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 |
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Robust Conformal Prediction Using Privileged Information | Accept (poster) | Summary: This paper focuses on conformal prediction with "privileged information" in constructing prediction intervals with missingness/noise. The proposed quantile-of-quantile approach handles the difficulty that the "privileged information" is not available for test data and achieves finite-sample coverage.
Strength... | Rebuttal 1:
Comment: We appreciate your review of our work and thank you for the helpful suggestions and interest in our work. In what follows we respond to your concerns in detail.
## The privileged information
We thank the reviewer for raising this point which is related to the one raised by Reviewer 3YMV. For th... | Summary: This paper introduces a method to create prediction sets with guaranteed coverage in the presence of training data that is corrupted by missing or noisy variables. The approach is an extension of conformal prediction that works by assuming access to privileged information available during training. This inform... | Rebuttal 1:
Rebuttal: We very much appreciate your positive feedback and interest in our work. We thank the reviewer for classifying our contribution as a novel one. We also thank the reviewer for their helpful comments and suggestions. In what follows, we address your comments in detail.
$ \newcommand{\indep}{\perp \\... | Summary: The authors introduce a calibration method called Privileged Conformal Prediction (PCP) to generate prediction sets that guarantee coverage on uncorrupted test data, even when the target label (Y) and/or input features (X) in the calibration data samples are corrupted (e.g., missing or noisy variables). The ke... | Rebuttal 1:
Rebuttal: We are very grateful for the time and effort you put into this review. We are also very appreciative of your encouragement and positive assessment of our work. Your feedback is very important to us! Thank you again for your support. | Summary: This paper is studying the problem of conformal prediction in the presence of data (covariate or label) corruption leveraging privileged data. They build upon the framework of weighted conformal prediction by introducing a novel leave-one-out weighting technique which produces a conservative (upper-bound) esti... | Rebuttal 1:
Rebuttal: We appreciate your positive and valuable feedback and suggestions. In what follows, we address your concerns in detail.
## The weights $w_i$
We thank the reviewer for raising this point. As the reviewer suggested, the real ratios of likelihoods, $w_i$, are required to provide the validity gua... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your time and effort in reviewing our submission and providing valuable feedback and suggestions.
In response to the reviewers' comment, we attached to this reply a PDF file containing the results of an ablation study analyzing the effect of $\beta$ on Algorithm 1 (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HydraViT: Stacking Heads for a Scalable ViT | Accept (poster) | Summary: This study proposes a training scheme called stochastic dropout training, thereby presenting its resulting models, called HydraViT. From the observation that ViT with a smaller version can be understood as a subset of a larger version, the authors propose to sample a subnetwork of a larger ViT. Although this s... | Rebuttal 1:
Rebuttal: We thank reviewer aEuL for their remarks and their thoughtful review. We answer the questions below:
> When we change the size of ViT, such as hidden dim, head size, and embedding dim, can we guarantee consistent behavior of ViT? In other words, if several dimensions of ViT change, ViT might exh... | Summary: Building on the concept of Matryoshka Representation Learning, the authors propose a novel variant of Vision Transformers (ViT) that improves quality-speed trade-offs. Specifically, the proposed ViT model, trains on subsets of embedding dimensions and associated attention heads, which are ordered by importance... | Rebuttal 1:
Rebuttal: We thank reviewer 1s2T for their remarks and their thoughtful review. We answer the questions below:
> It would be great to see an extensive discussion about how to efficiently implement such stochastic training
**Rebuttal**: This is a very interesting question. In HydraViT, we have modified all... | Summary: ViTs that allow users to dynamically select the amount of RAM consumed or latency at deployment are valuable. This paper proposes HydraViT, a training procedure that enables plain ViTs to perform relatively well when attention heads and embedding dimensions are dropped on demand. During training, HydraViT samp... | Rebuttal 1:
Rebuttal: We thank reviewer Yk3V for their remarks and their thoughtful review. We answer the questions below:
> Evaluation on Additional Datasets?
Thank you for your suggestion. We have evaluated our model and baselines on 5 ImageNet variants. Please see the general rebuttal (G.1).
> Comparison with Sort... | Summary: The paper proposes a new training scheme for ViTs to enable creating models with varying sizes (in the terms of the number of attention heads in multi-head attention layers). Full version of the model can be then used when more computational resources are available, and smaller portions of the model (with less... | Rebuttal 1:
Rebuttal: We thank reviewer 2Apv for their remarks and their thoughtful review. We answer the questions below:
> Q1. The paper emphasizes the adaptability of different subnetworks for various hardwares but only tests on an A100, where constraints are not applicable.
A1. We acknowledge this oversight and w... | Rebuttal 1:
Rebuttal: We would like to extend our sincere thanks to all the reviewers for their time and valuable feedback. Your insights help us to improving the clarity and quality of our work. In the following sections, we address the key points raised by the reviewers and provide clarifications and additional detai... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A generalized neural tangent kernel for surrogate gradient learning | Accept (spotlight) | Summary: The paper addresses the challenge of applying gradient-based training methods to neural networks with non-differentiable activation functions, such as binary and spiking neural networks. These networks use surrogate derivatives to enable gradient descent, but this approach lacks theoretical foundation. The aut... | Rebuttal 1:
Rebuttal: Thank you for your review and for your helpful suggestions. We appreciate the focus on the applicability of our work.
- While the mathematical details behind the NTK theory are generally complex and not particularly accessible to practitioners, the analytic NTK can be easily calculated for diffe... | Summary: The paper considers an extension of neural tangent kernel methods for the analysis of training of neural networks with non-differentiable activations with surrogate gradient learning. The basic approach is the define a generalized NTK (the SG-NTK) based on the quasi-Jacobian matrix (that is, the Jacobian cons... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and positive review and for your helpful suggestions.
- We have refrained from using logarithmic scales to keep the plot as simple as possible and to facilitate the comparison between Figure 1 and Figure 2. However, we agree that a logarithmic y-scale helps to illus... | Summary: This paper explore the neural tangent kernel (NTK) with regards to surrogate gradient learning for non-differentiable activation functions. The authors show that the standard neural tangent kernel is not equipped to deal with such activation functions and causes the kernel function to become singular. They pro... | Rebuttal 1:
Rebuttal: Thank you for your positive and thorough review and for your helpful suggestions.
- We apologise for any confusion caused by the use of the variable $m$ in lines 133 to 137 and will change the variable. The parameter $m$ is used in Definition 2.1 to be able to consider a limit by taking $m \to \in... | Summary: The paper adapts the neural tangent kernel framework to surrogate gradient learning (and so to learning in spiking neural networks).
Strengths: - The paper generalizes NTK to (some algorithms for) spiking neural networks, which is an important scenario for neuroscience and neuromorphic computing
- Like the o... | Rebuttal 1:
Rebuttal: Thank you for your encouraging, thorough and detailed review and for your helpful suggestions.
- **Short answer:** Indeed, by parameterizing all hidden layer widths with the parameter $m$ and considering $m \to \infty$, we cannot cover the sequential infinite-width limit as described in [1]. This... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thorough reviews and helpful suggestions.
We provide four additional figures in the attached PDF, where we have implemented the suggestions of Reviewer PShm and Reviewer Ljsu. The figures address the question raised by Reviewer L5ST. In Figure R1, we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration | Accept (spotlight) | Summary: The paper introduces UniKE, a novel multimodal editing method that addresses challenges in knowledge editing for Multimodal Large Language Models. UniKE unifies intrinsic knowledge editing and external knowledge resorting by vectorized key-value memories. By disentangling knowledge representations into semanti... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments. We will explain your concerns point by point.
**Q1:** The presentation is confusing. For example, in intrinsic knowledge editing, how do you actual edit the intrinsic knowledge in FFN?
**A1:** We apologize if our manuscript causes any confusio... | Summary: This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective for intrinsic knowledge editing and external knowledge resorting. On this basis, the authors combine both types of knowledge editing methods, executing them in the latent space with a unified paradigm. Furthermo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and high appreciation of our work! We are encouraged that our research is recognized as having significant implications for further studies on constructing more powerful MLLMs. We will address your concerns point by point.
**Q1:** In the NLP commu... | Summary: UniKE is a unified framework for multi-modal knowledge editing that includes three main aspects:
1.Knowledge Separation: UniKE divides knowledge into factuality and semantic spaces to manage and coordinate different types of knowledge more effectively.
2.Knowledge Collaboration: In the factuality space, UniKE ... | Rebuttal 1:
Rebuttal: Thank you for your kind feedback and valuable comments. We will explain your concern as follows.
**Q1:** Lacking a detailed discussion on computational speed and resource utilization.
**A1:** Thank you for the suggestion. In **Table 7 of the Rebuttal PDF**, we list the computational speed and re... | Summary: This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Within such a unified framework, the authors further promote knowledge collaboration by disentangling the knowledge representations ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments! We are encouraged to see that our work can enhance subsequent research endeavors. We will address your concerns point by point.
**Q1**: More foundation models should be compared (LLava). The improvement on BLIP2 seems incremental.
**A1**: Thank y... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find that:
(1) The motivation is **clear and reasonable**, supported by a well-structured article. *(Reviewer mp35, Reviewer w8aR, Reviewer V1xr)*
(2) UniKE establishes **a unifi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner | Accept (poster) | Summary: This paper proposes a novel insight to transform the “how to drag” issue into a two-step “what-then-how” by introducing an intention reasoner and a collaborative guidance sampling mechanism. They also raise the problem of image quality issues and design quality guidance to enhance performance. Experiments show... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We are encouraged that you find our ''what-then-how'' paradigm to be novel and effective. Here is our response to address your concerns.
> *W1: How were the single experimental results (like Figure 4) selected from the diverse results confo... | Summary: This paper aims to address the limitation of current dragging-based image editing methods that understand the intentions of users. To this end, the proposed method leverages the reasoning ability of LLMs to infer possible intentions, which are used to provide (asymmetric) semantic guidance in editing. Furtherm... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback. Below is our response.
> *W1: Despite the performance gain, there are observable artifacts.*
We acknowledge these artifacts, a common challenge for drag editing [DragDiffusion, Shi et al. [2024]], but they do not diminish the overall effectiveness ... | Summary: This paper presents a novel framework called LucidDrag for drag-based image editing. Compared to previous methods, LucidDrag first reasons the intention of the draging operation using LLM (GPT 3.5) and provides a semantic guidance for the following editing process. For the better image fidelity, the authors al... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We are encouraged by your recognition of the value of our work and your acknowledgment that our experiments sufficiently support our claims. Below is our response addressing your concerns.
> *W1: It's a good idea to introduce additional pro... | Summary: The paper introduces a novel framework for semantic-aware drag-based image editing. Specifically, to address the limitations in understanding semantic intentions and generating high-quality edited images, it utilizes an intention reasoner to deduce potential editing intentions and a collaborative guidance samp... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We are encouraged that you find our ''what-then-how'' paradigm and collaborative guidance mechanism to be novel and effective. Below are our responses to your concerns.
> *W1: How different LVLMs and LLMs perform on the task? Are the confid... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments which have helped improve our paper. We are encouraged that the reviewers found the importance of addressing the ill-posedness of dragging-based image editing which we aim to solve(Reviewer 17V7, 6UnY). We appreciate their positive ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Operator World Models for Reinforcement Learning | Accept (poster) | Summary: This paper extends policy mirror descent to the RL setting. They solve the problem of requiring a exact value function by formulating an approximate value function based on operators over the transition and reward functions, which yields a closed form solution. Then they use this approximate value function for... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
- __Experiments on more complicated MDPs__: we agree with the reviewer and we are very excited to test our methods on more complicated environments. As the reviewer pointed out, our main focus in this work was to prove the novel theoretical contri... | Summary: This paper proposes an approach (the first PMD approach adapted to RL setting) that can learn a world model using conditional mean embeddings (CME). The operatorial formulation of RL is used to express the action-value function in closed form via matrix operations. The proposed algorithm is proved to converge ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and kind words.
- __Line 88 - references__: the requested references are provided in the same sentence referred to by the reviewer, line 90. They are references [19,20] in the paper, namely:
[19] Beck and Teboulle. Mirror descent and nonlinear projecte... | Summary: (This review has been updated after revert of desk reject)
Strengths: (updated after revert of desk reject)
It's a solid theoretical paper with nice writing and experimental results on continuous control benchmarks.
Weaknesses: (updated after revert of desk reject)
The experimental results and more interpret... | Rebuttal 1:
Rebuttal: We kindly point out to the reviewer that the decision on desk rejection was reverted by the Program Chairs
(The initial desk rejection had been due to the automated checker failure to detect the NeurIPS checklist in the supplementary material. This happened to several papers this year).
Should ... | Summary: The paper presents a practical implementation of Policy Mirror Descent (PMD) for Reinforcement Learning (RL). PMD requires knowledge of the action value function of the current policy at each iteration. Existing methods that approximate the action-value function depend on the ability to restart the Markov Deci... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review.
In particular, we thank the reviewer for the additional references. We will add them to the paper with the following expanded discussion in the introductory section of our paper, on line 42 (we denoted with R1,R2,R3 the references suggested by the re... | Rebuttal 1:
Rebuttal: Thanking all reviewers for their feedback on our work, in this global reply we share our insights on the extension of our results to continuous action spaces, a question shared among reviewers. In addition, in the attached pdf we report Figure 1 updated according to the suggestions of reviewer __4... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy | Accept (poster) | Summary: For data owners, determining the appropriate privacy level based on the standard interpretation of differential privacy—which limits the likelihood of distinguishability from an adversary's perspective—can be challenging. This paper addresses operational interpretations of (pure) differential privacy by examin... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. To frame our response, we begin with two general points. First, our method is intended for the standard definition of DP, and we presume the agency will apply a DP algorithm with the selected epsilon. Thus, the DP guarantee associated with the selected epsilo... | Summary: This paper proposes a bayesian framework for determining the DP budget \eps. In particular, the authors develop a mathematical technique based on how much posterior risk the agencies are willing to accept given some prior risk and the \eps obtained through their formulation is unique.
Strengths: Although ther... | Rebuttal 1:
Rebuttal: Thank you for raising this point. A few works such as Kasivisiwanathan & Smith (2014) and Kifer et al. (2022) discuss Bayesian semantics of approximate DP, although the details differ from the semantic characterization we use in this work for pure DP. We imagine one could use these results as the ... | Summary: This paper proposes a novel method for selecting epsilon that comes with a natural adversarial interpretation. They characterize an adversary’s auxiliary knowledge by two prior probabilities: the prior belief that an individual participated in the dataset, and the prior belief that an individual’s record has s... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback regarding baseline clarity. On the question of demonstrating improvements over the baseline, the extended versions of Examples 1 and 2 in the supplement demonstrate the potential for substantial improvements over the baseline. For example, Table 4 and the accompan... | Summary: The paper proposes a novel framework for setting an appropriate privacy budget via controlling Bayesian posterior probabilities of disclosure. The connection is established through the risk profile, an upper bound on disclosure risk involving privacy parameters. Theoretical justification and empirical evaluati... | Rebuttal 1:
Rebuttal: Thank you for noting this omission. Generalization of our results from the discrete case to the continuous case can be accomplished by replacing sums with integrals and probability mass functions with probability density functions throughout the theorem statements and proofs. We focus on the discr... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning | Accept (poster) | Summary: This paper tackles the challenges of Offline RL, which involves learning effective decision-making policies from static datasets without online interactions. The authors introduce Generative Trajectory Augmentation (GTA), a novel approach that uses a diffusion model to enhance offline data by augmenting trajec... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, which can enhance our manuscript.
> **(Weakness 1)** Lack of theoretical guarantee to support the algorithm: The authors only provide empirical results, lacking theoretical analysis of the performance of the proposed method.
While existing work [1] proposes ... | Summary: To improve the quality of offline datasets, in this paper, a generative data augmentation approach is proposed by leveraging the diffusion models. Moreover, with the adoption of partial noising and denoising framework with amplified return guidance, the trajectory can be guided towards high-rewarding region. F... | Rebuttal 1:
Rebuttal: Thank you for your critical reviews and valuable feedback.
> **(Weakness 1)** The proposed approach is mainly a direct combination of current techniques.
Please note that we propose the hypothesis that augmenting the offline dataset with feasible and high-rewarding trajectories would boost the p... | Summary: This paper builds on ideas from SynthER but adds classifier free guidance to boost the returns of the generated trajectories. This makes sense as the resulting data has higher quality and the paper is a totally sensible next step in the series of works building upon SynthER, which is an exciting direction for ... | Rebuttal 1:
Rebuttal: Thank you for your positive review and valuable feedback!
> **(Weakness 1)** The authors could cite Ball et al 2021 "Augmented World Models" as another example of data augmentation in offline RL.
Thank you for pointing out crucial paper. The paper [1] augments learned model with simple transfor... | Summary: The paper introduces Generative Trajectory Augmentation (GTA), a data augmentation approach for Offline Reinforcement Learning (RL) that enhances the quality of static datasets by generating high-rewarding and dynamically plausible trajectories using a conditional diffusion model. GTA partially noises original... | Rebuttal 1:
Rebuttal: Thank you for your positive review and valuable feedback!
> **(Weakness 1-1)** Is there a principled argument on why diffusion models would learn the dynamics of the environment well? Especially where the goal is to create transitions outside the dataset. **(Question 1-1)** How do you ensure that... | Rebuttal 1:
Rebuttal: We sincerely thank the review committee for their detailed feedback. We appreciate the recognition of our paper's strengths, highlighted by the reviewers: **Originality** (CNng, ABdH), **Significance** (CNng, WvGZ, ABdH, UiuM), and **Extensive experiments** (CNng, WvGz, ABdH, UiuM). In response to... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents Generative Trajectory Augmentation (GTA) that is aimed at improving offline reinforcement learning (RL). GTA uses a diffusion model conditioned on high returns to generate high-rewarding trajectories. These trajectories are used to augment the static datasets used to train offline RL algorit... | Rebuttal 1:
Rebuttal: Thank you for the insightful review!
> **(Weakness 1, Limitation 3)** The performance of GTA is sensitive to the choice of hyperparameters, which might require extensive tuning for different tasks.
As we illustrated in tables in general response, we demonstrate that GTA outperforms other data a... | null | null | null | null | null | null |
Multi-Winner Reconfiguration | Accept (poster) | Summary: The authors study the multi-winner reconfiguration model in the approval setup. They focus on the following rules: AV, SAV, PAV, and CC. While AV and SAV can be solved in polynomial time, CC and PAV cannot. Therefore, the authors provide a more refined analysis of these latter two methods using the FPT approac... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful feedback. We wish to apologize for forgetting to remove the two resolved to-do notes in the appendix.
Weakness: “I also find one sentence in the ‘Conclusion’ section a bit unusual...”
We believe the experimental section is of interest to anyone... | Summary: The paper studies the multi-winner reconfiguration problem. The goal is to find a transferring path between two winner committees and not change/decrease the score too much in the path. An example of this problem is switching products for streaming providers. This paper study this problem under for voting rule... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful feedback.
Q1: "What is the most important difference between your modeling of multi-winner reconfiguration and the reconfiguration problems in the previous work?".
We’re not quite sure what reconfiguration problems in the previous work you woul... | Summary: The paper studies the multiwinner setting with approval preferences. The authors propose a new framework of multi-winner reconfiguration, where the goal is to select a sequence of committees such that (1) the subsequent pairs of committees do not differ too much from one another, (2) the final committee is bet... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and an interesting question.
Q: “do you have an idea how this setting can be generalized to the rules not based on maximizing committee score?”
There are two ways to extend our model to other voting rules.
- We could just follow the graph rec... | Summary: The paper studies an attractive reconfiguration problem in the context of multi-winner elections. Suppose we have an n-voter m-candidate approval election and work with a committee-scoring voting rule outputting size-k committees. Fix such a rule r (in the paper, this is either approval voting, proportional ap... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed and helpful feedback.
Q1: "Please clarify (*) above. What do you actually assume here?"
We assume that the voting rules are irresolute, as we look into the case where there are multiple optimal committees and we consider score-based voting rul... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity | Accept (poster) | Summary: The paper presents an analysis of the representation complexity, the necessary complexity of a circuit, between different paradigms in reinforcement learning. The authors show, with several reductions to well-known theoretical complexity classes, that MDPs exist in which representing a model is "easy" while re... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your feedback and will address each of your concerns individually.
---
**Q1:** The authors explicitly call the studied phenomenon a hierarchy, but I am uncertain if this is actually true. It seems intuitive that their should be MDP... | Summary: This paper studies three RL paradigms: model-based RL, policy-based RL, and value-based RL from the perspective of representation complexity. The authors demonstrate that representing the model emerges as the easiest task, followed by the optimal policy, with the optimal value exhibiting the highest representa... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your feedback and will address each of your concerns individually.
---
**W1:** From the perspective of MLP, using simple 2 or 3-layer MLPs to calculate approximation error to validate conclusions provides limited insights for moder... | Summary: This paper delves into understanding the inherent representation complexities associated with three different RL categories: model-based RL, policy-based RL, and value-based RL. By studying computational complexity theory and neural networks, MLP, the paper posits a hierarchy in which representing the underlyi... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your feedback and will address each of your concerns individually.
**Q1:** While the theoretical insights are significant, the paper does not extensively explore their direct implications for real-world RL applications.
**A1:** Tha... | Summary: This paper delves into the representation complexity in different RL paradigms. It focuses on the function class needed to represent the underlying model, optimal policy, or optimal value function.
Strengths: 1. The study uses time complexity and circuit complexity to theoretically analyze the representation ... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the positive feedback and appreciation for our work.
**Q1:** Do the findings regarding the representation complexity hierarchy hold consistently across different task settings, or do they vary significantly with various types of tasks?
**Response:** Yes, our f... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for reviewing our paper. We have provided comprehensive responses separately and demonstrate in our general response that our findings can be extended to deep reinforcement learning with **transformer** architectures.
Here are some informal theorems and proof sketc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation | Accept (poster) | Summary: Quantifying the uncertainty of the reward model output can mitigate the issue of reward over-optimization. This paper introduces a lightweight method for quantifying reward uncertainty in RLHF, which can be integrated into existing trained reward models.
Then the authors propose a distributionally robust opt... | Rebuttal 1:
Rebuttal: # Reply to Reviewer fmPe
> [Q1] "Clarification on uncertainty: (1) definition of uncertainty in this paper, epistemic or aleatoric uncertainty? (2) The proposed uncertainty measure has no statistically justification/guarantee and seems to only upper bound the difference between the true reward a... | Summary: This paper aims to tackle the problem of reward model overoptimisation in RLHF. To do this, they propose a new method for quantifying the uncertainty of the reward model on a given input, and penalise the reward of the policy during RLHF training based on this uncertainty estimation. The uncertainty estimation... | Rebuttal 1:
Rebuttal: # Reply to Reviewer NjZD
First, we would like to thank the reviewer for the comprehensive review of our paper and for acknowledging the novelty and benefits of our proposed AdvPO. We have carefully read through your review and added corresponding experiments. We hope the following clarifies any m... | Summary: This paper introduces uncertainty-based methods to tackle the over-optimization issue in RLHF. Drawing inspiration from neural bandits, the authors first propose a lightweight uncertainty estimator based on the final embedding layer. They then formulate the problem as an adversarial optimization task. Empirica... | Rebuttal 1:
Rebuttal: # Reply to Reviewer 2q4b
We would like to thank the reviewer for the thought-provoking questions to improve our manuscript. We would also like to thank the reviewer for acknowledging that our “experimental results demonstrate its effectiveness in mitigating over-optimization issues at the 3B and ... | Summary: This paper studies the reward model overoptimization problem in RLHF. Specifically, they introduce a lightweight approach using adversarial policy optimization, provide corresponding justifications, and extensive empirical study to verify the proposed approach.
Strengths: This paper studies important problems... | Rebuttal 1:
Rebuttal: # Reply to Reviewer JbJ9
First of all, we would like to thank the reviewer for encouraging comments as well as clarification questions. In particular, we would like to thank the reviewer for acknowledging that this paper “studies important problems in RLHF” and that the “effectiveness is clearly ... | Rebuttal 1:
Rebuttal: # Common Response
> [CQ1] "Comparison with ensemble-based approach with reference response incorporated."
Following the reviewers' suggestions, we incorporated a variant of ENS-s, called ENS-ref, that leverages the same set of reference responses as our proposed method AdvPO. More specificall... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space | Accept (spotlight) | Summary: In this paper, the authors introduce Coupled Oscillator Networks (CONs), extending the idea of coRNNs but with the difference that the generalized force is symmetric, enabling the definition of a potential energy expression which can be exploited for energy shaping based control methods. It is proven, that thi... | Rebuttal 1:
Rebuttal: # Response to Reviewer Ys99 (R1)
We thank the Reviewer very much for the kind words, for their interest in our research activities, and for the very insightful comments that they have provided.
Because of space constraints, we respond to the request for inference times and questions about the osc... | Summary: - This paper proposes Coupled Oscillator Networks (CONs); Networks consisting of coupled one-dimensional dampened harmonic oscillators, coupled through a neuron-like connection.
- It is shown that under some constraints, the unforced coupled oscillators have a single, globally stable equilibrium and the forced... | Rebuttal 1:
Rebuttal: # Response to Reviewer W9L3 (R2)
We thank the Reviewer for the careful reading and the encouraging comments.
In the following, we will respond to the questions raised by the reviewer, which also relate to the weaknesses mentioned by the reviewer.
## Application of CON to non-soft robots
> R2: H... | null | null | null | null | Rebuttal 1:
Rebuttal: # Global Rebuttal
## Performance of CON on non-soft-robotic datasets (R1 & R2)
> R1: Thus, a more detailed elaboration on the performance and limitations of CONs would be beneficial as it is evaluated for soft robotics data sets only.
> R2: Have you tried the method on other (non-soft) robots?
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
General Detection-based Text Line Recognition | Accept (poster) | Summary: This manuscript provides a new approach to line recognition of offline handwriting and printed documents with regard to multilingualism.
This method can recognize a line based on its simultaneously recognized features based on transformers.
This approach is interesting for document analysis.
Promising resul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and insightful questions. We appreciate the comments on the organization and clarity of the paper, as well as the recognition of the efficiency of our method in recognizing characters simultaneously. We address the concerns and questions raised below... | Summary: The paper introduces a novel transformer-based character detection for text line recognition. The authors use a diverse set of synthetic data to enable localization part of the detection network to generalize to unseen characters during training. The transformer-based detectors can identify all characters in a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and insightful questions. We appreciate their recognition of the originality of our method. Below, we address each of the reviewer's concerns and provide additional information to clarify our approach.
## Weakness
### W1: Several typos throughout th... | Summary: This paper presents a novel detection-based approach to text line recognition for both printed (OCR) and handwritten text (HTR), covering Latin, Chinese and cipher characters. Traditional detection-based methods have been largely neglected in HTR due to the difficulty of reading characters separately and the h... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We are glad they found the paper highly original, efficient, and adaptable. We address the concerns and questions raised below.
## Weakness
### Lack of new technical contributions.
We respectfully disagree with this assessment, as detailed in the c... | Summary: This paper treats text line recognition as an object detection task and proposes a two-stage training approach based on DINO-DETR. In the first stage, synthetic data with bounding box information is used to predict the bounding boxes and categories of text, due to the absence of character-level annotations for... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and positive feedback on the paper, in particular positive comments on the clarity of the paper and the thorough evaluation against strong baselines. We address the concerns and questions raised below.
## Weakness
### Abalation study is easy.
We eval... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We are pleased that our approach was found to be highly original (R9JMV), novel (RKLkw), efficient (R9JMV, RjpUH), rigorously evaluated (RhupU, RKLkw), and well written (RhupU, RjpUH). We address in the following main concerns, raised by severa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Scale Representation Learning for Protein Fitness Prediction | Accept (poster) | Summary: This work targets at the protein fitness prediction and introduces a sequence-structure-surface multi-modality (aka. multi-scale) self-supervised learning scheme. The results show that S3F outperforms baseline algorithms and achieves SOTA in the ProteinGym benchmark. I like the inspiration and favor S3F's prom... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. We have replied to each of your questions below and included additional analyses that we believe greatly strengthen our submission.
>**C1: Missing of some closely related baselines and relevant work.**
Thank you for pointing out additional ... | Summary: The paper presents a multimodal framework that integrates protein sequence, structure, and surface information together to predict protein fitness. The task of protein fitness prediction is a critical quality assessment of protein embeddings. Protein language models (pLM) are used for sequence representation w... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. We have responded to each of your questions below and included additional analyses that we believe significantly improve our submission.
>**C1: The idea of integrating surface into protein representation is not novel, for example [1], but is... | Summary: This paper proposes a new protein fitness prediction model, the Sequence-Structure-Surface (S3F) model, which integrates protein sequence information from a protein language model embedding, protein structure information processed through a Geometric Vector Perceptron (GVP) module, and protein surface informat... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. We provide detailed responses to your questions below.
>**C1: Lack of analysis for various hyperparameters of the model. It would be nice to see more in-depth experimentation and/or ablation of different parts of the S3F model..**
During m... | Summary: This paper augments a protein language model with two additional features, structure and surface information. The authors show that they can effectively use this information to predict protein function slightly better (though SOTA-level on relevant benchmarks) than sequence-only PLMs.
Strengths: Significance:... | Rebuttal 1:
Rebuttal: Thank you for the very thoughtful comments and suggestions. We address each of your questions below, including several additional analyses which we believe significantly strengthen our submission.
>**C1: The improvement is relatively small compared to models that only use sequence.**
The best fi... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely thank you for the time spent engaging with our paper and really appreciate the thoughtful comments. Based on your feedback, we have conducted additional experiments to further explore the strengths of our proposed approach, and have also clarified all points you had r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation | Accept (poster) | Summary: This paper aims to improve robust fairness in the adversarial distillation setting.
The proposed method adaptively assigns a smaller temperature for hard classes and a larger temperature for easy classes during the adversarial distillation training.
The smaller temperature means stronger supervision intensity ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We have taken great care to address all your concerns as follows:
**Comment1(weakness(1) or question(1))： What's the difference between the proposed method and re-weighting method? Why could the proposed method achieve better results?**
**Answer1:**
The... | Summary: The paper introduces Anti-Bias Soft Label Distillation (ABSLD), a method aimed at improving robust fairness in deep neural networks. This paper identifies the smoothness degree of soft labels as a critical factor influencing this imbalance. ABSLD mitigates the robust fairness problem by adjusting the class-wis... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We have taken great care to address all your concerns as follows:
**Comment1: The choice of hyperparameters (e.g., learning rate, temperature adjustments)**
**Answer1:**
Following your suggestion, we further discuss the hyper-parameter selection of the ... | Summary: This paper explores the issue of robust fairness in deep neural networks (DNNs), particularly focusing on the disparity in robustness between different classes in adversarial training (AT) and adversarial robustness distillation (ARD) methods. The authors propose a novel method called Anti-Bias Soft Label Dist... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We have taken great care to address all your concerns as follows:
**Comment1:In my humble opinion, I do not regard adversarial "fairness" is a critical problem. It is different from other fairness problem (e.g. demographic features) which brings social im... | Summary: This paper discusses the robust fairness problem, which is essential to solve for reducing concerns surrounding class-based security. The paper majorly analyzed the inheritance of robust fairness during adversarial robustness distillation (ARD). It is found that student models only partially inherit robust fai... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We have taken great care to address all your concerns as follows:
**Comment1: The evaluations on common corruptions is missing.**
**Answer1:**
We have chosen two common corruptions: Gaussian noise and color channel transformation. As in Table 8 in the o... | Rebuttal 1:
Rebuttal: This response contains mainly an overall response PDF with details as follows:
Figure 7: Information entropy change curve of teacher soft labels for hard classes and easy classes.
Table 8: Results on two common corruptions, for Gaussian Noise(GN) and Colour Channel Transformations(CCT).
Table 9... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SkipPredict: When to Invest in Predictions for Scheduling | Accept (poster) | Summary: The paper studies the effectiveness of machine-learned predictions for queuing systems, and falls into the area of learning-augmented algorithms / algorithms with predictions.
In particular it studies the M/G/1 queue with poisson arrival and i.i.d. service times with the objective to minimize the average respo... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback and understand your concerns. We believe the perceived "lack of surprise" in our results may stem from our presentation, which we will improve in the revision.
- While it may seem intuitive that the effectiveness of a two-stage algorithm depends on relative c... | Summary: This paper studies a scheduling problem that aims to minimize the expected response time, where jobs arrive online and the algorithm needs to decide the priority of jobs. This paper proposes a new algorithm called skip-prediction. Namely, the algorithm first uses a cheap prediction to partition the whole job s... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation behind our work and its potential impact on practical applications. We appreciate your concern about the clarity of the presentation, particularly the flow from problem definition to algorithm description. We understand this may have obscured the primary co... | Summary: Motivated by recent prediction based scheduling of ML jobs in data centers, the paper considers the problem of prediction cost aware scheduling to optimize mean response time. It considers two cost models - external and server time. The paper proposes a novel algorithm, SkipPredict, that uses a two level hiera... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and the thoughtful suggestions.
- We understand the concern about the relevance of our queuing theoretic approach to NeurIPS. Our approach relies on queuing theory to develop algorithms that efficiently manage resources in systems, opening new ... | Summary: The paper considers the job scheduling in the M/G/1 queueing model when the system has access to predictions regarding job lengths. The paper explicitly considers the cost incurred for obtaining the predictions in such a system. Two models are considered - (i) external cost: obtaining predictions incurs a fixe... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our paper.
We appreciate your feedback on missing references and we agree that these references are relevant to our study. In particular, we acknowledge the relevance of the paper 'Online algorithms with Costly Predictions', along with the related work 'Advice... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We are encouraged they found the paper raises an interesting question (R-oTX6, R-oree) and is the first to consider the important aspect of the cost of prediction in scheduling (R-ckzA, R-kmdF), which is relevant for learning-augmented algorith... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data Attribution for Text-to-Image Models by Unlearning Synthesized Images | Accept (poster) | Summary: The authors propose a new approach for data attribution for text-to-image models that utilizes machine unlearning. They then perform multiple experiments to demonstrate that the proposed method is competitive to other methods.
Strengths: - The paper is generally well-written, clearly structured, and easy to f... | Rebuttal 1:
Rebuttal: Thanks for the feedback and suggestions.
### **Can Textual Inversion be used for evaluation?**
In general response G3, we verify that Textual Inversion is not an ideal choice to check whether a concept is represented by leave-K-out models. | Summary: The paper discusses how we can identify influential examples by unlearning the synthesized images in the text-to-image generation models. Unlearning synthesized images leads to increase in the loss for the most influential training examples for the generation of synthesized images. The paper relies on a well-k... | Rebuttal 1:
Rebuttal: Thanks for the helpful suggestions and comments.
### **Sensitivity to subtle changes in captions**
We clarify that our work focuses on finding training images that influence a generated image, not how changes in captions affect generation quality. From our experience, the model is not very sensiti... | Summary: This paper proposes a novel method for data attribution in text-to-image diffusion models. The key idea is to unlearn a synthesized image by optimizing the model to increase its loss on that image, while using elastic weight consolidation to avoid catastrophic forgetting. The authors then identify influential ... | Rebuttal 1:
Rebuttal: Thanks for the helpful suggestions and feedback.
### **Efficiency comparison with other methods**
Following the reviewer’s suggestion, we compare our method’s efficiency with other methods. To proceed with the efficiency analysis, we provide a brief overview of each type of method.
- **Our unlea... | Summary: Authors propose a method for identifying training images that need to be removed from the training set of a generative model to prevent a single specific “bad” (undesired) output from occurring in its output. Authors propose to directly unlearn the synthetized “bad” image, evaluate how this unlearning changes ... | Rebuttal 1:
Rebuttal: Thanks for the thorough comments and suggestions.
### **Results Presentation**
**Tables.** Thanks for the suggestion. We include a table for baseline comparison in Tab. 1 of the response PDF, which corresponds to results in Figs. 4, 7, and 8 of the main text. We will include this, along with a s... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments. We are happy that reviewers found that our paper motivated the problem well (ZhUS, UBLR), provided an extensive literature review (ZhUS, UBLR), proposed interesting findings (ZhUS, YhLa), and conducted extensive experiments (8wwb, YhLa). We will ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe | Accept (poster) | Summary: The paper fine-tunes Pythia LLMs with different sizes for information retrieval (IR) applications. The paper investigates the effect of different model sizes, FLOPs, fine-tuning methods such as LoRA or bias tuning, and some important hyperparameters (e.g., dimension in LoRA) on loss and downstream applications... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their insightful feedback! Addressing your questions and concerns:
**(W1) The term Compute-Optimal [...] is very misleading because it only considers the training cost and ignores the inference cost [which is important in practice].**
We do agree that our usage of the “... | Summary: This paper proposes a new algorithm to automatically find optimal configurations of model sizes, data quantities, and fine-tuning methods for text embedding models at different computational budget levels by contrastive pretraining. Specifically, the paper analyzes the choices of different configurations and t... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort to assess our work! Below we address each of the concerns.
**(1a) The paper's generalization ability is limited. As mentioned in the limitation, the proposed computation law is only on the Pythia; additional language models might better reflect the generaliz... | Summary: This paper investigates how to effectively train text embedding models from pre-trained decoder-only language models while considering computational budget constraints. The authors explore the influence of model sizes, fine-tuning methods, and computational budgets on the performance of the embedding models. T... | Rebuttal 1:
Rebuttal: We thank the Reviewer for detailed and informative feedback. Below we address each of the concerns.
**(1) [Despite you mention so in lines 51-52] data quantity, a very crucial factor, is not investigated in the paper.**
In this paper, we focused on investigating the compute-bounded setting – the... | Summary: This paper focuses on the efficient contrastive training of text embedding models using pre-trained decoder-only language models. The main contribution is an algorithm that determines the best configuration of model sizes, data amounts, and fine-tuning methods for different computational budgets. Through exten... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort to assess our work! Below we respond to the two points of critique.
**(1) The paper does not introduce a new methodology. The techniques employed, such as scaling laws, extracting representations from transformers, and contrastive fine-tuning, are all well-e... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their effort and constructive feedback. We believe that it will lead to a substantially better version of our paper.
We are pleased that the Reviewers found
* the research question our work addresses relevant and practical [GCkf, 4Bw9, zJJS, xguk],
* our experimental s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Passive-Aggressive Framework for Online Regression with Side Information | Accept (poster) | Summary: The paper introduces a new method called passive-aggressive (PA) online linear regression with side-information regularization to solve the problem of stock prediction and allocation. The paper introduces PA-online regression as a variant to traditional online linear regression in two ways: 1. the parameter up... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on our paper. We appreciate your careful review and the constructive comments you provided. Below, we address your comments and questions in detail.
### Response to Weaknesses:
> The algorithm is not very well motivated. For example, I am not sure why the aut... | Summary: This paper proposes a novel adaptive version of the passive-aggressive (PA) method for online linear regression such that it incorporates additional side information within its optimization objective while adaptively updating the threshold above which the weight parameter in the regressor is updated. This meth... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review of our paper. We are pleased that you found our novel adaptive version of the passive-aggressive (PA) method for online linear regression to be an interesting and significant contribution, particularly in its ability to incorporate additional sid... | Summary: The paper presents a novel Adaptive Passive-Aggressive online regression framework with Side Information (APAS) framework that addresses the challenges faced by the traditional Passive-Aggressive (PA) method, such as selecting optimal thresholds and adapting to complex scenarios with additional metrics. The AP... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback on our paper. We appreciate your positive comments and the constructive suggestions you provided. Below, we address your comments and questions in detail.
### Response to Weaknesses:
> 1. Some mathematical derivations and theoretical exp... | Summary: This paper focuses on the online regression problems for handling large-scale streaming data. Passive-Aggressive (PA) method is a well established method for online regression but existing work struggles with determining optimal thresholds and adapting to complex scenarios with side information. To solve this ... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and the time you took to review our paper. We appreciate your recognition of the sound theoretical framework and the empirical validation of our method. Below, we address your comments and questions in detail.
> 1. Since $\lambda$ is the trade-off parameter ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models | Accept (poster) | Summary: This paper tackles the task of controllable image generation from bounding boxes and text prompts for training object detectors. To fine-tune diffusion models to specific domains effectively and deal with the challenge of concept bleeding, this paper proposes a method called ODGEN. In the proposed method, the ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We provide detailed responses below to resolve your concerns.
$\textbf{1. Ablations for fine-tuning on both cropped foreground objects and entire images:}$
The fine-tuning process is the basis of the training of the following object-wise conditioning module. Th... | Summary: This work is about generating synthetic (annotated) data for object detection with diffusion models that are tuned for a specific image domain. Generating synthetic annotated data can be useful for model training in situations where data is scarce. The authors propose a specific pipeline and new modules to gen... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We provide detailed responses below to resolve your concerns.
$\textbf{1. Comparison with InstanceDiffusion:}$
We didn't include InstanceDiffusion since it consists of an UniFusion module which at least requires bounding box labels and segmentation masks as tra... | Summary: ODGEN uses a diffusion-based generation model to create novel images to train object detectors. Object bounding boxes along with the object's textual description are given as a conditioning for the generation step. With these generated images they can improve the detector's performance.
Strengths: The propose... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We provide detailed responses below to resolve your concerns.
$\textbf{1. Generation cost:}$
Before training and inference, we can generate an offline library of foreground objects to accelerate the process of building image lists. With the offline library, we ... | Summary: This paper proposes the ODGEN method to generate high-quality images conditioned on bounding boxes. ODGEN fine-tunes Stable Diffusion (SD) on domain-specific datasets to enhance image quality in specialist domains, designing a novel strategy to control SD with object-wise text prompts and synthetic visual cond... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We provide detailed responses below to resolve your concerns.
$\textbf{1. Fine-tuning on foreground objects:}$
Both the model fine-tuning and the object-wise conditioning module are designed to enhance foreground object generation but not limited to small objec... | Rebuttal 1:
Rebuttal: We thank all reviewers for your efforts in reviewing this paper and providing so many valuable comments. We are glad that all reviewers acknowledge that the performance of our work is better than prior methods on the box-to-image generation task and most reviewers find our paper well-written and e... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: It proposes a novel data synthesis pipeline, ODGEN, which uses diffusion models to generate high-quality and controllable datasets for object detection.
They first fine-tune the pre-trained diffusion models on both cropped foreground objects and entire images. Next, they control the diffusion model using synth... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We provide detailed responses below to resolve your concerns.
$\textbf{1. Encoder architectures:}$
In this paper, we change the channel number according to the maximum object numbers that can be found in a single image. For datasets like MRI in which most imag... | null | null | null | null | null | null |
Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection | Accept (poster) | Summary: This paper proposes a Ti-FAD framework for the zero-shot temporal action detection (ZSTAD) task, where the goal is to locate and classify unknown action classes. The proposed Ti-FAD features a mutual corss=attention integration module for detection, and leverages text-related sub-action information to mitigate... | Rebuttal 1:
Rebuttal: > **W1,Q1) What exactly is the text used for the text feature extraction? The authors only mention they use a text encoder for text feature extraction (Lines 105-106) but do not mention the textual prompts.**
We simply use "{class name}" as the text prompt without any prefix or contextual text.
W... | Summary: This paper deals with the problem of Zero-Shot Temporal Action Detection (ZSTAD). The authors propose a simple cross-modal ZSTAD baseline with good performance. To address the issue that the cross-modal baseline over-focus on common sub-actions, the paper further proposed a Ti-FAD module to focus on text-relat... | Rebuttal 1:
Rebuttal: > **W1) The two branches in Fig. 2(a) and 3(a) should be cross-connected before cross-attention.**
We apologize for any potential misleading caused by Fig. 2(a) and 3(a).
As shown in Eq. (1) and (6), ${F'}^{(l)}\_{vid}$ and ${F'}^{(l)}\_{txt}$ are used as the inputs for the cross-attention, and t... | Summary: The paper provides a solution for zero-shot temporal action detection(TAD). It mainly addresses distinguishing between similar actions that share common sub-actions. The method introduces a cross-attention in the model and two actionness losses. They benchmark on the two standard TAD datasets, Thumos and Activ... | Rebuttal 1:
Rebuttal: > **W1,W2) ActionFormer detector without cross-modal modification. I think that would give a more fair view of the contributions of the cross-modal part.**
We appreciate your in-depth comment, which allows our cross-modal part to be viewed more fairly.
We conduct a comparative analysis of ActionF... | null | null | Rebuttal 1:
Rebuttal: Dear All Reviewers,
We sincerely appreciate the reviewers' thoughtful feedback on our paper.
We are grateful for the time and effort you have taken to review our manuscript.
All constructive comments allowed us to develop our paper even further.
In this global response, we address three aspects... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dimension-free Private Mean Estimation for Anisotropic Distributions | Accept (poster) | Summary: This paper tackles the problem of DP mean estimation for high-dimensional distributions exhibiting anisotropy, meaning the variances along different directions are highly non-equal. Prior works on this problem were plagued by a "curse of dimensionality", requiring sample complexities at least on the order of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and interest in our results.
- **Lower bound for the case of unknown covariance**: We are not certain that our results are optimal in the unknown covariance case, even assuming a diagonal covariance matrix, and we agree that identifying an intrinsic gap be... | Summary: The paper presents new differentially private algorithms for estimating the mean of high-dimensional data, addressing the inefficiencies of traditional methods that suffer from the "curse of dimensionality." The proposed estimators are tailored for anisotropic subgaussian distributions, where data signals are ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and suggestions to improve the presentation of our paper.
- We agree that having a conclusion would be preferable and since the final manuscript can be a page longer we will certainly add one (and move discussion for future work from the appendix) ... | Summary: This paper considers the problem of DP mean estimation and the focus is on the high dimensional settings where the distribution is nearly low rank (or tr(Sigma)<<d), and the error metric is l_2. Prior work in this setting still requires sqrt(d) samples to achieve any non-trivial error which is sub-optimal. In ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and interest in our results. | Summary: This paper presents a new method for estimating the mean of a subgaussian distribution, such that differential privacy is guaranteed (i.e., the final result does not provide too much identifying information about any individual sample). For the proposed method, in the case of known covariance, the sample com... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and suggestions to improve the presentation of our paper.
- Thank you for the suggestion on the presentation! We will consider including definitions from the preliminaries earlier in the introduction to formalize the intuition we want to convey. Pl... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper studies mean estimation for multivariate Gaussian distributions. It's well known that this problem under differential privacy suffers from a curse of dimensionality- the sample complexity of estimating the mean (in expected $\ell_2^2$ error) scales with $\sqrt{d}$ where $d$ is the dimension. However,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and interest in our results.
- **The case of unknown general covariance**: Our main result for unknown covariance (Theorem 1.3) includes the sum of the square-roots of the diagonal elements of the covariance matrix, $\sum_{i=1}^d \Sigma_{ii}^{½}$. ... | null | null | null | null | null | null |
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning | Accept (poster) | Summary: The paper addresses the challenge of identifying individualized state-transition processes in reinforcement learning (RL) when individual-specific factors are latent. The authors propose a novel framework, the Individualized Markov Decision Processes (iMDPs), to incorporate these latent factors into the state-... | Rebuttal 1:
Rebuttal: **Q1: The method might be challenging to generalize to all types of RL problems, especially those with instantaneous causal influences within states.**
**A1:** Thank you for pointing out this scenario. It is indeed possible that there are instantaneous causal influences within states in the consi... | Summary: The authors consider a problem where the underlying dynamical process is an MDP but with a time-invariant latent factor. The authors provide examples of such problems in the real-world. To solve these problems, the authors propose a new mathematical framework called Individualized Markov Decision Processes (iM... | Rebuttal 1:
Rebuttal: **A1:** Thanks for the helpful questions. Here is a brief summary of the differences. We also empirically compared our method with meta-learning and transfer-learning techniques and reported results in REBUTTAL.pdf.
- **Meta-learning** trains the model on a variety of tasks so that it can efficie... | Summary: The authors of this paper establish the identifiability of latent state-transition processes in reinforcement learning (RL) and propose a practical method for learning these processes from observed state-action trajectories. The focus is on personalized reinforcement learning (RL), where different individuals ... | Rebuttal 1:
Rebuttal: **Q1: Broader experimental scope:** Thank you very much for your suggestion. In the updated manuscript, we validated our algorithm in the AhnChemoEnv in DTRGym [1] and inventory management tasks [2]. AhnChemoEnv is designed to simulate cancer treatment through chemotherapy, allowing realistic mode... | Summary: The paper titled "Identifying Latent State-Transition Processes for Individualized Reinforcement Learning" addresses the challenge of optimizing individualized reinforcement learning (RL) policies by focusing on latent individual-specific factors that influence state transitions. This is particularly significa... | Rebuttal 1:
Rebuttal: **Q1: Discussion of time-varying latent factors:** Thank you for pointing out this interesting scenario. It is indeed possible for the latent individual-specific factor to be time-variant in the considered problem. We believe our framework can be extended to handle time-varying cases, although est... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time dedicated to reviewing the paper and valuable comments. We have revised the manuscript accordingly as described below. Concerns about the experiments are addressed collectively in the REBUTTAL.pdf. Your further feedback, if any, would be appreciated.
Pdf: /pdf... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages | Accept (poster) | Summary: Recent advances in large language models (LLMs) for code applications have demonstrated remarkable zero-shot fluency and instruction following on challenging code-related tasks, ranging from test case generation to self-repair. However, the authors note that these models struggle to compose syntactically valid... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Please see below for our answers to the specific questions posed.
**6oM2-Q1: Details of the semantic score?**
The semantic scores were manually judged by one professor with experience teaching formal methods (undergraduate and graduate level c... | Summary: In this paper, the authors present a novel approach called SPEAC (Synthetic Programming Elicitation and Compilation) to enable large language models (LLMs) to generate syntactically valid code for very low-resource programming languages (VLPLs). The approach involves creating a hallucinated library within a hi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Please see below for our answers to the specific questions posed.
**9mNj-Q1: Reorganization of the paper?**
We are happy to make all the suggested organization changes under the questions header. Specifically, we are happy to (1) reorganize th... | Summary: The paper presents a framework to generate programs given natural language and targeting very low-resource programming languages. They first choose a language well-represented in the training data (in this case Python), and then make the LLM generate in a subset of that language, and then compile the generated... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Please see below for our answers to the specific questions posed.
**9eZc-Q1: Would few shot prompting work better?**
Adding more examples improves both the baseline’s performance and Eudoxus’ performance (which currently uses no examples, see ... | Summary: The paper addresses the challenges of LLMs in generating code for very low-resource programming languages (VLPLs), which are not represented in their pre-training data. Traditional methods to enhance LLM efficacy in this domain include prompting, constrained decoding, and fine-tuning. The authors propose a nov... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Please see below for our answers to the question posed.
**tGyy-Q1: How is the existence of C determined?**
Determining the existence of C is a design process. It is possible that some target languages are so esoteric that no subset of a high-r... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators | Accept (spotlight) | Summary: This paper proposes a new position encoding technique CPG-PE for SNNs, which is inspired by the central pattern generator in the human brain and improves the ability of SNNs to process sequence data. Experimental results show that CPG-PE outperforms traditional SNNs in multiple fields such as time series predi... | Rebuttal 1:
Rebuttal: We are cheerful that our contribution is well recognized. And thanks for your valuable suggestions that truly enhance the quality of our paper and make it more understandable for the wider community.
Responses to your concerns are presented as follows:
### **1.ImageNet Dataset (W1, Q2)**
Thank... | Summary: This paper introduces a novel positional encoding method for SNNs called CPG-PE, inspired by central pattern generators (CPGs) in biological neural systems. The authors demonstrate both theoretically and empirically that CPG-PE can effectively capture positional information in sequential data while maintaining... | Rebuttal 1:
Rebuttal: We appreciate your comments and suggestions that enhanced the quality of our paper. Responses to your concerns and questions are hereby presented:
### **1.Typographical error (W1)**
Apologies for the confusion caused by this typo error. In line 158, the $X$ is the input spike matrix and it bel... | Summary: The lack of an effective and hardware-efficient spike-frm position encoding strategy in SNNs has been a consistent motivation for this study.
Drawing inspiration from the central pattern generators (CPGs) in the human brain, which produce rhythmic patterned outputs without requiring rhythmic inputs, this work... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions, which are valuable for enhancing our paper. We are pleased that our contributions are well recognized.
Responses to your concerns and questions are hereby presented:
### **1.Ablation experiments on parameters (W1).**
Thanks for your suggestion.
(1... | Summary: This paper introduces central pattern generators (CPGs) from neuroscience into the SNN framework as a novel method for position encoding. Through mathematical derivation, it is proven that the existing abstract PE methods in transformers are actually a particular solution for a specific type of CPG. The effect... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions, which are valuable for enhancing our paper. The follows are responses to your individual concerns:
### **1.Why is positional encoding important to SNNs (W1)?**
Currently, SNNs have been applied to a variety of tasks beyond image processing, including tim... | Rebuttal 1:
Rebuttal: # Global Response
We express our gratitude to all the reviewers for the valuable insights and acknowledging our contributions to advance the sequential modeling ability of SNNs through central pattern generators. We are encouraged by the comments highlighting the strengths of our work:
- Clear m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QGFN: Controllable Greediness with Action Values | Accept (poster) | Summary: The paper "QGFN: Controllable Greediness with Action Values" introduces a novel approach to enhance Generative Flow Networks (GFNs) by incorporating action-value estimates (Q-values) to control the greediness of sampling policies. This method, called QGFN, includes three variants—p-greedy, p-quantile, and p-of... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the time and effort you put into reviewing our work. Below, we address your questions and comments in detail. We hope this clarifies our approach and findings.
> QGFN variants matter. This paper does not provide a method to select different QGFN variant... | Summary: The paper focuses on improving high-reward sample collection, i.e., exploitation, in training GFlowNets. The motivation stems from the fact that the flow may pursue states that lead to many low-reward states rather than focusing on states that lead to high-reward states. To this end, the authors propose incorp... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper. We appreciate your questions and have provided detailed answers below.
> Are there failure scenarios where QGFN shows poor performance compared to GFN? (opposite of Figure 1)
We haven't encountered such scenarios in practice other than $p$ being ... | Summary: The paper proposes jointly learning a $Q$ function and a policy network $P_F$ to improve the search for high-valued states when training GFlowNets. To achieve this, the authors develop three sampling strategies for composing $Q$ and $P_F$, $p$-greedy, $p$-of-max, and $p$-quantile, and show that the resulting a... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the time and effort you put into reviewing our work.
Our work aims to provide an empirical analysis of the method we introduce, a novel idea that can be applied to any GFN. While we understand the reviewer's emphasis on theoretical guarantees, it is wort... | null | null | Rebuttal 1:
Rebuttal: We appreciate all feedback from the reviewers. To provide more analysis on QGFN, we have conducted an analysis using a bandit example. We also use some derivations to illustrate the general case. We hope these examples illustrate the behavior of QGFN. We will include this analysis in our revised m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing | Accept (poster) | Summary: This paper proposes a unified vision LLM for various downstream visual tasks, including understanding, generating, segmenting, and editing. The proposed framework addresses the problems with respect to images, videos, text, and human interactions. The authors also propose a hybrid method to integrate discrete ... | Rebuttal 1:
Rebuttal: We take your opinion “**There is no obvious technical weaknesses from my side**” as the greatest acknowledgment of the value of our work. Thank you very much! Your affirmation provides us with great motivation to further improve our research.
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**Q1: The authors put "pixel-level" in a highly... | Summary: This paper introduces a universal pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing both static images and dynamic videos. Technically, the authors propose using the LLM as the core brain, incorporating different encoders for images, videos, and pixel-level re... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and providing insightful comments that significantly enhance the quality of our paper. Here are our responses to your concerns and questions, and we hope to gain your further support.
---
**Q1: The authors may need to clarify the advantages of their work over e... | Summary: This paper proposed VITRON, a multimodal generalist that supports a wide range of vision tasks, treating images and videos as a unified entity.
It combines discrete textual instructions with continuous signal embeddings for effective function invocation.
The model is trained on fine-grained spatiotemporal visi... | Rebuttal 1:
Rebuttal: We feel honored for your many constructive comments, and appreciate you went through our paper so carefully. Below we try to address your concerns or misunderstandings. If you find our response effective, please consider generously increasing rating.
----
**Q1: The system is complicated, but pap... | Summary: This paper proposes a universal vision LLM, named Vitron, for comprehensive understanding, generating, segmenting, and editing of both images and videos. The model incorporates SOTA visual specialists as the backend to support various visual tasks. A cross-task synergy module is advised to learn to maximize th... | Rebuttal 1:
Rebuttal: We would like to thank you for your time in writing comments, especially for your strong recognition of our paper. Below we provide our response.
----
**Q1. Some SOTA methods are not included in the experiments. For instance, CM3leon [1] 7B achieves 4.88 FID on COCO-Captions. MAGVIT-v2 [2] reach... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
---
Dear Reviewers,
We sincerely appreciate the detailed and constructive comments you have provided on our work. We are fully committed to integrating your suggestions into our revision process. We feel very encouraged that the reviewers find our work novel,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Solving Sparse \& High-Dimensional-Output Regression via Compression | Accept (poster) | Summary: The paper proposes the Sparse & High-dimensional-Output REgression (SHORE) model to address challenges in Multi-Output Regression (MOR). SHORE introduces a two-stage framework to handle high-dimensional outputs efficiently. Theoretical analysis shows that the framework maintains training and prediction loss wh... | Rebuttal 1:
Rebuttal: ## Response
We sincerely thank the reviewer for their positive and encouraging feedback! Table 1 mentioned below can be found in the uploaded pdf file in 'global' author rebuttal.
**[Response to Comparison with SCO]**
Thank you for highlighting this aspect! If we understand correctly, the class... | Summary: In this paper, the author has proposed a new method for solving the high-dimensional output regression (MOR) problem through compression. The authors introduce a two-stage framework that incorporates output compression to achieve computational efficiency while maintaining accuracy. Theoretical results demonstr... | Rebuttal 1:
Rebuttal: ## Response
We sincerely thank the reviewer for their positive assessment of the paper's clarity, methodology, and theoretical contributions. Figure1 mentioned below can be found in the uploaded pdf file in 'global' author rebuttal.
**[Response to Comparison with Sparse Regression and related ref... | Summary: This manuscript proposes a new approach the authors refer to as Sparse & High-dimensional-Output REgression (SHORE) to tackle linear regression problems promoting sparse predictions. A major component of the author's approach is to reduce the computational cost with a random normal matrix compressing the signa... | Rebuttal 1:
Rebuttal: ## Response
Thank you for your positive feedback! Figure1 mentioned below can be found in the uploaded pdf file in 'global' author rebuttal.
**[Response to FISTA or ADMM]** Thank you for your comments. We have conducted some preliminary numerical experiments concerning comparison with FISTA, as ... | null | null | Rebuttal 1:
Rebuttal: ## Responses to All Reviewers & Area Chairs
We would like to thank the reviewers for their constructive and high-quality feedback. The manuscript has been revised based on the comments given in three reviewers' reports.
### Comparisons & Differences
We first highlight the differences between ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Chain of Thoughtlessness? An Analysis of CoT in Planning | Accept (poster) | Summary: This paper evaluates the effectiveness of CoT prompting on reasoning problems within the Blocksworld domain, revealing that performance gains from CoT are limited and heavily reliant on problem-specific prompts, with diminishing returns as problem complexity increases.
Strengths: - The study employs a well-de... | Rebuttal 1:
Rebuttal: Thank you for the thorough review.
Responses to questions:
1. The lexicographic stacking problem is a special case. For a given number of blocks, there is only one problem which requires stacking them all in lexicographic order, as the syntactic stringency of the order fully determines the probl... | Summary: This paper evaluates the efficacy of Chain of Thought (CoT) prompting in improving the reasoning capabilities of large language models (LLMs) in planning tasks. The authors analyze CoT's performance in the Blocksworld domain, a classical planning problem, and extend their findings to other synthetic tasks. The... | Rebuttal 1:
Rebuttal: Respectfully, we found this review highly inconsistent and in places incoherent. We wonder if there was some transmission/saving error when the reviewer posted it. Nevertheless, let us respond to the review as it was posted. We hope our clarifications persuade you to rethink your overall evaluatio... | Summary: The paper conducts a systematic study of claims that chain-of-thought (CoT) prompting unlocks reasoning abilities in LLMs. In particular, the paper evaluates the ability of LLMs to a) learn a simple algorithm from demonstrations annotated with reasoning steps ("thoughts") provided as part of the input prompt, ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
The progression proof CoT prompt does include a meta-prompt, but we failed to include it in the original draft. Here it is:
> The plan correctness is defined in terms of states resulting from executing the actions in the plan. An action is executable in a stat... | Summary: The paper aims to show that Chain of Thought style prompting does not result in generalisation of reasoning, instead relying on pattern matching to improve performance. It argues that if CoT results in language models learning algorithms in context, then prompts describing general procedures should result in s... | Rebuttal 1:
Rebuttal: Thank you for the thorough review.
Analyzing the data using only the tables is somewhat misleading. Figures 2 and 3, as well as the appendix figure A.1.1 show more clearly that the bulk of the improvement for all prompts is on the few-block problems, whereas if the procedure shown in these CoTs w... | Rebuttal 1:
Rebuttal: In addition to the reviewer-specific rebuttals, we provide this global response to a couple of common ways the reviewers misunderstood the claims/contributions of our paper.
**Central claim of our paper:**
The central claim of our paper is not that CoT can’t improve raw accuracy on static benchm... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearly Lossless Adaptive Bit Switching | Reject | Summary: The paper addresses challenges in model quantization for deep neural networks (DNNs), focusing on optimizing quantization-aware training (QAT) across multiple bit-widths with weight-sharing. To this end, this paper introduces a novel quantization method that exploits the highest integer precision to achieve ne... | Rebuttal 1:
Rebuttal: We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We are delighted with the identification of the novelty and effectiveness of the proposed method. We have carefully addressed a... | Summary: The paper proposes a QAT scheme to jointly optimize a single model with different precisions. The authors apply their scheme on various CNN-based models on CIFAR-10 and ImageNet datasets.
Strengths: 1. The paper is well-written
2. The ablation study is strong in my opinion and they evaluate various aspects of... | Rebuttal 1:
Rebuttal: We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have carefully addressed all comments, and below are our responses:
**W1**: The study should be done on larger models (LLMs... | Summary: This paper discusses advanced methods in multi-bit model quantization. Specifically, this paper proposes a method for one-shot joint training of multiple precisions. To this end, the authors introduce a double-rounding quantizer that leverages the highest integer precision to achieve nearly lossless bit-switch... | Rebuttal 1:
Rebuttal: We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We are delighted with the identification of the novelty and effectiveness of the proposed method. We have carefully addressed a... | Summary: The authors propose a bit-switching quantization method using Double Rounding, which applies rounding twice to achieve nearly lossless switching without storing a full-precision model. They also introduce Adaptive Learning Rate Scaling (ALRS) to adjust learning rates dynamically across precisions, ensuring con... | Rebuttal 1:
Rebuttal: We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which have significantly improved the presentation of our manuscript. We have carefully addressed all comments, and below are our responses:
**W1**: using a pretty straightforward idea of double r... | Rebuttal 1:
Rebuttal: We extend our sincerest gratitude to the AC and reviewers for their constructive comments, which greatly improve this work!
Pdf: /pdf/0e169581017f64d687bfabc48e0c1386332dca8f.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers | Accept (poster) | Summary: This paper presents a method using sequence-to-sequence transformers to find Lyapunov functions for dynamical systems, mainly polynomial systems. Even though it illustrates some new advances in LLMs in solving mathematical problems, the reviewer doubts the mechanism and the necessity of using large models such... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the paper.
**there are no equilibrium points in the three-body problem**
We agree, we mention this problem to emphasize the importance of stability in general. This said, there are periodic orbits which, up to a change of variables, are equilibrium points of a ... | Summary: In this paper, the authors propose a new method of generating synthetic training samples from random solutions, i.e. Lyapunov functions for dynamical systems in the forms of ordinary differential equations. They demonstrate that the sequence-to-sequence transformer training on such data can produce Lyapunov fu... | Rebuttal 1:
Rebuttal: Thank you for your interest and your in-depth reading of the paper.
**The novelty of the data generating methods, those described in Sec. 5, is not clearly stated and explained. Currently, it reads as if the results are obtained accidentally**
We agree, thank you for raising this. We provide a r... | Summary: This paper trains a transformer to predict Lapunov functions for both polynomial and nonpolynompial dynamical systems. They generate a dataset combining "forward-generated" problems — solutions discovered from existing solvers on randomly generated problems — with "backward-generated" problems — problems gener... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. Here are a few answers and clarifications.
**The algorithmic approach is not new**
We agree. See section A of the authors’ response for a clarification of our novel contributions. Summarizing,
* we demonstrate the applicability of backward generation to an... | Summary: This study addresses the problem of designing the Lyapunov function of dynamical systems via learning. The existence of the Lyapunov function is a sufficient condition of the dynamical system's stability. However, its design is not established except for systems with sum-of-square polynomials. The Authors prop... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
**Backward generation. it would be better to elaborate on the method a little more.**
We agree. See section B of the author rebuttal for a better presentation of our methods, explaining the motivation of the different steps. We will update the paper in thi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments an suggestions, which helped improve the paper. Here, we cover questions asked by several reviewers, and present the new experiments we ran for this rebuttal. The results are in the appended PDF file.
**A- On novelty, and comparison with Lample and Charto... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization | Accept (poster) | Summary: The paper proposes a novel algorithm for dynamic Bayesian optimization (DBO). DBO defers from traditional BayesOpt as the black-box function to be optimized is changing in time, and the goal of the optimization procedure is to keep track of the optimum across a continuous time index. The continuous time compon... | Rebuttal 1:
Rebuttal: Dear Reviewer NVX3,
Thank you for the detailed review. We are discussing below the weaknesses and questions you have raised. Also, please make sure to read the global response as we discuss some of your questions there.
**Lack of discussion of sparse Gaussian processes.**
Thank you for pointing... | Summary: The authors propose W-DBO, a statistical distance-based criterion for removing "stale" observations in the dynamic BO setting. Observations are removed based on their impact on the GP globally, as measured by an approximate integrated Wasserstein distance, for which the authors prove the approximation quality.... | Rebuttal 1:
Rebuttal: Dear Reviewer iNzn,
Thank you for the detailed review. We are discussing below the weaknesses and questions you have raised. Also, please make sure to read the global response as we discuss some of your questions there.
**On the usage of an ARD kernel.**
We make no mention of setting an ARD ker... | Summary: The paper proposes a new algorithm for dynamic Bayesian optimization (DBO). To develop this new algorithm, the authors first derive a Wasserstein distance-based criterion that is a way of measuring how relevant a given collected data point is during optimization. Since dynamic functions change over time, each ... | Rebuttal 1:
Rebuttal: Dear Reviewer JgwK,
Thank you for the detailed review. We are discussing below the weaknesses and questions you have raised. Also, please make sure to read the global response as we discuss some of your questions there.
**Direct comparison against other simpler (or even ad-hoc) methods for relev... | Summary: This paper addresses the challenge of optimizing a time-varying black-box function using GP-based Dynamic Bayesian Optimization (DBO). Unlike traditional Bayesian Optimization (BO), DBO seeks to handle dynamic functions where the optimum changes over time by incorporating time in the GP model covariance functi... | Rebuttal 1:
Rebuttal: Dear Reviewer mGHg,
Thank you for the detailed review. We are discussing below the weaknesses and questions you have raised. Also, please make sure to read the global response as we discuss some of your questions there.
**The scalability of W-DBO (e.g., in high-dimensional spaces and/or with lar... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you all for your detailed reviews. In this global response, we address some weaknesses and questions that were raised in more than one review. We also discuss the additional figures shown in the PDF uploaded with the global rebuttal.
**This is a high-throughput problem, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking 3D Convolution in $\ell_p$-norm Space | Accept (spotlight) | Summary: The paper proposes using the \( \ell_p \)-norm, specifically the \( \ell_1 \)-norm, to replace the classic squared \( \ell_2 \)-norm convolution in 3D tasks. The \( \ell_1 \)-norm kernel function relies on addition, reducing computational cost. Initial gradient implementation revealed insufficient gradient val... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are very happy to receive such an enthusiastic review!
# For Weaknesses:
***W1: Minor typos error***
Thanks for your advice! We have carefully rechecked all the writing issues and will revise them in the next version, including consistent capitalizati... | Summary: The paper proposes for using the $\ell_{p}$ norm in 3D convolution as a substitute for the inner product. Out of different choices, the authors pick the $\ell_{1}$-norm because it's faster and uses less energy than the inner product. This is because the $\ell_{1}$-norm relies on addition, which is simpler comp... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Answers to some of your questions are as follows.
# For Weaknesses
***W1: About baselines***
For the Classification and Segmentation task, we use PointNet and PointNet++ as the baselines. PointNet processes point clouds by embedding each point independen... | Summary: This paper addresses the challenge of enhancing the representational capacity of traditional convolution methods. To tackle this issue, the authors introduce a novel convolution approach based on $\ell_p$-norm and offer customized optimization strategies to expedite the training process. Extensive theoretical ... | Rebuttal 1:
Rebuttal: Thanks for the kind and constructive comments! Please see below for responses.
# For Weaknesses
***W1: About additional details***
We will add more details from the appendix in the revised version.
***W2: About the gap between theoretical guarantees and the empirical evidence***
Due to the spac... | Summary: This paper introduces a new convolution method based on the \( L_p \)-norm. The authors provide a theoretical foundation by proving the universal approximation theorem for \( L_p \)-norm networks and analyzing the robustness and feasibility of \( L_p \)-norms in 3D tasks. Several key findings are highlighted i... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments that help us improve our work. The following is a careful response and explanation about the weaknesses and questions.
# For Weaknesses
***W1: About Noise distribution.***
In this work, we focused on Gaussian noise because it is the most prevalent type of noise... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging | Accept (poster) | Summary: The authors propose to use a weighted average of previous layer outputs as an input for the successive layers. The weights are learned parameters and can also take negative values. The weighting module can be coarse: it is enough to insert it only in some layers, and it is enough to attend to a subset of previ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work.
1. **On the KV cache comment (line 133).** We thank the reviewer for bringing this to our attention and agree the statement line 133 is incomplete. As the reviewer rightfully pointed out, while we do not need to store the past layers’ ... | Summary: The work introduces DenseFormer, a variant of Transformer architecture using special connections between blocks. Each transformer block in DenseFormer may look at a weighted average of all previous blocks' outputs (instead of the simple output of the previous block). Authors run extensive experiments showing t... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your careful consideration of our work and your valuable feedback. We provide the following comments to further clarify our contributions:
1. **On the non-standard model shapes.** As you have correctly pointed out, using DenseFormer “alleviates the information capac... | Summary: The paper introduced DenseFormer -- a simple and effective architecture that boost transformer language model's performance by adding trainable residual connections to all the previous layers. The paper discusses intuitions behind the architecture (that it enhances information flow between earlier and latter l... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your consideration of our work and your valuable feedback. We make the following comments to provide further clarifications:
1. We completely agree on the ambiguity regarding the negligible memory overhead claim and thank you for bringing it to our attention. We poin... | Summary: The paper proposes to construct current transformer block input by weighted average of all inputs from previous transformer blocks. The weights are static and learned during training process. To reduce computation complexity, the method comes with a dialated version controlled by modulo and division relations.... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. We hereby answer the reviewer’s questions in order:
1. The number of additional parameters required by DenseFormer increases quadratically with the model’s depth. We mentioned 100B parameter models as those large scale models are typic... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fairness and Efficiency in Online Class Matching | Accept (poster) | Summary: This paper studies the online bipartite matching problem where the agents (offline nodes) are partitioned into multiple classes.
Upon the arrival of an item (online node), it needs to be immediately matched to a free agent or discarded, and the goal is to optimize fairness among different classes and efficien... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words with respect to our results and their presentation. We also appreciate the noted typos which have since been corrected.
For the noted weakness on comparisons to other price of fairness (PoF) results: the first set of results on the PoF refers back to Ber... | Summary: The paper studies the online bipartite matching problem from a fairness perspective. One set of the bipartition (agents) are known a priori, and the other set (items) arrive online, wherein edges to that item are revealed and the algorithm must (possibly) select an edge to include in the matching before the ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive feedback and kind words regarding the problem setting and approximate results (both upper and lower bound).
For the first noted weakness on the algorithmic contribution, we refer the reviewer to our general comments made to Reviewer gpQ6 on the appro... | Summary: The paper addresses the online bipartite matching problem with a focus on class fairness, proposing the first randomized non-wasteful algorithm that balances class envy-freeness, class proportionality, and utilitarian social welfare. It introduces the concept of the "price of fairness," highlighting the trade-... | Rebuttal 1:
Rebuttal: For an expansion on motivating examples of this problem setting: the study of online matching under fairness constraints is motivated by the challenges posed by the advent of Internet economics and new marketplaces, which demand solutions that are both transparent and fair, as highlighted in Mouli... | Summary: This paper studies the online bipartite matching problem with class fairness guarantee. In this problem, the offline vertices are divided into $k$ classes, and the challenge is to match each online vertex to an unsaturated offline vertex while providing guarantees on various fairness metrics (e.g., class envy-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work, highlighting the novelty of our analysis and impossibility constructions, as well as constructive feedback. We also appreciate the identification of a typo which has since been rectified. We here address the noted weaknesses and related ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Analysis of Elo Rating Systems via Markov Chains | Accept (poster) | Summary: The paper proves results about the Elo rating system, as used for instance in chess, using tools from probability and Markov chain theory. The Bradley-Terry-Luce model assumes a true ranking exists and that the Elo ranking evolves via a Markov chain when two players compete against each other. Although the sta... | Rebuttal 1:
Rebuttal: We thank the reviewer greatly for their extremely thorough review of our paper. We are pleased they found the results of interest and importance, as well as finding the presentation well-written. No general points were raised, but many (very helpful) minor points were. We just wanted to highlight ... | Summary: This paper provides convergence guarantees for the time-averages of a natural dynamics (Elo) modelized after the Bradley-Terry-Luce model of elo. If the system has $n$ players and $t$ is the number of time steps, the error is shown to be $(e^{4M}/\lambda_q)(n^{-\alpha} + \log(n) \sqrt{1/t})$ with probability $... | Rebuttal 1:
Rebuttal: We appreciate the reviewer has found our results interesting. We thank the reviewer for their comments about presentation and typography: we will definitely take them into consideration when preparing the revised version of our manuscript.
We now reply to their general questions.
1. We have not ... | Summary: The authors analyze Elo ratings under the BTL assumption and show that the Elo update system converges to the true ratings of the player with high probability. They do this analysis for an online setting compared to other work that has analyzed this for the offline setting (where a pool of data is collected). ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and in particular for highlighting that our work provides a thorough analysis of an algorithm with broad interest.
About the weakness highlighted, first, we believe that before improving upon a technique, it is important to understand that technique well. ... | Summary: The paper presents a novel theoretical analysis of the Elo rating system, a well-established method for ranking player skills in online gaming and sports contexts, particularly in chess. The authors analyze the Elo system under the Bradley-Terry-Luce (BTL) model, employing techniques from Markov chain theory t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and in particular for highlighting our paper offers "a thorough theoretical analysis of the Elo system". We first address the weaknesses highlighted.
1. We will try and add further explanations for the various quantities used in the paper in the camera-rea... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models | Accept (poster) | Summary: The paper studies spectral algorithms for recovery in the stochastic block model (SBM). Specifically, they consider a nonhomogeneous SBM where there are two communities $P_1, P_2$ and edges inside the communities occur (independently) with probability between $p$ and $\bar{p}$ while edges between the communit... | Rebuttal 1:
Rebuttal: Thank you for the helpful feedback! We address each concern here.
> The authors require an upper bound on the maximum edge probability that is comparable to $p, q$ -- this type of assumption isn't necessary for other algorithms such as SDPs.
When the degrees are sufficiently high (i.e., $p, q$ ... | Summary: The authors initially investigate the two-community spectral clustering algorithms applied to the 'Nonhomogeneous Symmetric Stochastic Block Model' (NSSBM). This model is characterized as a more encompassing semi-random framework, permitting a more lenient variation in the selection of in-cluster edge probabil... | Rebuttal 1:
Rebuttal: Thank you for the helpful feedback! We address each concern here.
> Adding the edge only inside the clusters or promoting the in-cluster edge probability will only increase the signal-noise ratio (SNR) for the SBM model(Abbe 2018).
This is typically the case for semi-random corruptions-- the SNR... | Summary: This paper considers several semirandom variants of the SBM, and investigates whether spectral algorithms achieve exact recovery. The authors give guarantees for the performance of spectral clustering from the unnormalized Laplacian in both a nonhomogenous model, and a model in which the adversary has control ... | Rebuttal 1:
Rebuttal: Thank you for the supportive review!
> Could you please give an indication of which parts of your analysis could be tightened to yield sharp constants?
In the current analysis, the Lemmas B.5 and B.6 have constants that we have not optimized, for the sake of clarity in exposition. While it is po... | Summary: Spectral clustering has been a popular unsupervised method among mathematical statisticians and theoretical ML researchers. The first analysis of spectral clustering using perturbation analysis (Ng et al., 2001) appeared more than 20 years ago, which, of course, has a lot of limitations: Sparsity is not accoun... | Rebuttal 1:
Rebuttal: Thank you for the helpful feedback and questions! We address each concern here.
> One of the biggest problems with keeping the number of clusters constant and then providing results for "n" large is that in most practical situations, the number of clusters grows with n. In that sense of weak cons... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their positive comments and their support of our paper. We are excited to hear the feedback and have responded to questions/provided some clarifications in comments directly responding to each review. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment | Accept (poster) | Summary: During training of Diffusion Models, images are corrupted with standard Gaussian noise and the Diffusion Model is tasked to denoise the corrupted images. The Gaussian noise used during training is often sampled independently of the clean image.
This submission presents the following "Immiscible Diffusion" met... | Rebuttal 1:
Rebuttal: We thank you for some of the constructive feedback on our work. We would like to present following updates:
For W1, we have revised our mathematical proof part in rebuttal to reviewer-neka - W1, taking into consideration your constructive suggestions.
For W1, we compute the average of the images... | Summary: This paper is motivated by the miscible phenomenon of physics and transfers it into the diffusion training process, which is very interesting. The author proposes a noise-assigned strategy based on distance, which is simple but powerful for faster diffusion model training. The experiment and the theoretical an... | Rebuttal 1:
Rebuttal: We sincerely thank you for your acknowledgement of our work. Besides, we also hope to emphasize that our method is one simple implementation to address the miscibility problem demonstrated to be important, into which we hope to inspire more work to address. We hope this work can benefit the diffus... | Summary: The authors propose an approach to mitigate the random correspondence of noise-data mapping in vanilla diffusion models. They first assign target noise by minimizing the total image-noise pair distance within a mini-batch, and then diffuse the data into noise. Experimental results seem to demonstrate the poten... | Rebuttal 1:
Rebuttal: Thanks for your acknowledgement on the potential of our method. Besides, we also hope to emphasize that our method is one implementation to address the miscibility problem, into which we hope to inspire more work to work on. Below we will address your concerns:
**W1 - Mathematical Proof**
We re-... | Summary: This paper shows that the current diffusion training strategy diffuses each image into the entire noise space, making it difficult to optimize the model and thus slow to converge. Inspired by the fact that miscibility can be changed according to various intermolecular forces in physics, this paper proposes Imm... | Rebuttal 1:
Rebuttal: We are really excited to hear that you agree with our strengths listed above, and we sincerely hope that the proposed perspective of image-noise matching can be realized and further discovered by our diffusion society. We hope to address your concerns below:
**W1-Practicality**
We thank you for ... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We are grateful for your time and effort in reviewing our work. We are glad to see that our work is recognized as reasonable (R-h6mk), novel and interesting (R-nEKA, R-y1v3), simple and effective (R-h6mk, R-nEKA, R-y1v3). We are also encouraged to hear that our experiments... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Saturation Effects of Spectral Algorithms in Large Dimensions | Accept (poster) | Summary: This paper concerns the convergence rate of spectral methods, particularly kernel ridge regression (KRR) and kernel gradient flow (KGF), in large-dimensional settings where the sample size $n$ is of the same magnitude as a power $\gamma$ of the input dimension $ d $, i.e., $ n \asymp d^\gamma $. It reveals a n... | Rebuttal 1:
Rebuttal: We sincerely thank you for taking the time to read our paper and for providing valuable feedback. We are pleased to see that you not only accurately described the contributions of our work but also gave it high praise. We would like to address the questions and comments you raised regarding possib... | Summary: In a large-dimension setting, i.e., the dimension $d$ of the input grows polynomially with respect to the sample size $n$, this manuscript rigorously proves upper and lower bounds for spectral algorithms and shows the dependence on the qualification and the interpolation index. Consequently, the manuscript pro... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable comments. Below, we address your concerns and questions in detail.
Concern 1: Please let us clarify our novelties and contributions below.
- Although the saturation effect has been observed for kernel ridge regression in a large-dimensional setting... | Summary: ### Summary:
The authors study the saturation of spectral algorithms (KRR & GF) in high-dimensions where $n,d$ are both large, meaning that when KRR can't achieve information theoretic lower bounds with over smooth the regression functions while kernel Gradient Flow (GF) can.
Theorem 3.1 states the opti... | Rebuttal 1:
Rebuttal: We sincerely thank you for your detailed review and thoughtful comments. We are grateful that you found our paper well-written and technically solid. Below, we address your concerns and questions in detail.
**Author's response to Concern 1:**
We appreciate your suggestion to present our results ... | null | null | Rebuttal 1:
Rebuttal: Following the recommendation of the Reviewer JAP1, we conducted two preliminary experiments using two specific kernels: the RBF kernel and the NTK kernel. Experiment 1 was designed to confirm the optimal rate of kernel gradient flow and KRR when $s=1$. Experiment 2 was designed to illustrate the s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Flexible Visual Relationship Segmentation | Accept (poster) | Summary: This paper proposed a flexible framework that can effectively handle human-object interaction (HOI) detection, scene graph generation (SGG), and referring expression comprehension (REC) tasks. The proposed method further addressed the problem of promptable visual relationship segmentation and enabled the capab... | Rebuttal 1:
Rebuttal: Thank you for your time and helpful feedback. Please refer to “The General Response to Reviewers” for the reply to the issue of the difference with previous methods and refer to the PDF for the added tables for PSG results and mask head only results. We respond below to your other comments and que... | Summary: This work presents a novel approach for visual relationship segmentation that integrates the three critical aspects of a flexible VRS model: standard VRS, promptable querying, and open-vocabulary capabilities. By harnessing the synergistic potential of textual and visual features, the proposed model delivers p... | Rebuttal 1:
Rebuttal: Thank you for your time and helpful feedback. Please refer to “The General Response to Reviewers” for the reply to the issue of backbone comparison, difference with previous methods and refer to the PDF for the added visualizations of masks generated from bounding box annotations. We respond below... | Summary: This work proposes an approach for visual relationship segmentation that integrates the three aspects of a VRS model: standard VRS, promptable querying, and open-vocabulary capabilities. The idea of the article is very good, but the performance seems to be lacking.
Strengths: Enhancing HOI from the perspectiv... | Rebuttal 1:
Rebuttal: Thank you for your time and helpful feedback. Please refer to “The General Response to Reviewers” for the reply to comparison of our backbone with ResNet's and refer to the PDF for visualizations of masks generated by SAM. We respond below to your other comments and questions.
1. **Performance s... | Summary: This paper propose a model to handle multiple visual relationship tasks, like HOI detection and Scene Graph Generation.
The proposed model is based on vision-language models similar to CLIP.
It handles different formulations like standard close-set, open-vocabulary, and prompted setting.
Strengths: - Unifie... | Rebuttal 1:
Rebuttal: Thank you for your time and helpful feedback. Please refer to “The General Response to Reviewers” for the reply to the issue of the difference with previous methods and refer to the PDF for illustration of the importance of using segmentation masks. We respond below to your other comments and ques... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. Our proposed Flex-VRS is commended for its ability to support a variety of tasks(Reviewer LLLd, EDLE, k2zH), its integration of flexibility and open-vocabulary capability into the VRS model(Reviewer LLLd, EDLE), its competitive performance(Revi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nesterov acceleration despite very noisy gradients | Accept (poster) | Summary: The paper studies the accelerated gradient method of Nesterov’s (NAG) for smooth and (strongly-)convex problems under the multiplicative noise model when the variance of the stochastic gradients behave as $O(\sigma^2 \| \nabla f(x) \|^2)$. The authors identify that the NAG in its original momentum formulation ... | Rebuttal 1:
Rebuttal: >I think it should be made clearer that the proposed method is as simple, modified version of NAG rather than a new framework as the only difference is using 2 step size sequences, which are only a multiplicative factor of
$\frac{1}{1+\sigma^2}$ apart from each other.
We agree that a key point i... | Summary: This paper presents a new accelerated gradient method for smooth (possibly strongly) convex functions. It is assumed that the available gradients are noisy, where the noise level is proportional to the norm of the gradient.
The authors show that Nesterov's accelerated gradient method (NAG) will converge in th... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their feedback and encouraging comments.
> I would have preferred if the authors had used different notation. For example, in (1), there are two sequences of points, $x_n$ and $x'_n$. But then, the authors use the notation $g_n=g(x'_n, \omega_n)$, i.e., the gra... | Summary: This paper proposes an accelerated gradient method which is applicable in stochastic convex optimisation with unbiased but highly noised estimator. The method is analysed in a continuous-time convergence framework. Special attention is paid to the application of the method in machine learning, corresponding nu... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their helpful feedback.
> The language requires proof-reading [...] and should be made more formal [...] To summarise, text is far from being publishable.
We understand that our presentation goes against the reviewer's stylistic preferences, and we are happy t... | Summary: This paper introduces and studies AGNES (Accelerated Gradient Descent with Noisy Estimators), a generalization of Nesterov's accelerated gradient (NAG) descent algorithm. First they show that NAG's guarantees break in the high noise regime. Then they prove that AGNES can accelerate gradient descent at any nois... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their helpful feedback and will be happy to incorporate it in a revised version.
> This should be highlighted more since currently the paper is written as presenting a new algorithm rather than presenting a better analysis.
We agree with the point that the nov... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable feedback!
The pdf attached contains an additional sweep over hyperparameters to compare AGNES and NAG as suggested by Reviewer 2pRK.
Details of the experiment: We trained ResNet34 on CIFAR-10 with a batch size of 50 for 40 epochs using NAG ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces ``Accelerated Gradient Descent with Noisy Estimators" (AGNES), a variant of Nesterov's accelerated gradient descent (NAG), and proves that AGNES achieves an accelerated convergence rate regardless of the noise level relative to the gradient in both convex and strongly convex cases. Additi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and comments.
>Does this imply that AGNES is essentially the same as MaSS or other previously suggested algorithms? Please clarify this point.
AGNES is equivalent to MaSS after a reparametrization (shown in Appendix B.2). We derived the algorithm ... | null | null | null | null | null | null |
Distributed-Order Fractional Graph Operating Network | Accept (spotlight) | Summary: This paper introduces a continuous Graph Neural Network (GNN) framework DRAGON that leverages distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON employs a learnable probability distribution over a range ... | Rebuttal 1:
Rebuttal: ## Weakness 1 & Question 4: Explanation of non-Markovian graph random walk with flexible memory
**Response:** Thank you for your valuable comments and suggestions.
**1. Further explanation for non-Markovian graph random walk with flexible memory:**
Lines 211-223 of the manuscript detail t... | Summary: The paper proposes the a novel framework called DRAGON, which uses a learnable probability distribution over a range of real numbers for the fractional order in graph dynamics, generalizing previous continuous GNN models. The paper provides a non-Markovian graph random walk interpretation for the DRAGON framew... | Rebuttal 1:
Rebuttal: ## Weakness 1: Implementation and complexity details
**Response:** Thank you for your insightful comments. We are pleased to provide additional explanation regarding the implementation and complexity of our framework.
Solving the distributed-order FDE Eq.(10) consists of two steps:
- **Ste... | Summary: This paper presented a new type of continuous GNN that extends and unified many continuous GNN variants. The paper mainly generalize the distributed-order fractional derivatives to continuous GNN dynamics, and it now supports a mixing of fractional derivatives within a range of continuous orders. The author al... | Rebuttal 1:
Rebuttal: ## Weakness 1: Hyperparameters chosen
**Response:** We appreciate the reviewer's comments.
Like GRAND, CDE, and FROND, we employ a grid search on the validation dataset to optimize common hyperparameters such as hidden dimensions, learning rate, weight decay, and dropout rate.
Some details are ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their insightful comments and valuable suggestions. We greatly appreciate the feedback and have thoughtfully addressed each point in our detailed responses.
In this "global" response, we provide a detailed analysis of the computational complexity of the DR... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach | Accept (poster) | Summary: This paper introduces TextStarCraft II, a text-based environment to evaluate the strategic decision-making and planning capabilities of large language models (LLMs) in real-time scenarios within StarCraft II (SC2). The study addresses the limitations of traditional Chain of Thought (CoT) methods by proposing t... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough review and insightful comments. Below, we address your specific concerns and outline the changes we plan to make in the revised version.
**Q1**:Human Interaction Assurance
We apologize for any confusion caused by our unclear explanation. To clarify, i... | Summary: The authors introduce TextStarCraft II, a framework for transferring the state of the Real Time Strategy (RTS) Starcraft II (SC2) into text form and Chain of Summarization (CoS), an extension of the traditional Chain of Thought (CoT) style prompting for condensing information and accelerating LLM inference. Th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Below is our response to your inquiry.
**Weaknesses**
We appreciate your suggestion about Starcraft-specific terminology. In our revised version, we will strive to make our paper more accessible to a broader audience by clarifying or simplifying Starcraft-s... | Summary: This paper introduces TextStarCraft II, a benchmark designed to assess long-term strategic decision-making in the context of playing StarCraft II. It models the game-playing process through pure textual representation and also develops a chain-of-summarization pipeline to aid LLM's decision-making to achieve v... | Rebuttal 1:
Rebuttal: Apologies for the lack of clarity in our methodology description.
**Q1**: Standard for Achieving Victory
In our TextStarCraft2 environment, the victory conditions mirror those of a standard StarCraft II 1v1 game, consistent with established AI benchmarks such as AlphaStar, ROA-Star, or DI-Star.... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Isometries: Taming Transformations for Equivariant ML | Accept (poster) | Summary: The paper proposes an autoencoder framework that encodes the input symmetries into isometries in the latent space. The equivariance in the latent space is captured by a functional map $\tau$, which is regularized to be an isometry. Instead of hard constraints, the equivariance of the system is encouraged via t... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting that our paper is well-written and easy to follow, novel, and competitive with baselines, and for providing valuable feedback! We further appreciate your commitment to reconsidering your score - we have tried our best to address your feedback, but please let ... | Summary: This paper introduces Neural Isometries, which is an autoencoder framework learning to map the observation space to a general-purpose latent space wherein encodings are related by isometries whenever their corresponding observations are geometrically related in world space. Several experiments, including camer... | Rebuttal 1:
Rebuttal: Thank you for validating our motivation and our communication - we appreciate it!
## Quantitative evaluation of equivariance: How equivariant is the model?
We are happy to report that we were able to quantitatively evaluate the equivariance of our model following standard procedures [6-7, 43]. ... | Summary: This paper proposes a generic equivariant ML framework by learning latent representations are are modelled to be related by an isometry. There are several important design choices made by the authors: (1.) An autoencoder framework that keeps the spatial structure (i.e. images get encoded into images) (2.) The ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, and for your kind words on our hypothesis being interesting and plausible, our writing enjoyable, and our validation with the laplacian helpful! We are happy to report that we executed your key task -- recovering the harmonics with spherical images – with exciti... | Summary: The main motivation behind Neural Isometry is as follows: most of real world transformations in vision and geometry processing lack identifiable group structure and therefore challenging for prior work in equivariant learning that assumes such knowledge apriori. The paper proposes an auto encoder framework tha... | Rebuttal 1:
Rebuttal: ## Limited experimental setup
We would like to respectfully push back on the notion that we are considering too few baselines. We note that there is very little work on equivariant machine learning *without* prior knowledge of the symmetry group - the NFT is the most relevant baseline in this spac... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading, and detailed and considerate feedback. We are glad that reviewers deem our paper “a clever idea”, “relevant”, “interesting and reasonable”, and “intuitive and interesting”, and the writing to be “very nice”, “easy to follow”, an “enjoyable read”, a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UQ-Guided Hyperparameter Optimization for Iterative Learners | Accept (poster) | Summary: The authors presented a novel scheme for hyperparameter optimization (HPO) in machine learning (ML) based on quantization uncertainty. The paper reflects a clear consequent way of discussion from introduction and literature overview to conclusions and very detailed appendixes. The authors demonstrated a strong... | Rebuttal 1:
Rebuttal: >**Comment 1: Has the UQ-scheme some limitations depending on the number of initial candidates for SH algorithm, for example? Could this be the case where UQ-scheme performance will be almost the same as in the original HPO algorithm, where regret between SH will equal SH+ with large or small vise... | Summary: The paper presents UQ-guided scheme, an approach for hyper-parameter optimisation (HPO) that quantifies uncertainty of each candidate configuration. This uncertainty quantification mechanism is applied to several state-of-the-art iterative HPO algorithms to prevent that promising configurations that have low p... | Rebuttal 1:
Rebuttal: >**Comment 1: Why do you claim that "data uncertainty is constant"? (This is in the beginning of section 3.1)**
Response: In our setting, each candidate is trained on the same set of training data so the data uncertainty among different candidates is the same.
> **Comment 2: Section 3.3. -- expl... | Summary: In the paper "UQ-Guided Hyperparameter Optimization for Iterative Learners" the authors present an uncertainty quantification method for optimizing the hyperparameters of iterative learners in a multi-fidelity setting. It is argued that the best possible candidate is often mistakenly discarded at some point an... | Rebuttal 1:
Rebuttal: > **Comment 1: The authors claim that their method is working for any kind of iterative learners. However, this claim is not supported in the experimental section as only deep learning methods are considered. Therefore, it is questionable whether the same quality of results could be observed for o... | Summary: This paper proposed an incorporate uncertainty quantification for hyperparamter tuning (learning rate, neural architecture). It is assumed the model performance metrics of interest, such as validation error, are Gaussian, and we can use the training output of first $N$ epochs to estimate the mean and variance ... | Rebuttal 1:
Rebuttal: >**Comment 1: On page 4, it is stated that
$\hat{v}$ and $\sigma^2$ are estimated with N=200 epochs. My understanding is that the first N=200 epochs are only for estimating $\hat{v}$ and $\sigma^2$ and the UQ-guided HPO are not used in these 200 epochs. However, I suspect we don’t have the luxury... | Rebuttal 1:
Rebuttal: We thank the reviewers for the insightful feedback and suggestions. We address the common concern of the reviewers here:
## Limitations of the current work
The key characteristic of the UQ method is the necessity to rank multiple learners
during the HPO process. Gradient-based HPO methods [1], ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion-Reward Adversarial Imitation Learning | Accept (poster) | Summary: The paper proposes Diffusion-Reward Adversarial Imitation Learning (DRAIL) an Adversarial Imitation Learning method where the discriminator is parameterized by the loss of a conditional diffusion model.
The diffusion model takes in a state-action pair $(s, a)$ and a binary label $c$ indicating whether the st... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> Hence, my understanding is that, in the context of these two prior works, DRAIL can be seen as a combination of DiffAIL and Diffusion Classifier (DC). Please feel free t... | Summary: This paper aims to address the training instability problem in generative adversarial imitation learning. The authors propose a diffusion discriminative classifier that helps achieve a more stable policy learning process by enhancing the smoothness and robustness of the reward model. Additionally, the author e... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> The advantages of the diffusion discriminative classifier compared to diffusion reward [1] are not well explained.
We extensively discuss how our proposed method differ... | Summary: The paper proposes a novel imitation learning framework that integrates a diffusion model into Generative Adversarial Imitation Learning (GAIL). The primary aim is to address the instability and brittleness associated with GAIL by introducing more robust and smoother reward functions for policy learning. The a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> Complexity: The introduction of diffusion models adds complexity to the framework, which may pose challenges in implementation and require significant computational reso... | Summary: Imitation learning (IL) is a research area that focuses on learning a policy from expert demonstrations. One of the most popular imitation learning techniques, General Adversarial Imitation Learning (GAIL), has been proposed to mitigate some of the issues that naive IL algorithms suffer from. Although GAIL has... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> The paper needs more novelty. DiffAIL proposed using diffusion as a reward, and DiffAIL also proposed using the training error as a reward signal.
> The authors do no... | Rebuttal 1:
Rebuttal: The attached PDF file contains the following content:
- **[Reviewer uNxe] The Effect of Denoising Time Step**: We experimented with computing rewards using different constant denoising time steps and reported the result in Figure R.1. The result shows that following the same denoising step samplin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metric Transforms and Low Rank Representations of Kernels for Fast Attention | Accept (spotlight) | Summary: This paper studies three seperate problems relating to metric transformations of kernel matrices via a new mathematical technique which the authors refer to as the representation theory of the hyperrectangle.
The first problem is about characterising the entrywise transformations which, when applies to a low-... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
Re: “the sections sometimes feel unrelated, and the paper lacks a clear narrative”: The sections are related through the unifying technique: the representation theory of the real hyperrectangle. Indeed, all our results (from LLMs to kernels) follow from this ... | Summary: The paper achieves three main results: Firstly, it demonstrates that polynomials are the only piece-wise continuous functions for which applying them entry-wise to a low-rank matrix results in another low-rank matrix. Secondly, it shows that if f is a Manhattan kernel, then it must be a completely monotone fun... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
Great question: one can show with some work that the same theorem statements hold for a broader class of metrics, including effective resistance distances from electrical network theory, or the class of graph metrics arising from all star graphs. Exploring thi... | Summary: This paper studies kernel functions with a particular interest to Manhattan distance kernels. The following three questions are studied:
1) For which functions f, entrywise transformation f(M) is guaranteed to not be full rank if M is any sufficiently low-rank matrix?
The authors show that if f does not have ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
To address weaknesses:
1. Thank you for pointing this out. We will take your suggestions for a final draft, expand on the proof technique and main novelties in the introduction, correct typos, and make statements (such as the ones you point to) more precise. W... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation | Accept (poster) | Summary: This work investigates the efficacy and robustness of Adversarial Collaborative Filtering (ACF) in recommender systems. The authors present theoretical analyses to demonstrate how ACF enhances traditional collaborative filtering (CF) by mitigating the negative impact of data poisoning attacks. They extend thes... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. We deeply value your recognition of our work's contributions, particularly its importance, clear explanations, novel theoretical insights, rigorous mathematical proofs, extensive experimental evaluations, and the effective combination of theoretical and empirical... | Summary: This work investigates adversarial collaborative filtering, providing theoretical evidence for the effectiveness of such methods and proposing a novel method based on the personalized magnitude of perturbation.
Overall, this work studies on an interesting problem and offer some theoretical insights. However, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive comments and recognition of the significance of our paper, including its theoretical contributions, novel methodology, and comprehensive experiments. In response to your feedback, we address your concerns and clarify some misunderstandings as follows:
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**... | Summary: This paper targets adversarial collaborative filtering and provides both deeper understanding and improvement. The paper provides a theoretical explanation for the ACF's improvement upon robustness and effectiveness. PamaCF, which is presented in Appendix, can more robustly performs CF, is evaluated on three r... | Rebuttal 1:
Rebuttal: Thanks for your detailed feedback and for acknowledging the readability and theoretical clarity of our paper. Below, we address each of your questions to clarify misunderstandings and address concerns. We hope this clarifies the contributions of our work and look forward your reconsideration.
---... | Summary: Adversarial training has been observed to degrade model performance on clean samples in the CV domain, however, ACF in recommender systems can not only enhance the robustness against poisoning attacks but also improve recommendation performance. This paper provides a comprehensive theoretical understanding of ... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. We sincerely appreciate your recognition of the importance, novelty, reasonable motivation, sound theoretical results, and the solid evaluations presented in our work. Below, we address each of your questions to resolve your concerns:
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**Q1: Can authors disc... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all reviewers for your valuable feedback and for taking the time to evaluate our work. We are very encouraged that our main contributions have been acknowledged by all reviewers. We have taken great care to address any concerns or misunderstandings... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IllumiNeRF: 3D Relighting Without Inverse Rendering | Accept (poster) | Summary: This paper introduces a new paradigm for the relightable 3D reconstruction: leverage a Relighting Diffusion Model to generate relit images and distill them into a latent NeRF. The proposed paradigm demonstrates superior performance compared to existing methods, which are based on inverse rendering, on both syt... | Rebuttal 1:
Rebuttal: ## About efficiency
We acknowledge that our approach currently prioritizes quality over real-time performance, as generating new samples with the RDM and optimizing a NeRF for each new lighting condition is computationally intensive (L252-254).
A potential solution to mitigate this limitation is ... | Summary: The paper proposes a method for novel view synthesis with novel lighting given multi-view observation of an object. The pipeline is composed of a learned single-image-based relighting model based on image diffusion models, and a latent NeRF model that reconstructs the appearance of the relit object by reconcil... | Rebuttal 1:
Rebuttal: ## Fig. 4.(a)’s generations are unrealistic
"More specularity than what is actually present in the hotdog scene" does not imply unrealistic results. Without known source lighting, the RDM samples from the entire distribution of plausible relighting outcomes. We showcased random samples from this d... | Summary: The paper proposes a method to relight NeRF representations from a set of multi-view input images. The paper first trains a NeRF model and recovers geometry using UniSDF from the input images. Then, from the mesh, they obtain radiance cues which are used to condition a diffusion model to sample relit images of... | Rebuttal 1:
Rebuttal: ## Method is limited to NeRF
Our approach introduces a novel 3D relighting paradigm, employing a Relighting Diffusion Model (RDM) as prior to optimize a relit 3D representation. This approach is adaptable to various 3D representations like NeRFs or Gaussian Splats, since both can be conditioned o... | Summary: The paper proposes a new method called illumiNeRF for 3D relighting — given a set of posed input images under an unknown lighting, illumiNeRF produces novel views relit under a target lighting. Most of existing methods use inverse rendering to first recover the material and lighting in the given images, and ap... | Rebuttal 1:
Rebuttal: ## Why is Z = 0?
It is unclear how we could use the optimal Z that best matches the actual material besides optimizing Z using the test set images, which does not seem fair. We found that setting Z to 0 yields good results across both our synthetic and real-world benchmarks (see Tab. 1 and 2, an... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort spent reviewing the paper.
We are grateful to hear that the reviewers agree that our work “addresses a long-standing challenge by introducing a novel paradigm" (mZi1).
We also appreciate the praise for our "probabilistic model of representing mult... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty | Accept (poster) | Summary: This paper is about uncertainty quantification in LLMs, specifically illustrating some results based on an assumption involving independence of the distribution for a correct output for a question given some “iterative prompting”. The proposed information-theoretic measure is intended to help measure epistemic... | Rebuttal 1:
Rebuttal: We would like to thank reviewer for many insightful suggestions, we will revise the
title, references, improve section 2.
## Scope, assumptions, and definitions
* **Q:** What is the scope of the work? Exactly what kinds of
tasks are the proposed methods suitable for?
* **A:** Our main motivation... | Summary: In the paper, the authors address the challenge of distinguishing between epistemic and aleatoric uncertainty in large language models (LLMs). They develop methods to decouple these uncertainties, which is crucial for handling queries with multiple valid responses. Their approach involves iterative prompting o... | Rebuttal 1:
Rebuttal: ## Definition of epistemic uncertainty, iterative prompting, and connection between them
* _**Q:** How the proposed iterative prompting strategy connected to
epistemic uncertainty is missing._
* **A:** Note that the definition of epistemic uncertainty is given in
Definition 4.4. It is a KL-di... | Summary: The paper considers both epistemic and aleatoric uncertainties and proposes a novel method to decouple them. This method employs iterative prompting based on its previous responses. Experiments demonstrate that the proposed approach effectively detects cases where only epistemic uncertainty is large for multi-... | Rebuttal 1:
Rebuttal: **Limited application scope:** on single-label queries, we should not expect to perform better than a competitive first-order method (such as S.E.), which is specifically designed for such queries. Please note that Fig.5ab in fact shows that our method performs essentially as well as the S.E. meth... | Summary: This paper proposes an iterative prompt-based approach to uncertainty estimation. They make a model generate multiple answers, and estimate the probability of each, estimating uncertainty for each. They argue this method easily adapts to aleatoric and epistemic uncertainty, and can be applied to multiple choic... | Rebuttal 1:
Rebuttal: **Weaknesses 1:** Although the assumption is stated in this form for simplicity, the assumption in our theory is only for a very specific type of prompt we consider which still seeks answer to the original question $x$, and hence the ground-truth response should not change, and we also only apply ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments. We are in the process of delivering more results with different LLM architectures and sizes, and we aim to provide the results during the discussion. We have already observed that small LLM models behave similarly.
Please find our responses t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Optimism: Exploration With Partially Observable Rewards | Accept (poster) | Summary: The paper studies the setting of finite state action MDPs with partially observable rewards. To formalize the framework they introduce Monitored MDPs. They introduce the algorithm that separates exploration and exploitation and prove that for a class of MDPs with finite goal-oriented diameter their algorithm i... | Rebuttal 1:
Rebuttal: Thank you for your helpful insights and suggestions. Below, we discuss the main points you raised.
1. We will make the distinction between Mon-MDPs and sparse-rewards MDP more explicit.
2. You are correct, the agent sees both states. We will make it clearer in the final version.
3. You are co... | Summary: This paper tackles the problem of exploration in MDPs where the reward is unobservable. For this, the authors perform goal conditioned exploration, i.e., the environment is explored according to how often a specified goal is reached. The authors propose an exploitation-exploration mechanism based on learning ... | Rebuttal 1:
Rebuttal: Thank you for your helpful insights and suggestions. Below, we discuss the main points you raised.
1. In sparse-reward RL, rewards are mostly 0 with very few exceptions ("meaningful rewards"). In Mon-MDPs, the agent cannot see the rewards, not even the 0s. While intrinsic rewards may improve expl... | Summary: The authors propose a novel exploration strategy for Mon-MDPs based on two policies; a goal-conditioned exploration policy and an exploitation policy which maximizes the underlying reward. The proposed algorithm alternates between the two policies, naturally trading off exploration and exploitation.
They show ... | Rebuttal 1:
Rebuttal: Thank you for your helpful insights and suggestions. Below, we discuss the main points you raised.
1. Thank you for the additional references, we will add them to the final version.
In particular, we think that [1] is indeed close to our approach in the use of SF. It is different, however, as it... | Summary: This paper considers the Reinforcement Learning problem in Monitored MDPs. Monitored MDPs are a formalism that has been recently introduced by Parisi et al. (AAMAS 2024), in which the value of the policy is computed on the rewards generated by the environment and those produced by a "monitor". However, the age... | Rebuttal 1:
Rebuttal: Thank you for your helpful insights and suggestions. Below, we discuss the main points you raised.
1. You are correct, we consider only "truthful monitors" (as formalized in *"Parisi et al., “Monitored Markov Decision Processes, 2023”*), i.e., monitors that either hide the reward or show it as is... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and helpful suggestions. We are pleased to see that all reviewers appreciate the core idea of our paper — a novel exploration algorithm for Mon-MDPs, where rewards are partially observable — and the thorough evaluation against different baselines on many e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators | Accept (poster) | Summary: This paper presents a method for selecting a conditional average treatment effect estimator using the distributional robust optimization (DRO) technique. The proposed method does not require specifying models for nuisance functions.
Strengths: 1. The result is strong, since this paper provides a method of eva... | Rebuttal 1:
Rebuttal: Dear Reviewer 1tzr,
We sincerely appreciate your positive recognition of the soundness of our theoretical results and the extensiveness and transparency of our empirical experiments. We are also grateful for your valuable suggestions in improving our presentation and bringing new insights to futu... | Summary: The paper addresses the challenging problem of Counterfactual Average Treatment Effect (CATE) model selection, where the goal is to develop a model selection metric using only observed data, as counterfactual labels are not available. Previous work has focused on model selection metrics that involve learning n... | Rebuttal 1:
Rebuttal: Dear Reviewer 9qbs,
Thank you for your thorough review of our paper. We are delighted that you recognize the novelty and significance of our DRM method in advancing CATE model selection, particularly its nuisance-free and robustness to distribution shift. Below we will address your comments.
* S... | Summary: The paper introduces a new metric for CATE model evaluation and selection. Specifically, they derive a distributionally robust metric (DRM) which is nuisance-free and robust against selection bias. They show and explain its robust performance in extensive benchmarking experiments against existing baselines.
S... | Rebuttal 1:
Rebuttal: Dear Reviewer e7g2,
We greatly appreciate your thoughtful feedback and insightful comments. Thank you for your recognition of our theoretical and experimental analysis! We will address each of your comments below.
**Q1.** Should more clearly discuss why and to which degree of unobserved confound... | Summary: The paper proposes a new model selection method for choosing an estimator of the conditional average treatment effect (CATE), namely, a Distributional Robust Metric (DRM). The proposed method, DRM, is nuisance-free: It does not require an additional estimation of the nuisance functions, unlike the majority of ... | Rebuttal 1:
Rebuttal: Dear Reviewer T6Zv,
We are grateful for your thorough summary and greatly appreciate your recognition of the motivation and novelty underlying our proposed method. Thank you for your time and effort in providing constructive and helpful feedback. We will respond to each of your comments below.
*... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We are grateful for your comments and suggestions, which are very helpful in improving our paper. Here are some general responses (GR) that might be useful for individual questions. In the separate rebuttal, we may remind you to refer to this general response. Thank you for your t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation | Accept (poster) | Summary: This work introduces an improvement over the previous method (DARC) with theoretical and experimental contributions for solving the off-dynamics transfer learning problem.
Strengths: - Using IL on states is a sensible idea to add
- The theoretical contribution is quite strong, as the authors were able to repl... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of your points.
---
>How many samples are used as rollouts in the target domain?
Our imitation learning step rolls out data from the target domain every 100 steps ... | Summary: This paper provides solution to a specific kind of domain adaptation, where the source action space is a subset of the target action space. In this setup, the source policy might fail in the target domain, because the policy would output arbitrary values for the action dimensions only active in the target doma... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and detailed feedback. We hope our response will fully address all of your points.
---
>Evaluation on a very specific kind of dynamics change.
1) In the general response, we provide additional experiment results under DARC setting and more general off-dynamics sett... | Summary: The paper considers the off-dynamic RL setting and identifies that existing approach, DARC, fails to obtain the true optimal policy in the target MDP. Leveraging ideas from imitation learning literature, the paper proposes a learning objective that takes into the account the dynamic shift between the source an... | Rebuttal 1:
Rebuttal: We first thank the reviewer for their time and comments. We now address your questions point by point.
1, **Notation of $\tau$.** $\tau$ represents the trajectory sequence of state-action pairs, so it is a notation for the trajectories. For example, we use $\tau_{\pi_{\theta}}^{\text{src}}$ to r... | Summary: The authors introduced the approach Domain Adaptation and Reward Augmented Imitation Learning (DARAIL) for off-dynamics RL. The work aim at generating the same trajectories in the target domain as expert trajectories learned via DARC in the source domain. With GAIL-style framework and reward augmentation, DARA... | Rebuttal 1:
Rebuttal: We first thank the reviewer for their time and comments. We now address your concerns point by point.
---
> How would DARC trajectory quality influence the overall imitation learning performance in the target domain?
We use trajectories generated by the DARC in the source domain as the expert t... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their time and constructive feedback on our paper. Since some reviewers referred to similar concerns, we would like to make a general response to address these questions.
---
> Experimental results on more general off-dynamics settings and more baselines (R_mGy8... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Physically Compatible 3D Object Modeling from a Single Image | Accept (spotlight) | Summary: This paper presents an optimization method that produces 3D shapes from a single images that considers about mechanical properties and external forces. The shapes of the generated results in a state of static equilibrium are able to match the input image. The proposed methods can be used with other single-view... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from the reviewer.
**Rebuttal Outline:**
1. **[Adjustment] (W1&W2&Q2)** Move Limitations and Discussions with Alternative Approaches to Main Text.
2. **[Discussion] (Q1)** Alternatively Optimizing the Hollow of the Solid Geometry.
3. **[Clarification] (Q3)** P... | Summary: This paper introduces the concept of physical compatibility into single-image to 3D generation. It proposes a post-optimization method for existing 3D generation pipelines, which takes the generated mesh as the target mesh and optimizes the deformation gradient to obtain a rest-shape geometry without external ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s insightful comments.
**Rebuttal Outline:**
1. **[Discussion] (W1)** Handling the generation of multiple coupled objects.
2. **[Discussion] (Q1)** Correcting errors in connectivity during the optimization process.
Please find below our detailed responses to you... | Summary: This paper introduces a physical compatibility optimization framework for reconstructed objects from a single image. The approach considers mechanical properties, external forces, and rest-shape geometry, integrating static equilibrium as a hard constraint. This framework improves upon existing methods by ensu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's effort in reading and evaluating our work carefully!
**Rebuttal Outline:**
1. **[Discussion] (W1)** Title.
2. **[Adjustment] (Q1)** $\textbf{x}_{static}$.
3. **[Clarification] (Q2)** 3D Printing Results of Flamingo.
4. **[Clarification] (Q3)** Evaluation on the numbe... | Summary: The paper presents a 3D mesh optimization framework to ensure physical plausible 3D object reconstructions from a single image. These 3D reconstructions should conform with global (e.g. gravity) and user-defined constraints (material stiffness) as well as match with a target image of the reconstructed, simulat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive questions.
**Rebuttal Outline:**
1. **[Adjustment] (W1)** Regarding the placement of the Related Work section.
2. **[Question] (Q1)** Elaboration on results of TetSphere.
Please find below our detailed responses to your comments and concerns.
---
**1... | Rebuttal 1:
Rebuttal: # General Response
We sincerely appreciate the detailed reviews and the thoughtful feedback provided by all reviewers and the Area Chair. In addition to addressing specific comments from each reviewer, we would like to outline our primary contributions.
- **[Motivation]** We tackle a critical is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Label Open Set Recognition | Accept (poster) | Summary: This paper addresses the problem of classifying instances with unknown labels in a multi-label setting (multi-label open set recognition). A
novel approach named SLAN is proposed which leverages sub-labeling information, and unknown labels are recognized by differentiating the sub-labeling information from hol... | Rebuttal 1:
Rebuttal: 1. There are existing tasks with similar settings. For example, in image tasks, there are zero-shot multi-label classification, and open vocabulary multi-label classification. What's the difference between the proposed task and these tasks? Is the setting more applicable?
- **Response 1:** Thanks... | Summary: This article introduces a new problem in multi-label open set recognition (MLOSR) and proposes a novel approach named Sub-Labeling Information Reconstruction for MLOSR (SLAN). SLAN utilizes sub-labeling information enriched by structural details in the feature space. Experimental results across various dataset... | Rebuttal 1:
Rebuttal: 1. There is concern regarding the robustness of the method. How stable is its performance when errors or irrelevant labels appear in the dataset?
- **Response 1:** Thanks to the comment. The detailed experimental results in terms of _ranking loss_ and _F-measure_ under different error rates are r... | Summary: The abstract discusses multi-label learning where instances can have multiple labels simultaneously. Traditional approaches assume a closed set scenario where test data labels are predefined during training. However, in real-world situations, new labels can emerge during testing, creating an open and dynamic e... | Rebuttal 1:
Rebuttal: 1. How does the SLAN algorithm perform on datasets with highly imbalanced label distributions?
- **Response 1:** Thanks to the comment. The following table summarizes the level of class-imbalance on data sets employed in the experiments including the minimum, maximum and average imbalance ratio a... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We greatly appreciate all of you for your thoughtful comments and valuable suggestions. These are very helpful for improving our paper. We have carefully referred to the questions and written the response. In addition to the text responses, we also report some figure results in th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations | Reject | Summary: The paper presents CogCoM, a novel approach to training large Vision-Language Models (VLMs) using a mechanism called Chain of Manipulations (CoM). This mechanism enables the model to solve visual problems step-by-step with evidence, inspired by human cognitive processes like marking and zooming into images. Co... | Rebuttal 1:
Rebuttal: Dear Reviewer, we are very grateful for the valuable time you have spent reviewing our paper and for your recognition of our work, which is of great significant to us. Concerning the issues your have mentioned in our paper, we will make the following improvements:
- **Design of Figures**: Thank... | Summary: This paper introduces the Chain of Manipulations (CoM) mechanism for data generation to enhance visual reasoning in VLMs. The authors developed a data generation pipeline, producing 70K high-quality samples, and created the CogCoM model. CogCoM achieves state-of-the-art results across nine benchmarks, demonstr... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for taking your valuable time to review our paper and the insightful review. In response to your suggestions and questions, we have conducted experiments and provided explanations, as follows:
- **The filtering strategy for quality control in data collection**: Thank yo... | Summary: Drawing inspiration from human cognition to solve visual problems through localizing, zooming, etc., this paper introduces a new framework called CogCom, which solves visual problems by automatically combining six types of basic manipulations. When facing a visual problem, CogCom can use reasoning to solve eac... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for taking your valuable time to review our paper and the insightful suggestions. We have added experiments (in the supplementary PDF) and supplemented content (in this page) of our paper, as detailed below:
- We have evaluated our model on the suggested new benchmarks,... | null | null | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for the time and effort they have invested in reviewing our paper. In response to the review comments, we have added **(1) evaluation on the suggested new benchmarks**, **(2) comparison with new baseline models**, and **(3) ablation experiment controlli... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Solving Minimum-Cost Reach Avoid using Reinforcement Learning | Accept (poster) | Summary: The papers coinsiders a reach-avoid problem under cost minimization objective, where a CMDP has to be solved under the goal and avoid constraints (the final state has to be in the certain set, and the trajectories must avoid an unsafe region). The authors derive an optimal control problem under these constrain... | Rebuttal 1:
Rebuttal: >## The paper considers ... where a CMDP has to be solved
We want to clarify **the minimum-cost reach-avoid problem is NOT a CMDP [1]** (e.g., as used in [2,3,4]), since it is NOT in the following form:
$$
\max_\pi \quad \mathbb{E}\left[ \sum_{k=0}^\infty r_k \right],\quad \textrm{s.t.} \quad \ma... | Summary: The paper introduces RC-PPO, an RL algorithm designed to solve the minimum-cost reach-avoid problem by reformulating the optimization problem on an augmented system. The paper addresses the limitations of current RL that mostly solve surrogate problems to fit to the problem setting. Furthermore, a comprehensiv... | Rebuttal 1:
Rebuttal: > ### In Section 5, the paper claims that RC-PPO remains competitive which is based on Figure 6. Can you either elaborate how this claim can be made from Figure 6, or maybe it should be Figure 4?
**You are correct, this should be Figure 4**.
> ### The presented experimental results are solid and... | Summary: This paper proposes Reach Constrained Proximal Policy Optimization which targets to solve the minimum-cost reach-avoid problem. The authors first convert the reach-avoid problem to a reach problem on an augmented system and use the corresponding reach value function to compute the optimal policy. Next, The aut... | Rebuttal 1:
Rebuttal: > ### What is the motivation of using two-phase PPO to solve this problem?
**The minimum-cost reach-avoid problem (1) cannot be *exactly* framed into problem structures that existing methods are able to solve.**
Our two-phase method _directly_ solves the minimum-cost reach-avoid problem (1). In c... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments.
We are excited that the reviewers identified that we provide a _novel_ ($\color{#E24A33}{\textsf{M8co}}$) and _elegant_ ($\color{#348ABD}{\textsf{zsEG}}$) solution to the minimum-cost reach avoid problem that _improves upon the optimized object... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient multi-prompt evaluation of LLMs | Accept (poster) | Summary: The authors introduce a novel method called PromptEval which permits efficient multi-prompt evaluation of LLMs across different prompt templates with a limited number of evaluations.
Strengths: - Theoretical guarantees that PromptEval has desirable statistical properties such as consistency in estimating perf... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your comments. | Summary: This paper introduces PromptEval, a novel method for efficiently evaluating the performance of large language models (LLMs) across multiple prompt templates. They propose a statistical framework based on Item Response Theory (IRT) for estimating LLM performance distribution across many prompts using limited ev... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your dedication to our paper. We addressed the issues you raised below. Please let us know if you have any other questions.
- **Use of IRT and alternatives:** We use (a generalization of) IRT because it is the model most suited to our data. Although there are other ps... | Summary: This paper introduces PromptEval, an efficient multi-prompt evaluation method for LLMs, showing its statistical consistency and effectiveness across benchmarks (MMLU, BBH, LMentry) and studying prompt sensitivity in 15 open-source LLMs.
Strengths: - The authors conducted a comprehensive theoretical analysis t... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your work on our paper. We addressed the issues you raised below. Please let us know if you have any questions.
- **New experiment with closed-source models:** We have conducted a new experiment using closed-source models. Due to the high costs associated with running... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your time reviewing our paper. We include new experiments suggested by reviewer PhKA in the extra pdf.
1. In the first experiment, we explore the concept of LLM-as-a judge. We used PromptEval to estimate the distribution of performances given by a closed-source LLM ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DRIP: Unleashing Diffusion Priors for Joint Foreground and Alpha Prediction in Image Matting | Accept (poster) | Summary: The paper describes a new alpha matting model, which builds upon Stable Diffusion v2, adding various blocks including a switcher for training for alpha and foreground color prediction using the same model, cross-domain attention and an alpha decoder. Unlike the usual emphasis on optimizing models for solely al... | Rebuttal 1:
Rebuttal: **W1: Absence of Baselines for Foreground Color Estimation**
Thank you very much for highlighting the need for a proper baseline in foreground color estimation. We agree that this aspect warrants further discussion and comparison.
1. **Related Work Discussion**
In the related work section, **we ... | Summary: This paper introduces DRIP, a novel image matting method that leverages pre-trained LDMs to jointly predict foreground color and alpha mattes. By integrating a cross-domain attention mechanism and a latent transparency decoder, DRIP addresses the limitations of traditional methods, achieving significant perfor... | Rebuttal 1:
Rebuttal: **W1: Computational Complexity**
Thank you for your valuable comments regarding the computational complexity of our model. We understand the importance of addressing computational demands, particularly for real-time or resource-constrained applications. **In the limitation section of our paper, w... | Summary: The paper introduces Drip, an approach to image matting that leverages vision priors from pre-trained latent diffusion models (LDM). Drip incorporates a switcher and cross-domain attention mechanism for joint prediction of foreground color and opacity, ensuring high consistency. A latent transparency decoder m... | Rebuttal 1:
Rebuttal: **W1: Contribution to Image Matting**
Our primary contributions and insights are not focused on the neural network architecture or block design. Instead, we concentrate on exploring how to leverage the priors learned by well-scaled image generation models to address the ill-posed problem of image... | Summary: The authors present the clear and straightforward method to improve image matting performance using an LDM-based model. The model is a conditioned LDM which can predict both the Alpha mask and the foreground, basically doing RGB-A prediction from an image and a trimap. To further adapt to the data domain, the ... | Rebuttal 1:
Rebuttal: **W1: Training Dataset Size and Model Generalization**
Thank you for raising concerns about our dataset size and model generalization. Our training dataset is substantial, comprising 431 foreground images and a background library of 82,783 images. Each foreground image is paired with 100 differen... | Rebuttal 1:
Rebuttal: **Summary of Revisions:**
To all reviewers,
We would like to express our sincere gratitude for your valuable efforts. We have meticulously reviewed all the feedback provided and made the necessary revisions to our paper. Below is a summary of the major changes incorporated into the final version... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs | Accept (poster) | Summary: This paper is concerned with the overoptimization issue in reward modeling: When optimizing a policy against a reward model, this leads to a distributional shift that can lead to an increase in the proxy score while the true score decreases. This paper addresses these issues by regularizing the hidden states i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments, and we provide clarification to your concerns as follows. We appreciate it if you have any further questions or comments.
**Q1:** This paper seems to view RLHF as the stage after reward modeling ... I think it's more typical to view reward modeling... | Summary: This paper introduces a method that retains the base model's language model head while incorporating text-generation losses to preserve the hidden states' text generation capabilities.
Strengths: * The paper is well-written, and the idea is straightforward.
* The code is easy to understand and implement.
* Th... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments, and we provide clarification to your concerns as follows.
**Q1:** Figure 3 (b) appears unusual, as the gold score decreases at the beginning of training. This could indicate suboptimal hyperparameter tuning or potential drawbacks in the pipeline.
**A1:** We ... | Summary: This paper proposes generalizable reward model (GRM) which modifies the standard reward-learning objective by adding an auxiliary task with a separate language modeling head. The auxiliary loss is either DPO or SFT. Experiments and ablations are conducted using mistral and gemma models and data from unified-fe... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments, and we provide clarification to your concerns as follows.
**Q1:** While the paper is generally comprehensive, there could be additional exploration of just using language modeling as an auxiliary task. It is not clear if the benefit of GRM is coming from lang... | Summary: The paper addresses the limitations of current reward models used in the reinforcement learning from human feedback (RLHF) framework, specifically their generalization capabilities to unseen prompts and responses. This limitation often leads to reward over-optimization, where the excessive optimization of rewa... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments, and we provide clarification to your concerns as follows.
**Q1**: The proposed method lacks innovation ... The inclusion of SFT loss in RM training has already been explored in previous works, such as InstructGPT and Anthropic's RM training.
**A1:** We woul... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments and are particularly grateful for their recognition of our work: 'sound and strong motivation' (Reviewers tSHJ, VYhZ), 'elegant idea' (Reviewer NAqK), 'thorough experiments' (Reviewers NAqK, VYhZ), and 'well-written' (Reviewer SDk2). We hope o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Node-Level Topological Representation Learning on Point Clouds | Reject | Summary: Large scale topological descriptors of data are leveraged to compute point/node-level descriptors, which encode to which large scale topological feature each point belongs to. For this, a combination of applied algebraic topology and applied harmonic analysis is used. More specifically, large scale homological... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough review and feedback! We believe we have significantly improved the paper based upon your comments! We will address the individual points:
# "Regarding unjustified claims":
1. By higher-order we mean of order 1 and above in the sense of homology. ... | Summary: This paper introduces TOPF, a topological feature extraction mechanism on point cloud data. The authors consider Vietoris-Rips/$\alpha$ filtrations over point clouds and compute the persistent homology. They propose a heuristic to select the “top” features from the barcodes. They consider the corresponding rep... | Rebuttal 1:
Rebuttal: We sincerely thank the review for their thorough review and feedback!
We will now address the points raised in the review:
> I do not fully understand the “learning” the representation here, because the representation is not particularly being learnt. It is being computed by using the persistent... | Summary: The paper introduces an approach to select and compute some point-level topological features for point cloud or general data set analysis.
The main ideas is to define a multi-scale simplicial complex representation, thus we can track how the homology modules change along the filtration and then select the homo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their feedback! We are happy to read that they find the paper sound and well-presented.
> Topological features are usually not localized, the idea of being able to bring back the topological descriptor to the relevant points is quite novel and impactful.
We ar... | Summary: The paper presents a novel method for extracting per point topological features - TOPF. The method builds on previous results in topological data analysis which described a shape or a point cloud with a single global feature, by generating per-point topologically-aware features. The paper presents a quantitati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their review! We are happy to hear that they find our work well-written and novel.
> it would be beneficial to include additional evaluation, qualitative and quantitative, on real-world data and additional applications
[Cf. Authore rebuttal:] We agree with the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their very valuable feedback and comments. Your work has already helped to significantly improve the paper. We will provide a brief summary of our changes here, and give detailed answers in the individual rebuttals.
Some reviewers asked us for more experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation | Accept (poster) | Summary: This paper studies latent Markov decision processes (LMDP) with M = O(1) number of MDPs. In other words, there exists a set of MDPs (unknown to the agent) and the environment randomly selects one MDP at the beginning of each episode. The selected MDP is not revealed to the agent and therefore the agent must in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and constructive feedback on our paper. We address the mentioned weaknesses below.
**Practicality of the Proposed Algorithm**
As the reviewer correctly pointed out, our focus was indeed on the purely statistical learning aspect of the problem. Ne... | Summary: This paper studies latent MDP, where the underlying dynamics and rewards are controlled by some latent states (not revealed to the learner), and the learner attempts to do learning and planning based on the trajectory data.
The algorithm builds from the optimistic MLE algorithm, which iteratively checked whe... | Rebuttal 1:
Rebuttal: We sincerely appreciate the encouraging comments and positive assessment of our paper. Below, we address an interesting question you raised:
**Can these results somehow adapt to the POMDP setting?**
Yes, we believe our results can be adaptable to the POMDP setting in two senses:
- Algorithmical... | Summary: This paper introduces a new version of the coverage coefficient for analyzing latent Markov Decision Processes (MDPs). It demonstrates how to link the proposed coverage coefficient with sample complexity using MDPs. Additionally, the paper presents an algorithm and provides a bound on the sample complexity of ... | Rebuttal 1:
Rebuttal: We are grateful for your insights and suggestions, which will help us improve our work. Below, we address the weaknesses you mentioned.
**Comparison to Previous Work on LMDPs**
We would like to highlight that our work is the first to propose a general exploration algorithm applicable to the ent... | Summary: The paper studies latent MDPs, an MDP framework with a set of MDPs and the environment samples a random MDP at the beginning of each episode. To avoid a $A^H$ sample complexity, previous works either assume separation or similarity of transitions. This work removes these conditions, and provide an algorithm wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thoughtful review and constructive feedback on our paper. We would like to start by emphasizing our technical novelty.
**About Technical Novelties**
Our LMDP-OMLE algorithm builds on the general framework of OMLE. The key novelty of the algorithm is the design of dat... | Rebuttal 1:
Rebuttal: We thank all reviewers for their effort, time, and their valuable feedback. While we respond to each reviewer on the specific concerns raised, we would like to emphasize the importance of studying the Latent MDP setting as well as the technical novelties in our work.
**Why Solving LMDPs is Imp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models | Accept (poster) | Summary: This paper proposes a novel method for test-time generalization of vision-language models. Specifically, the paper updates two prototypes, textual and visual prompts, online using test samples. Additionally, the authors learn task residuals by aligning the two different modality prototypes, further improving p... | Rebuttal 1:
Rebuttal: Dear Reviewer vqJd,
We greatly appreciate your valuable feedback on our paper. We address the raised concerns and questions below.
---
**Comment (1)**: “*More ablation studies are expected: What are the different impacts of the two loss terms in Eq 10?*”
**Response (1)**: Thank you for your va... | Summary: The paper introduces a novel test-time adaptation approach for vision-language models (VLMs) called Dual Prototype Evolving (DPE). The method effectively accumulates task-specific knowledge from multi-modalities by creating and evolving two sets of prototypes—textual and visual—during test time. This approach ... | Rebuttal 1:
Rebuttal: Dear Reviewer SVvN,
Thank you for your insightful comments and positive recommendation of our work. We provide point-by-point responses to address your concerns below.
---
**Comment (1)**: “*The introduction of learnable residuals and the dual prototype evolution mechanism adds complexity to th... | Summary: The paper proposes a novel test-time adaptation method for CLIP models, drawing inspiration from previous works on prototype learning and CLIP-based adaptors.
For each test sample, both textual and visual prototypes are optimized using learnable residual parameters based on alignment loss and self-entropy los... | Rebuttal 1:
Rebuttal: Dear Reviewer owvr,
Thanks for your valuable feedback!
---
**Comment (1)**: “*The method combines multiple existing techniques …*”
**Response (1)**: While our method shares some similarities in method details (e.g., multi-modal prototype residuals), we focus on a completely different test-time... | Summary: 1. This paper proposes a novel test-time adaptation method (DPE) for VLMs that captures multi-modal representations for target classes during test time.
2. This paper introduces and optimizes learnable residuals for each test sample to align the prototypes across modalities.
3. The results of this paper are pr... | Rebuttal 1:
Rebuttal: Dear Reviewer Sqqz,
We really appreciate your thorough review of our paper!
---
**Comment (1)**: “*Comparisons with DMN-ZS [1] and TPS [2] including the core idea design and performance should be provided*.”
**Response (1)**: Thank you for pointing this out. We acknowledge that our DPE method ... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We are sincerely grateful to you all for dedicating time and efforts in providing these detailed and thoughtful reviews, which helped us to improve the quality of our paper. We also want to thank all the reviewers for your **unanimous recognition and positive recommendation... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation | Accept (poster) | Summary: The paper proposes RFLPA, which deploys a robustness algorithm based on cosine similarity detection on SecAgg, defending against privacy attacks from the server and poisoning attacks from clients. Furthermore, the paper reduces communication and computation costs through packed Shamir secret sharing and dot-pr... | Rebuttal 1:
Rebuttal: Dear Reviewer VWie,
Thanks for your time and valuable comments. Hope our response below could address your concern.
**W1-a&W4-b: Additional communication cost of cosine similarity computation**
The additional communication cost introduced by cosine similarity computation is small compared to t... | Summary: The paper proposes a defense mechanism against poisoning attacks on federated learning while maintaining privacy guarantee. The solution is based on secure aggregation and evaluates the trustworthiness of client updates with cosine similarity. Information leakage during the computation of cosine similarity is ... | Rebuttal 1:
Rebuttal: Dear Reviewer WU56,
Thank you for your recognition and valuable comments. Hope our response below could address your concern.
**W1: Applicability to other federated learning models, such as those involving more dynamic and heterogeneous client populations**
Thanks for your comment. Our approac... | Summary: This paper aims to enhance the vulnerability of FL when dealing with poisoning attacks. A common strategy used in FL to avoid directly sharing local updates is called secure aggregation(SecAgg), which works with ciphertexts and is incompatible with defense techniques against poisoning attacks. To address this ... | Rebuttal 1:
Rebuttal: Dear Reviewer WU56,
Thank you for your recognition and valuable comments. Hope our response below could address your concern.
**W1 & Q2: Assumption on clean public samples**
Thanks for your comment. As discussed in our response to Q1 in global rebuttal to program chair, we proposed some remedi... | Summary: This paper presents a framework to address the dual challenges of privacy leakage and poisoning attacks in federated learning. The authors propose RFLPA, which integrates cosine similarity for robust aggregation with verifiable packed Shamir secret sharing to ensure secure aggregation without compromising on r... | Rebuttal 1:
Rebuttal: Dear Reviewer WU56,
Thank you for your recognition and valuable comments. Hope our response below could address your concern.
**W1 & Q4: Assumption that the server has a clean root dataset and that secure communication can be maintained without significant overheads**
Thanks for your comment. ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We want to express our profound gratitude for your insightful comments and valuable suggestions. We address some common questions in this global rebuttal section.
**Q1: The paper relies on the assumption that the server has trusted root of clean datasets.**
Please note that the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics | Accept (poster) | Summary: This paper presents a graph-ODE simulator for capturing rigid body dynamics with collision events. It introduces a design based on object-level and mesh-level representations. It also introduces an event module to capture collision and incorporate it into the network. The paper conducts an evaluation on two ri... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
> Q1. The Rigid-Fall and Physion examples are trivial and solved problems for modern rigid-body simulators. However, the paper did not include these sim... | Summary: The paper introduces EGODE, a method simulate rigid-body dynamics with contacts using a hierarchical representation. The main system consists of two parts: a mesh node representation and object representation that both are coupled inside a neural ODE that simulates the rigid body system dynamics. In addition, ... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
> Q1. Is there a reason to leave out FiGNet [1] as a baseline when its mentioned in the introduction and its limitations are discussed in Related Work? ... | Summary: The paper presents a Graph ODE framework to model rigid body dynamics. In a departure from previous works on Graph ODEs, the proposed framework incorporates a hierarchical structure by explicitly modeling both mesh-based representations and object-level representations of the rigid bodies. Furthermore, the fra... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, and your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1. The learnabl... | Summary: This paper introduces EGODE, a method for modeling rigid dynamics. To do this, they introduce a framework which integrates neural ODEs and GNNs, and they integrate this with an event module approach for collision modeling. They demonstrate superior performance to baselines on two standard benchmarks and demons... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1. It would be help... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces EGODE, a novel framework for modeling rigid dynamics that has applications in robotics, graphics, and mechanical design. The framework addresses the limitations of existing graph neural network (GNN) simulators by incorporating both mesh node representations and object representations with... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
> Q1. How does EGODE compare with traditional physics engines in terms of computational efficiency and accuracy?
A1. Thanks for your comment. The simu... | null | null | null | null | null | null |
Towards Croppable Implicit Neural Representations | Accept (poster) | Summary: This paper proposes Local-Global SIRENs, which partition the space into different regions and fit each region with smaller local INRs, leading to croppable INR by cropping the weights relative to the local regions. The model further proposes to use local and global feature extraction to improve the fitting per... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the constructive feedback and great reference. We appreciate the time and effort. Please see our comments and additional results below.
**[W1] Contribution**
While training an INR-per-partition does allow for cropping with a proportionate weight decrease, our extensi... | Summary: The paper proposes a method for learning patchwise INRs that are integrated with a global INR. The method is designed with cropping in mind, and this cropping can be achieved by pruning the relevant patchwise INR - similarly, cropping is limited to the pre-defined patches. This allows for novel post-training c... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the thoughtful feedback and insightful comments. We appreciate the time and effort. Please see our comments and additional results below.
**Weaknesses:**
**[W1]** We appreciate the reviewer’s observation regarding the rigid enforcement of the local-global distinction... | Summary: This paper proposes a new INR architecture to admit easy cropping of the target datum to a certain partition, allowing one to save memory and inference cost without any retraining. Comparing with training a new INR for the target partition, the approach lets one utilize the global context as well. The idea is ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the constructive feedback. We appreciate the time and effort. Please see our comments and additional results below.
**Utility of croppability**
The utility of our method stems from the inspiration for editable INRs. We began with the fundamental cropping operation an... | null | null | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their valuable feedback and insightful comments. We have carefully addressed each point raised in the individual reviews and provided detailed responses in the corresponding review replies.
Additionally, we recognize that the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks | Accept (poster) | Summary: This work develops a KFAC approximation for approximate second order optimization of physics-informed neural network (PINN) losses, in order to address optimization difficulties of PINNs.The first key idea is to use forward mode automatic differentiation for the different derivatives of the PDE and notice that... | Rebuttal 1:
Rebuttal: Dear Reviewer aiiA,
thanks a lot for the time and effort you put into reviewing our work.
Regarding readability, we agree that layer indices can be omitted and will do so in the updated version. We will also think about other ways to make the notation lighter.
**Weaknesses**
> [...] the loss c... | Summary: This paper generalizes the K-FAC method (which is well-known in optimization for deep learning) to enable it to train PINNs. The key idea is to combine Taylor-mode autodifferentiation with recent work on K-FAC with weight sharing layers. The proposed method is evaluated on several PDEs, where it outperforms fi... | Rebuttal 1:
Rebuttal: Dear Reviewer YHUU,
we would like to thank you for the time and effort you put into reviewing our work. In the following, address some of the points that were rightfully raised by you.
> How much additional memory is required [for our KFAC]?
> There is no per-step computational complexity provid... | Summary: This work considers the problem of optimising partial differential equations (PDE) with neural networks, in particular second-order optimization.
Even if simple models (multi-layer perceptrons) are used, for which KFAC approximations of the curvature matrix are well-known, it needs to derive new approximations... | Rebuttal 1:
Rebuttal: Dear Reviewer TQnM,
we would like to thank you for the time and effort you put into reviewing our work. In the following, we want to address some of the points that you rightfully raised.
---
> paper does not provide the complexity of the algorithm
> does the method scale poorly with the input... | null | null | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their thorough evaluation of our submission and provide extensive answers to the individual points raised by the reviewers below.
Here, we want to elaborate on our method's per-iteration complexity, both theoretically and empirically, as this concern was raised ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sequential Harmful Shift Detection Without Labels | Accept (poster) | Summary: The authors introduce an approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requires no access to ground truth data labels. They solution substitute true errors with the predictions of a learnt error estimato... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper.
We appreciate the suggestion to include a dedicated section on related work concerning distribution shifts and continuous production environments. Regarding the question of whether "requires no access to ground truth data labels" is a... | Summary: The paper introduces a sequential drift detector designed to identify drifts that may negatively impact model performance without requiring target labels in production. This is achieved by training an error proxy model on a calibration dataset, which can then be applied in a production scenario, thereby elimin... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and acknowledging our contributions.
> The claims regarding the properties of “traditional” drift detection methods are unfounded and can be seen as misinformation.
Regarding the issue raised about traditional shift methods (Lines 17-... | Summary: This paper proposes a new method for identifying harmful distribution shifts when no labels are available at test time. This work is a good contribution to the field of ML and has practical importance.
Strengths: - The paper proposes a theoretically motivated method for the detection of harmful distribution s... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and for providing valuable references that we were not aware of. We appreciate the opportunity to clarify our contributions in light of these references.
First, we note that paper [2] focuses on detecting distribution shifts in general... | Summary: This work is an extension of Podkopaev and Ramdas. They propose a framework to detect the harmful distribution shift without accessing the true labels during detection. To do that, the authors introduce an error estimator model to measure the error scores. Besides, the authors propose a strategy to manage fals... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review and acknowledge our work.
> In the experiments section, the authors only compare with a baseline scheme and ignore other existing distribution shift detection algorithms (e.g., [1-2]), although they may not claim to only detect harmful distribut... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms | Accept (poster) | Summary: The manuscript discusses a multi-task learning problem and proposes a hierarchical Bayesian (HB) bandit approach. In this approach, the agent maintains a meta-posterior distribution over the hyperparameters of the within-task bandit problems. In the HB bandit, each bandit task is characterized by a task parame... | Rebuttal 1:
Rebuttal: # Response to the Review by Reviewer rxXv (Part 1/2)
**Q1. Please broadly discuss the novelties summarized in the appendix in the main paper, as this is your main contribution that led to improved bounds.**\
A1: Thank you for this suggestion, we will broadly discuss the novelties summarized in th... | Summary: The paper improves the Bayesian regret bound for hierarchical Bayesian bandit algorithms in multi-task bandit and semi-bandit settings. Firstly, it improves the gap-independent bound by a factor of $\mathcal{O}(\sqrt{\log(mn)})$ for infinite action set, $m$ being number of tasks and $n$, the number of iteratio... | Rebuttal 1:
Rebuttal: # Response to the Review by Reviewer diEN (Part 1/4)
**Q1. The Gaussian distribution assumption for each layer of the hierarchy is highly restrictive since in many cases, the noise is not Gaussian (assuming sub-Gaussian would be better). This should at least be mentioned in the abstract since the... | Summary: This paper studies the multi-task Gaussian linear bandit and semi-bandit problems. It uses a Bayesian approach to maintain a meta-distribution over the hyper-parameters of within-task parameters. It provides an improved regret bound for the for multi-task for HierTS algorithm in the case of infinite action set... | Rebuttal 1:
Rebuttal: **Q1. The paper improves upon prior works in several directions; it proposes tighter regret bounds for HierTS, extends multi-task bandit algorithms to combinatorial semi-bandit setting.**\
A1: Thanks for your positive comments. we will continue to improve the quality of this paper.
**Q2. The pape... | Summary: This paper revisits the learning problems of (multi-task) Bayesian linear bandit/semi bandits, which is interesting. The improvement of the Bayesian regret bound for the multi-task Bayes regret bound of HierTS is very marginal. For the remaining presented upper bounds, there are still near-optimal up to some $... | Rebuttal 1:
Rebuttal: **Q1. The description “The gap between the cumulative reward of optimal actions in hindsight and the cumulative reward of agent 24 is defined as regret” seems not to be aligned with the learning problems to be solved. Instead, we care about the action with the highest mean given a problem instance... | Rebuttal 1:
Rebuttal: # Response to All Reviewers
We sincerely thank all reviewers for their detailed reading and constructive comments. We address below one concern that is common among reviewers and then we address the concerns of each reviewer individually:
**Common Question 1. Discuss the novelty in the technique... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Canonicalization Perspective on Invariant and Equivariant Learning | Accept (poster) | Summary: The paper introduces a canonization perspective for designing frames in neural networks. Canonization maps inputs to their canonical forms, allowing efficient and even optimal frame design. The paper shows the connection between frames and canonical forms, leading to the development of new frames for eigenvect... | Rebuttal 1:
Rebuttal: We thank Reviewer 8tUB for the constructive review and acknowledging our theoretical contributions. We address your main concerns as follows.
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**Q1.** The overall writing could be improved. The authors claim to present a unified and essential view of equivariance learning, but the application... | Summary: This paper highlights a one-to-one connection between frames over finite groups with ‘canonization’ over the space on which the group acts. This allows authors to prove non-universality of SignNets. Furthermore, authors claim that this view helps highlight equivalence of certain existing algorithms (MAP and FA... | Rebuttal 1:
Rebuttal: We thank Reviewer TcMZ for the careful reading and the constructive review. We acknowledged that this paper uses conventional notations from the GNN theory literature, such as hash function, without detailed explanations. We will definitely improve the clarity of the writing and the readability of... | Summary: The work establishes a significant connection between canonicalization and frame averaging, demonstrating an equivalence between the two concepts. By establishing such a relationship, the study efficiently compares the complexity of frames and determines the optimality of frames in relation to the symmetries o... | Rebuttal 1:
Rebuttal: We thank Reviewer MgpB for the constructive review. We address your concerns as follows.
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**Q1.** A more detailed empirical evaluation would make the work more *complete*. For example, considering another molecule dataset (e.g., Alchemy) and texture reconstruction task (section 4.3 SignNet an... | Summary: This work makes connections between two model-agnostic approaches to designing equivariant networks: frame averaging and canonization. It is first shown that any function obtained using frame-averaging can also be obtained using canonization. Then it is shown that canonization is computationally more efficient... | Rebuttal 1:
Rebuttal: We thank Reviewer y3Wh for the constructive review. We address your concerns as follows.
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**Q1.** Could you please provide a comparison of compute memory and time for all the networks in the experiments. Especially, comparison with non-frame-averaging methods such as GIN would give better ins... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Temporal Action Segmentation via Boundary-aware Query Voting | Accept (poster) | Summary: The paper introduces BaFormer, a novel Transformer network designed to improve the efficiency of Temporal Action Segmentation (TAS) while maintaining high performance. BaFormer tokenizes video segments into instance tokens and utilizes instance queries for segmentation along with a global query for boundary pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for investing time in reviewing our work and acknowledging our contributions.
**1. For Weakness 1**
(**utilizes existing frameworks**) We understand the reviewer's concerns regarding our use of existing frameworks; however, we employ them solely to achieve the desired funct... | Summary: The authors introduce BaFormer, an innovative single-stage boundary-aware Transformer network designed for temporal action segmentation (TAS). This method employs instance queries for instance segmentation and a global query for class-agnostic boundary prediction, achieving substantial reductions in computatio... | Rebuttal 1:
Rebuttal: Thank you for the valuable suggestions to enrich the analysis of our model, most of which are included in the existing supplementary. It looks like you didn’t find the supplementary material -- we double-checked and it’s not a problem, and other reviewers haven’t had this issue either. Please info... | Summary: This paper proposes a fully-supervised TAS approach that predicts both segment and frame predictions. Two levels of predictions are then combined to form the final action boundaries. The approach achieves competitive performance and at the same time reduces inference time for better effiency.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contributions and below is our response regarding the reviewer’s concerns.
**1.For Weakness**:
>However, given the design of voting between frame-wise and segment-wise predictions, one expected advantage of having access to segment prediction is to redu... | Summary: The paper introduces BaFormer, a boundary-aware Transformer network designed to enhance the efficiency of Temporal Action Segmentation while maintaining high performance. BaFormer tokenizes each video segment as an instance token for intrinsic instance segmentation and employs instance queries for segmentation... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide discussions and explanations about your concerns as follows.
**1. For Weakness1**
(**Query voting is very simple**) We thank you for acknowledging our contributions to Matching Strategies and Voting. Regarding your concern about the simplicity of the proce... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Advection Augmented Convolutional Neural Networks | Accept (poster) | Summary: The paper introduces Advection Augmented Convolutional Neural Networks (ADRNet), integrating a semi-Lagrangian push operator into CNNs to improve spatio-temporal sequence prediction. Mimicking the Reaction-Advection-Diffusion equation, ADRNet addresses challenges in long-range information propagation and expla... | Rebuttal 1:
Rebuttal: **We appreciate the Reviewer’s positive assessment of our paper. We’re pleased that our innovative use of the semi-Lagrangian push forward operator and its potential impact on spatio-temporal tasks, such as weather prediction and traffic flow, were well-received. We are grateful for the constructi... | Summary: This work is based on an analogy of CNNs to Advection-Diffusion-Reaction PDEs: Classical ResNets can be understood as pure Diffusion-Reaction PDEs, without an Advection Term. Thus, this work proposes to introduce a new layer resembling the Advection Term, to allow for long-range transportation of information. ... | Rebuttal 1:
Rebuttal: **Thank you for the positive, thorough, and detailed feedback, which combines profound knowledge from both ML and numerical PDEs perspectives. We address each of your comments and suggestions below and hope our responses are satisfactory, and that you will consider revising your score. We welcome ... | Summary: This paper proposes a method to enhance the performance of networks in solving PDE problems using CNNs. To achieve this, the authors present a computational approach for efficiently retrieving information from distant points in spatio-temporal datasets. Their main contributions can be summarized as follows: 1)... | Rebuttal 1:
Rebuttal: **We thank the Reviewer for the overall positive assessment of our paper. In particular, we are delighted to read that the Reviewer finds that our paper addresses existing interpretability issues in CNNs, that the paper is clear and easy to follow, and that our ADRNet demonstrates high performanc... | Summary: This study addresses spatio-temporal prediction problems in the physical sciences. Since existing methods for temporal prediction mechanisms based on convolutional neural networks (CNNs) often underperform when propagating long-range information, the authors proposed a physics-inspired architecture that mimics... | Rebuttal 1:
Rebuttal: **We thank you for the detailed and constructive feedback. We are glad you found our submission well-motivated and our approach interesting. We have addressed all your comments and suggestions and are open to further discussion. We hope you find our responses satisfactory and consider revising you... | Rebuttal 1:
Rebuttal: # Global Response:
We thank the four Reviewers for their detailed and constructive feedback, which overall was positive, stating that our method and paper are (i) **motivated and interesting** (*Reviewer 2i2R*), (ii) **addressing issues in existing CNNs, clear and easy to follow, and demonstrati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unified Graph Augmentations for Generalized Contrastive Learning on Graphs | Accept (poster) | Summary: This paper investigates the generality of graph augmentations across various types of graphs and tasks. Firstly, the paper presents a new interpretation that unifies the various graph augmentations into local attribute modifications of each node from a basic message-passing perspective. Then, the paper develop... | Rebuttal 1:
Rebuttal: ***Q1. There are a few typos and grammar mistakes in the paper that need fixing.***
R1. Thanks for your thoughtful reminder. We will polish our manuscript to elevate the quality and clarity.
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***Q2. While the authors offer the proof of generality, they should elucidate how the theorem relat... | Summary: This paper explores the characteristics of local attribute modification in current graph contrastive learning methods. It then integrates diverse augmentation strategies with attribute learning into a unified framework. This unified approach introduces a novel and straightforward graph contrastive learning fra... | Rebuttal 1:
Rebuttal: ***Q1. The symbols $\leftarrow$ used in many equations, such as Eq. (4), should be changed to $\rightarrow$ for convenience.***
R1. Following your advice, we will revise the equations to replace the symbols $\leftarrow$ with the symbols $\rightarrow$ to enhance readability.
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***Q2. It's unc... | Summary: The paper reconsiders the formulation of graph augmentations in graph contrastive learning, introducing a novel perspective on GA through message passing. The proposed UGA framework interprets graph augmentations as mechanisms for aggregation and propagation between nodes, highlighting the significance of loca... | Rebuttal 1:
Rebuttal: ***Q1. The paper lacks presentation on the interpretability of learned graph augmentation vectors.***
R1. To provide interpretation for Graph Augmentation (GA), we would first introduce the interpretable explanations for GNNs (i.e., graph encoders) on graphs [1]. Within the encoder-relevant comp... | Summary: The paper introduces GOUDA, a versatile framework for Graph Contrastive Learning that addresses the limitations of existing graph augmentation techniques. GOUDA proposes a unified graph augmentation module capable of simulating various explicit graph augmentations, enhancing the generality and efficiency of GC... | Rebuttal 1:
Rebuttal: ***Q1. The paper could provide more intuitive explanations and step-by-step derivations to enhance understanding.***
R1. Thanks for your valuable suggestion. We will further clarify the mathematical formulas with intuitive explanations in the revised manuscript. Besides, please refer to the Appe... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification | Accept (poster) | Summary: This paper introduces Medformer, a transformer variant designed to learn complex dynamics from medical signals. This is achieved in three key ways :
1. Cross-channel patching for token embedding;
2. Multi-length patching for coarse and fine feature processing;
3. Multi-granularity self-attention for informati... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our work！ We appreciate you carefully reading our method, equations, figures, and even the code! We are happy to receive your endorsement and feel that our paper is easy to follow! Again, thank you for your comments. Here are the responses to your question... | Summary: The authors have created a model that seems to perform especially well for high-frequency waveform classification. They have benchmarked their model over several datasets, and its overall rank is higher than that of other models.
Strengths: The model successfully combines three (seemingly pre-existing ideas).... | Rebuttal 1:
Rebuttal: Thank you for your suggestions to our paper! We are happy you interested in our design for experiments and carefully review our paper and code. Here are the detailed response for your questions and concerns.
***
**Q1**: The limitations should be discussed in the main paper, not appendix.
**A1**... | Summary: This paper proposes a multi-granularity patching Transformer for medical time series classification. The proposed model, Medformer leverages cross channel patching to learn inter-channel correlations. It utilizes multi-granularity embedding for learning temporal patterns. Finally, it takes 2 stage self-attenti... | Rebuttal 1:
Rebuttal: Thank you for your suggestions on our paper! We are happy you feel our paper is well-structured and easy to follow. We respond to each of your questions and concerns. If you do not feel we have sufficiently justified a higher score, please let us know your further concerns and how to improve. Than... | Summary: In this paper, the authors introduced a multi-granularity patching transformer tailored specifically for medical time series classification. To leverage the characteristics of medical time series data, the model incorporated three unique features : cross-channel patching to leverage inter-channel correlations,... | Rebuttal 1:
Rebuttal: Thank you for your feedback and concerns regarding our paper! We appreciate your thoughtful questions. Here are the reponses to your question and concern. If you feel our response does not fully justify a higher score, please let us know how we can further improve our work. Thank you again for you... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback provided by all the reviewers. We appreciate reviewer ApjC for recognizing the novelty of our work in being the first to integrate cross-channel patching and a new multi-granularity self-attention mechanism. In response to the reviewers' insightful comments, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LocCa: Visual Pretraining with Location-aware Captioners | Accept (poster) | Summary: This paper explored integrating region captions into caption-based language-image pertaining. Specifically, in addition to the autoregressive text modeling on global captions, the proposed LocCa also predicts constructed strings of "{bounding box}-{region caption}" and "{region caption}-{bounding box}". Althou... | Rebuttal 1:
Rebuttal: 1. **Weaknesses 1: Objectives of LocCa**
Thanks for pointing out the "dual-faceted" loss. The understanding is correct, and we will revise this section for clarity. By "traditional approaches," we are referring to methods like OFA [1] and Florence-2 [2], which we will appropriately cite in t... | Summary: The paper introduces LocCa, a new visual pretraining approach that incorporates location-specific tasks into image captioning-based vision language models, improving their ability to extract detailed information from images. The authors propose two location-aware tasks, automatic referring expressions (AREF), ... | Rebuttal 1:
Rebuttal: 1. **Weaknesses: Data quality and dependency on external models & Typos**
- To address the concerns about data quality and dependency on external models like OWL-ViT, we would like to emphasize our strategic use of simple filtering techniques to enhance data quality. Specifically, we employ ... | Summary: This work proposed a location-aware pre-training for vision-language learning. The pre-training contains two tasks: one has location input and output caption/text; the other one has text input and output location of the corresponding object. The model is trained from scratch in ~1B large-scale image-text pairs... | Rebuttal 1:
Rebuttal: 1. **Weaknesses 1&2: Comparison with location-aware MLLMs**
Thanks for highlighting the connections with existing location-aware MLLMs like Shikra, Ferret, and GLaMM which we will cite in our final manuscript. We discuss the differences between LocCa and the referenced work as follows:
... | Summary: This paper presents LocCa, a novel visual pretraining paradigm that incorporates location-aware tasks into captioners. Specifically, LocCa employs two tasks, bounding box prediction and location-dependent captioning, conditioned on the image pixel input. This multi-task training helps LocCa significantly outpe... | Rebuttal 1:
Rebuttal: Previous work, such as RegionCLIP, has also attempted to align image regions with corresponding textual descriptions using contrastive pre-training schemes. However, these approaches tend to be resource-intensive and less efficient:
- In terms of trainable parameters, RegionCLIP [1] uses a pre-tr... | Rebuttal 1:
Rebuttal: We appreciate the valuable advice and generally positive feedback from all reviewers. Specifically, Reviewers 88rJ, ris8, and HN67 find our paper clear and well-written. Reviewer HN67 considers our methodology straightforward and elegant. Reviewers gCZj, ris8, and HN67 acknowledge that our experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion | Accept (poster) | Summary: The paper “FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion” introduces a novel framework called FuseMoE, designed to tackle challenges associated with multimodal data in machine learning. This framework addresses key issues such as missing elements, temporal irregularity, and sparsity in data, w... | Rebuttal 1:
Rebuttal: Dear Reviewer `sZSN`,
Thank you very much for your constructive suggestions. We are encouraged by your acknowledgment that our idea is promising, the theoretical proof is rigorous, and the experimental design is comprehensive, containing a large amount of supplementary information. Below, we addr... | Summary: This paper proposes an MOE-based model to handle multimodal data fusion. It addresses two challenges: missing modalities and irregularly sampled data trajectories. A Laplace gating function is applied to the MoE Backbone. An entropy regularization loss is proposed to ensure balanced and stable expert utilizati... | Rebuttal 1:
Rebuttal: Dear Reviewer `6SUH`,
We are grateful for your positive feedback and insightful comments. It is particularly encouraging to hear that you found our manuscript to have strong motivation and address important challenges, with good theoretical analysis and comprehensive experimental results. In the ... | Summary: The Paper introduces “FuseMoE”, a novel mixture-of-experts (MoE) framework that can handle multi-modal data even in scenarios with missing elements, sparsity of samples and temporal irregularity. It proposes an innovative Laplace gating function with theoretical proof to enhance convergence rates and predicti... | Rebuttal 1:
Rebuttal: **W1**: Yes, we mentioned this as one of our limitations in line 316 of Section 5. This issue was observed during our empirical evaluation, where the Time2Vec method we employed transforms a univariate time series into a high-dimensional vector. This transformation helps capture trend/seasonality ... | Summary: This papers proposes a novel MoE arcchitecture for handling and fusing multiple modalities, along with two core contributions:
- a novel router design that can handle missing modalities;
- a laplace gating function that is theoretically proven to ensure better convergence.
Strengths: - 1 This paper is general... | Rebuttal 1:
Rebuttal: Dear Reviewer `P6x6`,
We deeply appreciate your insightful comments and positive feedback. We are heartened by your recognition that our paper is well-written, with well-elaborated motivations. We are also pleased that you acknowledge the theory and extensive experiments we provided to justify th... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
We want to thank you for your valuable feedback and insightful reviews, which have greatly contributed to improving our paper. The following endorsements are truly motivating:
- Writing: Our paper is well-written and easy to follow, with informative tables and fig... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer | Accept (poster) | Summary: The paper proposes MaskRegulate, a method that utilizes reinforcement learning in the refinement stage for time-efficient training and accurate reward generation. Additionally, the authors introduce a regularity metric that can be used alongside HPWL in chip placement. To validate their proposed method, the au... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive comments. Below please find our response.
### Q1 Figure 3(a) is not clear
We are sorry for the unclear figures.
The red macro $M^1$ denotes the macro that has been adjusted; the yellow macro $M^2$ is the current macro that is adjusting; the blue macr... | Summary: This paper utilizes an online RL algorithm to adjust the existing placement layouts, instead of placing the blocks on the scratch. Additionally, the paper introduces one heuristic concept regularity, which better regularity results in higher PPA performance. Besides, this paper tests the PPA performance using ... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive comments. Below please find our response.
### Q1 Technical details are not clear.
Thank you for carefully reading our paper and providing these detailed and valuable comments. We are sorry for the unclear presentations.
**Q1-1 Termination function.*... | Summary: This paper proposes a novel approach called MaskRegulate for the refinement stage of macro placement, using reinforcement learning (RL) methods. Specifically, it trains an RL policy to adjust the existing placement layouts, from which the policy can receive sufficient information, instead of placing from scrat... | Rebuttal 1:
Rebuttal: Thank you for your valuable and positive comments. Below please find our response.
### Q1 Discussion of regularity in other paper
Thanks for your valuable comments.
[1] proposes a multi-level approach for macro placement that can leverage the hierarchy tree and effectively explore structural i... | Summary: This paper introduces a Macro Regulator that uses reinforcement learning (RL) to optimize existing macro placements. It reconsiders the application of RL in macro placement and incorporates a regularity metric into the reward function. The paper presents a comprehensive set of comparative experiments to ultima... | Rebuttal 1:
Rebuttal: Thank you for your detailed and valuable comments. Below please find our response.
### Q1 Limited benchmark scope.
Thanks for your valuable comments. In our work, we use the ICCAD 2015 contest as our benchmark, which is currently one of the largest open-source benchmarks that allows us to eval... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewers for carefully reviewing our paper and providing constructive comments and suggestions. We have revised the paper carefully according to the comments and suggestions, but we cannot upload the paper due to the NeurIPS rules. Our response to individual reviewers ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents an application of the RL algorithm in chip design optimization. They formulate the chip design process as an MDP process to make decisions based on the current state of the chip macro arrangement so that they can apply PPO to optimize the policies. Compared with starting from scratch, they ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Below please find our response.
### Q1 Compared with MaskPlace, the contributions of this paper lie in the expertise or experiences in the expert chip design area .... One thing I am concerned about is that NeurIPS may not be a good venue to discuss this cont... | null | null | null | null | null | null |
LT-Defense: Searching-free Backdoor Defense via Exploiting the Long-tailed Effect | Accept (poster) | Summary: The paper proposes a backdoor defense method called LT-Defense for detecting whether a model is a backdoored model. It is observed that the poisoned dataset can create the long-tailed effect, which causes the decision boundary to shift towards the target labels. By using this observation, LT-Defense can detect... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. **We employ the symbol # to denote the labels in the additional pdf file (e.g., #Tab.1, #Fig.1).**
**Comment 1:**
The problem and method are not presented clearly. For example, in equation (1), T is not defined and loss function should be argmin instead o... | Summary: In this paper, the authors explore search-free defense strategies against backdoor attacks in language models.
For task-agnostic backdoor attacks, the paper proposes a Head-Feature Rate score to detect backdoored models based on the observation that backdoor mapping (triggers to pre-defined vectors) disrupts t... | Rebuttal 1:
Rebuttal: Thank you for your efforts and valuable suggestions. **We employ the symbol # to denote the labels in the additional pdf file (e.g., #Tab.1, #Fig.1).**
**Comment 1:**
The design choices of the proposed methods are unclear, and the selection of hyperparameters does not provide general guidelines ... | Summary: This paper proposes a searching-free backdoor defense method for language models, named LT-Defense. LT-Defense is inspired by the long-tailed property of target classes, where a backdoored model tend to have an increased predicting rate for target classes compared with benign model. Specifically, LT-Defense us... | Rebuttal 1:
Rebuttal: Thank you for your efforts and valuable suggestions. **We employ the symbol # to denote the labels in the additional pdf file (e.g., #Tab.1, #Fig.1).**
**Comment 1:**
- The effectiveness of LT-Defense depends on carefully chosen thresholds for the Head-Feature Rate (HFR) and Abnormal Token Score... | null | null | Rebuttal 1:
Rebuttal: # Global Rebuttal
We sincerely thank all the reviewers for your valuable feedback and insightful comments. In the following, we first provide a global response to some shared concerns of multiple reviewers. Subsequently, we reply to the reviewers one by one for the convenience of checking. **We e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Small coresets via negative dependence: DPPs, linear statistics, and concentration | Accept (spotlight) | Summary: This paper studies corsets and aims to construct them using determinantal point processes (DPPs). The authors show that DPPs can provably outperform independently drawn corsets due to linear statistics and better concentration of DPPs.
Strengths: - The topic of coreset problems is essential in machine learnin... | Rebuttal 1:
Rebuttal: Thanks for the review. Here are answers to your comments/questions that we hope will clarify things.
> The writing can be improved. There are many theorems and remarks in the paper, which makes it confused about the main contribution. The theorems are technical but without explanations. What are ... | Summary: The paper proves concentration inequalities for linear statistics of samples form a DPP. In particular, a guarantee for the coreset sampling problem is shown: If the coreset is sampled from a DPP then the loss over the coreset approaches the loss over the full dataset faster then when the coreset is sampled un... | Rebuttal 1:
Rebuttal: Thanks for the positive review. Here are answers to some of your comments/questions.
> The markers in Figure 1 (a) and 2 (a) are different sizes, but I can't find information on what a marker's size encodes. I would appreciate a clarification.
The size of a marker placed at $x$ is proportional t... | Summary: This paper presents a study on the use of Determinantal Point Processes (DPPs) for constructing coresets in machine learning tasks. DPPs are random configurations of points with negative dependence, making them suitable for subsampling tasks like minibatch selection or coreset construction. Therefore, it is na... | Rebuttal 1:
Rebuttal: Thanks for the positive review. Here are answers to your questions.
> What is the time complexity of DPP-based coreset construction?
In general, sampling a DPP of cardinality $m$ among a ground set of size $n$ is $O(nm^2)$ provided the kernel matrix has been diagonalized beforehand. Depending on... | Summary: This work improves on the concentration bounds on DPP for coresets by explicitly relying on the fact that loss based coresets are linear functionals. To this end, this paper also add the coreset results for Non-symmetric kernels, (including additive coresets) expanding on the previous results on DPP Coresets (... | Rebuttal 1:
Rebuttal: Thanks for the positive review! Here are answers to your comments.
> When going from Lipschitz (Peres and Pemantle) to linear functional concentration, what exactly changes which leads to the better bounds?
The Pemantle-Peres results aim to address general Lipschitz functions in several variable... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of the paper, and their detailed comments, to which we have responded point by point below. We would like to take this opportunity to elaborate on the fact that our submission is appropriately viewed in the context of a wider programme of leveraging... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting Score Propagation in Graph Out-of-Distribution Detection | Accept (poster) | Summary: In this paper, the authors attempt to tackle the task of detecting Out-of-Distribution (OOD) nodes in graph data. To this end, the authors propose an augmentation method, namely Graph-Augmented Score Propagation (GRASP). GRASP performs the task of OOD detection in graphs by increasing the ratio of intra-edges ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and insightful questions. Below, we address each point in detail:
>**W1&Q5. Suggestion on providing more discussion w.r.t the exisiting graph OOD works and comments on the paper's contribution position.**
We appreciate your comments and the oppo... | Summary: This study investigates the effectiveness of score propagation in graph raph Out-of-Distribution (OOD) detection. It explores the conditions under which score propagation can be beneficial and proposes an edge augmentation strategy (GRASP) to improve its performance. The authors provide theoretical guarantees ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and insightful questions. Below, we address each point in detail:
>**W1. it is strongly suggested to release the source code of experiments.**
Sure! We have made the source code available at the following link: https://anonymous.4open.science/r/... | Summary: The paper proposes a methodology called Graph-Augmented Score Propagation to improve OOD detection performance on graphs. The key idea of the paper is an edge augmentation strategy which selectively adds edges to a subset of training nodes, which is combined with score propagation for the OOD node detection ta... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and insightful questions. Below, we address each point in detail:
>**W1&Q1. Concern about using MSP score to select S_id and S_ood.**
Thank you for this insightful question! We acknowledge the concern that the model can sometimes exhibit overcon... | Summary: This work aims to detect out-of-distribution (OOD) nodes on a graph by exploring useful OOD score propagation methods. It introduces a novel edge augmentation strategy, with a theoretical guarantee. The approach's superiority is empirically demonstrated, outperforming OOD detection baselines in various scenari... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and insightful questions. Below, we address each point in detail:
>**W1. Relationship between GRASP and the in-distribution classification accuracy.**
Fair concern! GRASP is a post hoc OOD detection method that does not interfere with the classi... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We are grateful for the insightful comments and valuable suggestions from all reviewers. In the following, we would like to summarize the contributions and revisions of this paper. As abbreviations, we refer to Reviewer hQev as R1, Reviewer xmGd as R2, Reviewer aWhs as R3,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos | Accept (poster) | Summary: This paper tries to reconstruct 3DGS (3D Gaussian Splatting) with Dash Cam Videos, which introduces obstructions on windshields. An adaptive image decomposition is applied to learn transmission images and obstruction images separately. The former is modeled with 3DGS in G3 Enhancement and are rendered into 2D ... | Rebuttal 1:
Rebuttal: **1) Why is the opacity map modeled to be independent of car position?**
Thank you for pointing out that, according to Fresnel's law, the opacity of the window should also depend on the car's position.
However, in experiments, we found that most of occlusions are caused by the objects between da... | Summary: This paper presents a new Gaussian Splatting novel view synthesis method specifically for in-vehicle dash cam videos containing various obstructions. To the challenges caused by obstructions, the proposed method separately represents the transmission and obstruction part of the camera capture. The transmission... | Rebuttal 1:
Rebuttal: **1) Moderate novelty because the similar components in the proposed method can be found, in part, from other related work.**
We appreciate if the reviewer could be more specific about the components. Then we can provide detailed explanations during the discussion period.
While both 3DGS and hie... | Summary: This paper deals with the problem of novel view synthesis in outdoor scenes captured with a dashcam. The authors develop a 3D Gaussian Splatting based method that is robust to common obstructions observed in dashcam videos, mainly due to the way these videos are captured: Mobile-phone holders, reflections and ... | Rebuttal 1:
Rebuttal: **1) Details of $\boldsymbol{\mathcal{L}_{opacity}}$.** The formulation of opacity loss is shown in the global response. The opacity map is learned in a self-supervised way without relying on additional labels.
**2) Why only compare with general Novel View Synthesis methods?**
As the first work ... | Summary: This paper focuses on using dash cam videos for 3D Gaussian Splatting-based outdoor scene reconstruction. To address challenges such as reflections and occlusions on windshields, DC-Gaussian introduces an adaptive image decomposition module to model these effects in a unified manner. Additionally, an illuminat... | Rebuttal 1:
Rebuttal: **1) Concern about the practical value of using dash cam videos for Novel View Synthesis**
Dash cam videos have unique values for autonomous driving. Dash cam videos deeply reflect the diversity and complexity of real-world traffic scenarios. They are used to provide large-scale, diverse driving ... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their insightful and constructive suggestions. We are glad that reviewers found: (1) The problem setting is novel (Reviewer QCPG) and meaningful (Reviewer zoS8); (2) The proposed method is interesting, smart (Reviewer zoS8), and effective (Reviewer zoS8, N7Lh);... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables | Accept (poster) | Summary: The paper introduces the problem that unobservable confounders in data can mislead and negatively affect the quality of feature attribution explanations. Feature attribution methods do not consider unobservable confounders which poses the risk of misinterpreting the attribution scores found, which could have c... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and insightful comments. Please find our response below.
**Response to Weaknesses**
* W1: We will follow your suggestions to enhance the readability.
* Minor comments: Fixed. Thanks!
**Response to Questions**
* Q1: In our paper, when addressing the impact of unob... | Summary: The paper addresses the challenge of unobservable confounders in feature attribution methods, which can lead to misinterpretations. The authors propose a novel approach called "data-faithful feature attribution," which trains models free of confounders using instrumental variables (IVs) to ensure that feature ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and insightful comments. Please find our response below.
**Response to Weaknesses**
* W1: The implementation and computation complexity of the two-stage training process is similar to the model training in the model-faithful feature attribution. In the first stage, ... | Summary: This paper addresses the problem of estimating causal effects with feature attribution, by applying SHAP and Integrated Gradients (IG) to two-stage models with instrumental variables. On synthetic datasets, the proposed methods IV-SHAP and IV-IG can better recover the ground-truth causal effects, compared to S... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and insightful comments. Please find our response below.
**Response to Weaknesses**
* W1: For the case of discrete $\tilde{\boldsymbol{x}}$, $\hat{M_\phi}$ is trained as a DNN classifier with softmax output, where the output element represents the probability of t... | null | null | Rebuttal 1:
Rebuttal: We are very grateful to the reviewers for their encouraging and insightful comments. To address the concerns, we provide detailed point-to-point responses as follows. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridge the Points: Graph-based Few-shot Segment Anything Semantically | Accept (spotlight) | Summary: This paper proposes a graph-based approach for Few-shot Semantic Segmentation (FSS) based on the Segment Anything Model (SAM).
The authors propose a Positive-Negative Alignment module to select point prompts and a Point-Mask Clustering module to align the granularity of masks and selected points.
The proposed ... | Rebuttal 1:
Rebuttal: # Response to weaknesses
1.
>In L173, "This is the precondition for the efficacy of our PMC module, as even slight errors could significantly impact the clustering accuracy" The authors should provide some visualization examples or experiments to verify this statement.
To verify this statement,... | Summary: The paper first proposes graph-based approach for SAM-based few-shot semantic segmentation, modeling the relationship of SAM-generated masks in an automatic clustering manner. A positive-negative alignment module and a post-gating strategy based on the weakly connected graph components, enabling a hyper parame... | Rebuttal 1:
Rebuttal: 1.
>The captions are not detailed, especially in Figure I and 2.
We apologize for the brevity of the captions due to consideration of space constraints. As suggested, we will include more details in the captions.
Specifically, for Figure 1, the updated caption will be: "*Performance comparison... | Summary: This paper extends SAM to few-shot semantic segmentation tasks by proposing an approach based on graph analysis and representation learning. The contributions include a Positive-Negative Alignment module to generate the initial points prompt using DINOv2 features, as well as Point-Mask Clustering and Post Gati... | Rebuttal 1:
Rebuttal: 1.
>The proposed method is specifically tailored for DINOv2 and SAM, is it possible to apply it to other foundation models? This could broaden the impact of the method.
Thanks for the suggestion. Our approach can be easily applied to other foundation models, and as suggested, we further provide ... | null | null | Rebuttal 1:
Rebuttal: We appreciate the detailed and constructive comments from the reviewers. For each of the concerns/questions, we have provided replies, revisions, and additional experiments accordingly, and included **a global one-page PDF (as attached below)** for figures mentioned in our response.
Please let u... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gated Slot Attention for Efficient Linear-Time Sequence Modeling | Accept (poster) | Summary: The paper introduces Gated Slot Attention (GSA), an enhancement of Gated Linear Attention (GLA), aimed at improving the efficiency of sequence modeling. GSA incorporates a selective gating mechanism to manage memory updates, leveraging a two-pass GLA structure. This approach allows GSA to be more context-aware... | Rebuttal 1:
Rebuttal: **Q:** *While GSA presents improvements, it largely builds on existing GLA techniques. The
enhancements, though valuable, might be seen as incremental rather than revolutionary, potentially limiting the perceived impact of the work.*
**A:** We appreciate your feedback and respectfully argue that ... | Summary: A major challenge is storing information in a bounded number of memory slots. This work builds on ideas from ABC and gated linear attention. ABC recursively updates the bounded-memory keys and values states over time, and computes a softmax with the queries at timestep t to produce the output at t. Gated linea... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and insightful questions.
We promise to revise the paper to address your concerns and make the presentation more clear and comprehensive in the next version.
---
**Q:** *It would be interesting to know how the "context aware query vectors" help GSA on real... | Summary: The paper explores a new variant of attention with bounded memory to reduce the growing memory size and thus mitigates the memory-intensive challenges of Transformers. The key idea is to set a memory bound, with a predetermined number of usable memory slots, and a gating mechanism to select or mix KV vectors f... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and insightful feedback.
We will revise the paper accordingly in the next version to enhance clarity in the abstract, introduction, and discussion sections, polish the writing, fix potential errors, and improve the figures.
---
**Q:** *The paper coul... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thorough examination of our work and their insightful feedback.
Your thoughtful comments and questions have significantly enhanced our submission and have been addressed in detail in individual responses.
We have additionally run many new empirical resu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Statistical Multicriteria Benchmarking via the GSD-Front | Accept (spotlight) | Summary: The authors propose a novel way of comparing classifiers to assess their effectiveness. They posit (1) that comparisons should allow for different quality metrics simultaneously, (2) that comparisons should take into account the statistical uncertainty induced by the choice of benchmark suite, and (3) that the... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for their time and thoughtful consideration of our paper. We believe that the more direct mathematical notation suggested by the reviewer regarding the empirical measure as well as the suggested more detailed explanation of the test statistic will ... | Summary: The authors propose a method for multicriteria evaluation of classifiers which is more informative than the Pareto front. The new GSD-front and is based on the previously proposed generalized stochastic dominance ordering (GSD) for classifiers. The authors also provided a sound inference framework, included hy... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for their time and thoughtful consideration of our paper. We are grateful for the suggestion to add a toy example showing the differences between GSD-front and Pareto-front in a more didactic way and the suggestion to add more details on hyperparam... | Summary: This submission studies the problem of comparing multiple classifiers under multiple evaluation criteria. It presents the construction of the GSD-front, i.e., the set of GSD-undominated classifiers, and an empirical variant called the $\epsilon$-empirical GSD-front.
Then, theoretical aspects of the GSD-front ... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful consideration. We appreciate the suggestions to include end-user recommendations and comparative studies for the PMLB datasets. We follow both in the revision (recommendations go to the main paper, studies to the appendix). We are pleased you find our paper "... | null | null | Rebuttal 1:
Rebuttal: **Global response**
We sincerely thank all reviewers for their thorough, high-quality, and detailed assessment of our manuscript. We are encouraged by the very positive, affirmative reviews and feel most grateful for the very precise and constructive suggestions on how to improve our paper furthe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning | Accept (poster) | Summary: The paper introduces the Star-Agents framework, an innovative system designed to enhance the quality of datasets used for instruction-tuning of large language models (LLMs). The framework addresses the challenges of collecting high-quality and diverse data by automating the process through multi-agent collabor... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments.
**Q1**: Different numbers of agents or agent pairs on the results.
**A1**: We have conducted experiments to explore the impact of varying the number of agent pairs on the results. As shown in Table 1, we observed that as the number of agent pairs decreases,... | Summary: The paper introduces the "Star-Agents" framework, designed to optimize data for instruction tuning in large language models (LLMs). This system automates the process of enhancing dataset quality by employing a multi-agent approach. Empirical studies demonstrate that the optimized datasets lead to significant p... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments.
**Q1**：What is the overhead of this proposed method, like wall-clock time?
**A1**：Thank you for your insightful question regarding the overhead of the proposed method.
The computational overhead of our proposed method primarily depends on the inference comp... | Summary: The Star-Agents framework presents an advanced approach for enhancing data quality in instruction-tuning of large language models (LLMs) through multi-agent collaboration and automated assessment. By generating diverse instruction data using various LLM agents and evaluating it with a Dual-model metric, this f... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments.
**Q1**：What is the size and composition of the datasets used in your experiments?
**A1**：The datasets used in our experiments consist of 70,000 samples based alpaca evol instruct datasets [r1]. Each sample is paired with an instruction and a corresponding r... | Summary: The paper presents a framework for enhancing the quality of instruction datasets used for tuning large language models (LLMs). The proposed framework, Star-Agents, leverages multi-agent collaboration to generate, evaluate, and refine instruction data automatically. The approach comprises three main components:... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments.
**Q1**: Small Evaluation Datasets
**A1**: We followed several seminal works[r1,r2], and used widely accepted datasets such as Mt-bench, Vicuna-bench, and the WizardLM testset for our evaluations. These datasets are commonly utilized to assess the effectiven... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for the overall positive reviews and helpful feedback, which we have incorporated to improve our work. If any remaining doubts exist, we encourage the reviewers to engage in the discussion so we can clarify them. If all concerns have been resolved, we kindly ask the revi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks | Accept (spotlight) | Summary: This paper introduces a new approach called Robust Prompt Optimization (RPO) for defending large language models (LLMs) against jailbreaking attacks. The key contributions are:
1. Formalizing a minimax optimization objective for ensuring safe LLM outputs under a realistic threat model involving various attack... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and positive review. We're pleased that the reviewer found our work to have *significant contributions in formulating and addressing the challenge of defending LLMs against jailbreaking attacks* and appreciated our *combination of theoretical gro... | Summary: This paper introduces Robust Prompt Optimization (RPO), a novel method for defending LLM against jailbreaking attacks, which manipulate prompts to induce harmful behavior. Inspired by the Adversarial Training, RPO optimizes a suffix for the LLM prompt, ensuring safe responses even when the input is modified by... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and positive review. We are glad the reviewer found our use of optimization for defense innovative, theoretical analysis solid, and empirical evaluation elaborate and extensive.
> *Transferrability discussion*
We are glad the reviewer found the transferab... | Summary: The paper proposes a novel method, Robust Prompt Optimization (RPO), to enhance the robustness of large language models (LLMs) against jailbreaking attacks. Existing defenses, which operate during pre-training or fine-tuning stages, incur high computational costs. The proposed RPO method optimizes a lightweigh... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive assessment of our work. We greatly appreciate the reviewer's recognition of our paper's substantial improvements in reducing attack success rates and its computational efficiency. We're pleased that the reviewer found our theoretical analysis thoroug... | Summary: In this paper, the authors proposes a new defense, called Robust Prompt Optimization(RPO) to defend the jailbeak attack. It optimizes a secure suffix with min-max optimization. Experiments reveal that it can defend multiple existing attacks
Strengths: 1 This paper is well written.
2 The authors give theoreti... | Rebuttal 1:
Rebuttal: Thank you for the insightful and helpful review. We are glad the reviewer found the paper well-written, theoretical results meaningful, and method easy to understand. We address the concerns below.
> *RPO obtains high ASR against the JBC attack on Vicuna which is much worse than the Rephrasing de... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
We sincerely thank you for your thorough and insightful reviews of our paper on Robust Prompt Optimization (RPO). We appreciate the positive feedback and constructive suggestions that have helped improve our work.
We are pleased that the majority of reviewers were ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
High-Resolution Image Harmonization with Adaptive-Interval Color Transformation | Accept (poster) | Summary: This work proposes the Adaptive-Interval Color Transformation (AICT) method for harmonizing high-resolution composite images. The proposed AICT first uses a parameter network to predict multiple curves (as 3D LUTs) to perform pixel-wise color transformation at low-resolution. The AICT then adjusts the sampling... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
***W1 & Q1: Limited novelty of using two 3D LUTs for color transformation, which is a combination of ideas from existing LUT-based image enhancement methods [47, 48] but lacks necessary discussions and experimental verifications..... | Summary: This paper proposes an AdaptiveInterval Color Transformation method (AICT) for high-resolution image harmonization, which predicts pixel-wise color transformation and adaptively adjusts the sampling interval to model local non-linearities of the color transformation at high resolution.
Strengths: 1. The paper... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
***Q1: The authors should conduct experiments using the training/test set of HAdobe5k which focuses on high-resolution, and report the results under different resolutions (1024, 2048, etc). More baselines should be compared in this... | Summary: This paper presents a new method for harmonizing the color of foreground objects added to scenes with the colors of the background original image. The authors focus on a model able to present good results on high-resolution images. The idea is to learn two different sets of Look-Up-Tables. The first set aims a... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
***Q1:The authors abuse notation regarding 3DLUTs in color image processing.***
***A1:*** We thank the reviewer very much for pointing out the inaccurate usage of the term 3DLUTs. Following the reviewer's suggestion, we will ren... | Summary: This paper proposes a new method called Adaptive-Interval Color Transformation (AICT) for high-resolution image harmonization. The key ideas are:
1. Predicting pixel-wise color transformations using a parameter network that generates multiple 3D lookup tables (LUTs).
2. Separating the color transform into cas... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
***Q1: The effectiveness of AICT for ultra high-resolution images is unclear and should be validated with appropriate benchmarks.***
***A1:*** Thanks for this suggestion. In Table 1 of the paper, we have reported the results on ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our sincere gratitude to all the reviewers for their constructive feedback and for recognizing the performance and efficiency of our proposed method. We appreciate your valuable suggestions and have carefully addressed each point in our responses.
1.**Rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying General Mechanism Shifts in Linear Causal Representations | Accept (poster) | Summary: Recently, causal representation learning has drawn a lot of attention in the representation learning area, where it considers a causal relation among the latent generative factors. This work is under the setting of linear causal representation learning. Data $X$ comes from a linear mixing $X=GZ$ of the unknown... | Rebuttal 1:
Rebuttal: Thank you for your efforts on reviewing our paper! Below, we address your concerns.
### From weakness
1. We provided some motivation from Line 34 to Line 38 in the paper, and also included a toy example in Figure 1. Additionally, we provided a Psychometrics data application (Section 5.2) to refl... | Summary: This work studies the nontrivial problem of causal representation learning from the perspective of mechanism shifts within the latent SCM. Specifically, the authors relax existing restrictive assumptions in interventional causal representation learning, such as data generated from single-node perfect intervent... | Rebuttal 1:
Rebuttal: We thank the reviewer's valuable suggestions and for recognizing the novelty of our work. We next address the reviewer's concerns.
### From weaknesses
* Please refer to our global response regarding linear models.
* Thanks for pointing this out. We apologize for the typo in Proposition 2, line ... | Summary: This paper considers the setting of linear causal representation learning (CRL) with possibly multi-node interventions. Instead of focusing on the task of identifying the causal structure, which is recently shown to be impossible, the authors instead focus on the task of identifying mechanism shifts i.e. which... | Rebuttal 1:
Rebuttal: We thank you for your efforts in evaluating our paper. Below, we address your concerns.
### From weaknesses
1. In the population setting, step 3 will lead to identifiability provably, and it is proven in Theorem 3 that $L_i^{k,k'} = 0$ if and only if $Z_i$ is not a shifted node between environme... | Summary: This work studies the problem of detecting the mechanism shifts in a novel way by considering the latent nodes. The authors prove identifiability results based on assumptions softer from prior identifiability results for causal representation learning. Their method is based on ICA and is evaluated empirically ... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's recognition of our paper's novelty and contribution. We now address the reviewer's concerns.
### From Weaknesses
> Significance of contributions...
Please refer to the global response for this concern.
> Experiments...
The most recent CRL method with official code re... | Rebuttal 1:
Rebuttal: We appreciate the time and efforts all reviewers have invested in evaluating our manuscript. We are grateful for the constructive feedback and insightful comments. It has come to our attention that there are some common questions regarding our paper, particularly concerning:
1. Can existing CRL m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection | Accept (poster) | Summary: This paper makes a substantial contribution to the field of auditory attention detection (AAD) by presenting a more accurate and efficient model, the dual attention refinement network with spatiotemporal construction (DARNet). The authors effectively address critical limitations in current AAD algorithms, spec... | Rebuttal 1:
Rebuttal: # Rebuttal:
Thank you so much for your thoughtful comments and the time to provide constructive feedback!
### Weaknesses:
1. **Regarding grammatical errors and unclear sentence expressions in the paper:**
We are very grateful to the Reviewer for carefully reviewing the paper. We have thorou... | Summary: The paper proposes DARNet, a dual attention refinement network with spatiotemporal construction for auditory attention detection (AAD). The network captures spatiotemporal features and long-range latent dependencies from EEG signals, leading to improved classification accuracy and reduced parameter count compa... | Rebuttal 1:
Rebuttal: # Rebuttal:
Thank you so much for your thoughtful comments and the time to provide constructive feedback!
## Weakness:
1. **Supplementary cross-subject experiment results:**
Recently, we conducted additional leave-one-subject-out cross-validation experiments on the publicly available DTU an... | Summary: This paper proposes a new architecture for auditory attention detection (AAD) that consists of three key components: 1) Convolutional layers applied to the temporal and spatial dimensions of EEG signals in a sequential manner to extract features. 2) Two attention layers to process these features. 3) A feature ... | Rebuttal 1:
Rebuttal: # Rebuttal:
Thank you so much for your thoughtful comments and the time to provide constructive feedback!
### Weakness:
1. **A more in-depth discussion on why their spatial module is more effective than existing methods.**
Auditory attention decoding requires processing EEG signals under co... | Summary: The manuscript is aim to capture the and spatial distribution information and long-range dependencies in EEG signals. Two modules are designed to solve the upper two challenges, spatiotemporal construction module and dual attention refinement module. The experiment have shown the superiority of the proposed m... | Rebuttal 1:
Rebuttal: # Rebuttal:
Thank you so much for your thoughtful comments and the time to provide constructive feedback!
### Weakness:
1. **Innovation summary:**
We apologize for any confusion that may have led to the perception of a lack of novelty in our manuscript. We have clarified and summarized our ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions | Accept (poster) | Summary: In this work, the authors study the difference between underparameterised and overparameterised networks in terms of the collaboration between consecutive layers. They find that under-parameterized networks tend to foster co-correlation among layers to improve performance, whereas performance of over-parameter... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and careful review. Your detailed examination of the paper and identification of its flaws are greatly appreciated. My rebuttals are as follows:
## The reason why study co-correlation:
The motivation for studying co-correlation actually comes from th... | Summary: This paper investigates the implicit bias of gradient descent in over-parameterized neural networks, particularly focusing on the collaboration between consecutive layers. The study first introduces Dirichlet energy to evaluate the adversarial risk. Then it decomposes Dirichelt energy between layers and measur... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and careful review. The rebuttals are as follows:
## About the relation between co-correlation and adversarial robustness
The relationship between co-correlation and adversarial robustness is bridged by the Dirichlet energy, as depicted in Theorem 4.5 and Eq.... | Summary: This work focuses on studying the implicit bias of correlation between intermittent layers of neural nets and uses this metric to analyze the adversarial robustness of networks in under and over parameterized regimes. The authors further use these findings to suggest that in the under parametrized case, gradie... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and careful review, you indeed checked the details of the paper and pinpointed the flaws. It is highly valued feedback. My rebuttals are as follows:
## More experiments
### Experiments on ResNet50 and Wide-Resnet50
We conducted additional experiment... | Summary: In this work, the authors study the tradeoff between generalization and adversarial robustness from the perspective of collaboration between the layers of a neural network (NN). They adapt the concept of Dirichlet energy to analyze the robustness of different layers in the network. Decomposing this across laye... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and careful review. We appreciate the opportunity to clarify and expand on our work. Our rebuttals are as follows:
## Theoretical Implications
Although it may not be immediately apparent in the paper, the implications of our work are significant. From a theore... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their careful review.
## Motivation of the paper
This paper is mainly to address the research problem:
*Whether there a collaboration between layers against adversarial examples during Gradient Descent?*
To quantify this collaboration, we introduce a ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations | Accept (poster) | Summary: The authors present a new method for training diffusion models from corrupted data.
The presented method is an EM algorithm that in turn (i) uses the current model to sample clean images given the noisy input and then (ii) use these clean samples to train the diffusion model.
The authors show the results for d... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the constructive suggestions. We are happy that the reviewer recognizes our idea as "simple" and "elegant", our method "can have a substantial practical impact", and our paper "is well structured". We have provided explanations and clarifications regarding all th... | Summary: This paper we proposes an expectation-maximization (EM) approach to train diffusion models from corrupted observations. The extensive experiments show the effectiveness of the proposed method.
Strengths: 1. The experiments are extensive.
2. The proposed approach addresses a significant challenge in the field ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the insightful suggestions. We are happy that the reviewer recognizes our approach "addresses a significant challenge in the field", and our experiments are "extensive" and "show the effectiveness of the proposed method". We will release all the code and the chec... | Summary: Authors propose using the EM algorithm to train a diffusion model on corrupted data. To initialize the process, the proposed method requires access to a "limited number of clean samples." The authors claim the method allows convergence to the true data distribution.
Strengths: **Originality.** The idea of usi... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the thoughtful comments. We believe there may have been some misunderstandings regarding our work, which we will clarify. Below we carefully address all the questions from the reviewer.
**1. Quantifying the number of required clean images**
  We ... | Summary: The authors introduce a new framework for training diffusion models from corrupted data. Prior work on this research topic is based on the Ambient Diffusion framework or Stein's Unbiased Risk Estimate (SURE) idea. The authors propose an alternative methodology based on the Expectation-Maximization algorithm. T... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the invaluable feedback. As noted by the reviewer, our idea is "fresh" and "elegant," our algorithm is "agnostic to the type of corruption," and our results "outperform the prior state-of-the-art." Below we provide more explanations and additional results to add... | Rebuttal 1:
Rebuttal:
We thank all the reviewers for their professional and constructive feedback. We are encouraged by their recognition of our paper's technical importance and novelty (qpvz, Kirh, bEKL), broad applicability (qpvz, bEKL), impressive performance in extensive experiments (qpvz, Kirh), and high-q... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity | Accept (poster) | Summary: The paper consider the problem of optimizing second-order smooth and strongly convex functions where the algorithm is only accessible to noisy evaluations of the objective function it queries. Authors provide the first tight characterization for the rate of the minimax simple regret by developing matching uppe... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please find our responses below.
> Almost no mention is made of work motivation.
- The importance of stochastic optimization is clearly mentioned in our introduction.
- The motivation for this work stems from a lack of understanding of the sample complexity under ... | Summary: The paper studies zero order stochastic optimization (the learner has access to noisy function evaluation only) assuming the objective is $M$-strongly convex and has a $\rho$-Lipschitz (in forbenius norm) Hessian. Matching upper and lower bounds are presented, which establish a tight result of suboptimality $\... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback from the reviewer. Below are the point-to-point responses to the comments.
>Implication of bounded variance from 1-subgaussianity
We would like to clarify that our intention in mentioning both assumptions was to use the sub-Gaussian assumption to simplify ... | Summary: This work studies the convergence of zeroth order stochastic optimization for a class of strongly convex, second-order smooth objective functions. The authors assume that the noisy one-point feedback oracle is available, and the additive noise is subgaussian. Both the asymptotic upper bound and the matching l... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback from the reviewer and will state equation (6) formally as a Theorem. Below are the point-to-point responses to the comments.
>Necessity of the two-stage algorithm
The two phases of our algorithm are designed to handle distinct challenges. The first stage... | Summary: The paper studies the problem of zero-order optimization of a strongly convex function whose Hessian is Lipschitz continuous. The proposed algorithm exploits zero-order information from the oracle to estimate the Hessian and gradient of the function at each iteration. Using these estimates, the authors employ ... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback from the reviewer and have addressed each comment below.
> Why there is a need for two different gradient estimators?
The two gradient estimators are tailored to the two phases of our algorithm, which serve distinct purposes. The first stage of our algor... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nonstationary Sparse Spectral Permanental Process | Accept (poster) | Summary: Point process are generative models for datasets of points in (typically 1D, 2D, 3D Euclidean) space, e.g. datasets of rain drops, taxi pickup locations, and a Poisson process consists of an intensity function $ \lambda: \mathcal{X} \to \mathbb{R}^+ $ that roughly shows how likely a point is to appear at $x$. ... | Rebuttal 1:
Rebuttal: > Q: My only concern is the lack of substantial novel contribution......
I feel that it is an elegant although not-too-difficult extension of GSSPP and I do not feel the contribution is significant enough for publication at NeurIPS. I enjoyed the paper and I feel the contribution is certainly very... | Summary: Point process models are a commonly used technique for analysis of event-based data. Gaussian Cox processes are an example which use GPs to model the intensity function in a Cox process, which itself is a specific case of a Poisson process where the intensity function is a stochastic process. Generally, these ... | Rebuttal 1:
Rebuttal: > Q: As mentioned earlier, I think the rationale for using of a very small number of inducing points etc. for some of the baselines needs to be clarified in the text; is it the case that if you increase this number to 50+ that the baselines begin to outperform the models proposed by the authors?
... | Summary: The paper introduces an approach to modeling permanental processes by utilizing a sparse spectral representation of nonstationary kernels, termed as Nonstationary Sparse Spectral Permanental Process (NSSPP) and its deep kernel variant (DNSSPP). This method addresses the limitations of traditional permanental p... | Rebuttal 1:
Rebuttal: > Q: Performance comparison with a baseline that employs stacked mappings of stationary kernels.
A: Thanks for the suggestion. A baseline that employs stacked mappings of stationary kernels is an important baseline. To show the source of the performance improvement, we have re-compared with a de... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their efforts in providing insightful comments and constructive feedback. We are encouraged that reviewers recognize that our paper proposes an interesting extension on nonstationary permanental processes [R1,R2,R3], introduces an deep kernel variant to enhance... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference | Accept (poster) | Summary: This paper proposes to modify standard transformer architectures to use less allreduce operations from tensor parallelism. Specifically, the proposal is to allow each shard of the hidden state to operate as an individual complete hidden state during attention layers and each shard of the attention layer to ope... | Rebuttal 1:
Rebuttal: **Performance on ReCoRD:**
Please see the common response. This was due to a bug in the LM Evaluation Harness (Issue 1647 on GitHub).
**Results only up to 760M parameters:**
Indeed, it would have been great to evaluate models that are several billion parameters large. Unfortunately, we do not h... | Summary: The paper introduces a modification to the standard Transformer architecture aimed at reducing inter-device communication during inference in multi-device systems. By predetermining the degree of model parallelism, computations on each device can operate independently, allowing collective operators to overlap ... | Rebuttal 1:
Rebuttal: **Applicability to vision tasks:**
Yes, our approach will also translate to encoder-only Transformers including those used in vision tasks such as ViT[4]. Nonetheless, we narrowed the scope of this work to focus our resources on the decoder-only Transformer models that are typically the largest a... | Summary: This paper propose Kraken, a new evolution of the standard Transformer to reduce the communication cost of inference. Kraken overlaps the collective operations with computing, therefore achieves smaller pre-filling time cost. Kraken is specially designed for tensor parallelism on multi-device environments.
St... | Rebuttal 1:
Rebuttal: **Comparison to Related Work:**
Our approach is readily compatible with nearly all related work such as DeepSpeed, FlashAttention, and PagedAttention. Our evaluation (Figure 4) uses FlashAttention and TensorRT-LLM’s implementation of AllReduce which is even more performant than NCCL on systems equ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for reading our work, providing helpful feedback, and finding promise in our approach.
After submission, we learned of bugs in LM Evaluation Harness that affected the scores on some SuperGLUE benchmarks such as ReCoRD. We have evaluated all models again using the most r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Neuron Attributions in Multi-Modal Large Language Models | Accept (poster) | Summary: The paper describes a neuron attribution method for multimodal LLMs that the authors term "NAM". The method is broken down into two steps, a first step that uses image segmentation and a pretrained attribution algorithm, Diffusers-Interpret, to assign relevance scores to the final hidden state of the model. Th... | Rebuttal 1:
Rebuttal: Dear Reviewer gq3m:
Thank you very much for your comments. We sincerely appreciate the time and effort you have dedicated to reviewing our work! Below, we meticulously provide responses to each of your comments and outline the modifications made to the manuscript based on your suggestions. Hope t... | Summary: This paper proposes a method to attribute the multimodal output to neurons that are influential in the generation process. The approach considers image-text multimodal LLM where 1) the base LLM's internal representation is directly used to generate text, and 2) also passed to an image generation model. These t... | Rebuttal 1:
Rebuttal: Dear Reviewer VfTi:
Thank you very much for your comments. We sincerely appreciate the time and effort you have dedicated to reviewing our work! Below, we meticulously provide responses to your comments and outline the optimizations made to the manuscript based on your suggestions. Hope that our ... | Summary: The work introduces a novel Neuron Attribution Method (NAM) tailored for MLLM. The NAM approach aims to reveal the modality-specific semantic knowledge learned by neurons within MLLMs, addressing the interpretability challenges posed by these models. The method highlights neuron properties such as cross-modal ... | Rebuttal 1:
Rebuttal: Dear Reviewer VhwX:
Thank you very much for your comments. We sincerely appreciate the time and effort you have dedicated to reviewing our work! Below, we meticulously provide responses to each of your comments and outline the optimizations made to the manuscript based on your suggestions. Hope t... | Summary: Summary:
This paper introduces NAM (Neuron Attribution Method), a novel approach for attributing neurons to specific semantic concepts in multimodal large language models (MLLMs). The key contributions are: (1) A method to identify modality-specific neurons (text or image) that are crucial for particular seman... | Rebuttal 1:
Rebuttal: Dear Reviewer kLxz:
Thank you very much for your comments. We sincerely appreciate the time and effort you have dedicated to reviewing our work! Below, we meticulously provide responses to each of your comments and outline the modifications based on your suggestions. Hope that our responses could... | Rebuttal 1:
Rebuttal: Dear Reviewers:
We gratefully thank you for your valuable comments! We are truly encouraged by the reviewers' recognition of that our work has **addressed an important gap in MLLMs** (by all Reviewers), **provided interesting and instructive observations** (Reviewer VhwX and VfTi), and **conducte... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kermut: Composite kernel regression for protein variant effects | Accept (spotlight) | Summary: The authors introduce a family of Gaussian process based regression models for protein variant effect prediction. The "composite" kernel introduced makes use of structural information (i.e. closeness in 3d space) as well as pre-trained sequence and/or structure models like ESM2 and ProteinMPNN (via embeddings ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough insights and particularly the raised issues regarding the related works section as well as their comments on the methods section.
**Weaknesses:**
- "The description and discussion..."
- "For example, ..."
- "The authors should..."
We ack... | Summary: The authors suggest a method to predict the effect of mutations given sparse data. Their method is based on identifying the similarity of different sites on a protein by embeddings from large language models and structure. They show that their method performs state of the art mutation effect prediction. They a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough insights and ideas on kernel improvements.
**Weaknesses:**
- “Epistasis is only included through sequence embeddings; in particular, the impact of structure is purely linear.”
This is true and to some extent by design. With CoVES, Ding et ... | Summary: This paper proposes a kernel regression model to predict protein mutational effects. The model includes kernel functions crafted for the task. In specific, it includes a kernel that measures sequence similarity based on ESM-2 features, a local structure similarity kernel based on ProteinMPNN probability, and o... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their efforts.
**Weaknesses:**
- “Current formulation of Kermut does not provide transferability to different protein sequences, while previous zero-shot prediction methods are capable of predicting variant effects without prior experimental data on the sa... | null | null | Rebuttal 1:
Rebuttal: We would first and foremost like to thank all reviewers for their constructive, high-quality reviews. Based on these valuable inputs, we have made a series of alterations to our manuscript, which we will list here. These will be described in greater detail in the individual rebuttals:
- We have m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Certified Adversarial Robustness via Randomized $\alpha$-Smoothing for Regression Models | Accept (poster) | Summary: This paper considers randomized smoothing for (unbounded) regression models. An $\alpha$-trimming procedure is proposed in order to increase certification strength. Experiments to illustrate the effectiveness of the approach are conducted on synthetic datasets as well as camera re-localization tasks.
Strength... | Rebuttal 1:
Rebuttal: **The problem of scaling up certified robustness (e.g., via randomized smoothing-based methods) to regression models is an important one to consider, and is certainly of interest to the Neurips audience.**
The authors would like to express their gratitude to the reviewer for recognizing the signi... | Summary: Prior work extends the notion of a 'certified robustness radius' from classification tasks to a regression task. The prior state-of-the art for calculating these certified robustness bounds in the regression setting had a major shortfall: it exhibited major instabilities when applied to values with an unbounde... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time spent on this manuscript, as well as their comments on the contribution of the work. Below, we reply to each comment separately.
**The authors write that the method suggested by prior work in [17] fails for unbounded quantities in regression because i... | Summary: This work extends current randomized smoothing on the regression task via the $\alpha$-trimming filter. A new probabilistic certificate bound for is given against the $l_p$ norm attack for all regression models with the unconstrained output. Comprehensive synthetic simulations and evaluation on the real-world ... | Rebuttal 1:
Rebuttal: The authors appreciate the reviewer's recognition of the theoretical rigor in the paper. This work represents the first universal certification framework for regression models designed to defend against adversarial examples during the inference stage. Below, we provide a detailed response and furt... | null | null | Rebuttal 1:
Rebuttal: The authors thank the reviewers for their feedback and constructive comments. Please find visualisation of the probabilistic certicates vs $\alpha$ in the attached PDF (Reply to reviewer wW8d).
Pdf: /pdf/b0def38fe6ef72be294e84979ddb87496af8e2d1.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models | Accept (poster) | Summary: This study presents BeNeDiff that integrates behavior-informed latent variable models with state-of-the-art generative diffusion models to investigate neural dynamics during behavioral tasks. The methodology involves identifying fine-grained and disentangled neural subspaces and synthesizing behavior videos to... | Rebuttal 1:
Rebuttal: Dear Reviewer HCYS,
Thank you for your recognition of the work and insightful questions. We provide clarifications and new results that we have generated to address your questions below.
> This work primarily relies on a behavior LVM for neural activity disentanglement, which can be influenced b... | Summary: The paper proposed BeNeDiff to learn disentangled neural trajectories together with a generative diffusion mode for behavior. BeNeDiff leverages beta-VAE for learning of disentangled space space Additional behavior generation module is applied for interpretation of the neural subspace. It is interesting to see... | Rebuttal 1:
Rebuttal: Dear Reviewer CLKJ,
Thank you for your valuable comments. We would like to make the following clarifications. Hopefully these will resolve most of your concerns, and they can be taken into account when deciding the final review score.
> It is unclear to me if the behavior video generation part i... | Summary: The authors implement a VAE with behavioral labels to identify behavior-specific latent variables. They use these latents to then create a generative model for experimental video. They focus on the application of their model to a wide-field cortical recordings of a mouse during an experimental visual task. The... | Rebuttal 1:
Rebuttal: Dear Reviewer o3cJ,
Thank you for your detailed and constructive comments. We would like to make the following clarifications. Hopefully these will resolve most of your concerns, and they can be taken into account when deciding the final review score.
> Each of the primary contributions to neura... | null | null | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their insightful feedback and suggestions. We appreciate the positive comments which characterized our work as having an `"interesting and clever"` idea for leveraging video diffusion models to interpret inferred neural subspa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Control the Smoothness of GCN Features | Reject | Summary: The paper "Learning to Control the Smoothness of GCN Features" investigates the impact of activation functions, specifically ReLU and leaky ReLU, on the smoothness of node features in Graph Convolutional Networks (GCNs). It provides a geometric characterization of these effects, showing how altering the input'... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. In what follows, we provide point-by-point responses to your comments on the weaknesses and limitations of our paper and your questions.
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**1: The removal of white space in the paper makes it hard to read.**
**Response:** We appreci... | Summary: The paper addresses the challenge of balancing smooth and non-smooth features in graph convolutional networks (GCNs) for node classification. Building on previous work that highlighted the correlation between feature smoothness and classification accuracy, the authors propose a novel method to control the smoo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. In what follows, we provide point-by-point responses to your comments on the weaknesses and limitations of our paper and your questions.
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**1: The paper does not compare to established baselines from other competing methods. E.g. tab... | Summary: The paper studies how GCN smoothes node features in terms of unnormalized and normalized smoothness. The results show that adjusting projection can alter the normalized smoothness to any desired level. Based on this, the paper proposes a new method SCT to let GCN learn node features with a desired smoothness t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, valuable feedback, and endorsement. We appreciate your invaluable suggestions and will revise the paper accordingly. In what follows, we provide point-by-point responses to your comments on the weaknesses of the paper.
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**1. While I can see that normal... | Summary: This paper first shows that in GCN, the output of ReLU or LeakyReLU lies on a sphere whose input is characterized by components parallel and perpendicular to $\mathcal{M}$, the space spanned by eigenvectors for the maximum eigenvalue of a graph. As a corollary, this paper shows that these activation functions ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, valuable feedback, and endorsement. In what follows, we provide point-by-point responses to your comments on the weaknesses and limitations of our paper and your questions.
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**1: I need help understanding the explanation in Section 3.3. More specific... | Rebuttal 1:
Rebuttal: Dear reviewers,
We appreciate your thoughtful reviews and valuable feedback, which have helped us significantly improve the paper. We thank the reviewers’ praise for the originality, quality, clarity, and significance of our work. We are encouraged that reviewers found our proposed approach is we... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper deals with (Over-)smoothing is Graph Neural Networks. While it was previously known that GCN-type GNNs oversmooth, this paper reexamines the case for GCN-type architectures with Relu-type activation functions in terms of *normalized* smoothness. The authors show that the convergence behaviour of GCNs... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. In what follows, we provide point-by-point responses to your comments.
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**1. There is a slight improvement to be found in models with SCT, but this is not too surprising, as these models also have more parameters, and mostly imp... | Summary: This paper studies how ReLU and Leaky ReLU affect the smoothness of node features in graph convolution layers. The authors demonstrate that adjusting the input projection onto eigenspace $\mathcal{M}$ of the node feature matrix can achieve any desired normalized smoothness. Additionally, they propose a Smoothn... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review, valuable feedback, and endorsement.
Regarding the pointed-out weakness, we can avoid performing eigendecomposition by using the fact that the basis of the space $\mathcal{M}$ -- eigenspace associated with the largest eigenvalue of the message-pass... | null | null | null | null |
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