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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GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs | Accept (poster) | Summary: The paper presents a technique (called GraphMorph) for extracting tubular patterns as found for instance in retinal images (veins) and aerial images (roads).
The proposed pipeline is made of several modules: a segmentation network providing centerline probability and features,
a graph decoder for detecting n... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions! We will correct the typo error you raised in the revised version. Please find our reply to your questions below.
**Q1:** Some hyperparameters (e.g thresholds) have to be set. The methodology is not always clear to set these parameters. e.g. are experim... | Summary: In this work, the authors tackle curvilinear image segmentation. In particular, they move away from pixel-level prediction which has limitations when predicting thin structures. They propose GraphMorph which predicts location of endpoints of each branch and finds the optimal path between them. In this way, the... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We will answer your questions below.
**Q1:** For the performance mentioned in the tables, the authors should provide standard deviation to understand whether their method is statistically significant or not. The authors should conduct t-test to confirm th... | Summary: This paper proposes a method to extract tubular structures from images. Specifically, the authors have integrated graph extraction into the segmentation architecture. They have proposed a link prediction part to predict graph connectivity in tandem with the segmentation network. They combined the predicted gra... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! Please see our answers for your questions below.
**Q1:** Is the threshold in the SkeletonDijkstra algorithm dataset-dependent?
**A1:** $p_{thresh}$ is a hyper-parameter which is dataset-independent. It is set to 0.5 as the default setting across all experime... | Summary: This paper presented a method called GraphMorph, for tubular structure extraction to achieve more topologically accurate predictions. GraphMorph consists of a Graph Decoder and a Morph Module: 1) the Graph Decoder generates/decodes/learns a graph structure from the ouput of segmentation network and segmentatio... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We will respond to your concerns below.
**Q1:** The SkeletonDijkstra Algorithm is proposed by authors alone, any reference?
**A1:** The SkeletonDijkstra algorithm proposed by our paper represents an innovative adaptation of the classic Dijkstra's algorit... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We were encouraged to receive the following positive comments from reviewers: "The method is well-motivated"(CJeh), "The paper is well written with good clarity"(Ysem), "Their method is able to handle both FP and FN errors."(4DfY), "Experimental results against SOTA are co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimizing Automatic Differentiation with Deep Reinforcement Learning | Accept (spotlight) | Summary: This paper studies automatic differentiation in a computational graph. The topic is classic with wide applications in scientific research, e.g., computing gradients of a neural network or Jacobians needed in numerical optimization. Classic numerical methods like standard forward- and reverse-mode differentiati... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the typos and will definitely fix them for the final submission.
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Rebuttal Comment 1.1:
Title: Reviewer response
Comment: I remain positive about this work and will maintain my score. | Summary: The authors study the problem of computing the Jacobian of arbitrary computation graphs. The typical approach in most autodiff libraries is to implement the classic backpropagation algorithm, aggregating gradients from the end to the beginning. This is because such approaches are well suited for cases where th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful input to our work.
In particular, we agree with the reviewer that method is not entirely practical yet, but we intend to improve so that is becomes applicable to a wider range of problems.
Automatic differentiation is an ubiquitous tool that sees... | Summary: This paper considers the (combinatorially hard) optimization problem related to automatic differentiation algorithms represented via an "elimination order" based on prior work and proposes an RL formulation to solve it approximately. This follows in similar vein to recent celebrated results on matrix multiplic... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable feedback on our work and appreciate the time the reviewer took to double-check the equations. We will implement the corrections accordingly.
We partially agree with the reviewers assessment that our method requires retraining for every computat... | Summary: In this paper, the authors propose a novel method to optimize the number of necessary multiplications for Jacobian computation by leveraging deep reinforcement learning (RL) and a concept called cross-country elimination while still computing the exact Jacobian. The author present the search for the optimal el... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback of our approach and want to take the time to address some of the feedback more in-depth as compared to the general rebuttal.
Firstly, we noticed that our presentation of the obtained results is not optimal and invite the reviewer to have a quick... | Rebuttal 1:
Rebuttal: We thank reviewers for their thoughtful, detailed reviews and the additional literature suggestions. We first discuss two common themes, then address certain individual comments.
**Retraining is necessary for every function**
The best results for most of the graphs were indeed achieved with indi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes to learn an efficient computation of the Jacobian of a program by reinforcement learning.
The authors base their learning procedure on cross-country elimination, a classical scheme that progressively transforms a computational graph to a bipartite graph representing the evaluation of the Jac... | Rebuttal 1:
Rebuttal: We thank the reviewer for his constructive feedback and the careful study of our manuscript. We will correct the typos immediately.
Regarding the two questions posed by the reviewer:
The performances in table 2 are indeed given after JIT compilation of our AD algorithm. This was straight-forward... | Summary: This paper presents a novel method called AlphaGrad for optimizing computational graphs derived through automatic differentiation (AD) algorithms using deep reinforcement learning. The authors formulate the optimization of a computation graph as a single-player game where an AlphaZero agent aims to minimize th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, in particular for the additional references which we consider a useful contribution to shape the future of our project.
We agree with the reviewer's criticism regarding the use of the theoretical number of multiplications as a proxy for actua... | null | null | null | null |
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction | Accept (poster) | Summary: This paper focuses on improving SDF-based volume rendering and proposes a new pipeline to address issues stemming from SDF-to-density conversion and geometric regularization. First, it changes a global scale parameter to local adaptive value, allowing more flexible density values to be converted from SDF. Seco... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback. For more comprehensive ablation study, comparison with TUVR, results on DTU and more explanation of stochastic gradients, please refer to our global response. Apologies for the brevity due to word constraints.
### **Motivation of Local Scale Factor**
> Why an ... | Summary: The paper improved NeRF based surface reconstruction from two aspects: adaptive scale $s$ and a bias correction loss to reduce the bias, which encourages the SDF becomes negative after the maximum at $t^*$.
Strengths: The position $\mathbf{r}(t)$ based scale $s$ increases the degree of freedom, which has the ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We hope the following response can address your concerns.
### **Design of Explicit Bias Correction**
> The bias correction looks like partially. It doesn't punish negative SDF before $t^*$, so it is not completed.
As you mentioned, although aligning $t^*$ ... | Summary: This paper introduces an innovative two-stage framework, NeuRodin, for neural surface reconstruction that significantly improves upon previous SDF-based methods. Locally adaptive parameters for SDF-to-density conversion and a novel explicit bias correction are introduced to enhance the fine reconstruction of S... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. The issues we mentioned regarding SDF-based rendering, such as the bias problem, might indeed be addressed through improved SDF-to-density modeling. While it is possible to tackle these issues directly through mathematical modeling, we believe that our detai... | Summary: This article introduces NeuRodin, a Signed Distance Function (SDF)-to-density-based neural surface reconstruction method. The author summarizes the two main factors, SDF-to-density representation and geometric regularization, which cause low-quality performance in SDF-based methods and improve them in the pipe... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. Below, we address the concerns raised in the review.
### **Image Reconstruction Comparision**
> The authors only provide F-score for the evaluation metrics except for some single scenes in appendix. It will be good to provide more such as PSNR, SSIM, etc.
... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all the reviewers for their valuable insights and constructive feedback on our work. We truly appreciate your dedicated efforts and the time you have devoted to evaluating our work.
Here we address some common concerns raised by reviewers. We also have provided... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability | Accept (spotlight) | Summary: This paper aims to provide a unified framework for deriving lower bounds of interactive decision-making problems using an extended technique adapted from Chen et al., 2016 [11], which was established only for non-interactive estimation problems. The paper offers a quite general formulation of the minimax value... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and for dedicating time to review our paper!
> Q1. I'm not sure if it's appropriate to call Corollary 2 "Generalized Fano’s inequality"
In our proof of Theorem 1 on page 15, we actually prove Theorem D.1, which holds for general quantile $\delta\in(0,1)$ rat... | Summary: This paper develops the notion of Interactive Statistical Decision Making (ISDM), and a generic lower bound (Theorem 1) which can be instantiated to capture the standard Le Cam, Assaoud, Fano methods as well as recent lower bound results in interactive decision making. The authors further use Theorem 1 to deri... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive criticism! We will work on better presenting the intuition behind our results and revise our statements to make them more precise.
> Regarding "addressing remaining gap" and "complete characterization": the lower and upper bounds differ quadratic... | Summary: This paper proposes a unified framework for lower-bound methods in statistical estimation and interactive decision-making. The authors integrate classical lower bound techniques (Fano's inequality, Le Cam's method, Assouad's lemma) with recent minimax lower bounds for interactive decision-making (Decision-Esti... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and for dedicating time to review our paper! We respond to the specific questions as follows:
> 1. The proof of corollary 2 is skipped. It involves 3-4 steps, and therefore I don't think it is trivial, especially for those who are not experts in dealing with... | Summary: This work provides a unified perspective of existing techniques for deriving lower bounds. This viewpoint covers techniques that are useful for traditional statistical estimation (e.g., Fano's inequality, Le Cam's method, and Assouad's approach) as well as the recently proposed approach using decision-estimati... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and for dedicating time to review our paper!
> Q1. How realistic is Assumption 2? It seems to require that we have a model that is close to all models It is unclear why should such an assumption be true for a finite Similarly, how realistic is Assumption 3?... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Propensity Score Alignment of Unpaired Multimodal Data | Accept (poster) | Summary: This work addresses the problem of aligning unpaired samples from multimodal data, whereby the problem is to find sample $x_i$ from modality $i$ that is best “related” to sample $x_j$ from modality $j$, when those two samples come from the observation of the same phenomenon according to two different modalitie... | Rebuttal 1:
Rebuttal: **“Strong” Assumptions**
The problem that we address in this paper is obviously impossible in general: for example, if I gave you data from two modalities that are completely unrelated to each other (e.g. text from the complete works of Shakespeare, and images of cells under a microscope), there ... | Summary: The paper proposes a novel matching method for unpaired multimodal (bimodal) data. It aligns unpaired samples across modalities using a propensity score. Based on additional treatment information, the propensity score is learned and used to align multimodal samples.
The method is evaluated on three datasets: o... | Rebuttal 1:
Rebuttal: **Final purpose of the method**
We are most interested in (2) “learning from unpaired multimodal samples”. Our cross modality task (see Table 2) explicitly evaluate this and show that you do indeed see a significant improvement by learning from matched data (the difference in performance between ... | Summary: This paper presents a new method for pairing unpaired examples across different modalities using the labels of the examples. The method essentially trains a classifier for predicting labels for examples in each modality, then uses the classifier's logits across two modalities to calculate a similarity matrix. ... | Rebuttal 1:
Rebuttal: **Dependence on a classifier**
>There are two drawbacks with this approach: (1) the quality of matching depends directly on the choice of the classifier and its capacity/complexity.
It is true that the quality of the matching depends on the choice of classifier and its capacity, but because you c... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Localized Zeroth-Order Prompt Optimization | Accept (spotlight) | Summary: This paper focuses on the prompt optimization task. The authors first propose two insights: (1) Instead of pursuing the global optimum, this paper claims that local optima are usually prevalent and well-performing. (2) The input domain for prompt optimization affects the choice of local optima. Inspired by the... | Rebuttal 1:
Rebuttal: We thank Reviewer TMXX for taking the time to review our paper and appreciate the reviewer's feedback. We would like to provide the following response to address the concerns and hope it can improve your opinion of our work.
---
> [W1] This paper does not discuss the following recent prompt opti... | Summary: The paper titled "Localized Zeroth-Order Prompt Optimization" proposes a novel algorithm called ZOPO (Localized Zeroth-Order Prompt Optimization) aimed at enhancing the efficiency of prompt optimization in large language models (LLMs). The authors argue that local optima, as opposed to global optima, are more ... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer TQjw for the constructive feedback and for positively recognizing that our empirical study is **thorough** and our proposed algorithm is **well-designed**. We will incorporate the suggested discussion into our revised work. We respond below to their concerns and hope ou... | Summary: The paper proposes multiple contributions:
1. Establishes a new visualization technique for the objective landscapes of blackbox functions over prompts. This is done by converting the high dimensional embeddings of strings into 2D (via t-SNE), and visualizing the landscape in 3D. Using this, the paper finds s... | Rebuttal 1:
Rebuttal: We are highly encouraged by Reviewer GMgT's positive and constructive feedback! We appreciate that the reviewer positively recognizes that our visualization method is **insightful** and could be a very **important tool** for studying the black-box prompt optimization landscape, our designed algori... | Summary: This paper addresses prompt optimization for a black-box API LLM. This paper empirically investigated the objective function landscape of the prompt optimization problem and derived two insights: (I) local optima are usually prevalent and well-performed, (II) choice of the input domain affects the identificati... | Rebuttal 1:
Rebuttal: We thank Reviewer bDUw for recognizing that our algorithm is novel and efficient, and its performance is promising. We would like to address your concerns below and hope our response will improve your opinion of our work.
---
> [Q1] Validity of our insight
Thank you for your insightful comment. ... | Rebuttal 1:
Rebuttal: ## **Global Response**
We sincerely appreciate the insightful feedback provided by the reviewers, which has significantly contributed to enhancing the quality of our paper. We hope we have addressed all questions raised by the reviewers, providing our clarifications and additional results. In thi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LESS: Label-Efficient and Single-Stage Referring 3D Segmentation | Accept (poster) | Summary: In this paper, the authors propose a Label-Efficient and Single-Stage referring 3D instance segmentation method, which is under the supervision of binary mask. They propose Point-Word Cross-Modal Alignment, Query Mask Predictor and Query Alignment modules to achieve cross-modal alignment. Besides, the area reg... | Rebuttal 1:
Rebuttal: We appreciate that you recognize the significance of our work. We will respond to your concerns in the following:
## Q1: For the validation of TGNN and X-RefSeg with the RoBERTa backbone
Thanks for your detailed reading and pointing out this. We reimplement the TGNN and X-RefSeg with the same Ro... | Summary: The paper proposes LESS, a single-stage, label-efficient pipeline for referring 3D instance segmentation. It introduces fine-grained and coarse-grained cross-modal alignment to improve feature matching and employs additional losses to reduce irrelevant predictions. Experiments are conducted on the ScanRefer da... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your detailed and constructive comments.
## Q1: For the validation on other dataset
Thanks for your detailed reading and pointing out this. We also conduct the experiments on the Nr3d and Sr3d datasets, as shown at General Response and in the following table... | Summary: This paper proposes a label-efficient single-stage method for referring 3D instance segmentation. Specifically, this method enhances feature extraction by integrating multi-modal features, using only binary labels for supervision. It achieves fine-grained alignment between points and words, distinguishing poin... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. Next we address your questions.
## Q1: For the metrics of Referring 3D Instance Segmentation
Thanks for pointing out this question. For the metrics of Referring 3D Instance Segmentation task, our experimental metrics are constantly followed by ... | Summary: This paper addresses the problem of referring 3D Instance Segmentation, which segments all points belonging to an object in a 3D point cloud described by a query prompt. Previous methods use a two-stage approach requiring both instance and semantic labels, which is labor-intensive. The authors propose LESS (La... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. Next we address to your concerns as follows:
## Q1: For the novelty of the module design
The core motivation of our approach is to explore a novel single-stage R3DIS to fully embrace semantic concepts from text query and visual cues into a unifie... | Rebuttal 1:
Rebuttal: # General Response to all Reviewers:
Dear all Reviewers,
We would like to express our great thank and appreciation to all the reviewers for their constructive and thoughtful comments on our submission. In this rebuttal response, we address the questions raised by the reviewers and clarify how we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Autoregressive Policy Optimization for Constrained Allocation Tasks | Accept (poster) | Summary: This paper studies task allocation under resource constraints and proposes a new constrained RL algorithm based on autoregressive policy optimization with a novel de-biasing mechanism. Extensive simulations are provided demonstrating the improved performance.
Strengths: The paper is well-written and the algor... | Rebuttal 1:
Rebuttal: Thank you for the positive review and helpful comments. We are happy to address your questions below:
- W1: Please see our general response, where we address the theoretical guarantees in the form of a proof.
- W2: In theory, it is possible to learn the optimal policy regardless of the order as ... | Summary: he paper presents Polytope Action Space Policy Optimization (PASPO), a novel RL methodology tailored for strict linear constraint allocation tasks. By autoregressively decomposing the action space into manageable sub-problems, PASPO enhances compliance and efficiency without the need for corrective actions. Ad... | Rebuttal 1:
Rebuttal: Thank you for the positive review and helpful comments. We are happy to address your questions below:
- W1: Thank you for appreciating that we discussed this in our paper. Regarding the computational requirements, see also our response to reviewer QtCd question 1.
- W2: While several reviewers li... | Summary: This work considers constrained allocation tasks, like portfolio optimization or server scheduling and focuses on how to do policy optimization while respecting the given constraints.
In these problems, the core problem is that sampling over the polytope is challenging.
The work puts forward an approach for... | Rebuttal 1:
Rebuttal: Thank you for the positive review and helpful comments. We are happy to address your questions below:
- W1: The novelty of our approach lies in utilizing the properties of the polytope to efficiently parameterize a stochastic policy over it in an autoregressive way that can be optimized using st... | Summary: The paper presents a method for a specific setting of constrained RL method. The setting deals with constraints on the simplex of action space. The authors motivate this setting with resource allocation problems. The proposed method uses sequential conditional sampling of actions to impose constraints and also... | Rebuttal 1:
Rebuttal: Thank you for the positive review and helpful comments. We are happy to address your questions. We try to be as detailed as possible given the 6k limit.
- W1/Q1: Please see our general response regarding the theoretical guarantees.
- W2: Thank you for the suggestion. To assist readers in underst... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and constructive feedback. We will integrate many suggestions in the paper and are happy that our approach has been well received. Specifically, we are glad that reviewers find that:
- our paper is well-written and clearly presented:
- “PASPO is presente... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An engine not a camera: Measuring performative power of online search | Accept (poster) | Summary: The authors describe performative power, a pre-existing proposed measure of platform market power, and give an approach to measuring performative power using a browser extension. The browser extension perform random modifications to the search results page of results from target search engines, and measure cli... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback and comments. We hope to provide additional clarification and address your questions in the following.
**W1: Source code.** The extension has been published in the Chrome store. The code can be inspected using the developer console in the Chrome browser. We d... | Summary: This paper describes an online experiment seeking to measure how much power online search provides have in terms of impacting what content people consume. In short, the study attempts to measure the causal effect of small ranking rearrangements on click-rate for a population of web users. The authors present e... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback on our work. In the following we first discuss your questions and then provide some thoughts on the additional comments below.
**Applying method to other use cases.** This is an interesting point we have not discussed in our work. An important feature of our de... | Summary: The paper titled "An engine not a camera: Measuring performative power of online search" presents a study on the performative power of online search engines, specifically focusing on how they can influence web traffic through the arrangement of search results. The authors designed and executed an experiment to... | Rebuttal 1:
Rebuttal: Thank you for the feedback. Let us explain why we see the focus on measurement (rather than qualitative modeling) as an important opportunity of our approach, rather than a weakness.
**Qualitative insights.** We agree that qualitative insights are highly valuable. However, it is possible to measu... | Summary: The authors conduct a user study on the performative power of search engines, i.e., how much search engine providers can affect the information seen by the end user by tweaking the algorithmic ordering of results. In the specific context and assumptions formulated in this paper, this essentially amounts to mea... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We are glad you enjoyed reading the paper. We hope to clarify your questions by providing additional context related to the comparison points you mentioned. We will incorporate these discussions in the manuscript for a future version.
**Other types of biases.** As you... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the feedback. The attached PDF contains an additional robustness evaluation to support the author-reviewer discussion. We address questions below, responding individually to all reviewers.
Pdf: /pdf/8b1a1f50cf99873bcc04b527349d96274dd00a77.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency | Accept (poster) | Summary: The paper provide a fine-grained price of anarchy analysis for autobidders with non-uniform bid-scaling strategies in first price auctions, showing that first price auctions are more efficient when autobidders are less powerful and are more efficient with more balanced slices.
Strengths: 1. Concrete theoretic... | Rebuttal 1:
Rebuttal: Thank you for your insightful and detailed comments.
**Slice-based model**: first we'd like to note that our model is consistent with prior work on non-uniform bid scaling, e.g., "Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study" by researchers at Google. In ligh... | Summary: The paper considers the efficiency of auto bidders in first price auctions that use different shading factors on different slices of the items. Claims to show that the improved efficiency of the multi-slice system yields worse social welfare in equilibrium.
Strengths: The topic of auto-bidding and efficiency ... | Rebuttal 1:
Rebuttal: Thank you for your comments.
In words: all bidders are symmetric and use the same slices, which are given exogenously. Other than slices, we use the standard multi-item model, where essentially all items arrive at once at the very beginning, with the values given exogenously and publicly known (... | Summary: This paper investigates why non-uniform bidding in first-price auctions causes inefficiency while uniform bidding does not. The authors propose a new model that partitions auctions into slices. Bidders are allowed to bid differently across different slices but need to bid uniformly in each slice. They characte... | Rebuttal 1:
Rebuttal: Thank you for your insightful and detailed comments.
**Bidders have different slice partitions**: first we'd like to note that our model is consistent with prior work on non-uniform bid scaling, and in particular, our results can be viewed as a theoretical explanation for the recent empirical stu... | Summary: This paper studies the efficiency, measured by the price of anarchy, of a multi-round single-item first-price auction involving multiple bidders. The auctions are segmented into multiple slices, within each of which all bidders utilize uniform bidding strategies. While the bidding parameters of each bidder mus... | Rebuttal 1:
Rebuttal: Thank you for your insightful and encouraging comments.
**Practicality of (per-slice) uniform bidding**: while autobidders in reality are presumably more sophisticated, there is evidence that the advertising industry finds per-slice uniform bidding a reasonable approximation. In particular, the r... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Attack-Aware Noise Calibration for Differential Privacy | Accept (poster) | Summary: The paper proposes calibrating the noise in privacy mechanisms directly to MIA success metrics like advantage or TPR at low FPR instead of calibrating to a given $(\epsilon, \delta)$-bound and converting that to bounds on MIA success. The paper develops an algorithms for direct calibration for various MIA succ... | Rebuttal 1:
Rebuttal: We would like to thank you for such a detailed reading of our work, and especially the proof! We appreciate it.
## Theorem 3.4
> **Q1.** In the reasoning around lines 673-675, why is it not possible that $\alpha$ just happens to be one of the possible values of Eq. (42)? On line 659, it is said ... | Summary: This paper proposes new methods for calibrating noise in differentially private learning to achieve a given level of operational privacy risk, specifically focusing on the advantage and FNR/FPR of membership inference attacks. The methods reduce the noise scale compared to the standard two-step procedure (firs... | Rebuttal 1:
Rebuttal: Thank you for the review and suggestions!
**Weaknesses**
> I suggest the authors include more downstream tasks and utility metrics to further demonstrate the effectiveness of the theoretical results.
We have added a new experiment on a common use case of DP – private histogram release – which s... | Summary: This paper introduces a new method for improving the utility of privacy-preserving machine learning without sacrificing privacy protection. The authors develop efficient algorithms to calculate the trade-off curve between attack FPR and FNR using f-differential privacy (f-DP). They then show how to use this i... | Rebuttal 1:
Rebuttal: Thank you for your review and suggestions. We address some of points raised directly below, but they are also partially covered in our general response, to which we refer when relevant.
> The notation in this paper is heavy, could you provide notation tables?
We agree. See the [general response]... | Summary: Differential privacy (DP) mitigates privacy risks in machine learning by adding noise during training, balancing privacy and utility. Traditionally, the noise scale is set using a privacy budget parameter ε, which is then translated to attack risk. This two-step method often results in conservative risk assess... | Rebuttal 1:
Rebuttal: Thank you for the review and the suggestions! We respond to the comments and questions next. Note that we also address some of them in the general response, and we refer to it when relevant.
> The paper includes numerous definitions and symbols, which can be confusing for readers. Creating a tabl... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and feedback. We are glad the reviews found that our framework addresses an important technical problem (pcXQ), provides significant theoretical contributions (pcXQ), and compelling insights (JyJW). We are also glad that the reviews appreciat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Faster Algorithms for User-Level Private Stochastic Convex Optimization | Accept (poster) | Summary: This paper revisits the user-level private stochastic convex optimization (SCO) problem, where each user can possess multiple data points. The contributions of this paper are in two aspects:
1. They propose a linear-time algorithm that attains the same excess risk as the prior linear-time algorithm, but with ... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful feedback and positive assessment of our work. We respond to your comments below.
>*In the conclusion, it is stated that whether a linear-time algorithm with optimal risk exists for smooth losses is an open question. What about non-smooth losses?*
Good que... | Summary: This paper proposes new mechanisms for private SCO with user-level DP. Their approach extends prior work on this topic, and reduces the gradient complexity while attaining optimal excess risk. Three algorithms are proposed (1) a linear time algorithm that pushes state of the art (but does not achieve optimal... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful feedback and assessment of our work.
First, we would like to kindly remind you that our work focuses on understanding theoretical complexity bounds for a fundamental problem. This is both an important goal in its own right and also lays the foundation for... | Summary: This paper considers stochastic convex optimization under user-level differential privacy.
Algorithm 1 achieves the previous state-of-the-art excess risk of the linear-time user-level DP algorithm with milder assumptions. The algorithm is based on item-level DP-SGD algorithms. Previous paper AL24 shows that w... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful feedback and your positive assessment of our work. We respond to your comments below.
>*Section 1.1 may contain more information about the intuition of the novel techniques (e.g., what may be the key reason that outlier-iterate removal is better than outl... | Summary: This paper proposes new algorithms for stochastic convex optimization under user level differential privacy. This paper improves the computation complexity. The first algorithm achieves linear time complexity with suboptimal risk bound (the risk is SOTA among all linear time algorithms). The second algorithm a... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful feedback and your positive assessment of our work. We respond to your comments below.
>*Main ideas of the second algorithm (Section 3 in the paper)*
Our second algorithm is **inspired by the item-level accelerated phased ERM algorithm of [KLL21]**. Their... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contrastive losses as generalized models of global epistasis | Accept (poster) | Summary: This paper studies how the contrastive loss improves the estimation of fitness functions over the MSE loss and contributes in the following ways: (1) With noiseless data in Section 3.1, they found that the contrastive loss can estimate not only the correct ranking of fitness but also the exact fitness values; ... | Rebuttal 1:
Rebuttal: **Summary**: We thank the reviewer for their thoughtful and helpful comments, especially their recognition that our approach is a simple way to model epistasis without having to make explicit assumptions about the shape of the non-linearity (which is a limitation of current treatments). We addres... | Summary: The paper proposes using contrastive loss, specifically the Bradley-Terry (BT) loss, as an alternative to Mean Squared Error (MSE) for training global epistasis models in fitness prediction tasks. Experiments conducted on both complete and incomplete data sets, as well as benchmarked on FLIP tasks, show that t... | Rebuttal 1:
Rebuttal: **Summary**: We are deeply confused by the main criticism. Our focus is on supervised regression of fitness functions and Gene Ontology, Enzyme Commision and fold prediction are not fitness regression tasks. Rather, they are classification tasks on attributes of sequences mostly unrelated to fitne... | Summary: The authors study global epistasis models which are used to understand the fitness landscapes of biological sequences. They start with the observation that global epistasis is observed in real-world systems as a monotonic non-linear transform of an underlying fitness function on a genotype/sequence. The fitnes... | Rebuttal 1:
Rebuttal: **Summary**: We thank the reviewer for thoughtful and constructive feedback. As mainly bioML practitioners, we are bringing this work to NeurIPS partially to spawn discussion on theoretical connections. So far our efforts to create stronger theoretical connections have not yielded results, and the... | Summary: This paper addresses the problem setting of modeling and extracting the sparse interactions found in global epistasis, and proposes that the Bradley-Terry loss -- a loss used in ranking problems -- is a good alternative to MSE for ranking prediction in such settings, as it does not require assumptions on the f... | Rebuttal 1:
Rebuttal: **Summary**: We thank the reviewer for the positive and constructive feedback, especially their recognition that our work is in an area of bioML that, while not trend-following, would benefit the NeurIPS community in cross-pollinating theoretical and practical advances. Given that we are focused ... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing thoughtful comments. We are particularly encouraged by reviewer iMMK’s view that this paper would offer a somewhat unconventional, but valuable, perspective to the NeurIPS’ bioML community. We also recognize that multiple reviewers questioned the sufficiency of... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This study assumes a global epistasis model, where the observed experimental data represents a monotonic nonlinear transformation of underlying fitness, and explores the problem of learning these underlying fitness functions. Instead of directly learning fitness functions using Mean Squared Error (MSE) loss, t... | Rebuttal 1:
Rebuttal: **Summary**: We thank the reviewer for their comments. The reviewer’s ask to compare our argument for a generalized *supervised* loss function with unsupervised models suggests that we have miscommunicated the point of the paper. This paper is aimed at providing a straightforward, flexible way of ... | null | null | null | null | null | null |
Rethinking Optimal Transport in Offline Reinforcement Learning | Accept (poster) | Summary: This paper provided a novel view of offline RL, which roughly is maximizing return while keeping close to the data, as OT problem. The key contribution is demonstrating that partial OT can effectively address the challenge of stitching—a fundamental issue in offline RL—both theoretically and empirically.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your review and for a positive assessment of the paper! Your valuable feedback will help us improve the manuscript! Please find below the answers to your questions.
**Q1: In your method, stitching is done by the dynamic programing, application of bellman operator and your contributi... | Summary: The authors propose a novel perspective for offline reinforcement learning by formulating offline reinforcement learning as a partial optimal transport problem. They view the policy $\pi$ as a transport map from the state distribution $d^\beta$ to $\beta(\cdot\mid s)$ and show that the dual form of the partial... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and provide useful feedback. Your questions will help us improve the manuscript. Below are the answers to your questions. Please let us know if any issues remain!
**Q1: The authors formulate offline RL as a partial optimal transport problem betwe... | Summary: The authors address the problem of offline RL. They rethink offline RL using optimal transport. In offline RL, often the datasets consist of several sub-optimal trajectories that are needed to be stitched together. The authors use partial OT to incorporate stitching and a maxmin formulation of this partial OT.... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions. We have managed to address and improve the paper based on them. Below, we address each of your concerns: we include the W-BRAC comparison, provide an explanation of the conjunction, clarify the OT motivation, and provided details on the behavior policy visi... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity | Accept (poster) | Summary: This paper provides an in-depth error analysis of of kernel ridge regression (KRR) and truncated KRR (TKRR), where one replace the original kernel $K$ with its finite $r$-dimensional approximation $K^T$, such that their regressors $\hat{f}$ and $\hat{f}_r$ agree on the dataset. It is well known that KRR suffer... | Rebuttal 1:
Rebuttal: We appreciate your acknowledgment and positive feedback on this work. Your valuable suggestions and comments are very helpful and significantly improve this paper. Below are our point-to-point replies.
**Weakness 1 \& Question 1 (Part I): More discussion on the computational complexity of TKRR.*... | Summary: This paper investigated the impact of alignment between the target function of interest and kernel matrices.
To overcome the saturation effect, the TKM was introduced and its learning rate is analyzed.
Strengths: This paper is well-written and the theorems are solid.
This paper analyzed different alignment l... | Rebuttal 1:
Rebuttal: We appreciate your time in reviewing our work and thanks a lot for your question and valuable comments. We have carefully addressed your question below and provided some results for the exponential decay rate.
Indeed, Assumption 3.4 is needed if we want to derive the explicit upper bound of the a... | Summary: This paper conducts a comprehensive theoretical analysis on the learning rate of kernel-based machine learning methods under a general setting. It establishes the upper bounds for standard kernel-based estimators, and demonstrates that standard kernel-based estimators suffer from saturation effect at high targ... | Rebuttal 1:
Rebuttal: Your insightful comments and constructive suggestions are highly valued by us, and greatly contributed to the revision of this work. Below are our point-to-point replies.
**Weakness 1: TKM considered in this paper is not a very novel approach.**
**Answer:** Thank you very much for your comment.... | Summary: In this paper the authors consider both truncated kernel-based method (TKM) and standard kernel-base method (KM) and how their performance is affected by the target-kernel alignment (a.k.a. the smoothness of target function in RKHS). The authors show that they have the same effects in weak and just-aligned reg... | Rebuttal 1:
Rebuttal: Thank you very much for your nice summary and precious comments on this paper. Below are our point-to-point replies.
**Weakness 1: Some of the findings are also covered in Amini et al. (2022).**
**Answer:**
Thank you very much for your comment. We agree with you that some findings in this paper... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for your insightful comments and for the time you have dedicated to thoroughly reviewing our work. Your valuable and constructive feedback has significantly contributed to enhancing the quality of our work.
We have carefully considered all comments, concern... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates the impact of target-kernel alignment to mitigate the saturation effect, where the learning rate of kernel ridge regression plateaus when the smoothness of the target function exceeds certain levels. The kernel complexity function is used to establish the upper bounds for both the stand... | Rebuttal 1:
Rebuttal: We appreciate your time and great efforts in reviewing our paper and thanks a lot for your constructive comments and suggestions. Below are our point-to-point replies.
**Question 1 (part I): Can you clarify how the choice of loss function impacts your theoretical results?**
**Answer:**
Thank y... | null | null | null | null | null | null |
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment | Accept (poster) | Summary: The paper introduces DFA-GNN, a new framework designed to train Graph Neural Networks (GNNs) using Direct Feedback Alignment (DFA). Traditional methods like backpropagation (BP), though effective, have several limitations, including inefficiency, scalability issues, and a lack of biological plausibility. DFA-G... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the time taken to assess our work and for the valuable feedback. We address each point individually. “W/Q” numbers the weakness or question followed by our response.
$\textbf{\large Response to W1:} $
Thanks for your comments. Our method builds on some ingenio... | Summary: The authors propose to apply the Direct Feedback Alignment (DFA) algorithm for backpropagation-free training to graph neural networks. The DFA algorithm is combined with a pseudo-error generation mechanism to provide additional error signals for missing targets in the setting of semi-supervised node classifica... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and positive assessment of our work. After carefully reviewing your feedback, below we provide answers to the comments you raised.
$\textbf{\large Response to W1:} $
The standalone direct feedback alignment formulated in Eqs.5 and 6 could be faster than BP... | Summary: Recently, some studies have been exploring new optimization methods that can replace backpropagation, and one of the popular methods is direct feedback alignment (DFA). This paper adapts DFA to graph neural networks (GNNs). It replaces the gradient with a randomly initialized matrix multiplying the prediction ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and positive assessment of our work. Below, we provide individual responses to your questions.
$\textbf{\large Response to W1:} $
We take the GCN with ReLU activation function as an example to elaborate on our method since GCN is one of the most classic G... | null | null | Rebuttal 1:
Rebuttal: $\definecolor{mybgcolor}{RGB}{249,242,244}$
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Dear ACs, PCs and all reviewers,
We would like to express our grat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms | Accept (poster) | Summary: The present work studies the last iterate convergence of learning algorithms in zero sum games. Existing results have shown that although OMWU and OGDA both enjoy the $O(1/T)$ ergodic convergence rate, their best existing last iterate convergence rates exhibit a nontrivial gap. This paper constructs a particul... | Rebuttal 1:
Rebuttal: Thank you for the positive review and the constructive comments on the presentation. We will incorporate your suggestions in the revised version of the paper. Specifically, we will update the flow in section 3 and make Assumption 2, the discussion, and the proof sketch more reader-friendly. Below,... | Summary: The paper investigates the last-iterate convergence properties of several classes of algorithms employed in zero-sum matrix games. As a core contribution, it is shown that a large class of iteration rules can not exhibit instance-independent convergence in zero-sum games of dimension 2. The result is further g... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that we made a significant contribution to an important question and for the constructive comments on the presentation. We will incorporate your suggestions and improve the presentation in the next version of the paper. Below, we address your questions.
Q1: *Is it pos... | Summary: This paper shows a limitation of optimistic multiplicative weights (OMWU) with a fixed learning rate: the last-iterate convergence rate can be arbitrarily large, depending on a game-dependent constant in normal form games. This demonstrates that the current upper bounds on convergence are not loose and that th... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments!
Q: *Do you think using time-dependent learning rates might improve the last-iterate convergence of OMWU? If so, maybe it's worth discussing it or writing it as a limitation.*
A: We conjecture that slow last-iterate convergence of OMWU persists for time-de... | Summary: The authors, through some hard instances of games study the fundamental differences in convergence of broadly two classes of algorithms which are OOMD and OFTRL. Particular algorithms of interest include the OGDA and the OMWU algorithm respectively. They show that OFTRL (in particular OMWU) necessarily exhibit... | Rebuttal 1:
Rebuttal: Thank you for the positive review! Below, we address your questions.
Q1: *Please clarify whether Assumptions 1 and 2 are satisfied by the specific regularizers such as entropic, euclidean and log or general regularizers that are 1-strongly convex wrt to $\ell_2$ norm.*
A: We clarify that we onl... | Rebuttal 1:
Rebuttal: **Discussion on Dynamic Step Sizes**: Our negative results hold for OFTRL with *fixed* step sizes. We conjecture that the slow last-iterate convergence of OFTRL persists even with *dynamic* (time-varying) step sizes. In particular, we believe our counterexamples still work for OFTRL with decreasin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying Selections for Unsupervised Subtask Discovery | Accept (poster) | Summary: This paper addresses offline subtask discovery from a causal perspective by identifying subgoals as selections, targeting at solving long-horizon tasks and acquiring transferrable skills. The algorithm design is well-motivated and shows superior performance in offline subtask discovery.
Strengths: (a) The cau... | Rebuttal 1:
Rebuttal: We are profoundly thankful for your valuable feedback and the time you have dedicated to reading it, as they will surely improve the quality of this manuscript. In light of your suggestion, we incorporated additional discussions, as well as experiments to demonstrate the generalizability of our me... | Summary: The paper studies the subtask decomposing problem. The paper proposes a formal definition of subtasks as the outcome of selections. The proposed seq-NMF is introduced to learn the subgoals and extract subtasks, conforming with the proposed theory. The experimental results show strong results on transferring to... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and the constructive insights you provided, which will undoubtedly enhance the quality of our manuscript. In response to your comments, we have included several new discussions in the revised manuscript, and additional experiments, particularly addressing more ge... | Summary: This paper studies the problem of decomposing expert trajectories (in the context of imitation learning) into sub-trajectories corresponding to subtasks. First, the authors introduce a causal framework to understand and explain what subtasks mean in this context. Subtasks are then defined to be variables that ... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our work and for your insightful comments. Your valuable feedback has significantly enhanced the clarity of our manuscript.
**Q1**: *“One primary weakness is that some parts of the paper lack clarity. For instance, the mathematical objec... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for your dedicated time and insightful comments. We are happy to see that the novelty of this work is well-recognized by all reviewers, which lies in the idea of identifying subtasks as selections, as well as the soundness of the corresponding algorithms. We ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Challenges with unsupervised LLM knowledge discovery | Reject | Summary: This paper reveals novel pathologies in existing unsupervised methods aimed at discovering latent knowledge from large language model (LLM) activations. Instead of extracting knowledge, these methods tend to identify the most prominent features of the activations.
The paper theoretically demonstrates that arb... | Rebuttal 1:
Rebuttal: We thank the reviewer for their considerations and for highlighting how our work is useful and contains extensive experiments.
Weaknesses:
- We already considered three LLMs. We are somewhat limited by access to model internals to carry out this work (e.g. we can’t use APIs) and by licensing restr... | Summary: This paper presents a careful study on existing methods for discovering the latent knowledge from large language models (LLMs), especially Contrastive-Consistent Search (CCS). The authors prove that CCS might not actually discover the knowledge of LLMs, instead, it could fit any features that satisfy certain c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and for highlighting that our work provides useful information to the community about unsupervised knowledge discovery.
Weaknesses:
- Prompt sensitivity of CCS and other unsupervised methods (including PCA): this is a key point we were making in our pape... | Summary: This paper studies the failure modes of the method called "constraint-consistent search (CCS)" in knowledge discovery for language models. In particular, they showed: there is no unique identification on the minimizer of CCS, as there are a class of features achieves the optimal loss; demonstrated experimental... | Rebuttal 1:
Rebuttal: We thank the reviewer for their analysis. The reviewer mentions in the strengths that the CCS method is popular, but suggests in the weaknesses that analysis showing shortcomings of the method is unlikely to have long-term benefit, which seems slightly contradictory.
Weaknesses
- Focus on CCS: se... | null | null | Rebuttal 1:
Rebuttal: A common theme in the reviews was that it challenges only a particular method, CCS. In fact, our experiments involved multiple other unsupervised methods which all suffer the same issue, which is a general issue, and a hard one to solve. Similarly common was that our paper should have aimed to sol... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling | Accept (poster) | Summary: As the paper clearly outlines in the introduction, Optimal Transportation (OT) is widely used in various fields of machine learning, however, the cost for computing OT is computationaly expensive (quadratic scaling), even after Sinkhorn algorithm employing entropy regularization significantly alleviated the co... | Rebuttal 1:
Rebuttal: We thank reviewer 1xw8 for their careful reading of our work, and for their feedback.
> What this reviewer found missing is discussion of the case the optimal transport map is itself not low-rank. It is well-known that the optimal transportation plan is not necessarily low-rank (as indicated e.g.... | Summary: This work introduces a new low rank formulation of optimal transport based on the latent coupling decomposition introduced in https://arxiv.org/pdf/2012.11589 . Compared to previous formulations of low rank OT, this formulation allows easier extensions to unbalanced and Gromov Wasserstein settings.
Strengths:... | Rebuttal 1:
Rebuttal: We thank reviewer CGKE for their careful reading of our work, and for their feedback.
> from section 3.1 to 3.2, the new aspects of the approach compared to [https://arxiv.org/pdf/2012.11589](https://arxiv.org/pdf/2012.11589) should be stated more clearly. The contributions of the paper should be... | Summary: The paper introduces a novel framework called Factor Relaxation with Latent Coupling (FRLC) which is based on coordinate mirror descent to compute the low-rank LC factorization.The algorithm decouples the optimization into three sub-problems, offering greater flexibility, interpretability, and linear space com... | Rebuttal 1:
Rebuttal: We thank reviewer pnTm for their careful reading of our work, and for their feedback.
> While the empirical results are good, the paper could benefit from more extensive comparisons with additional baseline methods to solidify the claims of superiority.
The only OT methods solving for a low-rank... | Summary: The paper presents an approach for low-rank optimal transport (OT) leveraging a latent coupling (LC) factorization and solving it with mirror descent. This approach offers a new parameterization of the low-rank OT problem, providing advantages such as decoupling the problem into three OT problems and enhancing... | Rebuttal 1:
Rebuttal: We thank reviewer jx3E for their careful reading of our work, and for their feedback. We have abbreviated your questions due to space limitations for our responses.
> [I found ... novel method.]
Thank you for this comment indicating an area where we can strengthen our presentation. Our FRLC algo... | Rebuttal 1:
Rebuttal: Here we include information for all reviewers. We thank each reviewer for their helpful feedback and questions.
> Regarding time complexity raised by reviewers jx3E, CGKE:
The time complexity of FRLC $O(BLr^{2}(n+m))$, where $B$ is the number of Sinkhorn iterations, $L$ the number of mirror desc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fixed Confidence Best Arm Identification in the Bayesian Setting | Accept (poster) | Summary: This paper considers the best arm identification (BAI) problem in the fixed confidence setting and in the Bayesian setting, where the mean rewards of each arm is drawn from a known prior distribution. The authors formulate the problem and discuss the related literature. The authors provide the lower bound of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable review and comments. The following are the responses to the questions you raised.
> Q1) better upper bound using lil-UCB
We assume your question refers to the improvement of the confidence bound from $\sqrt{\log(N_i(t)/\delta)/N_i(t)}$ to $\sqrt{\log(\log(N... | Summary: This paper considers a best arm identification problem in Bayesian multi-armed bandit setting. The arms' distributions are generated according to the unknown prior, and the probability of error is averaged over this prior distribution. The paper makes two key contributions: first, a lower bound characterizing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the paper and the insightful comments. In the following, we address the question raised.
> The definition of ${\mathcal{H}}_{\mu}$ at the beginning of page 4 may not be precise...is it an event or a $\sigma$-algebra? In the former case, with contin... | Summary: In this paper, the focus is on the fixed-confidence best-arm identification problem within a Bayesian setting. The objective is to determine the arm with the largest mean with a certain confidence, given a known prior distribution. Existing work in FC-BAI is mostly in the frequentist setting. This paper shows ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the paper and the insightful comments. We will revise the typos and references in our final version. In the following, we address the main questions raised.
> W2) The authors do not discuss how they derived the constant L(H) and its significance. S... | Summary: The paper studies the problem of FC-BAI; the goal is to find the arm with largest mean with a given probability of correctness. It analyzes FC-BAI for a Gaussian bandit model where the arms reward mean is sampled from a known prior and the reward variances are known. It proves other (frequentist) FC-BAI algori... | Rebuttal 1:
Rebuttal: We are truly grateful for the encouraging evaluation and valuable comments. The following is the response to the question you raised.
>In Thm 4, the constants in the lower bound seem very small ($\frac{1}{16e^4}\approx 2^{-8}$), so it could entail a trivial lower bound in most cases, especially s... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers are Minimax Optimal Nonparametric In-Context Learners | Accept (poster) | Summary: This paper analyzed in-context learning of a transformer consisting of a DNN and a linear attention layer pretrained on nonparametric regression tasks. The authors derived a general bound on the generalization error consisting of the approximation error, in-context generalization error and pertaining generali... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and positive assessment of our theoretical contributions! Here are our responses to the comments.
**Weakness 1 & Limitations.**
While the reviewer mentioned in the Limitations section that our model does not include the MLP layer, we find it illuminating to con... | Summary: This paper explores the ICL capabilities of transformers from a statistical learning theory perspective. It focuses on transformers with a deep neural network and a linear attention layer, pretrained on nonparametric regression tasks from function spaces like the Besov space and piecewise gamma-smooth class. T... | Rebuttal 1:
Rebuttal: Thank you for your through review and helpful suggestions, which have helped us greatly to improve our paper!
**Response to Weakness 1.**
Unfortunately, Section 5 was completed last minute which resulted in the flow of the paper being rather disjoint. Together with improved lower bounds, we rest... | Summary: This work shows that ICL can perform non-parametric regression at an optimal rate.
Section 3 gives an upper bound on ICL error in terms of a metric entropy of the representation class. Section 4 instantiates the bound for DNN representations and shows it to be optimal. Section 4.3 explores ways to reduce the ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and insightful questions! Our responses are as follows.
* **It would be helpful to instantiate a target function class in the main paper, specify what $\alpha$ is, etc.**
(We answer this question first to help in understanding our paper.) **Throughout Section 4,... | Summary: This paper studies in-context nonparametric learning using transformers. In the setting used in the main result of the paper, the transformer is trained on a dataset consisting of multiple sequences/tasks. For each task, the target function $F_\beta$ is drawn from the span of a certain countable set of functio... | Rebuttal 1:
Rebuttal: Thank you for your through review of our work! We hope our responses can help clarify some important points.
**Weakness 1.**
Theorem 3.1 is indeed a standard result, as we have indicated it is a straightforward adaptation of Lemma 4 of Schmidt-Hieber (2020). While it is only the first step of th... | Rebuttal 1:
Rebuttal: ## 1. Extending to Multiple/Nonlinear Attention
As reviewers have noted, our setup builds on previous works which establish ICL of a single attention layer as linear regression in order to extend to complexity analysis of nonparametric regression. ICL of multiple/nonlinear attention layers can be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DALD: Improving Logits-based Detector without Logits from Black-box LLMs | Accept (poster) | Summary: This paper proposes a framework named Distribution-Aligned LLMs Detection (DALD) to improve the performance of surrogate models in detecting LLM-generated text from both closed-source and open-source models. The method enhances detection performance by aligning the distribution of the surrogate model to better... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review. Please check our response to your concerns.
**Response to Weakness 1**:
First, regarding the concern of the better performance of the model trained on multiple source data, a reasonable explanation could be that the optimization space of logits is la... | Summary: This paper proposes a method to improve black-box detection of machine-generated text, tackling the problem of performance degradation when a surrogate model's output is poorly aligned with the closed-source target LLM. By LORA fine-tuning the surrogate model on text generated by the target model, the authors ... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive and detailed reviews. We provide detailed responses to your concerns.
**Response to the question about different domains**:
We appreciate your interest in the effectiveness of DALD for different domains. It's worth noting that our training data is entirel... | Summary: This paper introduces Distribution-Aligned LLMs Detection (DALD), a novel framework for detecting AI-generated text from large language models (LLMs). DALD addresses limitations of traditional detection methods, particularly when dealing with black-box or unknown LLMs. It aligns surrogate models with unknown t... | Rebuttal 1:
Rebuttal: Thanks a lot for your detailed and careful reviews. We will give our response to your questions.
**Response to Weakness 1**:
We appreciate the suggestion of comparison with Ghostbuster and adding experiments comparing our method to Ghostbuster. We utilize the official code of Ghostbuster and ev... | Summary: The paper addresses the challenge of detecting machine-generated text from black-box LLMs without access to their logits. Traditional methods using surrogate models suffer from performance degradation due to misalignment with target model distributions, particularly as new models are introduced. The proposed D... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed reviews. Here are our responses to your questions:
**Response to Weakness 1**:
The detection of black-box commercial LLM models is a critical topic. The rapid updates in these models present a significant and ongoing challenge, as they lead to decreased det... | Rebuttal 1:
Rebuttal: **PDF Pages**:
This PDF page includes the ROC curves comparison with DNA-GPT and Fast-DetectGPT. We would like to invite all reviewers to check the Figure in the page. Thanks a lot.
Pdf: /pdf/725ff24e4c1b8b9a91534d4955a834ff3d79eaac.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation | Accept (poster) | Summary: This paper proposed PartCLIPSeg for open-vocabulary part segmentation. This framework leverages generalized parts and object-level contexts for generalization in fine-grained parts, integrating competitive part relationships and attention control techniques. Extensive experiments on three datasets demonstrate ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and valuable suggestions.
## **Weakness 1**: Fig. 3 Refinement
We refined Fig. 3 to make it clearer and to effectively emphasize our contribution.
Specifically, we had made modifications to distinguish between the modules involved in training, fine-tuning, and t... | Summary: This paper proposes PartCLIPSeg, which builds upon the CLIPSeg model and extends it to handle the unique challenges of part segmentation. This paper introduces several components to address the challenges in OVPS.
1. Generalized parts with object-level contexts: This approach combines generalized part informa... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions.
## **Weakness 1.1**: Impact of Object-Level and Part-Level Labels
We conducted additional experiments to verify the impact of object-level and part-level pseudo-labels.
We varied $\lambda_1$ and $\lambda_2$ in Equation 5, setting each to 0... | Summary: This paper identifies three key problems of current open-vocabulary part segmentation, namely, lack of generalization, ambiguous boundaries, and missing underrepresented parts. The paper then proceeds to propose solutions to fix these problems. Experimental results show that the proposed method outperforms pre... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions.
## **Weakness 1**: Discussion of Previous OVPS
We have now included a performance comparison with VLPart (ICCV 2023).
VLPart is a pioneering study in Open-vocabulary Part Segmentation (OVPS).
VLPart focuses on instance segmentation with ma... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate all reviewers for their thorough and insightful feedback.
The valuable reviews have significantly enhanced the overall delivery of the proposed method.
We are particularly thankful that all reviewers (Vaxb, LDit, KLx2) found our paper show promising result... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling | Accept (poster) | Summary: The paper analyzes the intricate relationship between optimal learning rate and batch size scaling for adaptive optimizers, such as Sign SGD and Adam. Building on prior analysis for SGD by McCandlish (2018), this work reveals a non-monotonic relationship between optimal learning rates and batch size. The optim... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful reviews. Below, we address your comments in detail.
> W1.
A1. Due to paper page limitation, we were unable to delve deeply into references [1-2]. We acknowledge this limitation and will provide a more com... | Summary: The paper presents a heuristic analysis of the scaling of the optimal learning rate with batch size for Adam-style optimizers in the framework of [1]. The analysis is accompanied by experiments to support the predictions. Notably the authors demonstrate that the optimal learning rate can decrease with batch si... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your valuable insights. We address your comments as follows.
> W1. The presentation is not great. A lot is assumed from [1], but it would make reading easier to make things more self-contained.
A1. Due to page constraints, we currently in... | Summary: The paper gives an optimal choice of learning rate and batch size for neural networks. Different from the previous results on SGD-style optimizers. The authors give such solutions for Adam-style-optimizers.
Strengths: 1. Batch size and learning rate will affect the performance largely and cost a lot to selec... | Rebuttal 1:
Rebuttal: We are grateful for the time and effort you have taken in reviewing our paper and for your thoughtful feedback. Here, we respond to your comments.
> W1. It seems that Lemma 1 can only apply to quadratic problems. In the appendix, the relation is approximately equal. But in Lemma 1, it becomes "e... | Summary: This work provides a scaling law between learning rate and batch size for Adam. Namely, this work finds that the optimal learning rate increases and then decreases as the batch size becomes larger; and, the peak of this curve corresponds to the trade-off between training speed and data efficiency.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive suggestions. We address your comments in the following paragraphs.
> W1. I think this paper could benefit from experiments with more popular architectures for the NLP tasks (e.g. maybe it would be useful to include some experiments on tasks with... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your reviews and constructive suggestions. We have incorporated additional experimental analyses to strengthen our conclusions.
**Experimental Analyses**
We made experimental analyses on both sparse MoE and dense structures. For the sparse MoE structure, in Experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Equivalence Between Static and Dynamic Regret Minimization | Accept (poster) | Summary: This paper demonstrates that dynamic regret minimization can be reduced to static regret minimization by embedding the comparator sequence into a higher-dimensional space, allowing the use of static regret algorithms for dynamic settings. It establishes a trade-off between the penalties associated with loss va... | Rebuttal 1:
Rebuttal: > My major concern is the main titile could be somewhat overclaimed, as
> the proposed reduciton is restricted in OLO
We actually believe that our title is carefully worded to avoid
overclaiming: we do indeed present *an* equivalence between static and
dynamic regret --- it is one which holds in ... | Summary: The paper addresses the problem of minimizing dynamic regret. The main goal is to provide a unified perspective on the problems of minimizing both dynamic and static regret. The first key contribution, presented in Proposition 1, is an interesting and straightforward observation demonstrating a general reducti... | Rebuttal 1:
Rebuttal: Thank you for the positive review, we are glad you enjoyed reading our
paper!
> I would like to know which measures of variability the authors expect
> to obtain with different choices of that yield reasonable and
> significant regret bounds.
So far the literature mostly revolves around the (uns... | Summary: This paper studied dynamic regret minimisation in convex optimisation. First, they proved some interesting equivalence between dynamic regret and static regret on extended decision space. Based on this observation, they showed a lower bound which implying that the hypothesis that optimal dynamic regret scales ... | Rebuttal 1:
Rebuttal: > My question: The lower bound is
> $\tilde\Omega (\max\_{M}G\\|\tilde u\\|\_{M^{-1}}\sqrt{\mathrm{Tr}(M)})$,
> and the upper bound essentially is
> $\tilde O(\min\_{M}G\\|\tilde u\\|\_{M^{-1}}\sqrt{\mathrm{Tr}(M)})$.
There seems to be a misunderstanding of the quantification of $M$ in the
lower ... | Summary: This paper presents a reduction from the dynamic regret minimization for Online Convex Optimization (OCO) to a static regret minimization problem over an extended domain. Using this reduction, the authors establish a lower bound that highlights the trade-off between the variation of the comparator and the vari... | Rebuttal 1:
Rebuttal: > The abstract claims that \"we show that dynamic regret minimization is
> equivalent to static regret minimization in an extended decision
> space.\" This claim is somewhat misleading to me, as the proposed
> reduction only applies to the online linear optimization problem (or
> OCO with lineariz... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and taking the time to carefully read our paper. Below, we address
some of their common questions.
A common point contention in the reviews was the significance of the
average squared path-length dependence guarantee achieved in Proposition
3. ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The main contribution of the paper is showing that, by `lifting' an online linear optimization (OLO) problem to a higher dimensional space, the dynamic regret in the original setting is equal to the $\textit{static}$ regret achieved in the higher dimensional setting. While simple, this clean reduction allows e... | Rebuttal 1:
Rebuttal: > Do you believe there are any implications of your reduction that are
> particularly useful from a practical/deployment perspective, or do you
> see the contribution as being a strictly conceptual one?
We believe that the most important implication is that the reduction
allows us to use any pres... | Summary: This paper investigates the problem of dynamic regret minimization in unbounded domains. The authors propose a novel lossless reduction from dynamic regret minimization to static regret minimization by treating the changing comparators in dynamic regret as a fixed one in another decision space with higher dime... | Rebuttal 1:
Rebuttal: > The authors stated that $P_T$ based dynamic regret is not favorable enough with
> an overly pessimistic diameter quantity.
>
> The relationship between the current averaged squared length and the
> desired squared length is not discussed
Given that the smoothed version of squared path-length i... | null | null | null | null |
Continuous Partitioning for Graph-Based Semi-Supervised Learning | Accept (poster) | Summary: In this submission, authors proposed a framework for graph-based semi-supervised learning based on continuous nonconvex quadratic programming.
Strengths: This paper studies the graph-based semi-supervised learning problem, which has attracted many attentions. In this submission, authors proposed a framework ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. We address reviewer comments below and will edit the final version of the paper according to the reviewer’s comments.
***Motivation of CutSSL***
In contrast to Laplace learning, which is degenerate in the low-label rate regime, we start with ... | Summary: The paper considers the task of semi-supervised learning under cardinality constraints and proposes a framework based on a reformulation into a non-convex constrained quadratic program. The authors provide sufficient conditions for the exact recovery of integer solutions. Moreover, they present an algorithm ca... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments. Below we comment on the choice of the hyperparameter $s$.
***Choice of s***
Thanks for pointing this out. This is an important question to address and we will provide additional clarification in the final version of our paper. We highlight a resu... | Summary: The paper proposes an approach for graph-based semi-supervised learning which is based on a cardinality-constrained extension of the classical Laplacian learning approach due to Zhu et al. Known theoretical limitations of standard Laplacian in the low-label rate regime motivates the approach. Theoretical prope... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work and for their positive and constructive comments and for appreciating the significance of our work. We address reviewer questions and suggestions below.
***Choice of S***
We refer to the response to all authors. In summary, Proposition 3... | Summary: The paper introduces CutSSL, a novel framework for graph-based semi-supervised learning (SSL). The authors address the limitations of Laplace learning algorithms, particularly their poor performance at low label rates and in imbalanced class scenarios. CutSSL leverages continuous nonconvex quadratic programmin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive comments and for appreciating the significance and originality of our work. We address reviewer questions and suggestions below.
***Theoretical analysis in low-label rate regime***
We agree that this is an interesting and important directi... | Rebuttal 1:
Rebuttal: We thank all reviewers for carefully reading our work and for their constructive suggestions and comments. We have added individual responses and several additional experiments suggested by the reviewers. We will incorporate these in the final version of the paper. Below, we summarize the main con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Scalable and Stable Parallelization of Nonlinear RNNs | Accept (poster) | Summary: This paper aims to address the challenge of parallelizing the evaluation of nonlinear Recurrent Neural Networks (RNNs). Key contributions include the introduction of quasi-Newton approximations and trust region-based methods to reduce computational complexity and improve numerical stability, respectively. Thes... | Rebuttal 1:
Rebuttal: Firstly, we thank the reviewer for taking the time to review our submission and for their positive comments! We were particularly pleased by the comments on the foundation, clarity and presentation of our work.
> Weaknesses
We discuss these in global review (“empirical validation” in Section 2 ... | Summary: In this paper, the authors propose to improve DEER, a previous method that evaluates non-linear RNN in parallel by viewing it as a fixed point problem. Specifically, instead of using Newton's method to solve the fixed point problem, the authors leverage quasi-Newton methods to approximate the procedure. This a... | Rebuttal 1:
Rebuttal: Firstly we thank the reviewer for taking the time to review our submission and for their positive comments! We were particularly pleased by the comments on the foundation, clarity and presentation of our work.
The central reservation of the reviewer is that our experimental results are “thin”. I... | Summary: This paper presents improvements over the approach of Lim et al. last year for the parallelization of nonlinear recurrences in the sequence length. The paper jumps right into the problem after a quick introduction, and presents a few improvements on Deer: Quasi-Deer (an approximate method), IKE (stable but exp... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper, for their positive feedback, and for providing some very interesting discussion points!
> Approximation quality:
This is a great observation. We have added Review PDF Figure 1 probing the compute or computational budget required to... | Summary: This work extends the parallel RNN (DEER) method to improve its efficiency and stability. The authors make two main modifications:
1. Replace the full Jacobian matrix inverse with its diagonal, allowing for linear-time inversion.
2. Introduce damping via Kalman filter to enhance the stability of Newton methods... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and providing some helpful feedback!
> Limited experimental scope
We point the reviewer to the global response for additional experiments we have added:
- In Review PDF Figure 1 we study the sensitivity to the $\lambda$ parameter (... | Rebuttal 1:
Rebuttal: Firstly, we thank all six reviewers for their positive feedback and insightful comments.
We present methods for parallelizing the evaluation of non-linear RNNs, building on a recent method, DEER, from Lim et al. We first proved the global convergence of DEER. We then ameliorate DEER’s two major ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a stable and scalable method to parallelize evaluation of an RNN across the sequence length. Furthermore, the paper presents a sketch of when the more recently proposed DEER algorithm converges. Alongside these contributions, the paper presents experiments showing the efficacy of the propos... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting some interesting clarification points.
> The result proving…
This is a really interesting line of thought that we will clarify in the paper. There are lots of points that your comment touches on, so we will try and answer them sequentially:
**Re: General... | Summary: This paper addresses the problem of parallel computation in RNNs, to be able to tap into the full potential highly efficient parallel machines (GPUs) that are available today: A naive inference of a recurrent model would apply each layer on the current hidden state, thereby requiring the length of the sequence... | Rebuttal 1:
Rebuttal: Thank you for your thorough and positive review!
We refer the reviewer to the global response for discussion of an ablation study on $\lambda$ (r.e. “As authors list”) and limitations (r.e. “In responses to” and “As also mentioned”). We now respond to your other comments:
> Contributions: The m... | null | null | null | null |
Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning | Accept (poster) | Summary: This study is motivated by accounting bugs in a real-world setting. The authors focused primarily on the Flash Loan attack in smart contracts, which cost $50 million due to eight accounting bugs in DefiLlama.
The proposed method uses a Large Language Model (LLM), GPT3.5-Turbo, to trace a smart contract in th... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 2yGP
### Answers to Questions of Reviewer 2yGP
**Q1. How were the rules derived?**
We are smart contract auditors with years of experience. We have studied many business models (and their variants). These rules are invariants (i.e., pro... | Summary: The work proposes a system to detect accounting bugs in smart contracts by combining large language models (LLMs) and rule-based reasoning. The system first annotates the source code with LLMs to identify the parameters and global variables that are relevant to accounting bugs. Then, the system uses rule-based... | Rebuttal 1:
Rebuttal: ## Response to Reviewer uXwt
### Answers to Questions of Reviewer F88y
**Q1. False positive (FP) rate when bugs are unknown**
Please see C2 in the global response.
**Q2. Code snippets included during prompting**
We prompt ... | Summary: The paper introduces ABAUDITOR, a hybrid system that combines LLMs and rule-based reasoning to detect accounting bugs in smart contracts. It leverages the semantic understanding capabilities of LLMs and rule-based logic for validating operations.
Strengths: - The proposed method is computationally inexpensive... | Rebuttal 1:
Rebuttal: ## Response to Reviewer F88y
### Answers to Questions of Reviewer F88y
**Q1. How many times were the experiments run?**
Three due to the cost involved. We will clarify.
**Q2. Origin of the rules**
We are smart contract audi... | Summary: This paper proposes a system to detect accounting error vulnerabilities in smart contracts. The key idea is a hybrid approach that combines LLMs and rule-based reasoning. In particular, it prompts LLMs to annotate the financial meaning of variables in smart contracts, and employs rule-based reasoning to propag... | Rebuttal 1:
Rebuttal: ## Response to Reviewer zmms
### Answers to Questions of Reviewer zmms
**Q1. Replacing with rule-based reasoning with SMT solving and expressiveness of the reasoning technique**
It is possible to enhance our system with an SMT solver. In lines 346-348 of our su... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments.
## Common Concerns
**C1. Baselines**
According to [1] published in 2023, accounting bugs are beyond existing tools. That is why we did not compare our tool with others. Following the reviewers’ ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
decoupleQ: Towards 2-bit Post-Training Uniform Quantization via decoupling Parameters into Integer and Floating Points | Reject | Summary: The paper introduces decoupleQ, a novel method that decouples model parameters into integer and floating-point parts. This approach transforms the quantization problem into a mathematical constrained optimization problem, avoiding the limitations of traditional heuristic quantization methods. DecoupleQ achieve... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading of our paper and your generally positive comments. And thank you for your accurate summary and for highlighting our strengths. We will try to respond to your questions in as much detail as possible, and we would be grateful if you could point out any om... | Summary: This paper proposes a linear and uniform quantization method, decoupleQ, which abandons the traditional heuristic quantization paradigm and decouples the model parameters into integer and floating-point parts, then transforming the quantization problem into integer and floating-point part. Experiments show dec... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper, and we respond to your concerns as follows:
# Weakness 1:
As we put in line 83, ***we focus on weight-only quantization***.
In the era of large language models, weight-only quantization has important industrial value because during the inference process with late... | Summary: The paper presents decoupleQ, a post-training quantization method that improves the accuracy of quantized models, particularly at very low bit-widths (2-bit). It achieves this by separating model parameters into integer and floating-point components and formulating the quantization process as a constrained opt... | Rebuttal 1:
Rebuttal: Thank you so much for taking the time to review our work and then give an accurate summary and outline our strengths. We will explain your concerns in detail. Due to page limitations, we put the responses to weaknesses at the "Official Comment" box.
# Question 1:
GPTQ, AWQ, OmniQuant are three ... | Summary: This paper proposes a novel post-training quantization method to achieve 2-bit uniform quantization on large language and speech models. The proposed method decouples the quantized values into integer and floating-point parts, which are then optimized via a constrained optimization problem that can be solved w... | Rebuttal 1:
Rebuttal: Thank you very much for reading our paper carefully and for your generally positive comments. We will respond to your concerns in detail as much as possible and would be grateful if you could point out any omissions.
# Weakness 1:
In traditional meaning, the whitepaper[1] explained in Sec 2.1 ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection | Accept (poster) | Summary: The paper addresses the challenge of distinguishing between normal and anomalous structured data. A new method designed to interpret and understand the structure of normal data distributions. It integrates anomaly detection model predictions into its splitting criteria to enhance the clustering process. In add... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your insights and suggestions, which have helped us to improve our paper.
> Comment 1: “The writing should be enhanced as some technical concepts are unclear. For example, a running example could be provided to explain the SCD tree and GBD algo... | Summary: This paper introduces the Segmentation Clustering Decision Tree (SCD-Tree) and Gaussian Boundary Delineation (GBD) algorithm to interpret black-box anomaly detection models in high-stakes domains. The method segments high-dimensional data, incorporates model predictions into decision criteria, and defines flex... | Rebuttal 1:
Rebuttal: Thank you! Your feedback has been invaluable in enhancing the clarity and impact of our work.
> Comment 1:The proposed method primarily builds upon some existing techniques. ... While the integration and adaptation of these techniques are innovative to some extent, the foundation lacks substantial... | Summary: The paper proposes a general method to extract interpretable rules from any anomaly detection model. A decision tree is learned from a black-box anomaly detector output/scores and the decision boundaries in the learned tree are further refined using Gaussian Processe framework.
Strengths: The paper tries to a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback on our paper.
> Comment 1: Lines 106-110: "In summary ... their operational logic." The paper has not systematically addressed attributes such as interpretability, non-reliance on oversimplified surrogate models.
> Comment 6:Line 282 -Th... | Summary: In high-stakes sectors like network security and IoT security, accurately distinguishing between normal and anomalous data is critical. This paper introduces a novel method to interpret decision-making processes of anomaly detection models without labeled attack data. It presents the Segmentation Clustering De... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful feedback.
> Question 1: While the method shows robustness across several datasets, it might not perform equally well in all types of data or anomaly detection scenarios.
**Response:**
We acknowledge the concern regarding the generalizability of our met... | Rebuttal 1:
Rebuttal: We are very glad that our efforts can clarify some of your concerns, and we really learn a lot from your valuable replies! Your suggestions are very meaningful and help us improve our work a lot!
We appreciate the reviewers' recognition of our paper's contribution to advancing anomaly detection i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation | Accept (poster) | Summary: This paper proposes diffusion guidance (Test-time Procrustes Calibration, TPC) for human image animation. TPC incorporates an auxiliary branch into the diffusion conditions process and provides a calibrated reference image latent. Experimental results show the effectiveness of the provided method.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your concerns below.
[**Q1**] I think the most direct and useful way is to find a way to align the two images, and I don't think it is very difficult to align the two images. So why do we need the proposed TPC method?
[**A1**] The direct way to in... | Summary: In this paper, the authors propose TPC, an alignment algorithm for human image animation systems. optimal precision is currently achieved only when the physical compositions of the human shapes in the reference image and target pose frame are aligned. Misalignment leads to a significant decline in fidelity and... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your concerns below.
[**Q1**] I suggest an algorithm table of the proposed Iterative Propagation.
[**A1**] Figure E of our attached PDF presents the algorithm table for Iterative Propagation. We will incorporate this into our manuscript. Thank you... | Summary: This paper starts from an interesting problem that what happens when the motion condition and the reference image are not well aligned. It analyses the robustness of an existing human animation network given different levels of misalignment, and tries to find the underlying cause through attention maps. Then t... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work. We address your concerns below.
[**Q1**] Demo results still contain some artifacts. But it is mainly due to the baseline MA or Disco. I recommend the authors to try some new methods as the baseline that have higher quality.
[**A1**] Yes, as shown in Fi... | Summary: This work proposes a method that combines exsiting human image animation method and Procrustes Warping, which improves the robustness of image animation approach. In addition to the explicit image warping, this work also proposes an interative propagation method to improve the temporal consistency. Experiment... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have addressed your concerns to the best of our ability.
[**Q1**] Procrustes alignment is popular in evaluating 2D/3D human pose estimation. This work introduces simple technique into the human image animation but technically contribution is not novel eno... | Rebuttal 1:
Rebuttal: We have uploaded a PDF file containing figures. Please refer to this PDF along with our rebuttal for a clear understanding.
Pdf: /pdf/193146ec3b25f737d240c01082ee1b465da9e6e4.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher | Accept (poster) | Summary: The paper proposes PaGoDA, an adversarial distillation method to support single-step generation of image resolutions higher than the teacher diffusion model. PaGoDA first solves the forward PF-ODE (data to noise) to collect noise from the dataset. Then, it gradually adds upsampling layers to the model and trai... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s constructive feedback.
**Weakness 1. LCM with lighter VAE may be more efficient than PaGoDA's upsampling blocks?**
**Ans.**
**[Lightweight PaGoDA]** The smaller VAE [A] decoder can be integrated into PaGoDA's super-resolution framework. If LCM's VAE is made ... | Summary: The paper introduces a novel GAN-based diffusion distillation approach that leverages ideas from PG GAN for effective high-resolution synthesis.
Strengths: * PaGoDA demonstrates state-of-the-art single-step generation performance on ImageNet, competing with or outperforming more expensive alternatives, includ... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for helpful reviews. Below, we faithfully answer the raised concerns, but we would be happy to provide additional experiments upon reviewer's request in our final revision.
**Weakness 1. Can we integrate PaGoDA with SD?**
**Ans.**
Yes, we can integrate P... | Summary: This paper introduces a method to distill a diffusion model into a one-step generator. The training process combines several loss functions. First, the authors use DDIM inversion to transform real images into latent noise, which is then fed into the generator. The generated images are supervised with a reconst... | Rebuttal 1:
Rebuttal: **Weakness 1.** To address the concerns about fair evaluation, we provide additional results below.
**W1-1. GAN's discriminator pretrained on ImageNet biases the FID metric.**
**Ans.**
**[Frechet Distance (FD)]** We agree with the reviewer that the use of ImageNet pretrained discriminator could... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their constructive and helpful feedback. For a clearer evaluation, we would like to highlight a high-level overview of the contributions in this paper.
**[Switch DDIM to DDIM Inversion]** PaGoDA proposes to utilize DDIM inversion for distillation. Unl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Certified Machine Unlearning via Noisy Stochastic Gradient Descent | Accept (poster) | Summary: The paper studies a practically important problem of machine unlearning and provides rigorous statistical guarantees for unlearning using the tools from the differential privacy. More specifically, it is shown that, for strongly convex losses, running Projected SGD on the new (adjacent) dataset guarantees that... | Rebuttal 1:
Rebuttal: We thank reviewer MVBT for their insightful comments and suggestions. We address the weaknesses and questions below.
**W1: ``Is the unlearning privacy bound for strongly convex settings trivial? Are our results for convex settings vacuous?``**
*Compare to retraining:* We agree with reviewer MVBT... | Summary: The paper proposes an effective and efficient machine unlearning algorithm based on projected noisy gradient descent (PSGD). The proposed methods can be extended to handle multiple unlearning requests. The theoretical unlearning guarantee is established when the loss is assumed to be convex and smooth. Exper... | Rebuttal 1:
Rebuttal: We thank reviewer DXXZ for their positive and thoughtful comments. We address the weaknesses and questions below.
**W1:``Smoothness of the loss is required``**
We agree with reviewer DXXZ that smoothness assumptions can restrict the applicability of our approach, for which will also list it as a... | Summary: The paper presents a simple scheme for machine unlearning. The algorithm has two parts: in the learning part, the models learns using the original dataset D, and in the second part it unlearns using a neighboring dataset D’. The algorithm uses 1) same batches at every epoch, and same batches for both learning ... | Rebuttal 1:
Rebuttal: We thank reviewer SMx2 for their careful reading, positive assessment, and thoughtful comments. We address the weaknesses and questions below.
**W1: ``No utility bound.``**
We thank reviewer SMx2 for the thoughtful comments. We agree that utility is an important aspect of the unlearning problem,... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction | Accept (poster) | Summary: This paper used the diffusion model to solve the widely known inverse problem in computed tomography. The paper is well-written and well-organized.
I reckon this paper has two main contributions:
1. The first diffusion model in CT considered z-axis consistency and used 3d data as neural network input.
2. A S... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the encouraging feedback! Below, we address the concern:
> **Q:** *Discuss related work with other regularization methods*
A:
We are aware of other works that apply external regularization between 2D slices with diffusion models such as [1,2] as we cited in o... | Summary: 1. The method proposed a method that learns the 3D-patch image prior incorporating the cross-slice dependency.
2. The method achieves state-of-the-art reconstruction results for 3D volumetric imaging for the task of ultra-sparse-view and limited-angle
3D CT reconstruction on "AAPM 2016 CT challenge" dataset a... | Rebuttal 1:
Rebuttal: Thank you for providing the valuable feedback. Below, we address the concern:
First, we address concerns regarding to additional experiments:
> **Q:** *Evaluate method's performance at various angles.*
A:
- We provide results on [20, 40, 60, 80, 100] views for our method (DiffusionBlend++) as w... | Summary: This paper proposes a novel method for learning 3D diffusion priors for CT reconstruction, which does not require large-scale data or computational resources. It presents two approaches: DiffusionBlend and DiffusionBlend++. The former learns a specific frame given adjacent slices, while the latter learns a 3D ... | Rebuttal 1:
Rebuttal: Thank you for providing the valuable feedback. Below, we address the concern:
> **Q:** *Extending the method to a broader field*
A: Our method is flexible and does not assume any specific data modality and forward models, so it should work for other modalities such as natural 3D images or videos... | null | null | Rebuttal 1:
Rebuttal: Firstly, we would like to sincerely thank the reviewers for taking the time to review our paper and providing constructive feedback. We are encouraged that the reviewers think that in our paper
- ```The first diffusion model in CT considered z-axis consistency and used 3d data as neural network i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model | Accept (poster) | Summary: The paper presents the DDSR model for Sequential Recommendation (SR), which better captures user interest evolution by using fuzzy sets of interaction sequences. Unlike traditional methods, DDSR effectively handles the unpredictability of user behavior and addresses cold start issues. Experiments on benchmark ... | Rebuttal 1:
Rebuttal: First of all, thank you very much for reading our work carefully and for your valuable comments and suggestions, from which we have greatly benefited. Next, I will explain and respond to the shortcomings you have pointed out, and we will make corresponding revisions in the official version of our ... | Summary: This paper studies the problem of fuzzy modeling for sequential recommendation. The work proposes to leverage the fuzzy sets of interaction sequences for modeling the nature of users' evolving real interests. It also introduces the discrete diffusion modeling specifically for the discrete data. The experiment ... | Rebuttal 1:
Rebuttal: Firstly, we sincerely appreciate your careful review of our work and the extremely valuable suggestions you have made! In response to the issues you have pointed out, we will improve our research and provide detailed answers to each of your questions, hoping to clearly address your concerns.
W1: ... | Summary: The paper presents a diffusion model-based sequential recommendation from a novel information-theoretical perspective, which operates on discrete structural state spaces along with semantic labels improving efficiency and tackling cold-start issues.
Strengths: Strengths*
1. Novelty. The paper uses a directed ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable time you have dedicated to our work and the encouragement you have given us! Thank you for thoroughly reading our paper and pointing out some issues, which indeed arose due to our oversight; for this, we deeply apologize. In the final version, we will carefully... | Summary: The paper presents a new model for sequential recommendation (SR) called DDSR, which aims to predict items of interest for users based on their past behavior. The authors critique conventional SR methods for not adequately capturing the randomness and unpredictability in user behavior. DDSR uses fuzzy sets of ... | Rebuttal 1:
Rebuttal: Firstly, we appreciate the valuable time you have invested in our work and the constructive suggestions you have offered; we will rigorously revise our paper based on your feedback. We hope the following explanations will somewhat alleviate your concerns.
W1: We find your suggestion to relocate S... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation | Accept (poster) | Summary: This paper introduces a method for generating realistic strand hair geometry using a frequency-decomposed representation. The approach constructs a hierarchical generative model for hair strands, leveraging discrete cosine transform (DCT) and k-medoids clustering to create coarse guide curves that effectively ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and suggestions, and your appreciation of our innovations.
**[W1] Technical contributions**
Please refer to the global rebuttal for a detailed discussion on our contributions. We will specify our innovations, and especially which components in our method ... | Summary: The authors propose reformulating hair strand generation by considering it in the frequency domain (DCT). This approach allows for decoupling high and low-frequency strands via frequency thresholding (low-pass filtering). The main idea is that we can first generate a set of sparse, low-frequency strands that c... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and appreciation of our method and innovations.
**[W1] On the effectiveness of DCT frequency decomposition, and separation of low- and high-frequency with varying threshold**
We appreciate the constructive suggestion.
The designing choice to learn first low... | Summary: This paper proposes a system to generate hair geometries in a coarse-to-fine manner via VAE. The paper demonstrates the effectiveness of the proposed method via some simple baselines such as grid-based methods. The paper is relatively easy to read. But it presents limited comparisons with existing SOTA methods... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive feedback.
**[S1 and W1] Contributions**
Please refer to our global rebuttal.
Although the methods of DCT and k-medoids are well established, these methods are never used in any prior work for hair modelling, because *how to employ DCT and k-medoids t... | Summary: The paper presents a representation for learning a generative model of hair strains. The suggested representation is hierarchical, going from low-frequency to high-frequency details. In turn, the suggested representation is incorporated into a VAE architecture. The method is evaluated on a dataset of synthetic... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
**[W1] Presentation quality and clarification**
We will improve the proof details for better readability. For Sec 3 and Fig 4, we reviewed them and think the information is technically precise and clear. However, we understand that, since the whole field of... | Rebuttal 1:
Rebuttal: We thank all reviewers for their precious time and effort they put into reviewing our work. We address some common concerns of unclear contributions and short of evaluation as follows. We will update the paper with evaluation results and other clarifications.
**Contributions related to existing ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Action Imitation in Common Action Space for Customized Action Image Synthesis | Accept (poster) | Summary: The paper introduces “TwinAct”, a novel method for separating actions from actors in few-shot action image generation using text-guided diffusion models (TGDMs). It creates a “common action space” to focus on actions alone, allowing for precise customization without actor-specific details. The process is strea... | Rebuttal 1:
Rebuttal: ### **For Reviewer** **2ZgR**
We are sincerely grateful to the reviewers for dedicating their time and effort to review our work, and we appreciate the recognition of the novelty of our approach and the significance of our given the impressive result. We will try to address reviewer's comments i... | Summary: This paper aims to tackle the few-shot action image generation problem by customizing text-conditioned diffusion models. To decouple action from actor, the proposed method introduces a common textual action feature space to avoid the interference of the actor's visual semantics during action generation. The ex... | Rebuttal 1:
Rebuttal: ### **For Reviewer** **Q179**
We are sincerely grateful to the reviewers for dedicating their time and effort to review our work, and we appreciate the recognition of the novelty of our approach and the significance of our given the impressive result. We will try to address reviewer's comments i... | Summary: This paper introduced an text-to-action generation framework. In this framework, the authors first abstracted the actions (represented by natural language phrases) with PCA technique into a common action space. Then these high-level action features are fed into a text-to-image transformer network. In order to ... | Rebuttal 1:
Rebuttal: ### **For Reviewer** **3NDQ**
We appreciate your time and effort in reviewing our work, and we have carefully considered your comments. We will be sure to incorporate your suggestions to enhance the overall quality of the paper. We hope the following clarifications can address the reviewer's conc... | Summary: Problem: Preserve the consistency of the reference action when generating images with new actors by decoupling the action and actor properly.
Key Idea:
The authors propose a method to disentangle actions from actors in text-guided diffusion models and generate new images that exactly replicate the action pose... | Rebuttal 1:
Rebuttal: ### **For Reviewer** **BXKD**
Thank you for recognizing our paper and recommending it for acceptance. Now, we will address the key arguments raised in the reviews.
### **Q1.The identity of actors**
It should be noted that two factors influence the identity of the actor.
(1) The first factor is... | Rebuttal 1:
Rebuttal: ### For ALL Reviews
We sincerely thank all the reviewers for their thoughtful feedback. We are glad to see that most of the reviews have recognized our work:
**[ALL Reviews]** **Robust and Superior Results:** We are grateful for the reviewers’ positive feedback on our impressive experimental res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs | Accept (spotlight) | Summary: The authors propose an algorithm for distributed optimization of a sum of nonconvex smooth functions, with partial participation. They obtain a O(1/T) rate.
Strengths: The study of convergence of algorithms adapted to federated learning is an important topic.
Weaknesses: * There is no discussion of the O(1/T... | Rebuttal 1:
Rebuttal: **W1 and W2: I don't think the method improves on the SOTA and compression must be used to reduce dramatically the number of communicated bits, which is the right criterion to study.**
The main contribution of this paper is neither proposing a theory that achieves faster optimization rates than ... | Summary: This paper investigates the issue of dual drift caused by the mismatch between primal and dual variables when using partially participated training in federated learning (FL). It proposes a novel method, A-FedPD, which employs virtual dual updates to mitigate these negative impacts. Comprehensive theoretical a... | Rebuttal 1:
Rebuttal: **W1: What is the variant A-FedPDSAM? I didn’t see an introduction to this algorithm in the text; please remind me if I missed this part.**
Due to space constraints in the main text, we have noted at the bottom of page seven (in the first paragraph where the Experiments section begins) that the m... | Summary: A primal-dual-based federated learning algorithm (A-FedPD) is proposed to mitigate the drift of local dual variables. In federated learning with partial participation training, the local dual variables in inactive clients can be drifted. To mitigate this issue, the propose A-FedPD would be effective since the ... | Rebuttal 1:
Rebuttal: **W1: The reviewer has serious concerns about this point. Without introducing SAM, can the superiority not be empirically demonstrated?**
FedSpeed method essentially uses **a variant of the local SAM optimizer.** To illustrate this, we can recall its implementation:
$$
g_{i,k,1}^t = \nabla F_i(x_... | Summary: This paper studies the primal-dual-based FL algorithm with partial client participation. The inactiveness of the local clients causes both local primal and dual variables to drift from their expected value and slows down FedPD's convergence. This paper provides a fix to the FedPD algorithm in the partial parti... | Rebuttal 1:
Rebuttal: **W1: The algorithm requires saving the local dual variable of all clients. When C is large, this might cause a large memory cost on the server, especially in the FL setting. This might restrict the proposed algorithm's use case. This is discussed in the limitations.**
Thank you very much for poi... | Rebuttal 1:
Rebuttal: **We thank the four reviewers for their valuable time and effort in reviewing our submission.** Reviewers 6Q4p, b9A4, and YYQB provided positive feedback with rates of 7, 7, and 5, respectively. After reading all the review comments, we noticed that the response from reviewer c2hH mentioned some c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Price of Implicit Bias in Adversarially Robust Generalization | Accept (poster) | Summary: This paper studies the generalization gap of robust empirical risk minimization for linear regression. The paper shows that the choice optimization algorithm or architecture affects the generalization gap of the trained linear model. In particular, a steepest descent algorithm w.r.t. $l_p$ norm finds the minim... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and for your positive evaluation of our work.
First, let us correct a small mistake on your summary of our work (since reviews might become public in the future and readers might get confused):
> This paper studies the generalization gap... | Summary: The paper studies the implicit bias of robust Empirical Risk Minimization (ERM) and its connection with robust generalization. In regularized classification, the authors discuss the choice of regularization for a given perturbation set to improve robust generalization. In the unregularized setting, they study ... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our work and help us improve it. We first reply to some of your points regarding the weaknesses of this paper:
> As the authors mention, the result of implicit bias in linear models is not surprising, and its proof is based on techniques from prio... | Summary: In this paper, the authors study the issue of large generalization gap with Robust ERM objective, they connect this with the implicit bias of optimization (including architecture and the optimization algorithm). The findings suggest that optimizing models for robust generalization is challenging since it is ha... | Rebuttal 1:
Rebuttal: Thank you for reviewing & positively assessing our work and for highlighting its strengths. We address your only concern:
> The theory studies might be still too limited
This paper is the first to consider the connection between the implicit bias of optimization in adversarial training/robust ER... | Summary: This paper explores a linear classification scenario, investigating the factors that contribute to the gap between empirical adversarial risk and expected adversarial risk. Furthermore, they discuss which type of regularization should be applied in different cases. There are also simulations results to support... | Rebuttal 1:
Rebuttal: Thank you very much for your critical and positive evaluation of our work. We respond to your questions:
1. > In Theorem 2.1, it is not clear whether the constant $\rho$ has influences on other constants shown in the theorem.
Thank you for the comment. The empirical margin $\hat{\rho}$ a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Benefits of Complex Parameterizations for Structured State Space Models | Accept (poster) | Summary: This paper provides a theoretical analysis of why SSM needs to be parameterized by complex numbers instead of real numbers. It shows that there exist complex LTI systems that could not be well-approximated by real systems of comparable size. Moreover, it proves that certain dynamics cannot be approximated by r... | Rebuttal 1:
Rebuttal: Thank you for your review, and for highlighting the thoroughness and preciseness of our theory. We address your comments and questions below. If you find our responses satisfactory, we would greatly appreciate it if you would consider raising your score.
## Significance of real diagonal SSMs:
Rea... | Summary: This paper considers learning LTI systems with bounded response. It shows that if we restrict to SSMs with diagonal dynamics, both real-valued and complex valued state sizes suffice. However, it shows that if we use only real-valued SSMs, to learn a sequence of length t we will need parameters that scale as $\... | Rebuttal 1:
Rebuttal: Thank you for your support, and for highlighting the importance of the question we study and the merit of our theory and experiments! We address your comments and questions below.
## Addressing selectivity:
We agree with you that addressing selectivity — i.e., theoretically supporting the success... | Summary: This work establishes a formal gap between real and complex parameterizations of stable, diagonal SSMs. While complex parameters can trivially express any real SSM, the converse is not true and real SSMs need an arbitrarily large number of parameters to approximate complex SSMs in at least two important cases.... | Rebuttal 1:
Rebuttal: Thank you for your support!
We conducted several additional experiments, including:
* Demonstration of the gap in practical learnability between real and complex SSMs in the theoretically analyzed setting (i.e., in the setting for which we established such a gap).
* Demonstration that complex pa... | Summary: The paper deals with an important question: is it possible to show a concrete advantage of complex diagonal RNNs compared to real diagonal RNNs? The motivation is clear, especially for people who studied the SSM literature. While modern SSM variants used in language modeling (e.g. S6-Mamba) do not make use of ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review, for highlighting that “with some tweaks, this can be a very good paper”, and for the willingness to “engage in the discussion so [as] to update [your] borderline score”. Below we address your comments and questions.
## Specificity of Theorem 1:
Theorem 1 readi... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and feedback, addressed per reviewer in our individual responses.
***Attached to this comment is a PDF presenting results of new experiments*** which will be added to the paper. These experiments include:
* Demonstration of the gap in practical learnability b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts | Accept (poster) | Summary: The authors propose a method which generalize effectively to covariate-shift regions while precisely identifying semantic-shift regions, i.e., domain generalization and OoD segmentation. They design a novel generative augmentation method to produce coherent images that incorporate both, various covariate shift... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: We thank the reviewer for the time and detailed feedback. We address the SMIYC benchmark comparison in the general response and will revise the paper to correct the noted typos. Below, we address your specific concerns.
# Weakness 2: Related Work & Novelty
**Anomaly Segm... | Summary: This work proposes a novel generative pipeline and fine-tuning method for anomaly detection under domain shift. The generative pipeline uses a semantic-map to image model that can leverage the labels from the Cityscapes dataset with some modifications which introduce novel unknown classes. The resulting images... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: Thank you for the thorough comments and many constructive suggestions. We appreciate the mention of the interesting work by Loiseau et al. [a1] and have discussed our differences, including [8] and [34], in the general response. We address the other concerns below.
# 1. ... | Summary: This paper aims to tackle both covariate-shift and semantic-shift in semantic segmentation. The idea is to use a generative augmentation method to produce coherent images that incorporate both anomaly objects and various covariate shifts at both image and object levels. The semantic segmentation model is then ... | Rebuttal 1:
Comment: Thank you for your time and constructive feedback. We discuss the novelty of our proposed generative-based augmentation in the general response. We address your other concerns below.
# Weakness 2: Hyperparameters for Relative Contrastive Loss
Our relative contrastive loss includes three terms, ea... | Summary: This paper addresses semantic segmentation in the presence of domain and semantic shifts. To enhance model robustness, the authors propose using a generative model guided by ground truth labels to generate domain-shifted images and further inpaint random negative data. Due to potential noise in the generative ... | Rebuttal 1:
Rebuttal: Thank you for the time and constructive feedback. We discuss the novelty of our generative-based data augmentation and additional comparison results on SMIYC in the general response. We address your other concerns below.
## Weakness 1: First Contribution
We thank the reviewer for highlighting th... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and constructive feedback. Below, we discuss shared concerns and reply to each reviewer with individual responses.
# The novelty of our Generative-based Augmentation
We address reviewers’ concerns about the novelty of our coherent generative-based data augme... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Loss Landscape Characterization of Neural Networks without Over-Parametrization | Accept (poster) | Summary: This paper proposes a new condition to describe the optimization landscape of deep neural networks. This condition alleviates restrictive consequences of alternative conditions such as PL, in particular the overparameterisation and absence of saddle points. A convergence for SGD (and other variants of first-or... | Rebuttal 1:
Rebuttal: **A to W (part 1):** For NGN the first three terms that appear in our Theorem 3 also appear in the convex setting; see Theorem 4.5 in [1]. The fourth (and last) term appears because of the $\alpha$-$\beta$-condition. We highlight that the first three terms shrink with a decreasing stepsize, but th... | Summary: A major challenge in Deep Learning optimization has been identifying structural conditions on the loss objective that ensure convergence of SGD and variants. As in practice despite the non-convexity, stochastic gradient algorithms have been tremendously successful in training neural networks. Many conditions h... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough reviews, insightful comments, and valuable
questions regarding our paper.
**A to W1:** The main reason why we used SGD for MLP, CNN, and ResNet experiments is the fact that SGD is known to be able to train those models, i.e. the last iterate of $... | Summary: This paper introduces a new regulatory condition named $\alpha$-$\beta$ condition. To motivate the necessity of such condition, it first show empirically that the aiming condition is not always satisfied in neural newtork training. The paper then support the $\alpha$-$\beta$ condition with $i).$ examples (incl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough reviews, insightful comments, and valuable
questions regarding our paper.
**A to W1:** Thank you for pointing this out. To ensure boundedness of $\mathcal{S}$, one can simply add L2 regularization. We provide a sketch of the proof and will change... | Summary: This paper proposes a novel class of functions and proves convergence of gradient descent (and some other optimizers).
Contrary to some previous classes, in relation to deep neural networks, this new class does not require extreme overparameterization.
In addition to theoretical convergence results, experiment... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough reviews, insightful comments, and valuable
questions regarding our paper.
***W1.*** "Some of the highlighted differences..."
***A to W1:*** This is a good comment and we will provide further detail on it in a revision. In Figures 1c-1d we observ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments and questions that allowed us to improve our paper.
**Non-vanishing term in the convergence rate:**
Three of the reviewers raised the question on the convergence of optimizers under the $\alpha$-$\beta$-condition. The main comment is about the p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles | Accept (poster) | Summary: The paper constructs the benchmark SPLAT to evaluate and elicit lateral thinking abilities in LLMs. It includes questions similar to brain teasers, which describe scenarios and ask non-obvious questions for guessing.
Example:
__Question/Story__: A hunter aimed his gun carefully and fired. Seconds later, he... | Rebuttal 1:
Rebuttal: **W1-1. "The dataset and framework are designed not only to evaluate but also to actively elicit lateral thinking in LLMs. Experiments show that using data and reasoning processes from our framework leads to improved performance of LLMs, even when applied to other lateral thinking benchmarks." Thi... | Summary: This paper focuses on lateral thinking, which is about creativity and viewing problems from multiple angles. To sovle the challenge that the complexity of assessing creative thought processes and the scarcity of relevant data, this paper introduces SPLAT, a benchmark leveraging Situation Puzzles to evaluate an... | Rebuttal 1:
Rebuttal: **W1. The size of the dataset is small.**
Please refer to General Response **G2**.
**W2+Q1. The reference answer is limited to 1, while the questions are one-to-many questions. Why not provide more than one answer for each question (I do not think it is challenging, there are multiple answers to... | Summary: This work focuses on creating a benchmark, as well as a modeling framework for evaluating lateral thinking of LLMs. The benchmark, called SPLAT, consists of 975 situation puzzles. The framework consists of a “judge” and a “player” (the LLM to be evaluated). The judge poses an open ended puzzle that is further ... | Rebuttal 1:
Rebuttal: **W1. Although the motivation and overall idea are strong, I am concerned about the experimental setup. The agreement rates for WizardLM-2 in Tables 2 and 3 don’t appear to be high enough (are around ~80\%) and 3 individuals seem to be fairly less. How does this fare with human agreement rates in ... | Summary: This paper introduces SPLAT, a novel benchmark for evaluating and eliciting lateral thinking abilities in Large Language Models (LLMs) using situation puzzles. The key contributions include: A new dataset of 975 graded situation puzzles across three difficulty levels.
A multi-turn player-judge evaluation frame... | Rebuttal 1:
Rebuttal: **W1. There's minimal discussion of potential biases in the dataset.**
During our data collection process, we also track user preferences for each situation puzzle, measured by a preference score (\%). We notice a pattern where puzzles (about 1\% of our dataset) with lower preference rates (30\%-... | Rebuttal 1:
Rebuttal: **General Response**
**G1. Results on more benchmarks than just RiddleSense.**
Besides the results on the RiddleSense (Section 5.3 in the submitted paper), we provide several results on another lateral thinking-focused benchmark, i.e., BrainTeaser [A], which features two main types of puzzles: S... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Least Squares Regression Can Exhibit Under-Parameterized Double Descent | Accept (poster) | Summary: The paper aims to understand the phenomenon of double descent in regression, and helps complement existing knowledge about the phenomenon by proving that double descent can occur even in the under-parametrized regime, going against previous intuition. They also prove that the peak in the norm of the estimator ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the paper's good contextualization, its original viewpoint, and its significant result.
We now address the reviewer's concerns.
> Clarity: Although the contextualization relative to prior work is good, I find that the paper lacks in clarity. In particular,... | Summary: The authors explore the double descent phenomenon, postulating that the location of the peak (that separates the "classical" and the "modern" interpolating regime) depends on the properties of the spectrum and the eigenvectors of the sample covariance. In particular, the authors show that the violation of one ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding that the paper tackles a very important research topic, is rigorous with relevant contributions, and is well written. We now address the reviewer's concerns.
> The conclusions of the paper are very brief and, from my point of view, not very informative (see sect... | Summary: In this paper, the authors focus on the generalization performance of linear least squares regression and show the existence of double descent generalization curve in the under-parameterization regime.
In particular, the authors argue, in the linear model in (1) under study (which is slightly different from s... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding that the problem we study is significant and that the findings are interesting. We now address the reviewer's concerns.
> Another issue is the contribution: while Theorem 1 is rather general ... It is thus difficult for me to evaluate the significance of this wor... | Summary: The paper considers the problem of linear least squares. Its main contribution is presenting two examples of double descent in the under-parameterized regime.
Strengths: - The paper is well written: Related works are sufficiently discussed (to my knowledge); the introduction is well-motivated and easy to foll... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our paper well-written and our perspective interesting. We now address the reviewer's concerns.
> Notation
We shall fix $\beta^T X$ to $X^T\beta$ to align the paper better with the prior convention. We shall change the font for the $u$ to clarify the difference... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, effort, and feedback.
The paper's main purpose is to attempt to understand the reasons peaks occur in the excess risk curve and their locations. Prior work has phrased this as occurring due to overparameterization and the location being on the boundary of t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors show several facts about the double descent phenomena for the linear regression model with L2 loss and Frobenius norm regularization. They show taking different assumption from previous work moves the peak of the risk of the problem from the interpolation point into the under-parameterized regime. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our analysis thorough, especially the reasoning for the trends. We now address the reviewer's concerns.
> The authors results which do not coincide with prior theory are based on different assumptions. Can the authors discuss how wider cases covered by their ass... | null | null | null | null | null | null |
End-To-End Causal Effect Estimation from Unstructured Natural Language Data | Accept (poster) | Summary: This work aims to the causal effect estimation from unstructured observational text data, proposing a pipeline based on LLM to extract the treatment, covariate and outcome from the unstructured data by LLM. It's a interesting exploration for causla inference and the pipeline process of the inference method is ... | Rebuttal 1:
Rebuttal: Thank you for your review and questions! Below, we will clarify all the questions in the review.
> (W1) some details are missing, leading to the poor readability.
Could the reviewer point to specific details that they found unclear? We are more than happy to provide clarification.
> (W2) Some ... | Summary: The paper introduces a family of causal effect estimators named NATURAL, designed to use LLMs for mining causal effect estimates from observational text data. The authors address the challenge of automating data curation and using LLMs to impute missing information, presenting a method that conditions on struc... | Rebuttal 1:
Rebuttal: Thank you for such a positive and encouraging review! Below, we address all the questions raised in your review.
> (W1) The original claims made in the abstract+intro seems to be too grandiose, but in fact it is about the use of LLMs to classify variables of interest from the text. I would change... | Summary: Estimating causal effects is costly and time-consuming. The authors propose to use large language models (LLMs) to mine unstructured text data for causal effect estimation. This paper introduces NATURAL, a family of causal effect estimators using LLMs to process unstructured text data. This seems to be a good... | Rebuttal 1:
Rebuttal: Thank you for your helpful review! Here, we will clarify and address the points brought up in your review.
> (W1) LLMs might inherit biases present in their training data or hallucinate.
As described in Section 5, this is an important limitation in the use of LLMs, in general as well as in the c... | Summary: The paper seeks to use LLMs for a workflow that extracts structured variables
from free text, filters the dataset, and then computes a causal estimate using
the imputed variables. The paper applies this methodology to two synthetic and
four real-world datasets and shows that it produces estimates that are
comp... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive feedback! Below, we address the questions in your review and describe corresponding changes to improve our paper.
> (W1) The paper at almost every turn relies on prompting an LLM and implicitly assuming that the model returns data from a desired distributi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and the effort they took to provide valuable feedback!
Overall, all of the reviewers appreciated the significance and novelty of our work, with oMqj calling it “ambitious work” and with oSFK calling it a “cool” and “properly formalized” methodology. Most rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Q-VLM: Post-training Quantization for Large Vision-Language Models | Accept (poster) | Summary: The authors present a novel post-training quantization framework for large multimodal language models to enhance inference speed. This method accounts for cross-layer dependencies that significantly impact overall model discretization errors and leverages activation entropy to effectively balance these errors ... | Rebuttal 1:
Rebuttal: We appreciate your suggestion and agree that comparisons with more advanced techniques and cross-attention based VLM architectures would provide a deeper insight into the effectiveness of our method. Below are our detailed responses.
**Q1: Ablation study about the projectors.**
**[Reply]** For q... | Summary: This paper proposes Q-VLM, a post-training quantization framework for Large Vision-Language Models (LVLMs). It aims to reduce the model complexity of LVLMs for practical deployment by replacing float numbers with quantized ones and substituting multiply-accumulate operations with integer arithmetic. The key in... | Rebuttal 1:
Rebuttal: Thank you for careful reading and valuable comments. We will check the paper carefully, and modify the presentation of the ambiguous parts in the final version. We provide answers to the questions as follows:
**Q1: Evaluation on Diverse Datasets.**
**[Reply]** LVLMs encounter diverse range of ta... | Summary: The authors propose a new post-training quantization method for LVLMs. The authors separate several layers in a LVLM into blocks and search for the optimal quantization bitwidth for each block individually. The authors also introduced a new objective function for quantizing the vision encoder. The extensive ex... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful reading and valuable comments. We address the questions and clarify the issues accordingly as described below.
**Q1: Confusion about the quantization strategies.**
**[Reply]** We apologize for the confusion. The detailed construction of the bas... | Summary: The paper introduces Q-VLM, a PTQ framework for VLMs that leverages entropy as a proxy to manage cross-layer dependencies for both language model and visual encoder. Experimental results on ScienceQA, VizWiz, VQAv2 datasets and LLaVA variant architectures validate the efficacy of the proposed method.
Strength... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the opportunity to clarify the points you raised regarding our methodology and its contributions.
**Q1: The benefit and motivation for using entropy as a proxy are unclear.**
**[Reply]** We are sorry for the confusion. For block-wise quantizati... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback and insightful questions provided by the reviewers. Below are our detailed responses to two common concerns raised by multiple reviewers. Detailed responses to other specific comments are provided under each individual reviewer's comments.
**Q1: Performance on ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension | Accept (poster) | Summary: The paper introduces and studies the problem of Semantic Keypoint Comprehension to evaluate the capability of Multimodal Large Language Models (MLLMs) in tackling fine-grained perception and comprehension. It does so by defining three related tasks: (1) keypoint semantic understanding, (2) Visual prompt-based ... | Rebuttal 1:
Rebuttal: ### Q1: The runtime, memory, and scalability of our model
During inference, our model processes multiple keypoints by stacking them into a batch. For the standard 17 keypoints, our model obtains their positions in 3.45 seconds, consuming 25,100 MiB of memory. It is important to note that in our i... | Summary: This paper proposes an LLM-based keypoint localization method that can detect keypoint by visual or text prompt and classify the keypoint category by given its coordinates. Experiments on several public benchmarks demonstrate the effectiveness of the proposed method.
Strengths: Adopting LLM to perform in-cont... | Rebuttal 1:
Rebuttal: ### Q1: The mutual benefit between textual prompt and visual prompt
In the main article, since textual prompt-based and visual prompt-based keypoint detection have their own benchmarks, combining both prompts for training would result in unfair comparisons. Here, we supplement the experiment by u... | Summary: The authors aim to enhance multi-modal LLMs with semantic keypoint comprehension. They introduce a hybrid visual prompting approach using a query and a support input image. In their pipeline, visual features from both images are extracted via a vision encoder model. Additionally, a support keypoint prompt, ind... | Rebuttal 1:
Rebuttal: ### Q1: Visual prompt-based keypoint detection is equal to few-shot keypoint detection
The visual prompt-based keypoint detection can be considered as the few-shot keypoint detection.
In our main article, we have explicitly illustrated the input requirements of visual prompt-based keypoint detect... | Summary: The paper proposes to build a MLLM for keypoint detection. They propose three main points to achieve this. First, the paper introduces a new benchmark that measures various types of keypoint detection including both text and visual-prompt-based, as well as traditional keypoint understanding. Next, they propose... | Rebuttal 1:
Rebuttal: ### Q1: Novelty and Contribution
**To Keypoint Detection Community:** To the best of our knowledge, this is the first work to
address the problem of semantic keypoint comprehension, which aims
to understand keypoints within different human-AI interaction contexts.
Previous keypoint detectors pri... | Rebuttal 1:
Rebuttal: We appreciate the efforts of all reviewers in reviewing our paper and providing insightful comments and valuable suggestions. The supplementary visualization results (To \# Reviewer Hwow) have been included in the rebuttal PDF.
Pdf: /pdf/2db4bc321cea9123f93ab0c567793c9867e97f3a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks | Accept (poster) | Summary: **Summary:** The paper proposes viewing the optimization of PINNs as a multi-objective optimization problem, with boundary and residual terms as potentially conflicting objective functions. To address conflicting gradients in the optimization process, the dual cone of the cone generated by the gradients of the... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and positive feedback.
> **Response to Q1.**
**Simple Answer**: As discussed in Section 3, it is empirically observed that gradient conflicts and imbalances between losses frequently arise during PINN training, which has led to the development of ... | Summary: The paper concerns with the training of physics-informed neural networks (PINNs). It observes that in a multi-objective setting a naive gradient update which decreases the total objective might not decrease every individual objective. This is used to explain the challenge of training PINNs which have a potenti... | Rebuttal 1:
Rebuttal: Thank you very much for your review and for raising questions from the perspective of readers who may be less familiar with optimization theory. We hope the following responses will clarify any misunderstandings and address your concerns.
> **Response to W1 (Theorem 4.2. is trivial).**
We respect... | Summary: In this paper, it is identified that PINNs can be adversely trained when gradients of each loss function exhibit a significant imbalance in their magnitudes and present a negative inner product value. To address these issues, the authors propose a novel optimization framework, Dual Cone Gradient Descent (DCGD)... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and suggestions.
- **Response to Weaknesses (W)**
> **W1.Visualization of PINN predictions and exact solutions.**
PINNs predictions and the exact solution, along with absolute error plots, can be found in Appendix C of the original manuscript. Plea... | Summary: PINNs have become commonplace in both scientific computing and machine learning communities and have found widespread applications. There have been many modifications to various aspects of PINNs, such as the loss functions, initialisations, initial and boundary condition representations, learning algorithms an... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, and for thoroughly checking details such as typos. We deeply appreciate your high-quality review.
- **Response to Weaknesses (W)**
> **W1. Comparison of Computational Complexity**
Please refer to the computational complexity discussion in our global resp... | Rebuttal 1:
Rebuttal: **Global response to all reviewers**
We would like to express our sincere appreciation for the insightful comments and valuable suggestions provided by all reviewers. We assure you that all comments and suggestions will be thoroughly addressed in the revised manuscript. In this global response, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Image Copy Detection for Diffusion Models | Accept (poster) | Summary: This paper studies a novel task of Image Copy Detection for Diffusion Models (ICDiff). Different from traditional Image Copy Detection task, ICDiff focuses on detect and evaluate the degree of image copy for images generated by text-2-image (T2I) generative diffusion models. This is an important and meaningful... | Rebuttal 1:
Rebuttal: *We sincerely appreciate your positive feedback and helpful suggestions. We hope our paper will meet your approval once we address the following concerns.*
**Q1. The large use of "refer to Section XXX in the Appendix".**
A1. Due to limited space, we indeed have placed the ***expected implementat... | Summary: This paper constructs a Diffusion-Replication dataset aiming to solve the image copy detection problem for diffusion models. This paper proposed a strong baseline named PDF-Embedding which transforms the replication level into a probability density function as the supervision signal. Extensive experimental res... | Rebuttal 1:
Rebuttal: *We sincerely thank you for your positive feedback and helpful suggestions. We address your questions below.*
**Q1. The selection rule and influence of $A$.**
A1. We thank you for this insightful question. We provide the selection rule and influence of $A$ here.
**Selection rule.**
***We selec... | Summary: This paper introduces a novel method named ICDiff for detecting whether images generated by diffusion models replicate the training set. The authors have constructed a new dataset called D-Rep and proposed a new embedding method called PDF-Embedding. This approach transforms the replication level of image pair... | Rebuttal 1:
Rebuttal: *We sincerely thank you for your positive feedback and helpful suggestions. We address your questions below.*
**Q1. The rationale behind using six vectors.**
A1. We appreciate this insightful question. As you say, when we understand our PDF-Embedding approach ***separately***, it seems counterin... | Summary: This paper proposed a new image copy detection model for diffusion models. A dataset of replication labels (0 to 5) is collected using human labelers and is used to train a model for replication grading. The proposed model estimates a pdf of replication labels and minimizes the error with a pdf representation ... | Rebuttal 1:
Rebuttal: *We sincerely thank you for your positive feedback and helpful suggestions. We address your questions below.*
**Q1. The use of continuous labels 0-1 will be more useful in practice.**
A1. Thanks. According to your suggestion, we implement this idea and find that it improves the original performa... | Rebuttal 1:
Rebuttal: **Thanks and Solve Common Concerns**
We sincerely thank the ACs and reviewers for their dedicated efforts in reviewing our paper. We also thank all reviewers for their positive, thoughtful, and helpful feedback, and we will add all these suggestions to the final version of our paper.
We are enco... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec | Accept (poster) | Summary: The authors proposed a novel method to improve the entropy encoding in deep image compression. The key idea is to introduce three contexts, local, region, and global, to capture contextual information at different scales while balancing the compute cost. The authors show that the proposed method achieves SOTA ... | Rebuttal 1:
Rebuttal: **[W1] Additional SOTA Methods**
Following your advices, we compare our method with six additonal SOTA methods. Two methods are employed in Tab. 1 of the uploaded PDF. For the experiment, we use the same structure of transforms (i.e., ELIC-sm) and different entropy models for fair comparison. The... | Summary: This paper proposes an entropy model that achieves higher compression rates than previously proposed models in the context of learned image compression. This model combines global, regional, and local information to make better (i.e., more accurate, and therefore requiring fewer bits) predictions with respect ... | Rebuttal 1:
Rebuttal: **[W1] Venue**
It is well known that entropy models in neural image codecs are highly related to generative models. We sincerely believe that our work will be helpful not only to compression experts but also to many researchers in the NeurIPS conference.
**[W2] Runtime Analysis**
Following your... | Summary: **Disclaimer:** My research direction is not related to image compression. I will try my best to review this work but could be biased.
The work studies neural image codec. The work proposes an entropy modeling framework that uses contents for forward adaptation without comprising the bit rate. The authors bui... | Rebuttal 1:
Rebuttal: **[W1] Presentation Issue**
We will revise it. Thank you for commenting.
**[Q1] Difference from Baseline**
First of all, we would like to say that the proposed entropy model, DCA, addresses the limitation of insufficient context for forward adaptation in the baseline (Li et al., 2023). We wo... | Summary: This paper presents a method for learned image compression called DCA, which stands for "Diversify, Contextualize, and Adapt". DCA is a more sophisticated approach for building the entropy model compared to previous methods. The entropy model is a key component in compression models and is optimized to predict... | Rebuttal 1:
Rebuttal: **[W1] Performance Improvements**
To explain the significant performance improvements, we clarify our goal $-$ To develop an **efficient** entropy model. As already mentioned by you, performance improvements can be easily achieved at the expenses of massively more compute and memory usage. Howeve... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments.
**Strengths.**
The proposed entropy model was appreciated for the performance improvement over state-of-the-art baseline methods (all reviewers: hsbA, 295Z, TpTZ, YQWe) with the well-motivated and intuitive method (hsbA). The reviewers recogniz... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks | Accept (poster) | Summary: The authors study the performance of ConvResNets trained with weight decay from the perspective of nonparametric classification. Specifically, the authors consider a smooth target function supported on a low-dimensional manifold, then prove that ConvResNets can adapt to the function smoothness and low-dimensio... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and for your positive feedback. I'm grateful for your engagement and would like to address the question you've raised with the following response.
**Weaknesses: Literature review is not sufficient. Minimax optimal nonparametric classification usi... | Summary: The paper studies the performance of an overparametrized convolutional residual network architecture on a nonparametric classification problem trained with weight decay. This model, known as ConvResNeXt, involves N residual blocks, where each residual block has a parallel architecture of M building blocks, and... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper. I would like to address the concerns you raised in your review.
**Weakness: The actual benefit of having parallel paths was not addressed fully… In Xie et at 2017, several experimental results focus on the positive effect of having higher cardina... | Summary: This paper builds the deep learning theory for studying convolutional residual neural networks with data lying on an embedded lower-dimensional manifold. Theoretical results on both approximation error and estimation error are provided, and interesting implications from the theory are discussed.
Strengths: 1.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and for your positive feedback. I'm grateful for your engagement and would like to address the question you've raised with the following response.
**Weakness 1: The theory of this paper highly depends on the exact manifold assumption since it need... | Summary: The paper provide theoretical analysis for the good prediction performance of convolutional residual neural networks, even overparameterized.
Strengths: 1. The theory they developed with overparamterized ConvResNeXts trained with weight decay is novel.
2. their theory does not suffer from the curse of dimens... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper. I would like to address the questions you've raised with the following responses.
**Weakness: In line 71, 72, the overparameterization is defined as "the number of blocks is larger than the order of the sample size n". Where is this definition fr... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking | Accept (spotlight) | Summary: This paper introduces Brain-JEPA, a self-supervised learning approach that leverages joint-predictive architecture to learn representations of brain fMRI images. The authors introduce two novel components on top of the JEPA architecture to adapt it to brain images: 1) Brain Gradient Positioning, which encodes ... | Rebuttal 1:
Rebuttal: > *Pretraining scheme of baselines (dataset and computational budget).*
Thank you for your inquiry regarding the pretraining process. We confirm that for self-supervised pretraining baselines like BrainLM, we used the same pretraining dataset and computational budget, specifically the UKB dataset... | Summary: In this paper, the authors train a foundation model on fMRI data. To this end, they combine multiple deep learning techniques in a novel way:
- they rely on a Joint-Embedding Predictive Architecture, and devise a specific masking strategy for brain data (referred to as spatio-temporal masking)
- they make use ... | Rebuttal 1:
Rebuttal: > *Full open-source code, preprocessing.*
We appreciate your important suggestions. We have supplemented the following materials/information:
**Code and Data Source:** We have now supplemented the codebase to include all downstream tasks on public datasets mentioned in the paper. The complete co... | Summary: The study introduces Brain-JEPA, an fMRI foundation model that leverages joint-embedding predictive architecture and a novel position-embedding approach based on brain gradient. This model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction, and ex... | Rebuttal 1:
Rebuttal: > *Ablation study on framework.*
Thank you for your feedback on our ablation study.
To thoroughly compare the performance between JEPA with anatomical locations (AL) and BrainLM (MAE), we have extended our comparison to include all the tasks except for the three in the current version, as well a... | Summary: This paper introduces a foundation model for fMRI time series, using a classic vision transformer backbone. It incorporates two main original developments: (1) a positional encoding for brain regions, using a “functional gradient” analysis (aka PCA on a Jacobian matrix derived from a temporal correlation matri... | Rebuttal 1:
Rebuttal: > *Code, data source, and preprocessing.*
Thank you for your valuable feedback.
We have supplemented code of all downstream tasks on public datasets to the codebase. The code and subject IDs are available via an anonymous link provided to AC in a separate comment, per author guidelines.
We uti... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in reviewing our work. We appreciate the positive feedback and great interest in our work from all reviewers, along with their insightful questions and suggestions. We are pleased to see that the reviewers acknowledge and appreciate the following as... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transparent Networks for Multivariate Time Series | Reject | Summary: GATSM effectively captures temporal patterns and handles dynamic-length time series while preserving transparency, outperforming existing GAMs and matching the performance of black-box models like RNNs and Transformers.
Strengths: * This paper is easy to understand.
* GATSM can be understood as a linear repre... | Rebuttal 1:
Rebuttal: **Q1**: It is not clear how multi-head attention works in Definition 3.1 to learn temporal patterns. My understanding is that the global feature interacts with the current feature in Eq.3, so why is it that the input to the attention is not but the transformed \tilde_x. And then, are temporal patt... | Summary: This paper introduces GATSM(Generalized Additive Time Series Model), designed for handling multivariate time series data with a focus on transparency and interpretability. Using independent networks to learn feature representations and transparent temporal modules to learn cross-time step dynamics, GATSM effec... | Rebuttal 1:
Rebuttal: **Q1**: The need for a large number of feature functions can limit scalability (even if it is reduced from TxM to B), particularly with high-dimensional data.
**A1**: A large number of feature functions in GAM are required to maintain its transparency, which is a key limitation. We will address t... | Summary: The paper introduces a Generalized Additive Model for time series, combining feature embedding and attention layer. The proposed solution is evaluated on forecasting, binary and multiclass classification, over 8 datasets, against black box and transparent models. Global, local, time-focused and feature focused... | Rebuttal 1:
Rebuttal: **Q1**: I am not sure if the work is original. The idea of using DNN first on the time axis without covariate interaction is not new, but wether there is a model similar to the proposed solution, I do not know. The review of previous works focuses on Generalized Additive Models, but a similar neur... | Summary: This work aims to build transparent models for the time series domain for better interpretability. Specifically, they proposed a work called Generalized Additive Time Series Model (GATSM) that consists of independent feature networks as well as a temporal attention module to learn temporal patterns. The corres... | Rebuttal 1:
Rebuttal: **Q1**: Black-box Time Series Models seem out-of-dated. The authors should consider better models such as TimesNet, PatchTST, FreqTransformer, or Informer for commonly used black-box models.
**A1**: We have added new experimental results comparing GATSM with two recent models, PatchTST and DLinea... | Rebuttal 1:
Rebuttal: ### **Response to all reviewers**
We appreciate all the reviewers for their helpful comments and discussion on our manuscript. The feedback provided was instrumental in improving the quality of the manuscript. We have addressed each of the reviewers' questions and concerns individually. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models | Accept (poster) | Summary: The paper presents a novel framework for generating 4D content, leveraging video diffusion models, and introduces motion magnitude reconstruction loss and 3D-aware classifier-free guidance to refine dynamics learning and generation. The framework is shown to enhance generation efficiency and 4D geometry consis... | Rebuttal 1:
Rebuttal: Dear Reviewer mXZW, many thanks to you for taking the time and effort to review our work and providing constructive comments. We are grateful for your recognition of the novelty and contributions of our research, noting that **it is the first work training a singular video diffusion model directly... | Summary: The paper tackles 4D asset generation. Its pipeline consists of two parts: a 4D-aware video diffusion model, where orbital videos circling the 4D assets can be seamlessly generated, and a coarse-to-fine 4D construction stage afterward. The video diffusion model is trained on a novel dynamic dataset curated by ... | Rebuttal 1:
Rebuttal: Dear Reviewer BwxH,
We would like to extend our great thanks for your time and effort in reviewing our work and providing insightful comments. We thank you for recognizing the values and contributions of our work, noting that **the paper is nicely written, the solution to the challenging task is ... | Summary: The paper presents Diffusion4D, a novel method for generating 4D content that maintains spatial and temporal consistency. It improves upon previous approaches by embedding spatial-temporal consistency within a single video diffusion model, enabling efficient creation of 4D assets. The technique uses a curated ... | Rebuttal 1:
Rebuttal: Dear Reviewer fabU, thank you very much for taking the time to review our work and providing constructive feedback. We also thank you for recognizing our paper with **novel method, valuable data contribution for subsequent research, good writing, and superior quantitative results**. We would like ... | Summary: This paper studies 4D object generation. Instead of using SDS, this paper proposes to use a video diffusion model for generating multi-view images first and then a 4D Gaussian Splatting representation is learned using the multi-view images. To facilitate the training of video diffusion model, this paper uses a... | Rebuttal 1:
Rebuttal: Dear Reviewer G7WS, we would like to thank you for taking the time to review our work and providing valuable feedback. We also thank you for recognizing the importance of our targeted issue, the effectiveness and versatility of our framework, and the value of our curated dataset.
We thoroughly c... | Rebuttal 1:
Rebuttal: Dear Area Chair and Reviewers,
We would like to express our sincere gratitude for your valuable time and efforts in reviewing our work and providing insightful feedback. We are encouraged that reviewers find that our paper is well-written (fabU, BwxH), the usage of video diffusion model to addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction | Accept (oral) | Summary: The paper introduces VAR, a novel autoregressive generative model for images that treats each scale in a multi-resolution feature pyramid as a token. Unlike traditional models that predict the next token from a rasterized grid, VAR predicts the next scale in a multi-resolution grid. This approach demonstrates ... | Rebuttal 1:
Rebuttal: Dear Reviewer dFai,
Many thanks to your professional, detailed, and valuable reviews. We're going to response to your concerns one by one.
-----------------
> [W1] I suggest that the authors add more details and clarification on VAR's training process, the residual tokenization, and the working... | Summary: The paper introduces Visual AutoRegressive modeling (VAR), which uses a coarse-to-fine approach for image generation. VAR drastically improves performance, reducing FID from 18.65 to 1.73 and increasing IS from 80.4 to 350.2, with 20x faster inference. It outperforms diffusion transformers in quality, speed, e... | Rebuttal 1:
Rebuttal: Dear Reviewer yx9H,
---------------------------
> [W1 Q1, about VQVAE]
[A1] We appreciate your detailed comments and will address them one by one.
1. VQVAE rFID: please see the overall Author Rebuttal part of "VQVAE concerns"
2. pre-training cost: please see the overall Author Rebuttal part o... | Summary: This work proposes a novel approach to image generation using an autoregressive decoder-only transformer model. Rather than decoding in a raster-scan their approach (VAR) decodes scales/resolutions conditioned on previously generated scales, reminiscent of traditional scale pyramids in computer vision. VAR dem... | Rebuttal 1:
Rebuttal: Dear Reviewer oCJa,
Many thanks to your valuable comments and questions, which help us a lot to improve our work. We address your questions as follows.
---------------------------
> [W1 Q1, about the efficiency evaluation] I'd like to see a throughput comparison in img/s as the batch size is in... | Summary: This paper introduces next-scale prediction autoregressive models that satisfy mathematical premises (unidirectional dependency of autoregressive model) and preserve the 2D spatial locality. The core method is to develop multiscale VQ-VAE. The proposed method is more efficient than the traditional autoregressi... | Rebuttal 1:
Rebuttal: Dear Reviewer Hisd,
Many thanks to your valuable comments and questions, which help us a lot to improve our work. We address your questions as follows.
---------------------------
> [W1] The second stage of training VAR transformers is too short and is hard for me to fully understand how it wor... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Vision-Language Models with Transduction | Accept (spotlight) | Summary: Summary:
The paper under review investigates the integration of contrastive vision-language pretraining (CLIP) with transductive learning methodologies. The authors are motivated by the efficacy of transductive learning in utilizing unlabeled data to enhance the performance of conventional supervised learning... | Rebuttal 1:
Rebuttal: **W1: Unclear motivation.**
While VLMs such as CLIP enable zero-shot predictions, they are far from being perfect (please refer to the zero-shot performances of CLIP for 6 different model sizes in Table 8 in the Appendix). Please note that CLIP receives unlabeled samples and makes class predictio... | Summary: The paper introduces TransCLIP, a transductive learning method designed to enhance the performance of vision-language models (VLMs) for zero-shot and few-shot learning scenarios. By incorporating a novel objective function constrained by Kullback-Leibler divergence, TransCLIP not only improves prediction accur... | Rebuttal 1:
Rebuttal: **W1: Balance of terms.**
We understand your concern. The terms in our objective function are not necessarily in conflict; they could be seen as a clustering (driven by the GMM term), which is regularized by the Laplacian term propagating the labels between neighboring samples, and by the KL-dive... | Summary: The paper proposes a transductive method to boost the performance of existing vision-language models by assuming that all unlabeled test samples are available during the training stage. Specifically, the paper models a Gaussian mixture model, where each class is represented by a Gaussian distribution. The meth... | Rebuttal 1:
Rebuttal: **Q1.a: Online setting.**
This is indeed a general limitation of transductive-inference approaches. Still, we believe this transductive setting is useful in a breadth of application domains and real scenarios, as pointed out by Reviewers NaRY and uSPt and evidenced by an abundant and recent liter... | Summary: The paper proposes a method named TransCLIP that performs transductive inference to boost classification performance of Zero-Shot & Pre-trained Few-Shot CLIP models. The proposed methodology is an extension of [1], but for VLMs. TransCLIP proposes to learn the class prototypes, in contrast to fixed-prototypes... | Rebuttal 1:
Rebuttal: **W1 and Q2: Discussion on Test-Time methods**.
We agree that the mentioned test-time methods also employ the transduction paradigm. However, their settings are in fact very different from the ones studied in our work (zero- and few-shot adaptation of CLIP, improving inductive few-shot methods). ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers' insightful and constructive comments, and are pleased that four out of the five reviewers voted towards acceptance.
We are also glad that the reviewers found the method useful for various domains and real-world scenarios (Reviewers NaRY and uSPt), the experim... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors explore fine-tuning VLMs to specific unlabelled/partially-labelled datasets in a transduction setting. The authors propose an objective function to carry out joint inference of labels for all the test samples simultaneously. The authors then propose an iterative block Majorie minimiz... | Rebuttal 1:
Rebuttal: **W1: Eq 2.**
It's an omission on our side; indeed, it should be $\mathbf{z}_i^\top \mathbf{z}_j$. Thanks for pointing that out! We will update the objective function accordingly.
**W2: Final prediction.**
Yes, we take the argmax for the final prediction. We should have made it clearer at th... | null | null | null | null | null | null |
In-Context Learning State Vector with Inner and Momentum Optimization | Accept (poster) | Summary: This work introduces the novel concept of the state vector. It encapsulates the information of ICL examples from separate tokens as anchors. Drawing inspiration from the duality between transformer attention and gradient descent, the authors implement inner and momentum optimization to generate task-specific s... | Rebuttal 1:
Rebuttal: Dear Reviewer `MvM2`,
Thank you for your review. According to the feedback from you and other reviewers, we have conducted additional experiments and analysis. We have uploaded a [Rebuttal PDF](https://openreview.net/attachment?id=ulPGXOjfvv&name=pdf) that contains new figures and tables. We pr... | Summary: The paper presents an in-depth analysis and optimization of In-Context Learning (ICL) within large language models (LLMs). In particular, it proposes inner and momentum-based optimization techniques to increase the performance of in-context learning.
Strengths: 1. In-depth analysis of ICL vectors across 12 di... | Rebuttal 1:
Rebuttal: Dear Reviewer `4gb3`,
Thank you for your review. According to the feedback from you and other reviewers, we have conducted additional experiments and analysis. We have uploaded a [Rebuttal PDF](https://openreview.net/attachment?id=ulPGXOjfvv&name=pdf) that contains new figures and tables. We pr... | Summary: The authors focus on the compressed vectors in In-Context Learning (ICL). They highlights the similarities between the compressed vectors and parameters trained via gradient descent, proposing the formulation of the state vector. Then they applies two optimization algorithm to progressively refine state vector... | Rebuttal 1:
Rebuttal: Dear Reviewer `yzeU`,
Thank you for your review. According to the feedback from other reviewers, we have conducted additional experiments and analysis. We have uploaded a [Rebuttal PDF](https://openreview.net/attachment?id=ulPGXOjfvv&name=pdf) that contains new figures and tables. We provide th... | Summary: This paper aims to revealing the working mechanism of the compressed state vector in context learning. They first prove the state vectors are similar with parameters trained via gradient descent. Then, they propose three methods to optimize such kind of parameters: (1) inner optimization averaging each vector ... | Rebuttal 1:
Rebuttal: Dear Reviewer `FZSw`,
Thank you for your review. According to the feedback from other reviewers, we have conducted additional experiments and analysis. We have uploaded a [Rebuttal PDF](https://openreview.net/attachment?id=ulPGXOjfvv&name=pdf) that contains new figures and tables. We provide th... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We greatly appreciate your insightful reviews and are delighted that you have acknowledged our paper's strengths. We briefly summarize them as follows:
- **Novelty:** "The methods proposed are quite novel.", "The research topic is significant and intriguing.", "The paper presents... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers the problem of using compressed vectors to replace explicit demonstrations, with potential benefits of providing new perspectives to better understand the mechanism of In-Context Learning (ICL), and addressing the issue of overly long demonstrations by summarizing them into a vector. The ma... | Rebuttal 1:
Rebuttal: Dear Reviewer `pJQh`,
Thank you for your review. According to the feedback from you and other reviewers, we have conducted additional experiments and analysis. We have uploaded a [Rebuttal PDF](https://openreview.net/attachment?id=ulPGXOjfvv&name=pdf) that contains new figures and tables. We pr... | null | null | null | null | null | null |
PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation | Accept (poster) | Summary: The paper introduces a Primitive-Driven Waypoint-Aware model for robotic manipulation tasks. Initially, the entire language instruction is broken down into specific sub-steps using VLM. Subsequently, each sub-step's corresponding feature ("waypoint") is predicted through image prediction to segment the entire ... | Rebuttal 1:
Rebuttal: We are grateful to see your recognition of the PIVOT-R experiment. But there are some serious factual errors in the review:
(1) VLM is **not equal to** the world model;
(2) AHE is **multi-threaded** rather than sequential execution;
(3) The task parsing of SayCan and PIVOT-R is NOT at the sa... | Summary: In this paper, the authors present PIVOT-R, a primitive-driven waypoint-aware world model for robotic manipulation. PIVOT-R comprises two key components: a waypoint-aware world model (WAWM) that parses primitive actions and predicts primitive-driven waypoints, and an Action Prediction Module that decodes low-l... | Rebuttal 1:
Rebuttal: We are grateful for your comprehensive and encouraging review! We respond to all the issues you pointed out in detail below. We hope our response and rebuttal revision will address your concerns.
**Q1. Real-world experiment.**
We have added additional real-world experiments in Table 3 of the reb... | Summary: This paper introduces PIVOT-R, an approach for language-guided robotic manipulation. PIVOT-R consists of a Waypoint-aware World Model (WAWM) and a lightweight action prediction module, along with an Asynchronous Hierarchical Executor (AHE) to improve efficiency. The model achieves state-of-the-art performance ... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and constructive comments. We are delighted to see your recognition of PIVOT-R's state-of-the-art performance on the SeaWave benchmark. We respond to all the issues you pointed out in detail below. We hope our response and rebuttal revisions will address your ... | Summary: The paper proposes PIVOT-R, a waypoint-based world model for robot manipulation. Concretely, given a language instruction, PIVOT-R converts it to a textual intermediate goal using a VLM and then feeds that as input into a scene prediction model that generates the waypoint. This waypoint is then fed into the ac... | Rebuttal 1:
Rebuttal: We are grateful for your comprehensive and encouraging review! We respond to all the issues you pointed out in detail below. We hope our response and rebuttal revision will address your concerns.
**Q1. The complexity and scalability of method.**
**(i) Model design & implementation.**
In fact, ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time, insightful suggestions, and valuable comments. We are happy that they appreciated our paper
- makes **important technical contribution** on combining large VLMs with scene prediction models (Reviewer XYkg);
- is **well-written, well-organized, and easy t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series | Accept (poster) | Summary: This manuscript proposes a spatial association reduction method for anomaly detection and diagnosis (SARAD) where anomalous features have low association values in reconstructing the multivariate time-series. The association values are derived from an attention between the features and exhibit changes with tim... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed and constructive reviews. Here are our responses:
**Weakness1:**
**Response:**
What distinguishes features 12 and 15 from features 9, 13, and 16 in Fig. 1 is that the spatial association reduction only occurs on the former features. Figs. 1... | Summary: In this paper, the authors aim to address the problem of anomaly detection and diagnosis for time series data. The authors consider that the existing methods may obscure or dilute the spatial information and the interaction between different variates, they propose the SARAD model to capture these interactions ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed and constructive reviews. Here are our responses:
**Weakness1:**
**Response:**
We agree that some temporal modeling methods, such as RNN, do not make feature independence assumptions. However, Lines 28-34 stated that temporal methods inclu... | Summary: In this paper, the authors focus on incorporating spatial information for time series anomaly detection and diagnosis. The proposed algorithm, SARAD, employs a transformer-based data reconstruction approach to capture inter-feature associations. By analyzing changes in these associations over time, the algorit... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed and constructive reviews. Here are our responses:
**Weakness 1:**
**Response:**
Apologies for not including all baselines being compared in the related work section. Lines 89-102 introduced and discussed the limitations of several baseline... | Summary: Paper proposes a deep learning based anomaly detection method for multi-variate time series data. The proposed method has two components in a single neural network. First component is a traditional auto encoder approach in which the input time series is broken into two parts (along time) and the model is train... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed and constructive reviews. Here are our responses:
**Weakness 1:**
**Response:**
Apologies for causing the confusion. We note that spatiality has different connotations in some AI literature, e.g., geographic locations or characteristics on ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metric Space Magnitude for Evaluating the Diversity of Latent Representations | Accept (poster) | Summary: This paper studies the diversity in representation learning. The authors propose a novel family of diversity measures based on metric space magnitude, a mathematical invariant that captures numerous important multi-scale geometric characteristics of metric spaces.
The main contributions are as follows:
1. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their questions and the positive assessment of our work.
**For revisions, we take this feedback as motivation to clarify and further explain the links between magnitude and diversity.**
___
> As mentioned in the paper, it is difficult to define the diversity. The pap... | Summary: This paper focuses on evaluating the diversity of latent representations. The authors develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalizing a novel notion of dissimilarity between magnitude functions of finite metric spaces. Moreover, they demonstrate th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are looking forward to discussing the questions raised as well as clarify them in the text during revisions.
___
> In section 4.3, what is the basis for the author to use the 5-NN classifier to predict the embedding model?
The 5-NN classifier is chos... | Summary: In this paper, authors introduce magnitude of a metric space to evaluate the diversity of the learned latent representation.
Extensive experimental results are proposed, which to some extent illustrate the effectiveness of the proposed criterion.
Strengths: 1. The expression of the paper is easy to follow.
... | Rebuttal 1:
Rebuttal: > The theoretical analysis lacks comparison.
> More theoretical comparison between existing methods will make the results more convincing. Is the proposed method superior over other counterparts theoretically?
We appreciate the interest in discussing theoretical analyses in comparison to existin... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their unanimous support of our work and contributions, as well as for their interesting questions and suggestions for further clarifications. We are confident that we can implement the required changes mentioned below by making **small amendments** to our manuscript. Mor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Selective Generation for Controllable Language Models | Accept (spotlight) | Summary: This paper introduces Neuro-Selective Entailing-Generation (NSEGen), a novel approach to enhance the trustworthiness of generative language models. The method extends selective classification to language generation tasks, utilizing textual entailment to measure semantic correctness between generated and true a... | Rebuttal 1:
Rebuttal: > The method introduces several new components and parameters, which may make it challenging to implement and tune. For instance, the algorithm involves learning an entailment set, designing neuro-selection functions, and tuning multiple parameters. This complexity could make it difficult for prac... | Summary: The paper investigates the trustworthiness of generative language models in critical decision-making systems, identifying deficiencies in current uncertainty learning methods, such as selective classification and conformal prediction, which fail to address metric misalignment in GLMs. The authors propose an en... | Rebuttal 1:
Rebuttal: > There is a bit of repetitiveness in the Experiment section when describing the GLMs and Datasets.
* Thank you for your suggestion. As you mentioned, there are redundancies in our descriptions of GLMs and datasets. The following is our revised version in a concise but detailed manner, and we will... | Summary: This work presents a method for selective generation from language models for question answering. Their approach functions as a secondary decision function on top of an existing language model, determining whether to accept the language model's generation or to abstain. Their method is approach is based on con... | Rebuttal 1:
Rebuttal: > Relying on single-directional textual entailment as a method for determining correctness of an answer is susceptible to accepting generations that produce that untrue fact or hallucinations in addition to the correct answer...
- We agree that the bi-directional entailment is the best choice in t... | Summary: The paper looks at a selective generative language system, meaning one which can produce a I-Don't-Know label rather than an answer, and calibrating it such that some guarantees on the precision of the system can be made.
The paper expands upon citation [1] on selective generation, to improve the efficiency b... | Rebuttal 1:
Rebuttal: > Say more about what language problems this could and could not be applied to. The key appears to be in being able to have an accurate entailment function. For how many types of language-generation problems are such obtainable?
Thanks for raising a nice point. We expect our method can be generali... | Rebuttal 1:
Rebuttal: We appreciate reviewers’ valuable feedback and constructive comments. In this global response, we delineate the structure of our responses.
* We provide individual responses to questions of each reviewer.
* We provide pdf, including requested experiment results.
* We also suggested an improved me... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Understand: Identifying Interactions via the Möbius Transform | Accept (poster) | Summary: The paper present a way to compute high-order interactions using Mobius transformation.
Strengths: The paper shows some interesting results on the sampling requirement for recovering the interactions among features, specially tailored for explainability with boolean functions (i.e., explanation game)
Weaknes... | Rebuttal 1:
Rebuttal: Thank you for your review. We believe that some aspects of this review may be based on misunderstandings, which may have resulted in some false or misleading statements in your review. In this rebuttal we will provide additional information and clarification that will hopefully resolve your concer... | Summary: This study proposes a new efficient method to compute the Möbius Transform. To understand the behavior of large complex machine learning models, various studies have used game theory concepts such as the Shapley value to measure the impact of input variables on the model's outputs. While Shapley values only fo... | Rebuttal 1:
Rebuttal: 1. $b = O(\log(K))$. In the appendix, we consider the constant $\eta = \frac{2^b}{K}$, which we refer to as the inverse load factor. Theoretically, if we carry through the density evolution analysis, it is possible to find the minimal $\eta$ to ensure convergence of the message passing asymptotica... | Summary: This paper proposes an algorithm to efficiently compute Mobius transform under the assumption that the function to be transformed is composed of sparse and low-degree interaction terms. The paper also provides an asymptotic analysis of the sample complexity, time complexity, and accuracy of the algorithm.
Str... | Rebuttal 1:
Rebuttal: Dear reviewer jcxh. Thank you for your through and helpful review. We are delighted that you are impressed with our contributions towards efficient Mobius Transform computation, and agree with our assumptions of low-degree interactions and sparsity. We believe that our work will in time be appreci... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers, ACs and Senior ACs.
We have posted individual rebuttals to all three reviewers. We believe we have addressed the concerns of all reviewers. We would particularly like to thank jcxh as an outstanding reviewer.
Taking into account the issues, which are now resolved, we are still ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CigTime: Corrective Instruction Generation Through Inverse Motion Editing | Accept (poster) | Summary: The paper introduces a novel approach to generating corrective instructional text based on motion editing and generation models. This work is particularly motivated by the applications in sports coaching and motor skill learning. The authors propose a method that takes a user's current motion (source) and the ... | Rebuttal 1:
Rebuttal: Thanks for your comprehensive review and constructive comments. We are grateful for your recognition of the novelty of the proposed approach and the importance of our work. In the following, we address your concerns on the off-the-shelf motion estimation and motion editing models, as well as the q... | Summary: This paper introduces an innovative method designed to generate corrective instructional text, guiding users to achieve desired motions. The approach utilizes existing frameworks to create a dataset of motion pairs and corresponding corrective texts. A new motion-language model is introduced, efficiently gener... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your supportive acknowledgment of the writing quality, the originality of the proposed task with potential impact, the effectiveness of the proposed model, and the novelty in the data collection pipeline. Also, thanks for the summary of the main ... | Summary: This paper works on generating corrective instructional text from source and target human motions. Specifically, it utilized existing motion editing framework to collect a dataset for this task. Then, an LLM instruction generation method was proposed to generate text from source and target motions. This paper ... | Rebuttal 1:
Rebuttal: Thank you for the constructive suggestions. We also appreciate your acknowledgment of the writing quality, the novelty and potential for real-world applications, and the effectiveness of the proposed method. However, it seems that the technical difficulty as well as the technical contributions of ... | Summary: This paper presents CigTime for generating motion corrective instructions. The key idea is to leverage motion editing to create datasets of motion triplets and use a fine-tuned language model to generate precise and actionable instructions.
Strengths: (1) Introduces a approach for converting motion discrepanc... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your positive comments on the proposed approach and its application potential, data generation efficiency, and the effectiveness of the method. Below, we address your questions regarding corrective instruction details, motion generation, practica... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the reviewers' careful evaluations and constructive feedback on our manuscript.
In response to the reviewers' feedback, we appreciate their recognition of the novelty in the task/approach presented, for generating motion corrective instructions (R2, R3, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources | Accept (poster) | Summary: The use of LoRA in FL is challenged by the heterogeneity of downstream tasks and available resources among clients. Traditional FL methods often use the smallest viable LoRA rank for all clients for aggregation compatibility, which makes it hard to capture the full diversity of client contributions and fully u... | Rebuttal 1:
Rebuttal: We appreciate the positive score and valuable feedback from the reviewer! We'd like to respond to the reviewer's questions and comments on the following aspects:
- Paper presentation comments: W1, Question, Limitation
- SVD contribution: W2
- Additional related work: W3
---
## Response to Paper ... | Summary: Under the framework of federated fine-tuning large model, this paper proposes a simple and effective LoRA aggregation method with different ranks, which mainly focuses on the problem of client resource heterogeneity. Specifically, the full-size LoRA is first obtained, and then after normal aggregation, it is d... | Rebuttal 1:
Rebuttal: We thank the reviewer's positive score and insightful feedback on our paper! In the following replies, we address all the reviewer's comments point-by-point.
## Response to W.1
> Does it increase the communication cost to convert matrix BA to full-size LoRA before uploading?
- The current desig... | Summary: This paper proposes FlexLora to tackle FL's heterogeneous resources and data distribution problem. FlexLora allows for dynamic adjustment of local Lora ranks by employing SVD for weight distribution, improving the global model’s generalization ability.
Strengths: 1. The experiments are comprehensive. The exp... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's acknowledge of our contribution! We respond the reviewer's suggestions in the following two replies.
## Response to W.1
> This paper claims to follow a cross-device setting, but cross-device setting usually involves thousands of clients, while the client number in th... | Summary: This paper proposes a LoRA-based federated fine-tuning algorithm called FlexLoRA. FlexLoRA keeps full-size LoRA modules on the server, and decomposes them into heterogeneous rank LoRA modules according to the client task and capacity. The heterogeneous local LoRA modules will be transferred back to full-size a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and their recognition of our work's contribution to addressing heterogeneity problems in FL! In response to the raised comments, we summarize the two main questions that the reviewer pointed out in the weakness section.
---
## Response to W.1
... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity | Accept (spotlight) | Summary: Differently from other papers in the field of federated learning, the authors consider the problem of uplink compression - from the server to the workers. The main contributions of the paper are MARINA-P and its variants. These optimization schemes leverage PermK sparsification to reduce the communication comp... | Rebuttal 1:
Rebuttal: Thank you for your review and for acknowledging the contributions of our research. We will now address each of your comments and provide clarifications.
__Weaknesses__
> The main weakness of the paper is the assumption of data homogeneity. While I understand that this assumption is necessary, it... | Summary: This paper presents methods to improve communication efficiency in distributed optimization, particularly focusing on the server-to-worker (s2w) communication costs, which are often overlooked. The authors first present a lower bound on the required round of communication. They used it to state that under no ... | Rebuttal 1:
Rebuttal: Thank you for your review and appreciating the strengths of our work. We would like to address your concerns and provide additional explanations.
__Weaknesses__
> The authors use the lower bound on the rounds communication to claim a lower bound on the communication load....
As far as we unders... | Summary: This paper addresses optimizing server-to-worker communication in distributed optimization by introducing MARINA-P, a novel method using correlated compressors for downlink compression. The study identifies inefficiencies in current downlink compression approaches and shows that MARINA-P can reduce communicati... | Rebuttal 1:
Rebuttal: We appreciate your comments and are grateful for highlighting the positive aspects of our work. We will now proceed to address the concerns you raised and provide clarifications.
__Weaknesses__
> The compressor model (Definition 1.4), while standard in the federated learning literature, is somew... | Summary: This paper deals with the downlink (server-to-client) communication cost in federated learning (FL). The main motivation of the paper is to provide a theoretical analysis of the total communication cost with downlink compression. This is achieved considering unbiased or a certain class of biased lossy compress... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for recognizing the strengths of our work. We will now address each of your comments in detail.
__Weaknesses__
> The lack of any empirical results is the main weakness of the paper.
We thank the reviewer for the suggestion. We would like to highlight that our emp... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes | Accept (poster) | Summary: The paper presents a novel approach namely MimicTalk to personalized talking face generation. Unlike previous methods that rely on individual neural radiance fields (NeRF) for each identity, the authors propose a more efficient and generalized framework using a person-agnostic generic model. MimicTalk introduc... | Rebuttal 1:
Rebuttal: We are grateful for your positive review and valuable comments, and we hope our response fully resolves your concerns.
> Q1: The experimental results presented are not sufficiently comprehensive or detailed to fully substantiate these claims. Although some video results are provided in the URL, t... | Summary: The paper presents MimicTalk, an approach to improve the efficiency and robustness of personalized talking face generation. Instead of using separate NeRFs for each identity, MimicTalk adapts a person-agnostic NeRF-based model for specific individuals. They also propose an in-context stylized audio-to-motion (... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and the positive remarks on our Soundness and Contribution. We acknowledge that your concerns are mainly about the experiment of this paper, and hope our response resolves your concerns fully.
> Q1: The head pose of the talking face video is not... | Summary: This MimicTalk work aims to bridge the gap between the person-agnostic one-shot TFG setting and the person-dependent small-scale TFG setting. A carefully designed static-dynamic-hybrid adaptation pipeline is proposed to achieve expressive, generalized, and efficient personalized TFG. Furthermore, an in-context... | Rebuttal 1:
Rebuttal: We are grateful for your positive review and valuable comments, and we hope our response fully resolves your concerns.
> Q1: There are several losses used in the pipeline. How to choose these loss weights and how these loss weights affect the performance could be further discussed.
- A1: Thanks ... | Summary: This paper targets to tackle efficient 3D realistic talking face customization. Rather than learning an individual neural radiance field for each identity, this work exploits a person-agnostic model to improve the efficiency and robustness of personalized talking face generation. A static-dynamic-hybrid adapta... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer fWzj (Part 1/2)
We thank the reviewer for the constructive feedback and the positive remarks on our proposed "generic-model-to-adaptation" framework. We acknowledge that your concerns are mainly about qualitative results and some technical designs, and hope our respo... | Rebuttal 1:
Rebuttal: # General Response
We would like to thank the reviewers for their constructive reviews! Following the comments and suggestions of reviews, we have performed additional experiments and revised the manuscript. We have also uploaded **4 new demo videos on the demo page** (Please kindly refer to our o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conditional Synthesis of 3D Molecules with Time Correction Sampler | Accept (poster) | Summary: This paper focuses on diffusion-model-based molecular inverse design (i.e., conditional molecular generation), and proposes a novel approach to address the inconsistency between target distribution and that after online guidance. Specifically, a time predictor is trained to predict the time of the manifold tha... | Rebuttal 1:
Rebuttal: Thank you for the constructive and helpful comments. We have addressed your comments below. We appreciate your positive comments that
- Clear motivation and a novel idea to the challenging problem.
- Extensive related works for the audience.
- Necessary ablation studies.
We initially address y... | Summary: This study utilizes a predicted time estimator to correct the data manifold during the guided generation of diffusion models for molecules, to mitigate the discrepancy between the forward and reverse distribution. The authors show that by adjusting the noised sample according to predicted time, as opposed to r... | Rebuttal 1:
Rebuttal: Thank you for the constructive and helpful comments. We have addressed your comments below. We appreciate your positive comments that
- Offers useful insights in reducing the exposure bias of diffusion models.
- Comprehensive analysis and ablation studies which could help future works.
- Demons... | Summary: This paper presents a framework for generating 3D molecules called Time-Aware Conditional Synthesis TACS. The proposed approach uses conditional generation with adaptively controlled plug-and-play online guidance into a diffusion model to drive samples toward the desired properties while maintaining validity a... | Rebuttal 1:
Rebuttal: Thank you for the constructive and helpful comments. We have addressed your comments below.
**W1** Generalizability of TCS
As detailed in [GR1], we have applied TCS to image generation using the CIFAR-10 dataset, demonstrating significant improvements in FID scores. These results show that our ... | Summary: This paper proposes Time-Aware Conditional Synthesis (TACS), a method that aims to improve the robustness of property-conditioned diffusion models for 3D molecule generation. The key idea is to mitigate the exposure bias of the conditional denoising process by training a time prediction model that matches samp... | Rebuttal 1:
Rebuttal: Thank you for the constructive and helpful comments. We have addressed your comments below. We appreciate your positive comments that our work
- Introduces a promising approach to the exposure bias
- Show improvements compared to baselines with desired quantum properties while maintaining stabili... | Rebuttal 1:
Rebuttal: General response
Dear reviewers and AC,
We sincerely appreciate your valuable time and effort in reviewing our manuscript. Your insightful feedback has been instrumental in improving our work.
Our paper introduces Time-Aware Conditional Synthesis (TACS), a novel approach for conditional 3D mole... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line | Accept (poster) | Summary: This paper try to achieve reliable TTA by tackling three bottlenecks, including performance evaluation without labeled data, some distribution shifts, and hyperparameter selection for TTA methods.
Strengths: 1. TTA is an important topic, and this work is a relevant and timely contribution.
2. The experiments... | Rebuttal 1:
Rebuttal: Dear Reviewer 1nU3,
We greatly appreciate your thoughtful reviews on our paper.
To address the concerns you raised, we made changes and clarifications as following:
+ Clarified our paper’s novelty,
+ Clarified notations on Algorithm 1,
+ Added theoretical intuition of using inverse of cumulative... | Summary: This papers shows that using test-time-adaptation improve the accuracy of the algorithms used for OOD performance estimation algorithms based on agreement-on-the-line and accuracy-on-the-line. This is shown through a series of experiments. Some justification has also been provided based on prior work,.
Stren... | Rebuttal 1:
Rebuttal: Dear Reviewer ZucK,
We greatly appreciate your thoughtful reviews on our paper. To address the concerns you raised, we made clarifications on:
+ Intuition of linear trend observations,
+ Our study’s contribution under cost-limited scenarios,
+ Condition 1 of the Eq. 2 of our original manuscript... | Summary: The paper presents a study on how test-time adaptation influences accuracy-on-the-line (ACL) and agreement-on-the-line (AGL). The authors empirically find that TTA methods significantly enhance the ACL and AGL, enabling better OOD performance estimation. Extensive experiments support their findings.
Strengths... | Rebuttal 1:
Rebuttal: Dear Reviewer JQG9,
We greatly appreciate your thoughtful reviews on our paper. To address the concerns you raised, we included additional results and clarifications as following:
+ Added T3A as non-BN method and its AGL visualizations and feature alignment analysis,
+ Clarified our paper’s resu... | Summary: This paper presents observations that TTA models exhibit strong agreement-on-the-line (AGL) and accuracy-on-the-line (ACL) phenomenon, which persists across a wide range of distribution shifts and models. Leveraging this observation, the authors apply methods to estimate OOD accuracy without labeled data and p... | Rebuttal 1:
Rebuttal: Dear Reviewer puv1,
We greatly appreciate your thoughtful reviews on our paper. To address the concerns you raised, we included additional results and clarifications as following:
+ Comparisons with five existing model selection baselines,
+ Best TTA methods selection results using our method,
+... | Rebuttal 1:
Rebuttal: We greatly appreciate all four reviewers' valuable feedback and thoughtful suggestions.
The reviewers highlighted the following strengths of our paper:
* Our paper investigates the under-explored but novel and important observation of TTA inducing AGL phenomenon (Reviewer puv1, Reviewer JQG9).
*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment | Accept (poster) | Summary: The paper introduces a novel approach termed "In-N-Out" for enhancing the performance of 3D object removal tasks by leveraging tuning-free latents alignment. The authors have addressed the challenges of geometric mismatches and color inconsistencies prevalent in existing methods.
Strengths: The authors have c... | Rebuttal 1:
Rebuttal: - **1. Quality of academic writing.**
Thanks for pointing out these typos. In line 172, Omega(·) is a decoder which is introduced in line 151, and $\hat{D}_{\phi}$ is the depth estimated by NeRF. We hope these typos do not affect the clarity of the ideas presented in this paper.
- **2. Comprom... | Summary: The paper deals with 3D inpainting problem in the NeRF setup with 2D diffusion models. To achieve multiview consistency, they took a "inpaint-outstretch" strategy. They first inpaint one key frame, and conditioned on which, generate a view-consistent inpainted image set. Finally, they use the multiview images ... | Rebuttal 1:
Rebuttal: - **1. Explanation of ILA**
(a) The reviewer's intuition of ILA is entirely correct. ILA primarily contributes to appearance (color) consistency. Geometry consistency is achieved by ELA (the alignment of initial noise) since this element serves as a foundational structure (semantic) for the inpai... | Summary: This paper presents how to tackle the challenge of 3D object removal by involving 2D diffusion prior. The approach involves pretraining NeRF with inpainting prior and then jointly optimizing it with latent alignment to align feature priors.
Strengths: 1. Introducing a 2D diffusion prior as a solution for the ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for providing these valuable comments.
- **1. Lacks a comparison with other baselines.**
Thank you to the reviewer for pointing this out. Unfortunately, the method "Reference-guided Controllable Inpainting of Neural Radiance Fields" (ICCV 2023) is not open-sou... | Summary: This paper proposes to solve multi-view or 3D inpainting problem. Given multiple posed views and the masks in each view specifying an object to be removed. It first trains a NeRF on the unmasked regions. To in-paint the masked regions, a seed view is selected and inpainted with a pre-trained diffusion inpainti... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the constructive feedback. Below we provide the answers to reviewer's concern:
- **1. Method over-simplified, and marginal difference compared to priors works.**
We would like to argue that while our motivation may appear straightforward, the solution we propos... | Rebuttal 1:
Rebuttal: We would like to thank 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 S5oa
- We have clarified the motivation and contribution compared to the priors work.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures | Accept (oral) | Summary: This paper states an hard-association EM algorithm for Gaussian mixture estimation, where the Gaussian components can be different and anisotropic. They also state theoretic bounds of the misclustering error rate.
Strengths: Originality: The theoretical association bound for different and anisotropic clusteri... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and remarks.
> Originality: A related work is
Thank you for pointing this out. Our hard-EM algorithm indeed shares similarities with the soft one. Specifically, our algorithm modifies the E-step of the soft-EM by implementing a maximization step that hard ... | Summary: This paper analyzes the minimax error rate in clustering anisotropic Gaussian mixture models. The authors establish a universal lower bound in two different models: different means, same covariance matrix (resp., different means, different covariance matrix) for every cluster and different covariance. For both... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and remarks.
> The paper misses some high-level intuition, and several proofs are tough to parse.
Thanks for pointing this out. In the final version, we will enhance our discussion to better articulate the derivation and significance of key terms. The "ide... | Summary: The paper provides a minimax lower bound of misclustering error rate for clustering under the anisotropic GMM. Then, the paper designs an adjusted Lloyd's algorithm which can obtain the minimax lower bound within log(n) iterations. The paper also conducts some experiments to show the performance of the propose... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and remarks.
> My major concern is about its experiments.
Thank you for your comment. We plan to include a new experiment in the final version of our paper using a real dataset from the Fashion-MNIST collection. This experiment will focus on the clustering... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their detailed and insightful comments. Each reviewer has provided valuable feedback from different perspectives. In recognition of this, we respond to each reviewer's comments individually to ensure that all concerns and suggestions are thoroughly addressed. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quasi-Bayes meets Vines | Accept (poster) | Summary: Quasi-Bayes, in the sense of building a model as a consistent sequence of predictive distributions, has recently received a lot of interest. The initial construction in [25] used products of bivariate copulas to build their predictives; the authors propose here to extend the construction to vine copulas, a fle... | Rebuttal 1:
Rebuttal: # Reply 1:
We did not mean to alienate readers, and will modify the text according to your suggestions. For the camera ready, we will use your phrasing for the abstract and use more neutral expressions in the introduction, citing papers emanating from [25] as evidence of an active area of research... | Summary: This paper develops the recently proposed quasi-Bayesian methods by applying vine copula (hence named QB-Vine) to the recursive Bayesian predicative distributions and bypassing the need for expensive posterior integration. The proposed method consists of two parts: independent recursion of marginals by bivari... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort in reviewing and offer answers to their comments below.
# Reply to weaknesses:
To showcase the scalability of our approach in higher dimensions, we have expanded the experiments from Table 6 in the paper with a study in $d=400,500,600$ dimensions on Gaussia... | Summary: The authors propose a novel method for modeling high-dimensional distributions (for density estimation and supervised learning), where they break the estimation task into estimation of univariate marginals and estimation of a multivariate copula. To expedite the univariate estimation tasks they utilize the nov... | Rebuttal 1:
Rebuttal: We appreciate your effort in reviewing our paper and are glad you found it well-written - thank you. We provide answers to your suggestions below.
# Reply Q1:
We thank the reviewer for pointing out this important oversight and have accordingly removed all $K$ and replaced them with $(n)$. We al... | Summary: Inspired by previous quasi-Bayesian (QB) methods, the recursive decomposition of Bayesian predictive posterior distributions, and vine copulas, the work introduces a new adaptation of QB to higher dimensions. The driving idea, introduced in Section 3 consists of making an adaptation of the previous decompositi... | Rebuttal 1:
Rebuttal: # Reply to weakness 1
We appreciate your recommendation on this. Following your and other reviewer's comments, we have (1) interchanged subscripts and superscripts between dimension and predictive step, (2) added an introduction of vines as a subsection in Section 2, providing a clearer overview t... | Rebuttal 1:
Rebuttal: #### We thank all the reviewers for dedicating their time and effort to the review of our paper. Below, we outline the main points raised by reviewers and how we address them. We also uploaded a pdf file with extra results and a figure showing the algorithm for the QB-Vine. Further, we have posted... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight | Accept (poster) | Summary: The paper introduces TALoS, a novel test-time adaptation method for Semantic Scene Completion (SSC) that leverages information from driving environments. The key idea is to use observations and predictions from different moments as self-supervision signals to adapt the pretrained model. Specifically, LiDAR poi... | Rebuttal 1:
Rebuttal: ### 1. Evaluation on the other datasets
| SSC method | Train dataset | Validation dataset | Baseline | TALoS |
|--------------|--------------|---------------|:----------:|:-------:|
| SSCNet | KITTI-360 | KITTI-360 | 17.0 | **17.4** |
| SCPNet | SemanticKITTI | KITTI-360 | 14.... | Summary: This work proposes a test-time adaptation method for 3d semantic scene completion from point clouds. Using the point cloud and predictions of past and future frames in a sequence the authors propose a self-supervision strategy to adjust the model weights during test time. They propose a completion loss based o... | Rebuttal 1:
Rebuttal: ### 1. Comparison with temporal fusion
We deeply appreciate the reviewer's valuable feedback.
As a response, we would like to clarify that TALoS indeed performs "something more" than a naive temporal fusion.
Besides, we also experimentally verified that this statement is still true even when usi... | Summary: The manuscript proposes an method for test time adaptation of semantic scene completion models. During test time the method updates two models: one causally based only on previous times and one (in a delayed way) on future times. The models are updated through harvesting freespace and occupied annotations from... | Rebuttal 1:
Rebuttal: ### 1. Experiments using other SSC models
| SSC method | Dataset | Baseline | TALoS |
|--------------|---------------|:----------:|:-------:|
| SSA-SC | SemanticKITTI | 24.5 | **25.3** |
| SSCNet | KITTI-360 | 17.0 | **17.4** |
Thank you for the constructive comme... | null | null | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's valuable feedback and will incorporate the suggestions into our paper revision.
We also sincerely thank the reviewer for acknowledging the following strengths of our paper:
* Novelty and soundness of TALoS (MQ39, xDnF, and 9T82)
* Comprehensive qualitative and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffeomorphic interpolation for efficient persistence-based topological optimization | Accept (poster) | Summary: The paper introduces a novel approach to topological optimization using diffeomorphic interpolation to tackle the sparsity and inefficiency of gradient descent in topological data analysis (TDA). The authors focus on optimizing point clouds by transforming sparse gradients into smooth vector fields. They achie... | Rebuttal 1:
Rebuttal: > topological registration losses is not demonstrated
See Global Rebuttal and companion pdf (Figure 2(b) and Table 1), where we showcased our methodology on a topological registration loss on a real-world dataset of single cells. While we do observe oscillations, we still note a global decrease o... | Summary: In "Diffeomorphic interpolation for efficient persistence-based topological optimization", the authors provide a novel method to compute interpolations of gradients of persistence diagrams. This approach makes optimisation of point clouds w.r.t. loss functions defined on the associated persistence diagrams fea... | Rebuttal 1:
Rebuttal: > 1. What is the intuition behind the proof of Theorem 2?
Our goal with this result was to quantify the smoothness of our proposed diffeomorphic interpolation (as characterized with its Lipschitz constant) based on the kernel it is computed from. In the case of the Gaussian kernel, we found that ... | Summary: The paper proposes a novel way based on diffeomorphic interpolation to deal with the problem of sparse gradients when performing topological optimisation, which is relevant in the intersection between Topological Data Analysis (TDA) and ML.
Strengths: (S1) The paper addresses a relevant problem in the TDA fie... | Rebuttal 1:
Rebuttal: > (Q1) color swap in Figures 6 and 7
This was accidental. Thank you for catching this error!
> (Q2) elaborate on (…) black-box AE models, (…) compare to other topological optimisation techniques
Sorry: we made a misleading mistake by accidentally naming “Vanilla” the competitor of “Diffeo” (our... | Summary: This paper proposes a new topological optimization scheme mainly due to the sparsity of topological gradients. The authors introduce the notion of diffeomorphic interpolation and use this to create a smooth vector field over the whole space, which gives a gradient descent algorithm. The authors show some theor... | Rebuttal 1:
Rebuttal: > Weaknesses 1. and 2. (Section 3 is notation intensive. // Proof of Proposition 3.1 can be moved to the appendix)
Thank you for the suggestion. We will alleviate Section 3, stating Proposition 3.2. more informally and deferring the complete, technical statement to the appendix. As far as the pro... | Rebuttal 1:
Rebuttal: # Global rebuttal
We thank the reviewers for their constructive reviews and overall positive comments on our work. We will take into account all comments on clarity, typo, etc., that have been reported by the reviewers in the revised version of our work.
In addition to individual responses, we p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Unsupervised Model Selection for Domain Adaptive Object Detection | Accept (poster) | Summary: The paper introduces the Detection Adaptation Score (DAS), an unsupervised approach for selecting optimal models in Domain Adaptive Object Detection (DAOD) without target domain labels. DAS is based on the flat minima principle and includes the Flatness Index Score (FIS) and Prototypical Distance Ratio (PDR) t... | Rebuttal 1:
Rebuttal: Thank the Reviewer XtC2 for your constructive comments and valuable feedback. We appreciate the recognition of our work on motivation and perspective. Now we answer the question raised by the reviewer as follows.
**Q1**: It seems that the proposed method is only tested on the Faster RCNN baseline... | Summary: This submission provides a strategy to select an appropriate domain adaptive object detection model without access to labels of target domain. The basic premise is the minima flat to instruct the selection, because minima flat means better generalization. Experiment indicate the effect of the design based on t... | Rebuttal 1:
Rebuttal: Thank Reviewer 9Lzv for the constructive comments. We appreciate the positive comments including clear presentation, rational strategy, and effective experiments. Following are the responses to the reviewer's concerns.
**Q1**: This paper may neglect a fact that in real application, target labels ... | Summary: This paper tackles the problem of model selection in unsupervised domain adaptation for object detection (DAOD). In DAOD, existing methods choose the models (checkpoints) using ground truth data on the target domain, which is impractical in real-world settings. To solve the problem, this paper proposes a model... | Rebuttal 1:
Rebuttal: Thank the Reviewer jxDH for your insightful comments. We appreciate the positive comments regarding the writing, method, and performance. Now we answer the question raised by the reviewer as follows.
**Q1**: Does the proposed method work well in other UDA tasks as well?
**A1** Yes. Our method ca... | Summary: The paper introduces a new metric ("DAS") for unsupervised model selection in domain-adaptive object detection. DAS consists of two components: a flatness index scores that approximates the flatness of the loss landscape in the target domain by measuring prediction agreement across perturbed model parameters, ... | Rebuttal 1:
Rebuttal: Thank Reviewer 4YNt for constructive comments and valuable feedback. We appreciate the recognition of our work including the support for the novelty, structure, and evaluation. Now, we address the reviewer's concerns as follows.
**Q1**: The writing quality should be improved.
**A1**:We sincerely... | Rebuttal 1:
Rebuttal: Dear reviewers,
We would like to thank all the reviewers for their insightful comments and constructive feedback which have significantly enhanced the quality of our work. We appreciate that the reviewer acknowledges the advantages of our work: "**This paper is well motivated. The proposed method... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper delves into an unsupervised evaluation problem in Domain Adaptation Object Detection (DAOD). To solve this problem, this paper proposes a method based on the flat minima principle, named Detection Adaptation Score (DAS), which can measure the flat minima without using target labels. Specifically, th... | Rebuttal 1:
Rebuttal: We thank Reviewer ood2 for the valuable feedback and insightful comments. We appreciate the reviewer's positive comments regarding motivation and method. We now clarify the reviewer's concerns as follows.
**Q1**: Explain why these methods fail to evaluate the object detection model in line49.
**... | null | null | null | null | null | null |
Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching | Accept (poster) | Summary: This paper introduces the Rough Transformer, a variant of the original Transformer that allows the processing of discrete-time series as continuous-time signals through the use of multi-view signature attention. Empirical comparisons shows that Rough Transformers outperform vanilla Transformers and continuous-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We are slightly surprised by some of their comments, which we believe are already addressed in the manuscript. We hope that this response will help to clarify any misunderstandings. In line with the reviewer's comments, we have carried out an extensive set o... | Summary: The paper proposes Rough Transformers, an attention-based model for long continuous-time signals. The model utilizes ideas from rough path theory to extract path signatures from the continuous-time signal (obtained by interpolation of the original signal). Two types of signatures are extracted: global and loca... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the engaging review and valuable feedback. We believe we have incorporated the reviewer's suggestions into our manuscript and hope our changes warrant an increase in the score.
**[W1] Need for stronger empirical analysis.**
We have added 19 new datasets, 8 extra baselin... | Summary: The paper proposes Rough Transformer (RFormer), an extention of the Transformer architecture towards operating on continuous-time representations of time-series data. RFormer employs a novel technique called multi-view signature attention, which performs attention on path signatures pre-computed from input dat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback, as it has been very relevant in updating our manuscript (especially the comment on oversmoothing). We hope that the reviewer will be satisfied with the additional experiments carried out and will be inclined to raise the score.
**[W1.1] Motivation of synthe... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable feedback. We have carefully reviewed their comments and incorporated their suggestions into a new version of the manuscript. Additionally, we are encouraged by the positive feedback provided by the reviewers on the novelty, efficiency, and pe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell RNA-Seq Data Imputation | Accept (poster) | Summary: This paper introduced a new scRNA-seq imputation method, scCR, utilizing both associating and dissociating gene-gene relationships to improve the accuracy of scRNA-seq imputation, especially on noisy dataset.
The method constructed comprehensive k-NN graph for both cell-cell and gene-gene. Standardize the val... | Rebuttal 1:
Rebuttal: > It would be helpful to include a detailed sensitivity analysis of key hyperparameters, such as k for k-NN, \alpha, \beta, and \gamma. The impact of hyperparameter selection, additional set of experiments would be perfect. But due to the time limit, theoretical analysis would also be very helpful... | Summary: This paper introduces a novel imputation method for scRNA-seq data that accounts for both associating and dissociating gene relationships by using a k-NN graph and negating the cell-gene matrix. The method standardizes gene value distributions and shows significant performance improvements in cell clustering a... | Rebuttal 1:
Rebuttal: > How do large models for cell clustering, such as SCGPT and GeneFormer, compare in terms of effectiveness?
Our method and large-scale models have **clearly different objectives; while our method tackles denoising scRNA-seq data, large-scale models (e.g., scGPT and Geneformer) targets learning ge... | Summary: The paper proposes an approach for single-cell RNA-seq data imputation. The data comes as a matrix capturing relationship between cells and genes. Zero values in that matrix represent unobserved gene expression that can result from technical omissions (known as dropouts) and true biological absence. The non-ze... | Rebuttal 1:
Rebuttal: > Imputation metrics might be dependent on dropout strategy and it would be good to discuss what kind of “random” strategies have been used and how likely they are to reflect the corruptions specific to single-cell RNA-seq. Is there any way to generate “challenging” splits that are better at refle... | null | null | Rebuttal 1:
Rebuttal: We 1) propose a novel imputation method that newly employs dissociating relationships in addition to associating relationships, 2) standardizes the value distribution of each gene to have standard distributions regardless of the gene, and 3) demonstrate that the proposed method
achieves exception... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Amortizing intractable inference in diffusion models for vision, language, and control | Accept (poster) | Summary: This paper studies the problem of training diffusion models to sample from an intractable posterior distribution, defined by a prior diffusion model and an arbitrary likelihood function. The contributions are summarized as follows:
- This paper proposes relative trajectory balance (RTB) for training diffusion... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive assessment of the paper! We answer your question below:
### Does Gaussian initial state violate assumptions?
Assuming the initial distribution is Gaussian does not violate the GFlowNet assumption, as it amounts to assuming the first generation step transi... | Summary: The paper looks at the problem of finetuning/training a generative model to sample from a desired posterior distribution when given access to a diffusion model prior. The experiments validated the generation capability across different tasks that diffusion models (and conditional generation) can be applied to ... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are happy to see that you appreciated the breadth of applications presented in our paper and the value of the contribution.
Below we have attempted to answer your questions and to clarify a few misunderstandings in the review. We note that most of the listed weakne... | Summary: This paper proposed a method to train a posterior $p^{post}(x)$ given a prior distribution $p(x)$ and some additional (possibly unnormalized) constraint function $r(x)$, when the prior is a pretrained diffusion model. Through the choice over $r(x)$, this setup can capture a wide range of tasks. The authors pro... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and good questions. We are glad you found the paper well-written and the empirical results compelling. We believe that the diversity of domains on which we showed the effectiveness of our approach makes this work both a valuable illustration of off-policy f... | null | null | Rebuttal 1:
Rebuttal: **We would like to thank all the reviewers for their comments. The reviewers all noted that the paper is well-written (4zDq, VfXT, Z1kk) and pointed out the the broad utility of the proposed approach (4zDq, VfXT, Z1kk), the theoretical justification for the proposed method (4zDq), its efficiency (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalizable and Animatable Gaussian Head Avatar | Accept (poster) | Summary: This paper presents a method for animatable facial avatar reconstruction from a single RGB image, and the reconstructed avatar is based on 3D Gaussian splatting (3DGS) to support real-time rendering. To this end, the proposed method generates pixel-aligned Gaussian point clouds to reconstruct the identity, and... | Rebuttal 1:
Rebuttal: Thank you for your review and helpful comments, which triggered deeper thinking about our approach. We would like to address your concerns in the following sections:
**How could the static points produced by the reconstruction branch model the dynamic expressions?**
* Your understanding is mostly... | Summary: The paper proposes a method to achieve one-shot head avatar animation with 3D Gaussian Splatting (3DGS). With 3DGS, the papers show high-fidelity animation with fast inference speed. To solve one-shot 3DGS reconstruction, the paper proposes a dual-lifting method with 3DMM regularization. Experiments justify th... | Rebuttal 1:
Rebuttal: Thank you for your positive review and helpful comments. We would like to address your concerns in the following sections:
**Is it possible to conduct a study by removing the reconstruction branch to better demonstrate the importance of dual-plane lifting?**
* Removing the reconstruction branch ... | Summary: The paper introduces a novel framework, GAGA, for one-shot animatable head avatar reconstruction from a single image. The key innovation is the dual-lifting method, which generates high-fidelity 3D Gaussians that capture identity and facial details by predicting lifting distances from the image plane to 3D spa... | Rebuttal 1:
Rebuttal: Thank you for your positive review and insightful comments. We are happy to address your questions in the following:
**Why not also predict position offsets for the expression Gaussians?**
* Our method emphasizes the efficiency of inference rendering. Predicting expression Gaussians offset for ea... | Summary: This paper presents "Generalizable and Animatable Gaussian Head Avatar" (GAGA), a method for one-shot animatable head avatar reconstruction using 3D Gaussians. Unlike existing methods that depend on neural radiance fields and require extensive rendering time, GAGA employs a dual-lifting method to generate high... | Rebuttal 1:
Rebuttal: Thank you for your positive review and valuable comments. We are pleased to address your concerns in the following sections:
**How sensitive is the model to image quality and lighting conditions?**
* We present more qualitative results with low-quality images or challenging lighting conditions i... | Rebuttal 1:
Rebuttal: Firstly, we would like to thank all the reviewers for their thorough review and valuable suggestions. We summarize the issues raised and indicate where we address them. Additionally, we provide some new visual results in the supplementary page.
We answered the following questions in our response ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Alias-Free Mamba Neural Operator | Accept (poster) | Summary: This paper introduces a novel neural operator called MambaNO (Mamba Neural Operator) for solving PDEs. The key contributions atleast to me are:
1. A new integral form called "mamba integration" with O(N) computational complexity that captures global function information. This is so cool! I give props to the a... | Rebuttal 1:
Rebuttal: 1. Most of the hyperparameters, including encoder layer, upsampling factor, scanning direction, and integration depth, have been ablated with results and practical settings given in subsection E of supplemental material. Note that for most NO applications, the dimension of state space is simply se... | Summary: This paper proposes a novel neural operator structure that applies the Vision Mamba architecture to neural operators. Additionally, it introduces an activation operator to mitigate the impact of standard neural network activation functions on bandlimited functions, thus reducing aliasing error. The method show... | Rebuttal 1:
Rebuttal: In the original texts, we presented the results of eight representative two-dimensional partial differential equations, demonstrating that MambaNO enjoys better accuracy yet with O(N) complexity. Thanks for your reminding of the new datasets and the two outstanding models. As suggested, we have ad... | Summary: The authors present a new operator architecture based on Mamba and convolutional integration. They show theoretically and empirically that this neural operator is discretization invariant and alias-free. The proposed architecture outperforms baselines across a variety of 2D benchmarks.
Strengths: The theoreti... | Rebuttal 1:
Rebuttal: 1. As suggested, we have provided the experimental data and their p-values in the uploaded PDF, showing that the differences between the experimental results are statistically significant. In other words, these differences are not due to any random fluctuations. Note that such tables are too redun... | Summary: The paper introduces Mamba Neural Operator (MambaNO) for solving Partial Differential Equations (PDEs) efficiently. Unlike existing methods, which are computationally expensive and often neglect global and local feature integration, MambaNO offers O(N) complexity. It balances global and local integration throu... | Rebuttal 1:
Rebuttal: 1. Alias-free is truly an important property of the framework. However, two reasons lead us to move it into the appendix. First, our innovation lies in proposing MambaNO that follows alias-free property. The alias-free framework is innovated by other work [1], not ours. Therefore, we put more effo... | Rebuttal 1:
Rebuttal: At the outset, we would like to thank all four reviewers for their thorough and patient reading of our article. Overall, four reviewers have all complimented some aspects of our study.
Specifically, Reviewer 1: The paper is well-motivated and presents a promising MAMBA-based alias-free framework... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs | Accept (poster) | Summary: The paper proposes a multi-dataset training approach that works on top of a unified output taxonomy mapped onto individual dataset-specific taxonomies. First, a multi-head model is trained. Then, a unified taxonomy is automatically constructed by merging semantically identical classes, determined based on segm... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful comments regarding our manuscript. Below are our responses to your questions and concerns.
**W1: Focus on benchmark performance vs. taxonomy quality**
We acknowledge the importance of reasoning within a unified label space. Our focus on multi-da... | Summary: This paper introduces a method using GNN to automatically construct a unified label space across multiple datasets, addressing the issue of label space conflicts in multi-dataset semantic segmentation. The method eliminates the need for manual re-annotation or iterative training, significantly enhancing the ef... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our work. Below, we summarize each of your questions and provide detailed responses.
**Q1 & Q2: Concerns about the lack of detailed explanation and the omission of the latest methods**
A: We respectfully disagree with your assessment. Our approac... | Summary: This paper introduces a method to automatically match and unify different label spaces for semantic segmentation. This allows to train a single model on multiple, differently annotated datasets. The authors can show that this can yield benefits in overall model performance, also compared to other multi-label a... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for highlighting the areas that require clarification in our paper. We appreciate your insights, which allow us to improve our manuscript. Below, We summarize each of your questions and provide detailed responses.
**Q1: Clarification on how graph connectiv... | Summary: The paper presents an approach to automatically train a semantic segmentation network using multiple datasets with individually different class label policies. A unified label space emerges during training automatically without direct manual label mapping definitions by using a graph neural network (GNN) which... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful inquiries and for pointing out crucial elements of our methodology. Your feedback is instrumental in refining our paper and addressing any weaknesses. Below, we summarize your questions and provide detailed responses.
**Q1: About the creation of textual des... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable comments and suggestions. The automatic construction of a unified label space using GNNs is the main innovation and contribution of our method. However, due to the limitations of our presentation format, we apologize for not being able to pro... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a novel approach for automated label unification across multiple datasets in semantic segmentation using Graph Neural Networks (GNNs). The proposed method aims to create a unified label space that allows semantic segmentation models to be trained on multiple datasets simultaneously without ... | Rebuttal 1:
Rebuttal: We sincerely thank for your thoughtful questions. Below are our responses to the specific questions raised:
**Q1: How do you select the unified label size N? Is it adaptive to different dataset labels, or preset hyper-parameters?**
A: We apologize for not clearly explaining the selection of the ... | null | null | null | null | null | null |
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning | Accept (spotlight) | Summary: This paper approaches the challenge of pluralistic alignment, which arises when the preferences among humans diverge across a population. The work identifies that current preference modeling assumptions do not account for multi-modal reward distributions, which is often the case for pluralistic preferences. Th... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate that you found the method interesting and agree that pluralistic alignment is a challenge that is mostly overlooked by the RLHF community. We have conducted 5 additional experiments to demonstrate scalability and include the results in the rebuttal PDF, ... | Summary: The primary objective of the paper is to design a multi-modal RLHF strategy to align diverse preferences with a latent variable model. The latent variable represents the users/topics and the reward model conditioned on the latent variable is learned for each user preference. The empirical results support the h... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing useful feedback regarding our work. We appreciate that the reviewer believes that our introduced approach is natural towards aligning models to diverse preferences. The reviewer brings up useful concerns regarding the scale of the experiments, demonstrating the ... | Summary: Instead of learning a unimodal reward model as in standard RLHF, this work aims to learn a reward that covers a diverse range of preferences. It assumes that user preferences are not explicitly given, such as through verbal descriptions in the prompt/instruction. Instead, preferences are implicitly provided th... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback. We appreciate that the reviewer recognizes the importance of the problem, and the realistic setup introduced to study it. The reviewer raised useful questions about the effectiveness of the user encoder with varying labels, the generalization performance, and the... | Summary: This paper introduces a new framework for preference learning which tailors to user-preferences. Human feedback with Variational Preference Learning (VPL) learns a latent reward / preference for each user at the test-time. They furthur show potential application of techniques from active learning and uncertain... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and are glad that they agree with the motivation of the problem and the novelty of the approach. We address the concerns raised as follows:
> **“Having a single-modal dataset [...] would help further ground the work and increase the experimental depth.”**
Th... | Rebuttal 1:
Rebuttal: [Disclaimer: All Figures and Tables referred to as **AM:X** are in the **A**dditional **M**aterial.]
We thank the reviewers for their careful reading and constructive feedback. We appreciate that all reviewers recognize the importance and relevance of the problem of pluralistic alignment in prefe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Road Network Representation Learning with the Third Law of Geography | Accept (poster) | Summary: This paper proposes a novel framework for learning road network representations. Different from previous approaches, the proposed framework leverages a particularly designed graph contrastive learning method to integrate the Third Law of Geography into the process of road network representation learning, which... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. We apologize for any confusion. We are pleased to inform you that all concerns have been addressed. Below are our responses to each comment.
Responses to Weaknesses:
1. To illustrate the Third Law of Geography, consider two roads surround... | Summary: This paper introduces a novel framework for road network representation learning, with the key innovation being the incorporation of the Third Law of Geography. This concept is implemented through a tailored graph contrastive learning objective, featuring geographic configuration-aware graph augmentation and s... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and suggestions. We apologize for any confusion. We are pleased to inform you that all concerns have been addressed. Below are our responses to each comment. As the reviewer has many concerns, we conduct more discussions to address them.
Response to Weaknesses... | Summary: This paper proposes a novel method for learning representations of road networks. It highlights the limitations of existing methods that primarily use the First Law of Geography, which emphasizes spatial proximity. The authors introduce a new framework, Garner, that incorporates the Third Law of Geography, foc... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. We apologize for any confusion. We are pleased to inform you that all concerns have been addressed. Below are our responses to each comment.
Response to the Weaknesses:
1. We have conducted the sensitivity test on the hyper-paramter $k$ o... | Summary: This paper introduces Garner, dual geographic-configuration-aware graph contrastive learning framework for road representation learning. Both the third law of geography and the first law of geography are considered by using street view images, geographic configuration aware graph augmentation, and spectral ne... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for providing constructive and detailed comments, which may significantly improve this paper. We are pleased to inform you that all your concerns have been successfully addressed. Please see the detailed response to each of your comments listed below.
Response to ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their careful review and constructive comments. Here, we briefly summarize our response to all the comments.
1. A case study can be found in the uploaded PDF file.
2. More ablation studies and concrete examples have been provi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel framework, termed Garner, for road network representation learning that leverages both the Third Law of Geography and the First Law of Geography. It emphasizes both spatial proximity and geographic configuration similarity. Street view images are used to capture similarities in ro... | Rebuttal 1:
Rebuttal: The authors would like to thank the reviewer for providing constructive comments. We have made the following clarifications to address your concerns.
Response to the Weaknesses:
1. The novelty of our paper is listed as follows:
- We introduce the **Third Law of Geography**, a fundamental prin... | null | null | null | null | null | null |
Wormhole Loss for Partial Shape Matching | Accept (poster) | Summary: The paper proposes an unsupervised optimization for partial shape matching between (near-)isometric shapes, relying on distance similarity. Defined as the "Consistent Pairs" a pair of points that present a similar geodesic distance between the full and the partial shapes, this work relaxes this definition by a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and for acknowledging the relevance and challenge of addressing partial shape correspondence in an unsupervised setting, as well as the applicability of our method to MDS embeddings. We will add to Figure 6 the ground-truths. We hereby respond to ... | Summary: The paper investigates the problem of shape matching in an unsupervised and partial setting. In particular, the paper extends an existing criterion for filtering out potentially inconsistent point pairs. This is done by simply including extrinsic information that bounds from below the distances between pairs o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and their appreciation of the paper's clear contribution and the soundness and clear presentation of our framework. In the following we relate to the reviewer's remarks and questions.
### Theoretical depth
Regarding the theoretical depth of our pap... | Summary: The paper presents a novel criterion for identifying consistent pairs based on geodesic distances between points on partial and full surfaces. This new loss function utilizes intrinsic geodesic distances, extrinsic distances between boundary points, and a consistency criteria to enhance the performance of part... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and for the appreciation of the strengths of our method when applied to the challenging partial shape matching problem and our significant improvements for this task. We hereby respond to the reviewer's remarks and questions.
### Reliance on the Eu... | Summary: This paper introduces a refined criterion for detecting pairs of points whose geodesic distance on a partial surface is equal to that on the full surface. The "wormhole" criterion is sound and less conservative than previous such criteria. The refined criterion improves results in planar embedding with multidi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and for recognizing the utility of the wormhole criterion in both MDS and partial-to-whole shape matching tasks. We are happy that the reviewer enjoyed the clarity, soundness, and contribution in our manuscript. We fixed the typos pointed out by the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GOMAA-Geo: GOal Modality Agnostic Active Geo-localization | Accept (poster) | Summary: The paper introduces GOMAA-Geo, a system designed for active geo-localization tasks where the goal can be specified in various modalities, including text, ground-level, or aerial imagery. The framework uses a combination of cross-modality contrastive learning to align representations across different modalitie... | Rebuttal 1:
Rebuttal: Thanks, we appreciate the reviewer's valuable feedback!
> **Q1**: Complex Integration: The integration of multiple complex components—contrastive learning, supervised pretraining, and reinforcement learning—while effective, can be challenging to optimize and maintain, potentially limiting its ad... | Summary: In this paper, the author proposes a direction classification task, which is for the drone path navigation.
My main concerns are about the task.
Usually we have the global satellite-view image, shall we estimate the direction?
Most works do the location retrieval, which is a common practise in the geolocati... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! We hopefully address all of your current concerns / potential misunderstandings below.
> **Q1**: My main concerns are about the task. Usually we have the global satellite-view image, shall we estimate the direction?
**A1**: This appears to be a misunderstandin... | Summary: This paper introduces GOMAA-Geo, a framework designed to enhance active geo-localization for the UAV, enabling them to find targets specified through various modalities like natural language or imagery. It addresses the challenge of efficient localization in dynamic environments without specific training on di... | Rebuttal 1:
Rebuttal: While we very much appreciate the feedback on our work, we were surprised to find that this reviewer has recommended rejecting the paper, given that the listed "Strengths" seem to clearly outweigh concerns listed under "Weaknesses" (where no technical concerns about our work were mentioned) nor un... | Summary: The paper introduces GOMAA-Geo, a novel framework for active geo-localization (AGL) that is capable of zero-shot generalization across different goal modalities. GOMAA-Geo is designed to assist agents, such as UAVs in search-and-rescue operations, to locate targets specified through various modalities (e.g., n... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are pleased to see that you found our proposed framework novel and that the new dataset will be valuable for future research in this field. Below, we address all your comments.
> **Q1**: While the framework shows promise, it may require further validation in real... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback and insightful comments. We are glad that the majority of the reviewers find the zero-shot generalizability across goal modalities to be a strong contribution of our work, and agree with the reviewers that extensive experiments have been condu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Domain Learning for Cross-domain Image Denoising | Accept (poster) | Summary: This paper introduce adaptive domain learning (ADL) for cross domain raw image denoising problem. The author also introduce a module to embed sensor information into the network. Experiments on public datasets with various smartphone and DSLR cameras demonstrate proposed model outperforms prior work on cross-d... | Rebuttal 1:
Rebuttal: We thank the reviewer G2YE for your valuable feedback. We now address your concerns below.
***- Novelty issue***
In this paper, we attempt to solve the problem of lack of RAW data for new sensors ***in a different way*** compared to prior work. The calibration method needs to collect data pairs ... | Summary: This paper proposes a novel adaptive domain learning (ADL) approach for cross-domain RAW image denoising problem. The ADL scheme allows to train models for target domains with limited data by leveraging data from other source domains. The harmful data from the source domain is automatically removing during tra... | Rebuttal 1:
Rebuttal: We thank the reviewer Xk6Q for your valuable feedback. We now address your concerns below.
***- Qualitative results and metrics in sRGB space should be provided for better comparison***
Since RAW data is hard to visualize and tell the difference, we use the error map for better demonstration. We... | Summary: This paper address the cross-domain image denoising problem with a small number of target domain training samples. The authors propose an adaptive domain learning (ADL) strategy that dynamically selects useful training samples from both source and target domains to improve performance. Additionally, the paper... | Rebuttal 1:
Rebuttal: We thank the reviewer e1pc for your valuable feedback. We now address your concerns below.
***- Lacking Clarity on Definitions and Practical Benefits***
Our ADL aims to solve the problem of data scarcity in RAW image denoising. To be specific, our ADL has benefits when the data pairs from the ta... | Summary: This paper addresses the challenge of cross-domain RAW image denoising due to varying noise patterns from different camera sensors (bit depths, color). The authors propose an Adaptive Domain Learning (ADL) scheme that leverages existing data from various sensors and a small amount of data from a new sensor to ... | Rebuttal 1:
Rebuttal: We thank the reviewer 4VKx for your valuable feedback. We now address your concerns below.
***- Compared with RAW2RAW mapping***
We mapped source domain RAW data to target domain data by the pre-trained model proposed in [1]. We utilize sensors "IP" and "S6" from SIDD dataset and replace the cor... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. Below, we address the concerns of Reviewer 4VKx, Reviewer e1pc, Reviewer Xk6Q and Reviewer G2YE.
Our paper presents a novel adaptive domain learning (ADL) method with a modulation strategy to solve the problem of data scarcity in RAW denoising.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Lagrangian Learning for Conic Optimization | Accept (poster) | Summary: The authors propose Dual Langrangian Learning for learning dual conic optimization problems. Dual conic completion, differentiable conic projection layers, and a self supervised Lagrangian training framework are discussed.
Strengths: - Presents a unique framework for the dual optimization. No dedicated framew... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and their appreciation of our work.
## Benefits of dual optimization
While the paper outlines the theoretical and computational building blocks of DLL, this methodology is meant to be used in tandem with primal techniques, e.g., primal optimizati... | Summary: This paper addresses the broad category of machine learning (ML) for optimization, where ML-based approaches are used to obtain solutions or bounds on the solutions of optimization problems. In particular, the paper addresses a particular subclass of problem - conic problems, and even more specifically, the du... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper, and their valuable feedback.
Please see our general rebuttal for comments regarding how DLL can be used in discrete and non-convex settings.
Detailed responses to the reviewer's questions follow.
## Implicit layers
We agree with the r... | Summary: This paper presents the Dual Lagrangian Learning (DLL) framework that utilizes a fully-connected neural network (FCNN) to provide high-quality dual-feasible solutions that can be used to generate valid Lagrangian dual bounds for linear and nonlinear conic optimization problems without the use of costly implici... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing our paper, and for their insightful comments.
Please see our general rebuttal regarding how DLL can be used in non-convex and discrete settings.
Detailed responses to the reviewer's questions follow.
## Non-convex constraints
Please see our general ... | Summary: The paper introduces Dual Lagrangian Learning (DLL), a machine learning framework designed for dual conic optimization problems. DLL utilizes conic duality and machine learning models to produce high-quality, dual-feasible solutions and corresponding Lagrangian dual bounds for both linear and nonlinear conic o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and their appreciation of our work.
Please refer to our general rebuttal for a detailed discussion of how DLL can be used in the non-convex and discrete setting.
Detailed answers to the reviewer's questions follow.
## Comparison with Lagrangian de... | Rebuttal 1:
Rebuttal: # General rebuttal
We express our gratitude to the area chairs and reviewers for their careful reading of our manuscript, their appreciation of our work, and their valuable feedback. In this general rebuttal, we comment on the applicability of the proposed framework to mixed-integer and non-conve... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding the Gains from Repeated Self-Distillation | Accept (poster) | Summary: This paper theoretically investigates the effect of multiple rounds of self-distillation (SD) in linear regression. Under some conditions on the ground truth model $\theta^{\ast}$ and the data matrix $X$, it is shown that multi-step SD can improve the risk bound by a factor of $r = \text{rank}(X)$. Specificall... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns below.
**Concern 1: Relaxing the strong assumption of $\theta^\star$ being perfectly parallel to $\mathbf{u}_1$**
Regarding the condition of $\theta^\star$ being perfectly parallel to $\mathbf{u}_1$, we address this in the shared response, sinc... | Summary: The paper analyzes the gains a model can achieve from multi-step self-distillation and presents a solid theory showing that the excess risk does not increase with more self-distillation steps. The synthetic task in the paper effectively proves this analysis.
Strengths: 1) The analysis of excess risk for the m... | Rebuttal 1:
Rebuttal: Thank you for your review! We address your concerns and questions below.
**Concern #1.1: Comparison to another relevant paper [1]**
Self-distillation has been explored in [1,2,3], and we do provide a comparison of our results with [2,3]. Thank you for pointing out the highly relevant reference ... | Summary: The paper tries to provide a theoretical analysis of gains from applying self-distiliation repeatedly, in particular by trying to show that it is important to optimally set the imitation paramter for each step rathen than having a fixed Value. The study is conducted on using ridge estimator for linear regressi... | Rebuttal 1:
Rebuttal: Thank you for your review! We address your questions and concerns below.
**Question 1: Analysis of a more complex model family**
We understand that linear regression is a relatively simple task, but we believe that the $\Omega(r)$ order-wise separation between $r$-step SD and $1$-step SD, althou... | Summary: This paper explores self-distillation from a theoretical perspective in the context of linear regression. Distillation is when a model is trained simultaneously to predict training labels and the predictions of another model that has already been trained on the data. Self-distillation is when the trained model... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review! Let us try to resolve some of your questions and concerns.
**Question 1: Gap between $2$-step SD and $r$-step SD**
The power of two choices in load balancing is related to two choices being the first non-trivial departure from the standard one choice algor... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback and valuable comments. Multiple reviewers (Uvvg, 2wSn) have raised **concerns about Assumption 2.2 being too strong**, which we address in this shared response. We want to emphasize two points.
- First, as we point out in lines 203-205 in the manuscr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random | Accept (poster) | Summary: This paper analyzes the relationship between bias, variance, and generalization bound of proposed general estimator with data missing not at random, and proposes a quantitative bias-variance joint optimization method to achieve bounded variance.
Strengths: - S1: The research question is good, and investigatin... | Rebuttal 1:
Rebuttal: **Response for Weaknesses**
A1. For the discussion of figures, the descriptions of theoretical results and their proofs, and experimants, according to comments from reviewers, we will add more explanation to avoid any ambiguity and improve the readability.
A2. Sorry for the unprecise description... | Summary: This paper addresses the challenge of handling data missing-not-at-random, which is prevalent in applications like recommendation systems and display advertising. The authors highlight the limitations of current regularization techniques and unbiased estimators, which often result in high variance and unbounde... | Rebuttal 1:
Rebuttal: A1. Thanks for your valuable comments. In the final version, we will add more metrics. Besides, the baseline approaches and the dynamic estimators are conducted on the KuaiRec dataset to verify the performance of the developed dynamic fine-grained framework. The experimental results will be merged... | Summary: In recommender systems, there are many ratings that are missing not at random, which leads to additional bias in RS when trained using only observed data. This paper first gives a general form of the estimator with regularization, then reveals limitations of previous regularization techniques and the relations... | Rebuttal 1:
Rebuttal: A1. Considering (1), for all $(u,i)$ pairs, we have $f(o _{u,i},\hat{p} _{u,i})e _{u,i}+g(o _{u,i},\hat{p} _{u,i})\hat{e} _{u,i}\ge0$ and $h(o _{u,i},\hat{p} _{u,i})$. To facilitate representation, $f(o _{u,i},\hat{p} _{u,i})e _{u,i}+g(o _{u,i},\hat{p} _{u,i})\hat{e} _{u,i}$ is denoted as $r(o _{u... | Summary: This paper first theoretically reveals the limitations of previous regularization techniques, such as unbounded variance and generalization bound. To address this problem, this paper defines the general form of the estimator with regularization. Then this paper develops a comprehensive dynamic learning framewo... | Rebuttal 1:
Rebuttal: **Response for Weaknesses:**
- In the final version, we will add the following analysis of Figure 1 to lines 212 and 220, respectively.
*''**Line 212:**.... The curves of objective functions under different designed functions $f(\cdot)$ are given in Fig. 1(c). It can be oberved that for a fixed ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Panacea: Pareto Alignment via Preference Adaptation for LLMs | Accept (poster) | Summary: # Summary
The paper presents Panacea, an innovative method for aligning LLMs with human preferences, by reconceptualizing the alignment task as a Multi-Dimensional Preference Optimization (MDPO) challenge to recover the entire Pareto front and adapt online.
# Contribution
Panacea marks a good advancement in... | Rebuttal 1:
Rebuttal: Thank you so much for your appreciation and the encouraging score. Sincerely, we would like to address your concerns as follows.
> W1: Scalability concerns: Although the paper claims scalability, there might be concerns about the computational cost and feasibility when scaling to even larger model... | Summary: This paper addresses the Multi-Dimensional Preference Optimization (MDPO) problem, which involves aligning multiple objectives that exhibit heterogeneity in preference in the population, such as the tradeoff between harmlessness and helpfulness. The authors propose a framework that identifies the Pareto fronti... | Rebuttal 1:
Rebuttal: Thank you for your appreciation. We address your comments as follows.
>W1
Please allow us to explain the two assumptions more clearly. We first discuss assumption 2. It states that for any preference vector $\boldsymbol{\lambda}$, the LLM policy space spanned by all $\theta$ can represent all out... | Summary: This paper introduces Panacea for LLM alignment that reframes it as a multi-dimensional preference optimization problem. Unlike traditional methods that use scalar labels, Panacea trains a single large language model capable of adapting to diverse sets of preferences in a Pareto-optimal manner without further ... | Rebuttal 1:
Rebuttal: Thank you so much for your appreciation and constructive feedback, which have motivated and guided us to further improve the paper. Sincerely, we would like to address them as follows.
> W1: The presentation of figure 1 is a bit confusing and hard to understand. It can be improved. Specifically,... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their precious comments, which have helped us enormously in guiding our revision and improvement.
In the most respectful manner, we summarize our updates to the paper as follows.
1. We present a pseudocode for the training procedure of Panacea. (Reviewer Z... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Multi-Fidelity Best-Arm Identification | Accept (poster) | Summary: This paper introduces qPOTS (Pareto Optimal Thompson Sampling), a novel approach for efficient batch multiobjective Bayesian optimization in the context of best-arm identification (BAI) with multi-fidelity bandits. The authors present a tight lower bound on the cost complexity of multi-fidelity BAI and propose... | Rebuttal 1:
Rebuttal: > ### "Narrow problem focus" and comparison of MF-BAI with MFBO
First, we note that the **multi-fidelity BAI is an established problem within the literature** (i.e., "Multi-fidelity best-arm identification", Poiani et al., NeurIPS 2022, and "Multi-fidelity multi-armed bandits revisited", Wang e... | Summary: This paper considers the fixed confidence BAI problem in a multi-fidelity setting, where each arm can be sampled at lower or higher fidelity levels, with a corresponding lower or higher cost. The objective is to declare the best arm, which is the arm with the highest mean at the highest fidelity level.
The a... | Rebuttal 1:
Rebuttal: > ### I don't see any shaded area denoting confidence intervals in Figure 3.
We thank the Reviewer for raising this point. There are actually shaded areas on the plot, but the confidence intervals are really small. This is also evident from footnote 8, where we measure the distance of ${\omega}(T... | Summary: The paper studies the problem of multi-fidelity best arm identification in the fixed confidence setting. The main contribution is an instance-dependent lower bound on the cost complexity. The authors demonstrate the bound's tightness by providing an algorithm with a matching upper bound in asymptotics, as well... | Rebuttal 1:
Rebuttal: > ### Writing suggestion
We thank the Reviewer for suggesting ways to improve the presentation of our algorithm. We have incorporated the symbols also within the algorithm box in a revised version of the manuscript.
> ### "Do the authors have any conjectures on the fixed-budget setting for mult... | null | null | Rebuttal 1:
Rebuttal: We thank the Reviewers for their efforts in reviewing our paper. We are happy that the reviewers have recognized our paper as a "clear improvement over previous work" (Reviewer bSBL) and that they appreciated the "non-trivial technical contributions to the state of the art in multi fidelity BAI" (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Information Theoretic Perspective on Conformal Prediction | Accept (poster) | Summary: The paper provides a series of upper bounds for the conditional entropy of the labels given the attributes. The right-hand side of the bounds depends on the coverage probability of the corresponding Conformal Prediction sets. The authors propose to use the bound as an alternative objective function for conform... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We address the main concerns below.
> The authors may explain more intuitively the role of the arbitrary distribution $Q$.
One can think of our results as way to upper bound the (irreducible) data aleatoric uncertainty $H(Y|X)$ of the true data ... | Summary: This paper observes a connection between the efficiency of CP (tightness of its prediction sets) and information theory. It uses this to derive 3 bounds on a CP's efficiency depending on the entropy of the label given a data record H(Y|X). It then uses these bounds as the optimization objective for the underly... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments and are encouraged by the positive feedback and recognition of our work's significance. We answer the remaining questions below.
> Bounds based on Fano's inequality may be loose.
That is definitely true of the simple Fano bound, where we assume a ... | Summary: The paper aims to relate the uncertainty measured by conformal prediction by the uncertainty measured by the conditional entropy $H(Y|X)$. Towards this end, they make the following contributions:
1. They observe that the miscoverage quantity from conformal prediction is the same as the decoding-error quantity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and interesting questions, which we discuss below.
> $H(Y|X)$ only measures aleatoric uncertainty whereas the conformal interval captures both aleatoric and epistemic uncertainty. So then, it is not surprising that the conditional entropy would be... | Summary: This work develops the upper bound of conditional entropy H(Y|X) of data generation process by information theoretic inequality. New objective for conformal training and upper bound for the inefficiency (size of prediction set) for a trained model. Including side information to improve the efficiency of CP is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and useful feedback. We address the main concerns below.
## Weaknesses
> Lack of clear notation clarification. For example, the subscripts of Q in Eq. (5) is not explained, making it hard to understand
The subscripts of $Q$ (and $P$) indicate the random var... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Biologically Inspired Learning Model for Instructed Vision | Accept (poster) | Summary: This paper proposes a novel and biologically plausible alternative to backpropagation. Their model is especially unique in how it incorporates visual attention through top-down processing mechanisms. Specifically, the model contains bottom-up (BU) and top-down (TD) networks that are symmetric to each other. Th... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and detailed review of our paper. Your feedback will be used, and we will update the final version of the paper according to your comments.
> W1
Thank you for the constructive comment; we will change the format to make it easy to parse.
> W2
Thank you for th... | Summary: This manuscript proposed the first biologically motivated learning model for instructed visual models that integrates bottom-up and top-down pathways mimicking the visual cortex. The model employs the TD pathway for both guiding attention and propagating signals. Experiments demonstrate its capability across m... | Rebuttal 1:
Rebuttal: Thank you for your review and your succinct and precise summary. We welcome any comments you have on the additional discussion in the global rebuttal. | Summary: This work focused on proposing an efficient biologically plausible learning framework. It is inspired from the cortical-like combination of bottom up and top down processing. The top-down part provide both guidance for visual process and feedback signals for learning part. It further introduce a "Counter-Hebb"... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your interest and constructive comments.
> W1 + Q2
Predictive coding is among the best biologically plausible models for learning from errors and for representation learning. We discuss some differences and novel aspects in the biological mech... | Summary: This study proposes a new biologically-motivated learning rule as an alternative to backpropagation. This “Counter Hebbian” rule separates the forward and backward path into two similar but non-identical pathways, called Bottom Up and Top Down by the authors, and allows the TD stream to gate the flow of inform... | Rebuttal 1:
Rebuttal: Thank you, we appreciate the time and effort and the comprehensive and helpful comments.
> W1 + W2
Additional experiments are included in the appendix and are only briefly mentioned in the main text due to capacity limitations. These experiments examine various components of CH learning, such a... | Rebuttal 1:
Rebuttal: Many thanks to the reviewers for their useful feedback, the time invested and your effort. Thanks for pointing out weak points, possible improvements, and positive feedback.
Guided vision: we add a general comment about the role of guided vision in our model compared with previous and recent bio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets | Accept (poster) | Summary: The paper presents HyperLogic, a pioneering framework that integrates hypernetworks into differentiable rule learning, enhancing the interpretability and flexibility of neural networks. It provides a strong theoretical foundation and backs it with extensive empirical evidence, demonstrating its effectiveness o... | Rebuttal 1:
Rebuttal: **Summary**
We are grateful that reviewer hMMc has a positive impression of the high-level design of our framework and the promising results of comprehensive experiments. To address your questions about the details of our method, we provide point-wise responses as follows.
**Q1. The introductio... | Summary: The authors consider the problem of learning interpretable rules for decision-making and propose a new neural rule learning framework to find these rules efficiently. In particular, they suggest to use a hypernetwork to learn a diverse set of network parameters for the rule-encoding network. On small binary da... | Rebuttal 1:
Rebuttal: **Summary:** We first thank reviewer F7a8 for the insightful comments, especially for the questions about our model design and experiment details, which helped us to clarify our paper. We would like to address the concerns one by one.
**Q1. The method is limited in network structure (2-layer arch... | Summary: This paper introduces HyperLogic, a novel differentiable framework for rule learning using neural networks. Instead of directly training network weights, HyperLogic extracts rules by generating the weights for a primary network through hypernetworks. These hypernetworks create diverse sets of weights, function... | Rebuttal 1:
Rebuttal: **Summary**
Many thanks to reviewer Q4Et for your positive comments and recognition of our contribution including an interesting framework, a robust theoretical foundation, comprehensive experiments, and a well-written paper. We would like to address your concerns one by one.
**Q1. Are there any... | null | null | Rebuttal 1:
Rebuttal: Dear esteemed reviewers,
We are grateful to all the reviewers for generously dedicating their time and effort to evaluating our paper. Their constructive feedback and valuable suggestions are helpful to further improve the quality of our work. We would like to answer general questions mentioned b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Approximating the Top Eigenvector in Random Order Streams | Accept (spotlight) | Summary: The paper studies the problem of finding the top eigenvector of a matrix in the streaming model. The problem is: given a sequence of d-dimensional rows a_1, a_2, ..., a_n of a matrix A, approximately compute the top eigenvector v_1 of A^TA. The algorithms gets a single pass over the stream of input rows, must ... | Rebuttal 1:
Rebuttal: We thank the reviewer for suggestions on citations for the random order model. We will add more references in the final version of the paper.
> Using substreams for quadratic approximation
When each row of the stream is independently sampled from a distribution, Hardt and Price analyze a simila... | Summary: The paper studies the problem of approximating the top eigenvector of $A^T A$ when rows of an $n \times d$ matrix $A$ are given in a stream. The authors consider worst-case inputs $A$ but assume the rows are presented in a uniformly random order.
They show that when the gap parameter $R = \sigma_1(A)^2 / \sigm... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> Separation in Practice
In section 5 of the paper, we give a synthetically constructed instance for which Price’s algorithm does not give a good approximation to the top singular vector. For instances observed in practice, it seems harder to show such a... | Summary: This paper is concerned with estimating a direction that is well-correlated with the top eigenvector $v_1$ of a matrix where the rows $a_1, \dots, a_n$ are streamed to us in uniformly random order. To make this well-defined, a gap assumption is required, i.e., that $R \coloneqq \lambda_1(A^{\top}A)/\lambda_2(A... | null | Summary: Given a stream of $n$ data-points $a_1, a_2, \dots, a_n \in \mathbb{R}^d$, this paper studies the problem of approximating the top eigen-vector of the matrix $A^T A$ at the end of the stream, where $A \in \mathbb{R}^{n \times d}$ is the data-matrix with rows of the matrix corresponding to the data-points. This... | Rebuttal 1:
Rebuttal: Thanks for the reference suggestions. We will add them to the introduction in the final version.
> How do the guarantees in this paper extend to the setting where you need to approximate not just the top eigen-vector, but the top $k$ principal components, for some arbitrary value of $k$?
In prac... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection | Accept (spotlight) | Summary: The paper focuses on a new architecture, Voxel Mamba, for 3D object detection using point clouds. It introduces a group-free approach utilizing state space models (SSMs) to address the limitations of previous voxel-based methods like small receptive fields and inefficient grouping. Voxel Mamba leverages asymme... | Rebuttal 1:
Rebuttal: **[Q1, Q2] Manuscript refinement.**
We apologize for the typos and presentation issues. We promise to thoroughly proofread the entire manuscript and correct the grammatical and typographical errors as much as possible. Considering that some concepts may be difficult for newcomers to this field, w... | Summary: The paper introduces a novel architecture for 3D object detection, addressing the limitations of current methods which rely on grouping operations. The proposed Voxel Mamba leverages State Space Models (SSMs) to capture long-range dependencies and process the entire scene in a single sequence, avoiding ineffic... | Rebuttal 1:
Rebuttal: **[Q1] Clarifying our motivation.**
Thanks for the question. Please kindly refer to our responses to **Q1 of Reviewer kB9g** for details.
**[Q2] Efficiency and performance for different model sizes and resolutions.**
We appreciate the reviewer's feedback and conducted additional experiments. ... | Summary: This paper proposes a novel backbone named Voxel Mamba for point cloud 3D detection. Different from previous methods that group the voxels into fixed-length sequences through padding, this paper adopt an interesting group-free strategy to sort all voxels into a single sequence through the space-filling curve. ... | Rebuttal 1:
Rebuttal: **[Q1] Comparison with other variants.**
Thanks for your insightful comments. To demonstrate the improvements of our method, we have conducted additional experiments comparing Voxel Mamba with group-based and group-free bidirectional Mamba. The group-based bidirectional Mamba uses the DSVT Input ... | Summary: The authors introduce Voxel Mamba, a 3D object detection method based on state-space models. Voxel Mamba introduces a efficient group-free approach to avoid inefficient grouping operations and prevent limitations on the receptive field for 3D object detection in point clouds. The authors also present Asymmetr... | Rebuttal 1:
Rebuttal: **[Q1] Motivation.**
Thanks for the question and we are sorry for not making the motivation clear enough. The motivation of our work is to leverage the advantages of SSM in linear attention for more effective feature learning in 3D point cloud based object detection. The advantage of SSM over... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for the valuable comments and suggestions. We first address the common concerns, followed by the detailed responses to each reviewer separately. We hope our responses can clarify the reviewers' concerns and make our contributions clearer.
**Q1. The latency compari... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new 3D detection architecture, Voxel Mamba. Voxel Mamba serializes voxels using a Hilbert Input Layer and then applies a forward SSM and a backward SSM at lower resolutions. It achieves state-of-the-art performance on both the Waymo Open Dataset and nuScenes dataset.
Strengths: Originali... | Rebuttal 1:
Rebuttal: **[Q1] Latency of generating curves.**
In our implementation, we record the traversal position in the space-filling curves of all potential voxels offline. The voxels are serialized by simply looking up these traversal positions. Thus, the latency for serializing voxels based on Hilbert and Z-ord... | null | null | null | null | null | null |
Selective Explanations | Accept (poster) | Summary: The paper presents a novel framework for explaining black box models through a selective explainer. The selective explainer unifies two different explanation methods: the former relies on amortized explainers, which are easier to compute but provide lower-quality explanations; the latter can provide higher-qua... | Rebuttal 1:
Rebuttal: Thank you for your review! It was really insightful and will definitely improve the final version of our paper. We will implement your suggested changes, and hope you engage with us during the discussion period. We address your comments below.
**Q1) “The terminology is misleading: the paper calls... | Summary: This paper introduces a novel method called selective explanations for improving the efficiency and accuracy of feature attribution methods for black-box machine learning models. The key contributions are:
- A zero-cost proxy to evaluate the adversarial robustness of deep neural networks without training.
- A... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate that you found our work novel. We also appreciate that you found our evaluation comprehensive and our method flexible. We answer your questions and comments next.
**Q1) “How does the performance of selective explanations scale with extremely large models ... | Summary: This paper proposed a feature attribution method that detects when amortized explainers generate low-quality explanations and improves the explanations with their linear interpolation of themselves and expensive high-quality explanations.
To detect the low-quality explanations of the amortized explainers, the ... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. Your feedback will positively impact the final version of our paper! We address your questions below.
**Q1) ”Although the experimental results show that the proposed method can improve the accuracy of explanations, those on computational efficiency ... | Summary: The paper proposes a method, termed "Selective Explanations," aimed at improving the quality of explanations generated by amortized explainers in machine learning. The authors introduce a technique that detects low-quality explanations and employs a combination of amortized and Monte Carlo methods to enhance t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Your feedback will positively impact the updated version of our paper and we will include all of your comments. We also appreciate that you found our manuscript well-written, our method intuitive and easy to understand, and that our idea of Selective Explanati... | Rebuttal 1:
Rebuttal: ### Global Rebuttal
Thank you very much to the reviewers for their effort! We are pleased that you found the paper well-written, the problem setting interesting, and our theoretical analysis sound (all reviewers), recognized that we are the first to propose "selective" feature attribution (review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exponential Quantum Communication Advantage in Distributed Inference and Learning | Accept (poster) | Summary: This paper studies distributed learning and inference based on graph neural networks but with quantum communications between the distributed agents. The authors show that quantum networks reduce the communication complexity inference and gradient computation of distributed models. This reduction is shown to be... | Rebuttal 1:
Rebuttal: > Can you elaborate on the run-time of the proposed method?
The run-time of gradient estimation based on shadow tomography is stated in Theorem 2. Asymptotically, this will dominate the cost of other parts of the algorithms such as state preparation. This polynomial scaling is based on the compl... | Summary: The paper investigates the exponential quantum advantage in communication complexity over the tasks of training or inference in multiple types of compositional distributed quantum circuit model. The authors also study the quantum communication advantage in a specific class of shallow graph neural networks, and... | Rebuttal 1:
Rebuttal: (1) We consider the problem of inference with graph networks in order to demonstrate the the communication advantage holds for model classes that can be trained on classical data. Note that in order to show the advantage, we must show both that any classical algorithm will require lots of communic... | Summary: This paper studies the quantum advantage on communication of distributed learning. It places a common quantum neural network model in the (two-party) communication scenario, where the data x is assigned to Alice, and the parameterized unitary operators of each layer are alternately given to Alice and Bob in or... | Rebuttal 1:
Rebuttal: > the upper bound in Lemma 1 should be O(L log N) instead of O(log N)?
The total communication is indeed linear in L, but for this reason the number of rounds required is linear in L. In each round, log(N) qubits should suffice in order to solve the problem.
> The claim of QUANTUM advantage on Q... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments and endorsement of the work. We will respond to individual reviewers below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models | Accept (poster) | Summary: This paper studies the universal adversarial attacks on large vision language models (LVLMs). It focuses on the black-box setting where only the model's response is available to the adversary. This paper proposed a query-based targeted attack that leverages a proxy model to obtain the similarities between the ... | Rebuttal 1:
Rebuttal: **Q1: Experiments investigating potential defense could make the paper more comprehensive.**
**A1:** Thanks for your suggestion. As shown in the following table, we evaluate the robustness of our adversarial patch with four popular defense methods. Specifically, PatchCleanser [a] is a state-of-th... | Summary: This research introduces a novel approach to creating universal adversarial attacks against Large Vision-Language Models (LVLMs).
It proposes the universal attacker against real-world LVLMs that operates with limited access to the model (only inputs and outputs).
The attack is designed to be task-agnostic, us... | Rebuttal 1:
Rebuttal: **Q1: Comparison with non-universal attacks.**
**A1:** Since existing LVLM attackers are implemented in different settings with different models/datasets, we have already provided detailed comparisons in Table 3 and 4 of the paper. Note that, existing methods are non-universal attacks and require... | Summary: This paper presents a novel approach to create a universal adversarial attacker for LVLMs. The proposed method focuses on two main aspects: restricting access to only the LVLM inputs and outputs, and devising a task-agnostic adversarial patch that can deceive multiple multimodal downstream tasks. The approach ... | Rebuttal 1:
Rebuttal: **Q1: The paper could benefit from clearer explanations of the technical details and processes.**
**A1:** Thanks for your suggestion. We will add more corresponding explanations in the revision:
(1) For adversarial patch initialization: to achieve successful targeted attacks, there is no prior k... | Summary: The paper proposes a universal adversarial attack method targeting large vision-language models (LVLMs) by designing a universal adversarial patch. This method restricts access to only the model’s inputs and outputs, creating a task-agnostic adversarial patch that can deceive various LVLM-driven tasks without ... | Rebuttal 1:
Rebuttal: **Q1: Can the attack achieve the same effect with a very small patch size without adding constraints to the perturbation?**
**A1:** Without adding perturbation constraints, a smaller patch can achieve a similar attack performance. We implement this perturbation constraint to make a fair compariso... | Rebuttal 1:
Rebuttal: Dear reviewers,
We much appreciate for your acknowledgment of our work and helpful, insightful comments. Following the reviewers' suggestions, we have carefully revised the paper and conducted a series of new experiments to address the reviewers' concerns. In the following, under each reviewer's ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TrAct: Making First-layer Pre-Activations Trainable | Accept (poster) | Summary: This paper presents TrAct, a new and novel training strategy to modify the optimization behavior of the first layer. Basically, it can achieve faster convergence and achieve better classification performance across different models. The effectiveness of TrAct is demonstrated across a range of 50 experimental ... | Rebuttal 1:
Rebuttal: We would like to thank you for your time reviewing and for helping us to improve our paper.
**Strengths:**
> 1. The technical elaboration of the proposed method is clear.
> 2. I really like the motivation for the proposed method, which is straightforward.
> 3. The evaluations conducted on the pr... | Summary: The paper introduces TrAct, a training strategy that modifies the optimization behaviour of the first layer. The proposed first-layer optimization enables a slightly better results for training the model at lesser number of epochs. TrAct is being demonstrated for a wider setup on image classification and have ... | Rebuttal 1:
Rebuttal: We would like to thank you for your time reviewing and for helping us to improve our paper.
**Strengths:**
> 1. TrAct enables faster convergence or can achieve slightly better performance for the same number of epochs.
> 2. The paper demonstrates applicability of TrAct in various possible scenar... | Summary: When training vision models, the update of the weights of the first layer is proportional to the input pixel values. This can make the model learn images with high contrast faster and damage learning efficiency. To reduce this dependency, the paper proposed to optimize the first layer embedding (before activat... | Rebuttal 1:
Rebuttal: We would like to thank you for your time reviewing and for helping us to improve our paper.
> The authors identified the fundamental problem of training vision models. Compared to modifying the input or the model architecture, targeting the update of the first layer weight is a more direct approa... | null | null | Rebuttal 1:
Rebuttal: Additional Figure PDF based on Reviewer yoFY's remark.
Pdf: /pdf/b4801e522730c57afe146073695fd6d76eec05b7.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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