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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Continual Audio-Visual Sound Separation | Accept (poster) | Summary: The paper proposes a novel continual audio-visual sound separation task, aimed at continuously separating new categories of sound sources while maintaining the performance of previously learned categories.
Strengths: 1. The structure of the entire paper is clear, and the expression is fluent.
2. The experimen... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments! We appreciate the reviewer highlighting the clear structure, fluent expression, and effective experimental results of our paper. We address the raised concerns below and are willing to answer any further questions.
> ### **Q1: Difference between the innovation... | Summary: This paper introduces a novel task termed "Continual Audio-Visual Sound Separation," aiming to address the practical challenge of separating sound sources for new classes in audio-visual scenarios while retaining performance on previously learned classes. This task is inherently challenging due to the inherent... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions! We appreciate your recognition of the novelty in our work's problem formulation and approach, as well as its effectiveness demonstrated in our experiments. We address your questions below and welcome any further inquiries.
> ### **Q1: Replace ... | Summary: The paper introduces ContAV-Sep, the goal is to continuously separate new sound classes while maintaining performance on previously learned classes, addressing the challenge of catastrophic forgetting in continual learning. ContAV-Sep employs a Cross-modal Similarity Distillation Constraint (CrossSDC) to prese... | Rebuttal 1:
Rebuttal: We appreciate the reviewer highlighting our proposed approach and writing. We address the raised questions below and are happy to answer further questions.
> ### **Q1: The performance improvement of ContAV-Sep (with iQuery) is not significant.**
Thanks for the comment! We would like to clarify t... | Summary: This approach defines an audio-visual sound separation task where sound separation is the task and during fine-tuning novel classes are added, in the regime of continuous learning. The goal is to avoid catastrophic forgetting which typically leads to decreased performance in task performance on classes which w... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and encouraging remarks! We address your questions below. If there are any additional questions, we are happy to address them and revise our paper.
> ### **Q1: Direct comparison between sound separation.**
Thank you for your comment! Indeed, our paper focuses... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a continual audio-visual sound separation framework to mitigate the catastrophic forgetting problem
Strengths: As I learned, this is the first work that focuses on the catastrophic forgetting problem in audio-visual separation task.
Weaknesses: 1. The font size in Figure 2 is too small, re... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions! We address the raised concerns below. If there are any additional questions, we are willing to address them and revise our paper.
> ### **Q1: The font size in Figure 2 is too small.**
Thank you for your suggestion! We have enlarged the font s... | null | null | null | null | null | null |
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation | Accept (poster) | Summary: The paper studied how to identify and rank agents who strategically manipulate their inputs to game machine learning models in multi-agent settings. A causally-motivated approach was proposed to address this challenge.
Strengths: The paper is an interesting follow-up to [1]. Provided that the proposed mytholo... | Rebuttal 1:
Rebuttal: We thank Reviewer Drhf for their comments. We appreciate that the reviewer found our approach to be practical, and applicable to other real-world problems such as college admissions mechanisms.
**Real-world validation.** Great point — real-world validation is an inherent limitation of the synthet... | Summary: This work studies the problem of identifying agents who would likely game a given system. When the gaming parameters are unknown, the authors show that identifying these parameters requires strong assumptions. In contrast, they show an ordering of agents based on their ranking order is learnable from a dataset... | Rebuttal 1:
Rebuttal: We thank Reviewer P2tW for their comments. We appreciate that the reviewer found the application area of U.S. Medicare to be interesting.
**Can we actually detect gaming/use it as a subroutine?** Good question — to that end, our Proposition 1 demonstrates that definitive gaming detection is not p... | Summary: The paper considers the problem of identifying agents with the highest values of scaling parameters in a stylized strategic adaptation optimization model under a wide range of assumptions. The paper casts this problem as a ranking problem via causal effect estimation and provides an algorithm to rank the param... | Rebuttal 1:
Rebuttal: We thank Reviewer dPj8 for their detailed comments, which improved our work. First, we address the motivation for our approach and clarify conceptual questions. We then briefly discuss connections to past work. Then, we address concerns about generalizability and our assumptions. We will add these... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments, which helped improve our work. Reviewers positively commented on the interestingness of our problem setting [R1, R3], as well as the practicality and realism of our proposed methodology [R2/R3]. We respond to requests for clarification from rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Does Video-Text Pretraining Help Open-Vocabulary Online Action Detection? | Accept (poster) | Summary: This paper explores the problem of open vocabulary video action detection in an online setting, where the action must be detected immediately once it appears in the video stream, vs. the more common offline setting that allows examining the entire video, past and future.
The authors propose a model with two m... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
### Q1: However such a claim would be stronger if supported by evidence or examples of frames being correctly clustering based on action semantics.
Here, we give evidence both quantitatively and qualitatively.
- Firstly,... | Summary: This paper addresses a challenging setting in video understanding: open-vocabulary online action detection. It leverages pre-trained visual language models with a proposed dual-encoder architecture to achieve successful zero-shot detection.
Strengths: 1. The proposed method follows a visual-text dual encoder ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
### Q1: I wonder if other VLMs, such as ActionCLIP, could mitigate these drawbacks for the proposed task.
Thank you for the suggestion. We plan to enhance our model's performance in future work by incorporating improved ... | Summary: The paper introduces OV-OAD, a novel zero-shot online action detection system leveraging vision-language models for open-world temporal understanding. The authors propose a Transformer-based model with an object-centered decoder unit, trained solely on video-text pairs without manual frame-level annotations. T... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
### Q1: The authors should consider using more recent OAD models.
Please refer to the response To-All-Reviewers-Q1 for detials.
### Q2: The improvements on the TVSeries dataset being relatively limited compared to THUMOS... | Summary: 1. The authors have proposed a new method for Online Open Vocabulary action detection by leveraging pretrained vision langugae models.
2. To that end they introduce 2 main modules, a distant neighboring frame transformer and an object centric Action clustering unit.
3. They train their model with three obje... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
### Q1: The statement "do not use any frame-level annotations" may appear contradictory.
Thank you for highlighting this inaccurate claim. We will rephrase it as follows: "avoid utilizing fine-grained temporal annotations... | Rebuttal 1:
Rebuttal: # To All Reviewers
We sincerely thank each reviewer for providing constructive comments for our paper, which are very helpful to improve our paper. Below, we address the general issues raised by the reviewers.
### Q1: Comparison with more online action detection models
We conducted a comparative a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding | Accept (poster) | Summary: This paper introduces an approach to enhancing document understanding capabilities by employing a hierarchical visual feature aggregation technique alongside pretrained Multimodal Large Language Models (MLLMs). The method utilizes a feature pyramid hierarchy integrated with cross-attentive pooling, which effec... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful recognition of our extensive ablation studies and the novel relative text-position prediction task, which validate our approach's robustness. Below are our responses to the main concerns.
**[W1] Robustness of the ablation studies**
As 35.17% represe... | Summary: This paper presents hierarchical visual feature aggregation for OCR-free document understanding, leveraging feature pyramid hierarchy with cross-attentive pooling to handle the trade-off between information loss and efficiency. Additionally, a relative text-position prediction task is proposed to address the t... | Rebuttal 1:
Rebuttal: We truly thank the reviewer for recognizing our key innovations in multi-scale feature fusion, novel instruction tuning, and comprehensive experimental results across different models. Below are our responses to the main concerns.
**[W1] Comparison to the recent works**
Table B: Comparison of d... | Summary: The paper analyzes the impact of features at different scales for document understanding. It proposes a method to combine multi-scale features without signifficantly increasing computational complexity. In addition the paper also proposes two new instruction tuning tasks that allow the model to better extract ... | Rebuttal 1:
Rebuttal: We are deeply grateful for the reviewer's insightful summary of our work, highlighting the key contributions of our multi-scale feature extraction approach, new instruction tuning tasks, and comprehensive experimental results. Below are our responses to the main concerns.
**[W1] Incremental nove... | Summary: The paper presents a novel approach to OCR-free document understanding using pre-trained Multimodal Large Language Models (MLLMs). The approach uses multi-scale visual features to handle different font sizes within document images. To address the high computational cost associated with multi-scale visual input... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's acknowledgment of our introduction of novel components, thorough ablation studies, and detailed model information. Below are our responses to the main concerns.
**[W1] Effectiveness of reconstruction loss**
Table A. Effectiveness of reconstruction loss on b... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SF-V: Single Forward Video Generation Model | Accept (poster) | Summary: This paper proposes a method to accelerate video generation model inference speed by distilling the multi-step reasoning of Singular Value Decomposition (SVD) into a single-step generation using adversarial networks. This approach achieves comparable results to multi-step SVD generation while significantly imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.
---
**Q1. Reproducing the results.**
A1. We plan to make our code and checkpoint public for the reproducing of our work.
---
**Q2. Training data.**
A2. Our dataset contains ar... | Summary: The paper tackles the task of distilling diffusion-based text-to-video models into single-step models, achieving much higher sampling speeds. To this end, they build a framework to fine-tune a pretrained video diffusion model. This fine-tuning is done in an adversarial setting in the latent space, whereby the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We provide detailed response in the following.
---
**Q1. About novelty.**
A1. We agree with the reviewer that certain design components, *e.g.,* adversarial training and constructing the discriminator initialized from the diffusion model, hav... | Summary: The authors propose a method to generate similar-quality as the original video diffusion model Stable Video Diffusion (SVD) in a single feedforward pass.
To this end, they take the pre-trained SVD model and fine-tune it with a reconstruction and adversarial loss. The discriminator uses a frozen copy of the SVD... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.
---
**Q1. About improving the writing for the preliminary section.**
A1. Thanks for the suggestion! We will revise the writing of the manuscript.
---
**Q2. About the approach i... | Summary: The paper proposes an idea of training a distillation approach using GAN based technique. The advantage which is suggested by the authors is that such distillation approach can reduce the computational cost associated with the sampling new samples during the inference time. Instead of taking multiple sampling ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.
---
**Q1. About generating real world videos.**
A1. Thanks for the suggestions. In this work, we fine-tune SVD, which is an image-to-video model, into single sampling step. Our m... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful comments and positive feedbacks. We appreciate their findings of the strengths for this paper, including:
- **our studied problem** (reducing the computational overhead for video diffusion models) is important (Reviewer R1mD) and well validate... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints | Accept (poster) | Summary: In this work, the authors address the problem of online DR-submodular maximization subject to long-term stochastic (linear) constraints. At each round $t\in[T]$, after committing an action $\x_t$, a random reward and an unbiased estimate of the point are revealed.
This paper focuses on the stochastic DR-submo... | Rebuttal 1:
Rebuttal: Thank you for your time and your encouraging review. We address your concerns in the following:
### Weaknesses
> The paper lacks empirical evaluation, which would be valuable in highlighting a use case and demonstrating the effectiveness of your proposed approach.
We agree with you that empiri... | Summary: The paper studied the problem of online monotone DR-submodular maximization subject to long term stochastic constraints. In detail, it explored the stochastic DR-submodular maximization setting, where the objective functions are i.i.d. sampled from a distribution. Previous works considered adversarial objectiv... | Rebuttal 1:
Rebuttal: Thank you very much for your review. Here we address the questions in "Weakness" and "Questions" as follows.
### Weaknesses
> - The authors did not motivate the stochastic DR-submodular setting well. It is not explained why previous works used adversarial setting and what the motivation is for ... | Summary: This paper investigates the online learning of DR-submodular with a stochastic budget (linear) constraint. The constraints vary from different rounds. In each round, the constraint is sampled from an unknown distribution independently, and the constraint in round $t$ can only be observed after the learner have... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and helpful suggestions. Next, we will address some of your concerns in "Weakness" and "Questions".
### Weaknesses
> 1. The main weakness is the technical contribution. The method used in this paper is a simple combination between non-oblivious PGA a... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for carefully reading the paper and their constructive suggestions. In the following we address some common questions.
> Regarding technical contribution.
Some reviewers have concerns about limited technical contribution. Here we emphasize two technical novelties:
1.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mutual Information Estimation via $f$-Divergence and Data Derangements | Accept (poster) | Summary: This paper introduces a new method for mutual information estimation using a discriminative training approach based on f-divergences. Notably, the authors address a well-known limitation for this class of estimators which exhibit exponential sample complexity in the strong dependence limit. The authors also de... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive feedback. We particularly appreciate that the Reviewer caught the key points of our contribution and highlighted the novelty and strengths of our paper. Regarding the questions, please find below our point-to-point response.
**Weaknesses**:
* We derive in Th... | Summary: The authors provide a new representation of mutual information (in term of a general $f$-divergence) which has the advantage that the corresponding estimator has a low variance, in stark contrast to the MINE estimator which has a variance which is exponential in the size of the mutual information. The new esti... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper. We understand the Reviewer’s main concern is centered around the permutation law $\sigma(Y)$ which renders $Y$ independent from $X$.
While it is true that such a function may not exist in practice and we assume its existence in the the... | Summary: This paper proposes a novel discriminative mutual information estimator via the form of f-divergence, which exhibits a bias/variance trade-off. Experimental results show that the proposed estimator is comparable to existing neural estimators.
Strengths: 1. The proposed MI estimator avoids directly computing ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper and for the valuable feedback. The overall opinion and strengths highlighted by the Reviewer are indeed a precise summary of our contribution. We hope that our replies below will help the Reviewer increase his/her good opinion about the ... | Summary: This paper investigates the long-standing task of estimating mutual information in high-dimensional data. The authors point out that mutual information estimation methods using variational lower bounds suffer from either high bias or high variance, and proposes an alternative solution leveraging the variationa... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper. In particular, we thank the Reviewer for having spotted the strengths of the paper, it is clear from her/his review that the key points and contributions of the paper have been captured. We provide below our answer to the detailed comme... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their valuable feedbacks.
Please find below the one-page PDF containing extra information supporting our point-to-point rebuttal.
Sincerely,
The Authors
Pdf: /pdf/4605d373fc70e48d9fe74e4fe8d2b4cd8d2fc605.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Policy Evaluation Across Multiple Different Experimental Datasets | Accept (poster) | Summary: This paper studies how to evaluate policies where source and target sites have distribution shifts. The authors introduce identification criteria for the effectiveness of policies, and develop doubly robust estimators that achieves fast convergence. The results are also generalized to multiple source datasets.... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We agree with your comment and have added another experiment using a real-world dataset.
---
> For example, the non-synthetic experiments are only conducted on ACTG 175 clinical trial dataset. Experimenting on other different datasets will enhance the empirical resu... | Summary: The paper develops novel graphical criteria and estimators for evaluating the effectiveness of various policies by integrating data from multiple experimental studies. Through theoretical analysis and empirical validation via simulations, the paper demonstrates the robustness and fast convergence of the propo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Please find below a point by point answer to all questions and concerns. Please let us know if we can clarify anything further.
---
> __(1)__ line 71, ‘variable $\mathbf{Z}$ For example’ -> ‘variable $\mathbf{Z}$. For example’; __(2)__ unify the notations if... | Summary: The paper studies off-policy evaluation in a transfer learning setting with multiple source datasets collected from observational and/or randomized studies. The objective is to evaluate the effect of a target policy on a possibly different target population. To achieve this, the author(s) assume at each time p... | Rebuttal 1:
Rebuttal: Thank you for your extensive review, we really appreciate the feedback. In the following, we comment on the mentioned weaknesses and address outstanding questions and concerns separately.
---
> __(1)__ Same number of studies to horizon __(2)__ While the paper studies multi-horizon dynamic treatm... | Summary: This work presents a method for evaluating effectiveness of policies across multiple domains using a new graphical criteria and estimators by combining data from multiple experimental studies. The authors report error analysis of the proposed estimators that gives provides fast convergence guarantee, and addi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insights. In the following, we aim to answer each one of your concerns in turn. Please let us know if we can provide more details.
---
> __(1)__ the empirical evaluations are very limited. There is little discussion on how this could impact realworld prob... | Rebuttal 1:
Rebuttal: Thank you again for your time and dedication in reviewing our work. In this global response, we describe additional results, illustrated with figures in the attached PDF, to address outstanding comments and questions. In particular, we attach three figures that are described below.
---
**Figure ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models | Accept (poster) | Summary: Paper presents DEQH model which is a Deep Equilibrium Model to solve quantum Hamiltonians. DEQH uses the deep equilibrium model because it converges to a fixed point, which matches the self-consistent nature of Hamiltonian solving. Paper presents architecture based on the QHNet, results and comparison with QHN... | Rebuttal 1:
Rebuttal: Thanks for your review. We will address each point in our response accordingly.
## Weakness 1:
Thank you for your feedback. DEQH is a general method specifically devised to instill self-consistency into pre-existing models, while QHNet is a distinct network that predicts the Hamiltonian directly.... | Summary: The paper introduces a novel neural network architecture DEQH, extending deep equilibrium models (DEQs) to improve predictions of quantum Hamiltonians. The architecture constrains solutions to ensure self-consistency of the Hamiltonian, thereby improve generalization capability and test accuracy.
Strengths: C... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of the innovation and significance of our work and will address each point in our response accordingly.
## Weakness:
Thank you for your insightful question. The Hamiltonian exhibits self-consistent iterative properties. Specifically, in the DFT s... | Summary: The authors introduce DEQH model, which combines deep equilibrium models with existing ML models to predict quantum Hamiltonian, and the author adopt QHNet as the backbone and further develop DEQHNet. The authors evaluate the proposed method on benchmarks MD17 and QM9, the results show some effectiveness.
St... | Rebuttal 1:
Rebuttal: Thanks for your review. We will address each point in our response accordingly.
## Weakness 1:
Thank you for your feedback. The DEQH model is designed to predict the Hamiltonian. Given the self-consistent iterative properties of the Hamiltonian, we regard these properties as a fixed-point iterati... | Summary: This paper introduces the DEQH (Deep Equilibrium Quantum Hamiltonian) model, which integrates Deep Equilibrium Models (DEQs) for predicting quantum Hamiltonians. By incorporating DEQs, the model captures the self-consistency of Hamiltonians without needing iterative Density Functional Theory (DFT) calculations... | Rebuttal 1:
Rebuttal: We are immensely grateful for your assessments. We will address each point in our response accordingly.
## Weakness 1:
Thanks for your feedback and suggestions. We provide a PDF document in the global rebuttal section, which includes a figure delineating the distinction between the off-the-shelf ... | Rebuttal 1:
Rebuttal: We are grateful for the valuable feedback provided by all the reviewers. We provide a PDF document in the global rebuttal section, which includes a figure delineating the distinction between the off-the-shelf model used for Hamiltonian prediction and the DEQH model. We hope this visual aid will en... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
L4GM: Large 4D Gaussian Reconstruction Model | Accept (poster) | Summary: This paper proposed L4GM, an efficient large 4D Gaussian reconstruction model to produce animated objects from videos by a single feed-forward. L4GM leverages ImageDream and LGM to achieve multiview images of the first frame as the input. The overall model is built upon the pre-trained LGM with cross-view and ... | Rebuttal 1:
Rebuttal: > Q1. The main concern is the novelty. Although the overall pipeline is convincing, this paper is more like an extension of LGM for 4D generation. Most techniques are very straight-forward, such as temporal and cross-view attention, and multi-view synthesis by ImageDream+LGM.
A1. As pointed out b... | Summary: This paper utilizes rendering of animated objects from objverse(-xl) to extend lgm into 4D generation. Specifically, L4GM uses four orthogonal images of an object and the object's monocular dynamic video to obtain 3D Gaussians at each moment, enhancing the consistency between different moments through temporal... | Rebuttal 1:
Rebuttal: > Q1. The paper discusses extensively how to use dynamic datasets for pre-training, which is also a very important part of this work and will have a significant impact on the community. Whether this dataset is open source is also extremely important for evaluating this work, but the paper does not... | Summary: This work introduces a novel framework for generating animated 3D objects from single-view videos. The proposed framework employs a feed-forward approach, thus eliminating the need for computationally expensive optimization. The core idea is to create a large-scale synthetic multi-view video dataset and train ... | Rebuttal 1:
Rebuttal: > Q1. Technical contributions:
A1. We appreciate that the reviewer agrees that “a straightforward solution to a new problem should be recognized”.We would also like to highlight that L4GM is the first feed-forward 4D reconstruction model, which could open up more possibilities for generating hig... | Summary: This paper proposes a model for 4D reconstruction from a single video, building upon dynamic 3D gaussians and LGM architecture [49] previously applied to static 3D scenes. By processing generated multi-view images (derived from the first frame using prior method) alongside the video, the model outputs 4D Gauss... | Rebuttal 1:
Rebuttal: > Q1. Claiming that model generalises “extremely well” to in-the-wild lacks empirical support (apart from cherry-picked qualitative results) and likely not true due to training assumptions (e.g. masks, static camera at 0 degree elevation). One possible evaluation to substantiate the claim would be... | Rebuttal 1:
Rebuttal: We thank reviewers for the encouragement and insightful feedback. We are glad that the reviewers found:
- (4G9y, UkTx, hQSp) This is the first work to achieve feed-forward generation of 4D assets from a given video. The task of 4D reconstruction is timely and of significant interest, demonstrating... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforcement Learning Guided Semi-Supervised Learning | Accept (poster) | Summary: This paper introduces Reinforcement Learning-Guided Semi-Supervised Learning (RLGSSL), a novel method that combines reinforcement learning (RL) with semi-supervised learning (SSL). By formulating SSL as a one-armed bandit problem, the authors employ a RL-based loss function to guide the learning process. Addit... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewer has dedicated to reviewing our work.
**About the rationality and advantage of using RL**: We use Reinforcement Learning (RL) to enhance the exploration of pseudo-labels. Traditional approaches in Semi-Supervised Learning (SSL) can encounte... | Summary: This paper presents a method called Reinforcement Learning Guided Semi-Supervised Learning (RLGSSL), which frames SSL as a one-armed bandit problem. The method features a reward function that measures the discrepancy between the model's predictions on mixed data and pseudo-labels, guiding the learning process.... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewer has dedicated to reviewing our work.
**About terminology**: The bandit problem can be viewed as a special case of reinforcement learning where there is only one transition in the trajectory. Given that the bandit problem is an older concept... | Summary: The authors proposes a novel Reinforcement Learning (RL) Guided semi-supervised learning (SSL) method, RLGSSL, that formulates SSL as a one-armed bandit problem and deploys an innovative RL loss based on weighted reward to guide the learning process of the prediction model adaptively. The core idea is to use R... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewer has dedicated to reviewing our work.
**About computational overhead**: In training deep models, backpropagation is typically the most computationally intensive step. Our method, RLGSSL, features a non-differentiable reward function and a st... | Summary: One of the bottlenecks for Semi-supervised learning (SSL) is achieving high performance with limited labeled data, as the model is often complex and needs multiple loss functions. Recently RL has been increasingly used in fine-tuning complex models with non-differentiable reward functions.
Thus with these o... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewer has dedicated to reviewing our work.
**About visualization**: We will add some figures to help visualize the results in future revisions of our paper. Nevertheless, the effectiveness of SSL is well captured in the test accuracy results repo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active preference learning for ordering items in- and out-of-sample | Accept (poster) | Summary: This paper proposes an active learning algorithm for selecting pairs of items for comparison in order to learn a ranking of the items.
The ranking error is measured by the (normalized) number of swapped pairs compared to the true ordering (Kendall’s Tau distance). The algorithm chooses pairs of items based on ... | Rebuttal 1:
Rebuttal: **R: The response is assumed to be: $P(C_{ij}=1) = \sigma(\theta_*(x_i-x_j))$.**
While this assumption underlies our theoretical analysis and motivates the current version of GURO, we believe that we have taken many steps to highlight the practical usefulness of our algorithm and that the results... | Summary: This paper considers the ranking problem based on pairwise comparisons. The goal is to get the best sampling strategy for the best ordering from a limited number of comparisons. Under a logistic model on the difference between scores, the authors provide the analysis for the upper bound on the ordering error, ... | Rebuttal 1:
Rebuttal: **R: Please provide more discussion on how difference the proposed method is comparing to logistic bandits.**
As mentioned in our submission (l109, l138), our theory builds on the same techniques as previous papers on logistic bandits. However, we want to highlight that the problem considered her... | Summary: Active preference learning is different from deriving a complete ordering from preferences. It focuses on “If we collect comparisons D_T, how good is the resulting model’s predicted ordering in the item set”. The paper proposes a sampling method in the active learning scenario. Theoretical analysis is also pr... | Rebuttal 1:
Rebuttal: **R: Baseline methods are weak. Though many related studies are mentioned in the related work section, performances of baselines are not shown in the experiments.**
* We argue that the baselines we have included are state-of-the-art and come from diverse fields: Active Preference Learning, Logist... | Summary: This paper considers the setting of learning an ordering between items according to some scoring rule. The assumption is that this ordering is determined by a contextual scoring rule, determined from the features of each item. Such contextual structure can aid in more rapidly learning an ordering, and generali... | Rebuttal 1:
Rebuttal: **R: The definition of "general preference learning'' and its distinction from the current setting**
We agree that this can be clarified further. This paper focuses on learning a map to recover a complete ordering, but we leverage active preference learning to achieve this. By ''the general pref... | Rebuttal 1:
Rebuttal: Dear reviewers and chairs, thank you for evaluating our work.
We are happy that a majority of reviewers found that the reasons to accept this paper outweigh the reasons to reject it. As strengths, the reviews pointed to the importance of the problem (3/4 reviews), the theoretical justification f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations | Accept (poster) | Summary: This paper presents an unsupervised method to enhance the representations of pretrained models. The authors observe that gradient features from various self-supervised objectives are helpful to enhance k-nearest neighbor (KNN) retrieval. The proposed method, FUNGI, combines the embeddings of a pretrained model... | Rebuttal 1:
Rebuttal: ### Other evaluations (linear probing, k-means, in-context learning)
We conducted further evaluations of FUNGI features for logistic regression, clustering and text classification via in-context learning. The results are discussed in the global response.
### What's the dimension of the gradient ... | Summary: The paper proposes a simple method named FUNGI - Features from Unsupervised Gradients, to improve the representations of Vision Transformers (ViTs). Specifically, FUNGI uses gradients from self-supervised objectives to augment the embeddings from pre-trained models. FUNGI involves three straightforward steps -... | Rebuttal 1:
Rebuttal: ### Missing backbones
We extend the evaluation of FUNGI to DeiT-III, MobileCLIP and CLIP and MoCov2 ResNet-50 backbones. For MobileCLIP, we extract gradient features from the attention output projection of the last token mixer, and for ResNet-50 models we use the last convolutional layer, i.e. `l... | Summary: - The draft introduces a feature enhancement technique called FUNGI (Features from Unsupervised GradIents) for vision transformers. -
- The core idea is to leverage the un/self-supervised loss gradients at an arbitrary hidden layer within a vision backbone (the default option being the attention output projec... | Rebuttal 1:
Rebuttal: ### Computational cost
Our method does indeed introduce a computational overhead, but we believe that in a retrieval setting, where the embeddings for the database are computed once and only query samples are encoded on the fly, the performance improvement may be very well worth the added computa... | Summary: The paper introduces an approach to augment the feature representations from ViTs, by utilizing the gradients from self-supervised losses. The gradients from the attention output projection of the last transformer block is extracted and projected into the output embedding space using random-projections and PCA... | Rebuttal 1:
Rebuttal: ### Can task specific loss functions help with adaptation?
In this work, we only use self-supervised losses that do not need human labels. If we understand correctly the reviewer's question, task-specific losses such as cross-entropy or a ranking loss are supervised and would require us to have a... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive feedback. In this general response, we address comments, shared by multiple reviewers, regarding the extension of FUNGI evaluation beyond k-NN classification and providing further insight into why FUNGI improves performance. Then, reviewer-specific respo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Group Robust Preference Optimization in Reward-free RLHF | Accept (poster) | Summary: This paper proposes a novel preference optimization technique, GRPO, which utilizes the group distributional robust optimization. Specifically, the method aims to maximize the worst-case group performance for improving the robustness of LLM models. This paper provides several theoretical aspects of GRPO when i... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the novel application of our technique to the RLHF setting, our insightful theoretical analysis, and the broad applicability of our proposed algorithm.
Next, we provide answers to **all** the questions posed by the reviewer.
**Regarding theoretical novelt... | Summary: This paper addresses the limitation of traditional reinforcement learning with human feedback (RLHF) approaches that indiscriminately optimize a single preference model, disregarding the unique characteristics and needs of diverse labeler groups. The authors propose a Group Robust Preference Optimization (GRPO... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive outlook on our work and for highlighting the clear motivation of our problem setting to address the limitations in traditional RLHF techniques.
**Regarding access to group information:**
We focus on settings with known groups, which are common in pluralis... | Summary: The work tackles the important problem of robust RLHF for diverse groups. Traditionally, RLHF assumes that a single model can fit the diverse feedback from multiple groups of users. In this paper, the authors introduce a method to learn a robust policy that maximizes for worst-case group performance. To achiev... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive opinion about our work recognizing the importance of our problem setting, broad applicability of our GRPO approach, and strong theoretical analysis of our proposed algorithm’s convergence properties.
**Regarding ablation for trade-off parameter between w... | Summary: This paper introduces GRPO, a method to optimize policy preferences across different groups in a robust way. GRPO looks at the worst group alignment loss by taking the maximum loss across all groups, ensuring the policy performs well even when there are group-specific differences or overlaps in prompts.
Stren... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive opinion about our work recognizing our thorough theoretical analysis and broad applicability of our GRPO approach.
**Regarding the groups in GlobalQA dataset experiment:**
Our proposed GRPO method can be applied to any set of finite groups. To demonstrat... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and for recognizing the strengths of our work. We appreciate the constructive comments raised by the reviewers and we believe we have addressed all of them in detail further strengthening the validity of our work.
In summary, the reviewers recog... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses the problem of improving fairness in preference optimization. It proposes a new loss function and algorithm. Experiments conducted show that the proposed algorithm indeed achieves better fairness. Additionally, the paper provides a theoretical analysis indicating the convergence of the opt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive opinion about our work recognizing the importance of the problem, clear and sound exposition of our algorithm and theoretical results.
**Regarding comparison with works in multi-objective preference optimization [1] and group optimization [2] and related ... | null | null | null | null | null | null |
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss | Accept (spotlight) | Summary: The paper investigates a type of PAC-Bayes bounds that makes use of sequential prior updates, without loosing the appearance of the cardinality of the whole training set.
Strengths: * Sequential updates feel natural in that context, not only statistically as demonstrated here, but also computationally. Indeed... | Rebuttal 1:
Rebuttal: General comments:
- We agree that Section 4 is too dense. We will take advantage of the tenth page offered for the final version to sparsify it.
- Concerning works on sequential posterior updates: we are sorry for brevity, let us elaborate. In works on sequential posterior updates the prior remain... | Summary: Data-dependent priors are crucial for the tightness of PAC-Bayesian bounds in several scenarios. However, using a fraction of the data to train the prior reduces the sample size of the bounds, which can sometimes be counter-productive. This fact also discourages sequential prior updates since the bounds can ra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
1. We agree that Section 3 and, even more crucially, Section 4 are too dense. We will take advantage of the tenth page offered for the final version to sparcify writing.
2. The application to streaming and online learning is essentially straightforward, b... | Summary: - The paper presents a novel PAC-Bayesian procedure that allows for sequential prior updates without splitting the training dataset.
- The proposed procedure is based on a novel decomposition of the expected loss of randomized classifiers, which rewrites the posterior loss as an excess loss relative to a downs... | Rebuttal 1:
Rebuttal: There are numerous and significant misunderstandings in the review, which we clarify below one-by-one.
The first sentence of the Summary states: “The paper presents a novel PAC-Bayesian procedure that allows for sequential prior updates without splitting the training dataset.” This statement is i... | Summary: PAC-Bayes bounds are extended to the recursive or streaming case by breaking the expected loss into ($a$) expected loss on the previous step times a discounting factor plus ($b$) current expected loss minus $a$. An extension of the kl inequality to many-valued (here 4-valued) random variables provides a bound ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and the feedback.
“sampling error between $\hat \pi_{t-1}^*$ and $\pi_{t-1}^*$” - It is true that this is a delicate point, but in fact no correction is required. We have explained it in lines 219-233, but we accept that the explanation might have been too dense.... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Transformers with Spectrum-Preserving Token Merging | Accept (poster) | Summary: This paper proposes Protect Informative Tokens Merging (PIToMe), a token merging method that safeguards crucial information-bearing tokens prior to the merging step. PIToMe focuses on the token-splitting step before token merging. Specifically, PIToMe defines and calculates an energy score for tokens to be mer... | Rebuttal 1:
Rebuttal: Thank you for your feedback and high score, and for recognizing the main contribution of our work. We would now address your concerns regarding the compression rate of PiToMe.
### 1. ToMe is actually capable of conducting token merging at a very high compression rate (see Table 10 of ToMe paper; ... | Summary: This paper introduces an energy-based approach to the token merging process, utilizing energy scores to avoid erroneous merges by distinguishing between informative or isolated tokens and redundant tokens. This method enhances the efficiency of heuristic merging approaches and preserves the spectral properties... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. In what follows, we will address your concerns individually.
### **1. The authors claim that is a fixed constant margin; however, according to the context, $m$ seems to be the threshold of the margin, and $1-m$ is the margin.**
We're sorry for the confusion. ... | Summary: This paper proposes a new strategy for reducing the number of tokens used by a vision transformer by merging similar tokens without. Such methods are very sensitive to the type of clustering/partitioning used on the tokens. The proposed approach, `PITOME` explicitly identify highly informative tokens, which sh... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for pointing out substantiated claims. We will now address your concerns one by one.
### 1. The experiments section is hard to parse
We will address these concerns in the final revision using the additional page. This will allow us to provide more detai... | Summary: This paper proposes a novel method called PITOME, which enhances the efficiency of Vision Transformers (ViTs) by merging tokens in a way that preserves crucial information. Unlike previous methods, PITOME uses an energy score to prioritize and protect informative tokens while merging redundant ones. This appro... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the strengths of our work. We appreciate your thoughtful comments. We will now address each of your concerns individually.
## 1. Performance comparison with Tome
We would like to emphasize that PiToMe demonstrates significant improvements o... | Rebuttal 1:
Rebuttal: First, we are grateful to the reviewers for their valuable comments and detailed feedback. We are pleased that the **reviewers recognize our energy-based token merging as a novel idea** (**Reviewer zfJf** and **Reviewer 4Z7M**) with a theoretical foundation explaining the underlying mechanism (**R... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning | Accept (poster) | Summary: The paper studies the relationship between geometric complexity (based on penultimate layer features) and the neural collapse phenomenon (especially, the variability collapse property). The main theoretical result is a bound on the CDNV-based NC metric using geometric complexity (Proposition 4.1). This is then... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are very happy that you found that the “unified analysis of neural collapse and geometric complexity presents an interesting line of research for the community [...] in terms of studying seemingly disjoint phenomena under a common lens.” We have addressed your comm... | Summary: This paper examines the relationship between geometric complexity (GC), neural collapse (NC), and transfer learning performance in deep neural networks. The key contributions are:
* Proposing geometric complexity as a measure that connects the flatness of the loss surface and neural collapse
* Deriving theore... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful review. We are very happy that you found our approach to be a “novel perspective” as well as an “original contribution” and that it “could open up new avenues for improving pre-training techniques” and “provide valuable insights into the mechanisms behind transfer learni... | Summary: The paper explores the relationship between neural collapse (NC) and geometric complexity (GC). It presents both theoretical and empirical evidence showing that geometric complexity is a robust metric across various variables. By substituting the NC metric with GC, the paper introduces a generalization bound b... | Rebuttal 1:
Rebuttal: Thanks for your review. We are very happy that you found the work “well-written” and “easy to follow”, making the interpretation of the theoretical results “clear” and “intuitive”. We are also glad that you found the relation between “GC” and “NC” to be “quite interesting” and that the “empirical ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for your thoughtful and thorough reviews. We are grateful that you found the paper "well-written" and "well-structured" and found our "novel perspective" to be an "original contribution" which you think “could open up new avenues for improving pre-training techniques” and “p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation | Accept (poster) | Summary: This paper tackles open-vocabulary semantic segmentation, a task aiming at per-pixel predictions of image inputs on any given classes. Existing methods are built on pre-trained vision-language models like CLIP. Different from CLIP that achieves coarse open-vocabulary classification, open-vocabulary segmentatio... | Rebuttal 1:
Rebuttal: # Response to Reviewer Rvjz
Thank you so much for acknowledging the strength of our method. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
**Q1. Unclear unique contributions.**
Please refer to **General Response-Q1**.
**Q2. Con... | Summary: The paper proposes a training and inference-efficient Relationship Prompt Network (RPN). This network leverages a layer-wise Relationship Prompt Module (RPM) utilizing tuning methods similar to VLM LoRA and an improved Linear Projection Module (LPM) without relying on a segmentation model. The authors conduct ... | Rebuttal 1:
Rebuttal: # Response to Reviewer Rcdd
Thank you so much for acknowledging the strength of our method. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
**Q1. Lack of a discussion on region-text relationships, focusing instead on directly lever... | Summary: This paper proposes relationship prompt module (RPM), which generates relationship prompt that directs VLM to extract pixel-level semantic embeddings suitable for OVSS. Moreover, RPM integrates with VLM to construct relationship prompt network (RPN), achieving OVSS without segmentation-specific networks. RPN a... | Rebuttal 1:
Rebuttal: # Response to Reviewer 2wFX
Thank you so much for acknowledging the strength of our method. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
**Q1. No efficiency comparison for ADE20K and Context dataset.**
We have verified the eff... | Summary: This paper primarily studies Open-Vocabulary Semantic Segmentation. The main contribution of this paper is the proposal of RPN, which employs relationship prompt learning solely to perform OVSS without any segmentation-specific networks. The authors claim that RPN attains state-of-the-art results on four publi... | Rebuttal 1:
Rebuttal: # Response to Reviewer DSMk
Thank you so much for acknowledging the strength of our method. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
**Q1. The claim that our methods achieved SOTA results lack persuasiveness.**
Thanks for ... | Rebuttal 1:
Rebuttal: # General Response
We would like to thank all reviewers for providing constructive feedback that helped us improved the paper. We are encouraged that reviews think our paper:
* Clear expression: *The paper clearly expresses its main research content and the proposed algorithm.* (Reviewer DSMk)
* ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicit Causal Representation Learning via Switchable Mechanisms | Reject | Summary: This paper studies weakly supervised causal representation learning with soft interventions.
It assumes that pairs of observations are provided that differ by a soft intervention on one variable in the latent space.
Additionally, the intervention target (and the total number of latent variables) are given.
Ide... | Rebuttal 1:
Rebuttal: The authors appreciate the reviewer’s valuable comments and provide the responses below:
**Diffeomorphic solution function deterministic mapping**
- In general if we do not account for **uncertainties** in a function, that function will not be a **deterministic map from a variable to another**... | Summary: This paper proposes a new approach **ICRL-SM** that performs implicit causal representation learning (mapping from noise to latent variables) by using causal mechanism switch variable to model the soft intervention effects.
### References
[1] Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, Abhishe... | Rebuttal 1:
Rebuttal: The authors appreciate the reviewer’s valuable comments and provide the responses below:
**Assumptions of a diffeomorphic causal mechanism** and **Gaussianity assumption of the causal and exogenous variables**
- We would like to add that CauCA [1] and dVAE [2] also assumes *diffeomorphic decod... | Summary: This paper presents a novel approach for learning implicit causal representations through switchable mechanisms, specifically designed to handle soft interventions which are more realistic but challenging compared to hard interventions. The authors introduce a causal mechanism switch variable to model the subt... | Rebuttal 1:
Rebuttal: The authors greatly appreciate the reviewer’s valuable comments and provide the responses below:
**Assumptions**
- We evaluated our model in the **real-world datasets (Causal-Triplet: Epic-Kitchen and ProcTHOR )** where some of our assumptions such as linearity of decoder and observability of $... | Summary: This work is in causal representation learning that utilizes interventional data. It involves two types of common interventions: hard interventions and soft interventions. It’s known that soft intervention is more general since it covers hard intervention. But it is also more challenging since parental relatio... | Rebuttal 1:
Rebuttal: The authors appreciate the reviewer’s valuable comments and provide the responses below:
**Unique theoretical contribution of switch variable**
- We have proposed switch variable to obtain **identifiability** in **implicit models** using **soft interventions**. Neither [38] nor [1] use implic... | Rebuttal 1:
Rebuttal: Our paper solves the more **general case** of a previously introduced problem in [1], which is **implicit** causal representation learning using **soft interventions**. We have used similar assumptions in [1] and relaxed the **hard intervention assumption**. Three new assumptions were added:
1... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transfer Learning for Latent Variable Network Models | Accept (poster) | Summary: This paper studies the problem of transfer learning for estimating the edge probabilities of random graphs under latent position models. In particular, given a fully observed graph on $n$ nodes generated from an independent-edge random graph model, with edge probabilty matrix $P$, and an $n_Q \times n_Q$ subma... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their very encouraging comments concerning the novelty of our theoretical guarantees, the great relevance of the problem we consider, and the quality of our writing. We address their comments in detail below.
### **Why we believe our Algorithm 1 will work for ... | Summary: In this paper, the authors address two topics in random network/graph models:
1. The transfer learning in latent variable network models. It proposed estimate of the distribution of target network from source network using a defined graph distance.
2. It also proves a minimax lower bound for Stochastic Block ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their feedback and for assessing our paper to have high impact. We address their comments in detail below.
### **Clarification of relative versus absolute graph distances.**
On line 105, we note that our rankings assumption (Definition 1.3) concerns relative,... | Summary: The work explores transfer learning in latent variable network models. In particular the work focuses on the setting of observing samples from an n x n probability matrix from a source P and a submatrix of the adjacency of a target Q. The goal is then to estimate Q, using information from P.
The authors pro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for highlighting the quality of our writing and the value of the problem we tackle. We address their comments in detail below.
### **We test our algorithms on two real-world transfer tasks.**
In Section 4, lines 271-287, we test our algorithms on two real-world t... | Summary: This paper investigates transfer learning in the context of estimating latent variable network models. Specifically, the goal is to estimate the edge probability matrix $Q$ of the target graph using (1) edge data from a source graph $P$ given by its adjacency matrix, and (2) edge data from a vanishingly small... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for highlighting the fundamental nature of our problem, and its numerous potential practical applications. We will fix all typographical errors in the revision.
We address their feedback in detail below.
### **Relevance and applicability of latent variable model... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their encouraging comments and for acknowledging the significance of our work. We will fix all typographical errors in the revision.
In this global rebuttal, we will mainly discuss the new experiments, attached as PDF.
### **New experiments quantify the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits | Accept (poster) | Summary: The paper focuses on stochastic combinatorial semi-bandit problems where a player selects from a power set of actions comprising subsets of d base items. It underscores the importance of adapting to the problem structure to achieve optimal regret bounds, emphasizing the use of covariance matrix estimation to e... | Rebuttal 1:
Rebuttal: ___Concerning dependance in $T$___
> The log(T) term has a power 3 in the regret bound, which can be improved.
> Do you think the $\log(T)^3$ factor can be reduced ? Why is there such a factor, whereas usually there is simply a $log(T)$ factor ?
> A sharp dependence on the horizon $T$ is more imp... | Summary: The paper "Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits" addresses the challenge of designing algorithms that adapt to covariance in the context of stochastic combinatorial semi-bandits, where decision-makers face a set of actions exponentially large in the number... | Rebuttal 1:
Rebuttal: ___Concerning the weaknesses___
> The assumption that the reward (Lines 45-46) for each base item ($Y_{t,i}$) is bounded by ($\frac{B_i}{2}$) may be strong, where reward distributions can exhibit significant variability and are not tightly bounded.
In many realistic settings, a player would be ... | Summary: This paper tackles the combinatorial semi-bandits problem and provides many theoretical results, including gap-free variance-dependent upper bounds for both deterministic and stochastic sampling strategies, and for the least, they also provide a corresponding lower bound.
Strengths: The paper is well-written ... | Rebuttal 1:
Rebuttal: ___Concerning the minor questions and remarks___
We first wish to thank the reviewer for pointing out our typos and inaccuracies.
> Be more precise in the formulation of the lower bound (Thm. 2): In which sense does the inequality hold, in expectation? Also, is policy restricted deterministic po... | null | null | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewers, ACs, SACs and PCs for their expertise and the time they are devoting to our submission. Their feedback and suggestions are very valuable and will be taken into account.
___General comments___
We were provided 3 high quality reviews that acknowledge... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scaling Laws in Linear Regression: Compute, Parameters, and Data | Accept (poster) | Summary: Large models often empirically satisfy the neural scaling law i.e. the error decreases when the number of data and the model size increase, however, this contradicts with the widely accepted belief in learning theory that the variance error (one of the decomposed errors) should increase with the model size. Au... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We address your comments as follows.
> Q1. The assumptions of the paper might be too strong with linear regression setting and Gaussian design assumption, is it possible to extend to the kernel setting and with relaxed assumption on features [1]?
A1. The Gaus... | Summary: This work examines neural scaling laws, in the simplified setting of linear regression trained by one-pass SGD. In particular, it attempts to explain the apparent mismatch between the statistical theory on one hand, which predicts that variance error increases with model size, and neural scaling laws on the ot... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and comments.
> Q1. It is not clear when the variance term is first given on page 2 that it is of higher order than the other terms. The reader has to find the more precise presentation in Theorem 4.1 in order to verify this for themselves. Clarif... | Summary: This paper sheds new light on the bounds of linear regression by framing them in terms of scaling laws.
Strengths: - The set-up is very clear, the story is well-narrated and the model, including some sketch matrix to play the role of the model-size, is well-thought.
- The authors try to be exhaustive with th... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Below is our response to your question.
> Q1. Perhaps the authors should put emphasis on the fact that there is absolutely no technical novelty and that the contribution relies in re-framing known bounds under the perspective of scaling laws.
A1. We respectfully disagree... | Summary: Motivated by the recent neural scaling law literature, this work investigates the generalization error rate of a sketched linear regression model trained on Gaussian covariates with power-law spectrum under one-pass SGD.
The main result are lower and upper bounds with matching rates, providing a detailed char... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will cite and discuss the relationship between the pointed papers and our work in the revision. However, there are several potential misunderstandings about our results, which we would like to clarify below.
> Q1. [1,2,3] have studied both single and multiple pass... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proves that the scaling laws that occur in deep learning also occur when solving linear regression with SGD.
Namely, suppose:
* that covariates x are Gaussian with a power-law spectrum
* the true labels are given by <w^*, x> for unknown w^*
* we run one-pass SGD to do linear regression on an M-dim... | Rebuttal 1:
Rebuttal: Thank you for supporting our work! We will fix the typo. We address your concerns as follows.
> Q1. How dependent are the results on a geometric step-size schedule? What if a constant schedule, or schedule decreasing as $\gamma_t = t^{-c}$ is used instead?
A1. The geometrically decaying stepsiz... | null | null | null | null | null | null |
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox | Accept (spotlight) | Summary: The paper explores CTS for the linear combinatorial semi-bandit problem with subgaussian rewards. It introduces a novel TS algorithm that avoids exponential regret scaling with problem dimensionality. Theoretical bounds and experiments are provided.
Strengths: The paper addresses a significant limitation in e... | Rebuttal 1:
Rebuttal: The Lemma 4 of (https://arxiv.org/pdf/2302.11182) is very interesting. It could replace lemma 10 from [9] and provide us with a better bound with a $\ln(m)$ instead of a $\ln(m)^2$. We will make sure to correct and add that in the final version of the paper.
$U^\star(s)$ is a scalar; we do not b... | Summary: This paper addresses Thompson Sampling for stochastic combinatorial bandits with sub-Gaussian rewards. In this area, previous work has identified the interesting phenomenon that some versions of Thompson sampling incur a per-instance regret which is exponential in the (maximum) number of arms pulled per round,... | Rebuttal 1:
Rebuttal: 1.
+ From a theoretical point of view :
We share the same leading term in $\log{T}$. However, for the exponential [18] vs. polynomial term, you need to compare $m^{8m}$ and $m^{20} \times d^{10}$. So, one can say that for $m>10$, our bound can be better. This does not take into account the te... | Summary: The authors present a new Thompson Sampling algorithm for linear combinatorial stochastic semi-bandits. The algorithm provably achieves a better finite-time regret than previous works, and specifically without an exponential dependency on the dimension of the problem. The authors also present a "paradox" that... | Rebuttal 1:
Rebuttal: We will provide the code in an open-source repository after the review process.
* In line 26, $X(t)$ could be any random variable that is bounded in $[a,b]^{d}$. However, the one that maximizes its variance is the half Dirac in $a$ and $b$. The latter gives us the most deconcentrated random varia... | Summary: This paper proposes a modified version of posterior sampling that achieves optimal asymptotic regret bound, providing an algorithm through the methodology of Thompson Sampling that achieves such a bound.
Strengths: This is a technical paper, and the message is clear and intriguing. This paper validates the me... | Rebuttal 1:
Rebuttal: The decomposition under a clean run (event $\mathfrak{A}$) is original. The decomposition of event $\mathfrak{A}$ into events $\mathfrak{B},\mathfrak{C},\mathfrak{D}, \mathfrak{E}$ is new. And how we handle event $\mathfrak{E}$ is original. However, the handling of event $\mathfrak{B},\mathfra... | Rebuttal 1:
Rebuttal: We want to thank the reviewers for their questions and remarks; we will take them into account to improve the paper's clarity. Here is a general answer to all the questions asked by the reviewers.
The main contribution of our paper was to find a way to circumvent the exponential term in the work ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training | Accept (poster) | Summary: This paper presents a novel scheme for reducing the communication cost of training LLMs using FSDP approach. To this end, the authors suggest to quantize the weights differences (instead of the weights themselves) and applying Hadamard transformations for making the gradients smooth. They show up to ~4x traini... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W2: To motivate the problem more, I would like to see some practical analysis of the communication volume based on the number of nodes. For example, how much data ...**
A: The practical communication volume for data parallelism is primarily determined by factors such as model si... | Summary: Thank you for submitting your paper to Neurips 2024. The paper proposes SDP4Bit, a communication quantization method that mitigates accuracy loss by weight difference quantization and 8-bit intra-node and 4-bit internode quantization. The authors provide convergence analysis to show that SDP4Bit is compatible ... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: Missing key ablation studies. The two proposed strategies in this paper are weight difference quantization and two-level int8-int4 all-to-all gradient averaging. So I suggest the authors add the following two experiments to compare the accuracies.**
- *Running ZeRO++ with two... | Summary: This paper proposed a 4bit quantization framework for sharded data parallelism training. It proposed to quantize the weight differences between iterations as the first method to reduce the accuracy degradation. It also proposed to mixed-precision quantization for intra- and inter- node communication. Experimen... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: The experiment results show training/validation loss on Pile for the proposed method and baseline (FP), QSDP, and other methods. The loss is an auxiliary variable to the performance of the model. A direct comparison on real datasets would be more convincing. For example, what... | Summary: The paper introduces a novel approach to reduce communication overhead in Sharded Data Parallelism (ShardedDP) for training large language models (LLMs). It proposes two key techniques: quantization on weight differences and two-level gradient smooth quantization, which effectively compress weights and gradien... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: The connection between the convergence analysis and the quantization schemes is not entirely clear. Specifically, it is not clear if the quantizers correspond to the biased/unbiased compressors in Algorithm 4 and if so which of them is biased and which is unbiased. It appears... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' critical assessment of our work. Below, we provide the relevant figures and results to address the questions and concerns raised.
**Contents in Rebuttal PDF**
1. Table of notations that explains the abbrevations of algorithms such as W4, Int4-Int4, TLq, etc.
2. Compa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Credal Deep Ensembles for Uncertainty Quantification | Accept (poster) | Summary: This paper presents Credal Deep Ensembles (CreDEs), an ensemble framework of Credal-Set Neural Networks (CreNets) to produce a high-quality epistemic uncertainty by using a credal set with the Distributionally Robust Optimization (DRO) technique.
Contributions are:
- A rigorous CreNet final layer in Section 2... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your valuable feedback.
**Response to W1:** We believe that lines 172-175 in the original paper are also an important result, which shows that cross-entropy loss (CE) can be applied to upper probability vectors as representatives of part of the boundary of the pr... | Summary: The paper proposes credal neural networks, which output probability intervals for each class as opposed to a single probability estimate. They also propose to use ensembles of these models, averaging the outputs of the members. Their ensembles of credal neural networks show a higher performance than traditiona... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your high recognition of our work and valuable feedback and suggestions. Below we address your concerns.
**Regarding W1**
**Response:** We fully agree that moving the additional results of DEs$^*$-5 (standard ensemble with DRO loss) from the appendix to th... | Summary: This paper introduces Credal Deep Ensembles, which are ensembles of Credal-Set Neural Networks designed to predict lower and upper probability bounds for each class, representing epistemic uncertainty.
Strengths: Novel approach to uncertainty quantification in deep learning. Empirically it seems that the prop... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate your efforts in reviewing our work and your recognition of our novelty. Below we address your concerns.
**Response to W1:** We believe that we have provided a comprehensive evaluation of our approach, which consistently shows significantly improved performance, uncer... | Summary: In this paper, the authors propose a novel method for uncertainty estimation in deep networks called Credal Deep Ensembles, which combines credal inference and ensembling approaches. During inference, the model predicts intervals (lower and upper probability values) for each class, resulting in a final output ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We express our sincere gratitude for acknowledging our work and your valuable feedback. In the following, we address your concerns.
**Regarding W4 and Q3: A more detailed explanation of the source of the improvements**
**Response:** We would like to make a clearer explanation bel... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We appreciate your efforts in reviewing our paper and recognizing the novelty and strengths of our work. In the following, we would like to address your concerns regarding the complexity of our Credal Deep Ensembles (CreDEs) method and the comparison baselines.
**Part 1: added co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Linear Causal Bandits: Unknown Graph and Soft Interventions | Accept (poster) | Summary: The paper considered a specific setting in the causal bandits problem where 1) the graph is unknown, 2) the causal model is linear , and 3) the action set consists of $2^{N}$ soft interventions.
To tackle the problem, the author proposed an algorithm that first learns the causal structure and then uses UCB-bas... | Rebuttal 1:
Rebuttal: **Comment:** The algorithm assumes that the causal graph does not contain latent variables. The algorithm requires access to identifiability parameters, which is an unrealistic assumption.
**Response:** We thank the reviewer for the thoughtful comment. In principle, we agree with the reviewer ab... | Summary: This paper studies the linear scm setting for causal bandits. In particular, there are two vectors associated with the linear response of every node, and the learner may independently choose which of the two vectors to use. The value as well as the graph are unknown. Hence, the action space is 2**number_nodes.... | Rebuttal 1:
Rebuttal: **Support of weights matrices:** We clarify that $B_i$ and $B_i^*$ always have the same support under soft interventions. as such interventions only change the conditional distribution of node $i$ without affecting the topology. The causal topology remains intact, leading to $B_i$ and $B_i^*$ havi... | Summary: The authors consider a stationary causal bandit problem for an unknown linear model with weight matrix $B \in \mathbb{R}^{N \times N}$ and noise vector $\epsilon \in \mathbb{R}^N$. In their setup, intervention on the node $X_i$ replaces all of the weights into $X_i$ with those from another unknown matrix $B^* ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful questions, especially those pertinent to the instance-dependant regret and hidden confounders. We provide more discussions and we hope these clarify the reviewer’s technical concerns.
**Dependence on $d$ and $L$:** The reviewer raises a good... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers about the comments about our assumptions and empirical evaluations. We would like to clarify the following:
**Theoretical contributions:** We remark that this paper significantly extends the scope of the causal bandit literature by entirely removing the assumption about kn... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem | Accept (poster) | Summary: The paper first shows the difficulties of obtaining the absolute optimal solutions to simple convex bilevel problems. The authors also present the lower bound of the first-order methods for solving simple convex bilevel problems. Moreover, the authors proposed a novel framework based on functionally constraine... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating their valuable time and effort to evaluate our manuscript. Below are our responses to the reviewer’s concerns:
### W1
Thanks for the suggestion! We will provide self-contained proofs for these lemmas in the revised version.
### W2
Actually, o... | Summary: This work studies simple bilevel problems with convex upper and lower level objectives. The paper studies the problem for both smooth and Lipschitz functions. The contribution is two folded: 1- first, it shows that no zero-respecting algorithm can achieve $(\epsilon_f,\epsilon_g)$ absolute optimal solutions fo... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating their valuable time and effort to evaluate our manuscript. However, there are some misunderstandings in the review, and we respond to your concerns one by one.
> Writing
Thanks for the valuable suggestions! We will incorporate your advice i... | Summary: This paper provides a theoretical proof that first-order zero-respecting algorithms are incapable of approximating the optimal solution for a simple bilevel optimization problem where both the upper-level and lower-level functions are convex. Then they propose a functional constrained reformulation to solve th... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating their valuable time and effort to evaluate our manuscript. Below are our responses to the reviewer’s concerns.
> W1: Can other baselines like [1], [2] find $(\epsilon_f,\epsilon_g)$ weak optimal solutions?
Actually, while some of the previo... | Summary: This work proposes a novel and near-optimal method to solve convex simple bilevel problems by finding weak optimal solutions. The author also provides theoretical and numerical guarantees of the convergence of this algorithm.
Strengths: 1. This paper is easy to follow, with techniques that are rigorously prov... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating their valuable time and effort to evaluate our manuscript. Below are our responses to the reviewer’s concerns.
> W1: Since this work focuses only on weak optimal solutions, the title Near-Optimal Methods for Convex Simple Bilevel Problems se... | Rebuttal 1:
Rebuttal: We are deeply grateful for the efforts and valuable feedback of the reviewers and area chairs in reviewing our manuscript.
Combining the suggestions of the reviewers, we conduct an additional numerical experiment to better compare the performance of $\texttt{FCB-BiO}$ with other methods. Followi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method to solve the bilevel problems where both upper and lower level objective functions are convex. The new algorithms, $FCB-BiO^{sm}$ combine bisection with sub-gradient or gradient methods to solve a reformulated problems to find a weak optimal solution. Convergence analyses show that ... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating their valuable time and effort to evaluate our manuscript. However, there are some misunderstandings in the review that we wish to clarify.
> W1 & W2: In the proof of Theorem 4.1/4.2, what if for the same problem, we allocate more budget of ... | null | null | null | null | null | null |
Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration | Accept (poster) | Summary: In this work, the authors propose an MIA for LLMs that utilizes prompt calibration to measure variation on model behavior for neighboring inputs. The authors also make a connection with the neighborhood attack from Mattern et al and show how their framework encapsulates such neighborhood-based attacks. Perform... | Rebuttal 1:
Rebuttal: Dear reviewer i2LA,
We deeply appreciate the time you took to review our work and your meticulous comments for improvement. Below we address the questions raised in your review, the responses to the weaknesses and minor comments, as well as the reference can be found in subsequent comments:
**Q1... | Summary: This paper presents a membership inference attack against causal language models, addressing the limitations of previous attacks, such as the inaccessibility of appropriate reference datasets and heavy reliance on overfitting. To overcome these limitations, the authors propose a self-prompt approach to extract... | Rebuttal 1:
Rebuttal: Dear reviewer saYA,
Thank you so much for your thoughtful review and your suggestions for improvement. Below we address the questions raised in your review, the responses to the weaknesses and the reference can be found in subsequent comments:
**Q1,W2: Does the proposed approach require training... | Summary: This paper proposes self-calibrated probabilistic variation (SPV)-MIA, a membership inference attack. The novel ideas that SPV-MIA introduces in the space of LLMs: 1) using paraphrasing to obtain samples around the target sample text in the sample domain, and using paraphrased texts to compute probabilistic va... | Rebuttal 1:
Rebuttal: Dear reviewer DLpC,
We deeply appreciate the time you took to review our work and your comments for improvement. Below we address the concerns raised in your review:
**Q1,W1: Questions about the PVA generalization.**
> **Q1.1: The paper claims the generalization of PVA but end up using the neig... | Summary: This paper studies the membership inference attack on large language models finetuned on private data. Instead of reusing other pre-trained public large language models, the paper proposes a way to generate a reference dataset and finetune this dataset to attain a reference model. With this reference model, th... | Rebuttal 1:
Rebuttal: Dear reviewer v2Kc,
Thank you so much for your thoughtful review and overall positive comments. Below we address the concerns raised in your review:
**W1: It is not fully in the black-box setting, since it assumes the knowledge of the same pre-trained model and the access of its parameters.**
I... | Rebuttal 1:
Rebuttal: We have attached the experimental results added according to the reviewers' requirements in the attached PDF file, which includes the following contents.
1. Table1: Evaluation of all baselines and SPV-MIA using AUC scores.
2. Table 2: Evaluation of all baselines and SPV-MIA using TPR@1%FPR.
3. Ta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Memory-Efficient LLM Training with Online Subspace Descent | Accept (poster) | Summary: The paper considers memory-efficient optimization for large language model. In particular, the focus is on optimizers leveraging low-rank projection. The paper first derives asymptotic convergence for such methods with arbitrary choice of projection matrix. Then the paper identifies the inefficiency of using S... | Rebuttal 1:
Rebuttal: ## Re: “Memory and efficiency”
Updating $P_t$ with AdamW will inadvertently increase the memory consumption. However, in practice, we observe that the increase in peak memory is minuscule compared to the overall size of the model. Meanwhile, we can enjoy the nice properties of online subspace desc... | Summary: The authors propose a variation of GaLore that doesn't require a fixed SVD for achieving a memory efficient LLM training. Instead, the authors project the gradients dynamically into a small sub-space using online PCA which depends on the evolving trace of the gradient landscape.
The authors tested their meth... | Rebuttal 1:
Rebuttal: ## Re: "Downstream tasks"
We did standardized GLUE evaluation for the above two 7B checkpoints with eval-harness.
| Method | MRPC | RTE | SST2 | MNLI | QNLI | QQP | AVG |
|--------|-------|-------|-------|-------|-------|-------|--------|
| GaLore | 0.6838| **0.5018**| 0.5183| 0.3506| ... | Summary: Utilizing low-rank structure has become a popular way for memory-efficient LLM training. The authors are the first to provide convergence guarantees for general low-rank updating rules. Furthermore, based on their theoretical result, they propose a family of optimizers called online subspace descent. The empir... | Rebuttal 1:
Rebuttal: ## Re: “Intuitions”
The rough intuition is that $P_t$ serves as a kind of preconditioning matrix in the Hamiltonian systems. But to arrive at the precise mathematical conclusion, we find that the best and quickest way to understand it is through the derivation in Eq (8), together with physical und... | Summary: The paper presents Online Subspace Descent, a memory-efficient modification applicable to a wide class of gradient-descent based algorithms where low-rank projections of gradients can be employed to reduce the memory overhead. Contrary to recent techniques such as GaLore, which require infrequent but costly up... | Rebuttal 1:
Rebuttal: ## l.1ß6 “Generally, Adam updates are memory-bound operations”
- This is precisely the bottleneck that this family of online subspace descent optimizers aims to tackle. By projecting into subspace, the memory footprint of Adam will be greatly reduced, allowing us to schedule another operation conc... | Rebuttal 1:
Rebuttal: We really appreciate our reviewers for their constructive reviews and suggestions. Here we summarize and highlight our response to a few main points of concerns. Hope that will help address the majority of those questions.
## Re: “System efficiency compared to Galore” (NLbB, FtMV, RBsr)
In terms ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? | Accept (poster) | Summary: This paper highlights problems with the Evidential Deep Learning (EDL) framework, analyzes these problems, and proposes possible solutions for some of them. The paper provides a new taxonomy and a unifying objective function for a wide range of EDL methods. The authors identify that aleatoric and epistemic unc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and address them in detail below.
**1. The difference between RPriorNet and VI, UCE cannot support the claim " the VI loss in Eq. (3) and UCE loss Eq. (4) turn out to be equivalent to the RPriorNet objective [...]"**
We acknowledge that this se... | Summary: The presented paper offers a (further) critique on EDL. They show that a range of EDL objective functions are largely equivalent by presenting a unified objective function that subsumes many existing objective functions. With this unified objective function some problematic properties are shown that prove that... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We address them in detail below.
**1. The central point of critiquing EDL is not novel.**
While novelty is in the eye of the beholder, and critiquing EDL is indeed a primary focus of this work, our contribution is non-trivial compared to exist... | Summary: The paper focuses on Evidential Deep Learning (EDL) models developed for uncertainty quantification in a computationally efficient manner. It identifies key limitations of the EDL methods: their inability to faithfully express both the epistemic and aleatoric uncertainties. The paper then proposes to integrate... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We address them in detail below.
**1. Is the proposed solution theoretically guaranteed to faithfully express the epistemic/aleatoric uncertainties?**
Please refer to global response 1.
**2. It is unclear if there is any benefit of the $p(\ps... | Summary: In this paper, the authors propose a novel analysis of existing evidential learning approaches, which have recently gained significant attention in the domains of uncertainty estimation and probabilistic modeling. The two major contributions of this work are as follows: First, the authors provide a clearer und... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We address them in detail below.
**1. Whether EDL’s behavior demonstrated in the paper would hold in other modalities and domains.**
We totally agree with the reviewer that analyzing EDL’s behavior in other domains and modalities is worth expl... | Rebuttal 1:
Rebuttal: # To ALL Reviewers:
We thank all the reviewers’ effort in reviewing our paper and providing thoughtful comments. We would like to take this opportunity to further clarify our contribution, and resolve some of the common concerns as follows:
**1. Theoretical justification of proposed Bootstrap Dis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GACL: Exemplar-Free Generalized Analytic Continual Learning | Accept (poster) | Summary: This paper proposes a new exemplar-free GCIL technique named generalized analytic continual learning (GACL). It adopts analytic learning (a gradient-free training technique), and delivers an analytical (i.e., closed-form) solution to the GCIL scenario, which is derived via decomposing the incoming data into ex... | Rebuttal 1:
Rebuttal: # Replies to Reviewer tsV2
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns
below.
### W1. Why the avg accuracy on CIFAR-100 is much lower than the last accuracy?
**Response to W_1**: As mentioned in Appendix G, tasks on CIFAR-100 are notably ... | Summary: Class Incremental Learning (CIL) faces the problem of catastrophic forgetting when training a network, i.e., the model loses previous knowledge when learning a new task. Generalized CIL (GCIL) aims to address more realistic scenarios, but existing methods are either ineffective or violate data privacy. This pa... | Rebuttal 1:
Rebuttal: # Replies to Reviewer qU32
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns
below.
### W1. Were the experiments in this paper also conducted in an online scenario?
**Response to W_1**: Yes, we follow the settings in Si-Blurry [1], which is a on... | Summary: This paper proposes a new exempler-free generalized continual learning (GCIL), named generalized analytic continual learning (GACL) technique. It does not depend on gradient-based tranining, which avoids the task-recency bias leading to the forgetting issue. It also delivers an closed-form solution to the GCIL... | Rebuttal 1:
Rebuttal: # Replies to Reviewer Xb3R
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns
below.
### 1. Comparing GACL with those methods (especially RanPAC)derives more solid results."
**Response to W_1**: Thank you for pointing out this important referen... | Summary: This paper deals with the generalized CIL (GCIL) problem where incoming data have mixed data categories and unknown sample size distribution. The author proposes generalized analytic continual learning (GACL) which adopts a pre-trained and fixed backbone and uses least squares to get a closed-form solution. Ex... | Rebuttal 1:
Rebuttal: # Replies to Reviewer vKCe
Thank you for your constructive and detailed feedback. We provide detailed responses to your concerns
below.
### W1. Most of the content on page 4 and page 5 (e.g., Theorem 3.1) in Section 3 is overly similar to existing ACL works. The main difference claimed by the aut... | Rebuttal 1:
Rebuttal: # General Response
We thank all the reviewers for their time, insightful suggestions and valuable comments. In summary, Reviewer vKCe, Reviewer Xb3R and Reviewer qU32 all appreciate that our writing is **clear** and **easy to follow**. Reviewer tsV2 appreciates that our method is **clear**, **wel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Monoculture in Matching Markets | Accept (poster) | Summary: The authors are focused on matching markets in which different firms use a single algorithm / evaluation criterion (monoculture) vs. markets where different firms may each have different evaluation algorithms / criterion (polyculture). This can be seen as a substantial generalization of the wonderful work of K... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback, and are glad you enjoyed the paper. We answer your questions below:
**Q1: How does Lemma 2 (Equal Cutoffs Lemma) relate to Theorem 1 part 1 from Azevedo and Leshno [10], which says that if has full support, then there is a unique stable matching?**
It is es... | Summary: This paper studies the monoculture problem in matching markets from a theoretical perspective. The authors found that on the firms' (colleges') side, monoculture may decrease the quantity of matched applicants; while on the applicants' side, monoculture may help matched applicants to match with higher-ranked f... | Rebuttal 1:
Rebuttal: We appreciate the reviewers comments, and are glad to hear that it challenged preexisting beliefs about monoculture. We address your comments below:
**Assumptions/Straightforwardness:** We note in our general response that we believe our computational experiments demonstrate that our conceptual i... | Summary: The paper considers a matching model with a continuum of students/applicants and m colleges/firms where the firms have a noisy estimate of the candidates' quality and compares the stable matching outcome in two situations: monoculture (where all firms have the same estimate) vs polyculture (where each firm has... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful feedback, and we discuss your concerns below.
**Assumptions:** We give detailed comments on our theoretical assumptions in the general response. We reemphasize here that we will incorporate your feedback in being clearer about assumptions in the introduction.... | Summary: This paper examines the effects of algorithmic monoculture in a large two-sided matching market, in which participants on both sides compete with each other and outcomes are determined by preferences on both sides.
It proposes a matching markets model to study monoculture and produces both expected and surpris... | Rebuttal 1:
Rebuttal: Thanks for the helpful comments. We’re glad to see that you found the results interesting, and at times surprising. We address your comments, mostly about the ML experiments, below:
“The results in Figure 5 seem to contradict the strong claims made about polyculture outperforming monoculture; the... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the thoughtful comments.
We’re glad to see that reviewers found the paper to be clear and well-written (R2, R3, R4), and the results to be surprising, countering past literature and expectations about algorithmic monoculture (R1, R3, R4). All reviewers noted how the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FlexCap: Describe Anything in Images in Controllable Detail | Accept (poster) | Summary: This paper introduce a versatile flexible-captioning vision-language model called FlexCap, capable of generating region-specific descriptions of varying lengths. The FlexCap use caption length to control the information density of the generated sentences. The paper also introduces a large-scale dataset of imag... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our work.
We address the concerns raised below:
> About the dataset building:1) It seems that there is no human involvement in the data construction process. I worry about the correctness and diversity of the generated sentences.
We... | Summary: This paper proposes a vision-language model termed as FlexCap, which given a specific region in the image represented as a bounding box, outputs a description of that region in a length-controllable fashion where the exact length of the generated description can be controlled via a prefix token. First the auth... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their insightful feedback and thorough review.
>However, how does the proposed method compare to [R1]?
In R1, the training dataset is generated by querying GPT4V with regions of interest found by SAM. While they do train OSPREY with this dataset, the... | Summary: This paper introduces a versatile captioner capable of generating region-specific descriptions with controllable information density. This functionality enables dense captioning tasks and enhances visual question answering (VQA) by integrating with a LLM. The paper also presents a large-scale dataset containin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and meticulous attention to detail in reviewing our paper.
We address the concerns raised by them below:
>Referring to FlexCap as a versatile flexible-captioning vision-language model might be an overstatement.
We used this description to highlight the ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors | Accept (spotlight) | Summary: The paper tackles the problem of enhancing 3GS novel views which are far from existing viewpoints (encountered in sparse-view settings), due to insufficient information in under-sampled areas. This work enhances the low quality views using a video diffusion model to maintain multi view and temporal consistency... | Rebuttal 1:
Rebuttal: **S1. Will this dataset be open sourced?**
Our 3DGS Enhancement dataset is generated based on the publicly available DL3DV dataset. We will provide the complete dataset generation code to ensure the community can quickly reproduce our results and explore new research opportunities.
**W1. Relate... | Summary: This paper utilizes a video diffusion model to enhance 3DGS rendering results. It proposes a 3DGS-Enhancer pipeline to reformulate 3D-consistent image restoration tasks and leverage it to generate high-quality and 3D-consistent images. They have enough experiments to prove the soundness of their method.
Stren... | Rebuttal 1:
Rebuttal: **W1. They can provide more comparisons on ablating the temporal consistency.**
As shown in Fig. 1 of the paper, the images rendered by 3DGS model trained on sparse view inputs often contain significant artifacts or blank areas. We observed that a image diffusion model trained solely on these im... | Summary: This paper presents a novel pipeline aimed at enhancing the quality of 3D Gaussian splatting (3DGS) representations, especially in scenarios with sparse input views. They propose a novel framework, 3DGS-Enhancer, that leverages video LDMs for generating high-quality and 3D-consistent images. Moreover, to mitig... | Rebuttal 1:
Rebuttal: **1.The writing of this paper is not very clear, and some typo errors exist in the submission, for instance, line147, it should be ${ I^{ref}_{i-1}, I_1, I_2, ..., I_T, I^{ref}_i }$; line216: it should be $I_c$, consistent to the equation (8).**
Thank you for the helpful comment. You are correct,... | Summary: This paper is working on the problem of novel view synthesis with sparse input views. The authors present 3DGS-Enhancer to enhance the rendering quality and address 3D view consistency problem using 2D video diffusion priors. The experimental results show that this work has achieved state-of-the-art performanc... | Rebuttal 1:
Rebuttal: **W1. Runtime is not discussed in this paper. I’m wondering how long it will take for one scene compared to the other baselines.**
As shown in the below table, we estimate the per-scene runtime and rendering FPS of different methods on the DL3DV test set (3 views) with one NVIDIA A100 GPU. Our me... | Rebuttal 1:
Rebuttal: The authors thank all reviewers for the careful review and constructive feedback. We are encouraged that all four reviewers appreciate the novel idea and excellent experimental results of this work. We address all the raised concerns in corresponding reviewer's rebuttal section. The code and datas... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QueST: Self-Supervised Skill Abstractions for Learning Continuous Control | Accept (poster) | Summary: The paper introduces an approach utilizing latent variable generative models in conjunction with FSQ quantization techniques to learn good representations of action sequences. It also proposes a prior network for autoregressive modeling at the level of behavioral representations. The superiority of the learned... | Rebuttal 1:
Rebuttal: Thanks much for your time spent reviewing and your thoughtful comments. We’ve uploaded an updated version of the paper to the website in case you’d like to review the changes we mention.
> I believe the main difference of this paper contains two points: (1) using FSQ to learn discrete latent spac... | Summary: - This work introduces a new method called QueST that uses a latent variable model to learn a set of discrete temporal action abstractions / motion primitives / skills for imitation learning. This is done by training an auto-encoder to encode a sequence of actions using a causality preserving encoder, discreti... | Rebuttal 1:
Rebuttal: Thanks much for your time spent reviewing and your thoughtful comments. We’ve uploaded an updated version of the paper to the website in case you’d like to review the changes we mention.
> Two of the baselines, VQ-BeT and PRISE, also learn temporal action abstractions. It would be nice to have a ... | Summary: The paper aims to capture skill abstractions through training a latent space through encoding and decoding actions. The resulting latent space is used for training a policy that converts observations into the latent space, and uses the trained decoder to output actions. The paper conducts experiments on manipu... | Rebuttal 1:
Rebuttal: Thanks much for your time spent reviewing and your thoughtful comments. We’ve uploaded an updated version of the paper to the website in case you’d like to review the changes we mention.
> Should probably include analysis on what the latent 𝑧’s ended up learning. Do they actually have some tempo... | Summary: This work develops a novel framework for learning generalizable skills from demonstration data. The author’s model uses a quantized discrete latent variable model that compresses skills into a sequence of latent variables and predicts temporal sequences of actions. Their approach decodes skill by cross-attendi... | Rebuttal 1:
Rebuttal: Thanks much for your time spent reviewing and your thoughtful comments. We’ve uploaded an updated version of the paper to the website in case you’d like to review the changes we mention.
> The only major weakness of the author’s work is the limited evaluations. If the authors can justify using ju... | Rebuttal 1:
Rebuttal: We are grateful for the insightful feedback from all reviewers. The reviewers have recognized the novelty of our approach in modeling the inherent structure of manipulation action data through temporal correlation and causal-masking. A particularly noteworthy aspect of our research is the learning... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DenoiseRep: Denoising Model for Representation Learning | Accept (oral) | Summary: This paper proposes DenoiseReID to improve feature discriminative with joint feature extraction and denoising, in which FEFDFA is developed to merge parameters of the denoising layers into embedding layers. Experimental results show the proposed DenoiseReID improves performance.
Strengths: 1. The proposed joi... | Rebuttal 1:
Rebuttal: Q1: **What constitutes this noise?**
A1: We appreciate your detailed review. Experimental results of Table 3 (FEFDUF) could empirically demonstrated the hypothesis of "the features obtained by backbone extraction are noisy". FEFDUF includes a well-trained person ReID model, and a denoise model wh... | Summary: This mauscript proposes a novel denosing model for representaetion learning and take person re-identification as a benchmark. It unifies the frameworks of feature extraction and feature denoising, where the former progressively embeds features from lowlevel to high-level, and the latter recursively denoises fe... | Rebuttal 1:
Rebuttal: Q1: **Clarify the difference between the proposed FEFDFA and reparameterization.**
A1: Thank you for your insightful comment. We appreciate the opportunity to clarify the differences between our "Feature Extraction and Feature Denoising Fusion Algorithm" (FEFDFA) and reparameterization.
- Repa... | Summary: This paper proposes a new method, Feature Extraction and Feature Denoising Fusion Algorithm (FEFDFA), which utilizes the denoising ability of diffusion models to denoise the features in the feature extraction layer, and fuses the parameters of the denoising layer with those of the feature extraction layer thro... | Rebuttal 1:
Rebuttal: Q1: **The authors need to enrich the Related Work.**
A1: We thank the valuable suggestions. Our proposed DenoiseReID is different from the related works [1-3]. The related works [1-3] apply the itermediate layer features of an existing pre-trained diffusion model to improve downstream task. Ours ... | Summary: This paper proposes a novel denoising model called DenoiseReID, designed to enhance representation learning in person re-identification (ReID) tasks. This approach combines traditional denoising processes with feature extraction through a feature extraction and denoising fusion algorithm (FEFDFA) that incurs n... | Rebuttal 1:
Rebuttal: Q1: **The authors missed an opportunity to benchmark their method against the latest advancements like CLIP-ReID.**
A1: Thanks for your valuable feedback. CLIP is a very strong vision-text encoder, which is trained with 400 million data. Beyond CLIP, CLIP-ReID pioneeringly adapts CLIP, a zero-sho... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What do Graph Neural Networks learn? Insights from Tropical Geometry | Accept (poster) | Summary: *Disclaimer*: I did not check proofs carefully and did not read the appendix. I am also by no means an expert in tropical geometry.
This paper studies expressivity of message passing neural networks (MPNNs) through the lens of tropical geometry. The paper has results on equivalences between classes of piecewi... | Rebuttal 1:
Rebuttal: Thank you so much for a detailed review and several excellent suggestions, all of which we will act on. Please see our response below.
**(Geometric complexity (GC) and generalization)**
GC characterizes the complexity of a neural network to approximate functions. In particular, a high value in... | Summary: This paper uses tropical geometry to understand MPNNs in the broader general context of GNNs. In the face of the WL framework which studies limitations of the GNNs, this paper proposes to use the rich and powerful theory of tropical geometry to uncover their potential. This paper makes some important contrib... | Rebuttal 1:
Rebuttal: Many thanks for such a detailed, constructive and thoughtful review. We're grateful for your acknowledgment of the contributions of this work, and share your enthusiasm for leveraging tropical geometry to better understand successful modern architectures.
Below we address all your questions, com... | Summary: This paper aims to characterize the class of functions learned by message passing Graph Neural Networks (GNNs) with ReLU activations through the lens of Tropical Geometry. Specifically, it characterizes the functions learned by ReLU-based Message Passing Neural Networks (MPNNs) by establishing their equivalenc... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We're glad to note your recognition of several contributions and strengths of this paper. We address your comments, concerns, and suggestions below.
`The theoretical analysis ... assumes that the ReLU MPNN processes the same graph structur... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Sample Complexity Bounds for Diffusion Model Training | Accept (poster) | Summary: This paper investigates theoretical guarantees for the performance of diffusion models. More precisely, while the previous works have mainly focused on the iteration complexity by assuming to have access to an accurate diffusion model, this paper targets the question of the sample complexity for learning an ac... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and valuable suggestions on the presentation of the paper. We appreciate the time and effort you have invested in providing this feedback.
Implications of our work
----
Our work is the first to show a polynomial sample complexity for learning a diffusion model... | Summary: In this paper, the authors analyze the sample complexity of training a score-based diffusion model. They show that, with a sufficiently expressive neural network, \tilde O(d^2 P D log \Theta \log^3 \frac{1}{\gamma} / \epsilon^3) samples are needed to learn an accurate diffusion model. Compared to the existing... | Rebuttal 1:
Rebuttal: We appreciate your comments and are glad that you like and support the paper.
**The assumption that networks can
represent the score is somehow strong.**
We agree that it is somewhat strong, but view this more as a statement
about data; analogous to "the data is sparse in X basis". Over the
pa... | Summary: The paper studies the sample complexity of training diffusion models. The bound derived in the paper is exponentially better than the previous results. The paper also discusses the difficulty of learning the score function in $L^2$.
Strengths: 1. The paper derives better sample complexity results for diffusio... | Rebuttal 1:
Rebuttal: Comparison to previous work
----
We would like to clarify that most of the prior work in the literature, including the works you mentioned, ask about the *approximation* power of neural networks for representing the score of *arbitrary* distributions, and/or make strong assumptions on the distrib... | Summary: In this paper, the authors studied the sample complexity of training diffusion models. By using a sufficiently expressive neural network, the authors showed an exponential improvement in the dependence on Wasserstein error and network width, which is expressed as $\tilde{O}(d^2PD\log\Theta \log^3(1/\gamma)/\va... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions. As you state, prior work
like Chen et al. have shown that "sampling is as easy as learning the
score." Thus the main open question is: how easy *is* learning the
score?
We think that question is clearly important enough for top tier
machine learning co... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback.
One common request was for a table placing our results in context of related work. Here it is:
| **Work** | **Sample Complexity** | **Notes** ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HGDL: Heterogeneous Graph Label Distribution Learning | Accept (poster) | Summary: This paper studies heterogeneous graph label distribution learning with the aim of predicting label distributions of unlabeled nodes in a heterogeneous graph. This paper elaborates the challenges for generalizing LDL into networked data, and proposes an LDL algorithm HGDL to overcome the challenges. Besides, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the reviewer's concerns below one by one in a Q&A fashion.
---
Q1: Why can't we just replace the output layer of existing heterogeneous node classification model with MSE (Mean Squared Error) or KL loss to learn the label distribut... | Summary: This paper studies the problem of heterogeneous graph label distribution learning. To deal with it, this paper proposes an HGDL method that optimizes meta-path graph topology and aligns it with nodal features for consistent message-passing, backed by theoretical support. Experimental results on five datasets d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the reviewer's concerns below one by one in a Q&A fashion.
---
Q1: Why only search for meta-paths connecting nodes of the target type rather than aggregating information from neighbors of different types?
A1: We justify our meta-pa... | Summary: This paper introduces a novel approach to Label Distribution Learning (LDL) specifically tailored for heterogeneous graphs, addressing the inherent complexities and challenges associated with this domain. By highlighting the necessity of LDL in heterogeneous settings and outlining the unique challenges involve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the reviewer's concerns below one by one in a Q&A fashion.
---
Q1: Limited comparison to the existing LDL methods.
A1: We would like to kindly point out that the existing LDL methods were not tailored for handling graphs (networke... | Summary: This paper advances Label Distribution Learning (LDL) into the realm of graph domains, specifically addressing the heterogeneous graph label distribution learning (HGDL) problem. The authors highlight that graph heterogeneity, reflected in node types, node attributes, and neighborhood structures, poses signifi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the reviewer's concerns below one by one in a Q&A fashion.
---
Q1: The paper lacks an analysis of the algorithm's complexity.
A1: We have conducted the complexity analysis, which has been deferred to the Appendix H.3 due to pa... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and constructive comments. Here, we summarize the major concerns and our responses.
(1) Add GLDL and HINormer as new rival models (suggested by Reviewers EYc1 and pXs2).
- We have added new comparative results with the two models, presented in Table 1 i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index | Accept (poster) | Summary: The paper proposes the Pandora's Box Gittens Index, a cost-aware acquisition function. Moreover, it questions the behavior of previous cost-aware acquisition functions, and motivates the behavior of PBGI, through connections to the Pandora's Box problem and the behavior under different, uniform costs. Theoreti... | Rebuttal 1:
Rebuttal: > Strengths:
> [...]
> Presentation: [...]
> Relevant Problem(s): [...]
> Thorough Experimental Evaluation: [...]
> Theoretical Results: [...]
> Visuals: [...]
Thank you for the kind feedback - we're glad you appreciated the paper!
> Weaknesses:
> Why does the algorithm work well? [...] wonderi... | Summary: This paper draws connections between cost-aware Bayesian Optimization (BO) and a problem from the economics literature, called the Pandora's Box problem. Based on these connections, the paper proposes a new cost-aware acquisition function, called Pandora's Box Gittins Index (PBGI). Numerical experiments show t... | Rebuttal 1:
Rebuttal: > Strengths:
> [...] interesting. [...]
> [...] well-written.
Thank you very much for your review! We are delighted that key strengths - specifically, **novelty, via a brand-new technical perspective on Bayesian optimization** - are recognized. We would like to draw your attention to these points... | Summary: The paper introduces the Gittins index, a novel perspective from the pandora box problem, to address the cost-aware optimization problem on unknown rewards. It offers a theoretical justification for adapting the Gittins index as an acquisition function and offers empirical results against previous works, demon... | Rebuttal 1:
Rebuttal: > Strengths:
> [...] novel perspective [...]
> [...] figures [...]
> [...] performance seems robust [...]
Thank you very much for your review! We are delighted that these key strengths - including **novelty, specifically a brand-new technical perspective on Bayesian optimization** - are recognize... | Summary: This work tackles cost-aware Bayesian optimization using the Pandora's box Gittins index. In particular, in this paper, the authors focus on expected budget-constrained cost-aware Bayesian optimization and cost-per-sample cost-aware Bayesian optimization. Then, they provide some evidence of the proposed method... | Rebuttal 1:
Rebuttal: > Strengths:
> [...] important research topic [...]
> [...] new perspective [...]
Thank you for your review! We are delighted the key strengths - (a) **the importance of the topic**, and (b) **novelty, specifically a brand-new technical perspective on Bayesian optimization** - are recognized. We ... | Rebuttal 1:
Rebuttal: # Summary
We thank all reviewers and the area chair for their time and thoughtful feedback and evaluating this work!
We are delighted that **all four reviewers recognized the work's key strengths**, including:
* **Importance of the topic (Reviewer ykvm)**.
* **Novelty in the form of a brand-new ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Relative Entropy Coding with Space Partitioning | Accept (poster) | Summary: The authors provide a formalization on how to introduce search heuristics for channel-simulation (sometimes called "relative entropy coding" in the ML literature). The encoder and decoder agree a priori on a binning scheme that divides the support of the prior/public distribution, which is used to control the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and insightful review. We are delighted that the reviewer recognizes the difficulty of this task and appreciates the contribution of our work. Below, we respond to the reviewer's concerns and questions. Should the reviewer find our answers satisfactory, we ... | Summary: **Global disclaimer:** I am very unfamiliar with the topic of the paper. I did my best to try and read the literature and understand as much as I could but my input may be very limited.
**Summary:**
The paper focus on relative entropy coding (REC) algorithms and propose to circumvent a major pitfall which is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their dedicated time and constructive review, which we feel largely enhances the quality of our paper. While the reviewer may not be familiar with this field, we believe they understand most of our manuscript and method well.
We are delighted that the reviewer found our... | Summary: The paper proposes a space partioning technique to speed up relative entropy coding.
In entropy coding the sender first transforms X into a representation Z that they encode.
This particular step can done by Poisson functional representation (PFR).
Unfortunately, PFR’s random runtime can be a significant dra... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer's time and detailed review and are delighted that the reviewer found our paper clear and appreciated our technical and theoretical contributions.
Below, we respond to the reviewer’s questions. We are happy to discuss any further concerns the reviewer might have.
... | null | null | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for their detailed and comprehensive reviews and for their time spent reviewing our manuscript. We are delighted that the reviewers found our paper easy to follow and recognized our method's technical and theoretical contributions. We addressed their co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Priors for Variational Likelihood Estimation and Image Denoising | Accept (spotlight) | Summary: This paper proposed a diffusion-based image denoising method. The method leverages the MAP framework with proposed adaptive likelihood estimation method and a pre-trained diffusion prior. Experiments on four real-world datasets validate the advantages of the proposed method.
Strengths: 1. The paper is well-wr... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work.
Q1: The title of the paper does not accurately reflect its contributions. The use of diffusion priors to address inverse problems is widely studied, as the author has noted, and this paper builds upon the framework presented in [1]. The significant contrib... | Summary: The authors propose a way to use diffusion priors for real-world image denoising where the noise statistics are complex and signal-dependent. They use variational inference to estimate the joint posterior of the noise precision and image throughout diffusion time. The result is a MAP estimate of the denoised i... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work. As some questions overlap with the weaknesses, we will integrate and answer them together.
Q1: The proposed method only provides MAP estimates (not posterior samples). How difficult would it be to adapt this method to provide posterior samples?
Reply: Emp... | Summary: This work considers the problem of using adapt diffusion models for solving real-world image denoising problem, that is, the noise is not assumed to be i.i.d Gaussian. The authors statistically model the real-world noise as independent, non-identically distribution noise, and then incorporate the adaptive MAP ... | Rebuttal 1:
Rebuttal: Thanks for your review of our paper. As there are some overlaps between Questions and Weaknesses, we will consolidate and answer them together.
Q1: The paper does not well motivate their choice of i.ni.d noise model. It is not clear why such an i.ni.d noise model can properly characterize the sta... | Summary: Overall, this paper presents a novel method in real-world image denoising by proposing a sophisticated method that combines adaptive likelihood estimation, MAP inference, and variational Bayes within the diffusion model framework.
Strengths: 1. Utilizing variational Bayes to dynamically infer the precision po... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work.
Q1: The method relies on hyperparameters (e.g., prior precision, temperature parameter, kernel scale etc.), which might require careful tuning for optimal performance for different datasets. Authors criticize previous methods for being dependent on hyperpa... | Rebuttal 1:
Rebuttal: We thank all reviewers for their review of our paper. The response to each reviewer has been posted separately in the following. The 6958_rebuttal.pdf contains figures related to responses to Reviewers UCEq and sawX.
Pdf: /pdf/ce75c835ed7817d00674791785eaf5562f3c70d2.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method to tackle real world noise using an adaptive likelihood estimation . For this the authors develop a technique to dynamically infer the precision posterior using variational bayes. The authors perform comprehensive evaluation on real world denoising datasets to show the effectiveness... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work. As there are some overlaps between Questions and Weaknesses, we will consolidate and answer them together.
Q1: The authors have utilized a gamma based hyperprior without reasoning the design choice or comparing with other possible prior distribution. In or... | null | null | null | null | null | null |
A Theoretical Understanding of Self-Correction through In-context Alignment | Accept (poster) | Summary: This paper investigates how large language models (LLMs) can improve their performance through self-correction without external feedback. The authors provide a theoretical framework for understanding self-correction as an in-context alignment process. They demonstrate that LLMs can refine their responses based... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and acknowledging our theoretical insights! We address your remaining concerns below.
---
**Q1.** This paper only focuses on two scenarios: social bias and jailbreak attacks. It lacks of showing effectiveness of this method on other types of tasks like reason... | Summary: This paper investigates the ability of large language models (LLMs) to improve their responses through self-correction from a theoretical perspective. Specifically, the authors prove the self-correction mechanism through in-context alignment formulation and analyze why self-correction naturally improves LLM pe... | Rebuttal 1:
Rebuttal: We thank Reviewer zSQV for appreciating the novelty of our theory and the solidness of our experiments! Below, we address your remaining concerns about the verification experiments.
---
**Q1.** In the proof, the author simplifies the concept by converting criticism into a reward, represented as ... | Summary: # Summary
This paper provides a theoretical analysis of self-correction from in-context learning, demonstrating that LLMs can refine their responses by using accurate self-examinations as feedback.
# Contributions
1. Theoretical Framework: The paper develops a theoretical framework that explains how self-co... | Rebuttal 1:
Rebuttal: We thank Reviewer jEFz for appreciating our theoretical insights, empirical verification, and real-world applications. We address your concerns below.
---
**Q1.** Validation Scope: The validation is primarily on synthetic datasets.
**A1**. For completeness, following your advice, we further val... | Summary: This paper analysis self-correction theoretically from the in-context learning perspective. It extends the theoretical analysis from previously over-simplified transformers to more realistic scenario: softmax attention, multi-head attention... It also provides experiments on how self-correction can serve in pr... | Rebuttal 1:
Rebuttal: We thank Reviewer bH8U for appreciating the novelty of our theory and our empirical verification. We address your concerns below.
---
**Q1.** In real-world experiment, the author provides several techniques: Multi-round Checking, Diverse Checking, Self-instruct. It's not mentioned that how these... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for careful reading and for giving positive feedback on our manuscript regarding the novelty and significance of our analysis. We have addressed the remaining concerns carefully in each response. Notably, besides synthetic tasks, **we further verify our theoretical... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper provides a theoretical framework for understanding self-correction in large language models (LLMs), framing it as a form of in-context alignment. The authors prove that a standard multi-layer transformer can optimize common ranking-based alignment objectives using self-correction samples, generating... | Rebuttal 1:
Rebuttal: We thank Reviewer pXYm for appreciating the novelty of our idea. We further address your main concerns on how models generate new responses below.
---
**Q1.** My main concern is about modeling the self-critic process as a ranking problem. Can you provide theory about the next round possible cand... | null | null | null | null | null | null |
Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces | Accept (poster) | Summary: This paper proposes a simple but effective method that can solve "fully dynamic k center problem with outliers" in general metric space. The fully dynamic setting requires the algorithm to adjust its output efficiently when deletion or insertion operations occur. This paper uses a "ball cover" strategy to obt... | Rebuttal 1:
Rebuttal: **Comment:** Time complexity. The amortized update time $\epsilon^{-2}k^6\log k\log \Delta$ is worse than the method in [5] (if $k$ is large), whose time complexity is $O(\frac{k^2\log\Delta}{\epsilon^2\tau}\log^2\frac{1}{\delta})$.
**Comment:** High update time complexity in the worst case. The ... | Summary: The paper studies the k-center clustering problem with outliers in the fully dynamic setting. Specifically, given a metric space (M,d), in the (k,z)-clustering problem, the goal is to find at most k balls minimizing the maximum ball radius while excluding up to z points from the clustering. In the fully dynami... | Rebuttal 1:
Rebuttal: **Comment:** Personally, I wouldn't agree that this is an improvement over the work of Chan et al. [5]. Indeed, it does achieve a better approximation ratio, but at the cost of increasing the runtime. The paper indeed provides a new, interesting trade-off, but I believe the abstract we should disc... | Summary: This paper studies the fully-dynamic $k$-center with outliers problem in the metric space. In this setting, operations (including insertion and deletion) appear over time. The performance evaluation of an algorithm is based on its cost approximation and the (amortized) update time. However, previous research h... | Rebuttal 1:
Rebuttal: **Comment:** the update time complexity $O(\varepsilon^{-2}k^6\log{k}\log{\Delta})$ is not competitive, particularly for large $k$.
**Response:** Our goal was to develop a dynamic algorithm with a low approximation guarantee and a simple, elegant data structure. We did not focus on optimizing the... | Summary: The paper gives a new algorithm for the dynamic version of k-center clustering with outliers. The algorithm works in the fully dynamic model with both point insertions and deletions allowed. The points can belong to an arbitrary metric space, compared to some previous algorithms addressing low-dimensional metr... | Rebuttal 1:
Rebuttal: **Comment:** The algorithm is randomized and works only against oblivious adversaries. This is a shared characteristic with the previous paper on this topic.
**Question:** Is there a good reason why the algorithm is randomized? What are the obstacles to obtaining deterministic algorithms in this ... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their insightful comments and valuable feedback. We also appreciate the time they dedicated to reviewing our work.
Here, we also provide a file with a figure illustrating our responses to reviewers CFX4 and fWP4.
Pdf: /pdf/9d3bb97f3532ac596d7e1e9d903c446952bf20f5.pd... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging Separated World Model for Exploration in Visually Distracted Environments | Accept (poster) | Summary: To address the challenge of visual distractions in unsupervised reinforcement learning, this paper proposes a bi-level optimization framework called SeeX, which utilizes a separate world model to mitigate the disturbance caused by visual distractions. The authors evaluate the proposed method in multiple tasks.... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. The term "task-irrelevant" on L51 should be "task-relevant". Here are our responses to your questions:
- **Strong assumption**
- Firstly, many Visual RL works use separation assumptions, such as Denoised MDP[3] and TIA[2], which have natural assumptions an... | Summary: This paper studies the problem of intrinsic-driving exploration in visually distracted environments, in the context of unsupervised reinforcement learning (URL). To address the issue that intrinsic rewards might be biased by distractors, the authors propose a method (called SeeX) that separates exogenous and e... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We have noted the typographical and formatting issues mentioned by the reviewer. BA on Line 157 refers to the Barber-Agakov bound[1], named using the authors' initials, which is a commonly used bound for mutual information. The second "=" on Line 164 should inde... | Summary: This paper considers the problem of unsupervised reinforcement learning (task-agnostic pretraining) from image observations in environments with visual distractors. The key technical contribution of this paper is a practical algorithm, SeeX, based on the world model framework common in MBRL literature. SeeX ex... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and valuable suggestions regarding our paper. We are pleased to hear that you find our work to have clear technical contributions and to perform well in experiments. In response to the questions and suggestions you have raised, we provide the following answers:
- ... | Summary: The authors propose a method for separating endogenous and exogenous latent states for unsupervised exploration under visual distractors. Motivated by a theoretical bound minimizing the regret under a latent world model, the algorithm learns both an endogenous and exogenous world model, as well as an explorato... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback on our work. We are very pleased that you found our algorithm theoretically motivated and experimentally successful. In response to your questions and suggestions, we have made the following points:
- **Exploration of Real-World Applications... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to give useful comments for our paper. We are glad that the Reviewers appreciate the correct motivation (Reviewer SZqk), original approach (Reviewer wcfo) and strong experimental results (Reviewer SZqk, wcfo). Reviewers pointed out the concerns and points... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The GAN is dead; long live the GAN! A Modern GAN Baseline | Accept (poster) | Summary: A lot of GANs papers have shrunk in quality since diffusion arose as a strong method. The authors goes against the grain by focusing on modernizing GAN baseline in a principled manner and in doing so they obtain stable, convergent, diversity, and high-quality images comparable to diffusion models. The authors ... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> “... still need to be scaled to large-scale text-to-image setting.”
For scaling, we are currently running ImageNet-64 experiments to include in our paper; please see our discussion in response to Reviewer 2Hzz. In general, we hope our work is a meaningful first ste... | Summary: This paper introduces R3GAN as a GAN baseline that simplifies and modernizes the architecture by replacing ad-hoc tricks with modern designs. R3GAN utilized a regularized relativistic GAN loss coupled with zero-centered gradient penalties on both real and generated data, to addresses mode dropping and non-conv... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> “The novelty of the method is somewhat limited, as both relativistic pairing GAN (RpGAN) and zero-centered gradient penalties (0-GPs) are previously proposed and validated approaches in the field of GANs.”
While these components have been proposed separately, none ... | Summary: The authors posit that the main reason GAN research has been slow in recent years is due to the most foundational StyleGAN2 not having undergone major architectural changes, essentially due to a lack of convergence guarantee in GAN objectives and being prone to mode collapse. This has limited the scope of arch... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> “there seems to be a lack of information about the training setup, hyperparameters used, number of inference steps, etc pertaining to the diffusion based models in Tables 4, 5 and 6.”
The diffusion model numbers in these tables are directly taken from reports in ex... | null | null | Rebuttal 1:
Rebuttal: Thank you everyone for your constructive feedback.
In summary, all reviewers found that the paper had strengths:
- The paper is clearly written.
- The claims are well supported with both theoretical and empirical evidence.
- The theoretical insights allow a method that is simpler than past GAN wo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MindMerger: Efficiently Boosting LLM Reasoning in non-English Languages | Accept (poster) | Summary: The paper introduces MergeMinds, a method that integrates LLMs with multilingual models to enhance reasoning capabilities across multiple languages. Through a two-step training scheme, MergeMinds effectively boosts performance in multilingual reasoning and language understanding tasks, particularly excelling i... | Rebuttal 1:
Rebuttal: Thank you for your encouragement of our work. We reply to your questions as follows.
**W1: From my perspective, the motivation of MergeMinds is extremely similar with LangBridge, both of which utilize the mapping layer to map multilingual encoder to existing MLLM.**
- In related works about MLLM... | Summary: This paper proposes a framework which merges LLMs with the external language understanding capabilities from multilingual models to improve multilingual reasoning performance. Specifically, the authors introduce a two-step training scheme: they (i) train the framework to embed the multilingual model into the ... | Rebuttal 1:
Rebuttal: Thank you for the valuable reviews. We have added some experiments and will include them in the next version of paper.
**W1. In Section 3.2, the authors mention that they use translation data and query translation task data generated from public translation models for the two-stage training; whil... | Summary: The paper propose a way to improve multilingual reasoning in LLM in own native languages without relying on pivoting methods such as translating to English. The method assume a hypothesis that LLMs have built in knowledge and reasoning abilities in a lower-resource language and not just common language like En... | Rebuttal 1:
Rebuttal: Thank you for your reviews.
The main concern is the performance of our model on the generation task. We believe that adding experiments on the generation task will help further expand the scope of our work.
We are grateful for the feedbacks on some writing improvement and we will revise them i... | Summary: The paper suggests incorporating an embedding block using external multilingual models to improve the models' understanding. Additionally, comprehensive experiments are conducted to demonstrate its efficacy.
Strengths: 1. The paper proposes a straightforward and easily implementable method to enhance models' ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to provide reviews. However, we would like to clarify some misunderstandings as follows:
**W1. To my understanding, the Embedding Layer of the model merely converts the query from text space to the token/embedding space. This transformation may not be int... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning | Accept (poster) | Summary: The paper introduces CORY, a novel reinforcement learning (RL) technique for fine-tuning language models that casts a multi-agent framework on the trained language model by duplicating it at initialization and then using the two copies to improve one another.
Essentially, a "pioneer" gives the first guess, use... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and time spent! We’re glad you found our idea original and likely to influence other work to build on it, our paper easy to follow, and the observations from the multi-objective RL perspective interesting. We would like to address your concerns below.
## **R4-... | Summary: This paper presents a Reinforcement Learning (RL) methodology to fine-tune LLMs based on Multi-Agent RL agents. One agent acts as the observer, and the other acts as the pioneer. They share knowledge through two interactions: transfer learning and role-switching. They named this methodology CORY (Coevolving wi... | Rebuttal 1:
Rebuttal: Thank you for your positive remark and insightful feedback! We’re glad you found our method viable for improving LLM RL fine-tuning, our paper is easy to follow, and modern and relevant benchmarks are used in our experiments. Below, we provide individual responses addressing your comments.
## **R... | Summary: This paper presents CORY, a novel approach for fine-tuning large language models (LLMs) using a sequential cooperative multi-agent reinforcement learning framework. Traditional methods, primarily based on PPO and its variants, often show suboptimal performance and risk distribution collapse. CORY addresses the... | Rebuttal 1:
Rebuttal: Thank you for your positive remark and insightful feedback! We’re glad you found our idea is both novel and compelling, our presentation is well-organized and clear, and our experiments are adequate and effectively support the proposed method. Below, we provide individual responses addressing your... | Summary: In this paper, the authors study a multi-agent organization for LLM learning. Specifically, they have two LLMs, with the second one responding to the same query given the query itself and the response generated by another LLM. They author shows this method can achieve a better tradeoff of the task reward and t... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback! We’re glad you found our proposed method interesting and the writing clear. We would like to address your concerns below.
## **R1-1** Dual-LLM Setup and Multi-LLM Learning
There has been some work studying the collaboration of multi-LLM (i... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their contributions and insightful comments. We appreciate that all the reviewers find our writing and presentation is clear and well-organized.
We are encouraged by the reviewers' appreciation that our idea is novel and interesting... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Questioning the Survey Responses of Large Language Models | Accept (oral) | Summary: The paper focuses on evaluating 42 different language models on the American Community Survey, highlighting how model responses are governed by ordering and label biases and in general the fact that any demographic correlation with specific subgroups is actually due to the fact that those subgroups aggregated ... | Rebuttal 1:
Rebuttal: Thank you for the feedback.
**What would you expect the models’ answers to be?** It is unclear what to expect models’ responses to be. Prior work has hypothesised that models may trend towards certain demographics; for example, younger demographics, which tend to be more present on the internet. ... | Summary: This paper prompts LLMs with 25 multiple choice questions (on basic demographic information, education attainment, healthcare coverage, disability status, family status, veteran status, employment status, and income) from the 2019 ACS. The authors use eight kinds of prompts which vary in additional context, in... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We hope to address your concerns and clarify some misunderstanding below.
> their experiment adds little value to further support their claim on "better represent subgroups whose aggregate statistics are closest to uniform."
We believe this to be a misunderstanding. W... | Summary: This paper critically examines possible pitfalls of using the responses of LLMs to survey queries to study the model alignment. They found substantial bias, e.g., with respect to the order of response option,
Strengths: The paper examines a very important methodological topic that has gained significant atten... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment and the feedback. Please note that we discuss in Appendix E how our findings for the ACS transfer to opinion surveys. We agree that our observations regarding models’ survey responses may be partially attributable to survey questions not having a “correct” ans... | Summary: This paper conducts experiments to verify the alignment between human and LLM responses to the ACS survey. Particularly, the paper questions existing literature suggesting that LLMs can be used as proxies for measuring responses to survey questions, suggesting instead that LLM choices are biased by the orderin... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will implement the suggested changes to improve the figures. Let us address your questions in the following.
**Selection of questions.** We chose 25 representative questions to achieve diversity over topics (e.g., educational attainment, healthcare status, employme... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback.
We hope to have addressed your concerns, and we are happy to answer any further questions you may have.
Thank you,
Authors
Pdf: /pdf/a0e4f4f00e81019ac67fcc15b866625951900655.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach | Accept (spotlight) | Summary: Mean-Field Langevin Dynamics (MFLD) framework is used to solve optimization problem over manifold. The main contribution appears to be reducing a general optimization problem over signed measures to probability measures using lifting or bilevel approaches. Convergence rate of MFLD, when applied to both approac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation, and address their questions and comments below.
- Thank you for pointing out the potential confusion about the scope of these ideas. In the final version, we will further clarify that the lifting and bilevel ideas are both always applicable for ... | Summary: The paper studies extension of mean-field Langevin dynamics (MFLD) to perform convex optimization over the space of signed measures. The paper considers the lifting and bilevel approaches and shows that the latter guarantees better convergence properties at a wider range of hyperparameters. MFLD-bilevel is sho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful assessment, and address their questions and comments below.
- **On compactness**: $\mathcal{W}$ is assumed to be without boundaries. This assumption is missing on line 20. Thank you for pointing this out.
The techniques presented in our work can be u... | Summary: This paper extends the well-known and recently extensively studied mean-field Langevin dynamics (MFLD) to optimization problems over signed measures (instead of probability measures). This has applications and relevance to the training of NNs or other problems in data science, such as sparse deconvolution, whi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed evaluation, and address their questions and comments below.
- Thank you for the feedback on this part of the introduction, it will be adapted.
- "$J$" on line 44 should be "$F$".
- Thank you for the feedback on this part of Section 2, we will add more disc... | Summary: Mean-field Langevin dynamics (MFLD) has been developed for optimizing convex functionals over the space of probability measures. This work extends MFLD to convex problems defined over the space of signed measures. The authors consider two approaches: lifting and bilevel approaches, and prove the superiority of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and encouraging comments. We address each point in "Weaknesses" and "Questions" separately.
- **On time-discretization over Riemannian manifolds**: So far, the theory for time-discretization of MFLD is established when $\Omega = \mathbb{R}^d$ [SWN23], or wh... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Observational Scaling Laws and the Predictability of Langauge Model Performance | Accept (spotlight) | Summary: This paper proposes observational scaling laws to align scaling laws of computing from different model families, which are trained on various recipes, by projecting model benchmark performance to surrogate compute. This enables applying scaling law analysis without actually training models. Using the observati... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We would like to address your remaining questions and concerns with the following responses.
### Capability predictability and cutoff selection
> *“The predictability of emergent abilities seems to be overclaimed… However, the feasibility o... | Summary: They propose using PCA decomposition of the performance of a range of models across a number of benchmarks to form an observational scaling law, which effectively predicts downstream performance across several different model families, including predicting post-training interventions like chain-of-thought. The... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We appreciate that you acknowledged that our work represents “very promising progress” towards an important problem with thorough experiments. We would like to address your remaining concerns with the following responses.
### Ablation study o... | Summary: This paper demonstrates the correlation, known as scaling laws, between training FLOPs and large language models’ (LLMs) downstream task abilities. The authors decompose performance metrics to fit this “Observational Scaling Law” and confirm its validity across emergent capabilities, agentic capabilities, and ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We appreciate that you acknowledged our paper provides sufficient validation and clear formalization. We would like to address your remaining questions and concerns in the following responses.
### Choice of using PCA
> *”Why using PCA to deco... | Summary: The paper proposes a generalized class of scaling laws that encompass multiple model families of different sizes. These resulting scaling laws are capable of predicting “emergent” behaviors, complex agentic performance, and inference techniques in an extrapolative manner, as seen with GPT-4. The observed laws ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We appreciate that you acknowledged that our work is “important to the whole community” with extensive experiments and strong extrapolation performance. We would like to address your remaining questions and concerns in the following response.
... | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful feedback and suggestions.
We are glad that the reviewers found our work offers a valuable contribution [V1YP] and very promising progress [oXqB] toward an important problem [oXqB, JzvB] with a comprehensive analysis [V1YP], extensive experiments [JzvB, M7... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes observational scaling laws, a method to use benchmark scores of LLMs to infer how their performance would change if the amount of training compute was scaled, without actually having to train additional models. The authors apply PCA to a model-task benchmark matrix in order to obtain latent ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We appreciate that you acknowledged our paper offers a “valuable contribution” with a comprehensive analysis and interesting insights. We would like to address your remaining questions and concerns in the following response.
### Interpretabi... | null | null | null | null | null | null |
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-Identification | Accept (poster) | Summary: This paper provides a unified perspective to consider the data augmentation strategies in cross-spectral re-identification. Based on the Lambertain model, the author finds that the cross-spectral discrepancy is induced by multiple local linear transformations. Furthermore, the authors propose a robust linear e... | Rebuttal 1:
Rebuttal: Thanks for your constructive and positive feedback which inspired us a lot. Below, we respond to your key concerns point by point.
>**Q1: As a data augmentation strategy, it will be better if the author can provide several visualization examples.**
**R1:** Thanks for your suggestion. We have pro... | Summary: This paper explores data augmentation strategies for cross-spectral re-identification. The authors find that non-linear modal differences arise mainly from different linear transformations occurring on various material surfaces; all data enhancement strategies for cross-spectral re-identification aim to simula... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback which helps us a lot. Below, we respond to your key concerns point by point.
>**Q1: Innovation is limited, and although the authors' introduction of the physical model is striking, the conclusions obtained can not actually guide the authors well in designing ... | Summary: This paper presents a unified perspective that reconsiders data augmentation strategies in cross-spectral re-identification. The authors identify that the main source of cross-spectral modality discrepancies stems from various local linear transformations due to material diversity. To address this, the authors... | Rebuttal 1:
Rebuttal: Thanks for your positive and constructive feedback which inspired us a lot. Below, we respond to your key concerns point by point.
>**Q1: Some details are simplified in the paper. For instance, visualization results of the proposed RLE are not shown, and the baseline structure could be included ... | Summary: This paper presents a novel approach to addressing the challenges in cross-spectral Re-ID, particularly the modality discrepancy between visible (VIS) and near-infrared (NIR) images. The authors propose a unified perspective based on the Lambertian reflection model to understand and categorize data augmentatio... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback. Below, we respond to your key concerns point by point.
>**Q1: Why is [lambda_r, lambda_g, lambda_g] set to [0.299, 0.587, 0.114]?**
**R1:** In fact, you may have misunderstood MRLE. As mentioned in Line 180~183, the [0.299, 0.587, 0.114] are not adopted fo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all reviewers for providing constructive feedback that helped us improve the paper. We are encouraged that reviews think our paper:
* "provides a unique and insightful framework for understanding the modality discrepancy between visible (VIS) and near-infra... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Clustering in Causal Attention Masking | Accept (poster) | Summary: This paper studies the representations or tokens generated by a sequence of causal attention layers. To this end, and following the example of prior works, the authors model such a sequence as a discretization of a system of ODEs. Each token in the input sequence is modeled as a particle and the evolution of e... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their positive feedback. We appreciate that they acknowledged the novelty and significance of our setting and are glad that they enjoyed our exposition **Quality and clarity: the paper is well-written, motivated, and clear**.
**Many results are either asymptot... | Summary: This paper strengthens the theoretical results from prior work by presenting causally masked attention used in AIGC.
The authors prove asymptotic convergence to a single cluster for arbitrary key-query matrices and an identity value matrix under causal self-attention. This significantly extends the results of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We are glad that they noted our perspective on the clustering phenomena in transformers **By linking the study to the Rényi parking problem, the authors provide a unique perspective on clustering phenomena in self-attention mechanisms** and the theoretical... | Summary: This work extends the work by Geshkovski et al. 23c, which analyzes the mean-field gradient flow of Transformer models and shows the emergence of clusters with full self-attention, to the ones with causal self-attention.
Transformer with causal self-attention is modeled as an interacting-particle system on the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We are encouraged that they appreciate the relevance of our work's subject: **The authors extend the analysis of full self-attention Transformers as interacting-particle systems to causal self-attention Transformers. As the current success of Tran... | Summary: This paper presents a theoretical framework where causal attention masking can be recast into an interacting particle system. The authors start by introducing the dynamics of the first token and extend it to $n$ tokens. They then discuss the token configurations as $t \rightarrow \infty$ (i.e., infinite number... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We are glad they think that **exploring the theoretical aspects behind the full attention and causal attention mechanisms is a very important topic in our understanding of how Transformers and modern LLMs/LMMs work** and that they enjoyed our presen... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Permutree Process | Reject | Summary: This article adapts the recently-developed combinatoric concept of the “Pemutree” into a machine learning context, making links with existing methods in Bayesian nonparametrics, and providing a pathway for how to make the abstract mathematical concept relevant to data-driven approaches and inference. The theor... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive comments. (To be more responsive to your important feedback, we would like to make additional remarks in 'Official Comment'.)
---
**[Q1] Generality of our framework.**
> “Data path modeling” (...) but might reveal the limitations of the model framew... | Summary: After giving an introduction to permutrees, a stochastic process on them is constructed by sampling the nodes according to an intensity function on the 2d unit interval and uniformly assigning the marks. It is shown how to add the edges to meet the requirements for the object being a labeled permutree. Paths f... | Rebuttal 1:
Rebuttal: We are grateful for your important comments and suggestions.
---
**[Q1] Task and goal of phylogenetic analysis.**
> What is the task in the phylogenetic analysis application? Do you have a set of DNA sequences where some of the letters are masked, and you want to predict the masked letters?
You... | Summary: The authors introduce a prior for Bayesian nonparameterics called the permutree. They apply it to model complex phylogenetic data with both coalescence and recombination, a setting that previous processes such as the Kingman could not model; the model seems to perform state-of-the-art phylogenetic inference. I... | Rebuttal 1:
Rebuttal: Thank you so much for your important comments. (We would like to be more responsive to your important comments, so let us make additional remarks in the 'Official Comment' section.)
---
**[Q0] More concise guidance.**
> The writing is very challenging.
Thank you for your helpful advice. By refi... | Summary: The authors describe the concept of permutrees, how to sample permutrees in a stochastic process, and how to model data with permutrees. They apply it to tracking DNA changes.
Strengths: Interesting new model that unifies permutations, trees, partitions, and binary sequences
Strong mathematical foundation
... | Rebuttal 1:
Rebuttal: We appreciate your helpful comments and recommendations.
---
**[Q1] Improvements to color schemes and size in figures.**
> the figures are small and hard to read when printed in gray-scale
Thank you for your important advice. We will improve the color scheme we used in our diagrams so that the ... | Rebuttal 1:
Rebuttal: ---
**Thanks to all involved. -**
We are very grateful to all the Reviewers who spend their valuable time to read our papers and give us constructive and favorable comments and suggestions. We are also deeply grateful to the Area Chairs (ACs) and Program Chairs (PCs) who, through their profession... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-Shot Transfer of Neural ODEs | Accept (poster) | Summary: The work explores the use of neural ODEs as basis functions for function encoders. This requires dealing with the additional integration step, with the weighted combination of obtained ODEs representing the behaviour of the function to approximate via its integral. With an inner product and its tractable Monte... | Rebuttal 1:
Rebuttal: **The assumption of orthogonality for the validity of the coefficients is vital. However, both the authors in [13] and in this paper do not share any analysis on the validity of this assumption. It would be interesting to see how well this holds in practice (computing inner product of obtained bas... | Summary: The paper presents a novel framework for the zero-shot transfer of neural ODEs by leveraging function encoders to represent a space of dynamical systems. It demonstrates the method's effectiveness in adapting to unseen environments without retraining, using MuJoCo and quadrotor experiments.
Strengths: The pap... | Rebuttal 1:
Rebuttal: **The paper presents a promising framework for zero-shot transfer of neural ODEs, but there are several areas for improvement. Firstly, the reliance on a large and diverse dataset for training is a significant limitation. The approach requires extensive data that spans the entire function space of... | Summary: This paper proposes a method to learn the dynamics of autonomous systems in a few shot manner. The core assumption is that the dynamics function dx/dt=f(x) of a new system can be modeled by a linear combination of basis dynamics functions. The method involves two stages. In the offline stage, the method learns... | Rebuttal 1:
Rebuttal: **My main criticism of the paper is the technical contribution. The problems this system can solve seem to be constrained to systems limited variation in parameter, where the offline dataset & online system share a high level of similarity. While the authors explained how they apply NODE very clea... | Summary: The paper aims to address the challenge of zero-shot transfer and adaptation. The authors propose tackling this challenge by learning a dynamics space spanned by neural ODE basis functions, which can then be used for rapid identification and adaptation to dynamics at inference time without additional training.... | Rebuttal 1:
Rebuttal: Thank you for your feedback. See below for responses to your suggestions.
**To place the results in context with prior work, it would be additionally helpful for the robotics experiments to show comparisons to other methods that enable adaptation (e.g. training a model free method with domain ra... | Rebuttal 1:
Rebuttal: # Response to all Reviewers:
We thank the reviewers for their comments and keen insights. We have made the following major changes to the paper in response to their feedback.
## **1. Hyper-Parameters**:
We have added an ablation on both the number of basis functions and the number of example... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Resource-Aware Federated Self-Supervised Learning with Global Class Representations | Accept (poster) | Summary: This paper introduces a novel approach for enhancing global representation models in resource-adaptive federated self-supervised learning through a multi-teacher knowledge distillation framework, named FedMKD. The proposed method addresses the challenges posed by heterogeneous architectures and extreme class s... | Rebuttal 1:
Rebuttal: We thank the reviewer hPCk for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to Weakness:**
Thank you very much for the insightful comments.
- First, we survey some **federated transfer learning (FTL)** methods, and add the following discu... | Summary: The authors propose a multi-teacher knowledge transfer framework, FedMKD, based on two challenges in resource-adaptive federated self-supervised learning: deviated representations abilities and inconsistent representations. This framework uses an adaptive knowledge integration mechanism and a weighted combinat... | Rebuttal 1:
Rebuttal: We thank the reviewer sGaU for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to Weakness1 & Question1:**
Thank you very much for your recognition. As you mentioned, we have two mechanisms to transfer knowledge, but they are not duplicated. F... | Summary: This paper proposes a multi-teacher knowledge distillation framework named FedMKD for resource-adaptive federated self-supervised learning (Fed-SSL). The method aims to address the challenges of global representation learning in Fed-SSL caused by heterogeneous architectures and imbalanced class distributions. ... | Rebuttal 1:
Rebuttal: We thank the reviewer 24fB for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to Weakness 1:**
Thank you very much for the insightful comments. In the related works section (Lines 94-99), we have surveyed several existing studies that address... | Summary: This paper studies the problem of federated self-supervised learning. A multi-teacher knowledge distillation framework is proposed to address the two challenges: deviated representation abilities and inconsistent representation spaces. Specifically, the adaptive knowledge integration mechanism is designed to l... | Rebuttal 1:
Rebuttal: We thank the reviewer de59 for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to Weakness 1:**
Thank you very much for the insightful comments.
As you said, knowledge distillation (KD) has been widely used in Fed-SSL, such as FedX and other ... | Rebuttal 1:
Rebuttal: We thank all the reviewers' valuable comments and feedback, which are great helpful in improving the quality of this paper. We try our best to address the concerns including making preliminary experiments as long as time allows. As the **reviewer** **QsUu**, **de59**, **24fB** concerned, we try ou... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: - The paper proposes a framework called FedMKD which is a multi-teacher knowledge distillation framework for federated learning.
- To allow for different clients to have different resources, a resource adaptive approach is designed. The approach handles class skew and different architectures.
- The knowledge... | Rebuttal 1:
Rebuttal: We thank the reviewer QsUu for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to Weakness 1:**
Thank you very much for the insightful comments. Our proposed FedMKD is designed for resource-aware settings, allowing each client to choose an app... | null | null | null | null | null | null |
Decomposable Transformer Point Processes | Accept (poster) | Summary: The work designs a novel transformer-based approach for modelling time series (e.g. predicting next event). The main novelty is the decomposition of the log-likelihood into a conditional probability mass and density functions. The former, implemented with a transformer, models the distribution over the event t... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We respond to your concerns below:
- Even though we do not present the full thinning algorithm in the main text due to space restrictions, we present the exact algorithm in the appendix on page 17. As we explain in lines 76-83, the expression of the two log-likelihood... | Summary: This paper presents a novel framework for modeling marked temporal point processes (MTPPs) using Transformer-based architectures. The authors address the limitations of traditional methods that rely on computationally intensive thinning algorithms by proposing a decomposable approach that partly uses a Transfo... | Rebuttal 1:
Rebuttal: Thank you for your feedback and your suggestions. We respond to your concerns below:
1. Notice that we do not learn any intensity functions since our framework is based on the decomposition in Eq. (2). Given the black-box nature of the transformer-based architecture we refrained from including vi... | Summary: The paper introduces a Decomposable Transformer Point Process (DTPP), a novel framework for modeling marked point processes. It maintains the advantages of attention-based architectures while avoiding the computational intensity of the thinning algorithm. The model uses a mixture of log-normals for inter-event... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We respond to your concerns below:
1. Using the decomposition in Eq. (2) is equivalent to using the standard log-likelihood based on $λ_k^∗(t)$ in Eq. (1). For more details, see JG Rasmussen, 2018, "Lecture notes: Temporal point processes and the conditional intensit... | null | null | Rebuttal 1:
Rebuttal: We provide an ablation study on the influence of the number of mixture components M.
Pdf: /pdf/bd6d281468db416422bc2b4b9858cd08847aba76.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ST$_k$: A Scalable Module for Solving Top-k Problems | Accept (poster) | Summary: This paper addresses the Top-K problem by introducing a new loss function. Building on the Average Top-K Loss, the authors incorporate a smoothed ReLU function to create the $ST_k$ loss, which is fully differentiable. Through experiments on various datasets, they demonstrate the effectiveness of their approach... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and feedback. Below is a detailed point-by-point response addressing your main concerns and questions.
**For your concerns:**
> lack of significant innovation
We hold a different view on this.
* The sorting optimization algorithm proposed by [1] has been ar... | Summary: This paper proposes a differentiable module for solving the top-k problem. Specifically, the paper proposes to approximate the hinge function with a new differentiable function.
Experiments on binary classification, long-tailed classification, and regression tasks with $ST_k$ loss show some improvements ove... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and feedback. Below is a detailed point-by-point response addressing your main concerns and questions.
> Explanation of why ST$_k$ outperforms AT$_k$.
* ST$_k$ and AT$_k$ are not equivalent. Reference [1] proves the equivalence between AT$_k$ and MAT$_k$, as s... | Summary: The authors proposed a differentiable layer to approximate the top-k loss in deep learning. The proposed layer is motivated from Eq. (1), replacing the ReLU with a smoothed ReLU (SReLU in Eq. (2)). The authors showed that the proposed layer is point-wise convergent to top-k loss. Numerical experiments validate... | Rebuttal 1:
Rebuttal: We appreciate your feedback.
**For your concerns:**
> Limited dataset scale.
We hold a different view on this.
* We conduct experiments on datasets such as ImageNet-LT and Places-LT, which, to the best of our knowledge, are the largest unbalanced visual classification datasets available.
* ... | Summary: Authors introduce a novel differentiable module (ST_k) for efficiently solving top-k problems. Their method relies on optimizing a differentiable form of an equivalent optimization problem, by proposing an approximation of ReLU differentiable everywhere. This equivalent optimization problem contains a single p... | Rebuttal 1:
Rebuttal: Your positive feedback is very encouraging.
**For your concerns:**
> Although the improvements shown by the authors are consistent, they are also marginal - only improving slightly in for example CIFAR-100-LT classification and ImageNet-LT classification compared to average aggregation.
We ackn... | Rebuttal 1:
Rebuttal: We appreciate all your comments.
Here is a summary of the strengths and a general response to the concerns received:
**Strengths:**
Firstly, we appreciate that all reviewers think our paper to be clearly written and easy to follow.
- Reviewer 8VJU noted that our framework is efficient and integ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer | Accept (poster) | Summary: This work studies learning to defer, focusing on the single-stage and single-expert setting. It introduces a family of surrogate losses based on comp-sum losses [Mao et al., 2023b] and establishes their realizable H-consistency (under mild conditions). In addition, when the base loss is the logistic loss $\Psi... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**Weaknesses:**
**1. The realizable H-consistency result (Theorem 4.1) only applies to a subset of comp-sum losses.**
**Re... | Summary: This paper considers the problem of learning to defer (L2D), where a classifier is allowed to defer a decision to an expert (possibly expensive to query) and trained to accurately predict while minimising the expert cost.
A major contribution of this paper is establishing consistency guarantees for surrogat... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**Weaknesses: Due to my lack of experience in the field, I find it challenging to adequately adjudicate the significance of ... | Summary: The authors provide a framework of surrogate loss functions for learning to defer under the multi-class classification problem. By examining the deferral loss function and choosing different surrogates for the indicator functions, the authors provide a novel class of surrogate loss functions for learning to de... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We have carefully addressed all the questions raised. Please find our responses below.
**Weakness 1: The proposed loss is not practical ... the derivation of equation (2).**
**Question 1. The major concern ... mitigate the training cost of the model?**
**... | Summary: This paper proposes considers the setting of learning to defer: a machine learning system can choose to either classify an instance or defer the decision to an expert which incurs a variable cost. The objective is to minimize the deferral loss of the system. To solve this problem, prior work has proposed surro... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We will take your suggestions into account when preparing the final version. Below please find responses to specific questions.
**Weaknesses:**
**1. There are no weaknesses with regard to the theory in this paper beyond the generalizability of the app... | Rebuttal 1:
Rebuttal: Dear reviewers,
We would like to express our appreciation for all your constructive suggestions and insightful comments. We have attached a PDF that includes additional experimental results for both the realizable case and the non-realizable case with general cost functions.
Figure 1 shows syste... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Spurious Correlations via Disagreement Probability | Accept (poster) | Summary: To address the issue of spurious correlations when bias labels are unavailable, the work proposes a new method to mitigate the spurious correlations by minimizing the maximum average loss over bias-aligned and bias-conflicting groups. Additionally, they introduce the disagreement probability/sampling probabili... | Rebuttal 1:
Rebuttal: We are very grateful for your constructive comments. We have provided answers to each comment. Please let us know if you need any clarification or have additional questions.
> **Q1**: Originality of DPR.
**A1**: The following differences exist between JTT, CNC, and our proposed DPR. JTT identifi... | Summary: The authors mainly target fairness without accessing bias labels. They suggest a new learning objective that minimizes the loss of the bias group showing the highest ERM and demonstrate that minimizing this objective decreases the upper bound of the expected average loss. To utilize this loss when the bias lab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We have provided answers to each comment. Please let us know if you need any clarification or have additional questions.
> **Q1**: The effectiveness of the proposed learning objective itself.
**A1**: As you mentioned, to demonstrate the effect... | Summary: This paper addresses the critical issue of spurious correlations in machine learning models, where models often rely on easy-to-learn bias attributes rather than the intended target features, leading to poor generalization on minority groups where these spurious correlations are absent.
The authors propose a ... | Rebuttal 1:
Rebuttal: Thank you for pointing out the strength and originality of DPR, which uses a loss function directly derived mathematically from setting an objective. We also appreciate your high regard for the various experiments, theoretical analyses, and ablation studies that support the effectiveness of DPR. W... | Summary: This paper proposes a re-sampling approach based on disagreement between bias predictions and target label predictions. First, a biased model is trained using generalized cross-entropy. Then, sample-wise weights are determined by calculating the probability of disagreement between the bias predictions of the b... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We've carefully considered them and provided responses. Please let us know if you need any clarification or have additional questions.
> **Q1**: The differences between the proposed DPR and other existing approaches using bias predictions.
**A1**: As outl... | Rebuttal 1:
Rebuttal: We are very grateful to all the reviewers for their valuable comments.
The additional figures showing the experimental results of group identification for BFFHQ are in the PDF file.
Pdf: /pdf/230092823703aa0d62429c7da1bc43865c55becc.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometric Trajectory Diffusion Models | Accept (poster) | Summary: A new diffusion-based generative model for modeling complex 3D geometric structures with time-evolving trajectories is proposed. By introducing the SE(3) equivariance property, temporal attention, and learnable geometric prior into the discretized diffusion model, the proposed model can achieve high performanc... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and suggestions! We provide point-to-point response below.
> **[Q1] Since the transition kernel and the prior are modified with extra restrictions, will it be difficult to redesign the framework (both forward and backward) into the continuous diffusion ODE/S... | Summary: In this paper, the authors propose geometric trajectory diffusion models (GeoTDM) to model temporal distribution of geometric trajectories
while keeping the desirable physical symmetry of the trajectories.
Strengths: The authors impose certain constraints of SE(3)-invariant on the prior and transition kernel ... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and suggestions! We provide point-to-point response below.
> **[W1] I am concerned that dynamic system and rotation translational invariant has already been studied in the previous research (listed below, but not quoted here), not like the authors claimed th... | Summary: The paper introduces geometric trajectory diffusion models for generation of particle, pedestrian, or molecular trajectories. The architecture consists of EGNN layers within a temporal frame and temporal attention across frames computed with relative temporal ecodings. The architecture is shown to possess the ... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and suggestions! We provide point-to-point response below.
> **[W1] The work does not score highly in conceptual novelty. While all architecture choices are sensible, they are relatively straightforward and do not seem surprising, insightful, or inspired. An... | Summary: The paper proposed the first diffusion model for modeling the temporal distribution of 3D geometric trajectories, while previous works only operate on static structures. It demonstrates the equivariant temporal kernels can lead to density with desired symmetry and develop a novel transition kernel leveraging S... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and recognition of our work! Please let us know if you have any questions and we are more than happy to answer. | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Amortized Eigendecomposition for Neural Networks | Accept (poster) | Summary: In this paper, the authors proposed a novel framework to integrate eigendecomposition efficiently into neural network training, which is typically costly in the backward process.
The key insight is to compute the decomposition together with training (since the decomposition also changes during training). Rath... | Rebuttal 1:
Rebuttal: We appreciate reviewer fsNU for the constructive feedback on our paper. Here are our responses.
***Q1 It seems amortized eigendecomposition would converge slower w.r.t iterations, compared to traditional eigh/svd (because it's a rather indirect way of optimization). And I wonder how much time one... | Summary: This paper proposes a method that decouples the eigendecomposation calculation from training process, instead, they use a eigen loss to jointly optimize it with the training loss of the neural network as a nested optimization loop. The proposed method can speed up the training process of problems that incorpor... | Rebuttal 1:
Rebuttal: We would like to thank reviewer WiHy for the constructive comments on our paper. Here are our responses.
***Q1 Whether the accelerate method can maintain the performance.***
In the manuscript, we try to answer this question through two experiments:
- Latent-space Principal Component Analysis: As... | Summary: This paper proposes a method named "amortized eigendecomposition" to replace the computationally costly SVD operation in settings where an eigendecomposition is required during neural network training. The proposed method introduces a loss term ("eigen loss") and replaces the full SVD with the less computation... | Rebuttal 1:
Rebuttal: We would like to thank reviewer 5kBi for his acknowledgement and valuable comments of our paper. Here are our responses.
***Q1 Regarding the scalability.***
For additional experiments on large-scale setups, please refer to the general response provided to all reviewers.
***Q2 Comparison with ra... | Summary: The paper describes a novel method to circumvent the need for explicit eigendecomposition during neural network training. The central insight is that one can simply learn estimates of the eigenvectors (parameterized by a QR decomposition) of interest alongside the original loss function via gradient descent.
... | Rebuttal 1:
Rebuttal: We thank reviewer 6W8h for the constructive comments and valuable suggestions. Here are our responses.
***Q1 Regarding the scalability.***
For additional experiments on large-scale setups, please refer to the general response provided to all reviewers.
We would like to emphasize that, as shown ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their in-depth comments and valuable suggestions, which have significantly improved the quality of our paper. As many of the reviewers mentioned the scalability of our approach, we would like to provide a general response on this aspect.
We conducted an additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Local to Global: Learning Dynamics and Effect of Initialization for Transformers | Accept (poster) | Summary: This paper focuses on training a single-layer linear attention transformer with low-rank parameterization on first-order Markov chain data. By reparameterization, they can reduce this problem to a 3-variable learning dynamics and comprehensively characterize the trajectory and local/global minimizers. This pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and insightful comments. We address the individual questions below:
- **Generality of the insights**: For the two-state Markov chain, while our analysis capitalizes on the canonical parameters and linear attention, our guidelines and insights from th... | Summary: The paper seeks to characterize how single layer transformers learn (two symbol) markov chains. The analysis relies on a reparametrization of the transformer that assumes the weight matrices are rank 1. The primary results are characterizing when gradient flow on this reparameterized transformer leads to globa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and suggestions to improve the paper and the code which we are planning to incorporate in the revised version. We refer to the global response regarding our results across repeated trials for various $(p,q)$ and the measure of low-rankness. We address... | Summary: The paper investigates how transformers learn first-order Markov chains, focusing on the role of parameter initialization and providing a comprehensive analysis of the learning dynamics. It proves that transformer parameters can converge to global or local minima based on initialization and data properties, an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and insightful comments. We will update the revised version with information about the attention in the main text. We address the individual questions below.
- **Rank-1 input sequence……:** Please note that the input is a first-order Markov chain, wit... | Summary: This paper investigates the learning dynamics of transformer models, specifically focusing on first-order Markov chains and single-layer transformers. The authors aim to understand how transformers learn Markov chains and the impact of initialization on their training outcomes. They provide a comprehensive the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and helpful suggestions to improve the paper. We will add a separate section with prerequisite background details in the revised version. We refer to the common response for experiments on multi-state Markov chains. We address the individual conc... | Rebuttal 1:
Rebuttal: ## **Generality of the insights: Our insights and conclusions hold for all pairs of $(p,q)$, across repeated trials, and for non-linear soft-max attention**
We thank the reviewers for the constructive feedback. We address the common concerns here regarding the error bars across repeated trials, e... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper theoretically studies the effect of initialization on the gradient dynamics of a single-layer transformer trained on Markov data. It considers a simplified model by: (1) using a binary input alphabet and (2) reducing the many parameters of the single-layer transformer to just two or three scalar para... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and insightful comments. We refer to the global response regarding our results across repeated trials for various $(p,q)$ and the measure of low-rankness. We address the individual concerns below.
- **Binary and large alphabet**: This paper primarily... | null | null | null | null | null | null |
MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity | Accept (poster) | Summary: In this paper, authors record multi unit activity (MUA) from macaque ventral stream (V1, V4 and IT) and train a CNN decoder to reconstruct the visual stimuli. The authors present three variations decoding attempts: a baseline CNN decoder that maps MUA directly to image space, and two U-Net based decoders that ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We appreciate the opportunity to clarify and address your concerns.
**Addressing Mentioned Weaknesses:**
**Text and Figures:**
- **Line 267 and Table 1**: We apologize for the oversight. There was a mistake in the header of Table 1. ... | Summary: SETTING: Decoding (reconstructing) observed images via Utah-array recordings made from the visual cortex of a macaque.
APPROACH: A GAN, but taking (transformed--see below) neural data as input, and with some additional losses. In particular, in addition to the standard adversarial loss, the generator/decoder... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review. We appreciate your positive feedback on our contributions and the detailed suggestions for improvement. Your insights on our decoding approach are invaluable. We are glad to hear that our retinotopic mapping approach is recognized for its importan... | Summary: The authors present a CNN-based decoder that illuminates the distinct information encoded in V1, V4, and IT neuronal populations. The decoding results are remarkably good, and their accomplishment in decoding natural images from neuronal-level signals is unprecedented and the best so far.
Strengths: Their nov... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive review. We appreciate your recognition of our novel space-time resolved decoding technique and the data collected from 15 Utah arrays. Regarding the mentioned weakness, our primary aim was indeed to demonstrate the feasibility and beauty of using these ad... | Summary: This paper proposes a CNN-based decoder to reconstruct naturalistic images from macaque brain signals. To this end, the paper presents the Learned Receptive Field (LRF) layer to enhance the reconstruction and understanding of the model's structure and interpretive capacity. Here, the work aims to interpret bra... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We appreciate your positive comments and are pleased to hear that you recognized our model's adaptability. Below, we address your points in further detail:
**Weaknesses:**
1. **Data-specific model**: While our model was trained on the THINGS dataset, which ... | Rebuttal 1:
Rebuttal: We have included additional results in the form of figures as requested. The ablation study on various losses is presented in the figure titled "Model Ablations," along with the corresponding model losses for each ablated model in the figure titled "Ablation Losses." Although these were run with f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sparse maximal update parameterization: A holistic approach to sparse training dynamics | Accept (poster) | Summary: This work (similarly to \muP method) proposes a specific parameterization for weights, gradients and updates such that the optimal training hyperparameters (i.e. learning rate) are transferrable across different sparsity levels. The approach is validated on the task of language modeling within wide range of sp... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. It is gratifying to know that you found the method theoretically sound and intuitive, and that the experimental results were a strength of the paper.
```
Whereas all the components in S$\mu$Par seem to be important, the importance of each individua... | Summary: A parameterization method is proposed to ensure that the sparse models with different weight sparsity share the same set of optimal hyperparameters. The sparse models with S$\mu$PaR achieve lower validation accuracy using the same hyperparameters the dense model uses.
Strengths: - The paper is well-written an... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive feedback, and your kind words regarding the paper’s writing, experimental results, and core idea.
```
SuPar is discussed and evaluated only with the random unstructured sparsity pattern, which is hardly used in practice to speed up the DNN training and... | Summary: The paper introduces Sparse Maximal Update Parameterization (SµPar), a novel approach designed to address challenges in training sparse neural networks. Sparse networks, despite their computational advantages, often struggle with signal propagation and optimal hyperparameter (HP) tuning across different sparsi... | Rebuttal 1:
Rebuttal: Thank you very much for the helpful comments, and for your kind words regarding the paper's key idea, motivation, and clear and well-structured presentation.
```
This paper has many supporting experiments reporting the training loss, validation loss, and transfer loss for SµPar. However, loss val... | Summary: This paper studies the effect of various hyperparameters on static sparse training while having a holistic approach. It highlights that there is a correlation between their settings and neural network performance (practically loss function values). The experiments are performed on smaller and larger models, in... | Rebuttal 1:
Rebuttal: Thank you for your very useful suggestions, your positive comments regarding the promising/interesting findings, and your note on the potential for impact in the community.
```
This is an interesting study with a relatively low level of originality (in my opinion). It practically puts together a... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to read our submission and provide helpful feedback. Please find attached our 1 page PDF containing additional results. We believe these additions help address many reviewer concerns and strengthen our submission. Here we provide a discussion of the resul... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can large language models explore in-context? | Accept (poster) | Summary: In this study, the authors investigate the impact of diverse prompt designs, including environmental descriptions, summarizing interaction history, and chain of thought reasoning, on the exploration behavior of three distinct LLMs in a multi-armed bandit task. The results demonstrated that none of the models e... | Rebuttal 1:
Rebuttal: > *Additionally, the authors have not adequately cited related works [1-8].*
Thanks for the references! We actually became aware of some of these between the submission and now and have already incorporated appropriate discussions into the manuscript. But we’ll be sure to discuss them in a revisi... | Summary: This paper investigates whether large language models (LLMs) like GPT-3.5, GPT-4, and LLAMA2 can perform exploration in reinforcement learning settings without additional training. The authors focus on in-context learning, where the environment description and interaction history are provided entirely within t... | Rebuttal 1:
Rebuttal: > *While the focus on multi-armed bandits provides a clean experimental setup, it may not fully represent the challenges of more complex reinforcement learning problems. The generalizability of the findings to broader settings is unclear.*
Failures on a fundamental special case such as MAB plausi... | Summary: This paper investigates the exploration capabilities of contemporary large language models. The authors use LLMs as agents in multi-armed bandit environments, describing the environment and interaction history in-context without any training interventions. Their experiments involve a variety of configurations ... | Rebuttal 1:
Rebuttal: > *In line 161, it is mentioned that no parameter tuning is performed for the baselines with tunable parameters. In this case, we are not sure that the baselines are performing to their highest capacity and therefore, the comparison might not be fair.”* (Also, Q1)
This is rather standard in bandi... | Summary: This paper investigates whether popular LLMs can engage in exploration in a in-context manner (all experiences are stored as context/prompt). To achieve, the paper deploys LLMs (GPT-3.5, GPT-4, LLAMA2) as agents in multi-armed bandit environments, using various prompt designs to specify the environment descrip... | Rebuttal 1:
Rebuttal: > *As an experimental article, the findings are naive and obvious: the LLMs are not designed for solving decision-making tasks. It is consistent with intuition that LLMs can explore in-context when CoT with summarized history.*
Whether LLMs are “designed” for decision-making tasks is not very rel... | Rebuttal 1:
Rebuttal: Thanks for taking the time to review our submission. To summarize our contributions, we perform a systematic analysis of the extent to which LLMs are capable of exploration, a core component of reinforcement learning/decision-making, by deploying LLMs as agents in multi-armed bandit environments. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching | Accept (poster) | Summary: The paper introduces CoMat, an end-to-end diffusion model fine-tuning strategy for text-to-image generation that addresses misalignments between text prompts and generated images. This method integrates a novel image-to-text concept activation module and an attribute concentration module, aimed at improving te... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Complexity of Implementation**
Thank for your feedback. Indeed, we acknowledge the intricate nature of our approach. However,... | Summary: The paper breaks down the misalignment problem in T2I two: concept ignorance and concept mismapping, and propose a fine-tunning strategy to enhance the prompt understanding and following.
The methods include two modules: The concept activation module to maximize the posterior probability; An attribute concent... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Training cost is disproportionate**
Thank you for your feedback. We will open-source our training code for reproducibility.
W... | Summary: This paper proposes a fine-tuning strategy for text-to-image diffusion models to improve the alignment of generated images to text prompts. The solution components are summarized as follows:
1. Concept Activation Module: This module helps the model focus on ignored text concepts by leveraging an image-to-text ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Differences between Class-specific Prior Preservation Loss with Fidelity Preservation**
Thank you for your advice. Indeed, ou... | Summary: This paper studies the prompt-following issues within text-to-image generation models and proposes a very simple yet effective solution by supervising the diffusion models with recognition models like BLIP for image captioning and Grounded-SAM for image segmentation. The authors also propose a fidelity preserv... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Limited technical novelty**
Thank you for your feedback. We respectfully disagree with the reviewer's assessment. Indeed, our ... | Rebuttal 1:
Rebuttal: Overall author rebuttal:
We thank all reviewers for their thoughtful comments. We greatly appreciate all the reviewers' acknowledgment that our method is **effective and achieves excellent results**. We have added new evaluations and visualization in our author rebuttal PDF.
The main concerns r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction | Accept (poster) | Summary: This paper proposes a Hybrid Language Model workflow for citation prediction, where core citations are predicted from superficial citations and non-citations rather than using a simple binary classification approach. This method can handle candidate sets of up to 100K papers and demonstrates better performance... | Rebuttal 1:
Rebuttal: # Response to reviewer 7Ub3
**Q1.** *Design of unchanged candidates in the LLM decider.*
**Response:** Thank you for carefully reading through the appendix. We apologize for not clearly explaining the the LLM decider's working process. Here, we explain the detailed designs.
With the retrieval si... | Summary: The paper proposes a framework, HLM-Cite (Hybrid Language Model) for scientific citation prediction based on incorporating generative language models embeddings and LLMs as agents. The pretrained text embeddings are used to retrieve high likelihood core citations (a term the paper introduces to define as more ... | Rebuttal 1:
Rebuttal: # Response to reviewer b1Uz
**Q1.** *Novelty of the core citation idea.*
**Response:** Thanks for the literature. We agree that citation classification is not new. Existing works classified citations with traditional ML [2,3,5] and DL [1,4] according to the roles in the context (background, metho... | Summary: The authors introduce the concept of core citations to distinguish important citations from superficial ones and non-citations. This shifts the citation prediction task from simple binary classification to the more subtle approach of identifying core citations. They then propose HLM-Cite, a hybrid language mod... | Rebuttal 1:
Rebuttal: # Response to reviewer 3KQH
**Q1.** *Overly dense terminology in Section 2.1.*
**Response:** Thanks for pointing out this. In this paper, we follow previous computational social science [1,2] studies and define the core citations of a query paper from the citation network. We apologize for making... | Summary: This paper studies text-based citation prediction by exploring the varying roles of paper citations from foundational to superficial. The authors introduce the concept of core citations, emphasizing the most important citations over superficial ones. Then, they propose HLM-Cite, a hybrid language model workflo... | Rebuttal 1:
Rebuttal: # Response to reviewer tEdQ
**Q1.** *Missing baselines, e.g., SPECTER 2.0 and SciMult.*
**Response:** Thank you for providing these up-to-date baselines for scientific texts. We test SPECTER 2.0 [1] and SciMult (both vanilla and MoE) [2] on our tasks as suggested. Also, we investigate new baselin... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thanks for taking the time to review our paper. We greatly appreciate your valuable feedback and insightful suggestions. Please find our detailed one-on-one responses to the raised questions in 'Rebuttal' following the reviews. In addition, we have attached here 'result.pdf', whic... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Perplexity-aware Correction for Robust Alignment with Noisy Preferences | Accept (poster) | Summary: The paper proposes Perplexity-Aware Correction (PerpCorrect) to improve the alignment of large language models (LLMs) by detecting and correcting noisy preferences (NPs). PerpCorrect identifies NPs using the differences in perplexity (PPLDiff) between chosen and rejected responses. The method involves aligning... | Rebuttal 1:
Rebuttal: Many thanks for your comments! Please find our replies below.
``[Reply to W1] Please refer to our general author rebuttal [R3]. ``
``[Reply to Q1] We provide a discussion about this phenomenon as follows. ``
We found that the two models have similar proportion of NPs in the denoised datase... | Summary: This paper presents a novel method called PerpCorrect for robust alignment in large language models, particularly in the presence of noisy preferences in training data. PerpCorrect addresses noisy preferences by evaluating the perplexity difference (PPLDiff) between selected and rejected responses. By leveragi... | Rebuttal 1:
Rebuttal: Many thanks for your comments! Please find our replies below.
``[Reply to W1] Please refer to our general author rebuttal [R3].``
``[Reply to W2] Please refer to our general author rebuttal [R2].``
``[Reply to W3] Please refer to our general author rebuttal [R1].``
``[Reply to Q1] We validate... | Summary: The paper proposed a novel method to mitigate the preference noise in alignment. The authors first provide insights into how the PPLDiff can recognize the noisy preferences and then use the PPLDiff to select and correct noise preferences. Extensive experiments demonstrate that the method can significantly impr... | Rebuttal 1:
Rebuttal: Many thanks for your comments! Please find our replies below.
``[Reply to Q1] The overlooked difference is that NPs have incorrect labels, which can be identified using PPLDiff.``
Both cDPO (ref to Eq. 9) and rDPO (ref to Eq. 10) use a universal loss to treat CPs and NPs. They overlooked the dif... | Summary: The paper introduces Perplexity-aware Correction (PerpCorrect), a method for robust alignment of large language models (LLMs) with noisy preferences (NPs). PerpCorrect detects and corrects NPs by analyzing the perplexity difference (PPLDiff) between chosen and rejected responses. The approach involves aligning... | Rebuttal 1:
Rebuttal: Many thanks for your comments! Please find our replies below.
``[Reply to W1] Please refer to our general author rebuttal [R1].``
``[Reply to W2] Please refer to our general author rebuttal [R2].``
``[Reply to W3] We provide a discussion of the assumption as follows.``
**Technically**, this ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments and suggestions. Please find our replies below.
``[R1] We discuss the complexity and additional computation time of method as follows.``
The additional computation time is primarily due to PerpCorrect. Both the theoretical and practical times ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics | Accept (poster) | Summary: The paper proposes a novel data quality control technique to enhance the quality of data from different private domains in collaborative training settings, such as model merging. The technique scores each training sample by tracing gradients of low-rank adapters and filters out low-quality data locally. The mo... | Rebuttal 1:
Rebuttal: **W1 & Q1**:
> Please explain how the proposed method performs on datasets from different domains beyond the medical field.
As mentioned in s1, our method by nature can be adapted to various models ‘without the need for task-specific adjustments’. We conduct comprehensive evaluations on differen... | Summary: This work proposes a method for finetuning LLMs in federated or collaborative training scenarios that selects the most informative examples for each client to train on such that their local parameter updates are likely to improve the global, merged model's performance on a public (shared) test set. The propose... | Rebuttal 1:
Rebuttal: Thanks so much for your valuable comments and feedback.
**W1**:
> Detailed algorithm definition
Thank you for your valuable suggestions! Following the reviewer's constructive feedback, we have included our pseudo-code algorithm in the PDF.
**W2 & L1**:
> Evaluation
We appreciate your question... | Summary: This paper proposes a novel approach for data quality control in collaborative fine-tuning of large language models (LLMs), particularly in settings where data cannot be directly shared between different silos due to privacy concerns. The authors introduce a method that scores training samples based on tracing... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and positive feedback!
**W1 ans Q1**:
> Although the methodology is thoroughly described, the authors could improve the clarity of Figure 3 by providing a more intuitive explanation of how training dynamics enhance collaborative training processes.
Figur... | Summary: This paper proposes a data quality control technique for the collaborative training of large language models from filtered private heterogeneous data domains via a quality score function that tracks the gradient of each training sample. The proposed framework is tested in medical and multilingual settings to d... | Rebuttal 1:
Rebuttal: **W1**
> The assumption of homogeneity for model architecture
We clarify that we follow the commonly agreed assumption of previous work on collaborative learning (federated learning, model merging) [1, 2, 3]: in order to aggregate the model in the parameter space, we should have the same model a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and appreciate the great efforts made by all reviewers, ACs, SACs, and PCs. We are grateful that the reviewers have multiple positive impressions of our work, including:
* *[Motivation]* **studies an important issue** (Xfnf, igLM)
* *[Method]* *... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers | Accept (poster) | Summary: This paper proposes a method for training transformer-based policies for multi-task meta-RL settings, where the task distribution has multiple tasks each of which has parametric variation and the training and test sets both include all tasks. Focus is on developing a method that handles the scale variation in ... | Rebuttal 1:
Rebuttal: Thank you for the review and for the constructive comments on limitations and writing. We will discuss your suggestions below.
> Framing issue: The considered setting is interesting and a fine target for research. However, parametric variation is not the grand challenge in meta-RL and the paper, ... | Summary: In this paper, the authors investigate to utilize the Transformer architecture for multi-task RL tasks by giving the trajectories from multiple previous episodes as input tokens. More specifically and technically, they addressed the issue of the imbalanced rewards from different task (e.g., For Atari games, so... | Rebuttal 1:
Rebuttal: Thank you for the review and comments on the experimental results. The evaluation domains in our work are computationally expensive, so it was not possible for us to fully address your questions with finalized results during the rebuttal window. We have begun work on these experiments and will upd... | Summary: This paper addresses the challenge of scaling meta-reinforcement learning (meta-RL) to handle multiple tasks without explicit task labels. It introduces a method where both the actor and critic objectives are converted to classification terms, decoupling optimization from the scale of returns. This approach bu... | Rebuttal 1:
Rebuttal: Thank you for your review. We will try to address your questions and would be happy to continue the discussion.
> By converting the actor and critic objectives to classification terms, the method effectively decouples optimization from return scales, which is a novel and practical approach.
To c... | Summary: This paper studies multi-task reinforcement learning using a context-based Transformer policy without task labels. To address optimization difficulties caused by imbalanced losses across different tasks, it proposed replacing actor and critic losses with classification losses. Ablation studies on several bench... | Rebuttal 1:
Rebuttal: Thank you for your review. We will discuss your concerns below.
> Multi-task RL using in-context adaptation without task labels is not new. Some recent works [1,2] learn in-context meta-RL policies that adapt to diverse tasks
We are focused on end-to-end online RL while Raparthy et al. belongs ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their comments, and we will respond to individual questions and concerns below.
Several reviewers asked for an expanded discussion of our method’s limitations, which we will add to our conclusion. The main technical limitation of our technique is tha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Power of Small-size Graph Neural Networks for Linear Programming | Accept (poster) | Summary: This paper explores the effectiveness of small-sized GNNs in solving LP problems, addressing the discrepancy between theoretical predictions requiring large GNNs and empirical observations showing small GNNs' capability. The authors provide a theoretical foundation for the success of compact GNNs by proving th... | Rebuttal 1:
Rebuttal: > **Q1: It has been observed that previous empirical studies predominantly utilized general-purpose GNNs, such as GCNs. Could similar theorems be achieved with general GNN backbones?**
A1: Yes, we can establish similar theorems with general-purpose GNNs. We can still derive polylog-depth in the b... | Summary: This paper examines the expressive power of GNNs in representing linear programs (LPs). The authors first introduce a first-order iterative algorithm for packing and covering LPs, conceptualizing this algorithm as a GNN called GD-Net, applied to these LP types. They then provide a convergence rate of the propo... | Rebuttal 1:
Rebuttal: > **Q1: The idea of conceptualizing iterative algorithms as GNNs is not novel. The convergence rate of first-order algorithms for LPs is not new. Given existing linear rates, the sublinear rate of GD-Net is not good enough.**
Thanks for raising this insightful comment. We would like to make seve... | Summary: The paper investigates the capability of small-sized Graph Neural Networks (GNNs) to solve linear programming (LP) problems, specifically focusing on polylogarithmic-depth, constant-width GNNs. It provides both theoretical proofs and empirical evidence demonstrating that these GNN architectures can effectively... | Rebuttal 1:
Rebuttal: > **Q1: Could the authors elaborate on potential modifications or extensions of the GD-Net architecture that might enable it to handle a broader range of LPs or even mixed integer linear programming problems?**
A1: Intuitively, GD-Nets can be viewed as unrolling the gradient descent on a carefull... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable comments. We provide detailed responses individually to each reviewer. Note that more than one reviewer suggested additional numerical experiments to bolster the robustness of our findings. We conduct these experiments and summarize as below.
--... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parallel Backpropagation for Shared-Feature Visualization | Accept (spotlight) | Summary: This paper presents an innovative method for explaining why an IT neuron that is supposed to tune to a particular object class or category would respond to outside-of-category stimuli. The proposed method uses parallel backpropagation to highlight the features in the out-of-category stimuli that are shared by ... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and the encouraging feedback. Regardings questions/criticisms:
> The presentation of the scientific results is not very systematic [... and] a bit preliminary.
We agree that the results on feature selectivity in macaque body patches can be expanded on to get a ... | Summary: A deep-learning-based approach is proposed in the paper "Parallel Backpropagation for Shared-Feature Visualization" to visualize shared visual features in high-level visual brain regions, which are typically thought to respond selectively to particular categories like bodies or faces. Despite this selectivity,... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We highly appreciate your confidence in our work. We agree that the mentioned limitations are largely adressed in the paper, but for the sake of completeness we post some remarks here.
> The ability to generalize to other categories such as face patches was ... | Summary: The paper shows that a hypothesis for the brain (category-selective neurons are actually selective to generic lower-level features that are present in those categories, not necessarily features specific to those categories) can be reproduced and visualized in a ResNet trained to predict neural activity. If I u... | Rebuttal 1:
Rebuttal: Thank you for reviewing the manuscript, and for your positive feedback.
> The results are interesting and intriguing to look at (Figure 4), however at the end of the day it comes across as a bit anecdotal as opposed to giving us insight into general principles of shape or object representation. T... | Summary: The authors proposed a deep learning based method to visualize shared features in neurons that are selective to specific categories, such as faces or bodies, when they respond to out-of-category stimuli. The method identifies visual features driving the selectivity of neurons by modeling responses to images ba... | Rebuttal 1:
Rebuttal: Thank you for review and comments. We hope to be able to increase your confidence in recommending the manuscript for publication.
> The paper primarily applies existing deep learning techniques rather than introducing new machine learning algorithms or models. This might be seen as a limitation f... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and their valuable insights. We were glad to see that all reviewers found the presentation of the work clear and understandable, which is also reflected by the fact that all summaries clearly capture the main points of the paper. We also did not ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ordering-Based Causal Discovery for Linear and Nonlinear Relations | Accept (poster) | Summary: This paper studies the causal discovery problem in mixed functional relations data, where both linear and non-linear relationships exist in the causal graph. The author presents a Jacobian score-based method (essentially a score-matching method) to identify leaf nodes and thereby recover the causal order. The ... | Rebuttal 1:
Rebuttal: Your insightful advice is greatly valued, as it can contribute to the progress of our work. We hope that the answers provided below have resolved your inquiries.
**Q4.1:** non-decreasing variance too strong.
**A4.1:** Before answering this question about assumptions, we want to highlight some fa... | Summary: This paper proposes an ordering based causal discovery method when the underlying causal model has both linear and nonlinear causal relationships. Starting with a method to iteratively find leaf nodes, this paper proposes to use parent score for better pruning. Results show that the proposed method outperforms... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback, as it can help us improve our work. We trust that the following answers have clarified your questions.
**Q3.1:** Results are not great on real-world datasets.
**A3.1:** Yes, CaPS achieves the best performance on Sachs dataset but only the second best in ... | Summary: The authors propose an ordering-based causal discovery algorithm designed to handle both linear and nonlinear causal relations in an SEM. In contrast to existing methods that assume purely linear or nonlinear relations, CaPS introduces a unified criterion for topological ordering and a new "parent score" to qu... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions, as they can aid in enhancing our work. We hope that the responses below have addressed your concerns.
**Q2.1:** non-decreasing variance of noise
**A2.1:** The first thing we need to emphasise is that CaPS works under (i) **or** (ii) in assumption 1. So, ... | Summary: This paper addresses the challenge of ordering-based causal discovery, which involves first determining the topological ordering of variables (typically by recursively identifying sub-leaf nodes) and then identifying the parent set for each variable.
Existing methods often focus on either nonlinear or linear ... | Rebuttal 1:
Rebuttal: **Preliminaries**
Thank you very much for your valuable comments. Before answering the three question about assumptions, we want to highlight some facts of ANM. Without interventional data, all ANM-based models ($y=f(x)+\epsilon$) have to make some assumptions on $f$ or $\epsilon$ due to the prob... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning | Accept (poster) | Summary: The paper presents DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach designed to handle environments with evolving latent states. The authors propose the Dynamic Latent Contextual Markov Decision Process (DLCMDP) model to capture the temporal structure of episodes where the latent state changes at ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback regarding the formulation of DLCMDP and application in many real-world scenarios compared to prior methods. We are pleased that the reviewer found the experimental setup to be “detailed” and “comprehensive” and as a result enhanced the credibility of ... | Summary: The authors introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach formulated as a dynamic latent contextual MDP (DLCMDP). This framework allows the latent context of an episode to change multiple times and at varying rates within a single episode, making it more general than both POMDPs and l... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback regarding the DLCMDP model and its applicability in many real-world scenarios. We are especially grateful for the reviewer’s remarks stating that our “theoretical and experimental aspects … make it [the paper] of high quality”. Moreover, the reviewer ... | Summary: The paper proposes a special variant of non-stationary MDPs, DLCMDP, where the latent context information changes according to an unknown transition function. Then the authors present DynaMITE-RL, a meta-RL approach to handle environments with evolving latent context variables. Experiments are conducted on Gri... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback and that they found the figures and diagrams helpful for understanding the paper.
**W1: DLCMDP Definition**
The reviewer correctly points out that according to the Equation after line 125, when the latent context changes, the next state is sampled ... | Summary: This paper introduces a meta-RL method for environments with evolving latent variables. To this end, the authors introduce the notion of dynamic latent contextual MDPs, a generalization of POMDPs, which they use to model the environments. The basic idea is to have latent variables that are sampled and remain f... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback that the paper is “well-written and pleasant to read”. We are glad that the reviewer finds the DLCMDP problem setting “well-motivated” and our extensive set of baseline comparisons and ablation studies strongly supports our technical claims. We furthe... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback. We appreciate positive comments regarding the novel problem formulation of slowly evolving latent context variables in DLCMDPs, clarity of writing and presentation, and strong, comprehensive empirical results of DynaMITE-RL against state-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning | Accept (poster) | Summary: This paper aims to address the challenge of utilizing unlabeled data in SSL, especially when there is a mismatch in class distributions between labeled and unlabeled data. The authors propose to leverage diffusion models to convert unlabeled data, especially out-of-distribution data, into in-distribution sampl... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback.
**W1.** To investigate trade-offs between performance gains and computational cost, we conducted additional experiments on the CIFAR-10/100 tasks, varying U-Net depths $d$ (i.e., the number of residual blocks per downsample) in the diffusion model. The re... | Summary: The paper proposes an approach that leverages a diffusion model to enrich labeled data using both labeled and unlabeled samples to try to solve the traditional ssl method struggling in the real-world scenarios, i.e., a large number of irrelevant instances in the unlabeled data that do not belong to any class i... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback.
**W1.** Thank you for bringing up the different diffusion model network structures and training methods for further improving our work. Given that DPT [A], which is closest prior work to our methodology, has demonstrated a remarkable performance with U-Vi... | Summary: This paper proposes DWD, a new OSSL method that trains a diffusion model to transform OOD unlabeled data to ID images for SSL. DWD can mitigate the class mismatch problem in the OSSL task, which affects SSL performance.
Strengths: While previous OSSL methods attempted to distinguish between ID and OOD data th... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback.
**W1.** We agree that a more extensive set of experiments can help validate DWD’s effectiveness. We thus conducted additional experiments that are common in recent OSSL methods: 1) different ratios of ID and OOD classes, and 2) various sizes of labeled data... | null | null | Rebuttal 1:
Rebuttal: ## **General response**
We appreciate all the reviewers for taking the time to provide constructive feedbacks on our paper. We are very encouraged that the reviewers have recognized the following strengths in our work:
1) Proposition of a novel perspective by addressing the class distribution mis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection | Accept (poster) | Summary: This paper proposes KF-GVD, a novel Knowledge Fusion-based Graph Neural Network (GNN) model designed for detecting vulnerabilities in C/C++ source code. KF-GVD employs Code Property Graphs (CPGs) to represent the structure and semantics of the source code. Based on the CPGs, KF-GVD flexibly extracts features a... | Rebuttal 1:
Rebuttal: Thank you very much for the detailed review and the professional opinions you have provided, which are highly valuable to us. Below are our responses to the questions you raised, and we hope they address your concerns.
# Answer for Q.1
+ **A.1.1)** In most cases, Joern can successfully build CPG. ... | Summary: The paper proposes KF-GVD, a vulnerability detection method that integrates task-specific vulnerability knowledge into a graph neural network model. KF-GVD aims to guide the model to learn vulnerability patterns tailored to the target task, rather than relying solely on a generalized approach. Experiments show... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments. We have carefully considered the issues you raised and provided our responses below, hoping to address your concerns.
# Answer for Q.1
Based on the research content of the paper "Interpreters for GNN-Based Vulnerability Detection" (hereafter referr... | Summary: This paper proposes KF-GVD, a Graph Neural Network (GNN) model that integrates specific vulnerability knowledge into its feature learning process to enhance vulnerability detection accuracy in source code. Unlike traditional deep learning methods that optimize for general performance, KF-GVD uses knowledge fus... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our paper , which is very helpful to us. Below are the answers to the questions you have raised, hoping to solve your doubts.
# Answer for Q.1
Some Sota static analysis tools may detect such vulnerabilities. In our comparative experiments... | Summary: This paper introduces KF-GVD, a knowledge fusion-based vulnerability detection method integrating specific vulnerability knowledge into the Graph Neural Network (GNN) feature learning process. Traditional VD methods apply uniform feature learning, which can miss diverse vulnerability types or functional module... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and valuable suggestions. We have taken your comments into consideration and respond to the raised points below.
**1) P-values Evaluation**
We evaluated the improvements obtained by using the model fine-tuning strategy (GVD-ft) and the vulnerability detection m... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics | Accept (poster) | Summary: The authors study the problem of probabilistic inference's scalability in the integer arithmetic setting. Their method is based on the representation of integer-valued random variables probability mass functions (PMFs) as vectors and the observation that PMFs of different operations applied on said random vari... | Rebuttal 1:
Rebuttal: We thank the reviewer for their supportive review. Please allow us to address your experimental concern below.
Our experimental evaluation tackled two of the most prominent neurosymbolic benchmarks, where PLIA$_t$ outperformed both exact and approximate state-of-the-art methods for neurosymbolic ... | Summary: The paper presents a framework, PLIA_t, to solve the generally intractable problem of probabilistic inference using the fast Fourier transform (FFT) for integer-valued random variables. The paper shows how to use the log-sum-exp trick to solve the numerical stability issue in the FFT setting and defines arithm... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort, and for their suggestion of formalizing PLIA$_t$. For the camera-ready version we propose to add the following subsection at the end of the Section 2:
### 2.4. Formalizing PLIA$_t$
**Definition** *(probabilistic linear arithmetic expression)
Let ... | Summary: The paper addresses key challenges in applying neurosymbolic AI techniques, specifically in integer arithmetic. Leveraging the power of tensor operations and the fast Fourier transform (FFT), the authors propose a novel approach to perform probabilistic inference on integer-valued random variables. Central to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and appreciate the raised concerns. We will start below by answering the main questions:
1. Our precise parameter settings are detailed in Appendix E for the neurosymbolic experiments (cf. Section 5.2), being learning rate, number of training epochs... | Summary: This paper presents an approach to compute probability distributions over sums of random variables. To achieve this, the authors replace as slow quadratic computation with a Fourier transformation and a Hadamard product that can be computed in log-linear time.
The authors introduce the log-sum-exp trick to imp... | Rebuttal 1:
Rebuttal: Thank you for your review and for bringing the work of [1] to our attention!
Firstly, we would like to clarify that we do not introduce the log-sum-exp trick as this is indeed a well-known trick to avoid numerical instabilities when summing probabilities in log space. Instead, we introduce a simi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments and suggestions to improve the paper. Our rebuttal to the individual reviews can be found directly under each review. We also attached a plot (as pdf) used to address Reviewer Ltsw's concern regarding memory usage.We discuss this issue just below.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When is Multicalibration Post-Processing Necessary? | Accept (poster) | Summary: The paper investigates the necessity and effectiveness of multicalibration post-processing across data of different modalities and machine learning models. It finds that models which are calibrated out of the box tend to be multicalibrated without further post-processing, while multicalibration can help inhere... | Rebuttal 1:
Rebuttal: We address the weaknesses and questions below.
>**W1**: Regarding observation 3. The association between the accuracy and the calibration fraction is somewhat clear for HMDA but it’s definitely unclear of the other datasets (Appendix F.3). Accuracy seems to even increase as the calibration fracti... | Summary: The authors provide a large and broad set of evaluations of how useful it is to supplement empirical risk minimization procedures with multicalibration (and/or calibration) post-processing. Their experiments span a wide variety of settings and datasets, broadly falling into the tabular data, image data, and la... | Rebuttal 1:
Rebuttal: We agree that recent theoretical consequences of multicalibration have provided more than sufficient motivation for an empirical analysis. In light of this observation, we point out that another contribution of our work is the benchmarking repository containing all experimental code (submitted wit... | Summary: This work presents an empirical analysis of multi-calibration post-processing applied to a variety of models for binary classification, ranging from decision trees to transformers. They perform experiments with tabular, image, and text data on datasets of varying sizes. They compare model group calibration to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback. We will address the weaknesses and questions.
>**W1**: The paper could be more self-contained. While it's understandable for some results to be in the appendix given the number of experiments run in this paper, I worry that too much ma... | Summary: The paper investigates the effectiveness of multicalibration post-processing across various datasets and machine learning models. The study finds that models which are inherently calibrated often exhibit multicalibration without post-processing, while uncalibrated models benefit from multicalibration technique... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback. Please kindly refer to the global response for **Q2** and **Q5**. We address the other weaknesses and questions below.
**W1**: We agree with the reviewer that we do not introduce any new algorithms. The expressed purpose of our work is... | Rebuttal 1:
Rebuttal: **Global author response**
We thank all the reviewers for the thorough reviews and detailed comments. We respond to two common reviewer comments here.
1. Firstly, reviewers **Zyam Q5**, **nkHR W2**, and **M2Di W2** all had questions and comments about group selection. We have combined our discu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements | Accept (poster) | Summary: The paper studies unconstrained (strongly)-monotone finite sum minimization problems. They introduce a new scheme called SEG-FFA which:
i) for a given epoch runs stochastic extragradient (SEG) with possibly two different stepsizes
ii) uses flip-flop shuffling per epoch
iii) uses the average of the epoch initi... | Rebuttal 1:
Rebuttal: We appreciate the effort made by the reviewer in inspecting our manuscript.
1. **Monotone Case: Why Not Use Full Batch? (W1):**
This point, raised by the reviewer, is valid. However, in practice, shuffling-based stochastic methods are prevalent; it is not an exaggeration to say that they ... | Summary: In this paper the authors study stochastic extragradient methods for solving unconstrained minimax convex-concave problems with a finite sum structure. In particular, various shuffling schemes (random reshuffling without replacement, flip-flop, uniform sampling with replacement) are investigated and it was sho... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive feedback and thoughtful comments.
1. **On the Comments on the Notations Used (W1, W2):**
The reviewer has made valid points, and let us share our thoughts about the comments made by the reviewer. The second set of equations in (2) may cause a bit... | Summary: This paper proposes a new algorithm, SEG-FFA, which converges for the convex-concave minimax problem, while existing algorithms like SEG-RR and SEG-FF diverge for the same class of problem. Moreover, the authors show that SEG-FFA can better approximate EG in comparison to SEG-RR and SEG-FF.
Strengths: - It pr... | Rebuttal 1:
Rebuttal: We appreciate the effort made by the reviewer in inspecting our manuscript.
1. **On the Lipschitz Hessian Assumption (W1):**
As both our manuscript and the reviewer have mentioned, the Hessian Lipschitz assumption is a somewhat unusual one, not widely assumed in the literature. Still, it is... | Summary: The paper studies various same-sample SEG algorithms under different shuffling schemes, including SEG-US, SEG-RR and SEG-FF. The three algorithms all can diverge when $f$ is convex-concave. Furthermore, the authors discuss the underlying cause for the nonconvergence of the three algorithms. Moreover, the autho... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the constructive feedback and thoughtful comments.
1. **Importance of Initial Point due to the Anchoring Step (W1):**
For any optimization method, its behavior is more or less influenced by the choice of the initial point, and our SEG-FFA is not an exception. ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback. Here, we would like to discuss some important
issues commonly raised by the reviewers.
1. **On the Convergence Rate in the Strongly Monotone Setting ([k7BS] W2, [hmmd] Q1, [19M7] W2):**
One of the common questions raised by the rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting | Accept (spotlight) | Summary: This paper proposes a method named Frolic to improve the zero-shot performance of vision-language models like CLIP. The method focuses on two key challenges: enhancing prompt distribution learning and correcting inherent label bias in pre-trained models without relying on labeled data. Experimental results acr... | Rebuttal 1:
Rebuttal: **[Q1]** Does the paper utilize the validation dataset in experiments, if not, this should be clarified.
**[A1]** We do not use a validation set because our method involves no hypermeter searching.
***
**[Q2]** How does the proposed method compare with the methods[1][2] in the few-shot settings?
... | Summary: This work aims to enhance the zero-shot performance of pre-trained vision-language models. Specifically, three strategies, including label-free prompt distribution learning, adaptive calibration and correction pre-training label bias, are proposed and work together to improve the performance on different downs... | Rebuttal 1:
Rebuttal: **[Q1]** The work works with a uniform prior that classes are balanced distributed. It is better to discuss the scenario with imbalanced prior.
**[A1]** We consider that most of the datasets such as ImageNet and its variants are uniformly distributed, leading us to assume $\pi_j=\frac{1}{K}$ in ... | Summary: In this paper, the authors introduce a promising method for enhancing zero-shot vision models through the utilization of prompt distribution learning and bias correction. The method is particularly notable for being training-free and label-free, which greatly simplifies the implementation process. The authors ... | Rebuttal 1:
Rebuttal: **[Q1]** The computation of second-order moments and the covariance matrix from the marginal distribution as discussed in Eq.(3) and (5) might be computationally intensive.
**[A1]** We have evaluated the running time as presented in Table 5. The results show that while Frolic requires slightly mo... | Summary: This paper presents Frolic, a label-free prompt learning methods aiming to improve zero-shot visual recognition of vision-language models like CLIP. The method is built upon estimating distributions over prompt prototypes to capture diverse visual representations and further bias correction. Experiment result... | Rebuttal 1:
Rebuttal: **[Q1]** the downstream task is balanced (line 134), which is not always hold true in reality. The long-tail nature of real world does not guarantee that the testing class distribution will be balanced even though the benchmarks do.
**[A1]** We acknowledge that real-world data often exhibits an... | Rebuttal 1:
Rebuttal: Dear Program Chair, Senior Area Chair, Area Chair, and Reviewers,
First of all, we gratefully thank all the reviewers for their thoughtful comments and feedback.
In this paper, we propose label-Free prompt distribution learning and bias correction, dubbed as Frolic, framework to boost the perfor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying Causal Effects Under Functional Dependencies | Accept (spotlight) | Summary: The paper studies the identifiability of causal effects in the presence of functional dependencies. Section 4 starts with an engaging discussion on the interaction between positivity constraints and functional dependencies, during which it defines the concept of F-identifiability. Then in Section 4 its introdu... | Rebuttal 1:
Rebuttal: > The paper presents numerous theorems that share a similar objectives. I wonder if it's possible to consolidate them into two general, elegant rules, one one for reducing F-identifiability to F-identifiability from simpler graphs and one for reducing F-identifiability to identifiability. However,... | Summary: The paper addresses a novel problem in causal effect identifiability, introducing the concept of functional dependency among variables. It proposes a new elimination approach for removing redundant variables from the graph while preserving the identifiability of the target quantity. The main contribution inclu... | Rebuttal 1:
Rebuttal: > I didn’t understand when your theorems fail to recognize whether a causal effect in a graph is id. Can you provide some examples where the conditions of Theorem 15 are not satisfied, but the causal effect is identifiable (or not)?
We suspect that Theorem 15 will hold under weaker positivity con... | Summary: Existing causal effect identification algorithms such as the ID algorithm, require strict positivity constraints on the observed distribution that can get violated in cases where some variables (observed or hidden) are deterministic functions of their parents. This paper takes a step towards finding conditions... | Rebuttal 1:
Rebuttal: > I believe the current version is dense with results with the page restriction limiting a better style of presentation. There are multiple corollaries that deserve to be highlighted separately that appear in the middle of the text. I would also prefer adding proof sketches of the main theorems an... | Summary: This paper investigates the identification of causal effects in the presence of functional dependencies, where some variables are determined by their parents. The study demonstrates that unidentifiable causal effects can become identifiable and that certain functional variables can be excluded from observation... | Rebuttal 1:
Rebuttal: > What are the implications when the treatment/target variable is a functional variable?
If each treatment and target variable has some hidden parent and these are the only functional variables (after perhaps eliminating other functional variables by Theorems 13 & 15), we can reduce F-identifiabi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and suggestions. Please see individual responses below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mini-batch kernel $k$-means | Reject | Summary: The authors present the first mini-batch algorithm for kernel k-means. The algorithm itself is simple and works the way one would expect mini-batch kernel k-means to work. The authors improve the running time of an iteration of kernel k-means from $O(n^2)$ to $O(n(k+b))$ for the mini-batch version of the algor... | Rebuttal 1:
Rebuttal: Thank you for your questions and suggested improvements.
We agree with your observation that our main contribution is for practitioners, and that our work can benefit from additional experiments. We ran additional experiments on graph kernels (see our main rebuttal for more details). Please let u... | Summary: This paper proposes the first mini-batch kernel $k$-means algorithm, which significantly reduces running time compared to the previous kernel $k$-means methods relying on the full datasets. With the proposed mini-batch kernel $k$-means algorithm, each iteration can be executed in time $O(n(k+b))$, improving th... | Rebuttal 1:
Rebuttal: Thank you for taking the time to comb through are paper. You have been very generous with your time and have given us some new ideas to think about.
We understand your concern regarding the novelty of the theoretical analysis. However, as reviewer mEhQ pointed out, our main contribution is in ma... | Summary: In this paper, the authors propose the first mini-batch kernel k-means clustering algorithm. It is a variant of Lloyd's algorithm that was introduced by Sculley that takes a batch of random b points instead of the full set of points and a weighted avaerage with the current centers while updating the centers. T... | Rebuttal 1:
Rebuttal: Thank you for taking the time to go through our paper.
We understand your concern regarding the novelty of the theoretical analysis. However, as reviewer mEhQ pointed out, our main contribution is in making a big step towards making kernel k-means usable in practice by reducing the running time ... | Summary: The article presents the first mini-batch kernel k-means algorithm, which significantly improves running time compared to the full batch kernel $k$-means with only a minor negative effect on solution quality. The proposed algorithm runs in $O(n(k+b))$ time per iteration, as opposed to $O(n^2)$ for the full-bat... | Rebuttal 1:
Rebuttal: Thank you for your comments. Regarding your points:
- The performance of the algorithm does not require careful tuning of the learning rate as you suggest since $\alpha_i^j$ is totally determined by the formula given at the end of page 6. Please can you clarify what you meant?
- The approximation... | Rebuttal 1:
Rebuttal: Following the reviewer comments, we ran additional experiments with graph datasets. Please find the details of the experiments below and a PDF with the results attached.
An advantage of kernel k-means compared to (non-kernel) k-means is its ability to handle graph datasets. Specifically, we can ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning | Accept (poster) | Summary: To address the high variance issue of model-based offline RL methods, which rely on sampling-based uncertainty estimation, this manuscript proposes MOMBO, a model-based policy optimization method based on moment matching. MOMBO learns a Q-function using moment matching and deterministically propagates uncertai... | Rebuttal 1:
Rebuttal: Thank you for this thorough review. We will address your concerns below.
> 1.There is a clear necessity for the authors to enhance their writing proficiency.
Could you be specific as to where the writing proficiency is suboptimal?
> 2.The manuscript lists many contributions, which obscures it... | Summary: This paper proposes a new uncertainty estimation method for model-based offline reinforcement learning, which uses moment matching to deterministically propagate uncertainties through the Q-function, rather than relying on sampling-based uncertainty estimation. The resulting model, Moment Matching Offline Mode... | Rebuttal 1:
Rebuttal: Thank you for the detailed review.
> The novelty of this method seems relatively weak. It appears to be a minor modification of the uncertainty quantification method used in MOBILE.
MOPO, MOBILE, and MOMBO are three algorithms derived from the meta-algorithm known as Pessimistic Value Iteration... | Summary: ** I am unfamiliar with the methods/related works in this paper.**
This paper proposed a model-based method for offline RL, based on moment matching. Improved numerical results are presented.
Strengths: The method of moment matching is quite novel, which aims to improve the accuracy of the first two moment e... | Rebuttal 1:
Rebuttal: Thank you for your thorough reading despite the unfamiliarity.
> I feel the presentation of the paper can be improved.
Given the overall feedback provided via the reviews, we updated the section title and presentation for Section 3, and the _discussion and results_ paragraph in Section 5 (see, ... | Summary: This work addresses the issue of sampling for uncertainty propagation that is the standard practice in offline RL and identifies the high variance of sampling-based estimates as an obstacle to better performance of uncertainty-aware offline RL methods. As an alternative, the authors propose propagating uncerta... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback.
> While the authors conjecture that sampling based approaches to uncertainty propagation has inherent flaws, it would be nice to see more evidence, e.g. empirical evidence that digs into this phenomenon
We include an additional evaluation in our general ans... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback.
We address all the reviewer comments in our individual responses. To summarize the main changes during the rebuttal phase:
- We conducted an experiment to compare the quality of uncertainty quantifiers among the baselines and our MOMBO, which supports o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes | Accept (poster) | Summary: The paper presents a new method for scaling Bayesian Optimisation to large datasets by employing sparse GP in a way that facilitates BO. The proposed method of Focal BO fits a sparse GP at different scales, optimises the acquisition function for each of them and selects the next point to query by sampling wit... | Rebuttal 1:
Rebuttal: Thanks for your appreciations of our problem setting and algorithm design. We address your concerns below. Please find the rebuttal Figures by opening the rebuttal PDF file.
**Baselines comparison**
In **Figure R4**, We run the "keep closest N TuRBO" baseline (denoted as NN GP TuRBO) on both rob... | Summary: Operating in the context of Bayesian optimization, the authors propose to train a surrogate model which focuses on a specific sub-region of the input space by weighting the log-likelihood contributions of each datapoint relative to their distance to that region. They also propose an algorithm for choosing this... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We are improving the paper based on your suggestions. We'd also like to clarify your concerns in the following paragraphs. Please find the rebuttal Figures by opening the rebuttal PDF file.
**GP comparison in Figure 1**
We would like to highlight that our goal... | Summary: This paper focuses on scalable Bayesian optimization, where the training data size is large and the search space is high dimensional. This work proposes a new kind of sparse GP model by designing a variational loss function that allows for adaptively focusing on interesting regions throughout the optimization ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of this work! We address your question below. The rebuttal figures can be found by opening the rebuttal PDF file.
**Better way to center the search region**
A plausible alternative would be to instead center the search region at the maximum of the chosen acquisiti... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for helpful and insightful reviews. In addition to addressing the comments below, we will incorporate the suggestions and new figures into the camera-ready version. Our additional experimental results are attached in the rebuttal PDF below.
Pdf: /pdf/e6613a2d10... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Stable Representations for Protein Interface Prediction | Accept (poster) | Summary: This work focuses on protein interface prediction, i.e., determining whether a pair of residues from different proteins interact. It notices the conformational change within the protein upon binding and regards the flexibility as an attack on the trained model. An adversarial training framework named ATProt is... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough review and insightful feedbacks. We also appreciate the reviewer's recognition for the core idea and contribution of this paper. We will respond to these comments point-by-point.
**[Cons. 1 Problem Formulation]**
Thank you for your professional com... | Summary: This paper first identifies a commonly overlooked issue in protein docking: protein flexibility, and proposes an improved method to address it. This approach utilizes an adversarial training framework to maximize Lipschitz continuity. Experimental results demonstrate the effectiveness of this method.
Strength... | Rebuttal 1:
Comment: Let me add that I believe the method proposed by the author is a form of regularization rather than an adversarial training approach. If I am correct, please adjust the relevant writing accordingly.
---
Rebuttal 2:
Comment: Also, please add the ablation study and baselines (e.g. EBMdock) as revie... | Summary: This paper considers the generalization issue caused by the conformational changes of two proteins before and after binding in the PIP task. The authors view protein flexibility as an attack on the model and aim to defend against it for better generalization. Hence, an adversarial training framework for protei... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer **kBXn**'s recognition of our paper and valuable comments. We will respond to reviewer's insightful suggestions point-by-point.
**[Cons 1. Novelty of Model's Architecture]**
Thanks for the valuable comment. We sincerely clarify that the novelty of this paper i... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely appreciate your valuable time and constructive feedbacks.
**Please see the attached one-page PDF with a summary of added experimental results.** It contains:
Figure 6: Visualization of the changes in interface prediction results before and after using stable regula... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation | Accept (poster) | Summary: This paper introduces M3GPT, an advanced Multimodal, Multitask framework for motion comprehension and generation. M3GPT operates based on three fundamental principles. Firstly, it aims to create a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for mu... | Rebuttal 1:
Rebuttal: **W1: In Table3, some comparation results are missed for text-to-motion task.**
Thanks for your suggestions. We will add the related works mentioned, MoMask [1], PRO-Motion [2] and MotionLCM [3], in revised version.
(1) To ensure fair comparison, we reproduce **MoMask** and **MotionLCM** on Mo... | Summary: The paper presents M3GPT, which creates a unified representation space for different motion-relevant modalities, including text, music, and motion/dance. The framework employs discrete vector quantization for these modalities, enabling integration into a large language model (LLM) with a single vocabulary. The... | Rebuttal 1:
Rebuttal: **W1: The proposed framework is complex and may require significant computational resources to implement.**
To ensure reproducibility, we will open-source the training and inference code of proposed $M^3$GPT framework.
For computational resources, please refer to **reply to Q2 in Global Respon... | Summary: This paper presents an advanced Multimodal, Multitask framework for Motion comprehension and generation. It aims to create a unified representation space for various motion-relevant modalities and model the connections and synergies among different motion tasks. M3GPT consists of multimodal tokenizers and a mo... | Rebuttal 1:
Rebuttal: **W1: For supplementary videos, it's hard to judge the quality of music-motion generations without combining them in one video; Long-term dance generation on aist++ is only a little longer than 5s.**
(1) Due to the rebuttal period restrictions on video uploads, we will present music and dance in ... | Summary: In this paper, the authors introduce $M^3$GPT, a multimodal multitask framework designed for both motion comprehension and generation. Utilizing discrete vector quantization, $M^3$GPT establishes a discrete semantic representation space for various modalities. To avoid information loss during discrete de-token... | Rebuttal 1:
Rebuttal: **W1: There is still doubt how can text-to-motion or music-to-dance tasks benefits from multimodal framework.**
We argue that text-to-motion and music-to-dance tasks benefits from multimodal framework in two main aspects:
1. **A shared tokenizer for motion and dance data:** The shared tokenizer ... | Rebuttal 1:
Rebuttal: ## **Global Response**
We sincerely thank all reviewers and ACs for reviewing our work. Some common questions are answered.
### **Q1: The evaluations for different size of T5 (Reviewers #ZwnC, #Wkeh, #KMVC)**
We conduct experiments on different sizes of T5: T5-small (60 M), T5-base (220 M), T5-l... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration | Accept (poster) | Summary: The paper introduces a new unsupervised outdoor point cloud registration method called INTEGER, which dynamically integrates high-level contextual information and low-level geometric information to generate reliable pseudo-labels, addressing the issue of poor performance in complex outdoor environments seen in... | Rebuttal 1:
Rebuttal: We first would like to thank the reviewer for giving us valuable comments.
**Q1:** *Robustness of Initial Teacher Model: the strategies used for teacher initialization and its robustness in adverse conditions or with suboptimal initialization.*
**A:** The initialization strategy is detailed in S... | Summary: This paper introduces an unsupervised framework for point cloud registration that generates reliable pseudo-correspondences using both low-level geometric and high-level contextual information. It employs a widely used teacher-student architecture and proposes Anchor-Based Contrastive Learning to facilitate ro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable comments. **For concerns about the efficiency and effectiveness of FCEM, please refer to the "6. Efficiency and effectiveness of FCEM" part in the general response.**
**Q1:** *Similarity to EYOC.*
**A:** Our method **significantly differs** fr... | Summary: This submission proposes an unsupervised framework for point cloud registration. The key contribution is a two-stage training scheme, which first trains a teacher network on synthetic data that extract features in a density-invariant manner, and then trains a student network with pseudo label produced by the t... | Rebuttal 1:
Rebuttal: We first would like to thank the reviewer for giving us valuable comments.
**Q1:** *The key insight is more presented as an empirical observation, is there any chance that such can be justified in a more concrete way, even on some toy examples?*
**A:** Thanks for your valuable comments. Fig.1 in... | Summary: This paper focuses on unsupervised point cloud registration in 3D computer vision. To tackle the problem, it leverages the observation that in the feature space, points of latent new inlier correspondences tend to cluster around respective positive anchors. Based on that, this paper proposes a novel unsupervis... | Rebuttal 1:
Rebuttal: We first would like to thank the reviewer for giving us valuable comments.
**W1:** *Problems with the figures. For example, Fig. 1 is not very easy to understand. Fig.2 and Fig.3 include too much content and make it hard to focus.*
**A:** Thanks for pointing out the figure issues. A simplified v... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their careful, valuable, and insightful reviews. The reviewers acknowledge our work with good writing(Bwh6,FNCk,2VKD), novel design(Bwh6,SEpR), and competitive performance(Bwh6,FNCk,2VKD). We are particularly delighted to see that all reviewers recognize the no... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Honor Among Bandits: No-Regret Learning for Online Fair Division | Accept (spotlight) | Summary: The paper studies online fair division of indivisible items, where items arrive one at a time, and each item must be permanently assigned to a player upon arrival. Player utilities are initially unknown and must be learned via bandit-style feedback: there are a finite number of types of goods, and each player’... | Rebuttal 1:
Rebuttal: Thank you for all of your helpful feedback! We will make sure to incorporate your suggestions into the final version. Below, we address your questions about the motivating example.
> Do you agree with my criticisms of the food bank analogy? Is there a different real-world situation that you belie... | Summary: The authors study an online fair division problem where items arrive online and need to be assigned to agents to maximize welfare while satisfying one of envy-freeness or proportionality. The novel consideration in their model is that the item valuations are drawn from an unknown distribution. They approach th... | Rebuttal 1:
Rebuttal: > Is it possible to achieve non-trivial regret in the setting where the valuations of the agents are not independent? In the motivating example of the food pantry, if a delivered food item is of bad quality, the agents might all lower their valuation of this item together.
This is definitely an i... | Summary: In this work, the authors consider the problem of maximising the total utility in online fair allocation settings, where:
(i) T items having types in set [m] arrive online in T distinct rounds; (ii) the value $V_{i}(t)$ that each agent $i$ assigns to each item $t\in [T]$, when its type is $k$, is independentl... | Rebuttal 1:
Rebuttal: > I believe that the results could be written and presented more effectively (see the major comments below). [...] -It should be highlighted from the beginning that the allocations returned by the algorithm are envy-free in expectation, but with high probability (that is, not always). -The matrix ... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and comments! We respond to specific comments in the individual rebuttals below.
Shortly after submitting our paper, we found a simple lower bound which shows that for our setting, no algorithm can do better than the ones we presented. We pl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Barely Random Algorithms and Collective Metrical Task Systems | Accept (spotlight) | Summary: This paper introduces and studies the problem of designing and analyzing randomized algorithms for Metrical Task Systems (MTS) using only limited randomness, that is, a number of random bits that only depends on $n$ (the number of states in the MTS), rather than the length of the sequence, as in the case of th... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their interest and their thoughtful questions.
* To the best of our knowledge, the use of the Birkhoff-von Neumann theorem in the context of competitive analysis is new. We build upon this theorem in Proposition 2.2 of Section 2.2. As the reviewer points out, the purpose... | Summary: The current paper addresses the problem of metrical task systems on a general space with $n$ points and proposes a technique to reduce the amount of randomness used by any random algorithm. More precisely, the authors prove that any "fully random" algorithm, which uses an unlimited number of random bits, can b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interest and their careful reading. We note that the reviewer's appreciation of our results seems very positive, and that their only concern is whether the paper is a good fit for NeurIPS. We attempt to convince the reviewer that this is indeed the case, in the hope... | Summary: Authors study randomized algorithms for Metrical Task Systems (MTS)
which need only a small number of random bits which, in particular,
does not depend on the length of the time horizon.
They also interpret this in terms of an average performance of
a cooperating group of several deterministic algorithms
and i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and suggestions. We answer the reviewer's questions below.
* The reference "Online Computation with Advice" by Emek et al. pointed out by the reviewer is very relevant and will be added to the paper: Emek et al. study the situation where $b$ b... | Summary: The paper bounds the randomness required to achieve the asymptotically tight randomized competitive ratio for metrical task systems, a fundamental model of online computing (essentially, prediction with expert advice endowed with a metric cost function for switching among experts). It shows that $2\log n$ rand... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging review! | Rebuttal 1:
Rebuttal: We thank the reviewers for their interest, their careful reading, and their feedback to improve the paper.
We quote all four reviewers who unanimously appreciated the content and the presentation of the paper:
* **Reviewer Ddub**: "The result answers a fundamental question in online computing."
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Abrupt Learning in Transformers: A Case Study on Matrix Completion | Accept (poster) | Summary: The paper explores the behavior of Transformer models in the context of low-rank matrix completion, treated as a masked language modeling (MLM) task. The authors train a BERT model to solve matrix completion and analyze its performance, particularly noting an algorithmic shift characterized by a sudden drop in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and are excited to know that they found our approach interesting!
- *The paper lacks a deep theoretical ...*
We would like to point out that apart from the analysis in our paper, to the best of our knowledge there has been no work on analyzing such an al... | Summary: The authors study how encoders perform at the task of matrix completion.
They generate synthetic data and train encoders of different sizes to predict masked out tokens.
They observe an interesting behaviour where initially the loss appears to be at a plateau but then drops.
Their hypothesis -which they inv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and are glad that they found our problem formulation and analysis interesting!
- *I think the related work could be improved. I think the paper would benefit from a deeper discussion on the experiments and findings of relevant works cited (e.g., 4, 6, 20... | Summary: The paper explores the behavior of BERT on matrix completion tasks. The authors show that the model's loss shows a phase transition, where the model switches from copying tokens for filling masked tokens to predicting the masked entries accurately. The authors also conduct probing studies to understand the str... | Rebuttal 1:
Rebuttal: We thank the reviewer for their extensive suggestions, and are happy to know that they found our work important and interesting!
- *How does convergence of BERT …*
Thanks for this suggestion! We have now performed experiments with a 4-layer, 8-head model on rank–1 matrices of size 5x5, 7x7, 9x9... | Summary: The paper applies a BERT-style transformer encoder to do low-rank matrix completion. To frame it as a masked language modeling problem, they restrict the domain of the problem to smaller matrices and discretize the domain.
They find the model can solve the problem. The training dynamics are interesting. Initi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and are glad to know they found our results interesting!
- *The main weakness is how significant the contribution is. They have some interesting results … how the transformer does math.*
We re–emphasize that our focus is studying the sudden drop in loss... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback and are excited to see that they find our work interesting! To address raised questions, we have performed several new experiments, as described below. We will add these results to the final version of the paper.
**Larger matrices.** We show tha... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper examine the ability of the transformer model to solve a low-rank matrix optimization problem. The numerical matrices with missing values are tokenized and flattened as sequences with masked values, and a BERT transformer is trained to predict the original sequence with both masked and unmasked values... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments, and are happy to know they found our work interesting!
- *Though using matrix completion as an "abstraction" of MLM is interesting and novel … interpretation and insights.*
We emphasize that our goal while casting matrix completion as an abstr... | null | null | null | null | null | null |
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning | Accept (poster) | Summary: This paper introduces a novel training debugging concept aimed at enhancing efficiency, robustness, and balance during the graph training process. It employs trainable prototypes to dynamically select appropriate samples for each training iteration. The concept is innovative and intriguing. However, the experi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful comments and thorough understanding of our paper! Here we give point-by-point responses to your comments and describe the revisions we made to address them.
---
> **Weakness 1 & Question 1**: Is the proposed method extendable to other data domains?
To addr... | Summary: This paper presents a novel method for graph neural network training called Graph De-Redundancy. This method aims to enhance the efficiency, balance, and robustness of GNN training. It constructs a hyperspherical embedding space using trainable prototypes to maintain a balanced subset of the training data, add... | Rebuttal 1:
Rebuttal: Thank you immensely for your time and efforts, as well as the helpful and constructive feedback! Here, we give point-by-point responses to your comments.
---
> **Weakness 1**: How are the prototypes initialized? I didn't see a note about it from the Algo. 1. Does the initialization of prototypes ... | Summary: This paper addresses the computational and memory challenges posed by large datasets in the training of graph neural networks (GNNs). The authors propose GDeR, a dynamic soft-pruning method that leverages trainable prototypes to regularize the graph embedding distribution. This approach aims to maintain a bala... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer RKXd for the thoughtful and constructive reviews of our manuscript! Based on your questions and recommendations, we give point-by-point responses to your comments and describe the revisions we made to address them.
---
> **Weakness 1**: Though the authors claim that GD... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We extend our sincere gratitude for your dedication to the review process. We are truly encouraged by the reviewers' recognition of several positive aspects of our paper, including **a novel and significant data pruning method** (Reviewer `RKXd`, `kNzQ`, `zhEK`), **high-quality p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity | Accept (poster) | Summary: The paper studied distributed asynchronous SGD with heterogeneous communication and computation times for non-convex stochastic optimization problems. In particular, the paper proposed a new algorithm called Shadowheart SGD and analyzed his time complexity. The time complexity is proven to be optimal in the fa... | Rebuttal 1:
Rebuttal: Thank you for the comments! Let us respond to the weaknesses:
> The paper was not well written and is very hard to read. It reads like a summary of the results that are in the appendix. In addition, the intuitive explanations are very few and many notations were not well explained.
We are sorry ... | Summary: The paper proposes a method (Shadowheart SGD) for centralized asynchronous optimization with compression. The lower complexity bounds are proposed, as well. It is shown that Shadowheart SGD achieves the lower bounds, meaning that the method is optimal.
Strengths: The main strength is the development of an opt... | Rebuttal 1:
Rebuttal: Thank you very much for the review! Let us clarify the weaknesses and questions:
> Personally, I did not understand the discussion of equilibrium time well. I understand that it comes from the analysis; also the examples are illustrative.
Indeed, the equilibrium time, in some sense, is a "mixin... | Summary: This paper considers distributed and centralized smooth non-convex optimization when workers have different computation and communication speeds. These different speeds on the workers (and even possibly the server) characterize the problem's device heterogeneity. The authors provide a new algorithm that uses u... | Rebuttal 1:
Rebuttal: Thank you for your very positive comments! We now respond to weaknesses:
> 1. I am not sure if the lower bound actually holds for all unbiased compression schemes.
In the lower bound (Theorem O.5), we chose the particular compressor, Rand$K.$ Thus, indeed, our lower bound works only with one c... | Summary: The paper presents a novel method for non-convex stochastic optimization in an asynchronous centralized distributed setup, focusing on improving time complexity in heterogeneous environments. Additionally, the authors demonstrate that the proposed method achieves theoretically optimal time complexity under com... | Rebuttal 1:
Rebuttal: Thank you for the positive review! Let us address the weaknesses and questions:
> The definition and calculation of equilibrium time are complex and not very intuitive. The implicit nature of this definition might make practical implementation and understanding challenging.
Unfortunately, with ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes | Accept (poster) | Summary: The paper presents D-AdaST, a distributed adaptive minimax method designed to address non-convergence issues in nonconvex-strongly-concave (NC-SC) minimax problems caused by inconsistent locally computed adaptive stepsizes in distributed settings. The method incorporates a stepsize tracking mechanism, which en... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. Please see below for a detailed point-by-point response.
>__Q1:__ How does D-AdaST compare to other existing adaptive minimax methods in terms of computational time efficiency?
__Response:__ We note that, as discussed in the Introduction sect... | Summary: This paper proposed a decentralized stochastic first-order method for nonconvex minimax optimization. For nonconvex-strongly-concave setting, the proposed D-AdaST has the convergence rate of $O(\epsilon^{-4+\delta})$ for any small $\delta>0$.
Strengths: see questions
Weaknesses: see questions
Technical Qual... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. Please see below for a detailed point-by-point response.
>__Q1:__ This paper states D-AdaST achieves the near-optimal convergence rate, while the definition of optimality is unclear, e.g., problem setting and algorithm class. I’m not sure whe... | Summary: The authors introduced a new distributed adaptive minimax method, D-AdaST, to address the issue of non-convergence in nonconvex-strongly-concave minimax problems caused by inconsistencies in locally computed adaptive stepsizes. D-AdaST employs an efficient adaptive stepsize tracking protocol that ensures time-... | Rebuttal 1:
Rebuttal: >__W1:__ The experimental section has some deficiencies, as it only includes a GAN experiment on the CIFAR-10 dataset. I believe it would be beneficial to supplement the paper with results from additional datasets or other minimax optimization problems to provide a more comprehensive evaluation.
... | Summary: The paper introduces a method for distributed minimax optimization, for the scenario that various "agents" each hold part of a data set locally, and aim to coordinate to find the minimax solution of some criterion. Here, the criterion consists of the average of local cost functions, which are assumed to be smo... | Rebuttal 1:
Rebuttal: Thank you for the insightful and valuable comments. Please see below for a detailed point-by-point response.
>__W1:__ Whilst I think the problem that the authors address is interesting, and within their scope they provide satisfying answers, I also think that this scope is rather limited. There a... | Rebuttal 1:
Rebuttal: __General Response to All Reviewers__
We would like to express our gratitude to all the reviewers for evaluating our work positively and providing their insightful and valuable comments that have helped us greatly improve the quality of our paper.
We have carefully considered each of the review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling | Reject | Summary: After rebuttal
While I think there are still some problems with this paper, e.g. the short training duration, and the slight exaggeration of claims (that SHED outperforms UED). I think, however, that the idea is nice, and getting RL environment design to work better is a good goal.
-----
This paper aims t... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable time and feedback,
- **W1.** The results.
**A1.** It is crucial to note that SHED demonstrates more consistent and superior performance in the more complex BipedalWalker and Maze, as depicted in Figure 3,4. This suggests that SHED offers better generalizabili... | Summary: The paper presents a novel approach to Unsupervised Environment Design (UED) that addresses the challenges of efficiency by introducing a hierarchical MDP framework and using synthetic data. This framework involves an upper-level RL teacher agent that generates training environments tailored to a lower-level s... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable time and feedback, and we kindly request the reviewer to consider our clarifications.
- **W1.** The proposed method introduces significant complexity, particularly in the implementation of the hierarchical MDP and the generative modeling components. This might l... | Summary: This paper considers the Unsupervised Environment Design problem, where a teacher agent seeks to design environments to train a student. Methods such as PLR, PAIRED and ACCEL have recently shown promising performance for random, RL and evolutionary generators. This paper proposes a handful of modifications, us... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable time and feedback, and we kindly request the reviewer to consider our clarifications.
- **S1.** The idea of combining this with Genie is incredibly exciting.
**A1.** Genie is the first generative interactive environment trained in an unsupervised manner using... | Summary: The authors of this paper use hierarchical MDP formulation and a teacher agent trained by RL to perform curriculum learning. To address the sparse data available for the teacher agent, this paper uses diffusion models to synthesize datasets for training. This paper performs experiments on lunar lander and bipe... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable time and feedback, and we kindly request the reviewer to consider our clarifications.
- **Q1.** Fingerprint Policy Optimization (Paul et al, 2019) also models the learning process of the student agent. It would be interesting to explain more about how this paper... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable feedback! We present the new results in the attached PDF. All feedback will be incorporated into the updated manuscript.
Pdf: /pdf/02a4e962b8a8d43cbe20b901e8e6f4b7a9d12d1c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis | Accept (poster) | Summary: This paper introduces Hyper-SD, a novel framework designed to mitigate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). The proposed framework addresses the limitations of existing distillation techniques, which focus on either ODE Trajectory Preservation o... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our method, experiments and writing. We would like to answer every question you have in the following.
W1:
We would like to clarify that our RLHF training is separated from TSCD and we apply RLHF through LoRA merging. So there would be no excessive memory usage i... | Summary: The paper introduces a novel framework named Hyper-SD, which enhances diffusion models’ efficiency by combining consistency distillation, human feedback learning, and distribution matching. Hyper-SD performs consistency distillation in segments to preserve the original ODE trajectory, maintaining high-quality ... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our method and main results. We would like to answer every question you have in the following.
W0:
We fully understand your primary concerns about ablation studies. To further validate the effectiveness of our proposed method in multi-step or one-step generation,... | Summary: This paper presents an approach for distilling a diffusion model into a multi-step generator. Previous distillation methods typically fall into two categories: those that preserve the ODE (Ordinary Differential Equation) trajectory and those that match the teacher model at the distribution level. This research... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our writing, experiments and reward optimization approaches. We would like to answer every question you have in the following.
W1:
As for technical novelty, we would like to highlight and summarize as follows:
1. We're the first to split the acceleration objectiv... | Summary: This paper studies the distillation problem of diffusion models. Specifically, it introduces Trajectory Segmented Consistency Distillation to progressively perform consistent distillation within pre-defined time-step segments, which facilitates the preservation of the original ODE trajectory. Besides, the huma... | Rebuttal 1:
Rebuttal: Thanks for your kind words about our writing, intuition and experiments. We would like to answer every question you have in the following.
W1:
Firstly, the randomness of t_end that broadened the boundary condition of the original consistency distillation in CM[1] (Theorem 1) has been proven to ... | Rebuttal 1:
Rebuttal: Dear all,
For each question from different reviewers, we have responded individually with a targeted rebuttal under.
We put all the figures and tables into the pdf file submitted here.
Hope this address your concerns and we welcome more discussion at any time.
Regardless of the final decision,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? | Accept (poster) | Summary: This paper introduces a novel approach called CIDM to address the challenge of Concept-Incremental Flexible Customization in text-to-image generation. The authors identify two key issues in CIFC: catastrophic forgetting of previously learned concepts and concept neglect during multi-concept composition. The pr... | Rebuttal 1:
Rebuttal: Q1: The symbol definitions are too complex which are hard to follow.
A1: Thanks for your valuable comment. We will carefully polish the symbol definitions and introduce a table about notation definitions to better understand this paper in the final revision process.
Q2: This paper introduces s... | Summary: This paper explores a novel and practically significant problem, namely how custom diffusion models can continuously learn new personalized concepts while avoiding catastrophic forgetting and concept neglect. The authors developed a concept consolidation loss and an elastic weight aggregation module to explor... | Rebuttal 1:
Rebuttal: Q1: It would be better to mention the comparison methods of experiments in the related work, such as LoRA-M, LoRA-C, CLoRA and L2DM, and emphasize the differences between them and the proposed method.
A1: Thanks for your insightful comment. We will carefully polish the related work in the finial ... | Summary: This paper introduces the Concept-Incremental text-to-image Diffusion Model (CIDM), which addresses the Concept-Incremental Flexible Customization (CIFC) problem. This approach represents one of the first explorations into learning new customization tasks incrementally, effectively navigating the dual challeng... | Rebuttal 1:
Rebuttal: Q1: I recommend the authors include details on memory consumption or the number of stored parameters after each task during the rebuttal process.
A1: Thanks for your constructive suggestion. We conduct comparison experiments to evaluate memory consumption and training parameters of each task, whe... | Summary: This paper tackles the problem of continually adapting text-to-image diffusion customization models. The proposed method employs a novel concept consolidation loss, elastic weight aggregation module, and context-controllable synthesis strategy. Extensive experiments demonstrate that the proposed method perfor... | Rebuttal 1:
Rebuttal: Q1: Please include a table that compares the training, parameter, and memory costs of your method against other methods. This comparison is crucial for a comprehensive evaluation of your method.
A1: Thanks for your insightful comment. As shown in the following results, we use Stable Diffusion (S... | Rebuttal 1:
Rebuttal: Dear reviewers and area chairs:
We extend our gratitude to all the reviewers and area chairs for dedicating their time and effort to evaluating our paper. We also thank the reviewers for their positive and insightful comments, which can help us improve our work.
We are encouraged that:
$\bullet... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising | Accept (poster) | Summary: This paper presents a novel transformer architecture for real-world image denoising under a self-supervised framework. Following blind-spot networks, they introduced directional self-attention (DSA) module and Siamese architecture to prevent performance degradation from the masked region.
Strengths: Superior ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Please find our response below.
**[W1]** *Regarding novelty over [27] and knowledge distillation (KD) used in recent self-supervised denoisers with BSNs.*
**Novelty and contribution clarification**
- As acknowledged by **Reviewer JdHm**, our approach "builds u... | Summary: This paper proposes a transformer-based model for self-supervised denoising. The model employs two pseudo-siamese sub-networks during training, but only one is used for inference. One sub-network uses a grid-based DSA for blind-spot learning, while the other utilizes full grid-based attention to mitigate the b... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and valuable feedback. Please find our response and answers below.
**[W1]** *In Table 3, 'w/o DSA' and 'only SelfFormer-F' both lack a blind-spot scheme, but why is there a large gap between them?*
Thank you for pointing this out. We sincerely apologize for the co... | Summary: This paper explores self-supervised image denoising using only single-shot noisy images. The authors enhance the blind-spot technique by harnessing the transformer's ability to manage long-range pixel interactions, which is essential for eliminating noise dependencies between pixels. Experimental results verif... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and valuable feedback.
**[W1]** *Regarding novelty \& difference from PUCA and LGBPN.*
Our work focuses on studying the blind-spot mechanism in a transformer model, with an emphasis on spatial self-attention. Both PUCA and LGBPN also study effective blind-spot mecha... | Summary: This paper presents a novel approach for real-world image denoising using self-supervised learning. The method leverages the transformer's ability for long-range pixel interactions, combined with a sophisticated blind-spot structure through grid self-attention and directional self-attention (DSA) modules. Addi... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and valuable feedback. Please find our response and answers below.
**[W1]** *The evaluation is limited to SIDD and DND datasets only. More diverse datasets could further validate the method’s robustness.*
The SIDD and DND are widely recognized datasets for benchma... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback and insightful suggestions. We appreciate the constructive nature of this review process and are committed to thoroughly addressing each aspect.
Pdf: /pdf/ca729db0974868fd6a01f78e9db80491c2cf0c82.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Comparison between Multi-modal and Single-modal Contrastive Learning | Accept (poster) | Summary: In this paper, the authors performed theoretical analysis between single-modal and multi-modal contrastive learning. The authors proved theorems that indicates single-modal contrastive learning tend to perform worse on test datasets after converging on the training set, while multi-modal contrastive learning t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed and constructive feedback on our paper. Below, we provide detailed responses to the main comments
---
**W1(a)** Incorrect use of big-O-like notations.
**R1(a)** We appreciate the reviewer's attention to the details of our notation. We have... | Summary: This paper presents a theoretical framework to understand the differences between multi-modal and single-modal contrastive learning approaches. It emphasizes the impact of signal-to-noise ratio (SNR) on the generalizability of these learning methods in downstream tasks. The authors argue that multi-modal learn... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive feedback and thoughtful comments on our paper. We appreciate the opportunity to address the points raised and clarify our contributions. Below, we provide detailed responses to the main comments and questions.
---
**W1**: Strong assumpti... | Summary: This paper provides a theoretical analysis comparing single-modal and multi-modal contrastive learning. The authors develop a unified framework to analyze the optimization dynamics and generalization capabilities of both approaches. Key findings include:
- Both single-modal and multi-modal contrastive learnin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive feedback and insightful comments on our paper. We appreciate the opportunity to address the points raised and clarify our contributions. Below, we provide detailed responses to the main comments and questions.
---
**W1**: Can you provide... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs
Thank you all for your time and constructive feedback! We are truly encouraged to see many positive comments on our work, such as the the *unified framework for an interesting problem* (Reviewer bcfY, Reviewer 7UFF, Reviewer nN5z), *thorough theoretical analysis* (Reviewer ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graphcode: Learning from multiparameter persistent homology using graph neural networks | Accept (poster) | Summary: This paper introduces graphcodes, biparametric persistence summaries that are empirically efficient to compute yet not topologically invariant. It also provides a dedicated C++ library for calculating graphcodes. Graphcodes are structured as layered graphs; each layer contains vertices representing points in p... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments.
**Weaknesses:** Please also see "Expressivity and non-invariance of graphcodes" in the overall rebuttal above.
Given a fixed size dataset one can create multiple graphcodes corresponding to different choices of bases for each instance. This is a way to te... | Summary: This paper introduces "Graphcode", a new representation for summarizing the topological properties of datasets filtered along two parameters. Graphcodes are based on persistent homology but aim to provide a more interpretable and efficient summary than existing multi-parameter topological descriptors. The key ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments.
**Question 1:** The standard graphcode algorithm chooses a basis based on the reduction performed by the slicewise persistence algorithm. So in some sense the algorithm chooses a specific basis. In the uploaded software we also added an option (do-exhausti... | Summary: The paper introduces "graphcodes," a novel multi-scale summary of the topological properties of datasets using graph neural networks. Unlike traditional persistent homology, which uses a single parameter, graphcodes handle datasets filtered along two real-valued scale parameters, resulting in a more informativ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments.
**Question 1:** The construction of the graph is explained in Section 3, Appendix B and D. The vertices are the points of the persistence diagrams (topological cycles) of the individual slices where two points in consecutive slices are connected by an edge ... | Summary: This paper proposes a computationally fast method to extract information from a 2-parameter persistence module. The authors consider a 2-parameter persistence module as slices of 1-parameter persistence modules. Each 1-parameter persistence module can be represented as barcodes. The authors consider these barc... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments.
**Question 1 and 2:** The idea of choosing a GAT network is that topological (homological) features that are persistent across both scale parameters are reasonable candidates for the topological signal and that the network should learn to pay more attention... | Rebuttal 1:
Rebuttal: At first, we want to thank all reviewers for their comments. As some points came up in more than one review we want to address them here in a general rebuttal and address more specific questions in individual rebuttals below.
**Expressivity and non-invariance of graphcodes:** The most important p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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