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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Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency | Accept (poster) | Summary: This paper designs two self-consistency methods for formalizing mathematical statements in natural language into Isabelle --- symbolic equivalence and semantic consistency. In particular, symbolic equivalence measures whether the formalized statements are equivalent (judged by the Isabelle’s built-in automated... | Rebuttal 1:
Rebuttal: **Dear Reviewer GLbW**
Thank you for your review and detailed feedback. In the following, please let us address the concerns and questions you've pointed out.
**[Weakness #1] The scope of our framework and the significance of statement autoformalization**
First, we would like to clarify that, t... | Summary: This paper targets the task of autoformalization for mathematics, which inputs an informal theorem statement (in English) and outputs a formal theorem statement (inside an interactive theorem prover). Prior work has used LLMs in-context to produce autoformalizations. The work is motivated by a disparity betwee... | Rebuttal 1:
Rebuttal: **Dear Reviewer LZ2a**
Thank you for the insightful feedback on our paper. We appreciate the time and effort you have put into reviewing our work, and we are grateful for encouraging comments such as interesting idea, effective results, and general applicability. We have carefully read your revi... | Summary: This paper introduces two methods metrics, symbolic equivalence and semantic consistency, to determine the equivalence relation between formal statements. They design novel technical solutions to measure each method and show that this improves autoformalization by allowing self-consistency-based clustering.
S... | Rebuttal 1:
Rebuttal: **Dear Reviewer zk1U**
Thank you for the insightful feedback on our paper. We appreciate the time and effort you have put into reviewing our work, and we are grateful for the recognition of the importance of our research problem and encouraging comments such as clear writing, easy-to-understand, ... | Summary: The paper presents a novel framework aimed at enhancing the accuracy of autoformalization in mathematics using LLMs. It addresses the challenge of translating natural language mathematical statements into formal language by introducing a two-pronged approach based on symbolic equivalence and semantic consisten... | Rebuttal 1:
Rebuttal: **Dear Reviewer 37Eh:**
Thank you for the insightful feedback on our paper. We appreciate the time and effort you have put into reviewing our work, and we are grateful for encouraging comments such as clear presentation, comprehensive evaluation, and wide applicability. We have carefully read you... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable and insightful comments, which help improve our paper accordingly. Here, we summarize our responses to the major issues raised by the reviewers.
Reviewer **37Eh** and **LZ2a** request a further discussion about the performance of semantic consistency.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms | Accept (poster) | Summary: Minimum Bayes risk (MBR) decoding is a widely used technique for machine translation (MT) that involves generating $N$ candidate translations that are then scored according to a utility function, typically an automatic reference-based MT metric. In practice, this is done by comparing each candidate in the set ... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback.
We limited the paper to 4 different language pairs because precomputing the full 1024x1024 utility metrics is very expensive (It requires around 2000 TPUs for a full day per language pair). We definitely agree that performing a survey across all the efficient ... | Summary: - this paper uses alternating least square method for matrix completion when only a small part of the utility matrix in MBR decoding is calculated to decrease the computational cost for full MBR decoding and at the same time guarantee the translation quality
- the authors first justify the use of their method... | Rebuttal 1:
Rebuttal: Thanks for the insightful review.
We mainly focused on ALS because of its simple implementation and efficiency. We agree that optimizing the matrix completion algorithm further could potentially improve the performance. As you suggested, using an adaptive sampling[1] approach to pick the most rep... | Summary: This work proposes an approximate method for minimum Bayes risk (MBR) decoding by employing matrix completion. Basic idea is to fill-in only a fraction of the score matrix to compute MBR scores, and to leverage alternating least squares (ALS) algorithm to estimate the empty slots assuming that the utility scor... | Rebuttal 1:
Rebuttal: Thanks for the insightful review.
We agree that a further analysis on the convergence of the matrix completion algorithm will help us understand better the behavior of our approach and potentially optimize it further. We think this is an interesting future direction of research and we we will poi... | Summary: The paper proposes yet another method for speeding up the utility computation of MBR decoding. The algorithm exploits the structure of the problem that the utility matrix tends to be lower rank as the utility is kind of a similarity metric. The experiments are compared against the standard MBR and show that it... | Rebuttal 1:
Rebuttal: Thanks for the insightful review.
We definitely agree that performing a survey across all the efficient MBR methods will be beneficial for the community and we are interested in running this for future research. We will point this out in the limitations section.
We address your questions in orde... | Rebuttal 1:
Rebuttal: Thank you all for the reviews. We attach a pdf that includes more results and details that were requested. The plots in the pdf are referenced in the individual replies with [fig #number] format.
Pdf: /pdf/273554f80434d979a830709921bf694ad0ae083b.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Evaluating alignment between humans and neural network representations in image-based learning tasks | Accept (poster) | Summary: This article assesses how AI embedding can predict human behavior on 2 different tasks. In those tasks, humans have to learn continuous relationships and categories of natural images. Overall the authors tested the predictability of 77 retrained neural networks, including supervised, semi-supervised, and multi... | Rebuttal 1:
Rebuttal: > (...) claiming that 'multimodality better predicts human performance' is a bit overstated.
We agree that many factors contribute to alignment, as detailed in Figure 4. However, the comparison of SLIP, SimCLR, and CLIP models in Figure 6 suggests image language training is important, as archite... | Summary: This paper explores the alignment between human cognitive processes and neural network representations in image-based learning tasks. The authors evaluated 77 pretrained neural network models to see how their representations aligned with human learning patterns in two tasks: category learning and reward learni... | Rebuttal 1:
Rebuttal: > It would have been very insightful to include a comparison with generative models.
Thanks for the great suggestion! We now included 3 masked autoencoder models[1] trained on ImageNet in our comparison. We chose these specific models because they are SOTA classification models trained in a gener... | Summary: The authors propose a novel representational alignment metric tied to sequential human behavior and leverage it to analyze factors in NN design and training that contribute to increased alignment with humans.
Strengths: - Great analysis of factors contributing to alignment of NNs with humans
- Well-written ma... | Rebuttal 1:
Rebuttal: > The authors claim that their alignment metric is better than existing alignment metrics as it requires "generalisation and information integration across an extended horizon".
We believe that our method is not necessarily better than other metrics but that alignment is multifaceted. We will cl... | Summary: The authors evaluate alignment between humans and 77 pre-trained neural network vision models using learning tasks. Previous work has focused on comparing alignment between humans and models with similarity judgments alone; instead, here the authors asked participants to perform simple category learning and re... | Rebuttal 1:
Rebuttal: > Did you consider fitting a mapping between the model features and participant responses more directly? If not, why not?
We agree with the reviewer that fitting the linear/logistic regression models directly to human choices is an interesting thought (as it has for example recently been applied... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive and helpful feedback. Their input was immensely valuable to further improve our manuscript. The reviewers’ assessment was overall positive, with each reviewer recommending an initial score of at least borderline accept:
- Reviewer pzQG saw our work “a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Generation of Adversarial Examples Against Safety-aligned LLMs | Accept (poster) | Summary: This paper introduces two modifications to gradient-guided LLM attack algorithms that can improve its effectiveness and efficiency. First, they skip skip connections in the transformer when propagating gradients. Second, they modify the optimization objective to also include a term for making the latent repres... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Our responses to the comments are given as follows.
> Improve the writing and making the zero line dark in Figure 3.
**Answer:**
Thanks for the effort to fathom the contribution of our work. We will carefully revise the writing to make our contribution more cle... | Summary: This paper studies and improves white-box suffix-based language model jailbreaking. The authors treats the gradient-based discrete optimization problem involved in jailbreak suffix generation as using a continuous surrogate model to attack the discrete real model. By drawing parallels between this observation ... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Except for the comments about the discussion on perplexity and the concurrent related work, which are answered in our global response, all comments are replied to as follows.
> Regarding the Mistral-7B-Instruct model, Table 1 shows that only the combination of L... | Summary: The paper explores methods to enhance the effectiveness of adversarial prompt generation based on GCG against LLMs. By leveraging previous transfer-based attack techniques, originally used for image classification models, the authors adapt the Skip Gradient Method (SGM) and Intermediate Level Attack (ILA) to i... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Except for the comments about the experiments of reducing the gradients from skip connections, which is answered in our global response, all comments are replied to as follows.
> Though I believe the method shown in the paper can be applied to other models, the ... | Summary: The paper takes inspiration from the adversarial attack literature in computer vision to improve the common GCG attack algorithm for LLMs. The paper focuses on the ideas from SGM and ILA in particular. The former enables the author to improve the gradients being used in GCG to be more informative. The latter l... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Except for the comments about the experiments of reducing the gradients from skip connections, the discussion on perplexity and some concurrent related work, which are answered in our global response, all comments are replied to as follows.
> • Lack of error bar... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the effort they spent reviewing our paper and providing valuable feedback. Our responses to some common questions are presented as follows. In addition, we provide a PDF that contains figures.
> The experiments of reducing the gradients ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Block Coordinate Descent Methods for Optimization under J-Orthogonality Constraints with Applications | Reject | Summary: The paper introduces the JOBCD (J-Orthogonal Block Coordinate Descent) algorithm, a novel method designed to tackle optimization problems under J-orthogonality constraints. JOBCD includes two variants: GS-JOBCD (Gauss-Seidel strategy) and VR-J-JOBCD (Jacobi strategy with variance reduction). Theoretical analys... | Rebuttal 1:
Rebuttal: Dear Reviewer hZf8, we appreciate your dedication to reviewing our manuscript. In the following, we will respond to your concerns point by point.
**Question 1. Some proofs for Section 4 are hard to follow.**
Response: We now provide the proof sketch for the global convergence of the JOBCD algor... | Summary: This paper proposes two Block Coordinate gradient descent methods(BCD) for solving J-orthogonal constrained problem. One is Gauss-Seidel type, the other one is Jocobi type as well as addressing finite sum problem using variance reduction strategies. Convergence guarantees are proved with KL conditions. Numeric... | Rebuttal 1:
Rebuttal: Dear Reviewer swMz, thank you for your efforts in evaluating our manuscript. In the following, we will respond to your concerns point by point.
**Question 1. This paper is based on the paper " [51] Ganzhao Yuan. A block coordinate descent method for nonsmooth composite optimization under orthogon... | Summary: The paper proposes two block coordinate descent methods for minimization of a finite-sum subject to the J-Orthogonality constraints — one based on Gauss-Seidel strategy, the other based on variance reduction and Jacobi strategy. The convergence is proved, with a global convergence rate of O(N/\epsilon) and O(\... | Rebuttal 1:
Rebuttal: Dear Reviewer JyL5, we appreciate your dedication to reviewing our manuscript. In the following, we will respond to your concerns point by point.
**Question 1. For instance, the parameter theta is used in the algorithms but I’m not sure where it is introduced**
Response: $\theta$ is first defin... | Summary: This paper proposes a block coordinate descent method for solving optimization problems with J-orthogonality constraints. Several variants of the method are introduced within this framework, and convergence results are established. Extensive numerical results are also presented to demonstrate the efficiency of... | Rebuttal 1:
Rebuttal: Dear Reviewer TAHW, thank you for your efforts in evaluating our manuscript. In the following, we will respond to your concerns point by point.
**Question 1. My major concern is that the novelty of this paper might be insufficient since the row-based approach is very similar to that in [51], even... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for taking the time to review our paper.
Your valuable feedback and constructive comments are greatly appreciated. Please, find the answers to your questions below.
**Please note that we have added tables and figures in the attached pdf to support our responses to the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training | Accept (poster) | Summary: The paper discusses three drawbacks of traditional N:M sparse training and suggests using soft-thresholding over hard-thresholding for 2:4 sparse pre-training. It introduces the idea of rescaling sparse weights with a fixed scaling factor per tensor. Results from experiments in machine translation, image class... | Rebuttal 1:
Rebuttal: Dear Reviewer WZj6,
Thank you for the acknowledgment of the potential and effectiveness of our work and the detailed constructive comments. Below we provide a point-to-point response to all comments.
**Weakness 1:** Most of the techniques are discussed in other tasks, and the paper is only appli... | Summary: This paper presents a framework to circumvent the common challenges associated with STE-based 2:4 pre-training due to pruning function discontinuity. In particular, their framework addresses 3 aspects of this behavior: descent direction, amount of descent, and sparse mask oscillation.
Strengths: * This paper ... | Rebuttal 1:
Rebuttal: Dear Reviewer 73MA,
Thank you for the acknowledgment of the potential and effectiveness of our work and the detailed constructive comments. Below we provide a point-by-point response to all comments.
**Weakness 1:** This work is centered on the potential of continuous pruning schemes for enablin... | Summary: The authors address the challenge of pre-training models, with 2:4 sparsity, to high quality (ideally matching a densely-trained model). Building on existing methods, the authors point out three issues with discontinuous pruning functions: gradients move in the wrong direction, weight updates do not match exp... | Rebuttal 1:
Rebuttal: Dear Reviewer JQrs,
Thank you for the acknowledgment of the potential and effectiveness of our work and the detailed constructive comments. Below we provide a point-to-point response to all comments.
**Weakness 1:** **Quality** A missing experiment is one that would show the relative importance ... | Summary: This work studies an efficient method for pre-training, identifying three significant limitations in previous 2:4 sparse pre-training approaches: incorrect descent direction, the inability to predict the extent of descent, and oscillations in the sparse mask. Subsequently, the authors introduce a novel trainin... | Rebuttal 1:
Rebuttal: Dear Reviewer 18hX,
Thank you for recognizing the potential and effectiveness of our work and for providing detailed constructive comments. Below, we address each point raised.
**Weakness 1:** The acceleration of S-STE is demonstrated solely in terms of theoretical gains. Seems like S-STE will i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas | Accept (poster) | Summary: This paper examines the impact of emotional prompts on various tasks involving large language models (LLMs), such as ethical dilemmas and strategic games. The authors present a framework for evaluating LLMs under different emotional states. The experimental results demonstrate that emotional prompts can signif... | Rebuttal 1:
Rebuttal: **W1 The performance of various LLMs… with different emotional prompts.**
LLMs are trained on human data and frequently inherit human biases [1,2]. These biases (like less ethically correct behavior of the majority of LLMs under anger) cause the deterioration of performance.
As for LLM architectu... | Summary: This paper introduces the EAI framework to evaluate the impact of emotions on large language models (LLMs) in ethical and game-theoretical contexts. The framework includes game descriptions, emotion prompting, and game-specific pipelines. Extensive experiments were conducted using various LLMs like GPT-4, GPT-... | Rebuttal 1:
Rebuttal: **W1: On the main finding of this paper:**
Thank you for your valuable feedback. The aim of our research is not to prove that emotion can influence LLM decision-making abilities. We agree that this statement already has scientific grounding. Instead, we aim to advance this line of research by expl... | Summary: This paper studies the impact of emotion prompting in LLMs when playing strategic games. The paper introduces a framework for integrating emotion modelling, and provides a large empirical evaluation under multiple different emotions.
Strengths: - I am pleased to see that the authors study a wide range of LLMs... | Rebuttal 1:
Rebuttal: **W1, Q1: Prompting strategy used for experiments and comparison of different strategies.**
Thank you for the question! We'll gladly clarify the details. The results from the main text of the paper presented in Table 1 and Figures 2-4 were obtained using a “simple” strategy of prompting.
We also... | Summary: The paper "EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas" explores the integration of emotion modeling into large language models (LLMs) to assess their behavior in complex strategic and ethical scenarios. It introduces the EAI framework, which incorporates emotions into LLM de... | Rebuttal 1:
Rebuttal: We are grateful for the high appreciation of our work and valuable feedback!
**W1, Limited Practical Applications:**
We start from the need for practical applications in our study. Emotional AI, aligned with human behavior, has significant practical implications, particularly through LLM-based... | Rebuttal 1:
Rebuttal: Thank you very much for your comments, which allowed us to address the shortcomings and refine the presentation of the proposed approach.
1. We received questions about the comparison of prompting strategies and the direct influence of emotions themselves.
- To provide a solid and well-argued r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Extensive-Form Game Solving via Blackwell Approachability on Treeplexes | Accept (spotlight) | Summary: This paper introduces a method, predictive treeplex Blackwell, for using Blackwell approachability directly on the treeplex in order to perform regret minimization in extensive-form strategy sets. They show that their algorithm achieves $O(\sqrt{T})$ regret (where $O$ hides polynomial factors in the game size)... | Rebuttal 1:
Rebuttal: We thank for your time reviewing the paper and for your encouraging comments. We answer your questions below.
### Response to questions.
* *My most significant issue is simple: although the method is conceptually interesting, it is unclear if it carries any advantages over CFR (see also question... | Summary: This paper studies a Blackwell's approachability method for solving extensive-form game. Rather than applying it at each infoset as in the CFR approach, it applies it globally, which allows for a $\mathcal{O}(1/T)$ convergence rate with a predictive version.
Strengths: This paper is well written and well pres... | Rebuttal 1:
Rebuttal: Thanks for your positive reviews. We answer your questions below, quoting them in italics and answering in regular font. We remain available during the rebuttal period if you have any other questions/comments.
* *The experiments are a bit hard to read.*
In the camera-ready version, we will use o... | Summary: This paper designs a new algorithm for computing minimax equilibria in two-player zero-sum extensive form games. It is well-known that one way to do this is to have both players run no-regret algorithms against each other and take the time-average of their strategies. The main innovation of this paper is desig... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper. We quote your comments in italic and we respond to them in regular font.
## Response to your questions.
* *But it is not clear to me that “based on Blackwell approachability” is really a well-defined concept (perhaps you could recover some existing... | Summary: This paper studies computations via regret minimization of Nash equilibria in zero-sum extensive form games (EFG) with the perfect recall assumption.
The actions of the players are a sequence of polytopes (treeplexes).
In prior work (counterfactual regret minimization framework), this was solved with methods ... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper and for your positive review. If you have any questions about our work, we remain available during the rebuttal period. | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal and Approximate Adaptive Stochastic Quantization | Accept (poster) | Summary: The paper is concerned with quantization,
that is encoding the components of a vector $X\in \mathbb{R}^{d}$ in a finite
alphabet $Q$ of given size $s$. The goal is unbiased,
stochastic quantization, where for a given component $x$ the encoding value $%
\hat{x}$ are chosen at random such that $E\left[ \hat{x}\r... | Rebuttal 1:
Rebuttal: Thank you for the review.
> I am a bit bewildered about calling $X$ a vector, since the ordering is appearantly irrelevant. Also multiple values do not change anything. "Set of real numbers" seems more appropriate to me.
We agree that the input can be considered as a multiset (and not a set - a ... | Summary: The paper presents an algorithm to solve the Adaptive Stochastic Quantization (ASQ) problem that is claimed to be more computationally efficient than existing solutions. The paper also presents simulations showing the improved efficiency.
I have read the authors' responses to my comments and made changed the ... | Rebuttal 1:
Rebuttal: Thank you for the review.
> What is the source of redundancy that makes quantization possible without affecting too much the performance?
The source of redundancy depends on the ASQ use case. Here are two specific examples, while other use cases may have a different motivation.
*Example 1: Gra... | Summary: This paper studies the Adaptive Stochastic Quantization (ASQ) problem and presents a dynamic programming based algorithm that improves time and space complexities.
Strengths: The algorithm is compared with peer dynamic programming based algorithm called ZIPML. Quiver Algorithm introduced in this paper is show... | Rebuttal 1:
Rebuttal: Thank you for the review.
Thanks for pointing out the typos, we will fix these.
> In 106 is Q in X ?
We write that there exists an optimal solution for which $Q\subseteq X$ (and credit [24] for this observation). That is, there exists an optimal solution where all quantization values are entri... | Summary: The paper proposes the QUIVER algorithm, Accelerate QUIVER (an accelerated variant when s = 3), and Apx QUIVER (a variant that utilizes approximations for better speed) to solve the Adaptive Stochastic Quantization (ASQ) problem. The paper also provides theoretical guarantees to their algorithm, improving the ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We first address the questions in detail and *relate to the weaknesses in the comment that follows*.
**Q1** While SMAWK has the optimal $O(d)$ time complexity for the problem, there are simpler approaches. For example, by leveraging the fact that if MSE[i,j] i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learnability of high-dimensional targets by two-parameter models and gradient flow | Accept (poster) | Summary: This paper studies the problem of learning a target function $f$ lying in some $d$-dimensional Hilbert space $\mathcal{H}$ via gradient flow on a $W$-parameter model with $W < d$. The main result, Theorem 5, is that for $W = 2$, given a distribution over $\mathcal{H}$ there exists a parametric map $\Phi : \mat... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our paper and your useful feedback and critique.
* "*I struggle to understand the relevance of the problem studied in this paper to the NeurIPS community.. The construction for $\Phi$ in Theorem 5 is quite pathological..*"
That's true, the construction in ... | Summary: The paper studies the *learnability* of finite dimensional space of functions $\mathcal{H}$ of dimension $d$. The learnability criterion is related to the gradient flow according to the canonical $L^2$ error, associated with a certain $\Phi$-parametrized family of dimension $W$, that can be chosen. The authors... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and a very positive evaluation of our work!
* "*What the set of learnable functions look like... Doesn't Theorem 5 implies a sort of density of learnable functions?*"
The right geometric picture for the set of learnable functions in Theorem 5 would be a multidim... | Summary: This paper analyzes when it is possible to define a map $\Phi: \mathbb{R}^W \to \mathbb{R}^d$ such that any point $y \in \mathbb{R}^d$ is ``learnable'' via gradient flow on the square loss $\|y - \Phi(w)\|^2$. They show that for any distribution over $y$, there exists a map $\Phi: \mathbb{R}^2 \to \mathbb{R}^d... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and a positive evaluation of our work!
* "*The construction and proof sketch for Theorem 5 and the diagrams in Figure 2 are very difficult to follow.*"
Thank you for this feedback. We admit that some elements of the proof sketch may be not clear enough. We want... | Summary: This submission studies the learnability of high-dimensional targets with models having fewer parameters. The manuscript considers training with Gradient Flow and studies when the target -- identified by a general d-dimensional probability distribution -- can be learned by $W-$ parameter models, with $W <d$. F... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our paper and many useful comments.
* "*I would enlarge the discussion part [of Section 5]*"
Thank you for this feedback. Indeed, we agree that it can be useful to add some comments and maybe a graphic illustration here.
* "*Am I correct to understand that g... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for a careful reading of our paper and many useful comments and suggestions.
Reviewers XxKo and qWpD ask about a possible extension of our main Theorem 5 to infinite-dimensional target spaces. We believe that this can be done, but there are some subtleties that w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms | Accept (poster) | Summary: In this paper, the authors propose a variant of MDS that is able to deal with data with a non-Euclidean structure. The main idea is to generalize the inner product to a bilinear form, hence admitting some relationships corresponding to "negative eigenvalues," interpreted as in an inner product form $u^TAv$. Al... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful reading and valuable feedback. In the following we address the concerns.
- ### Regarding complex values
In Line 99, we apologize for the confusion. $D$ is the squared dissimilarity matrix in general. We will emphasize it in the revision.
Regarding the c... | Summary: The paper introduces Neuc-MDS, a novel extension of classical Multidimensional Scaling (MDS) designed to handle non-Euclidean and non-metric dissimilarities in datasets. The goal is to create accurate low-dimensional embeddings while minimizing the STRESS (sum of squared pairwise error)
Strengths: 1. Neuc-MDS... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful reading and valuable feedback. In the following we address the concerns.
- ### Regarding Computation and Scalability
We discuss the computational complexity and provide more ideas (e.g. using ideas similar to landmark MDS) on reducing the runtime for very l... | Summary: The authors introduce Non-Euclidean-MDS (Neuc-MDS), an extension of Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric outputs, efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS. The results seem to be promi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful reading and valuable feedback. In the following we address the concerns.
- ### Regarding the problem definition (Definition 3.1) and description
For a generic bilinear form $f_A(u, v)=u^T A v$, consider the induced dissimilarity $D_A(p_i,p_j):=f_A(p_i-p_j,... | Summary: This paper introduces Non-Euclidean Multidimensional Scaling (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that can handle non-Euclidean and non-metric data. The key ideas and contributions are:
1. It generalizes the inner product to more general symmetric bilinear forms, allowing the u... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, we respond to your comments as follows.
- Computational Complexity
Thanks for raising the concern. One-line argument is both Neuc-MDS and classical MDS run in $O(n^3)$ time due to eigen decomposition, therefore, no extra cost asymptotically. We also have mor... | Rebuttal 1:
Rebuttal: We appreciate the efforts by reviewers and would like to clarify a few questions and answer to raised concerns. We are glad that this submission has been found to be:
- Well-written (Reviewer wzYh, EWdH)
- Showing solid/thorough experimental results (all reviewers)
- Accompanied with convincing... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection | Accept (poster) | Summary: The paper pioneers the utilization of Mamba in the anomaly detection field. Specifically, the core contribution of the proposed MambaAD is a Locality-Enhanced State Space module, which utilizes Mamba for global information and CNN for local information. Whereas the motivation and the core technical contributio... | Rebuttal 1:
Rebuttal: **Q1: Motivation**
*(1)* Since the model needs to learn the **data distribution among samples with significant differences in a multi-class anomaly detection task, it requires a global modeling capability**, such as that provided by Transformers. UniAD, as the first model to propose a multi-class... | Summary: This paper employs a pyramid-structured auto-encoder to reconstruct multi-scale features, utilizing a pre-trained encoder and a decoder based on the Mamba architecture. The experimental results show SoTA performances on several commonly used datasets.
Strengths: 1. As far as I am concerned, MambaAD is the fir... | Rebuttal 1:
Rebuttal: **Q1: Compared with Transformer-based Method**
*(1)* As shown in Fig. 1 of the main text, although UniAD is a Transformer-based method, it employs a **single-scale approach**, whereas our MambaAD, based on Mamba, utilizes a **multi-scale approach**. Specifically, **single-scale methods model and ... | Summary: The paper "MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection" introduces MambaAD, a novel framework for multi-class unsupervised anomaly detection using Mamba-based models. The framework consists of a pre-trained encoder and a Mamba decoder that integrates Locality-Enhanced S... | Rebuttal 1:
Rebuttal: **Q1: Further Exploration of Scanning Methods and Direction Selection**
***(1) Scan Directions***
Firstly, we further investigate the impact of different scanning directions on Params, FLOPs, training time over 100 epochs, training memory usage, and final performance, as shown in the table below... | Summary: This paper introduces a method for multi-class unsupervised anomaly detection utilizing a CNN-based encoder and a Mamba-based decoder. It incorporates an LSS module and a HSS block within the Mamba decoder to enhance performance. The effectiveness of the method is validated through experiments conducted on thr... | Rebuttal 1:
Rebuttal: **Q1: From a methodological perspective**
*(1)* We adopt a **reconstruction-based framework**. Anomaly detection methods based on reconstruction can be broadly categorized into **image reconstruction and feature reconstruction**. Methods such as GAN and diffusion-based approaches primarily fall u... | Rebuttal 1:
Rebuttal: **We would like to express our sincere gratitude for the constructive comments and suggestions from the reviewers. We appreciate the efforts of all reviewers in evaluating and helping to improve the quality of our manuscript.**
***The primary contributions of this paper are as follows:***
This p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Reinforcement Learning with General Utility | Accept (poster) | Summary: The authors combine the topics of robust RL with general non-linear utility. They use policy gradient formulae from general utility RL and combine those with gradient algorithms for minimax problems due to Lin et al. The authors claim to have a convergence theory for the sample based algorithm that ensures con... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript and providing valuable feedback. Below is a response to the review questions/comments. We will revise the manuscript accordingly after the review discussion period. Please let us know if further clarifications are needed.
**Q1:** The weakness in th... | Summary: In this submission, the authors tackle a new problem: how to train a policy with a general utility when one needs to be robust to some uncertainty in the environment. More precisely, they are looking for the solution of min-max problem: the minimization over a set of parametric policies of the worst case over ... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript and providing valuable feedback. Below is a response to the review questions/comments. We will revise the manuscript accordingly after the review discussion period. Please let us know if further clarifications are needed.
**Q1:** The nonconvex case... | Summary: This paper aims to incorporate robustness in reinforcement learning environments by allowing the transition kernel to be within a polyhedral s-rectangular uncertainty set to handle general utility functions. It applies a stochastic gradient descent with (gradient sampling subroutines) algorithm designed for gl... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript and providing valuable feedback. Below is a response to the review questions/comments. We will revise the manuscript accordingly after the review discussion period. Please let us know if further clarifications are needed.
**Q1:** Modeling motivatio... | Summary: The paper studies the problem of robust RL with general utility function, which is looking at maximizing a general (possibly non-convex) utility function with the worst-case possible transition kernels in an ambiguity set. The paper provides convergence analysis for a wide range of utility functions and ambigu... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript and providing valuable feedback. Below is a response to the review questions/comments. We will revise the manuscript accordingly after the review discussion period. Please let us know if further clarifications are needed.
**Q1:** I think the author... | Rebuttal 1:
Rebuttal: **About Motivation**
Thank the reviewers for bringing the motivation issue to our attention. **Our revision will include the following special cases and application examples of our proposed problem. First, our proposed *robust RL with general utility* can be applied to improve the policy robustne... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling | Accept (poster) | Summary: The paper introduces an autoencoder-like nonnegative matrix co-factorization framework (AE-NMCF) to enhance predictions of student exercise performance and assessments of knowledge proficiency even when data is sparse. The authors offer a projected gradient method employing block coordinate descent and Lipschi... | Rebuttal 1:
Rebuttal: Thanks for your attention and comments on our paper. Your valuable feedback means a lot to us. Regarding the questions you raised, we have carefully considered each point and have made the following responses:
>**Q1.** The matrix B seems to make parameter estimation difficult.
**A1.** Despite th... | Summary: The authors present a novel model of student cognition to improve the prediction of student exercise performance and the estimation of their knowledge proficiency in a subject. Current approaches such as matrix factorization perform well in predicting student performance on exercises, but the knowledge profici... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback to enhance the quality of our paper. As you suggested, we have conducted ablation experiments to verify the encoder-decoder architecture in AE-NMCF. In the ablation study, we use two variants of AE-NMCF, including (*a*) AE-NMCF w/o Decoder that removes the deco... | Summary: The authors propose a novel approach to student cognitive modeling based on nonnegative matrix factorization via the use of sparse autoencoders. The authors propose an algorithm to solve the estimation problem in their framework, and provide a formal analysis including a convergence proof of the propose approa... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. We are grateful for the suggestions for improving the quality of our manuscript in terms of writing and presentation. We have considered all of your concerns, including but not limited to enriching the background, updating the figures, simplifying the expression,... | Summary: The paper studies the problem of predicting student grades based on the past student response to the question. The proposed method falls into the research of matrix completion. The novelty lies in (1) a new matrix co-factorization, and (2) the proof of the convergence of the proposed gradient descent method.
... | Rebuttal 1:
Rebuttal: We would like to thank for your positive evaluation. For your concerns regarding the performance comparison, we compare our model (AE-NMCF) with several variants of collective matrix factorization (MF) methods suggested by Reviewer Px4s, including CMF [Singh2008], GNMF [Lee2009], and NMMF [Takeuch... | Rebuttal 1:
Rebuttal: We deeply appreciate all the reviewer's evaluations and comments on our paper. Their thoughtful feedback has provided valuable insights that have significantly contributed to improving the quality of the manuscript. In the global response, **we have included an attached global file**, where some f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities | Accept (poster) | Summary: This paper shows that CLIP-style pairwise contrastive objectives on multiple modalities (more than two) fail to capture the total dependencies of different modalities, and proposes to use total correlation, a higher-order generalization of mutual information as the new objective to optimize multimodal contrast... | Rebuttal 1:
Rebuttal: We greatly appreciate your helpful and constructive comments! We are glad that you found our contributions well motivated and clearly explained, and that you consider our solution to be simple and effective. We have addressed your questions regarding suitable baselines for Symile both below and in... | Summary: The paper introduces Symile, a new contrastive learning objective designed to accommodate any number of modalities, addressing the limitations of pairwise CLIP which fails to capture joint and conditional information between modalities. Symile targets total correlation, capturing higher-order dependencies amon... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and questions! We're thrilled that you found our work valuable and our examples and theoretical contributions effective. Please let us know if we can provide any further clarifications. If our response has resolved your questions, we would greatly appreciate ... | Summary: This paper proposes Symile, a new contrastive learning objective that addresses the challenge of contrasting multiple modalities simultaneously by considering total correlation. Symile captures the statistical dependence between multiple variables and uses a generalized inner product to derive a lower bound on... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback, and are glad that you found our work conceptually and theoretically compelling. We address your questions below, but please let us know if we can provide any further clarifications. If we have successfully addressed your primary concerns, would you be willing ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive and careful comments, which will significantly strengthen our work. We are glad they found that:
* our paper "effectively identifies and addresses a significant limitation of CLIP, presenting a novel and important contribution to the field" (HxmV)
* ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level | Accept (poster) | Summary: This paper proposes two CUDA kernel optimization techniques: batched GEMM and fusion for neighborhood attention. On average, batched GEMM optimization provides 895% (548%) and 272% (193%) improvement in full precision (half precision) latency compared to existing naive CUDA kernels for 1-D and 2-D neighborhood... | Rebuttal 1:
Rebuttal: We thank you for your feedback.
To answer your questions:
1. Our performance evaluations are limited to runtime, mainly because the naive kernel is already not utilizing anything other than occupancy (through launching as large of a wave as possible and assigning a single dot-product’s worth of w... | Summary: They implemented a fused neighborhood attention kernel (N Atten). N Atten is very useful in reducing the computational cost of various tasks because sequences usually attend to nearby (e.g., Mistral and StreamingLLM).
However, previous implementations of N-Atten kernels are very inefficient because of a lack... | Rebuttal 1:
Rebuttal: We thank you for your feedback.
To answer your questions:
1. Yes; we can link to or upload an anonymized version of our code if you would like, but our intention is to open source all of our code and integrate into the existing neighborhood attention package. Given the volume of the code it did n... | Summary: The paper introduce a method to improve the performance of neighborhood attention mechanisms. The authors present two new implementations: GEMM-based kernels and fused kernels. These implementations aim to reduce the latency and memory footprint of neighborhood attention in deep learning models, particularly i... | Rebuttal 1:
Rebuttal: We thank you for your feedback.
To respond to your questions:
1. To clarify, our GEMM-based approach's FP16 performance is significantly affected by the gathering and scatter of attention weights, not Fused Neighborhood Attention. FNA does not store or load attention weights to global memory, and... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and their valuable feedback and suggestions.
We've posted individual rebuttals, and hope to have answered their questions and concerns.
Please let us know if there are any more questions, and we would be happy to elaborate further. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pricing and Competition for Generative AI | Accept (poster) | Summary: The paper presents a theoretical analysis of pricing strategies for generative AI models in a competitive market. It investigates how companies can optimally set prices for AI models that are used across various tasks, considering the impact of competition from other firms. The study uses a game-theoretical ap... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the relevance and timeliness of our work, clarity of our formulation and writing, as well as the key insights drawn from our work.
---
> W1) From the perspective of a stylized model, the work is solid and interesting. But I am not sure about the practice r... | Summary: * The paper explores the optimal pricing problem for generative AI services in a two-firm Stackelberg competition setting.
* In the setting, two generative AI firms compete over users. Each generative model is characterized by a fixed price $p$ per query, and success probabilities $(V_1, \\ldots, V_T)\\in[0,1]... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our motivation, clarity of writing, and overall presentation of our work.
---
> W1) The analysis seems to rely on the assumption that a full taxonomy of tasks is available, and that demand curves for each task are independent. However, due to the general-p... | Summary: This paper studies the pricing and competition of companies providing services using generative AI models. This paper proposes a stylized economic model that abstracts away from the technical details, and in particular, considers two companies entering the market sequentially. This paper assumes that the custo... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the interest and timeliness of our work.
---
> Q1) Is the sequential order of companies true? Would simultaneous competition be more reasonable? Furthermore, if so, do they get any first-mover advantage? The current model assumes that the first company is ... | Summary: The paper identifies some unique characteristics of modern generative AI software which affect their pricing. It uses a notion of user cost-effectiveness to compare two models, capturing the cost per prompt and the number of prompting rounds needed to reach a satisfactory answer. The authors propose to model t... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the uniqueness of our problem as well as the simplicity and comprehensiveness of our model.
---
> W1) The setup seems far from reality and loosely related to generative AI. First, in real life even if we assume two companies, there is nothing prohibiting t... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their positive comments:
- Originality, novelty, and timeliness of our study (`USe1, U4W8, 6DrW, CY6H`)
- Broad applicability of our model & insightful analysis/guidelines/conclusions drawn (`USe1, U4W8, aKR2, CY6H`)
- Clarity of our written presentation (`USe1, aKR... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper develops a theoretical model of users deciding which generative AI system to use based on the price and probability of performing a task satisfactorily. Based on this theoretical model, the paper then explains how a firm should price their generative AI model in response to other firms, and then how... | Rebuttal 1:
Rebuttal: Thank you for your clear summary of our work, your positive comments on the originality and significance/broad applicability of our work, as well as on the clarity of our writing.
---
> Q1) Is there an alternative way of modeling task performance that does not assume that the AI system will alw... | null | null | null | null | null | null |
Saliency-driven Experience Replay for Continual Learning | Accept (spotlight) | Summary: Inspired by some neurophysiological evidence, the author proposed a novel method for online continual learning dubbed as SER, which utilizes visual saliency to modulate the classification network so as to alleviate catastrophic forgetting.
Specifically, the network architecture follows a dual-branch design wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insight. We first address the major weaknesses (indicated with W1 and W2) identified by the reviewer and then respond, point by point, to their raised questions (indicated with Q1 and Q2). We will also review the whole paper to correct the identified typos.
**W1 - ... | Summary: In this paper, the authors propose Saliency-driven Experience Replay (SER) a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. More concretely, they propose to employ auxiliary saliency prediction features as a modulation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the provided comments. In the following we respond to the raised questions (indicated with Q1, Q2, and Q3).
**Q1 - Class-Incremental with Repetition**
To clarify, in our approach, the tasks $D_i$ and $ D_j$ are indeed considered disjoint. We acknowledge that our current... | Summary: This paper draws inspiration from neurophysiological evidence and proposes a biologically-inspired saliency-driven modulation strategy named SER to mitigate catastrophic forgetting in online continual learning. SER works by regularising classification features via predicted saliency and is comprised of a class... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insight. We first address the weaknesses (indicated with W1, W2, and W3) identified by the reviewer and then respond, point by point, to their raised questions (indicated with Q1, Q2, Q3, and Q4).
**W1 - Generalization to other models:**
To address this concern, we... | Summary: The paper propose to use saliency prediction features as a guidance to stabilize training in online continual learning settings. The method is motivated by the observation that saliency detection remains stable with training over new tasks continually. The proposed SER method is model-agnostic and improves sig... | Rebuttal 1:
Rebuttal: We thank the reviewer for the provided comments. We here address point by point the weaknesses (indicated with W1 and W2) identified by the reviewer.
**W1 - Activation maps per layer**
We have tested GradCAM at different layers of the network, as well as LayerIntegratedGradients, as suggested. O... | Rebuttal 1:
Rebuttal: We appreciate the feedback from all reviewers and provide an overview of our responses to the major concerns raised. Detailed responses are addressed individually for each reviewer.
**Reviewer U9b5**
**1. Activation Maps per Layer (W1):**
We have investigated per-layer activation maps using Grad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning | Accept (spotlight) | Summary: This paper presents a thorough analysis of imitation learning (IL), focusing on the gap between offline and online IL. The authors demonstrate that behavior cloning (BC) with logarithmic loss can achieve horizon-independent sample complexity under specific conditions, providing valuable insights into the theor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> The theoretical results have a gap with practice. For example, the reviewer finds it odd that the conclusion of a 'horizon-free' guarantee is obtained by assuming the ... | Summary: This paper analyzes the sample complexity of offline behavior cloning w.r.t. the trajectory horizon. The theoretical result reveals that offline behavior cloning actually does not suffer more from the long horizon than the online BC, under two assumptions.
Strengths: - The theory to further understand the com... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> The result is intuitive since with "parameter sharing", we assume the policy generalizes to longer horizon states.
While it is certainly intuitive that smaller policy... | Summary: This paper studies the horizon dependence in imitation learning (IL). In particular, the authors analyze the sample complexity of Behavioral Cloning (BC) with logarithmic loss and general policy class $\Pi$.
For deterministic experts, they first present a sharp horizon-independent regret decomposition and th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> Some formulas and claims in this paper are misleading. In the logarithmic loss of Eq. (5), the policy variable is written as a stationary policy, which does not includ... | Summary: The paper proposes theoretical results on whether and when can offline imitation learning (IL) match online imitation learning in sample efficiency. The paper connects the results of existing works and demonstrates that offline IL can indeed match online IL under certain conditions. The paper further provides ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> The paper only considers log loss, which I understand is very commonly used, but I am curious about how much of these analyses transfer to other losses.
This is a gre... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How does PDE order affect the convergence of PINNs? | Accept (poster) | Summary: - Building upon the work of Gao et al. [22], the authors extend the analysis of the gradient flow (GF) of PINNs (two-layer MLP is assumed) to general $k^\mathrm{th}$ order partial differential equations (PDEs) and the $p^\mathrm{th}$ power of Rectified Linear Unit (ReLU) activation function with general $p$.
-... | Rebuttal 1:
Rebuttal: In order to provide a concise response to theoretical questions, we structured our proof:
* (S1) Determine the condition for the positive definiteness(PD) of the initial Gram matrix.
* (S2) Establish an upper bound for the initial loss in terms of a polynomial of the initial weights.
* (S3) Identi... | Summary: **Summary:**
The paper discusses the convergence of the gradient flow of a PINN loss function for arbitrary-order linear PDEs. The analyzed neural networks are shallow MLPs with a power-of-ReLU activation function. The results established are of the NTK convergence type in the overparameterized regime and tak... | Rebuttal 1:
Rebuttal: **Q1**. There is no sharpness of the lower bound $m>C$. Thus, ...
**R**: We acknowledge the reviewer’s concern regarding the sharpness of the bound we presented in our paper. We concede that we have not demonstrated the sharpness of the bound in our theorems. However, it is important to note that... | Summary: This paper presents a theoretical analysis of the relation between PDE order and the convergence of PINNs. A tighter bound is obtained than the previous work. Inspired by the importance of reducing PDE order, the authors propose the VS-PINN, which employs the variate splitting strategy. Both theoretical analys... | Rebuttal 1:
Rebuttal: **Q1**. Practical efficiencies are expected ...
**R**: Table A1 in the attachment shows GPU memory, running time (the mean of the 50 epochs), and the number of model parameters that correspond to experiments presented in our paper.
Becasue VS-PINNs need as many networks as auxiliary variables, fi... | Summary: This paper provides a theoretical understanding about the behavior of PINN when dealing with high-order or high-dimesional PDEs. Variable splitting is then proposed to decomposes the high-order PDE into a system of lower-order PDEs and facilitate the convergence of PINN.
Strengths: 1. This paper extends Gao e... | Rebuttal 1:
Rebuttal: **Q1**. The numerical results pertaining to the parameter ...
**R**: The theoretical results presented in this paper indicate that networks with a lower power $p$ have a higher probability of convergence. A reduction in $p$ would facilitate optimization, which may be observed as an acceleration i... | Rebuttal 1:
Rebuttal: **Response to All Reviewers**
We sincerely appreciate all the reviewers for their invaluable comments, recommendations, and suggestions. The opinions of the reviewers have been carefully considered and responding to their questions has enhanced the paper. We first introduce additional experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kolmogorov–Smirnov GAN | Reject | Summary: This paper first generalizes the Kolmogorov–Smirnov (KS) distance from one-dimensional spaces to multidimensional spaces and proposes the Kolmogorov-Smirnov GAN, which formulates the generative model by minimizing the Kolmogorov-Smirnov (KS) distance. Theoretical results are also given in this paper and the ex... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find answers to the posed questions below:
Q1. “In Line 66-67, what's the meaning there are $2^d - 1$ ways of defining a CDF on a d-dimensional space?”
Please find the answer to the question in reference [35] which we cite after the next sentence in L68 - middle... | Summary: This paper proposes a novel variant of the generative adversarial network that uses the Kolmogorov-Smirnov distance to align the generated distribution with the target distribution. This distance is calculated using the quantile function, which acts as the critic in the adversarial training process. Experiment... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find answers to the posed questions below:
Q1. “Could the authors explain or provide more evidence about line 40, "The Bayesian inference community has been reluctant to adopt adversarial methods"?”
Our intention was to provide references [8] and [40] to support... | Summary: The authors introduce a generalized KS distance applicable to high dimensional spaces, formulate the corresponding dual problem, and use adversarial training to construct a generative model that minimizes the GKS between data and generated distributions.
The paper is well presented and appears technically cor... | Rebuttal 1:
Rebuttal: Thank you for your review. As there are no questions but there are suggestions instead, we would like to thank you for those. We will consider them in our future work.
We would like to clarify one thing regarding the comment “The authors claim there that they don't need to maximize the supremum i... | Summary: The paper proposed a new kind of GAN training method called KS-GAN. The method is based on minimizing the Kolmogorov–Smirnov distance. The KSGAN updates the generator by minimizing an upper bound of the generalized KS distance. It updates the discriminator (or the critic network) by using energy-based model tr... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find answers to the posed questions below:
W1. “[...] I think it would strengthen the paper a lot if the authors could somehow show strong performances of KSGAN using StyleGAN2's architectures and implementation techniques. [...]”
We are aware that the best gene... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their comments and suggestions. A recurring issue in all reviews is the insufficient performance of our method in the results we presented. Therefore, we will address it in a global response.
We would also like to emphasize that, according to the official ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD | Accept (poster) | Summary: The paper gives a new concentration method to improve the bounds for the convergence of clipped-SGD when the noise has bounded second moments in the online/streaming setting. The method has also applications in streaming heavy-tailed statistical estimation, including streaming mean estimation and regression. T... | Rebuttal 1:
Rebuttal: $\newcommand{\Tr}{\mathsf{Tr}} \newcommand{\deff}{d_{\mathsf{eff}}} \newcommand{\vx}{\mathbf{x}} \newcommand{\vz}{\mathbf{z}} \newcommand{\vw}{\mathbf{w}} \newcommand{\vn}{\mathbf{n}} \newcommand{\bE}{\mathbb{E}} \newcommand{\bR}{\mathbb{R}} \newcommand{\dotp}[2]{\left\langle #1, #2 \right \rangle... | Summary: This paper addresses the problem of high-dimensional heavy-tailed statistical estimation in a streaming setting, which is more challenging than the batch setting due to memory constraints.
The authors cast the problem as stochastic convex optimization (SCO) with heavy-tailed stochastic gradients.
They demo... | Rebuttal 1:
Rebuttal: $\newcommand{\Tr}{\mathsf{Tr}}$ Thank you for your helpful feedback. We hope to address your concerns below:
### Missing Reference
Thank you for pointing out this helpful reference. The work is indeed quite relevant and we have updated our draft to include the citation in Section 1.2.
### Exten... | Summary: The paper introduces significant advancements in the field of statistical estimation and optimization for heavy-tailed data. It improves the convergence complexity of the widely-used clipped-SGD algorithm in both strongly convex and general convex settings, achieving near-optimal sub-Gaussian statistical rates... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We hope to address your concerns below:
### Proof Sketches
Thank you for this helpful pointer which has helped improve our work. We have updated our draft to add a detailed proof sketch in the beginning of our Appendix. This new section expands upon the proo... | Summary: The paper studies the clipping SGD algorithm and shows a refined analysis to improve the dependence on the variance. The authors also provide different applications of their new results.
Strengths: The new concentration bounds are interesting, which is the key novel part of the work.
Weaknesses: 1. The key d... | Rebuttal 1:
Rebuttal: $\newcommand{\Tr}{\mathsf{Tr}} \newcommand{\deff}{d_{\mathsf{eff}}}$ Thank you for your insightful feedback. We hope to address your concerns below:
### Problem Dependent Parameters
While our results assume knowledge of some problem-dependent quantities, we emphasize that such assumptions are *... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributional Successor Features Enable Zero-Shot Policy Optimization | Accept (poster) | Summary: The paper presents a novel approach called Generalized Occupancy Models (GOMs), which aims to address the challenges of model-based RL and successor features in transferring across tasks with various reward functions. GOMs learn a distribution of successor features from a stationary dataset, enabling the quick... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and finding our approach “novel”. We address your questions below.
> (W1) In the experiments I would like to see actual changes of the underlying reward function of performing a completely different task in the same setting.
We conduct additional experiments ... | Summary: The paper describes a method for using distributions of successor features for fast transfer across reinforcement learning tasks. The method combines the long-term predictions and fast transfer properties of successor features with a "readout policy" (conditioned on achieving a particular successor feature) an... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! It is encouraging to hear that our paper is addressing “an important problem.” We reply to your questions below.
> (W1) The paper could do a better job explaining how this is providing more diversity of experience than typical policy-dependent successor featu... | Summary: This paper proposes an approach to zero-shot reinforcement learning through learning the distribution of successor features in deterministic MDPs allowing for the efficient computation of approximately optimal policies. The authors perform an empirical evaluation comparing their method to other zero-shot RL, m... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and for finding our paper “novel” and “well-written”. We address each point below.
> (W1) I don't think the term generalized occupancy model is appropriate here.
We name our method “generalized occupancy model” because successor features capture a notio... | Summary: The paper proposes a method based on successor features for modeling possible long-term outcomes in the environment based on data, together with a learned policy to achieve those outcomes. After training from offline data, the model can produce a policy for a given new reward function without any additional in... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for finding our approach “promising.” We address your comments below.
> (W1) Model-based RL can generate novel action trajectories whereas GOM is constrained to the dataset distribution.
While model-based RL can generate novel trajectories when queried wi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading and constructive feedback. We appreciate the reviewers for finding our approach “promising” and “novel, our paper “well-presented” and “easy to read,” and our experiments “encouraging.” We address some common questions here and defer more detailed r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation | Reject | Summary: The paper presents H-CLIP, a novel framework for open-vocabulary semantic segmentation using the CLIP model. The framework addresses three key challenges: high computational cost, misalignment between CLIP's image and text modalities, and degraded generalization ability on unseen categories when fine-tuning fo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback. We will include all new results and clarifications in the revised version.
### Weaknesses
> W1: Formula 5 does not specify how to interact with the $\boldsymbol{R}$ matrix.
**A1:** The interaction is introduced in Section 4.3. According to form... | Summary: The paper presents H-CLIP, a novel approach for parameter-efficient fine-tuning of the CLIP model in hyperspherical space, specifically for open-vocabulary semantic segmentation. H-CLIP includes the introduction of a symmetrical parameter-efficient fine-tuning strategy, leveraging hyperspherical energy princip... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and hope our following clarifications and responses could clear your concerns.
### Weaknesses
> W1: The introduction lacks transitions from existing problems to the approach of this paper, such as introducing the advantages of Hyperspherical Spac... | Summary: This paper proposes H-CLIP, a symmetrical parameter-efficient fine-tuning (PEFT) strategy conducted in hyperspherical space for both of the two CLIP modalities. The PEFT strategy is achieved by a series of efficient block-diagonal learnable transformation matrices and a dual cross-relation communication module... | Rebuttal 1:
Rebuttal: Thanks for your instructive comments. We will include all new results and clarifications in the revised version.
### Weaknesses
> W1: Limited novelty.
**A1:** We would like to emphasize that the novelty of our proposed POF mainly lies in its task-oriented design. Most previous OVSS methods opt ... | Summary: This paper proposes a novel method called Parameter-Efficient Fine-Tuning in Hyperspherical Space for efficiently solving the open-vocabulary semantic segmentation problem. The method introduces a series of efficient block-diagonal learnable transformation matrices and a dual cross-relation communication modul... | Rebuttal 1:
Rebuttal: We truly thank you for the insightful comments and suggestions. We hope our responses can address your concerns.
### Questions
> Q1: Training time efficiency.
**A1:** We compare the training time of our method with other representative PEFT methods based on ViT-B/16 backbone, which shows compar... | Rebuttal 1:
Rebuttal: ## Common Response for fine-tuning CLIP on other tasks
We thank all reviewers for their insightful comments. We will include all new results in the revised version.
Since all reviewers (Q2 of Reviewer YGpA, W5 of Reviewer WXoe, Q1 of Reviewer zhUR, and Q1 of Reviewer MnaP) are curious about the p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations | Accept (poster) | Summary: This paper introduces a method that can add a novel generated or customized object into a 3D reconstructed scene. To enable this, it proposes a Erase-and-Replace strategy. The first step is to remove an object queried by a text prompt: it segment out the target object using existing language based model and fi... | Rebuttal 1:
Rebuttal: We are grateful to reviewer ZFSz for their review! However, we are concerned that the reviewer has misunderstood our proposed method, as the summary contains multiple factual inaccuracies. In particular, in their summary, the reviewer states that we:
“Fine-tune the existing NeRF model based on inp... | Summary: This paper introduces a novel method that utilizes the Erase-and-Replace strategy for text-guided object replacement in 3D scenes. Given a collection of multi-view images, camera viewpoints, text prompts to describe the objects to be replaced and to be added, this method first optimizes the background scene wi... | Rebuttal 1:
Rebuttal: We are grateful to reviewer MnXs for their review! We are glad that they find our method “novel”, that it “enables localized 3D editing and obtains more realistic and detailed editing results”, that our experiments are “extensive”, and that our “ablation study validates the effective role of the k... | Summary: The authors introduce a method for replacing 3D objects within 3D scenes based on text descriptions. They do this by using in-painting approaches for the set of images that are used for down-stream novel-view synthesis.
Strengths: The paper introduces an interesting and important application of distilling 2D... | Rebuttal 1:
Rebuttal: We are grateful to reviewer MzLe for their review! We are glad that they found our application “interesting and important”, that our Halo region method is a “nice solution”, and that the baselines we compare to are “sensible”. We now address the reviewer’s specific concerns and questions:
“Replac... | Summary: This paper studies instruction-guided scene editing. They introduce ReplaceAnything3D (RAM3D, not sure why using this abbreviation), a two-stage method to first erase the object to be replaced and in-paint the background in the scene, and then generate the replace-to object and compose it to the scene after st... | Rebuttal 1:
Rebuttal: We thank reviewer NhGj for their review! We are glad that they found that our ‘visualization results look good’, and that the pipeline is ‘novel and well-motivated’. We now address the reviewer’s specific concerns and questions:
“No ablation study was provided about HiFA” - we have now added this... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback! We are glad that reviewers NhGj, MzLe, MnXs found that our proposed method is novel and well-motivated, and that MzLe and MnXs in particular praised the quality of our results.
A concern that was raised by both reviewers NhGj and ZFSz was that Instru... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WeiPer: OOD Detection using Weight Perturbations of Class Projections | Accept (poster) | Summary: The paper introduces WeiPer, an post-hoc method for out-of-distribution (OOD) detection that leverages weight perturbations in the final layer of neural networks to enhance detection performance.
Contributions:
1. Introducing linear projections of the penultimate layer by perturbing the final layer's weight... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and analyzing the strengths and weaknesses of our approach.
> "Related Work Missing Strong Baselines: Data Depths and Information Projections"
Many methods have been developed for OOD detection, even when only considering image classification. The pap... | Summary: This work proposes a component, WeiPer, that can benefit OOD detection. WeiPer adds random perturbations (sampled from standard normal) to the class projection weight vectors and essentially expands the output dimension (compared to the original logit space). WeiPer can be combined with multiple existing OOD d... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to assess our paper and following up with a thought-provoking question.
> Since WeiPer essentially expands the output logit space and is expected to encode richer information, is it possible to leverage WeiPer for other tasks such as OOD generalization, b... | Summary: The manuscript proposes a post-hoc OOD detection method, which is broadly applicable and can improve existing OOD detection methods.
Strengths: - The methodology is post-hoc, making it more practical.
- Results for near OOD evaluation are promising.
- The method can be combined with existing OOD detection s... | Rebuttal 1:
Rebuttal: We thank reviewer for examining our paper, highlighting strengths, and indicating weaknesses to improve our contribution.
We provide a memory and time analysis comparing WeiPer+KLD to its closest competitors in Table 1 and Table 2 in the rebuttal pdf. Our method is on par with the other methods. ... | Summary: The paper introduces "WeiPer," a method that improves existing out-of-distribution (OOD) detection techniques by perturbing class projection weights. This method leverages the class-discriminative ability of pre-trained neural network classifiers by introducing weight perturbations in the final fully connected... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to assess our work.
> WeiPer introduces additional computational complexity and memory requirements, which might limit its applicability in resource-constrained environments.
That is true for virtually every method. WeiPer+KLD is comparable to other meth... | Rebuttal 1:
Rebuttal: Memory and time comparison to other methods.
Pdf: /pdf/af2ad2f0a405cc7bd583bd8c833244f54321d8c6.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scale Equivariant Graph Metanetworks | Accept (oral) | Summary: The paper considers the emergent and fascinating field of learning over weight spaces, that is, neural nets that process other neural networks. The processing NN is referred to as a metanetwork. Previous approaches showcased the importance of accounting for the input NN’s symmetries by designing equivariant ar... | Rebuttal 1:
Rebuttal: *For weaknesses: 1,3,4,5,7 and question 3 please refer to Comment.*
### Weaknesses
>*Weakness 2. Scaling data augmentations*
An effective method to get such augmentations is to sample scaling matrices for every training datapoint - diagonal sign matrices for sign and diagonal positive scaling... | Summary: This paper addresses the emerging and fascinating field of deep-weight space learning where neural nets used to process weights and biases of another deep model. The authors have introduced new methods based on GNN architecture called ScaleGMN and ScaleGMNB for Scale Equivariant Graph MetaNetworks. The latter ... | Rebuttal 1:
Rebuttal: ### Weaknesses
>*Weakness 1. Writing Style*
We thank the reviewer for their suggestions. Please refer to the global response about the writing pace and style.
>*Weakness 2. Equivariant tasks*
To evaluate the performance of our method on tasks that require permutation and scale equivariance, we... | Summary: This work develops new GNN-based metanetworks that are equivariant to scaling symmetries induced by nonlinearities in input neural networks. Their ScaleGMNs extend metanetworks, which are typically only permutation equivariant (if at all equivariant), to also account for other symmetries in input neural networ... | Rebuttal 1:
Rebuttal: ### Weaknesses
>*Weakness 1. It would be interesting to see how ScaleGMNs perform on different tasks, especially an equivariant task (rather than just invariant tasks) such as INR editing.*
Metanetworks can indeed find interesting applications that require our model to be permutation and scale ... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thorough evaluation of our paper and their constructive feedback, which helped us improve our empirical evaluation to further corroborate our claims and identify potential future directions. In the following comments, we gather the strengths pointed o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large Language Model Unlearning | Accept (poster) | Summary: This paper studies how to perform unlearning on large language models. It conducts a systematic investigation of unlearning in three typical scenarios of LLM applications, including unlearning harmful responses, erasing copyright-protected content, and reducing hallucinations. In all scenarios, the proposed un... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We discuss the comment one by one.
**W1 (Nonsense Output)**: We thank the reviewer for the concern. As mentioned, this is our design choice. Since the scenario we target is when we are only given negative samples, there is no way we can output the ... | Summary: This paper explores the concept of unlearning in large language models (LLMs) as an alternative approach to aligning AI systems with human preferences. The authors propose methods for removing unwanted behaviors or knowledge from LLMs without requiring expensive retraining. They demonstrate the effectiveness o... | Rebuttal 1:
Rebuttal: We thank the reviewers for the feedback. We address the comments one by one.
**W1 (Stability)**: We respectfully disagree with the statement that merely based on the reviewer’s previous experience of unlearning in the **traditional models**, unlearning would not work in our case.
First, we empir... | Summary: This research paper explores techniques for inducing large language models (LLMs) to selectively forget pre-learned information or undesirable behaviors through an unlearning paradigm. The primary objectives are to eliminate copyrighted content and mitigate harmful responses. The authors propose machine unlear... | Rebuttal 1:
Rebuttal: We thank the reviewers for the positive feedback. We address the comments one by one.
**W1 (Novelty)**: We agree gradient ascent is a simple method but we think this method warrants the development for this new LLM unlearning problem we introduced. We would also like to stress that one of our con... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and valuable feedback. We provide our response to each reviewer individually, summarized below:
* In response to reviewer EdQC, we have included additional experiments compared to DPO. Our method requires less cost than DPO (Figure 1 in the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence | Accept (poster) | Summary: The authors here introduce a new correlation statistic here they call the
"rearrangement correlation:" $r^{\sharp}$. The work argues this statistic
captures non-linear, monotonic relationships between two samples. The
authors prove a couple theoretical results for this new statistic and then
conduct a large em... | Rebuttal 1:
Rebuttal: ---
Q:The main weakness in the current form of the paper is when to utilize this statistic... it is not clear how much better it is compared to using the RSS of a LOWESS regression.
A:Yes, the main utility of the proposed coefficient is to measure the strength of monotonic relationships. The role... | Summary: The authors refined Pearson’s r, and proposed a new correlation coefficient, i.e., rearrangement correlation. They showed that this coefficient is able to capture arbitrary monotone relationships, both linear and nonlinear ones. With simulation, they showed the rearrangement correlation is more accurate in mea... | Rebuttal 1:
Rebuttal: Comments:
The contribution of this rearrangement correlation doesn't seem significant enough or at least the authors can clarify more on this. For example, Theorem 1, which was already proposed in the existing literature, seems enough to motivate this rearrangement correlation thus lacking some si... | Summary: The paper proposes an adjustment to Pearson’s r to measure nonlinear monotone relationships, resulting in a new coefficient called the rearrangement correlation. The rearrangement correlation can capture both linear and nonlinear monotone relationships more accurately than traditional measures.
Strengths: Thi... | Rebuttal 1:
Rebuttal: Comment: This paper presents a new inequality tighter than the Cauchy-Schwarz Inequality. The proposed Rearrangement Correlation is well-defined but could use more intuitive explanations.
Response: I am deeply grateful for your kind acknowledgement, and we will resort to permutahedron to explain ... | Summary: This paper proposes a new variant of Pearson's r coefficient to capture arbitrary monotone relationships between random variables.
Traditionally, Pearson's r is used to capture linear dependence between variables. The relation of "being linearly dependent" is stronger than the relation of "being monotone dep... | Rebuttal 1:
Rebuttal: Q:Can this method be generalized beyond one-dimensional data? I.e., is there an analog of "rearrangement correlation coefficient" for a proper definition of monotone dependency between $X$ and $Y$ in $R^d$?
A:Thank you for your comments. Your suggestion means a lot to us. We believe that there s... | Rebuttal 1:
Rebuttal: # Main theoretical contributions
The main theoretical contribution is Theorem 1 and Theorem 2, along with their corollaries and propositions.
Theorem 1 establishes the connection between two famous inequalities, i.e., the Cauchy-Schwarz Inequality and the Rearrangement Inequality. It is revealed... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization | Accept (poster) | Summary: This article proposes a new method for optimisation of risk under uncertainty, using Dirichlet Processes to introduce an extra degree of robustness to modelling uncertainty on the data generating process. The method is generally applicable to many statistical learning problems through the use of loss functions... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing our work and for the insightful comments, which we will incorporate in the next version of our paper by emphasizing the points brought up in the review.
First, we would like to address the weaknesses pointed out by the reviewer as follows (we def... | Summary: The authors use Dirichelet process and a smooth ambiguity aversion model to approximate the solution of risk minimization problems. They demonstate the consistency of their approach, along with finite sample guarantees. They additionally give a practical means of applying their procedure and apply the procedur... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing our work and for the insightful comments, which we will incorporate in the next version of our paper by fixing the typos/mistakes pointed out by the reviewer and by emphasizing the points brought up in the review.
As for the minor problems pointe... | Summary: This paper introduces a new robust risk criterion that integrates concepts from Bayesian nonparametric theory, specifically the Dirichlet process, and a recent decision-theoretic model of smooth ambiguity-averse preferences. They show the relationships between their criterion and traditional regularization met... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing our work and for the insightful comment, which we will incorporate in the next version of our paper by emphasizing its main point.
Specifically, we believe that the reviewer makes a good point that, at first sight, it could be desirable to estima... | Summary: This paper proposes a novel robust optimization criterion for training machine learning and statistical models by combining Bayesian nonparametric theory and smooth ambiguity-averse preferences, addressing distributional uncertainty to improve out-of-sample performance. The authors demonstrate theoretical guar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and time spent reviewing our work. If any further questions should come up during the next phases of the reviewing process, we will be happy to engage in further discussion with the reviewer. | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a Bayesian method for optimizing stochastic objectives under a given finite sample. More precisely, The paper assumes the Dirichlet Process for data generation and then it proposes to optimize the mean of the stochastic objective over posterior distributions given the sample. The paper prove... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing our work and for the insightful comments, which we will incorporate in the next version of our paper by emphasizing the points brought up by the reviewer.
Specifically, we would first like to address the weakness, kindly pointed out by the review... | null | null | null | null | null | null |
Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models | Accept (poster) | Summary: Taking inspiration from stratified sampling, this paper proposes a method named Stratified Prediction-Powered Inference (StratePPI).
With appropriate choices of stratification and sample allocation, StratePPI can provide substantially tighter confidence intervals than unstratified approaches. The experimenta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We provide answers to specific questions and remarks (quoted) below.
> In practical applications, how should we stratify data to ensure that StratPPI is better than PPI or PPI++?
In general we should stratify data so that ex... | Summary: The paper proposes an extension of prediction-powered inference (PPI) - a method for calculating confidence intervals with narrower confidence bands. The data is separated into subsets and the bias rectification term in the confidence calculation is weighted based on the similarity to the particular subsets. E... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We provide answers to specific questions and remarks (quoted) below.
> Line 107 defines $Y_i$ as the target output, which in the case of QA would normally be a textual answer. Line 112 then defines $Y$ as the binary gold rat... | Summary: This paper introduces the stratified PPI method, designed to reduce evaluation bias from autoraters by aligning them with a few human annotations. The key novelty lies in identifying that the autorater's performance varies across different example groups, with biases being smaller for some examples and larger ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We provide answers to specific questions and remarks (quoted) below.
> The evaluations are conducted on simple evaluation problems where the evaluation is binary.
Our Stratified PPI method works for general M-estimation prob... | Summary: The paper extends the predictive power inference methods to leverage data stratification strategies, and demonstrates that stratification can be used to obtain unbiased statistical estimates, while reducing variance (with worst case being as bad as prior approaches PPI++, and in practice considerably better). ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and thoughtful comments. We provide answers to specific questions and remarks (quoted) below.
> It would have been good for the paper to have a stronger focus on evaluation of the numerous publicly available (large) language models.
As suggested, we... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for taking the time to read and comment on our work. We were pleased to receive several good suggestions, and have taken this feedback into account. Please see our uploaded pdf for supplemental results.
Specifically, we have added another very realistic experiment ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling | Accept (poster) | Summary: This paper focuses on the task of phenotype imputation and proposes utilizing multi-modal data to gain insights that facilitate the evaluation of patients' overall health status. Specifically, the authors design a framework based on view decoupling, which involves segregating the modeling of biological data an... | Rebuttal 1:
Rebuttal: Dear Reviewer dJb6
Thank you for the review and valuable comments. We respond to your questions below.
**1. Time complexity.**
The time complexity of data quantization for each patient is composed of three primary components. The first component is the encoder, which has a time complexity of $O... | Summary: The work integrates multi-modal biological data into the task of phenotype imputation, addressing the challenges of heterogeneity and imprecision inherent in fusing biological and phenotype data. This paper introduces the MPI model, a novel approach that incorporates multi-modal data through a view decoupling ... | Rebuttal 1:
Rebuttal: Dear Reviewer W1JA,
Thank you for the review and valuable comments. We respond to your questions below.
**1. Other modalities.**
Yes, our method can be certainly applied to other modalities if data is available. For each modality, a distinct encoder and decoder can be utilized to uncover the la... | Summary: This paper introduces a machine learning (ML) approach aimed at addressing the challenge of phenotype missing data in clinical datasets. Specifically, the authors propose utilizing multi-modal biological data to enhance patient health information, thereby improving the accuracy of phenotype imputation. To acco... | Rebuttal 1:
Rebuttal: Dear Reviewer 8HRK,
Thank you for the review and valuable comments. We respond to your questions below.
**1. Residual quantization**
We respectfully highlight that our proposed biological data quantization is not for reducing noise in biological data.
The motivation behind residual quantizati... | Summary: This paper introduces MPI, a framework designed to improve phenotype imputation in EHR by leveraging multi-modal biological data through view decoupling. The method consists of quantizing biological data to identify the latent factors, using them to create a correlation graph, and a separate graph which models... | Rebuttal 1:
Rebuttal: Dear Reviewer QunD,
Thank you for the review and valuable comments. We respond to your questions below.
**1. Sparsity/missing rates**
The biological modalities used in this study exhibit significant missingness at random, with approximately 90% missingness in proteomics and 50% in metabolomics.... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes the MPI framework for phenotype imputation. Focusing on the detrimental effects of heterogeneity and inaccuracies in phenotype imputation, the proposed framework separates the biological view and the phenotype view for model learning and integrates them afterward. In experiment results usin... | Rebuttal 1:
Rebuttal: Dear Reviewer wLvH,
Thank you for the review and valuable comments. We respond to your questions below.
**1. General graph neural models**
We respectfully highlight that our experiments compared general graph neural models, specifically GraphSage and GIN. The models you referred to are designed... | null | null | null | null | null | null |
On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks | Accept (poster) | Summary: The authors explore node individualization schemes and argue that they can improve the expressiveness of shallow GNNs and provide bounds on the sample complexity of these methods. This allows node individualization schemes to be compared in this context. The theoretical findings are then substantiated with exp... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and the importance of our work.
We also would like to thank you for pointing out some potential weaknesses of our paper that we didn't spot before. We are confident that both of them can be addressed by adding a discussion in the paper, which we will do by mak... | Summary: In this paper, the authors investigate the generalization properties of node-individualized Graph Neural Networks (GNNs). Specifically, they aim to differentiate between various individualization schemes based on their generalization properties. To achieve this, they employ two techniques: VC dimension and cov... | Rebuttal 1:
Rebuttal: We thank you for your insights on how the paper could be improved, and for acknowledging the high relevance of our work. We think that, by addressing your comments on the lack of clarity of some sections, the paper will be indeed easier to understand. Please find below both the answers to your com... | Summary: This paper proposes sample complexity bounds for message-passing graph neural networks (GNNs) with node individualization schemes, i.e., the assignment of unique identifiers to nodes. The authors first introduce a mathematical framework which describes node individualization schemes as a relabeling process. Ba... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and the potential impact of our work to the graph learning community.
Moreover, we want to thank you for your extremely thorough and insightful review, which is certainly not to be taken for granted.
Your comments made us spot and address some sections of t... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
we would like to express our appreciation for the positive comments on our paper, recognizing
- the novelty of our work;
- the strong significance of our work for the community, potentially guiding future architectural design decisions;
- the strength of experimental results, pa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline Behavior Distillation | Accept (poster) | Summary: This paper emphasizes on the offline data generation to enable rapid policy learning in reinforcement learning. A new surrogate loss function is proposed for the data generation. Theoretical analysis shows the superiority of the proposed method in the performance gap.
Strengths: 1. A new objective for data ge... | Rebuttal 1:
Rebuttal: **Q1:** *Related work on offline data generation of RL should be provided and compared. The proposed Av-PBC method is only compared with random policy, are there other related works? Is this paper the first work of offline data generation in RL?*
**A1:** Thanks for your comments. To the best of o... | Summary: The paper considers the offline behavior distillation (OBD) for reinforcement learning. The problem is to distill a synthetic dataset given a large dataset sampled by a sub-optimal policy. The key challenge of the problem is to design a good distillation objective. The authors first give two native objectives... | Rebuttal 1:
Rebuttal: **Q1:** *Corollary 1 and Corollary 2 compare the policy for dataset distillation to the policy of offline RL, but we are more concerned about the performance of any policy trained on the distilled dataset comparing to the optimal policy. It would be better to propose a new metric to evaluate the p... | Summary: The paper introduces Offline Behavior Distillation (OBD), a method to synthesize expert behavioral data from sub-optimal reinforcement learning (RL) data to enable rapid policy learning. The authors propose two naive OBD objectives, Data-Based Cloning (DBC) and Policy-Based Cloning (PBC), and introduce a new o... | Rebuttal 1:
Rebuttal: **Q1:** *The dependency on an offline RL algorithm makes Algorithm 1 fails to claim that OBD could improve training efficiency when compared to direct application of the offline RL with the whole dataset.*
**A1:** Thanks for your comments. Though Algorithm 1 incorporates a complete offline RL pro... | Summary: This paper formulates offline behavior distillation in order to enable fast policy learning using limited expert data and thereby leveraging suboptimal RL data. The authors run extensive experiments on D4RL benchmarks to support their findings.
Strengths: 1. The linear discount complexity is a vast improvemen... | Rebuttal 1:
Rebuttal: **Q1:** *There are no major weaknesses, but ablation studies and further corollaries would strengthen the paper.*
**A1:** thanks for your acknowledgement and suggestion. We have conducted additional empirical studies during the response period to strengthen our paper:
1. We explored the effect ... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for their enormous efforts and constructive comments. We will make sure to incorporate the parts that you suggested for clarity and reflect your feedback on the revised paper. We have compiled the results of additional experiments related to the reviewers' questions... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion | Accept (poster) | Summary: The paper introduces SOFTS (Series-cOre Fused Time Series forecaster), an efficient multivariate time series forecasting model that addresses the gap between channel independence and channel correlation in a novel way. By utilizing a centralized STAR (STar Aggregate-Redistribute) module, SOFTS creates a global... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work and your thoughtful feedback. We are pleased to know that you found our approach and presentation to be excellent and that you see the value of our contributions to the field. We have carefully considered your comments and here are our responses.
... | Summary: This paper presents an efficient MLP-based model, the Series-cOre Fused Time Series forecaster (SOFTS). SOFTS incorporates a novel STar Aggregate-Redistribute (STAR) module to aggregate all series to form a global core representation, which is then dispatched and fused with individual series representations to... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and constructive comments on our paper. We appreciate your recognition of our work's strengths and have carefully considered your suggestions for improvement. Below, we provide detailed responses to each of your points.
> The paper lacks significant innovation... | Summary: This paper proposes to use a global representation to capture the channel correlations for multivariate time series forecasting. Specifically, it uses stochastic pooling to get the global representation by aggregating representations of individual series and then concats the global representation and individua... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our paper. We appreciate your feedback and have made corresponding responses to address your concerns.
> It is unclear why different datasets and metrics are used for different ablation studies, e.g., the datasets used in Table 3 and 4 are different, MAE is u... | Summary: The authors present a framework for modeling correlations between channels in a multivariate time series forecasting task. This framework concatenates each channel embedding with a ‘global core embedding’ which contains information from all channels in the lookback window. The authors present experiments that ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your constructive feedback. We appreciate your recognition of the importance of time series forecasting and your acknowledgment of our work's potential impact. We have carefully considered your comments and suggestions and have made revisio... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate the time and effort you have dedicated to reviewing our paper and for providing valuable feedback. We are delighted that the majority of the reviewers (3 out of 4) have given positive evaluations of our work. Our work is said to "of broad interest" (gtby), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials | Accept (poster) | Summary: This paper aims to assign material information to 3D objects that already have a base color. They do not generate material maps through training generative models but instead use a pre-established material library and build an automated recognition and indexing system to index from the material library. To use... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful comments. We will address your questions and concerns below and in the revised paper.
***
### W1. Importance of Addressed Task for Material Painting
While the derendering task mentioned by the reviewer is important, we believe the task of adding... | Summary: This paper presents a novel framework leveraging MLLM priors (GPT-4V) to build a material library and proposes an automatic pipeline to refine and synthesize new PBR maps for initial 3D models with diffuse albedo only. The pipeline integrates existing tools, such as GPT-4V and Semantic-SAM, while introducing n... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful comments. We will address your questions and concerns below and in the revised paper.
***
### W1. A clarification of novelty of Make-it-Real
Thank you for the comments. Our key innovation lies not in the MLLM model technology itself, but in how ... | Summary: The proposed work leverages GPT-4V to extract and infer materials in albedo-only scenarios, utilizing existing material libraries to generate SVBRDF maps with a region-to-pixel algorithm. This approach enhances 3D mesh realism, ensures precise part-specific material matching, and is compatible with rendering e... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful comments. We will address your questions and concerns below and in the revised paper.
***
### W1. Realism and Application of Generated PBR Maps
Thank you for pointing this out. We acknowledge that the generated PBR maps are not true representati... | Summary: This paper introduces the large-scale multimodal language models to realistic material rendering of 3D objects. Specifically, this paper employs MLLM to retrieve materials from a material library for different parts of . By combining 2D-3D alignment and diffuse reflection reference techniques, it generates and... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful comments. We will address your questions and concerns below and in the revised paper.
***
### W1. Differences between Make-it-Real and MaPa and the superiority of our method.
- **MaPa**: Introduces a text-driven segment-based procedural material... | Rebuttal 1:
Rebuttal: We thank all reviewers and appreciate the constructive comments and the recognition of novelty, and we are grateful for that most reviewers score our work with positive initial ratings (two accept, and two borderline accept). Our work introduces the MLLM into the texture inpainting pipeline **[mcg... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper tackles the problem of recovering materials for 3D meshes with known geometry and base color. The proposed solution consists of three steps:
- Utilizing Semantic-SAM to segment the 3D objects, identifying and isolating various material regions.
- Using hierarchical prompting to retrieve and match m... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and insightful comments! We will address your questions below and in the revised paper.
***
### W1: More Comparisons with Artist-Created Materials:
Thank you for the insightful suggestion! We conducted an additional user study comparing materials generated by our M... | null | null | null | null | null | null |
Benign overfitting in leaky ReLU networks with moderate input dimension | Accept (spotlight) | Summary: This paper stuides the benign overiftting of two-layer neural networks (only training the first layer) with leaky ReLU for binary classification, which relaxes the dimension condition on the dimension from $d = \Omega(n^2 log n)$ to $d = \Omega(n)$.
The considered problem setting is
- data generation process:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive feedback on our work. We are confident we have addressed each of the issues raised and hope the reviewer might consider raising their score in light of our responses.
>Linearly separable data
Indeed, a linear classifier is sufficient to learn this pr... | Summary: The paper studies benign overfitting in leaky ReLU networks trained with hinge loss on a binary classification task. The paper gives the conditions on the signal-to-noise ratio under which benign or harmful overfitting occurs for leaky ReLU networks. Unlike the previous related works, this paper does not requi... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper and for raising their questions and comments. We are confident that we can clarify the issues raised and hope in light of our responses below that the reviewer will consider raising their score.
> The paper may lack some experiments. The results are full... | Summary: This paper studies the benign overfitting of two-layer leaky ReLU network for binary classification with only mild overparameterization under a simple Gaussian mixture model assumption. First, the paper proves that for sufficiently small initialization, gradient descent with hinge loss converges in a polynomia... | Rebuttal 1:
Rebuttal: We are glad the reviewer appreciates the contribution of our work and thank them for highlighting the two related works on linear models [1,2]. We are happy to cite these and discuss them in future revisions. We also thank the reviewer for spotting the two typos listed, we will do a further thorou... | Summary: This paper studies benign overfitting in a two-layer leaky ReLU network trained with hinge loss for a binary classification task. This paper proves that in a finite iteration, the leaky ReLU network can reach zero training loss through gradient descent, and the network weight matrix after convergence will appr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions. It appears that the primary concern lies in the fact that our results only hold for shallow networks and linearly separable data. We emphasize that these are limitations also present in the existing literature on benign overfitting in non-lin... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and efforts in providing feedback on our work. Two actions we can take forward in future revisions are as follows.
- **Improving the presentation**: We will better connect Section 3, where the key results are presented, with the proof sketch given in Section ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-supervised Transformation Learning for Equivariant Representations | Accept (poster) | Summary: This paper is concerned with the learning of expressive representations in a self-supervised fashion. In particular, it aims at learning representations that
Strengths: - Presentation: The paper is clearly written, well-structured, easy to follow, and overall is in a good state
- Proposed approach: the idea ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our manuscript.
In response to your detailed feedback, we have gone to great lengths to address and accommodate every single one of your comments.
We would greatly appreciate it if you could review our responses to your comments and the submitte... | Summary: The authors propose Self-supervised Transformation Learning (STL) to learn equivariant representations. The core of the method is to not use augmentation information but instead use transformation representations obtained from pairs of data. In this sense, instead of knowing the transformation information, it ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our manuscript.
In response to your detailed feedback, we have gone to great lengths to address and accommodate every single one of your comments.
We would greatly appreciate it if you could review our responses to your comments and the submitted ... | Summary: The paper introduces STL, a method for learning self-supervised equivariant representations. It suggests replacing transformation labels with representations derived from data pairs. The proposed pretext task promotes learning invariant and equivariant representations alongside transformation-related informati... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our manuscript.
In response to your detailed feedback, we have gone to great lengths to address and accommodate every single one of your comments.
We would greatly appreciate it if you could review our responses to your comments and the submitte... | Summary: This paper proposes a new way to learn equivariant representations by directly learns the transformation representation. They enforce that different transformations have their own input-agnostic representations. To obtain this, they learn an encoder that takes pairwise representations of the same image to extr... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our manuscript.
In response to your detailed feedback, we have gone to great lengths to address and accommodate every single one of your comments.
We would greatly appreciate it if you could review our responses to your comments and the submitte... | Rebuttal 1:
Rebuttal: Thanks for
Dear Reviewers and Area Chairs,
We thank the reviewers for their constructive feedback. We are glad to take various helpful reviewer comments to clarify and complete our work. Reviewers agreed on the originality, motivation, soundness, and significance of the paper. In here, we breif... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models | Accept (poster) | Summary: This work aims to provide a theoretical analysis about the uncertainty-perception trade-off in generative models, corresponding to the fidelity-naturalness trade-off of the generated images. By defining the inherent uncertainty and formulating a uncertainty-perception （UP) function, the authors proves that the... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our manuscript and providing constructive feedback. Below, we respond to the specific points raised by the reviewer.
**Weaknesses**
1. While our initial analysis utilized full-reference metrics (LPIPS, MSE, PSNR, SSIM) due to their widespread acceptance, we ack... | Summary: This paper presents a theoretical perspective towards hallucinations and reveals a tradeoff between uncertainty and perception for image restoration problem. Additionally, the paper points out that uncertainly-perception tradeoff can induce the well-known perception-distortion tradeoff.
Strengths: 1. The pape... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in evaluating our manuscript. We have taken the feedback into consideration and responded to the specific points raised below.
**Weaknesses**
1. In our context, perception is defined as the probability $p_\text{success}$ of a human observer to succe... | Summary: The paper employs information-theory tools to characterize a tradeoff between uncertainty and perception in image restoration. They prove that high perceptual quality leads to increased uncertainty and the uncertainty-perception trade-off induces the distortion-perception trade-off. The theoretical results are... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and constructive feedback. Below we address in detail the major concerns raised by the reviewer.
**Weaknesses**
1. We acknowledge the extensive literature on the perception-distortion tradeoff, particularly the seminal work demonstrating the i... | Summary: Deep generative models have achieved remarkable performance in image restoration, resulting in generated images of high visual quality. However, these models often produce high-frequency details that are not consistent with the ground-truth images. Such hallucinations introduce uncertainty in the generated con... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the reviewer has dedicated to reviewing our manuscript. Below we address in detail the major concerns raised by the reviewer.
**Weaknesses**
1-2. *Experiments* - We acknowledge the reviewer's concerns regarding the experiments on image super-resolution using the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their efforts in evaluating our manuscript, their overall positive feedback, and their constructive criticisms, which have significantly strengthened our contribution. We have provided detailed responses to each reviewer individually. In the following, we summa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction | Accept (poster) | Summary: This work introduce a new system for the FSMPP tasks, which includes: context adapter in GNN, context graph, and weight consolidation. With these three techniques, the Pin-Tuning method achieved promising results on many FSMPP benchmarks.
Strengths: - FSMPP tasks are important and have grate research value. ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer nYSV
We thank the reviewer for your constructive feedback. Please find detailed responses below.
> `W1` The novelty of our MP-Adapter for parameter efficient tunning.
Based on your valuable feedback, we have provided a detailed description in the `Global Response` to cla... | Summary: This paper proposes Pin-Tuning, a parameter-efficient in-context tuning method for few-shot molecular property prediction (FSMPP), mitigating the parameter-data imbalance and enhancing the contextual perceptiveness of pre-trained molecular encoders. This method treats the embedding layers and message passing l... | Rebuttal 1:
Rebuttal: # Responses to Reviewer u7hY
We thank the reviewer for your constructive feedback. Please find detailed responses below.
> `W1` `Q1` Add details on pilot experiment in Figure 1.
Thank you very much for your reply. We apologize for the lack of details about the pilot experiment due to the length... | Summary: The authors propose a strategy for few-shot drug discovery scenarios. The authors propose to fine-tune representations retrieved from encoder layers with adapters. In addition to the initial representations from the message passing layers, the adapters are provided with property representations and "context-aw... | Rebuttal 1:
Rebuttal: # Responses to Reviewer W6tN (1/3)
We thank the reviewer for your constructive feedback. Please find detailed responses below.
> `W-O1` `W-Q2` The novelty of our adapter-based parameter-efficient tuning.
We greatly appreciate your comments. Indeed, there are several adapter-like methods used to... | Summary: This paper introduces a novel Pin-Tuning method. Focusing on improving the fine-tuning process of pre-trained molecular encoders, especially for the task of Few-Shot Molecular Property Prediction (FSMPP), Pin-Tuning skillfully balances the contradiction between the number of tunable parameters and the limited ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer 1DKF
We thank the reviewer for your constructive feedback. Please find detailed responses below.
> `W1` `Q2` Compared to adapters in NLP, the specific design and advantages of the proposed MP-Adapter.
Based on your valuable feedback, we have provided a detailed descript... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely appreciate all the reviewers for your valuable feedback on our paper. In this global response, we aim to address the reviewers' concerns regarding the novelty and empirical advantages of our MP-Adapter. Specifically, we intend to answer the following questions:
>`T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff | Accept (poster) | Summary: In this paper, the authors study the problem of learning stochastic contextual MDPs using a realizable and finite models class $\mathcal{M}$ accessed via an offline density estimation oracle, which have the guarantee of minimizing the expected error measured by the squared Hellinger distance. Under the assumpt... | Rebuttal 1:
Rebuttal: >> In my opinion, the writing requires some improvements. For instance, the algorithm is hard to understand due to some definitions that appear after it (instead of before it). An intuitive explanation of the use in the trusted occupancy measures set is missing, the use of them to derive a multipl... | Summary: This work studies low-rank contextual decision processes (CMDPs) in offline settings. The authors introduce a novel algorithm that leverages the structure of low-rank CMDPs to achieve efficient learning with limited data. The proposed method, O-LRCDP, is designed to minimize the dependence on large-scale data ... | Rebuttal 1:
Rebuttal: >> The presentation of technical details, particularly in Section 3.2, is quite dense and may impede overall readability. While the mathematical rigor is appreciated, the main text could benefit from focusing more on intuitive ideas and key insights that directly address the technical challenges. ... | Summary: The paper studies the stochastic Contextual MDP problem under an offline function approximation oracle. Concretely, the authors assume access to an offline density estimation oracle for a (realizable) class of CMDPs. Under this (minimal) assumption, they prove a rate-optimal regret bound, I.e., that scales wit... | Rebuttal 1:
Rebuttal: We greatly appreciate your comments and have written a detailed technical overview as a general rebuttal. We believe it would be helpful for you to check before the detailed rebuttal.
>> The offline density oracle is a bit vague. Can it be implemented using ERM on the log loss as in previous work... | Summary: This paper studies a statistical and computational reduction from the general Contextual Markov Decision Process (CMDP) problem to offline density estimation. They propose an efficient algorithm called LOLIPOP which achieves a near-optimal regret with minimal oracle calls $\mathcal O(H log log T$) if $T$ is kn... | Rebuttal 1:
Rebuttal: >> This paper is purely theoretical. It would be interesting if the authors provide some experimental results to demonstrate the performance of the proposed algorithm. """
This paper presents a purely theoretical result. We look forward to seeing experiments in future works.
>> In Eq (3), to con... | Rebuttal 1:
Rebuttal: General rebuttal:
We thank the reviewers for their positive reviews and will make sure to address the writing issues mentioned. Here, we clarify the technical challenges and our methodology. We will include a paragraph/section regarding this in the updated version of this paper.
Technical chall... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning | Accept (poster) | Summary: This work takes a function space view of the posterior distribution, and places a GP on the likelihood with neural network predictors. This generate a posterior distribution, and the authors attempt a Laplace approximation as a which they combine with matrix-free linear algebraic methods to aid in computationa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We are happy that they found the paper was "well written" and that our method "performs well".
> "Laplace approximations are justified by the Bernstein-von Mises theorem which states that [...] the posterior distribution concentrates around its ... | Summary: This paper proposes a functional Laplace approximation approach which is able to incorporate function space prior instead of weight space prior used by existing Laplace approximation methods. The function space prior is more meaningful than weight space ones in terms of expressing prior knowledge about the und... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive feedback. We are glad that they found our paper was "well-written", "easy to follow" and had a "smooth flow".
> "The comparative experiments are not sufficient. Various efficient LA methods should be compared. [...] It would be better to include s... | Summary: The paper proposes a new method (FSP-Laplace) to place priors in the function space of deep neural nets and develop scalable Laplace approximations on top of it. The ideas are inspired in MAP estimation theory and the fact that an objective function that is actually regularising the neural network in the funct... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive feedback. We are glad that they found that the "quality of the manuscript is [...] high", that they "like the spirit of the work" and that our method has "good performance".
> "Context points in section 3.2 are kind of an obscure part of the A... | Summary: This paper proposes a method to calculate the Laplace approximation of the posterior of neural networks with a prior defined directly in the functional space (Gaussian processes). Due to the absence of Lebesgue densities in infinite-dimensional functional spaces, the notion of weak mode is used to obtain an an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback and time. We are glad that they found our "method is well-motivated", our "techniques [...] are solid" and that the paper was "very well-written" and "easy-to-follow".
> "My main concern is the relation between this paper and Sun et al. (2019) [8]. ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and feedback. Some reviewers requested a comparison with FVI (Sun et al., 2019) and more details on the effect of the context points. We ran these additional experiments and wish to share them with all the reviewers.
**Comparison with FVI (Sun et al., 2019):... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning from Pattern Completion: Self-supervised Controllable Generation | Accept (poster) | Summary: Conditional generation in the era of diffusion models has been positively impacted by ControlNets, which allows for the fine-tuning of diffusion models using additional image input allowing for fine-grain conditioning. Popular ControlNets rely on pose, edge, or segmentation map conditioning which requires addi... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and constructive feedback.
**Q1**: Presentation/Clarity/Rigor
**R1**: We sincerely apologize for the confusion caused in the introduction and methods sections. Our aim was to bridge concepts from neuroscience with controllable generation in AI, hoping... | Summary: The authors train a modular auto encoder with an auxiliary custom equivariance objective, which enables them to get independent sets of representations of an image. The authors then use some of these representations to condition a ControlNet.
The authors find that the auto encoder’s submodules learn to encod... | Rebuttal 1:
Rebuttal: Thank you very much for your efforts and valuable feedback on our paper.
**Q1**：What are we learning from pattern completion?
**R1**：Pattern completion focuses on the relationships between different module features at a global scale. By learning these relationships, SCG can utilize information f... | Summary: This paper proposes a self-supervised controllable generation (SCG) framework with two training stages. The first stage exploits equivalence invariance to learn a modular autoencoder, aims to extract different visual pattens from input images, each extracted (learned) visual pattens can be regarded as a kind o... | Rebuttal 1:
Rebuttal: We are grateful for your fondness of our idea and are truly encouraged by it.
**Q1**：Evaluation may lead to a serious unfair comparison because the original ControlNet is not trained on MS-COCO.
**R1**：The ControlNet we used as baseline is trained from scratch with the same setting as the propos... | Summary: Presents a self-supervised approach for learning multiple distinct representations from images through a loss leading to specialized modules, each learning different aspects of the data without manual design. Demonstrate that those modules may be useful for the conditional generation of images through a contro... | Rebuttal 1:
Rebuttal: Thank you very much for your affirmation of our originality!
**Suggestion**：Better use "controllable generation" rather than “pattern completion” .
**Response**：Thank you for your suggestion. “Controllable generation” is indeed a more accurate and understandable term in the field of AI, as it cl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their efforts and constructive comments on our manuscript, which is very valuable and enlightening to us. We have revised our manuscript according to reviewers’ concerns. We would like to make an overall response before specific response to reviewers.
# GR1：Significance... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation | Accept (poster) | Summary: The paper focuses on layout-to-image (L2I) generation from the perspective of rich-context scenario where the object descriptions are complex and lengthy. In the framework design, it introduces a novel regional cross-attention module to enhance the representation of layout regions. In the evaluation for open-v... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her review. We use W to denote bullets in weaknesses
**Answer to W1**: In Rebuttal Section A, we compare the throughput of L2I methods using SD1.5 and SDXL baselines. It is noteworthy that the overall throughput of our method is not significantly hampered. In a typic... | Summary: In this work, the author views both training and evaluation of the layout-to-image synthesis task. They propose regional cross attention to addresses the issues of previous works and they also introduce a new evaluation protocols for this task.
Strengths: - I agree with the author's discussion on the desired ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her review. We use W and Q to denote bullets in weaknesses and questions
**Answer to W1**: Our proposed two metrics may not seem exceptionally special, however, we are the first to repurpose these metrics to evaluate L2I performance. They offer significant value for ... | Summary: The field of paper is open-set layout-to-image (L2I) generation. They propose to apply regional cross-attention module to enrich layout-to-image generation, slightly surpassing existing self-attention-based approaches. The paper also proposes two new metrics to assess L2I performance in open-vocabulary scenari... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her review. We use W and Q to denote bullets in weaknesses and questions
**Answer to W1, Q2**: Our solution, like GLIGEN and InstDiff, requires inserting additional parameters into the pre-trained model and enhancing L2I ability through training. Indeed, our module i... | Summary: The paper proposes a layout-to-image generation method based on cross-attention control. It also proposes evaluation metrics for the task.
Strengths: * The paper highlights the potential effectiveness of cross-attention control and designs a learning framework using this insight.
* The framework shows some e... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her review. We use W and Q to denote bullets in weaknesses and questions
**Answer to W1**: The backbone of the current SoTA L2I method, InstDiff, is based on SD1.5. Our proposed method has been validated using both SDXL and SD1.5 backbones (Ln 201). Therefore, our co... | Rebuttal 1:
Rebuttal: The figures, tables, and pseudo-codes for the rebuttal are presented in the PDF file. We appreciate the reviewers for taking the time to read and consider them.
Pdf: /pdf/5406210c09a6c6e53a6275c7f7038c82ae19e5c5.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning | Accept (poster) | Summary: The Weighted Safe Actor-Critic (WSAC) is a new Safe Offline Reinforcement Learning algorithm designed to outperform any reference policy while ensuring safety with limited data. It uses a two-player Stackelberg game to achieve optimal convergence and safe policy improvements. In practical tests, WSAC surpasses... | Rebuttal 1:
Rebuttal: Thank you for your comments on our paper. Please find our point-by-point response to your questions below.
- **Response to contributions:** We respectfully ask the reviewer to evaluate our theoretical contributions. We would like to mention that our approach has significant differences from ATAC.... | Summary: For safe RL methods, a desired property is Safe Robust Policy Improvement(SRPI), which means the learned policy is always at least as good and safe as the baseline behavior policies. But it's not achieved yet.
Also, the traditional Actor-Critic framework may suffer from insufficient data coverage, which may f... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ positive evaluation of the novelty of this paper. The current formulation can be applied to the case with $0-1$ cost, indicating whether a constraint is violated or not at each step. Nevertheless, if we only get feedback over the entire trajectory whether it is... | Summary: This paper proposes weighted safe actor-critic, and provides corresponding theoretical analysis on its optimal statistical rate. Some interesting technical tools were introduced. The authors also implement a practical version of WSAC and evaluate it against SOTA offline safe RL baselines in continuous control ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper! We appreciate your support and comments. Please find our point-by-point response to your questions below.
- **Response to clarification of "Adversarial":** The adversarial training in this paper is designed based on the concept of relative pessim... | Summary: This paper introduces a principled approach for safe offline reinforcement learning (RL), aimed at robustly optimizing policies beyond a given reference policy, particularly when constrained by the limited data coverage of offline datasets.
The traditional constrained actor-critic methods face challenges inclu... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper! We appreciate your support and comments. Please find our point-by-point response to your questions below.
- **Response to the reference policy:** We would like to mention that extracting the behavior policy from an offline dataset is not difficu... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their thoughtful evaluations. In this global rebuttal, we point out our main contributions and address the common concerns of the reviewers. We respond to each individual reviewer in each individual rebuttal separately as well.
**Our Contributions**:
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifiability Analysis of Linear ODE Systems with Hidden Confounders | Accept (poster) | Summary: This paper provides the identifiability analysis of linear Ordinary Differential Equation (ODE) systems, particularly in scenarios where latent variables interact with the system. In detail, it investigates two specific cases. In the first scenario, latent confounders do not exhibit causal relationships, but t... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have addressed each of your comments as follows. Additionally, we have revised our manuscript in accordance with your suggestions, and we believe that the quality has been significantly enhanced as a result of your insightful input.
## Answers to weaknesse... | Summary: This paper studies the problem of identifiability analysis of linear Ordinary Differential Equation (ODE) systems. It focuses mainly on the scenarios where some variables in the system remain latent to the learner. This paper aims to address this challenge by studying identifiability analysis in two classes of... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have addressed each of your comments as follows. Additionally, we have revised our manuscript in accordance with your suggestions, and we believe that the quality has been significantly enhanced as a result of your insightful input.
## Answers to weaknesse... | Summary: The paper focuses on the (parameter) identifiability problem of linear ODE systems. The identifiability results of the existing work has been limited to fully-observable systems, i.e., with no latent variables. The paper analyzes the parameter identifiability of partially-observable linear ODEs with certain st... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have addressed each of your comments as follows. Additionally, we have revised our manuscript in accordance with your suggestions, and we believe that the quality has been significantly enhanced as a result of your insightful input.
## Answers to weaknesse... | null | null | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their thoughtful and constructive feedback on our manuscript. Below, we summarize the modifications made to the manuscript based on your comments:
- To Reviewer 1wF2:
- We have added two real-world linear ODE examples that align ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging | Accept (poster) | Summary: This paper investigates a dynamic and compressive merging method for adapting large-scale models to multiple tasks. The authors claim that adjusting the ratio between shared knowledge and exclusive knowledge is crucial for high-performing model merging, and they devise an algorithm that learns proper coefficie... | Rebuttal 1:
Rebuttal: # Response to Reviewer `LqLU`
> Q1. Lack of technical details on the router (Model architecture and training configurations ), validation set for router training. Does the validation set consist of the integration of downstream tasks? or general text corpus?
Thanks for your suggestion, we will ... | Summary: This paper attempts to resolve an issue of destructive interference with model merging techniques. It proposes to maintain a shared base model and separate task-specific knowledge structures that can dynamically be combined at test time.
The paper presents some a nice to buttress the drive home their methodol... | Rebuttal 1:
Rebuttal: # Response to Reviewer `2gMR`
> Q1. Why this is a viable alternative to just keeping the base model and LoRA low-rank vectors? Why we would use this method over keeping separate task low-rank adapters and just using these at test time ?
Table 2 has proven that our knowledge modularization techni... | Summary: `Twin-Merging` proposes a method for task merging which tackles two issues:
* Task interference: The proposed `Twin-Merging` explicitly model shared vs task-specific knowledge to potentially reduces redundancies across the task vectors, which may lead to subpar task merging results
* Dynamic merging: Usual... | Rebuttal 1:
Rebuttal: # Response to Reviewer `Q3uE`
> Q1. The design does not support batching
While the router process supports batching, the dynamic merging process currently handles inputs sequentially. However, **it is straightforward to extend the merging process to support batching**. As detailed in the "Infere... | Summary: In this paper, the authors introduce the Twin-Merging to merge language models, aiming to close the performance gap between conventional model merging techniques and fine-tuned models, while improving adaptability to data heterogeneity. By modularizing and dynamically merging shared and task-specific knowledge... | Rebuttal 1:
Rebuttal: # Response to Reviewer `vkK5`
> Q1. The proposed method dynamically merges the models with the varying inputs, which can be extremely time-consuming in practice.
Please refer to the "Inference Efficiency" section of our global rebuttal. Our method achieves superior performance (95.33 vs 94.04) ... | Rebuttal 1:
Rebuttal: # Global Rebuttal
Thank all four reviewers for their constructive feedback which has helped us to improve the clarity and contribution of our work.
The below contains a rebuttal for remarks that are common to most reviewers.
## 1. More Baseline (To `Q3uE`, `2gMR`)
To compare with baselines that... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models | Accept (poster) | Summary: This paper is devoted to the use of diffusion models to solve inverse problems, arguing for treating the inverse process in a diffusion model as a function and proposing a new plug-in approach called DMPlug. DMPlug addresses the issues of feasibility and measurement feasibility of manifolds in a principled man... | Rebuttal 1:
Rebuttal: We thank reviewer uJfQ for the detailed review and insightful comments!
### RE Weakness 1:
We appreciate this insightful comment! Indeed, the two papers have similar ideas to ours from a high level, and we will definitely add and discuss them in our revision. However, we still want to highlight ... | Summary: This paper presents a plug-and-play method DMPlug for solving inverse problems with diffusion models. DMPlug utilizes a pre-trained diffusion model as a deterministic function that generates images from latent seeds and solves MAP problems by optimizing the seeds. Experiments show that their method beats curre... | Rebuttal 1:
Rebuttal: We thank reviewer y52g for the detailed review and insightful comments!
### RE Weakness 1:
Please refer to GR3.
### RE Weakness 2:
We totally understand this concern that using different DMs may lead to some unfairness concerns, but it is not clear which model the unfairness pertains to. Actua... | Summary: This paper proposes an optimization-based method for optimize the initial noise for data consistency. This method is evaluated on a variety of linear inverse problems and non-linear inverse problems, and achieves SOTA results. The method also show robustness for unknown noise levels with a technique called ES-... | Rebuttal 1:
Rebuttal: We thank reviewer wUnV for the detailed review and insightful comments!
### RE Weakness 1 & Question 1:
Please refer to GR2.
### RE Weakness 2 & Question 4:
Please refer to GR4.
### RE Weakness 3 & Question 2:
This is a very good suggestion! We use the standard DDIM/DDPM and LDM models. Th... | Summary: The authors propose a new framework, called DM-Plug, to solve inverse problems with pre-trained diffusion models. Most of the prior works on this topic propose approximations for the conditional score function. The authors propose an alternative approach that is closely related to techniques for solving invers... | Rebuttal 1:
Rebuttal: We thank reviewer yL1C for the detailed review and insightful comments!
### RE Weakness 1:
We agree that allowing posterior sampling for uncertainty quantification and other purposes would be great. This is, unfortunately, not what the MAP framework can offer. We follow most current DM-based met... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful and constructive comments about our manuscript!
### GR1: Difference between solving inverse problems and condition generation with the classifier guidance (CGwCG)
As suggested by Reviewer uJfQ and hinted by other reviewers, we ablate the number of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention | Accept (poster) | Summary: This paper introduces SwitchHead, a novel MoE architecture for the attention layer. Unlike the existing MoA approach, which computes the output of the attention layer as a weighted average of the top-k heads determined by a learnable routing mechanism, SwitchHead independently applies expert mixtures to the he... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review and for positive comments on the clarity and methodology of the paper. Please find our responses as follows:
> similar techniques for transforming attention heads into MoE have been proposed in … authors can consider mentioning these... | Summary: This paper introduces SwitchHead, a Mixture of Experts (MoE) method for improving the efficiency of the self-attention layer in Transformers. Unlike traditional MoE methods focused mainly on feedforward layers, SwitchHead effectively reduces both compute and memory requirements, achieving significant wall-cloc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review and for positive comments on the clarity of the paper. Please find our responses as follows:
> A key distinction lies in its use of a non-competitive activation function (sigmoid) instead of SoftMax. This design choice is important t... | Summary: This paper presents SwitchHead, a Mixture of Experts (MoE) method applied to the self-attention layer in transformer blocks. By applying MoE to the self-attention layer, SwitchHead reduces computational and memory costs while maintaining language modeling performance comparable to traditional dense models. It ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review. Please find our responses as follows:
> … it would be beneficial to see results on a broader range of NLP tasks. How does SwitchHead perform on tasks such as document summarization[1] or open-domain question answering[2]?
> Have y... | Summary: This work proposes a novel Multi-Head-Attention (MHA) mechanism which is more efficient than the standard MHA used in most transformers. The method---called SwitchHead---is relying on Mixture-of-Experts (MoEs) to save computation while retaining the same model capacity and performance. To achieve this, instead... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review and for positive comments on the clarity and methodology of the paper. Please find our responses as follows:
> I could not find the sequence length used in your experiments, which prevents me from computing the number of tokens used ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning | Accept (poster) | Summary: The authors introduce the IRL-SE problem, which which uses multiple experts, including sub-optimal ones, to enhance reward function learning. Given a bounded performance gap between experts, the authors provide a theoretical analysis on shrinking the feasible set of compatible reward functions. Using a PAC fra... | Rebuttal 1:
Rebuttal: > ### It would be powerful to see it actually employed in a case study to see how such a technique would be used. Even outside of empirical evidence, it would aid in readability of the paper to see how the notations used in the theoretical sections are actually used in practice. To that end, even ... | Summary: This paper aims to mitigate the intrinsic reward ambiguity in IRL problems. The authors propose that incorporating sub-optimal expert demonstrations can lead to a more accurate estimate of the feasible reward set. Initially, they formulate the IRL with Sub-Optimal Experts (IRL-SE) problem and explore the theo... | Rebuttal 1:
Rebuttal: > ### How can we utilize the feasible reward set in a real-world scenario?
We thank the Reviewer for raising this point. How to make the best use of the feasible reward set to learn a policy in RL is currently an open problem, even for the single-agent IRL formulation of the feasible reward set ... | Summary: This paper studies unregularized IRL involving one optimal expert and $n$ suboptimal experts. The authors show that the additional suboptimal experts can help mitigate the reward ambiguity in IRL that arises due to the unknown suboptimality of unvisited state-action pairs. They present an analytical expression... | Rebuttal 1:
Rebuttal: > ### The paper assumes access to a generative model of the experts' policies. However, in practical IRL a finite data set of precollected expert demonstrations is the norm
We thank the reviewer for raising this point. We agree with the reviewer that the generative model represents a limitation. ... | Summary: The paper develops a theory to address inverse reinforcement learning (IRL) from sub-optimal expert demonstrations. The authors assume a set of experts with known degrees of sub-optimality. Rather than learning a single reward model, as is often done in standard IRL, they provide an explicit characterization o... | Rebuttal 1:
Rebuttal: > ### The main weakness of the setting is that it assumes having access to a generative model for the optimal and sub-optimal experts during sampling and that there is a performance gap between the sub-optimal and optimal experts
We thank the Reviewer for raising this point. Extending this work t... | Rebuttal 1:
Rebuttal: We thank the reviewers for the time they spent reviewing our paper. Specifically, we are happy that the reviewers considered our work "original" and with "novel technical contribution" (Reviewer Euvz), with "notable contribution to IRL" (Reviewer zNGu), and a "clear and comprehensive theoretical a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data | Reject | Summary: This paper proves that the probability ratio that appears when computing the time reverse rate matrix for an absorbing state diffusion model has a simple form composed of the conditional distributions of clean data given partial masking scaled by an analytic time dependent weighting. They exploit this form to ... | Rebuttal 1:
Rebuttal: # Response to Reviewer vbN2
Thank you for your extremely thorough review and constructive feedback on our paper. Below, we address your concerns and suggestions.
### Correction of Theorem 2 and Corresponding Experiments
- **Modification on Section 3.3**: We acknowledge the error in Theorem 2. E... | Summary: This paper proposes a simplified discrete diffusion model to improve upon prior language diffusion models.
Strengths: * The method is simple and scalable. It is overall a nice insight, and the authors do a good job in extracting the relevant and impactful applications of this.
* The method seems to improve u... | Rebuttal 1:
Rebuttal: # Response to Reviewer 9MAq
Thank you for the detailed review and acknowledgment of our contributions. Below we address specific questions.
- **Q1: Misleading speeds up declaration.**
- A1: We apologize for any confusion caused by the initial claim of our speed-up results. We will revise our stat... | Summary: This work derives a new interesting connection between the concrete score and conditional target densities in absorbing diffusion models, which decomposes the time-dependent ratio between marginal probabilities (of two transitive states) as a conditional distribution on clean data scaled by an analytic time-de... | Rebuttal 1:
Rebuttal: # Response to Reviewer 7sHz
Thank you for acknowledging our contributions. We have tailored our rebuttal to address the points you raised.
### Weaknesses:
1. **Problem Formulation:**
We will add more intuitive explanations and illustrations regarding the problem formulation. Below are the c... | null | null | Rebuttal 1:
Rebuttal: # Overall Response
We would like to thank all the reviewers for taking their time to review our paper and provide high quality feedback. We have updated the results of RADD to better demonstrate the effectiveness of RADD, addressing the common concerns from Reviewer 7sHz,9MAq, and vbN2.
## Upd... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos | Accept (poster) | Summary: This paper introduces a novel technique, Quantized Efficient Encoding (QUEEN), to achieve streamable free-viewpoint videos. Unlike methods that directly optimize per-frame 3D-GS given multi-view videos, QUEEN learns the residuals of Gaussian attributes between continuous frames by decoding learnable latent emb... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please refer to our shared rebuttal (texts and PDF) for additional discussion and results. We address the specific questions below.
***
## Q: Details and ablation studies regarding the quantization process.
Thank you for the suggestion. We will add a detai... | Summary: This paper presents a novel method for free-viewpoint video learning and streaming based on 3D Gaussian splatting (3DGS). The proposed method learns the attribute residuals of the raw Gaussian points in a frame-by-frame fashion, and the learning process is incorporated with both latent quantization and sparsit... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please refer to our shared rebuttal (texts and PDF) for additional discussion and results. We address the specific questions below.
***
## Q: The idea of exploiting temporal redundancy via encoding attribute residuals is not new.
Thank you for confirming ... | Summary: This paper proposes a framework called QUEEN, based on Gaussian splatting, for compact free-viewpoint videos. The data size is reduced to around 0.7MB per frame while achieving fast training speeds. QUEEN encodes the Gaussian attribute changes between consecutive frames. Specifically, it uses latent codes to e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please refer to our shared rebuttal (texts and PDF) for additional discussion and results. We address the specific questions below.
***
## Q: 360-degree rendering.
We evaluate on N3DV and Immersive datasets as they are widely-adopted standard benchmarks f... | Summary: This paper introduces QUEEN, a framework designed to enable fast encoding (training) and decoding (rendering) for online free-viewpoint video (FVV) streaming using 3D-GS. Achieving high frame generation quality alongside real-time seamless streaming and rendering is challenging due to the intensive computation... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Please refer to our shared rebuttal (texts and PDF) for additional discussion and results. We address the specific questions below.
***
## Q: Experimental settings.
- (a) The number of bits is dependent on the scene content as it relies on the amount of m... | Rebuttal 1:
Rebuttal: We thank the reviewers for the insightful comments and for acknowledging the novelty of our design (jnXV, 9x86), a “solid submission” (L5E8), superior performance (all 4 reviewers) and
extensive evaluations (all 4 reviewers). We address the common issues in this **shared response** and we will ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation | Accept (poster) | Summary: The paper proposed a unified image compression method with multi-path aggregation for joint human-machine vision tasks. The authors utilized a lightweight predictor to generate masks to allocate features into main and side paths. By leveraging the pre-trained main path module, shared features can be reused to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing and acknowledging the strengths of the proposed approach. We will address the raised concerns below:
# Response to the Weaknesses
1. **Comparisons to TinyLIC and other SOTA baselines.**
**[Reply]**
We thank the reviewer for the valuable suggestion. It is in... | Summary: This paper explores image coding for multi-task applications and introduces Multi-Path Aggregation (MPA), integrated into existing models to facilitate joint human-machine vision through a unified architecture. The MPA employs a predictor to distribute latent features among task-specific paths according to the... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to you for the considerate feedback. We appreciate your recognition of the clarity and quality of our paper, particularly in articulating the problem, proposed method, and significance of our contributions.
We want to affirm that your understanding o... | Summary: The paper introduces a Multi-Path Aggregation (MPA) architecture designed to unify image coding for both human perception and machine vision tasks. By integrating the side path, the authors aim to optimize performance across various tasks while maintaining efficiency in terms of parameter and bitrate usage. Th... | Rebuttal 1:
Rebuttal: First of all, we really appreciate the reviewer's careful comments. We offer the following response to the reviewer's concerns:
# Response to the Weaknesses
1. **Comparison to SpotTune.**
**[Reply]**
We appreciate the reviewer's attention to the differences between MPA and SpotTune. It is essen... | null | null | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their recognition of the strengths of MPA, as well as their insightful feedback and constructive suggestions. We have identified several common themes in the comments and would like to address them comprehensively in this global response.
# Advantages of MPA and Co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Guided Masked Autoencoder | Accept (poster) | Summary: This paper aims to enhance the Masked Autoencoder (MAE) approach for self-supervised learning in computer vision. The authors discovered that MAE intrinsically learns pattern-based patch-level clustering from the early stages of pre-training. This finding led them to propose a self-guided masked autoencoder, w... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' positive comments and constructive feedback. We have made efforts to address each of the concerns raised.
__[W1] Quality of informed masks__
We acknowledge the concern that initial clusters may not be well-formed for some images. Although we have shown that MAE prope... | Summary: This paper proposes a novel masking strategy, Self-Guided Informed Masking for pre-training MAE. The authors found that MAE learns patch-level clustering of visual patterns at the early stages of training, which is demonstrated through an in-depth analysis. Based on this insight, the authors suggest bi-partiti... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have tried to alleviate all the concerns raised.
__[W1] Additional baselines__
We agree that it would be valuable to compare our method with others that rely on external pre-trained models or additional modules, as long as the comparison is fair. We inclu... | Summary: This paper demonstrates that Masked Autoencoders (MAEs) learn pattern-based, patch-level clustering and that they obtain this ability in the early stages of pre-training. Based on this finding, it proposes a self-guided Masked Autoencoder method, which utilizes self-attention map information to mask out meanin... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback; we have carefully considered your comments and made efforts to address each of the concerns raised. Due to length limitations, we have had to use references from the main paper. We apologize for any inconvenience this may cause.
__[W1]... | Summary: Masked autoencoding (MAE) based pre-training has been widely adopted recently. However, it is not fully uncovered what and how MAE exactly learns. In this paper, the authors provide an in-depth analysis of MAE and discover that it learns pattern-based patch-level clustering from early stages of pre-training. U... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for positive comments and constructive feedbacks. We have tried to address each of the concerns raised.
__[W1] Reason for the analyzing MAE with 400 pre-training epochs__
To use a 1600-epoch pre-trained MAE for analysis, we would need to train MAE, MoCo, and ViT for 16... | Rebuttal 1:
Rebuttal: Dear all reviewers,
Thank you for your time and effort in reviewing our paper. We have carefully addressed all the concerns raised and incorporated the valuable suggestions. We kindly request that you re-evaluate our paper in light of these rebuttals.
Pdf: /pdf/d45d067320c599db8a3b5e92af4d00745e3... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates the properties of MAE and introduces a better masking strategy for MAE. Analyses using similarity scores and attention show that MAE learns to cluster the patches at the early epochs. On the other hand, analyzing exploitation rates of various layers implies that the encoder is trained s... | Rebuttal 1:
Rebuttal: We appreciate the detailed feedback and valuable insights provided by the reviewer, and we have tried to address all concerns and suggestions.
__[W1-2] Connection between the explotation rate analysis in Sec. 3 and our method__
We thank the reviewer for this constructive feedback. This will be e... | null | null | null | null | null | null |
Learning Diffusion Priors from Observations by Expectation Maximization | Accept (poster) | Summary: This paper tackles the problem of learning a diffusion model from incomplete and noisy observations. The problem is modelled as follows; the distribution of the noised and incomplete data is assumed to be $p(y) = \int p(y|x) q(x) dx$. The authors introduce a parametric version of it $p^\theta(y) = \int p(y|x) ... | Rebuttal 1:
Rebuttal: Thank you for your review and the legitimiate concerns you have raised.
* **W1** (Experiments) We follow your suggestion and benchmark MMPS against previous methods (DPS and $\Pi$GDM). We invite you to consult the global rebuttal regarding these additional experiments.
Concerning Figures 1 and... | Summary: This paper focuses on training diffusion models using incomplete or noisy data only, which is obtained through a linear measurement operator $A$. While prior works assume full rank of $E[A^TA]$ or $E[A^+A]$ and change the denoising score matching objective while training diffusion models, this paper uses a mom... | Rebuttal 1:
Rebuttal: Thank you for your review and the legitimate concerns you have raised.
* **W1** Indeed, we are not the first to use the covariance $\mathbb{V}[x \mid x_t]$ to improve the approximation of the likelihood score. We believe that Finzi et al. [24] were the first to propose it, shortly followed by Boy... | Summary: This paper proposes a method to learn generative models from noisy and incomplete data. As opposed to general recent methodologies which require a clean unconditional dataset to solve inverse problems, the paper aims at providing a method to learn the prior from noisy data.
Strengths: The paper is on an inter... | Rebuttal 1:
Rebuttal: We sincerely apologize for the inconvenience caused by the writing of our manuscript. We believe that most of your questions stem from the lack of clarity of Section 4.2, where we fail to state that MMPS is not bound to the EM context and can be applied to any diffusion prior. We propose to clarif... | Summary: This paper proposes a novel solution to a specific class of Empirical Bayes problems. The method is especially useful when the latent variable and the observations are closely related, such as in cases where the observations are incomplete or noisy latent variables. The major technical obstacle is modeling the... | Rebuttal 1:
Rebuttal: Thank you for your review and the pertinent questions you have asked.
* **W1** Although we agree that a benchmark with previous empirical Bayes methods would be valuable for the statistical inference community, we don't think it would be relevant to justify our work. First, our method is based on... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the quality and pertinence of their reviews. We are glad that all reviewers found the topic of our work interesting and timely.
Reviewers **3yec**, **XeV5**, **mKff** and **tnYw** found the method sound and well presented. Theirs concerns mainly regard the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a new framework for training diffusion models from corrupted data, based on the Expectation-Maximization algorithm. Before this work, there were two approaches to the problem of learning from corrupted data: Ambient Diffusion and SURE. Ambient Diffusion was designed for linear inverse probl... | Rebuttal 1:
Rebuttal: Thank you for your in-depth review and the legitimate concerns you have raised.
* **W1** Indeed, this is one of the limitation of our method, which we mention in Section 3. However, we would like to mention that in our pipeline we start each training step with the previous parameters, which reduc... | null | null | null | null | null | null |
Online Posterior Sampling with a Diffusion Prior | Accept (poster) | Summary: The paper studies online learning and proposes to approximate the updating prior with a diffusion model, rather than the more simple, less expressive Gaussian approximation. The main application is contextual bandits with a linear or GLM model. For the latter case, the authors derive a version of the IRLS algo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for positive feedback, which recognizes multiple contributions of our work. Our rebuttal is below. If you have any additional concerns, please reach out to us to discuss them.
**Avoid $\approx$ in Theorems 2 and 4**
A great comment. We agree that it is cleaner... | Summary: This paper introduces novel posterior sampling approximations tailored for diffusion model priors, specifically designed for use in contextual bandits and applicable to a broader range of online learning problems. The methods are developed for linear models and generalized linear models (GLMs), emphasizing enh... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and praising our execution. Our rebuttal is below. If you have any additional concerns, please reach out to us to discuss them.
**Q1: Score issue in posterior sampling**
There may be a misunderstanding. The diffusion prior does not solve ... | Summary: The paper presents approximate posterior sampling methods for contextual bandits with a diffusion prior. A key weakness of existing methods designed to work on noisy data is that rely on using the score function is that it becomes unstable as the number of observations grows. Instead, the authors propose to us... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for detailed feedback, and praising our contributions and execution. Our rebuttal is below. If you have any additional concerns, please reach out to us to discuss them.
**Some claims are imprecise**
The reviewer is right that the posterior in (3) is only propo... | Summary: The authors propose an algorithm for sampling from a generalised linear model posterior where the prior is defined through a diffusion model. This is achieved by utilizing the Laplace approximation, and is shown to be asymptotically consistent. This model and inference scheme is then applied to contextual band... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback. Our rebuttal is below. If you have any additional concerns, please reach out to us to discuss them.
**Experimental setup of Chung et al. (2023)**
Chung et al. (2023) experiment with computer vision problems. Their algorithm is unstable i... | Rebuttal 1:
Rebuttal: We wanted to thank all reviewers for positive reviews and recognizing our contributions. There were three common comments that we want to address jointly: limitations of the work, technical challenges, and a non-bandit evaluation.
**Limitations of our method**
We point out limitations of our wor... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces new posterior sampling approximations for contextual bandits with diffusion model priors, allowing the capability to handle complex distributions beyond traditional Gaussian priors. It contributes by developing efficient sampling algorithms, proving their asymptotic consistency, and valida... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for detailed and positive feedback. Our rebuttal is below. If you have any additional concerns, please reach out to us to discuss them.
**Differences from Hsieh et al. (2023)**
There are multiple differences:
* The posterior approximation in Hsieh et al. (202... | null | null | null | null | null | null |
Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features | Accept (spotlight) | Summary: The paper addresses the critical task of enhancing feature selection in diffusion models to improve performance in discriminative tasks such as semantic correspondence, semantic segmentation, and label-scarce segmentation. Previous methods often overlooked many potential activations within diffusion models, le... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Weakness 1:**
Some more comparisons are recommended. While the authors claim to find unique properties in diffusion U-Nets, it lacks a detailed comparison with traditional U-Nets. A more comprehensive a... | Summary: This paper highlights the importance of considering a broader range of activations within diffusion models. The authors propose three universal properties of diffusion U-Nets that aid in qualitatively filtering out activations that are clearly sub-optimal. On top of this, the authors can improve the efficiency... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **W1:**
This paper could benefit from introducing more notations to clearly define and differentiate between various activations and components within the diffusion models, which would further enhance the... | Summary: Diffusion models have achieved significant success in image generation and show great potential for various discriminative tasks. The authors rethink the foundational problem of feature selection within these models. To this end, they analyze the properties of diffusion models, including asymmetric diffusion n... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Weakness 1:**
The authors acknowledge the uncertainty about whether the findings can generalize to newer models like DiT (Diffusion Transformer) due to architectural differences.
**Response:**
Thanks... | Summary: This paper proposes a new feature selection method for diffusion models by evaluating a broader range of activations, particularly those in embedded Vision Transformers (ViTs). The authors identify the limitations of current approaches that consider only a narrow range of activations and introduce a qualitativ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Weakness 1**:
As pointed out by the authors, the observations and methods proposed do not necessarily generalize well to recently developed DiT models since their architecture is markedly different from... | Rebuttal 1:
Rebuttal: Dear SAC, AC, and reviewers,
Thank you for your invaluable feedback. Based on your comments, we have revised the details and now offer a global response to some common questions.
> **Question 1:**
As we have admitted, the conclusion of this study can fail to extend to DiT models. Can this be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Learning-based Capacitated Arc Routing Problem Solver Comparable to Metaheuristics While with Far Less Runtimes | Reject | Summary: The paper introduces a learning-based method to address the Capacitated Arc Routing Problem (CARP). It involves breaking undirected edges into directed arcs and utilizing a graph attention network to build a Direction-aware Attention Model. In the training process, supervised learning is used to create the ini... | Rebuttal 1:
Rebuttal: ***Q1: Converting the graph G from arcs to nodes represents a common approach in many heuristics for addressing CARP. This process adds complexity to the problem and increases its scale. The proposed method appears to lack enough novelty, with most components bearing resemblance to neural models d... | Summary: The authors skillfully address challenges posed by non-Euclidean graphs, traversal direction, and capacity constraints with their novel NN-based solver in solving capacitated arc routing problem. The introduction of the direction-aware attention model and a supervised reinforcement learning scheme is particula... | Rebuttal 1:
Rebuttal: ***Q1: It's better to redraw the first part of Figure 1 to enhance its aesthetic quality.***
>**A1**: Thank you for the great suggestion, we will carefully redraw the paper if it is accepted. We would like to see if you have any concise suggestions for improving the aesthetic quality.
***Q2: Th... | Summary: The paper proposes a new learning-based constructive heuristic for capacitated arc routing problems. In contrast to node routing problems such as the TSP and VRP, arc routing problems received comparably little attentition. To address the specific
challenges in the capacitated arc routing problems, the authors... | Rebuttal 1:
Rebuttal: ***Q1(abbreviation): The abstract inaccurately describes the CARP as finding a "minimum-cost tour" rather than finding a set of routes.***
>**A1**: Thanks for the valuable suggestion. This vague description does confuse readers when they are trying to understand. We promise to correct all ambigui... | Summary: This paper presents a novel neural network-based CARP solver that uses a direction-aware attention model to incorporate directionality into the embedding process. It then applies supervised reinforcement learning for subsequent fine-tuning.
Strengths: 1. A learning-based CARP solver is proposed.
2. The perf... | Rebuttal 1:
Rebuttal: ***Q1: The comparison algorithms were published five years ago, and there is no discussion of existing methods aimed at big data.***
***Q2: The experiments only tested the self-constructed dataset and did not evaluate on public datasets.***
>**A1&A2**: The suggestions are greatly appreciated! Th... | Rebuttal 1:
Rebuttal: ***General Response***:
Great thanks to all the reviewers for their time and effort in reviewing this paper. In this review, most reviewers wanted to see the experiments on this method with more comparison algorithms (from Reviewers **NUZV**, **DrmY**) and on more public datasets (from Reviewer *... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HENASY: Learning to Assemble Scene-Entities for Interpretable Egocentric Video-Language Model | Accept (poster) | Summary: This paper proposed HENASY, a novel framework for learning egocentric video-language models where texts are grounded to visual scene entities. Its main idea is to use both global and local visual encoder to encode video features so that nouns and verb phrases in the paired text could be matched individually. T... | Rebuttal 1:
Rebuttal: We appreciate your acknowledgment of our well-written paper, the importance and novelty of our approach, and the extensive experiments.
## **1: Comparison with HelpingHands**
We recap the key improvements of our work over HelpingHands as below
| Property | Our Work | HelpingHands |
| --- | --- ... | Summary: The paper introduces a novel framework for improving interpretability and performance in video-language models, specifically for egocentric videos. The framework, Hierarchical ENtities ASsemblY, employs a spatiotemporal token grouping mechanism that assembles and models relationships between dynamically evolvi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer of their acknowledgment that our approach is groundbreaking with strong interpretability, comprehensive experiments. We would like to address concerns and questions raised by the reviewer as follows:
## **1: Scalability and computation**
**Scalability**: In our submiss... | Summary: The paper introduced HENASY, a novel framework for enhancing video-language understanding in egocentric videos. It utilized multi-grained contrastive losses from alignments of video-narration, noun-entity, and verb-entity, to improve interpretability and performance. The method showed competitive results acro... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing that our paper is well-organized, easy to follow, competitive results in real-world applications.
## **1: Comparison with [1, 2]**
We provide the comparison as below:
| Property | Ours | [1] | [2] |
| --- | --- | --- | --- |
| Method | Explicitly models vid... | Summary: This paper presents HENASY (Hierarchical ENtities ASsemblY), a pretraining framework to learn scene-entities representations for egocentric videos. The authors proposed to learn compositional and hierarchal video representations by three levels: 1) global video features from a global video encoder. 2) entity f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful evaluation and acknowledgment that our approach is well-motivated, novel, and effective under extensive experiments, providing intuition and experience for future model design. We will fix all noticed typos in our final version. Below, we would like to ad... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for their valuable time and constructive feedback. We are grateful for their recognition about the **significance, inspiration and impact** of our grounded and **interpretable** work (Reviewers EqYJ, cNiH, 4RbK), the **novelty** of our method (Reviewers EqYJ, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning | Accept (poster) | Summary: This work describes two new type of sparse autoencoders (SAEs) that are trained with two new loss functions, L_{e2e} and L_{e2e + ds}. L_{e2e} loss penalizes the KL divergence of the original model vs. when the SAE is inserted, as opposed to existing SAE training techniques which instead optimize MSE with the ... | Rebuttal 1:
Rebuttal: Thank you for your evidently detailed read of our paper and your thoughtful comments.
## Comments on Weaknesses
Regarding evaluation metrics, we've taken your criticism on board and run a set of evaluations on a set of downstream tasks. These are documented in the Author Rebuttal at the top (resu... | Summary: This paper proposes to train Sparse Autoencoders (SAEs) with an additional loss function term: The KL divergence between the original output distribution and the output distribution obtained when using model with the inserted SAEs. This additional loss pushes the SAEs to focus on functionally important feature... | Rebuttal 1:
Rebuttal: Thanks for your comments and your typo spotting and notation improvement suggestions!
## Addressing main misunderstanding
After reading your review, it appears we failed to convey the core benefit of our method. You state
> the authors state that "locally trained SAEs are capturing information ab... | Summary: The authors present their observations on modified sparse autoencoder (SAE) learning. In the proposed method SAE is trained to reconstruct original model weights (features). SAE is optimized with the KL-divergence loss between the model output and the output at the same location in the original model. An addit... | Rebuttal 1:
Rebuttal: Thanks for you comments and suggestions
## Comments on Weaknesses
> The interpretability is not well defined...
The interpretability provided by an SAE contains two main components: The average number of SAE features required to interpret any particular model output (L0 - lower is better), and ... | Summary: This paper proposes a way to train sparse autoencoders (SAEs) that encourages them to learn features that are causally relevant to the model's output. This is done by replacing the usual SAE reconstruction objective - an L2 penalty between original activations and their reconstructions - by the KL divergence b... | Rebuttal 1:
Rebuttal: Thank you for engaging with our paper and giving well-considered feedback.
Regarding the main limitation you’ve mentioned, we agree that it would be helpful for the paper’s narrative to have other metrics of functional importance than KLDiv, including evaluations on an individual feature level. H... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewers and ACs for their time. We're delighted to hear that our work "makes some progress on ... an important open problem" (R2), "provides extensive analysis of their results" (R3), "provide an intuitive solution" (R4), introduces "exciting techniques" and has some "extr... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes new framework to improve the interpretability of large language models. Inspired by the work proposed in Anthropic's "", the proposed work replaces the original reconstruction loss in the sparse auto-encoder (SAE) with KL-div loss, and additionally add further constraint to minimize the erro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We believe there may be a core misunderstand of our method which we clarify in the response to Weakness 2.
## Response to Weaknesses
### Weakness 1
> W1: The authors may further explain the intuition between the evaluation metrics. The curre... | null | null | null | null | null | null |
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation | Accept (poster) | Summary: This work proposes the two randomized RL algorithms, RRL-MNL and ORRL-MNL, for the MNL transition model that for the first time achieve both computational and statistical efficiency without stochastic optimism. The complexity of ORRL-MNL has better dependence on the large problem-related constant $\kappa^{-1}$... | Rebuttal 1:
Rebuttal: Thank you for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question:
---
### __[W1] Regret bound__
It is generally well-known that the regret bound of randomized algorithms is higher than that of optimism-based algorithms. For example,... | Summary: The paper considers the MNL MDP setting and provides several regret bounds. The MNL setting is one where the transition probabilities are modeled as a soft-max over a linear transformation of state-action-next-state features. The work improves the dependence on an important problem parameter. The main results ... | Rebuttal 1:
Rebuttal: Thank you for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question:
- - -
### __[W1] Compact characterization of the value or policy__
We believe that the lack of a compact characterization of the value or policy should NOT be conside... | Summary: This paper studies reinforcement learning of an MDP with multinomial logistic (MNL) function approximation, which approximates the unknown transition kernel of the underlying MDP. The setting is the finite horizon episodic MDPs. Two algorithms are proposed. The first algorithm is statistically efficient with a... | Rebuttal 1:
Rebuttal: Thank you for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question:
- - -
### __[W1] Presentation__
> there are many displayed equations that are very long
The comprehensive nature of our analysis required the inclusion of detailed proo... | Summary: The paper introduces a computationally efficient randomized algorithm for MNL-MDPs, addressing limitations of previous exploration algorithms. It presents the RRL-MNL algorithm, focusing on online transition core estimation and stochastically optimistic value function design. This paper provides a frequentist ... | Rebuttal 1:
Rebuttal: Thank you for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question:
- - -
### __[W1] Generalizability of the proposed algorithms__
We respectfully disagree that the stated point is a weakness of our work. For instance, in the contextual ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Flipping-based Policy for Chance-Constrained Markov Decision Processes | Accept (poster) | Summary: The paper is dealing with a chance constrained MDP problem, where the authors constrain the probability of staying in the safe set throughout an episode. The authors show that the original problem can be recast using a flipping-based policy, where the optimal decision to satisfy the constraint is conditioned o... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's encouraging comments. Your feedback and suggestions are valuable and help us improve the quality of our manuscript. All responses are given in a point-to-point way.
**Proposition 1 [Question 1 and Weakness 1].**
We will restate Theorem 1.3 from [21]. We proved a weak ... | Summary: The paper presents a number of optimality results for flipping-based policies in safe reinforcement learning. It first establishes that flipping-based policies are overall optimal for joint chance constrained MDPs, which represents a significant reduction of the original optimization formulation. Then, acknowl... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive comments and insightful questions. We feel the reviewer has been patient and given many suggestions to help us improve the quality of our manuscript and questions to guide us in reflecting on our research. Except for your professional advice, you... | Summary: The study proposes a flipping-based policy for managing chance constraints in Markov Decision Processes (MDPs). It introduces a probabilistic approach where actions are selected by flipping a "distorted coin", which is helpful in handling uncertainties in safety-critical environments. The authors establish a t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's helpful comments and insightful questions. The questions and comments raised by the reviewer are addressed point-to-point as follows.
**Neural networks for action and flip probability [Question 1].**
Thank you for this good question! As the reviewer mentioned, it is ... | Summary: This paper introduces a new policy called the flipping-based policy for Chance-Constrained Markov Decision Processes (CCMDPs), which is useful in safe reinforcement learning. The policy uses a coin flip to choose between two actions, depending on the state. The authors establish a Bellman equation for CCMDPs a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback and comments. We will answer the questions first and then address the comments about the weakness. All the concerns are addressed in a point-to-point way as follows.
**Sudden change behavior [Question 1].**
Thank you for the interesting question! T... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
The authors deeply thank all the reviewers for their insightful comments and constructive suggestions.
1. We have conducted new experiments based on the reviewers' comments. Additional experimental results are provided in a One-Page PDF file containing new figures. The On... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model | Accept (poster) | Summary: The paper proposes FASTopic, a topic model using pre-trained document embeddings and embedding transport between documents and topics as well as words and topics. The minimized objective function is then a combination of the DSR and ETP which is optimized via finding topic, and word embeddings.
Strengths: - I... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews! We appreciate you find our work interesting and results potential.
We hope our responses can address your concerns and improve your rating.
__Q1: comparison to more models__
We emphasize __we have included the newest baselines__: HyperMiner(NeurIPS2022), ... | Summary: This paper introduces a fast, adaptive, stable, and transferable (FAST) topic modeling paradigm by using dual semantic-relation reconstruction (DSR) to model topic-document and topic-word relations. It enhances topic modeling by incorporating a embedding transport plan (ETP) method to address relation biases. ... | Rebuttal 1:
Rebuttal: Thank you for your comments! We're glad that you believe our paper is well-written, our experiments are comprehensive, and our model is straightforward and fresh.
We hope our responses can address your concerns and improve your rating.
__Q1: how document embeddings affect the method__
Thank y... | Summary: This paper proposes a fast, adaptive, stable, and transferable topic model, FASTopic. Instead of using the VAE or clustering method, it incorporates a new model structure named Dual Semantic-relation Reconstruction (DSR). DSR learns topics by directly optimizing the semantic relations among topics, documents, ... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback! We're glad that you appreciate our well-written paper, neat method, and extensive experiments.
We sincerely hope our responses can address your concerns and improve your rating.
__Q1: comparison to earlier NSTM (2022) and WeTe (2023)__
Thank you for your ... | Summary: The author found that existing methods (VAE-based or clustering) suffer from low efficiency, poor quality of topic words, and instability. To address these issues, this paper proposes a novel topic modeling paradigm called Dual Semantic-Relation Reconstruction (DSR) for efficient modeling of semantic relations... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We're happy that you appreciate our clear writing, effective method, and extensive experiments.
We sincerely hope our responses can address your concerns and improve your rating.
__Q1: difference between ETP and the ECR of ECRTM (Wu_2023)__
We clarify that __the E... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ContactField: Implicit Field Representation for Multi-Person Interaction Geometry | Accept (poster) | Summary: A novel implicit field representation is designed for multi-person geometry modeling, which manages to estimate the occupancy, identity, and geometry simultaneously. Moreover, to alleviate the occlusion issue, an additional 3D scene representation module is designed. A synthetic dataset with multi-view multi-h... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on the synthetic dataset and our experiments. Below are our responses to weaknesses and questions in your comments.
## **Weakness 1:** details about datasets
We outline the relevant information about the dataset below.
**Data Source**:
Initially, we acquired ... | Summary: This paper proposes a method to reconstruct close interactions from multi-view images using an implicit representation. The occupancy and ID fields are directly regressed from multi-view images, which can then be used to infer contacts. To fuse multi-view information, a transformer-based module is introduced t... | Rebuttal 1:
Rebuttal: Thank you for your valuable question. Below are our responses to raised weaknesses and questions.
## **Weakness 1:** multi-person interaction with SMPL
Thank you for your valuable feedback. In the case of DeepMultiCap (DMC) [1], which uses the SMPL human prior, reconstruction is performed one pers... | Summary: The paper focuses on the problem of multi-person reconstruction from multi-view images in the face of close interactions (e.g., in cases where are in contact). The objective of this work is to propose a method to reconstruct one mesh per person that is able to capture both fine grained details (e.g., garments,... | Rebuttal 1:
Rebuttal: We greatly appreciate your insight and agree that including an ablation experiment to support this statement is crucial for the integrity and quality of our paper.
## **Weakness 1:** missing ablation on grouping loss function
In response to your suggestion, we are currently conducting an ablation... | Summary: In this paper, the authors introduce an implicit field representation for multi-person interactive reconstruction. As they said, it can simultaneously reconstruct the occupancy, instance identification (ID) tags, and contact fields. The local-global feature learning methods are used. They also propose a datase... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the contributions of our work.
First, we will revise our paper to discuss related works including single-view and multi-view settings mentioned in [1, 2, 3]. We emphasize that the mentioned methods [1, 2] primarily focus on single-view reco... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thorough constructive comments on our paper. We earnestly responded to your concerns; please see the respective comments. Before answering your questions and concerns, we would like to highlight our contributions by quoting your comments.
### **Strength... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QTIP: Quantization with Trellises and Incoherence Processing | Accept (spotlight) | Summary: The paper introduces Trellis-based compression for quantization. Essentially, the method applies incoherence processing on the weight matrices, turning the approximately into Gaussians, such that the best compression method available to compression roughly Gaussian data can be applied to it. In this case, the ... | Rebuttal 1:
Rebuttal: ## QTIP’s significance (W1)
Tables 3 and 4 show QTIP’s strengths. Table 3 indicates that QTIP *w/out fine-tuning* consistently beats VQ-based approaches *w/ fine-tuning*, showing that there is significant value in better quantizers. QTIP also succeeds where fine-tuning “fails.” Fine-tuning does n... | Summary: This paper proposes QTIP, a new method for efficient post training quantization of LLMs. QTIP is a vector quantization method inspired from Quip# [1], but with no limitation in the dimension of the codebook. The work’s fundamental contributions are: 1) Propose to adapt trellis quantization to the compression o... | Rebuttal 1:
Rebuttal: ## QTIP's contribution (W1)
The focus of QTIP is on *what to quantize with* (e.g. VQ, TCQ), and not *how to quantize* (e.g. GPTQ, fine-tuning). Choosing a good quantizer is hard since LLM weight matrices have outliers and small-batch LLM inference is memory bound, necessitating fast decoding. Whi... | Summary: This paper introduces trellis coded quantization (TCQ) into large language model (LLM) quantization, achieving ultra-high-dimensional quantization with less inference burden compared to traditional vector quantization (VQ) methods. The main innovations are a hardware-efficient "bitshift" trellis structure and ... | Rebuttal 1:
Rebuttal: ## Updated Inference Speeds on More Devices and More Bitrates
Below are throughput numbers for decoding 1024 tokens on the RTX 3090, RTX A6000 Ampere, and RTX 6000 Ada, averaged over 8 runs. The kernels were not re-tuned for each device, so they could be made faster. These numbers were run using ... | Summary: This work presents a new method of weight-only post-training quantization (PTQ) that uses trellis-coded quantization (TCQ) to achieve ultra-high-dimensional quantization. Although TCQ was introduced by Mao and Gray, QTIP transfers it into the LLM space and introduces new algorithms to make the method hardware ... | Rebuttal 1:
Rebuttal: ## Ablations and fast inference (W1 and W2)
The main components of QTIP that enable fast inference are the bitshift trellis and compute-based codes. To understand why these are necessary, let us look at the L, K, and V parameters in trellis coding. L is the trellis size, K is the bitrate, and V is... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed reviews. As multiple reviewers noted, QTIP is well motivated and novel (1CRX, 3PHK, ZTfk), and simultaneously achieves strong quality and fast inference (1CRX, F569, ZTfk). Below, we have written individual responses to reviewers. We have also run a number... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Power of Resets in Online Reinforcement Learning | Accept (spotlight) | Summary: This paper study the online RL having access to a local simulator with general function approximation. Their results unclock new statistical guarantees. First, $Q^*$ realizability together with coverability assumption are enough for sample-efficient online RL under this setting. Second, their results further i... | Rebuttal 1:
Rebuttal: **Novelty relative to prior work:**
The algorithm presented in Section 4 is far from a simple adaptation of previous techniques. We now highlight some of the key algorithmic innovations:
- RVFS builds on the DMQ algorithm by [1]. Unlike the latter, we use a core set of state-action pairs instead ... | Summary: The authors show that local simulator access removes Bellman completeness for MDPs with bounded coverability. This generalizes the results of existing works that are limited to low-rank or linear structures.
On the statistical front, the authors analyze the sample complexity of SimGOLF using coverability, b... | Rebuttal 1:
Rebuttal: **Technical novelty of SimGOLF in light of [1]**
- We agree that the main technique behind our first result, SimGOLF, is quite simple, but we view this as a positive.
- In particular, our result shows that the challenging coverability + realizability setting, which was explicitly left as an open ... | Summary: The paper introduces the SimGolf algorithm, which leverages local simulator access to reset to previously visited states and sample multiple trajectories. This approach enhances sample efficiency and accuracy in value function approximation, particularly in high-dimensional MDPs. The SimGolf algorithm uses loc... | Rebuttal 1:
Rebuttal: **Paper format:**
Thank you for your feedback. We understand the importance of adhering to a standard format; however, we chose to prioritize a detailed explanation of our novel and complex algorithm to ensure that its intricacies were fully conveyed. We believe this approach still aligns with th... | Summary: The paper presents some theoretical results for new reinforcement learning algorithms with a sophisticated approach to a simulator environment.
Strengths: Paper presents an extensive theoretical study.
Weaknesses: Practical applications of the algorithm remain questionable.
The modifications themselves migh... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LAM3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image | Accept (poster) | Summary: The paper addresses the problems of multi-view consistency and geometric detail in image-to-3D generation. The proposed method, LAM3D, adopts a two-stage approach for training. In the first stage, the authors train a plane encoder and decoder to compress point clouds into a latent tri-plane representation. In ... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and suggestions. We have provided our responses below:
***Q1. Texture***:
We acknowledge the reviewer's concern about LAM3D's current limitation in generating textured meshes, as mentioned in the paper. While our primary focus was on achieving accurate ge... | Summary: This paper introduces a new 3D generation framework, LAM3D, which uses point cloud data and image-point-cloud feature alignment method to improve the geometry of the generation results. Authors use triplanes as representation, combining image feature as well as point cloud feature. For point cloud compression,... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and suggestions. We have provided our responses below:
***Q1. Comparison***
We thank the reviewer for highlighting these related works. Shape-Image-Alignment is not a novel concept, and has been explored in works like Michelangelo and ULIP [58]. The essenc... | Summary: This paper introduces the Large Image and Point Cloud Alignment Model (LAM3D), a novel framework that enhances 3D mesh reconstruction from single images by utilizing point cloud data. LAM3D integrates a point-cloud-based network for generating precise latent tri-planes, followed by an Image-Point-Cloud Feature... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and suggestions. We have provided our responses below:
***Q1. Omission of Texture and Texture Extension***
We appreciate the reviewer's observation regarding the omission of texture mapping. Our research primarily focused on addressing the limitations of p... | Summary: The paper proposes a two-stage 3D reconstruction method that first uses a transformer-based 3D point cloud feature extractor to initialize hierarchical latent triplanes (XY, XZ, YZ) and reconstructs the 3D mech. Next, it presents an image-point cloud feature alignment approach that leverages initial latent tri... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and suggestions. We have provided our responses below:
***Q1. Model Capacity***
As shown in Tab. 1 of the global response, we provide parameter size comparisons between our model and SOTA methods. It is worth noting that recent large reconstruction models ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and the time invested in reviewing our work. We take all suggestions seriously and are committed to carefully revising the paper based on your feedback.
In the attached PDF, we have included one table and three figures for your reference:
+ **... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos | Accept (poster) | Summary: This paper introduces a novel and generic representation of 3D planes called AlphaTablets. AlphaTablets represent 3D planes as rectangles with alpha and RGB channels, enabling accurate, flexible, and consistent modeling. The paper also proposes a differentiable rasterization method on top of AlphaTablets, as w... | Rebuttal 1:
Rebuttal: Thanks for the comments. All responses below will be put into revision.
**[W1: break down of time budget and analysis]**
Below is a breakdown of the time budget for the optimization process of a single scene:
| **Stage** | **Task** | **Time (s)** |
| -------------------... | Summary: The paper presents a light-weight 3D scene representation, which utilizes oriented 2D rectangles in 3D space with associated 2D texture and alpha maps (AlphaTablets).
For the task of 3D indoor scene reconstruction and 3D plane decomposition from multi-view posed RGB images (keyframes of a monocular video), th... | Rebuttal 1:
Rebuttal: Thanks for the comments. All responses below will be put into revision.
**[W1:Baseline setup]**
Thanks and we appreciate the opportunity to clarify:
1. Seq-RANSAC: For 3D volume-based methods including Atlas, NeuralRecon, PlanarRecon, and Metric3D with TSDF fusion, we followed PlanarRecon to us... | Summary: The paper presents a novel scene representation, AlphaTablets, for planar scene reconstruction. AlphaTablets are bounded plains with a texture map and an alpha channel, which can be optimized through a differentiable rendering scheme. By applying conventional photometric losses with regularization, AlphaTablet... | Rebuttal 1:
Rebuttal: Thanks for the comments. All responses below will be put into revision.
**[W1: Notion unclear]**
We will revise Figure 1 to include a more intuitive display with 3D effect that demonstrates how multiple layers of AlphaTablets are composed to render each pixel in the final image.
**[W2: Detailed... | null | null | Rebuttal 1:
Rebuttal: Please see the attached PDF file for figures and captions.
Pdf: /pdf/9b1303a22c6ef677088f2582d82503092dc52575.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting Ensembling in One-Shot Federated Learning | Accept (poster) | Summary: This work proposes a one-shot federated learning method where, instead of simply aggregating the models at the end of training, a non-linear ensemble of the (frozen) models is trained with an iterative federated learning method. In other words, after training the models locally, the server trains a shallow neu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and constructive comments. We address the reviewer's questions below.
--------------
> Q. Does the communication cost comparison in Figure 2 assume quantization? If so, does the communication cost for the iterative FL methods also use quantization... | Summary: This paper introduces FENS, a One-shot FL (OL) approach with the aim of improving the globl model's accuracy without significantly increasing the communication cost of canonical OFL. Different from existing OFL methods, FENS employs and iteratively trains a prediction aggregator model stacked on top of the loc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and comments. We address the reviewer's questions below.
--------------
> Q. How to determine the best number of iterations for the aggregator model?
$\rightarrow$ This is determined as in standard FL schemes by stopping when the validation loss... | Summary: This paper proposes a 2-phase mechanism for learning models across clients in federated learning settings with minimal communication. The method, FENS, first uses a one-shot communication to learn copies of a base model across clients' local data. It then uses standard iterative FL approaches to learn a smalle... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and the suggestions for improvements.
We address the reviewer's questions below.
-----------------
> Q. Why does the MoE approach to aggregation require such a large communication overhead? Can't the size of the dense
layers used fo... | Summary: This paper focuses on the one-shot federated learning with the model ensembling to assist the model aggregation. Specifically, after only one-round of model uploading, the authors provide a new aggregator based on a shallow neural network for the global model. The performance indicates that the proposed method... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and insightful comments. We noticed that there was a confusion regarding the usage of public dataset in our algorithm. We also noticed that most of the identified weaknesses seem to arise from this confusion. Below, we clarify our algorithm design and address... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Performative Control for Linear Dynamical Systems | Accept (poster) | Summary: This paper presents the new framework of performative control based on the performantive prediction concept. The performative stable control (PSC) problem is formulated and its solvability conditions are derived. This paper also presents the algorithm of finding the PSC solution and addresses the convergence ... | Rebuttal 1:
Rebuttal: Thank you for your work in reviewing our manuscript, and for providing thorough feedback. We address your concerns as follows.
**Weaknesses:**
**[W1] "The disturbance-action policy...":** We want to clarify that the reviewer has a misunderstanding regarding the measuring of disturbance $\mathbf{... | Summary: The authors introduce the problem of performative linear control whereby one aims to control the evolution of a linear dynamical system whose dynamics are influenced by the choice of control policy. Apart from defining the problem, they define the concept of a performatively stable controller, extending the de... | Rebuttal 1:
Rebuttal: We are glad that the reviewer liked our paper and recognized our exciting idea.
**Weaknesses:**
**[W1] "While the motivation...":** Thank you for recognizing and appreciating our key idea that control policies affect dynamics. To fully develop this idea, we have moved the main illustrative examp... | Summary: The work presents a new approach for linear dynamical systems and highlights how control policies can directly affect the system’s dynamics thereby resulting in policy-dependent changes in system states. It also outlines specific conditions under which a stable control strategy can be achieved and introduces a... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and kind suggestions!
**Weaknesses:**
**[W1] "The concepts and algorithms introduced...":** Our primary focus is the thorough theoretical investigation
of performative dynamical systems. The low complexity customized implementations
can be exciting future rese... | Summary: The paper introduces the framework of performative control in the context of linear dynamical systems, where the control policy chosen by the controller impacts the underlying system dynamics. This interaction results in policy-dependent system states with temporal correlations. Inspired by the concept of perf... | Rebuttal 1:
Rebuttal: Thank you for the comments which inspired us to explore our contributions more.
**Weaknesses:**
**[W1] "I feel I does not get the punchline...":** We want to clarify that the reviewer has a significant misunderstanding of our work compared to the original performative prediction (PP).
**1. Con... | Rebuttal 1:
Rebuttal: We thank all reviewers for recognizing that our paper provides a thorough and mathematically rigorous theoretical investigation of performative linear control, where control policies can directly affect the dynamics of the system (reviewers wfwc, JGbs, nkz4, e5pZ), which fills a gap in traditional... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization | Accept (poster) | Summary: The paper studied the problem of steering large language models through steering vectors on the neural network activations. The motivation is to find an effective steering method than state-of-the-art approaches (e.g., contrastive activation addition) and preserves the utility of the original LLM. To achieve t... | Rebuttal 1:
Rebuttal: ## Response To Reviewer sXZi
Thank you for your encouragement and insightful comments!
**Q1:** Tedious layer selection.
**A1:** We concur that current steering vector methods typically necessitate conducting sweeping trials across different layers to identify the optimal layer. Typically, it is ... | Summary: This paper introduces a novel method called Bi-directional Preference Optimization (BiPO) for producing more effective steering vectors to control the behavior of large language models (LLMs). It includes the demonstration of personalized control over model behaviors by adjusting vector direction and magnitud... | Rebuttal 1:
Rebuttal: ## Response To Reviewer EQVZ
Thank you for your constructive comments!
**Q1:** Theoretical analysis.
**A1:** We appreciate your suggestion. The primary reason our method outperforms other heuristic baselines is that we formulate it as a **direct optimization objective** reflecting the steering e... | Summary: This paper focuses on how to better steer the behavior of LLMs.
Specifically, they followed the activation engineering method and introduced the so-called "bi-directional preference optimization" to create more effective steering vectors.
Unlike previous activation addition methods that rely on the differen... | Rebuttal 1:
Rebuttal: ## Response To Reviewer XMbM
Thank you for your valuable comments!
**Q1:** Activation engineering has many heuristic designs.
**A1:** We are sorry for the confusion. We would like to clarify that most existing works on activation engineering indeed involve many heuristic designs, such as token ... | null | null | Rebuttal 1:
Rebuttal: ## General Response to All Reviewers
We thank all the reviewers for your valuable comments!
**We have incorporated all experiments suggested by the reviewers:**
1. (XMbM, EQVZ) Computational Cost -- We have assessed the number of trainable parameters and training time, which demonstrates that o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations | Accept (poster) | Summary: This proposed a DeltaDEQ method, which is designed to enhance computational efficiency for implicit models represented by deep equilibrium models. This method is inspired by the authors' observation of the heterogeneous convergence phenomenon prevalent in implicit neural networks, where differen dimensions of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. In the reply below, we use for example $\textbf{W1}$ for the reply to point 1 in Weaknesses, etc.
$\textbf{W1 and W4: }$ We thank the reviewer for the reminder. For the realization of the delta rule and sparse processing in RNN and CNN, we provide... | Summary: This work provides a finer-grained analysis of the dynamics involved in updating hidden states and identifies a phenomenon of heterogeneous convergence in implicit models, where certain dimensions of hidden states converge much faster than others. Based on this observation, the forward pass update of DEQ model... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments!
**W1.1:** In comparison to 'layer skipping' or 'dynamic gating' methods including the Adaptive Computation Time (ACT) work[C1], our method exploits much finer-grained delta activation sparsity, because ACT can only decide to halt the entire global recurrent u... | Summary: The authors begin by presenting the observation that in fixed-depth networks, only three dimensions are sufficient to explain the trajectory of a dimension-20 hidden state. With this motivation in mind, they introduce the notion of heterogeneous convergence, which can be summarized as the concept that differen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the in-depth reviews! Our reply is arranged as $\textbf{W1}$ for the first point in Weakness etc.
$\textbf{W1.1: }$ The delta rule is not limited to the vanilla fixed-point iteration (FPI) form as in Eq.3. Methods like Broyden’s solver as used in the original DEQ work ca... | Summary: The paper proposes the DeltaDEQ framework to accelerate the forward pass of the Deep Equilibrium Model. Initially, the paper identifies the heterogeneous convergence phenomenon, which shows that different dimensions of the state converge at uneven speeds in the forward pass of DEQ framework. Inspired by the ph... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and the precise summarization of our work! For points 1 and 2 in Weaknesses, the corresponding answers are under $\textbf{W1}$ and $\textbf{W2}$ respectively.
$\textbf{W1: }$ We thank the reviewer for the suggestion! We conducted new experiments exploiting the ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time in reviewing our work!
We first clarify the common concerns among some of the reviewers:
$\textbf{G1: }$In this work, we present the heterogeneous convergence phenomenon, which is a finding that shows different dimensions of the hidden states c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Unsigned Distance Fields from Local Shape Functions for 3D Surface Reconstruction | Reject | Summary: This paper introduces a new method to learning unsigned distance functions. Specifically, the method leverages local shape priors which brings geometry priors and is also able to handle noises and outliers. The results demonstrate that the proposed method outperforms previous baselines, especially in the corru... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our paper. We also appreciate your valuable suggestions.
In the following, we address your main concerns. Please refer to the supplementary one-page PDF for figures and tables.
**Q1: The visualization is not quite convincing in the geometry details.... | Summary: The paper proposes an approach to reconstruct 3D surfaces from point clouds, using unsigned distance fields. The proposed approach consists in training a specific neural network architecture to predict UDF values from local point cloud patches, which can be triangulated using UDF meshing methods. The paper mat... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our paper. We also appreciate your valuable suggestions.
In the following, we address your main concerns. Please refer to the supplementary one-page PDF for figures and tables.
**Q1: The intuition to design the network architecture and more ablation... | Summary: The paper proposes a novel training strategy to learn Unsigned Distance Fields from local shapes. The idea is to train the model on a dataset of point cloud patches characterized by mathematical functions representing a continuum from smooth surfaces to sharp edges and corners. Although trained only on synthet... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our paper. We also appreciate your valuable suggestions. In the following, we address your main concerns. Please refer to the supplementary one-page PDF for figures and tables.
**Q1: The inference time of the method compared to other baselines.**
We ... | Summary: The authors propose a method for open surface reconstruction from 3D point clouds. They train a network to predict unsigned distance functions (UDFs) from point cloud patches using only synthetic data of quadratic surfaces. Evaluation shows that the trained network generalizes well to other complex patterns an... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty of our method. We also appreciate your valuable suggestions. In the following, we address your main concerns. Please refer to the supplementary one-page PDF for figures and tables.
**Q1: Analyses on using the synthetic patches to approximate the real local 3D... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. We reiterate our method introduces a novel strategy for learning UDFs from local shape functions. It requires only a single training session on a synthetic dataset composed of local patches represented by simple mathematical functions, and it demonstrates stron... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition | Accept (poster) | Summary: The author proposes a novel optimization strategy, Gaussian neighborhood minimization prompt tuning (GNM-PT), for VPT under long-tailed distribution. This method is improved based on the SAM optimizer. Specifically, during each gradient update, GNM-PT searches for a gradient descent direction within a random p... | Rebuttal 1:
Rebuttal: > Q1. Why apply GNM to VPT?
**A1:** We acknowledge that most deep models encounter the issue of sharp local minima. We select VPT as a representative method. The primary reason is that it facilitates direct comparison with existing methods tailored for long-tailed learning, which employ the same ... | Summary: This paper proposes Gaussian neighborhood minimization (GNM) to enhance prompt tuning methods in long-tailed recognition. GNM is inspired by a recent work SAM, which aims to achieve flat minima by capturing the sharpness of loss landscape. Theoretical evidence shows that GNM can achieve a tighter upper bound a... | Rebuttal 1:
Rebuttal: >Q1. Comparison with MHSA-based model incorporated with SAM-based methods.
**A1:** We implement the experiments using CIFAR-100-LT with an imbalance ratio of 100. Since the re-balancing strategy employed in the second stage can influence the performance of optimization methods on a per-class leve... | Summary: The paper proposes an optimization approach called Gaussian neighborhood minimization prompt tuning (GNM-PT) for long-tailed visual recognition. Compared to sharpness-aware minimization (SAM), it excels in lower computational overhead, tighter upper bound for loss function and superior performance. GNM-PT util... | Rebuttal 1:
Rebuttal: > Q1. Experimental results with LDAM.
**A1:** Thank you for pointing out this issue. GCL is a logit adjustment method with a rationale similar to LDAM. Since LDAM is one of the baseline methods for GCL, and GCL performs better than LDAM on long-tailed visual recognition, we chose to compare our m... | Summary: This work addresses the long-tailed learning problem by adding a tight upper bound on the loss function of data distribution and improving the generality of the model through flattening the loss landscape.
Strengths: 1. Long-tailed visual recognition is an inevitable problem and is desired for conducting in-d... | Rebuttal 1:
Rebuttal: > Q1. Does GNM help improve generalization through flattening the loss?
**A1:** Thanks for pointing out this problem. Flattening the loss landscape is one aspect to consider. As observed in Figures 1 and 4, GNM exhibits a relatively large "area" at the minimum, particularly compared to the origin... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the PCs, SACs, ACs, and all the reviewers for their effort to enhanc our work and for their positive feedback. For example, Reviewer veiW pointed that our work *"includes theoretical justifications, detailed algorithm descriptions, and experimental... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Initializing Variable-sized Vision Transformers from Learngene with Learnable Transformation | Accept (poster) | Summary: This work proposes Learnable Transformations (LeTra) for improving learngene-based model initialization. In particular, a set of width transformations is learned to produce weight matrices of varying dimensions, and a set of depth transformations is learned to change the number of layers in the model. In the l... | Rebuttal 1:
Rebuttal: **[Presentation]**
**1) The description of transformations and notations.**
Thank you for pointing this out!
In the revision, we will simplify the notations of Section 3 and highlight the core idea behind the two types of transformations.
**2) How to choose the start and step size in "step-wis... | Summary: This work builds on top of a learning paradigm called Learngene (introduced in an earlier work), which focuses on providing effective initializations for training multiple target models of different sizes. In this paradigm, a compact module, referred to as learngene, is first leaned from a large well-trained n... | Rebuttal 1:
Rebuttal: **[Presentation]**
**1) Simplification of notations in Sec 3.1.**
Thank you for pointing this out! In the revision, we will simplify the notations of Sec 3.1 to describe the method more clearly.
**2) Initialization process in Figure 3.**
Thank you for raising this confusion!
During the first ... | Summary: To avoid unafforadable trainining cost, a new training paradigm like Learngene framework is proposed. Unlike previous work that mainly focus on the depth, the authors proposed Learnable Transformations, which is able to adjust the learngene module along both depth and width dimension for flexible variable-siz... | Rebuttal 1:
Rebuttal: **1) Performance improvements with increased depth.**
Thank you for pointing this out!
In Table 4, we present the results of LeTra with 2-epoch tuning (L315), which aims to demonstrate the effectiveness of our proposed depth transformation rather than performance improvements with increased dept... | Summary: This research adopts the learngene learning paradigm; the core idea of learngene is to transform a well-trained ancestry model (Ans-Net) to initialize variable-sized descendant models (Des-Net). The authors pointed out two limitations of previous works: (1) the original learngene paradigm lacks the provision o... | Rebuttal 1:
Rebuttal: **1) Scaling Des-Net bigger than Aux-Net.**
Please see the relevant discussions in G1 of General response.
**2) Combination with other Learngene strategies.**
Thank you for your insightful question!
We could combine LeTra with other Learngene strategies. For instance, we could replace the dept... | Rebuttal 1:
Rebuttal: ### General response
**G1. Size diversity of Des-Nets.**
We appreciate the valuable comments regarding the size diversity of Des-Nets, *e.g.*, scaling bigger than Aux-Net or smaller than learngene.
Firstly, we would have to emphasize that the primary focus of this paper is on initializing varia... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Invisible Image Watermarks Are Provably Removable Using Generative AI | Accept (poster) | Summary: This paper investigates the resilience of invisible watermarks embedded in images against removal attacks. The authors propose a new class of attacks, called regeneration attacks, which combine adding random noise to the image and then reconstructing the image using generative models. The study demonstrates th... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback and the opportunity to address your concern.
> "While invisible watermarks are highly vulnerable, semantic watermarks are less affected by the proposed attacks."
The primary purpose of this paper is not to propose an attack that can remove any type of... | Summary: The main idea proposed in this paper is that regenerating images using other (pretrained) generative AI (e.g., vae, diffusion models) can provably remove any invisible watermarks embedded in a given image. Accompanying this, the paper can be divided into the following parts: (1) intro of the proposed regenerat... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We appreciate the opportunity to clarify our contributions and address your concerns.
> W1: Experiment settings and evaluation are not rigorous. The authors may consider profiling the quality and detectability tradeoff.
We appreciate your suggestion. We hav... | Summary: This paper proposes regeneration attacks, which adds destructive Gaussian noise to the latent representation of the watermarked image, and then reconstructs the corrupted latent to reconstruct the original clean image. The paper provides theoretical guarantee that shows the trade-off function between the Type ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the opportunity to address the specific points raised and provide further clarification.
### 1. Calibration of $\sigma$ and Attack Performance
> "Relies on the upper bound $L \geq L_{x,w}$ to effectively calibrate $\sigma$… this could pot... | null | null | Rebuttal 1:
Rebuttal: To address reviewer n9vc's question 2 and reviewer V7G3's several concerns, we have included more figures in the attached PDF.
## Figure 1: Quality-Detectability Tradeoff
During the rebuttal, we conducted a comprehensive evaluation with various parameter settings for each attacking method.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge | Accept (poster) | Summary: I'm not an expert in this field. I'm familiar with organic retrosynthesis prediction but not familiar with inorganic retrosynthesis planning.
This paper first trains a retriever to determine which materials to reference. Then this paper trains a model for material selections.
Strengths: 1. This inorganic re... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and for acknowledging the novelty of our work in inorganic retrosynthesis! We are more than willing to address any questions in detail.
---
**[W1]**
As the reviewer suggested, describing the differences between organic retrosynthesis and inorganic retrosynt... | Summary: This paper lies in the domain of AI for chemistry, and this paper proposes RetroPLEX for inorganic retrosynthesis planning. The proposed approach is comprised of two components: masked precursor completion retriever and neural reaction energy retriever.
Strengths: The writing is mostly clear.
The experiment ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work! We are more than willing to address both the weakness and the question in detail.
---
**[W1]**
While we have endeavored to thoroughly explain the training process of RetroPLEX, the limited space of the submission may not have allowed for complet... | Summary: The manuscript presents a approach, RetroPLEX, for inorganic retrosynthesis planning. The authors propose RetroPLEX, a method that implicitly extracts precursor information from reference materials using attention layers. Additionally, they incorporate domain expertise by considering the thermodynamic relation... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work! We are more than willing to address each of the specific weaknesses and questions in a detailed manner.
---
**[W1&Q2]**
Indeed, we have provided the complexity of the model in terms of model training and inference in Appendix E.5. Upon the revie... | Summary: The authors address the domain of inorganic retrosynthesis by better leveraging existing inorganic retrosynthesis data. They employ attention learning techniques to establish relationships between chemical formulas and precursor formulas. Additionally, they utilize a neural reaction energy predictor to forecas... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work and for recognizing our efforts to address inorganic retrosynthesis using thermodynamic factors! We are more than willing to address each of the specific weaknesses and questions in detail.
---
**[W1&Q4]**
As the reviewer pointed out, different c... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for your valuable comments on our work. We are more than willing to address each of the weaknesses and questions in detail. Additionally, we have attached a PDF file that includes a qualitative analysis, experiments, and pseudocode for the rebuttal.
Pdf: /pdf/2d72f145eca0... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces RetroPLEX, a method for inorganic retrosynthesis planning. It extracts precursor information from retrieved reference materials implicitly. The authors use attention layers to extract information from the reference material and design a neural reaction energy (NRE) retriever to provide com... | Rebuttal 1:
Rebuttal: Thank you for your high praise and acknowledgment of the novelty of our work in inorganic retrosynthesis. We are more than willing to address your two questions in detail.
---
**[Q1]**
As the reviewer noted, actual material synthesis processes often involve multiple possible synthesis routes for... | null | null | null | null | null | null |
Revisiting the Message Passing in Heterophilous Graph Neural Networks | Reject | Summary: 1. The paper unifies existing heterophilous graph neural networks (HTGNNs) into a Heterophilous Message-Passing (HTMP) mechanism.
2. The authors reveal that the effectiveness of HTMP is due to increasing differences among node representations belonging to different classes.
3. Guided by this revelation, the pa... | Rebuttal 1:
Rebuttal: Thanks for your insightful and constructive review of our work. The following are our detailed responses to the reviewer’s thoughtful comments. We are expecting these could be helpful in answering your questions.
> Question #1 & Weakness #1: The connections and differences between CMGNN and exist... | Summary: This work revisits the message-passing mechanisms in existing HTGNNs and reformulates them into a unified heterophilous message-passing (HTMP) mechanism. Based on HTMP, the authors propose a new framework named CMGNN. Experiments on 10 datasets with 13 different baseline models demonstrate the effectiveness of... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review of our paper. Below, we address the reviewer's concerns point by point, hoping that a better understanding of every point can be delivered.
> Question #1: The contributions of benchmark datasets and unified codebase.
Answer #1:
To address the issue... | Summary: This paper aims to address the question of "why does message passing remain effective on heterophilous graphs" and proposes a unified framework called heterophilous message-passing (HTMP) mechanism. It extensively reviews the architecture of existing heterophilous GNNs under this framework. It then moves on to... | Rebuttal 1:
Rebuttal: We would like to thank you for your deeply thorough review. We have carefully considered your comments and suggestions, and the following are our detailed responses.
> Question #1 & Weakness #1: The connections and differences between the observations in this paper and prior works.
Answer #1:
Th... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful and valuable review points. We add the figure and algorithm of the proposed CMGNN and the results of empirical runtime comparisons in the **PDF file** attached to this global response. We appreciate that some reviewers suggested we provide theo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits | Accept (poster) | Summary: The article establishes upper and lower bounds for the average degree of navigable graphs in high-dimensional case. In particular, a method for constructing a navigable graph with average degree $\mathcal{O}(\sqrt{n \log n})$ for any set of $n$ points is provided. In addition, they provide a random point set f... | Rebuttal 1:
Rebuttal: Thank you for the review. Regarding your question, your understanding is exactly correct. Some point sets admit sparser navigable graphs. For example, if all points lie on a $d$-dimensional hyperplane, then the Arya, Mount result discussed in Section 1.1 implies that we can find a navigable graph... | Summary: This paper studies the problem of constructing navigable graphs over high-dimensional point sets. Specifically, a randomized algorithm and a deterministic algorithm are given to construct such graphs within almost the same time complexity. Besides, theoretical results demonstrate that both algorithms can achie... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We provide responses to the specific questions below:
Q1: There has been little formal work on connecting the property of navigability to near neighbor search. Indeed, the standard definition of navigability (which we study in our paper) only ensures that greedy... | Summary: This paper analyzes graph construction for greedy graph-based nearest neighbor search. First, the very general setup assuming an arbitrary similarity function is considered. In this case, it is shown that it is possible to construct a graph with an average degree at most $2 \sqrt{n \log n}$ which guarantees th... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We address a few of the points raised below:
- We agree with the reviewer that an important next research direction is to look beyond the graph’s degree, and at more accurate proxies for the efficiency of near-neighbor search. For example, a natural metric might be t... | Summary: The paper provides a theoretical framework for understanding and constructing navigable graphs for high-dimensional nearest neighbor search, and the authors establish some of the first upper and lower bounds for high-dimensional point sets.
Strengths: S1: The article establishes both upper and lower bounds fo... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. We address the two specific questions below.
Q1: A desirable property of an ANNS algorithm is that, if the vector queried, $q$, is in the dataset, then the algorithm should return exactly that vector. This behavior is also required for any algorithm to give ... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their thoughtful reviews and feedback. We address all specific questions in our individual responses below.
In general, we want to emphasize that our work can be viewed as a meaningful starting point to obtaining a better theoretical understanding o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features | Accept (poster) | Summary: The paper introduces a self-supervised learning framework named DistillNeRF for understanding 3D environments from limited 2D observations. This framework is designed for autonomous driving and leverages per-scene optimized Neural Radiance Fields (NeRFs) and features distilled from pre-trained 2D foundation mo... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable constructive comments! We address your specific questions below and will incorporate your suggestions into the paper. We have also included our source code during rebuttal (see the joint response), to enhance reproducibility and allow for inspectin... | Summary: This paper presents a method for 3d understanding from 2d observations for autonomous driving. The main technical contribution is a feed-forward model, which is trained by distilling RGB and depth from a per-scene optimized NeRF model. The proposed model predicts 3d feature volumes that enable volumetric rende... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and the insightful comments! Our detailed response for each question is below. Note that we have also included our source code during rebuttal (see the joint response), to enhance the reproducibility and allow for inspecting every detail of our model.
> Q1. Dis... | Summary: Appears to present a method for online 2D feature distillation in an autonomous driving configuration. Method appears to use some kind of pre-trained depth prediction network to build a frustum aligned grid of 2D features, which are somehow rasterized into a canonical sparse volumetric grid. There appears to b... | Rebuttal 1:
Rebuttal: Thank you for your comments about clarity and reproducibility. To address your concerns, we have included the source code (see the joint responses), which allows for inspecting every detail of our model and reproducing results. We also address your specific questions below and will incorporate the... | Summary: This work aims to enhance the understanding of 3D environments from limited 2D observations in autonomous driving scenarios. It achieves this by proposing a new generalizable NeRF pipeline, trained using distillation from per-scene NeRFs and foundation models. This pipeline can transform input RGB images into ... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and constructive comments! See the detailed response below, which will also be updated in our paper.
> Q1: The new insights of this work are somewhat unclear. Formulate the challenges, and analyze which design is useful for them.
Instead of object-centric indo... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the recognition of our work and the constructive feedback!
The majority of reviewers recommended accepting our work, and evaluated the method to “exhibit good soundness” (R-T1bA), “presents some SotA metrics” (R-WxYD), is “extensively evaluated on NuScene... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels | Accept (poster) | Summary: The paper presents Vidu4D, a reconstruction model that can accurately reconstruct 4D (sequential 3D) representations from single generated videos. This method addressing key challenges and enabling high-fidelity virtual content creation. The proposed techniques, such as Dynamic Gaussian Surfels (DGS) and the ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and insightful suggestions. We address all your comments below. If our response has addressed the concerns, we will highly appreciate it if the reviewer considers raising the score.
**1. Foundation model:** Our foundation model is Vidu. We believe... | Summary: The paper presents Vidu4D, a reconstruction model that excels in accurately reconstructing 4D (i.e., sequential 3D) representations from single generated videos, addressing challenges associated with non-rigidity and frame distortion. At the core of Vidu4D is a proposed Dynamic Gaussian Surfels (DGS) techniqu... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and insightful suggestions. We address all your comments below.
**1. Anonymous link:**
Thank you for your feedback. We believe the issue with the anonymous link might be due to a temporary network problem and we appreciate it if you could try agai... | Summary: Video generation models have shown great power recently. Transforming generated videos into 3D/4D representations is important for building a world simulator. This paper proposes an improved 4D reconstruction method from single-generated videos. The key component is the dynamic Gaussian surfels (DGS) technique... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and insightful suggestions. We address all your comments below. If our response has addressed the concerns and brings new insights to the reviewer, we will highly appreciate it if the reviewer considers raising the score.
**1. Motivation/necessity... | Summary: The paper proposes a technique called Dynamic Gaussian Surfels to effectively reconstruct 4D reqpresentation from a single generated video. DGS optimizes time-varying warping functions to transform Gaussian surfels and the authors adopt Neural SDF for initialization and proposes a geometry regularization techn... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and insightful suggestions. We address all your comments below.
**1. Novelty:** To the best of our knowledge, our method is the first to generate 4D content using a text-to-video model. We primarily focus on addressing the spatial-temporal inconsi... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers's efforts as well as very detailed and insightful suggestions. We find there are common concerns to our paper, and we'd like to clarify them here.
We also add a **PDF file** with more experiment results and visualizations.
_**Q1: From the 4D reconstruction persp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dissecting the Failure of Invariant Learning on Graphs | Accept (poster) | Summary: The authors propose a novel regularization and alignment mechanism for invariant graph learning with the goal of obtaining better out-of-distribution (OOD) generalization. Besides proving that existing methods tackling OOD generalization in other domains do not transfer to graphs, they propose CIA (environmen... | Rebuttal 1:
Rebuttal: We thank Reviewer nx4e for your careful reading and detailed comments! We'd like to address your concerns in the following points:
****
Q1: **G in Fig. 1 is not mentioned in the text**
A1: Sorry for the lack of clarity. $G$ refers to the graph data.
Q2: **limitation: the definition of the GNN ... | Summary: The paper investigate the failure case of IRMv1 and VRex for graph data, and develop a novel approach CIA for node-level OOD generalization. To adapt for the scenarios without environment labels, the authors propose CIA-LRA for the datasets without environment labels.
Strengths: 1. The paper is well-written ... | Rebuttal 1:
Rebuttal: We thank Reviewer yNy3 for your careful reading and detailed comments! We'd like to address your concerns in the following points:
****
Q1: **The author may want to clarify the reasoning behind the two SCMs corresponding to the two types of distribution shifts, as this connection is not entirely... | Summary: This paper addresses OOD generalization in node-level graph neural networks. The authors theoretically analyze why popular invariant learning methods like IRM and VREx fail on graph data, and propose two novel solutions: CIA and its environment-label-free variant, CIA-LRA. These methods enforce intra-class sim... | Rebuttal 1:
Rebuttal: We thank Reviewer LzeD for your careful reading and detailed comments! We'd like to address your concerns in the following points:
****
Q1: **Limited comparison to recent node-level OOD method [1].**
A1: We've added the evaluation of **CIT** [1], the results are in Table B of the rebuttal PDF. ... | Summary: This paper analyzes the failures of standard invariant learning techniques for node classification.
Through theoretical analysis, they argue that methods such as IRM and VREx will learn spurious features on graph data. Using this as motivation, they design a new method, CIA, that introduces additional invarian... | Rebuttal 1:
Rebuttal: We thank Reviewer empj for your careful reading and detailed comments! We'd like to address your concerns in the following points:
****
Q1: **The authors seem to leave out some baselines in their experiments. The authors should better motivate which baselines they compare against and which they ... | Rebuttal 1:
Rebuttal: We list the references of our rebuttal here (We start numbering from [4] to avoid conflicts with the numbering used by some reviewers):
[4] Spurious Feature Diversification Improves Out-of-distribution Generalization (ICLR 2024)
[5] Towards Understanding Generalization of Graph Neural Networks (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models | Accept (poster) | Summary: The paper addresses the challenges of using large language models (LLMs) as prompt encoders in text-to-image diffusion models. It identifies two primary issues: the misalignment between LLM training objectives and the requirements of discriminative prompt features in diffusion models, and the positional bias i... | Rebuttal 1:
Rebuttal: Dear reviewer nE1N,
Thanks for your comments. We will address your concerns below.
## Q1: The training and inference cost.
We train the LI-DiT-10B model on a GPU cluster with 1024 NVIDIA A100-80G. The training framework is implemented with Pytorch. We use the gradient checkpointing, mixed preci... | Summary: This paper presents an investigation into the integration of Large Language Models (LLMs) into text-to-image diffusion models. It identifies issues with using LLMs as prompt encoders, namely misalignment between next-token prediction training in LLMs and the need for discriminative prompt features in diffusion... | Rebuttal 1:
Rebuttal: Dear reviewer NTY6,
Thanks for your comments. We will address your concerns below.
## Q1: The contribution and novelty of our work.
Please refer to the Q1 of our global response.
## Q2: Comparing with other methods adopting LLMs.
Please refer to the Q2 of our global response.
## Q3: The risk... | Summary: In the context of text-to-image (T2I) generation, this work addresses the problem of leveraging representations from state-of-the-art decoder-based LLMs for conditioning image generation. The authors highlight challenges of leveraging existing LLMs, namely - misalignment in representations due to differing tra... | Rebuttal 1:
Rebuttal: Dear reviewer yHpu,
Thanks for your comments. We will address your concerns below.
## Q1: Analyses on the choice of text encoders.
Analyzing the choice of text encoders from encoder-only model, decoder-only model, and encoder-decoder model is one of the core contributions of our paper. In the ... | Summary: This work identifies two main reasons for degraded prompt-following ability in image generation with decoder-only transformers: the misalignment between pretraining objective and diffusion's need of discriminative prompt feature, as well as the intrinsic positional bias for decoder-only transformer. The soluti... | Rebuttal 1:
Rebuttal: Dear reviewer Nr61,
Thanks for your comments. We will address your concerns below.
## Q1: The contribution and novelty of our work.
Please refer to the Q1 of our global response.
## Q2: Comparing with other methods adopting LLMs.
Please refer to the Q2 of our global response.
## Q3: The siz... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable time and effort all reviewers have dedicated to review our work. We are pleased to learn that the reviewers generally acknowledge and commend our contributions, including:
- The importance of LLM-infused diffuser in integrating decoder-only LLMs into the diffu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration | Accept (spotlight) | Summary: The authors propose to use relative representations to unify the tokenization across different models for an effective ensemble. They first transform the prediction of each model to a relative space. Then, after averaging the relative predictions, they perform a gradient-based optimization to find the averaged... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, which greatly help us improve our paper. We are glad to have this discussion to address your concerns.
**Concern-1: The proposed method requires gradient updates to project back to the original space at every generation step. This can cause extra overh... | Summary: The paper introduces DEEPEN, a novel training-free ensemble framework designed to leverage the complementary strengths of various large language models (LLMs). The key innovation of DEEPEN lies in its ability to fuse informative probability distributions from different LLMs at each decoding step, addressing th... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which have greatly helped us improve our paper. We appreciate the opportunity to discuss and address your concerns.
**Question-1: How well does DeePEn generalize to unseen data or across different types of tasks beyond the evaluated benchmarks?**
W... | Summary: The paper introduces a method that maps output distributions of different LLMs to and from a universal relative space to aggregate them, based on which the next token is determined.
Strengths: - The paper is structured well and written clearly.
- It proposes a novel method of ensembling the heterogeneous outp... | Rebuttal 1:
Rebuttal: We would like to thank you for your constructive feedback. We appreciate the opportunity to address your comments.
**Question-1: Comparison between DeePEn with other ensemble methods in experiments beyond the main experiment.**
Thanks for your insightful suggestion! We have followed your... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Multiclass U-Calibration Error and Beyond | Accept (poster) | Summary: This paper studies online forecasting algorithms for achieving multiclass U-calibration. In the standard setting of online forecasting, a forecaster must produce a prediction p_t each day for an event with K possible outcomes (p_t is a distribution over K outcomes). The event then occurs (with outcome x_t), an... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments and evaluation of the manuscript. About your comment on the technical perspective, we would like to highlight a result that seems to be overlooked by the reviewer (since it is not mentioned in the summary of the review): our Theorem 4, an $\Omega(\log T)$ l... | Summary: This paper studies the calibrating for multiclass distribution forecasting while considering all proper functions simultaneously and contributes minimax optimal errors for a variety of settings.
Strengths: Originality:
The work is a novel combination of known techniques, especially Kleinberg et al. (2023).
... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments and evaluation of the manuscript. Regarding the weakness in originality and significance, we would like to highlight that our Theorem 4, an $\Omega(\log T)$ lower bound for any algorithm when learning with the squared loss, is the most technical part of our... | Summary: This paper closes an open problem regarding U calibration, left by Kleinberg et al. (2023). It is shown that a modified version of Kleinberg et al's algorithm recovers a classical FTPL algorithm of Daskalakis and Syrgkanis (2016), which improves the pseudo U calibration error in Kleinberg et al (2023) from $O(... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments and evaluation of the manuscript. | Summary: This works considered the the problem of making sequential non-contextual probabilistic predictions over $K$ classes with low U-calibration error.
The authors improved the upper bounds for U-calibration error from $O(K\sqrt{T})$ to $O(\sqrt{KT})$ after $T$ rounds, and they showed an existing algorithm, Follow-... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments and evaluation of the manuscript. Your questions are addressed below:
>L25: Based on the regret definition, it seems the it can be negative because the best prediction in hindsight is fixed for all time steps? For example, an oracle $p = \text{arg}\min_p\s... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms | Accept (poster) | Summary: Please see Strengths and Weaknesses.
Strengths: 1. Rigorous theoretical analysis of the proposed formulation
2. Detailed empirical evaluation.
Weaknesses: 1. Formatting in Figure 2 can be improved. The text near smaller cubes is not readable.
Technical Quality: 3
Clarity: 3
Questions for Authors: NA
Conf... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper and providing constructive feedback.
`Q-1:` Formatting in Figure 2 can be improved. The text near smaller cubes is not readable.
`A-1:` We thanks the reviewer for the suggestion. In the revised version, we modify Figure 2 as follow... | Summary: This work gives some theoretical analysis for mask optimization and DIP-based SCI recovery methods. The work claims that the proposed SCI-BDVP achieves SOTA performance among UNN methods.
Strengths: 1. This work provides some theoretical analysis, compared with the conventional work is rare.
2. Bagged DIP is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper and providing constructive feedback.
`Q-1:` Application of DIP hypothesis to all untrained networks and the relevance of the Lipschitz condition.
`A-1:` As mentioned by the reviewer, not all untrained neural nets (UNNs) satisfy the DIP hypot... | Summary: The focus of this paper is on developing recovery algorithms of snapshot compressive imaging (SCI) using untrained neural networks (UNNs). Besides, the paper introduces the concept of bagged-deep-image-prior (bagged-DIP) to create SCI Bagged Deep Video Prior (SCI-BDVP) algorithms, which are designed to address... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper and providing constructive feedback.
`Q-1:` Discussion about computational complexity and runtime.
`A-1:` We have made the following changes to the paper: In the main body of the paper, in Section 5.1, we have added the following exp... | Summary: This paper leverages untrained neural networks UNN (deep image priors DIP or deep decoder DD) as a prior to solve Snapshot Compressive Imaging (SCI), a technique used in ($n_1$ x $n_2$ x B)-dimensional 3-D imaging where the captured measurements lie in a 2-D plane ($n_1$ x $n_2$). The application of UNNs in th... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper and providing constructive feedback.
`Q-1:` Discussion about Computational complexity.
`A-1:` We have made the following changes to the paper: In the main body of the paper, in Section 5.1, we have added the following explanation on t... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback and thoughtful comments. We have carefully considered their suggestions and revised the paper accordingly. In the following, we address the main comments/questions from each reviewer..
The attached pdf file includes an improved version of Figure ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Asynchronous Perception Machine for Efficient Test Time Training | Accept (poster) | Summary: The authors propose a novel test-time training method called the Asynchronous Perception Machine. This method leverages knowledge distillation from other pretrained networks, such as CLIP. The main contributions of this approach are its robust accuracy in classifying corrupted images and its computational effi... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing us the chance to improve our work. Please find our responses:
```On improving related-work``` As reviewer KZWi points out, we will discuss more recent tta/prompting/source-free domain-adaptation approaches. We have noted to add differences w.r.t feature refine... | Summary: This paper introduces a new algorithm for test-time training where the “test-time” task is overfitting for per-image CLIP / DINO feature distillation. The associated “downstream” task involves directly using the per-image network feature to perform image classification using dot product in CLIP space. Given an... | Rebuttal 1:
Rebuttal: We thank the reviewer for giving us their valuable time. Please find our responses:
``` On using the local patch variable $I_{xy} in the fig1 , but not in the methods. ``` Fig 1 meant to be a general case of APM's operation. In methods, APM doesn't use $I_{xy}$. We were showcasing that positional... | Summary: The authors propose a new test time training method (architecture + self-supervised task) called Asynchronous Perception Machines (APMs). APM is computationally efficient, and empirically matches or improves performance on OOD image classification tasks versus prior test time inference approaches. The approach... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in evaluating our work and providing positive and constructive feedback. We will be happy to address your comments:
```Illustrative example, “self-driving car trying to make sense of the visual world while it is raining, but there were no such training scenario... | Summary: This paper proposes a computationally-efficient architecture for test-time-training. APM can process patches of an image in any order asymmetrically, where it learns using single representation and starts predicting semantically-aware features. The experiment results demonstrates the effectiveness and efficien... | Rebuttal 1:
Rebuttal: We thank the reviewer for helping us improve our work. Please find our responses below:
```[1] On processing patches of an image one at a time/lack of novelty```
We apologize that the novelties of our work were not clear. A CNN filter in any layer could process any patch by directly sliding on t... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We appreciate the positive feedback from the reviewers for our work. The reviewers acknowledged several aspects such as the creativity [Reviewer PHih], novelty [Reviewer doB6], effectiveness [Reviewer X1eH], first practically-viable approach towards GLOM's [10] ideas [Reviewer doB... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation | Accept (poster) | Summary: This paper analyses the AIL problem in the context of general function approximation. Specifically, authors propose an algorithm which is both sample efficient and computationally efficient. Finally, the paper concludes with an empirical validation of the results.
Strengths: - The paper analyses the AIL probl... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review and check our paper, and for your insightful comments. The references mentioned in this response can be found in the global response section.
**Question 1:** Typos.
**Answer 1:** We have fixed these typos and thoroughly revised the paper.
**Question 2:** ... | Summary: This paper introduces optimization-based adversarial imitation learning (OPT-AIL), a novel method for online AIL with general function approximation. OPT-AIL combines online optimization for rewards and optimism-regularized Bellman error minimization for Q-value functions. Theoretically, it achieves polynomial... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper, and for your insightful comments. The references mentioned in this response can be found in the global response section.
**Question 1:** The complexity measure and main idea of algorithm is not entirely novel, which is based on GEC and a series o... | Summary: This paper studies adversarial imitation learning (AIL). From a theoretical perspective, it proposes a new algorithm OPT-AIL which works in the context of general function approximations, accompanied with a provable sample efficiency guarantee. The advantage of the proposed theoretical algorithm is that it can... | Rebuttal 1:
Rebuttal: We appreciate your time to review and provide positive feedback for our work. The references mentioned in this response can be found in the global response section.
**Question 1:** The idea and techniques in this paper seems direct given the existing theoretical works on AIL and RL with general f... | Summary: This paper explores the theory of adversarial imitation learning (AIL) using general function approximation. The authors introduce a novel approach called Optimization-Based AIL (OPT-AIL). OPT-AIL employs a no-regret subroutine for optimizing rewards and minimizes the optimism-regularized Bellman error for Q-v... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing us with your valuable feedback. The references mentioned in this response can be found in the global response section.
**Question 1:** The practical algorithm itself is not highly innovative. The idea of running a no-regret algorithm... | Rebuttal 1:
Rebuttal: Here we list all the references that appeared in the responses to reviewers.
References:
[R1] Juntao Ren et al. "Hybrid inverse reinforcement learning." arXiv: 2402.08848.
[R2] Gokul Swamy et al. "Of moments and matching: A game-theoretic framework for closing the imitation gap." ICML 2021.
[R... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary | Accept (poster) | Summary: This paper addresses the manipulation of explanations in AI-assisted decision-making, presenting a comprehensive study that explores how human behavior models can be used to adjust explanations provided by AI systems. The aim is to understand if these manipulations can nudge decision-makers towards specific ou... | Rebuttal 1:
Rebuttal: Thanks for the review! Below we address your questions.
> Could the authors please provide more detail on which covariates were found to be significant in the regression analyses? It would be helpful to understand not only which variables significantly impacted the model outcomes but also how th... | Summary: This paper proposes to train a computational model to predict how humans would respond to model predictions and their explanations to make the final decision. Using this model, the authors then demonstrate that it could be used to manipulate explanations for both good and bad purposes, specifically to steer hu... | Rebuttal 1:
Rebuttal: Thank you for your review! We noticed that you are particularly concerned with the ethics of this study. We hope the clarifications below satisfactorily address these issues, and we are open to discussing any further concerns you may have. We are fully committed to ensuring that our work meets all... | Summary: This paper proposes a novel method that manipulates human decision-making by manipulating AI explanations in human-AI interaction scenarios. By utilizing human behavior models and minimizing the cross-entropy function between human and AI agreement with constraint to generating the same AI recommendation outco... | Rebuttal 1:
Rebuttal: Thanks for the review! Below, we address your questions.
> Only particular features are selected in each task. This may weaken the effectiveness of the manipulation.
- Thank you for your insightful feedback. Firstly, we’d like to clarify that in Section 5 (Evaluation I, when AI explanations are ... | Summary: The authors show that by modeling a human decision maker they can manipulate the provided information in ways that reliable influence their decisions towards even non-benign outcomes.
Strengths: # originality
Manipulating Mturk works is a well studied area of research, but this is a unique approach and highl... | Rebuttal 1:
Rebuttal: Thanks for the review! Below, we address your questions.
> Are the manipulations working possibly just due to the bar plots being being bigger, and numbers being clearer? So no behavioral model needed.
- Our behavior model-based manipulation demonstrated that changing the bar length in some tas... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Do's and Don'ts: Learning Desirable Skills with Instruction Videos | Accept (poster) | Summary: The paper introduces "DoDont", an instruction-based skill discovery algorithm designed to learn desirable behaviors and avoid undesirable ones through unsupervised skill discovery (USD). The method uses instruction videos to train an instruction network that distinguishes between desirable (Do’s) and undesirab... | Rebuttal 1:
Rebuttal: Dear reviewer DB1p,
Thank you for your insightful feedbacks and the positive support. We have provided a detailed response to your concerns below. If you have any further comments, please let us know.
>**Question 3.1**
The instruction network is trained using in-domain video data, which might no... | Summary: Unsupervised skill discovery is an RL task to learn interesting behaviors without rewards from environments. However, since there is no specification of desired behavior either, a lot of learning is wasted on acquiring skills that people may not be interested in eventually. The paper studies a setting where a ... | Rebuttal 1:
Rebuttal: Dear Reviewer oJKL,
Thank you for your valuable feedback on our paper. We have carefully considered your concerns and would like to address them as follows. Please let us know if you have further questions or feedbacks.
>**Question 2.1**
The idea of combining unsupervised RL with some form of ta... | Summary: This paper proposes a method, DoDont, to avoid hand-crafting reward functions in unsupervised skill discovery. DoDont first learns a reward function from labelled instruction videos that discriminates desired and undesired behaviors, and then use the reward function in unsupervised skill discovery. The authors... | Rebuttal 1:
Rebuttal: Dear reviewer AtB3,
Thank you for your constructive comments. We have provided a detailed response to your comments below. Please let us know if you have further questions or feedbacks.
>**Question 1.1**
I think the major weakness here is using a implicit reward function instead of explicit hand... | null | null | Rebuttal 1:
Rebuttal: **Response to All Reviewers (General Response)**
We deeply appreciate the thoughtful feedback and valuable suggestions from all three reviewers. R1, R2, and R3 correspond to reviewer AtB3, reviewer oJKL, and reviewer DB1p, respectively.
The reviewers highlighted the following strengths in our su... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Progressive Entropic Optimal Transport Solvers | Accept (poster) | Summary: In this paper, the authors propose a new entropic optimal transport solver as an alternative to the commonly used Sinkhorn algorithm named ProgOT. This solver has three main properties:
(i) It is less sensitive to the choice of the entropy-regularized parameter than the Sinkhorn algorithm;
(ii) When computin... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We have fixed the notation and typos issues, thank you for pointing them out.
> **Since the optimal transport map is unknown in practice, the assumption (A.2), which says that the inverse map has at least three continuous derivatives, is quite st... | Summary: This paper introduces a new class of entropic optimal transport (EOT) solvers called PROGOT. This work aims to address the challenges of selecting entropic regularization strength $\epsilon$ for original EOT. As we know $\epsilon$ is significant to the performance of EOT like computation time and convergence r... | Rebuttal 1:
Rebuttal: Many thanks for your careful review, we thank you for your positive score, encouraging comments and questions. We did our best to answer them.
> **… One may find this less favorable, as it replaces one hyper-parameter with several others.**
While we certainly agree with this assessment, one mess... | Summary: The choice of appropriate entropic trade-off term is one of the main headaches for finding maps between data distributions with sample access when considering Optimal Transport (OT) with entropic regularization. While the selection of sufficiently small regularization terms leads to unstable learning, the pick... | Rebuttal 1:
Rebuttal: We would like to thank you for your review, and for the many thought provoking questions you have asked. We did our best to answer all of them.
> **a crucial shortcoming of this method is scalability. […] severely limits its practical usability.**
There might be a confusion: Theorem 3 proves a t... | Summary: The authors proposed ProgOT, a method to solve a sequence of EOT problems so that practitioners do not have to tune the entropic regularizer parameter $\varepsilon$ and strike a good balance between computational and statistical complexity.
Strengths: The manuscript is well-written and it was easy to understa... | Rebuttal 1:
Rebuttal: Many thanks for your review and for your comments. Our response follows.
> **I think the contribution of this work is rather limited since the only benefit is to avoid the tuning of the parameter $\varepsilon$.**
In light of your comment, we will delete the sentence *“Setting $\varepsilon$ can b... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking the time to read about our new method. We take pride in seeing many appreciative comments, notably from reviewers `Z1nb`, `mHCn` and `9mT1`:
> *I am sure that other researchers might apply this methodology for Flow matching or Schroedinger bridge met... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Derivatives of Stochastic Gradient Descent in parametric optimization | Accept (poster) | Summary: The paper studies stochastic optimization problems where the objective depends on a parameter, and more specifically the derivatives w.r.t. that parameter of the SGD iterates. The paper makes various quite strong albeit common assumptions, and for various more specific settings concrete convergence rates are e... | Rebuttal 1:
Rebuttal: **Motivation:** We made a common response to all reviewers regarding the motivation. We will modify this section to include explicit references to works which consider differentiating SGD sequences or propose it as a relevent research venue.
**Vanishing derivative at initialization:** this remark... | Summary: The authors consider parametric stochastic optimization problems of the form $\min_x F(\theta,x)$ where $F(\theta,x)= \mathbb E_\xi [ f(x, \theta, \xi)]$ under the condition that $f$ is strongly convex in $x$ for any fixed $\theta, \xi$. This ensures that for fixed $\theta$, there exists a unique minimizer $x^... | Rebuttal 1:
Rebuttal: **Title:** We propose to add ''in parametric optimization'' at the end of the title if you believe it better illustrates our results. An alternative would be to name it "Derivatives through Stochastic Gradient Descent".
**Numerical section:** Thanks for pointing out this possible confusion, we wi... | Summary: The paper considers stochastic optimizations where the objective depends on some parameter. Instead of the SGD, the paper considers the derivatives of the iterates of the SGD with respect to that parameter in the context where the objective is strongly convex.
Convergence analysis is obtained for the derivat... | Rebuttal 1:
Rebuttal: ### Weaknesses:
**(1) Discussion of the assumptions:** The crucial assumption for our results is strong convexity. The rest of the assumptions are typically satisfied in applications such as hyper parameter tuning. We point out that both examples in the numerical section satisfy our assumtions a... | Summary: This is a theoretical paper on iterative process differentiation. The paper analyzes the behavior of the derivatives of the iterates of SGD (Stochastic Gradient Descent). Based on a set of assumptions, the paper establishes the convergence of the derivatives of SGD and conducts numerical experiments to validat... | Rebuttal 1:
Rebuttal: **About the motivation in stochastic hyperparameter optimization and assumptions:** We made a common response to all reviewers regarding theses two points. We will modify the appropriate paragraphs to include explicit references to works which consider differentiating SGD sequences or propose it a... | Rebuttal 1:
Rebuttal: Dear AC, dear reviewers,
We are sincerely grateful for your time and input. We reply to each of your questions and comments in a separate point-by-point thread below. We will of course integrate all applicable points in the next revision opportunity. We start with two general comments regarding m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models | Accept (poster) | Summary: The paper presents a pipeline for creating a 3D model of a full body avatar given a single input image.
While the previous methods show a single image to 3D using a 2D diffusion model such as ImageDream, as a 2D model, they suffer from 2D inconsistencies. This paper combines the benefit of both the large-scal... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognition of our method’s strengths, **including its generalization, potential extensions, performance, and clarity**. We maximize our effort to answer every comment seriously and hope our response can address the reviewer’s concerns. If there are any rem... | Summary: This paper proposed an image-conditioned 3D-GS generation model for human 3D reconstruction. 2D diffusion models fall short in offering 3D consistency for multi-view shape priors. To address this, the authors introduce a method that combines the strengths of 2D multi-view diffusion and 3D reconstruction models... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for **recognizing our insight, motivation, and the interesting idea of the proposed framework**. We notice that the reviewer has concern about the performance comparisons with SOTA methods and thus looks forward to more comparison and results. We address these conce... | Summary: The paper introduces a framework that combines 2D Multi-view Diffusion model and Gaussian Splatting to achieve the task of 3D clothed human body reconstruction from a single view. The focus of the paper is to deal with the 3D inconsistency present in 2D multi-view diffusion models.
Strengths: A novel framewor... | Rebuttal 1:
Rebuttal: ### **Q1: Point-to-surface (P2S) metric not reported in the paper**
A1: Thanks for the question. We would like to point out that the chamfer distance (CD) reported in the paper is a bidirectional point-to-mesh distance. It measures the distance from both Point-to-Surface (P2S, reconstructed mesh t... | Summary: In this paper, the authors propose to create realistic avatar representations by coupling the 2D multi-view diffusion and 3D reconstruction models which complement each other. Specifically, the 3D Gaussian Splatting (3D-GS) reconstruction leverages the priors from 2D diffusion models and produces an explicit 3... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the **importance of our task** and highlighting that our paper is **well written**, and our method **improves existing methods**. We address the concerns raised below and are open to further discussion and questions.
---
### **Q1: Performance of Human Reon... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
We sincerely thank all reviews and ACs for their time and insightful feedback. We are glad that they found our work novel and addressing an important task (R1) and appreciating the technical contribution of of integrating 3D Gaussian Splatting generation (R3, R4) w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal, Efficient and Practical Algorithms for Assortment Optimization | Reject | Summary: This paper considers the Adaptive Optimal Assortment (AOA) problem a.k.a. Utility Maximization with Subset Choices. The goal of this problem is to find the optimal profit-maximizing subset of size up to m (Top-m-Objective) or its weighted variant (Wtd-Top-m-Objective). Given a selected subset, the feedback fol... | Rebuttal 1:
Title: Rebuttal by authors
Comment: Thanks for your review
## Weaknesses --
> Q1. Non supported claims in line 21
-- Sudies in brain, psychology and cognitive neuroscience corroborate the fact. We will add references such as:
- Kahneman and Tversky. The psychology of preferences. Scientific American 19... | Summary: The authors consider the online MNL assortment optimization problem, where the goal is to learn MNL parameters while suggesting assortments, with the goal of either learning the top-m highest utility items or learning the maximum revenue set with m items. They use a UCB-based approach on pairwise win rates to ... | Rebuttal 1:
Title: Rebuttal by authors
Comment: Thanks for your positive detailed review and the insightful questions.
> Q1. Missing terms in the regret bounds in Table 1
-- You are right. We only included the main leading term for conciseness, ignoring logarithmic and constant terms. We will clarify this in the... | Summary: The paper addresses the problem of active online assortment optimization problem with preference feedback, which has been extensively studied. The paper argues that the previous studies have some unrealistic assumptions such as: there is a ‘strong reference’ which is always included in the choice sets; the sam... | Rebuttal 1:
Title: Rebuttal by authors
Comment: We thank the reviewer for the comments.
## Weaknesses --
> Q1. "Drawbacks may not be real. .. very limited value"
-- We respectfully but absolutely disagree. We strongly believe the comment 'the drawbacks may not be real' only depicts a very personal viewpoint of the ... | Summary: The paper studies the problem of active assortment optimization in MNL model.
In the problem of assortment optimization, we have a large universe of products i=1,2,\dots N, each of which generates a given revenue r_i for the seller. In MNL model each product i has a value \theta_i to the customers and when... | Rebuttal 1:
Title: Rebuttal by authors
Comment: Thanks for your review. Please see our responses below. Please note the misunderstanding and we
tried our best to clarify it. We will be glad to clarify any further questions.
> Q1. Clarification of variable $x$ in In Eq3
-- $x$ is the input to the algorithm. It can be... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedbacks. We make every effort to address any concerns raised and hope that our response will clarify any questions you may have. We emphasise here our contributions with respect to existing work.
**Description of existing algorithms [2,1,24] (as given in Tab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prediction-Powered Ranking of Large Language Models | Accept (poster) | Summary: The paper studies uncertainty estimate in the LLM ranking problem, where the task is to rank LLMs based on their response quality. Ideally the labels should be from humans, but due to the cose, people use models such as GPT-4 as auto raters. There lacks a good study of uncertainty estimation in the problem. Th... | Rebuttal 1:
Rebuttal: **[Application of PPI]** To the best of our knowledge, existing work has always applied PPI to construct confidence intervals for numerical quantities. In contrast, our work is the first to apply PPI to construct rank-sets and does so in a very timely domain, LLM evaluation.
**[Rank set vs numer... | Summary: This paper proposes a statistical framework to rank a collection of LLMs according to how well their output aligns with human preferences. This framework does this using a small set of human-obtained pairwise comparisons from LMSYS Chatbot Arena platform and a larger set of pairwise comparisons by a "strong" L... | Rebuttal 1:
Rebuttal: **[Small set of human pairwise comparisons]** The small set of human pairwise comparisons has length = $n > 1$ and we evaluate the performance of our computational framework for different values of $n$ in Figure 2.
**[Erroneous human comparisons]** We agree that it would be interesting to explor... | Summary: - Focuses on uncertainty in rankings using a small set of human pairwise comparisons and a large set of model estimated comparisons using a concept of rank sets. A rank set is a set of ranks a specific model can take. A large rank set indicates high uncertainty in ranking position and vice-versa a small set im... | Rebuttal 1:
Rebuttal: **[Evaluation metrics]** In our work, we focus on rank-sets as a measure of uncertainty in rankings and thus our experimental evaluation aims to assess the quality of the rank-sets estimated using our method and several baselines. In this context, we think that the ranking metrics proposed by the ... | Summary: The paper tackles an interesting problem of evaluating ranking large language models automatically using a strong LLM as alternative to human preference estimates. The work primarily focuses on modelling uncertainty in such a ranking generated when compared to the distribution of human preference rankings. Sin... | Rebuttal 1:
Rebuttal: **[Other benchmarks]** Since our computational framework is rather generic, it is true that it could be readily applied to other benchmarks. However, this would require significant funding and time, and given the on-going discussions about the reliability of LLM evaluation, we believe the NeurIPS ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their careful and insightful comments, which will help improve our paper. We include point-by-point responses to each reviewer in individual rebuttals. Moreover, in what follows, we provide details of an additional evaluation of our framework using a synthe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE) | Accept (poster) | Summary: Whilst useful for many downstream tasks, CLIP’s vision-language representations are notoriously hard to interpret. The paper proposes to represent CLIP representations in a sparse, non-negative overcomplete basis of learnt interpretable directions using a standard dictionary learning technique. Not only is int... | Rebuttal 1:
Rebuttal: Thank you for your comments! We appreciate your feedback and address your concerns below.
**Sparsity/interpretability-accuracy tradeoff.** We thank the reviewer for their detailed thoughts on the interpretability-accuracy tradeoff claims and for their willingness to increase their score if this i... | Summary: This paper introduces a method for building sparse embeddings from CLIP in order to improve the interpretability of CLIP’s latent space. They formulate their objective as a sparse reconstruction problem, and under certain assumptions, demonstrate when finding a sparse representation is possible. Empirically,... | Rebuttal 1:
Rebuttal: Thank you for your comments! We appreciate your feedback and address your concerns below.
**Benefits of sparse CLIP embeddings.** Thank you for highlighting this confusion, we hope to clarify this in our response. The primary benefit of SpLiCE is the insight it provides into interpreting the sema... | Summary: This paper presents a method to explore semantic concepts in multimodal models of text and images. Specifically, the paper formulate semantic concept discovery problem as one of sparse recovery and build a novel method, Sparse Linear Concept Embeddings (SpLiCE), for transforming CLIP representations into spar... | Rebuttal 1:
Rebuttal: Thank you for your comments! We appreciate your feedback and address your concerns below.
**Connection to Multi Modal Topic Models.** This is an interesting connection! MMTMs such as [A] are trained on a corpus of data and use tf-idf statistics to generate multimodal clusters of topics, such as c... | Summary: This paper introduces a method to transform CLIP representations into sparse linear concept embeddings that are interpretable to humans. SpLiCE uses task-agnostic concept sets, demonstrating its versatility over prior works. SpLiCE provides interpretability without compromising zero-shot classification perform... | Rebuttal 1:
Rebuttal: Thank you for your comments! We appreciate your helpful feedback and address your concerns below. We will also correct the typo you mentioned.
**Efficacy of task-agnostic concept sets.** As requested by the reviewer, we include in the additional results concept decompositions of randomly chosen s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thorough assessment of our paper and the AC for facilitating the discussion of our work. We appreciate the reviewers’ recognition that our paper is an “important study toward understanding the latent space of vision-language models like CLIP” and that it ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes to decompose the representations of the CLIP model using dictionary learning; where the components of the dictionary are composed of human understandable concept directions. The procedure here is as follows: a concept list is constructed from a filtered set of unigrams and bigrams from the ... | Rebuttal 1:
Rebuttal: Thank you for your suggestions! We appreciate your feedback on how to improve this work and address your concerns below.
**Assumptions not justified.** Thank you for this comment. Regarding Assumption 2, it is true that our experiments demonstrate the presence of non-semantic concepts in our deco... | null | null | null | null | null | null |
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction | Accept (poster) | Summary: The paper presents SMART, a self-supervised representation learning approach that tries to tackle the problem of missingness in EHR data. It proposes a novel self-supervised pre-training approach which is able to reconstruct missing data representations in the input space and makes use of both temporal and var... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of SMART, especially its novelty and effectiveness. We address your concerns and answer your questions below.
---
**W1: During the pre-training stage, since it is based on reconstruction, access to the full dataset with all observations is required. The me... | Summary: This paper presents SMART, a novel model designed to tackle the challenges of missing and irregular data in electronic health records (EHRs). Utilizing a two-stage training strategy, SMART first pre-trains to handle missing data in the latent space and then fine-tunes for specific clinical tasks. The model's i... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of SMART, especially its effectiveness and novel designs. We address your concerns and answer your questions below.
---
**W1: Lack of comparative analysis with state-of-the-art models beyond the specific baseline models mentioned.**
Thank you for your que... | Summary: The authors propose a novel approach to handling missing data in an attention-based module in a method that is geared for predicting downstream health-related outcomes given multivariate time series patient data in EHR settings. Specifically, the proposed module, termed as the MART block, biases the attention ... | Rebuttal 1:
Rebuttal: **W1: Why handling imputation in the latent space is expected to be a better approach than prior approaches?**
Thank you for your insightful questions about why our approach works. We have shown in lines 59-60 that reconstruction in the latent space can help it better learn higher-order data patt... | Summary: The paper presents a strategy to account for missing data in EHR called SMART. This is broken down in 2 stages: pretraining and fine tuning. Pretraining learns a hidden state representation which is done by randomly making the input and predicting the label, while fine tuning uses this hidden state representat... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of SMART, especially its effectiveness and novel designs. We address your concerns and answer your questions below.
---
**W1: Can you discuss the limitations of SMART by showing more areas of impact by looking at conditions where missing data could cause m... | Rebuttal 1:
Rebuttal: We thank all reviewers for their high-quality comments and for recognizing the strengths of SMART. We have addressed all the concerns and answered questions in the rebuttal.
Here, for the commonly asked question of why reconstruction in latent space is more effective than imputation in input spac... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Harmless Rawlsian Fairness Regardless of Demographic Prior | Accept (poster) | Summary: Although importance of group fairness appreciated, most existing works have required demographic information for debiasing. The paper introduces a novel method, VFair, to achieve fairness with minimum sacrifice of the utility under no prior demographic information scenario. VFair aims to achieve harmless Rawls... | Rebuttal 1:
Rebuttal: # Reply to weakness
1. Experimental results. We compared the regression and classification performance among six benchmark methods across six datasets. Additionally, we discussed the results of using F1-score as utility metric, randomly splitting groups, methods using demographic prior during the ... | Summary: The authors propose VFair: an approach to Rawlsian, demographics-agnostic fairness where the variance over each data point's loss term is minimized together with the mean loss during training. These objectives are clearly often at odds with eachother, so they include a principled dynamic weighting scheme for t... | Rebuttal 1:
Rebuttal: # Reply to weakness
1. Rigor of expression. Thank you for your careful reading and rigorous derivation. We will update Proposition 1 and the assumptions regarding u as you suggested.
2. Confusion caused by lines 169-180. Sorry for any confusion on this point. Lines 169-180 offer a deeper reflect... | Summary: The authors suggest a framework aimed at enhancing the fairness guarantees of classifiers in scenarios where sensitive information is unavailable. Their approach seeks to identify a classification rule that minimizes the variance in losses from the training sample, while ensuring that the overall average utili... | Rebuttal 1:
Rebuttal: # Reply to weakness
1. Reproducibility issues. We will provide more training details in the appendix. (1) Throughout experiments, all methods are set with a batch size of 32 and a learning rate of 0.01. As for the training epoch, as mentioned in lines 555-559, to ensure all baselines comply with t... | Summary: The paper proposes a novel view of Rawlsian fairness for scenarios where no demographic information is provided. The core proposal of the paper is VFair, a method for reducing the variance of the predictive loss across a dataset, with the core tenet that a well-concentrated loss distribution would assign simil... | Rebuttal 1:
Rebuttal: # Reply to weaknesses
1. Computational costs. Your comment on two backward passes is correct. We will include this extra computation cost as one of the limitations of our work. Since the backward pass is the bottleneck of the total computation, we found that VFair requires approximately twice the... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from the reviewers. In response to some issues that required additional experiments and illustrations, we added new experiments and figures in the supplementary PDF.
Pdf: /pdf/6169aeed5762831275d7eff402da966f250a40e4.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper presents the first result on provably efficient randomized exploration in cooperative multi-agent RL. This paper focuses on parallel MDP where the transition kernel assumes an approximately linear structure. To this end, two Thompson-sampling-type algorithms are propose which leverages the perturbed... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and providing positive feedback on our work. We hope our response addresses your question.
---
### Q1. Extension of the algorithm beyond linear MDP setting
Empirically, we would like to clarify that our algorithm is designed for general function approximation. Fo... | Summary: This paper studies provably efficient randomized exploration in parallel MDPs setting. The authors consider the linear setting, and propose two Thompson Sampling-style algorithms and establish the regret guarantees and communication cost. After that, they also conduct some experiments for evaluation.
Strength... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and providing positive feedback on our work. We hope our response will fully address all of your points.
### Q1. Discussion on multi-task RL and multi-agent RL
Our work focuses on parallel MDPs, which have been categorized as multi-agent RL in the literature [1... | Summary: This paper considers randomized exploration in a multi-agent reinforcement learning setting called parallel MDPs. Two Thompson sampling-type algorithms are provided with a regret bound and communication complexity bound. The algorithms are empirically validated in multiple environments.
Strengths: * The paper... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and effort in providing feedback on our work. We hope our response will fully address all of your points.
---
### Q1. Detailed explanation about challenges in theoretical analysis.
We explain the specific improvements we made in our theoretical analysis here.
1... | Summary: This paper investigates multi-agent reinforcement learning in cooperative scenarios. The main contribution is the extension of randomized exploration methods, including perturbed-history exploration and Langevin Monte Carlo exploration, to the multi-agent cooperative setting. The authors offer a regret analysi... | 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.
---
### Q1. Explanation for the configuration of the contenders
All the baselines as well as our two proposed methods are run under the unified fra... | Rebuttal 1:
Rebuttal: ## General response
We would like to thank all reviewers for your insightful and detailed reviews and comments. We have addressed your comments and revised the manuscript accordingly. In the following, we would like to provide general responses to several common questions raised by reviewers.
--... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes | Accept (spotlight) | Summary: This paper studies the implicit regularization of large learning rates in gradient descent. The setting is univariate linear regression with two-layer ReLU neural networks. The authors show that, if GD converges to a local minimum, then the function implemented by the neural network at this local minimum has a... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**The paper considers a univariate case, some comments on the extension to the multivariate case are given at the end of Section 1.1.**
Since this is the first paper to consider minima stability ... | Summary: This paper studies the generalization properties of two-layer ReLU neural networks in a univariate nonparametric regression problem with noisy labels. It proposes a new theory for local minima to which gradient descent (GD) with a fixed learning rate $\eta$ stably converges. The paper shows that GD with a cons... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**The analysis in this paper has little focus on optimization. There is no rigorous theoretical evidence that GD will find the solutions that satisfies the assumptions (though I know this is an op... | Summary: The paper studies uni-variate regression with two layer networks and builds on the following observation: If the basin (derived form a quadratic approximation) around a given minimum is too narrow, gradient descent with a fixed step size $\eta$ will escape it, only sufficiently wide basins can capture the iter... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**Regarding the ''optimized'' assumption.**
First of all, we politely point out that we actually made some assumptions on the smoothness of $f_0$. In line 312 of Theorem 4.4, we assume an upper b... | Summary: This paper studies the generalizability of stable local minima in univariate regression with shallow ReLU networks. Along the way, the authors provide a bound on a weighted total variation norm of networks corresponding to stable solutions which in turn provide a tighter generalization bound.
Strengths: The p... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**It is not clear to me why the experiments (e.g. figure 3) corroborate the theoretical results in the paper. ... The point is that the actual learning rate of GD that was used to find stable sol... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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