bringing it up! This is an interesting result. For `torch.nn.Linear(in_features, out_features)`, the only const...	Summary: This paper investigates the ability of large language models (LLMs) to implicitly learn and apply numeric parameter constraints in data science (DS) APIs, focusing on PyTorch and NumPy libraries. The authors conduct a comprehensive study across 28 representative APIs, evaluating LLMs in three settings: full pr...	Rebuttal 1: Rebuttal: Question-1: Why did you limit your study to PyTorch and NumPy? We chose PyTorch and NumPy due to their wide adoption in the data science community: PyTorch is used by over 480k open-source GitHub projects [a] and NumPy is installed more than 300 million times monthly [b]. Focusing on these tw...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments and suggestions to improve the paper! We address the main questions and concerns in this rebuttal. Furthermore, we also plan to revise the paper accordingly to address all other minor suggestions and comments. We have also attached a PDF co...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper constructs a benchmark, DSEval, of 19600 programs across 28 data science (DS) library APIs with numerical constraints and uses the benchmark to invesitigate the capability of LLMs in generating valid DS programs which satisfy those numerical constraints. Additionaly, this paper categories the constr...	Rebuttal 1: Rebuttal: Question-1: Why only 28 or 12 APIs are studied given a large number of APIs? What does it mean by representative? Good question! Please note that although there are a large number of APIs, the commonly used ones (e.g., `Conv2d`) are not that many. For example, the widely-used benchmark DS-100...	null	null	null	null	null	null
Beyond Accuracy: Tracking more like Human via Visual Search	Accept (poster)	Summary: The authors touch upon a very important topic in visual tracking. They try to build upon the Central-Peripheral Dichotomy (CPD) theory that talks about how humans use visual information to track targets in complex environments. To this end, they propose a tracker named CPDTrack and STDChallenge Benchmark (for ...	Rebuttal 1: Rebuttal: Thank you for your review and feedback! We will release our code upon acceptance and include the new analyses below in the revision. Here are our responses to the following points: IRB: Previous studies [a] have demonstrated that such experiments only involve interaction between human subject...	Summary: This paper presents a novel approach to visual object tracking by drawing inspiration from the Central-Peripheral Dichotomy (CPD) theory. The proposed CPDTrack aims to improve tracking performance by emulating human visual search mechanisms, particularly under challenging scenarios involving spatio-temporal di...	Rebuttal 1: Rebuttal: Thank you for your review and feedback! We will include the new analyses below in the revision. We provide responses to the specific points below: As described in the paper, compared to existing algorithms, CPDTrack's advantage lies in its more robust performance in STDChallenge and its behavior ...	Summary: - The authors generalize the visual tracking problem as human dynamic visual ability task, and propose a new benchmark named STDChallenge to evaluate the visual tracking algorithms. This new benchmark includes challenging scenarios with spatio-temporal discontinuity, where previous short-term tracking oriented...	Rebuttal 1: Rebuttal: Thank you for your review and feedback! We will include the new analyses below in the revision. We provide responses to the specific points below: Differences with VideoCube: VideoCube is a benchmark specifically designed for the GIT task, consisting of 500 sequences characterized by frequent...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their efforts and invaluable suggestions. This work is inspired by the cognitive science theory of the central-peripheral dichotomy (CPD) and introduces a tracker, CPDTrack, designed to address the STDChallenge. Its effectiveness and similarity to human behavior have b...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Kernel Perspective on Distillation-based Collaborative Learning	Accept (poster)	Summary: This work presents an analysis of a distillation-based collaborative learning algorithm called FedMD from a nonparametric perspective. The method is to adopt an operator theoretic approach to obtain an upper rate for the expected generalization error of local models. Then, the authors propose DCL-KR to achieve...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the detailed comments and hope to address all of the questions and concerns raised by the reviewer. > The paper's organization and structure are not clear. > The writing looks like combining different parts of different papers without a clear clue. We attempted to ...	Summary: The authors propose a nonparametric version of FedMD, a distillation-based collaborative learning methodology. They also propose a neural network variant of the nonparametric approach as an extension for heterogeneous local neural network models. They provide both theoretical results on the nonparametric appro...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the detailed comments and hope to address all of the questions and concerns raised by the reviewer. > (1) The need to perform learning rate scaling to ensure the impact of local iterations is consistent across models introduces additional complexity. The exact metho...	Summary: The paper investigates the theoretical underpinnings and practical implementation of distillation-based collaborative learning (DCL) from a kernel regression perspective. The authors propose DCL-KR, a nonparametric version of the FedMD algorithm, which achieves nearly minimax optimal convergence rates in massi...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the detailed comments and hope to address all of the questions and concerns raised by the reviewer. > The paper lacks a discussion on the privacy risk and communication efficiency of DCL-KR and DCL-NN. Basically, our work shares the typical (widely used) assumption...	Summary: This paper theoretically proves a nonparametric version of the most standard distillation based collaborative learning algorithm (named DCL-KR) is nearly minimax optimal in massively distributed statistically heterogeneous environments. Extensive experiments demonstrate their theoretical results and show the p...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the detailed comments. We briefly describe the main contribution of our work as below: * Applying kernel regression theory, we analyze the most representative DCL algorithm, FedMD, from a non-parametric perspective (called DCL-KR). Compared with the existing studies,...	Rebuttal 1: Rebuttal: We appreciate all reviewers for the detailed comments. In this general response, we provide additional explanations to help the reviewers better understand our work. In detail, we (1) briefly outline the overall flow of our manuscript, (2) provide additional explanations on the connection between ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: In this paper, the authors perform a study of distillation-based collaborative learning (DCL) in massively distributed statistically heterogeneous environments. In particular, the study focuses on analyzing DCL-KR, a non-parametric version of FedMD, and proves its near-minimax optimality. The authors proposed ...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the detailed comments and hope to address all of the questions and concerns raised by the reviewer. > The paper does not perform any formal privacy analysis. As such, I believe using terms like "privacy-preserving" can be misleading in this context. > Could the aut...	null	null	null	null	null	null
Dealing with Synthetic Data Contamination in Online Continual Learning	Accept (poster)	Summary: This paper investigates the impact of AI-generated images on the performance of online continual learning (CL) models. It introduces a novel method called Entropy Selection with Real-synthetic similarity Maximization (ESRM) to mitigate the negative effects of synthetic data contamination. ESRM leverages entro...	Rebuttal 1: Rebuttal: Thank you for your feedback and valuable comments. ## Weaknesses 1. We indeed tested our method with a limited set of generative models, due to the computation constraint. Also, it is true that almost all of the existing work in online Continual Learning (CL) is limited to Class-Incremental Lear...	Summary: Image generation has been showing promising achievements in the last few years, but the generative models may not be able to keep up with the distribution of the real samples. Due to low diversity, synthetic images perform poorly in downstream learning tasks. This paper tackles this problem by diversifying the...	Rebuttal 1: Rebuttal: Thank you for your feedback and valuable comments. ## Weaknesses 1. In our experiments, we prioritize the importance of diffusion-based contamination. There are two reasons: Firstly, training with synthetic images generated with GAN-based or autoencoder-based methods yields catastrophic perform...	Summary: This paper investigates the negative impact of synthetic data contamination on existing online continual learning methods. An entropy selection with the real-synthetic similarity maximization method is proposed to alleviate the performance deterioration. Strengths: 1. Detailed analysis of synthetic data conta...	Rebuttal 1: Rebuttal: Thank you for your feedback and valuable comments. ## Weaknesses 1. We would like to include more detailed information on the synthetic dataset generation process. For Stable Diffusion and VQDM, we use source code and model snapshots from huggingface, as mentioned in Table 12 in the appendix. F...	null	null	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their comments and suggestions, which enabled us to improve the manuscript significantly. We respond to each reviewer individually. Here, we introduce the rebuttal experiments in the attached PDF file. ## 1. Results with LLM Enhanced Prompts As suggested ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Secretary Problem with Predicted Additive Gap	Accept (poster)	Summary: The paper examines the value maximization secretary problem, where values $w_1 \geq \ldots \geq w_n$ are observed in a uniformly random order, and investigates how the optimal competitive ratio of $1/e$ can be improved in a learning-augmented setting with predictions about an additive gap. Specifically, the au...	Rebuttal 1: Rebuttal: Concerning tightness of our results: the mentioned implication of the two-best secretary yields an upper bound on $0.5736$ for exact additive gaps. In addition, we will of course address your minor weaknesses in the final version. --- Rebuttal Comment 1.1: Comment: I thank the authors for thei...	Summary: This paper studies the famous secretary problem under the setting with predictions: some extra advice (presumably generated by some machine learning algorithm) that enables the algorithm to do well when this advice is accurate (consistency), but will not force the algorithm to do poorly if it is inaccurate (ro...	Rebuttal 1: Rebuttal: The additive gap is a useful piece of advice when we are concerned about data privacy; for example, when we only have access to a translation of previous instances: instead of seeing $w_i$, we see $w_i - X$ where $X$ is some random shift of all the values. In this way, the maximum weight element i...	Summary: This work considers the classical secretary problem in an online decision making with hints setting, where the objective is to select the element with highest weight from an online, random-order stream of elements with adversarially chosen weights, where the decision to select or reject elements is irrevocable...	Rebuttal 1: Rebuttal: Our main contributions are twofold: From a conceptual point of view, our work emphasizes and justifies to study weak prediction models. This question can of course also be asked for other online (learning) problems. For example, it is easy to see that the canonical $1/2$-tightness instance in Prop...	Summary: This paper considers the value maximization version of the secretary problem with additional information of the gap between the best and second best values. The first main contribution of this paper is that this additional information makes it possible to design a $0.4$-competitive algorithm. The second main c...	Rebuttal 1: Rebuttal: Please find a plot for the Pareto frontier of the robustness-consistency trade-off in the attached document. When being (close to) zero-robust, we obtain a consistency which is close to the mentioned $0.5736$ upper bound. On the other hand, we obtain guarantees of $\approx 1/e$ robustness and cons...	Rebuttal 1: Rebuttal: We would like to thank all four reviewers for their highly valuable feedback and appreciate the positive spirit concerning the comments and remarks. Concerning the question on guarantees as a function of an unknown error from reviewers rRpH and jGey: When underestimating the gap, Algorithm 1 has...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Zero-shot Image Editing with Reference Imitation	Accept (poster)	Summary: The paper proposes a method to modify specific parts of the image with the content from the reference image and consistently with the original content. It combines the technique of dual diffusion to replace the key and value features, which is used in MasaCtrl, of the source image with the reference image. The...	Rebuttal 1: Rebuttal: ``W1. The proposed method is not actually zero-shot as it requires training. It requires expensive resources for the training model.`` This comment is erroneous. “Zero-shot” is different from “training-free.” Zero-shot means that we train the model on some examples, and the model’s ability ca...	Summary: To achieve more precise imitative editing, the paper proposed a training framework, called MimicBrush. It randomly selects two frames from a video clip, masks some regions of one frame, and learn to recover the masked regions using the information from the other frame. Also, it constructs a benchmark, consisti...	Rebuttal 1: Rebuttal: Thank you for your acknowledgment and constructive suggestions. We will follow your recommendations to polish our paper for the revision. `` W1. The definition of inter vs. inner is unclear.`` Inter-instance refers to cases where the source and reference images are of different instances, s...	Summary: The paper introduces a novel approach called imitative editing aimed at enhancing user creativity in image editing tasks. Traditional image editing often involves matching references to the source image, which can be challenging. In contrast, imitative editing allows users to directly draw inspiration from in-...	Rebuttal 1: Rebuttal: Thank you for your acknowledgment and constructive suggestions, we will follow you suggestions to polish our paper for the revision. ``How the model distinguishes between referencing parts and textures.`` During inference, users have the option to enable "depth control." The depth map serves as ...	Summary: The paper proposes a new form of image editing, termed imitative editing. In this scenario, the user can edit the local area of the source image with a similar area to the reference image. This requires the system to automatically figure out what to expect from the reference to perform the editing. To achieve ...	Rebuttal 1: Rebuttal: Motivation & Novelty We disagree. There exist misunderstandings about our motivation and novelty. The "structure of dual U-Net" and the "data difference" are not our contributions. Please refer to A. Core contributions and novelty in the global rebuttal to see our motivation. ``1. Motiva...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable suggestions. We emphasize the motivation and novelty in the global rebuttal and respond to each reviewer individually. Additionally, we include more figures and tables in the attached PDF file. --- ### A. Core contributions and novelty. We would li...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
LLM-Check: Investigating Detection of Hallucinations in Large Language Models	Accept (poster)	Summary: The paper addresses the issue of hallucinations in large language models (LLMs). They propose using special scores based on internal model states such as attention maps, hidden activations, and output prediction probabilities to identify hallucinations within a single response in both white-box and black-box s...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer appreciates the efficacy of the proposed method extensively demonstrated across diverse detection settings and datasets. We respond to the questions raised below: **Theoretical insights and comparisons with IN...	Summary: This paper proposes LLM-Check, a hallucination detection method that requires a single response for both black-box and white-box settings. Specifically, LLM-Check inspects the internal hidden representation, attention map, and output token uncertainty of an auxiliary LLM and derives scores for detecting halluc...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer appreciates the efficacy of the proposed method across diverse detection settings and datasets, and the significant performance improvements achieved while requiring only a fraction of the computational cost (sp...	Summary: The paper explores the challenge of hallucinations in large language models (LLMs), which are outputs that appear plausible but are inaccurate or fabricated. The paper conducts a comprehensive investigation into the nature of these hallucinations and proposes novel, compute-efficient methods for detecting them...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We are encouraged that the reviewer appreciates the compute-efficiency of the proposed method, and its practicality and versatility towards real-time hallucination detection within a single model-response across diverse settings. We respond to the...	Summary: The paper presents a comprehensive study on the detection of hallucinations in outputs produced by large language models (LLMs). The authors propose a method, LLM-Check, which aims to identify hallucinations within a single response of an LLM by analyzing internal hidden states, attention maps, and output pred...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We are glad that the reviewer found the proposed method to be novel, and is effective towards hallucination detection in various settings, while being extremely efficient computationally. We respond to the questions raised below: > The paper lack...	Rebuttal 1: Rebuttal: A note to all Reviewers We sincerely thank the reviewers for their valuable feedback and constructive comments on our paper. We are glad to note that the reviewers appreciate the comprehensive study into the nature of hallucinations in LLMs, and the practicality and effectiveness of the novel...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Towards Effective Planning Strategies for Dynamic Opinion Networks	Accept (poster)	Summary: This manuscript investigates intervention planning, a critical issue in complex social networks. To address this challenge, the authors introduce a novel ranking algorithm and a reinforcement learning-based dynamic planning framework. Three cases of opinion and trust values are considered, enhancing the method...	Rebuttal 1: Rebuttal: Thank you for recognizing our contributions and acknowledging our effort to cover various scenarios in our evaluation. We are also thankful for the constructive feedback and suggestions for future work. Yes, Figure 19 is correct. The barplot displayed in Figure 19 depicts the mean infection rate...	Summary: The paper investigates intervention planning aimed at disseminating accurate information within dynamic opinion networks using learning strategies. It introduces a novel ranking algorithm to identify key nodes for disseminating accurate information and develops a Reinforcement Learning (RL)-based dynamic plann...	Rebuttal 1: Rebuttal: Thank you for pointing out the novelty of the ranking algorithm and the thoroughness of the analysis presented in the paper. In the following, we address the specific concerns on novelty, comparisons with existing literature, and the question on scalability. Remarks 1 & 3: Novelty Existing ...	Summary: This paper studies strategic planning for disseminating credible information within dynamic opinion networks. The main two contributions are (1) a ranking algorithm to identify influential nodes to spread accurate information, and (2) an RL-based framework for adaptive intervention strategies. Strengths: 1. P...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. Thank you for pointing out that the paper is well written and for your valuable suggestions for improving our paper. With regards to the infection rate calculation - it is calculated as the ratio of the number of infected nodes to the total number of nodes wi...	Summary: This study explores intervention strategies aimed at curbing the spread of misinformation in dynamic social networks. The authors propose a novel ranking algorithm to identify influential nodes for disseminating accurate information and a RL-based framework to address the computational complexity associated wi...	Rebuttal 1: Rebuttal: Thank you for acknowledging the technical contributions and novelty of our work and for providing valuable feedback. Remark 1: Real-world network models In our work, we develop planning algorithms and analyze their efficacy using synthetic data in controlled settings. The synthetic data use...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable feedback and suggestions. In this rebuttal, we have tried to address all the specific concerns and comments of individual reviewers. To support our response to the reviewers' comments, we include two additional tables in the attached PDF that are not part ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
STL: Still Tricky Logic (for System Validation, Even When Showing Your Work)	Accept (poster)	Summary: The paper investigates the efficacy of formal specifications (specifically, Signal Temporal Logic (STL)) for human validation of autonomous systems. The authors distinguish between verification (whether an implemented policy adheres to a formal specification of its behaviors) and validation (whether the sy...	Rebuttal 1: Rebuttal: High Level Objectives and Experiment Setup (Weakness 1 and Limitation) We appreciate the reviewer's observations regarding the perceived incongruity between the high-level objectives delineated in our introduction and the specific experimental task employed in our study. Upon reflection, we recog...	Summary: This paper studies the claim of Signal Temporal Logic (STL) specifications being human interpretable and provides results from an experiment with human participants studying a potential active learning technique to improve explainability metrics. Results show that while human engagement is improved, system val...	Rebuttal 1: Rebuttal: Fit for NeurIPS (Weakness 1) As the reviewer notes, our work tackles an important topic that is ignored by many studies in logic: validation of behavior via interpretable specifications. The implications of our work relate broadly to logic and XAI, and future work in this area will improve human-...	Summary: The paper presents the results of a human study exploring the intuitiveness and interpretability of formal logic—in this case, signal temporal logic (STL)—in expressing policies for autonomous systems. Specifically, the authors study the effect of active learning, a pedagogical approach for human learning, on ...	Rebuttal 1: Rebuttal: List of Specifications Used (Weakness 1) We have included a full list of specifications and corresponding maps in the rebuttal's attached PDF. To clarify the nature of these specifications in the context of STL, we offer the following: All specifications were expressed as location constraints. T...	Summary: This paper examines the challenges of using Signal Temporal Logic (STL) for validating autonomous systems and finds that human validation accuracy remains loweven with active learning techniques. Using the ManeuverGame interface, the study tests three conditions—no active learning, active learning, and active ...	Rebuttal 1: Rebuttal: Human Supervision (Weakness 1, Question 1) We appreciate the reviewer's comment regarding the lack of necessity for human supervision in our experiment and acknowledge that model checking could be used given the the concreteness of our objectives. As described in lines 155-158 of the manuscript, ...	Rebuttal 1: Rebuttal: We thank the reviewers for their critical assessment of our work. As a note, we appreciate one of the reviewers pointing out that we did not include the example formulas and maps show to subjects. We have included this content in the rebuttal's attached PDF and will also include it in the final ma...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems	Accept (poster)	Summary: This paper introduces a new approach to model the low-dimensional latent dynamics of a collection of neurons over time. Their approach balances the two desiderate of capturing complex nonlinear dynamics while remaining interpretable. Specifically, they introduce the Gaussian Process Switching Linear Dynamical ...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to read our submission and for noting the strengths of our work! We are especially pleased that the reviewer praised our work's clear motivation, solid experimental results, and relevance to the neuroscience community. ## Weaknesses **Re. Runtime comparis...	Summary: This paper proposes a new model called Gaussian Process Switching Linear Dynamical System (gpSLDS). The model is more interpretable and infers more stable latent compared with the alternative rSLDS. Particularly, the weird oscillations of the latent can be avoided due to the newly proposed Smoothly Switching L...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to read our submission and for providing helpful feedback! We are especially pleased that the reviewer found our methodology to be intuitive and supported by solid experimental results. ## Weaknesses Re. Complexity analysis > Lacks some complexity ana...	Summary: The paper explores latent state inference and parameter learning within a switching stochastic dynamical system. In this context, the dynamics are represented by a stochastic differential equation, with the drift function modeled as a Gaussian process. Notably, the paper introduces a novel kernel for this Gaus...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to read our submission and for providing insightful feedback. We especially appreciated that the reviewer found our submission to be clearly written and a "fresh perspective" on SLDS models! ## Weaknesses Re. Prior independence assumption\ We thank th...	Summary: This paper introduces the Gaussian Process Switching Linear Dynamical System (gpSLDS), a novel approach for modeling latent neural dynamics. This model extends the Gaussian process stochastic differential equations framework by incorporating a new kernel function that supports smoothly interpolated locally lin...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to read our submission and for noting the strengths of our work! We are especially glad to see the reviewer’s comments on the originality and soundness of our method, as well as the clarity of our submission. A large part of the review centers around que...	Rebuttal 1: Rebuttal: We thank the reviewers for taking the time to read our submission and for providing thoughtful and insightful feedback! We were pleased that the reviewers unanimously supported our submission as a valuable contribution to the NeurIPS community, citing that it was **1) easy to follow, 2) clearly mo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models	Accept (poster)	Summary: This paper investigates the interaction between attention heads at different layers in a transformer. They primarily study the “inhibition-mover subcircuit”, a previously identified attention head interaction from circuit analysis work [1,2]. They show the interaction between heads can be characterized by a lo...	Rebuttal 1: Rebuttal: > The study is limited to fairly small transformers (GPT2-small, and Pythia-160m in appendix), and it’s hard to know whether the results will generalize to more complicated tasks or methods will scale to larger models. Additionally, It would also be helpful to know whether larger models also strug...	Summary: The paper investigates the communication between attention heads across different layers in Transformer-based language models. First, it establishes that a previous composition metric, which has been shown to be useful in toy settings, is noisy in larger models when tested on the Indirect Object Identification...	Rebuttal 1: Rebuttal: Thank you for the review. We are happy that the reviewer enjoyed the paper and we appreciate the encouraging comments regarding static weight analysis, we think there is a lot of interesting work to be done in this area. > The proposed composition score does not work for models using relative po...	Summary: This paper explores the routes and mechanisms of information transfer between heads of transformer large language model. They hypothesize the presence of low-ranking communication channels between attention heads from different layers within residual connections. The authors propose a method based on Singular ...	Rebuttal 1: Rebuttal: Thank you for the review. We are very happy to hear that the reviewer found the results interesting, promising for facilitating future work, and the paper easy to follow. The reviewer had some concrete questions and concerns about the generalizability of the method to new models and results to new...	Summary: Building upon prior work in Transformer circuits (Elhage et al., 2021; Wang et al., 2022), the paper identifies novel, low-rank communication channels between attention heads across layers via the Composition Score (a generalized cosine between the read-out weights of a lower-level layer and the read-in weight...	Rebuttal 1: Rebuttal: Thank you for the review. We are glad the reviewer found the methods interesting and well-motivated but disappointed the paper was difficult to read. This is a complicated topic that needs to fit in 9 pages, so it is difficult to catch everyone up and make a significant contribution in that time. ...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their thorough and thoughtful comments and suggestions. We are glad the reviewers shared our interest in the results on static weight analysis, as we’re hopeful for continued progress in this area, and we’re pleased that there was mostly consensus that are ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper primarily employs low-rank communication channels to elucidate how internal layers within the Transformer model transmit information. Initially, the study utilizes existing research to identify duplicate heads, inhibition heads, and mover heads. The objective is then to explore the interactions amon...	Rebuttal 1: Rebuttal: > “It is well known that the matrix of the attention head is sparse, which naturally suggests using Singular Value Decomposition (SVD) to compress the original matrix… the conclusion that inter-layer communication is low-rank seems evident due to the inherent low rank of the attention head weight ...	null	null	null	null	null	null

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