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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups | Reject | Summary: I am unable to review this paper as it lies outside my area of expertise.
Strengths: I am unable to review this paper as it lies outside my area of expertise.
Weaknesses: I am unable to review this paper as it lies outside my area of expertise.
Technical Quality: 3
Clarity: 3
Questions for Authors: I am u... | null | Summary: The authors aim to create a discrete diffusion model that generates permutations. This model can then be used to solve combinatorial problems including jigsaws and travelling salesman problems. To formulate their model they cover a range of forward shuffling strategies and discuss how to parametrize the revers... | Rebuttal 1:
Rebuttal: Thank you for the insightful and constructive comments. We appreciate your positive feedback and address the questions below.
> **Q1:** You later say that you can merge steps of the forward process if each individual step does not induce enough mixing and so the stated weakness that these styles ... | Summary: This paper proposes a discrete diffusion model to learn distribution over the finite symmetric group $S_n$. The forward process is built off of random walks on finite groups (in this case, card shuffles), and the paper learns to reverse this diffusion process with standard discrete diffusion arguments.
Streng... | Rebuttal 1:
Rebuttal: Thank you for the insightful and constructive comments. We appreciate your positive feedback and address the questions below.
> **Q1:** The model proposes to directly learn the reverse transition densities $p_{\theta}(X_{t-1}|X_t)$. The issue with doing this for standard diffusion models is that ... | Summary: This paper introduces SymmetricDiffusers, a new approach to learning complex distributions. It works by breaking down the problem into simpler steps: learning how to reverse a transformation using deep neural networks. The authors identify a particularly effective method for this reversal step (the riffle shuf... | Rebuttal 1:
Rebuttal: > **Q1:** In Robin Winter et al.'s paper, the authors have studied the symmetric group and my understanding is disentangling invariant features and equivariant groups enables the design of flexible diffusion models since you only need to perform diffusion modeling in the invariant latent space.
> ... | Rebuttal 1:
Rebuttal: ## General Replies
We thank the reviewers for their insightful and constructive comments. We have addressed all the questions in the individual responses. Here, we'd like to highlight a common question from the reviewers:
**Q: Why can’t we use $q(X_{t-1}|X_t,X_0)$ and the KL divergence form of t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality | Accept (poster) | Summary: This paper concerns the overfitting behaviour of Gaussian kernel ridge regression (KRR) with varying bandwidth or dimensionality. The contribution is two-fold:
1. In fixed-dimension, the ridgeless solution of Gaussian KRR is not consistent with any varying or tuned bandwidth.
2. In high dimension with inpu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Below we address the main comments:
1. Regarding the wrong predictions of the Gaussian Universality Ansatz (GUA): There is ample empirical evidence that the eigenframework holds well for the Gaussian kernel. For example, Figure 1 in [38] shows the predicte... | Summary: This paper discusses the generalization ability of Gaussian kernel interpolation, an interesting topic. However, the authors sidestep the most challenging part by assuming the 'Gaussian universality ansatz,' which significantly simplifies the problem. With this assumption, their work essentially consists of el... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Below we address the main comments:
1. The goal of the paper is to study the kernel interpolation problem by understanding its behavior under common settings. It achieves this goal in two ways. First, we consider the common Gaussian kernel with varying ba... | Summary: This paper looks at the problem of kernel (ridgeless) regression with Gaussian kernel and data on a sphere. The paper studies two cases, first we fix the dimension $d$ and are allowed to send the number of data points $m \to \infty$ and the width of the kernel $\tau_m \to 0$, second, we are allowed to scale $d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. Below we address the main comments:
1. Regarding the claim about Theorem 1:
$\beta_i$ in this case do not depend on $m$. Changing $m$ does change the bandwidth $\tau_m$, but the eigenfunctions remain the same despite the kernel changing - the eig... | Summary: The paper investigates the overfitting behavior of Gaussian Kernel Ridge Regression (KRR) in high-dimensional settings, focusing on how the model performance is influenced by the choice of the bandwidth parameter and sample size.
They aimed to provide a more comprehensive picture of overfitting with Gaussian ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Below we address the main comments:
1. Regarding the experiments: There is ample empirical evidence about the eigenframework holding well for the Gaussian kernel. For example, Figure 1 in [38] shows the predicted vs. true test risk for Gaussian... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their thoughtful feedback.
In the attached PDF, we provide empirical evidence that our predictions are correct. We focus on the finite-dimensional case because of computational constraints. In Figure a, we consider the first case of Theorem 1, namely when the band... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors derive estimates of the population risk of the kernel (ridgeless) regression estimator for spherical data when either the kernel bandwidth or the dimension of the data depends
on the number of training points $m$. To do so, authors rely on the eigenframework and prior work, which provides (under as... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. Below we address the main comments:
1. Ridge vs Ridgeless: We will change the title to use “ridgeless” instead and reword other relevant parts of the paper.
2. Assumptions for the eigenframework: There is ample empirical evidence that the eigen... | null | null | null | null | null | null |
You Don’t Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning | Accept (poster) | Summary: This paper examines the question of whether data augmentations, specifically hand-crafted domain-specific augmentations, are needed for self-supervised learning. This question has relevance as the existing state of the art methods use such augmentations in the standard natural image domain, which leaves open ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive reception of our work. In the following we will be addressing each of your comments.
**W1**) Please refer to the general rebuttal. We add an analysis on MOCOv3 (a more modern version of the SimCLR paper) and explain why our choice is constrained to DINOv2... | Summary: Traditionally, it is believed that the effectiveness of Joint-Embedding Architectures (JEAs) such as SimCLR lies in their ability to map augmented views of the same image to the same representation in the latent space, thus requiring specific data augmentations that lead to superior downstream performance. Mo... | Rebuttal 1:
Rebuttal: We would like to thank you for recognizing the importance of scaling SSL on other domains. In the following we extended our experiments, including also other imaging domains, making our conclusions more solid.
**W1**) You indicated that the main motivation for this work, which is to allow using ... | Summary: This paper demonstrates the possibility of training JEA SSL encoders using limited augmentations (not 0 augmentations as in the title) compensated by significantly increasing the size of the training data. This is built on the claim that most SSL works have relied on augmentations for SOTA performance. The exp... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the importance of the role of scale in self supervised learning and for their questions that made us think more deeply about the semantics and experiments we used throughout our paper. In the following we will be addressing each of your comments.... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful questions.
To recapitulate our paper, we summarize our contributions:
- We can train a powerful and scaled self-supervised model with no domain-specific data-augmentations, in contrast to all alternative approaches. This model provides an existence ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models | Accept (poster) | Summary: The paper introduces RouterDC, a novel LLM query router that employs contrastive learning losses to train an encoder and LLM embeddings for routing queries efficiently. The motivation behind this approach is the variability in LLM performance across tasks and domains, and the computational efficiency of routin... | Rebuttal 1:
Rebuttal: We sincerely thank you for the detailed and positive comments.
We carefully address your concerns below.
Please let us know if you have any follow-up questions.
---
> Q1. parameter efficiency, scalability, training cost is likely significant when the number of LLMs scales up (each training quer... | Summary: The authors propose a routing between different LLMs, based on classification of embeddings from a fine-tuned transformer model (mDeBERTaV3-base), on a per-query basis. To sharpen the classification, they chose positive and negative samples among the training tasks, and to stabilise training they add a loss t... | Rebuttal 1:
Rebuttal: Thanks for your efforts and useful comments.
We take all comments seriously and hope that our reply can resolve your concerns.
Please let us know if you have any follow-up questions.
---
> Q1: the meat of the router task is to see whether it's possible to classify these different tasks via a sm... | Summary: This paper studies the problem of assembling off-the-shelf LLMs to harness their complementary strengths. The authors propose a novel query-based router by Dual Contrastive learning (RouterDC), i.e., a sample-LLM contrastive loss and a sample-sample contrastive loss. The former contrastive loss aims at trainin... | Rebuttal 1:
Rebuttal: We sincerely thank you for the detailed and positive comments.
We take all comments seriously and do our best to address every concern raised.
Please let us know if you have any follow-up questions.
> Q1. In Line 157, the authors claim that “The reason is that some similar queries can have dissim... | Summary: This paper aims to improve the ability of LLMs by assembling them. The proposed method use a contrastive learning strategy. It uses a sample-LLM contrastive loss which pull the query embedding closer to the top-performed LLM embedding. It also employs a sample-sample contrastive loss, which learns the distribu... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments.
We really appreciate your efforts to help us improve our paper.
We carefully addressed your concerns below and sincerely hope that our reply resolves your concerns.
Please let us know if you have any follow-up questions.
> Q1. the relation between the two cont... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We sincerely thank all the reviewers and ACs for your insightful and valuable comments.
We are delighted that reviewers find that:
- our work **addresses a significant challenge** in LLM utilization (`Reviewer 4ok1`).
- our method is **intuitive** (`Reviewer ytgM`), **nov... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Doubly Mild Generalization for Offline Reinforcement Learning | Accept (poster) | Summary: The paper proposes an algorithm to allow for some mild generalization to OOD actions in offline RL by proposing constraints on which actions to generalize on and by constraining bootstrapping signal to avoid overestimation. It includes results and ablations on common offline RL benchmarks.
Strengths: 1. The i... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: More random seeds and use of confidence interval in evaluation.**
Thanks for the kind suggestions and this nice reference. [1] provides a comprehensive resou... | Summary: The paper introduces a novel approach called Doubly Mild Generalization (DMG) to tackle the well-known issue of over-generalization in Offline Reinforcement Learning (RL). Offline RL often suffers from extrapolation errors and value overestimation due to the generalization of value functions or policies toward... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: Dependence on Continuity Assumptions.**
Thanks for the comment. Indeed, our work relies on continuity assumptions about the learned Q function and transition... | Summary: The offline RL community has recently shown a surge of interest in in-sample offline reinforcement learning algorithms. However, these algorithms can sometimes be too restrictive, unable to leverage the generalization capability of deep neural networks. As a remedy, the authors proposed an algorithm called Dou... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: The constants $C_1$ and $C_2$ of Theorem 1 are state-dependent.**
Indeed, they are state-dependent. It is worth noting that Theorem 1 provides insight and mo... | Summary: This paper studies the problem of extrapolation error and value overestimation in Reinforcement Learning (RL). The authors exploit generalization in offline RL by using mild action generalization and mild generalization propagation.
Strengths: 1. The problem studied is on interest : offline RL typically suffe... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: Assumption 11 looks very restrictive.**
We apologize for the lack of a detailed explanation of this assumption in the paper. To our knowledge, Assumption 11 ... | Rebuttal 1:
Rebuttal: ### **Global Response**
We thank all the reviewers for taking the time to read our manuscript carefully and for providing constructive and insightful feedback. We are encouraged by the positive comments of the reviewers, such as:
- Meaningful research problem, innovative approach, and nice insig... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Great Minds Think Alike: The Universal Convergence Trend of Input Salience | Accept (poster) | Summary: This paper studies the resemblance of the functions obtained by training neural networks across various network depth and width. They do so by looking at the gradient field of the learned functions $f_1, f_2$, and defining their similarity by taking the cosine similarity between the gradients, then taking inde... | Rebuttal 1:
Rebuttal: ## Response to Reviewer We4a
We thank the reviewer for acknowledging our contribution. Regarding the reviewer's concerns, we clarify that there is some communication in the empirical verifications.
First, we would like to answer the clarification questions:
**[Eq. 5 (W4)]**
We thank the review... | Summary: This paper studied the uncertainty introduced in stochastically optimized DNNs via the input saliency maps. By empirical evaluations, the authors discovered that 1) within the same model architecture, models with different capacities tend to align in terms of their population mean directions of the input salie... | Rebuttal 1:
Rebuttal: ## Response to Reviewer RiRS
We thank the reviewer very much for acknowledging our work! We now answer the reviewer's questions as follows to make sure that all remaining concerns are resolved.
**[Figure 1 Clarifications (W1)]**
We appreciate the reviewer for pointing out the potential ambiguit... | Summary: This paper studies the distribution of input saliency maps of trained neural networks at varying depth and width. The authors observe that as the model capacity increases, these distributions converge towards and become concentrated around a shared mean direction. The authors also state two hypotheses for the ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer fWRt
We appreciate the reviewer very much for the acknowledgment of our work. To further strengthen the reviewer's confidence in our findings, we address the reviewer's remaining questions as follows.
**[Fixed Initializations with Random (W1)]**
We thank the reviewer for... | Summary: This paper investigates the distribution of trained deep neural networks through the lens of input saliency maps. It empirically shows that as the capacity of either of two stochastically optimized models increases, they tend to resemble each other more. Therefore, the authors hypothesize that within the same ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer YpLK
We thank the reviewer for the invaluable feedback. We address the questions as follows.
**[Novelty (W1)]**
We summarize the work in [5,6] and how the novel contributions of our work on deep ensembles differ from them. It should also be noted that exploring the mechan... | Rebuttal 1:
Rebuttal: ## Global Response to all Reviewers
We appreciate the reviewers very much for the invaluable feedback and insightful suggestions! First, we address the questions shared by reviewers as follows. All images are shown in the attached PDF file, indexed by the prefix `R` (e.g. Fig. R1).
##Different S... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Social Welfare Functions | Accept (spotlight) | Summary: The authors study the learnability of social welfare functions given decisions data by a central decision-maker that is taking into account their constituents' welfare. They discuss PAC bounds according to a number of settings with a focus on weighted power mean functions. These settings include cardinal utili... | Rebuttal 1:
Rebuttal: > I'd be interested if you could discuss some implications or interpretations of your work. You obtain PAC bounds for the various settings and you summarize your results in Table 1. I am not immediately sure what the quality of these results are, how they compare to prior work, and how they would ... | Summary: This paper studies the learnability of social welfare functions -- which are functions over the utility of a group of voters and an outcome. Under varying information schemes, they address the question of how well it is possible to learn the social welfare function being used by a decision maker. The first set... | Rebuttal 1:
Rebuttal: At the risk of slightly abusing the rebuttal, we note that overall you seem to have a positive view of the paper. You write that the problem "seems like it may be interesting" and it "provides an interesting contribution in its own right," the methodology "seems quite reasonable," the results "app... | Summary: This work studies learning the social welfare function from a power mean function class.
- They first consider the cardinal social welfare setting, where the data distribution is over the utilities and social welfare values. They provide the upper bounds on the pseudo-dimensions of the function class and then... | Rebuttal 1:
Rebuttal: > The availability of labels in the real world: In the cardinal setting, the label of the data point is the true social welfare value. I am curious if there really exists any such labeled data set. I have the same question in the pairwise comparison setting.
Let us start with pairwise comparisons... | Summary: The paper is about learning social welfare function which belongs to a well-studied family of weighted power mean function, as a way to understand a policy maker's rationale. In particular, the paper focuses on two settings: 1) when the input is the vector of utilities/social welfare, and 2) when the input is ... | Rebuttal 1:
Rebuttal: > I find some of the results hard to interpret, for example, the bounds in Theorem 3.2. It would be great if the authors could add intuitions behind the result and better demonstrate the influence of each term.
We appreciate the reviewer's comments and will add further intuition about the role of... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uncovering the Redundancy in Graph Self-supervised Learning Models | Accept (poster) | Summary: This paper presents new insights on graph self-supervised learning models. Namely, the parameters, as well as the learned representations, of graph self-supervised learning models are highly redundant. The paper also proposes a novel pre-training and fine-tuning paradigm, SLIDE, which achieves better performan... | Rebuttal 1:
Rebuttal: We sincerely thank you for all the comments and it is a great honor for us for your enjoying our paper. We have addressed all your questions below and hope they have clarified all confusion you had about our work.
> [W\#1] I in particular wonder why “Although SLIDE significantly reduces the numbe... | Summary: This paper studies the redundancy in graph self-supervised learning models. The authors discover that even randomly removing a number of parameters, the performance of graph self-supervised learning models is still comparable, revealing the redundancy problem. Then the authors propose to simultaneously fine-tu... | Rebuttal 1:
Rebuttal: We are deeply grateful for your insightful feedback and constructive suggestions. Your thorough review has significantly strengthened the quality of our manuscript.
>[W\#1] About RFF
**Response:** Thanks for your good question about Random Fourier Features (RFF).
Deep neural networks exhibit co... | Summary: This paper is the first to uncover that graph self-supervised models exhibit high model redundancy at both neuron and layer levels, providing two key perspectives for graph pre-training and fine-tuning framework.
This paper proposes SLIDE to achieve a pre-training and fine-tuning paradigm with fewer parameters... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude for the thoughtful review and constructive feedback. Your thorough evaluation has undoubtedly contributed to enhancing the robustness of our findings.
>[W\#1] The task evaluated in this paper is only node classification. I’m curious what about other tasks? It migh... | Summary: This paper studies the redundancy issue of GNNs that are pre-trained in the self-supervised manners. Examples of these methods include GraphMAE and GRACE. The first part of this paper shows that the numbers of parameters of these models can be reduced by half, while their performance wouldn’t change much (only... | Rebuttal 1:
Rebuttal: We sincerely thank you for valuable opinions and concerns about our work. It is our obligation to describe more details and give more explanations. We really hope that these further efforts can alleviate your confusion.
>[W\#1] About the issue of redundancy
**Response:** To demonstrate that redu... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing | Accept (poster) | Summary: This paper proposes a ViSu framework to enhance text recognition by viewing and summarizing. For the viewing process, the authors generate various text images to lead the model to focus on different text styles. For the summarizing process, they analyze the drawback in existing character consistency loss and p... | Rebuttal 1:
Rebuttal: Many thanks for the feedback. In the following we address the weaknesses and questions pointed out by the reviewer.
> Q1: To further verify the effectiveness of OGS. Authors should compare the performance of replacing OGS with normal synthetic data.
A1: Replacing OGS with normal synthetic data m... | Summary: This paper focuses on the character morphologies and proposes to boost the scene text recognizer through viewing and summarizing paradigm. In the viewing process, the Mean Teacher framework is used to train with unlabeled data. In the summarizing process, the proposed method theoretically proves the mistakes i... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive review. We hope these responses will address your concerns appropriately.
> Q1: There is a lack of clear description in the paper on how CRNN-ViSu and TRBA-ViSu in Table 1 are set up and trained.
A1: For a fair comparison, CRNN-ViSu and TRBA-ViSu use... | Summary: Existing scene text recognition (STR) methods struggle to recognize challenging texts, which originates from the insufficient exploration of character morphologies. To address the issues, the paper proposes to facilitate the contrastive learning-based STR framework in a self-motivated manner by leveraging synt... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and address each concern below.
> Q1: The overall framework follows a mean teacher framework, which is not new for semi-supervised scene text recognition.
A1: While certain methods, such as Zheng et al.[54], also employ the mean teacher framewor... | Summary: This paper addresses the problem of insufficient exploration of character morphology in scene text recognition and proposes a new framework comprising an Online Generation Strategy (OGS) and Character Unidirectional Alignment (CUA) Loss to enable the model to learn from unlabeled real data. OGS mitigates the i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback provided. Below, we address the identified weaknesses and questions.
> Q1: The writing quality needs improvement.
A1: We appreciate your observation regarding the writing quality. We will refine and enhance the clarity of the writing.
> Q2: Th... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
COLD: Causal reasOning in cLosed Daily activities | Accept (poster) | Summary: This paper proposes the COLD (Causal reasOning in cLosed Daily activities) framework, aiming to bridge the gap between open-ended causal reasoning and symbolic representation-based question answering.
The framework leverages human understanding of daily real-world activities to reason about the causal nature... | Rebuttal 1:
Rebuttal: We thank you for your detailed and insightful review, pointing towards some suitable directions to make our work more impactful. We would like to address the raised concerns below:
* We want to mention that the human/expert annotations were done to construct the underlying causal graph, capturing... | Summary: This paper proposes causal reasoning in closed daily activities, which combines the causal reasoning works of open-ended causal reasoning via causal commonsense reasoning and symbolic representation-based question answering for theoretically backed-up analysis. By creating a dataset containing about 8 million ... | Rebuttal 1:
Rebuttal: Thank you for providing your insightful comments. Please find the response to the clarifications below.
* Given the wider scope of this work, we agree that the paper might have become too dense, affecting the presentation quality. We would love to hear some more feedback from you to incorporate in... | Summary: The paper proposes a dataset for evaluating the causal reasoning capabilities of LLMs by grounding evaluation in human understanding of real-world daily activities. The authors address the gap between open-ended causal commonsense reasoning and symbolic question answering by introducing the COLD framework, whi... | Rebuttal 1:
Rebuttal: Thank you for pointing out some of the important directions.
* The scope of this paper was limited to commonsense knowledge and we agree that the data used is simplistic in nature (easily to reason about by humans), and does not deal with events beyond commonsense knowledge. We also highlight th... | Summary: This paper presents a new causal reasoning dataset for LLMs. The main motivation of the dataset is to bridge the gap between casual commonsense reasoning datasets and symbolic representation-based causal reasoning datasets. Specifically, the proposed dataset collects crowd-sourcing observations, and constructs... | Rebuttal 1:
Rebuttal: We thank you for your detailed and insightful response. Please find the response to the mentioned weakness/clarifications below:
* **Lack of Human Performance:** We would like to mention that validating human performance is a little challenging due to the nature of the causal reasoning task. The... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed reviews and suggestions. We are pleased that the reviewers found our work **novel, helping bridge the gap between open-ended causal reasoning and symbolic representation-based question answering (Reviewer iBzV, Reviewer LejL)**. We are also happy that ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers | Accept (poster) | Summary: This paper studies the convergence rate of a variant of the boosting algorithm AdaBoost.MH, which is named factorized AdaBoost.MH. Factorized AdaBoost.MH replaces the one-against-all base classifier by the factorized base classifier which consists of a binary and a vote vector. This paper solves an open proble... | Rebuttal 1:
Rebuttal: Thank you for your recognition. We are glad you like our paper.
**For the weakness:** Thank you for your reminder. We use the presentations that are similar to that in [1] in order to make them consistent and make it convenient for the readers that have read [1] before to understand our paper. We... | Summary: The paper addresses an open question posed by Kegl, 2014 regarding the convergence properties of AdaBoost.MH, a boosting algorithm designed for multi-class classification problems. Specifically, Kegl, 2014 noted that it is challenging to prove the convergence of this algorithm due to the weighted sum of binary... | Rebuttal 1:
Rebuttal: Thank you for your suggestions. We now provide explanations about the issues in weaknesses and questions.
### **For weaknesses.**
**For the significance of our work.** Firstly, we claim that the main contribution of our work is solving an open problem in [1] and providing a convergence rate for t... | Summary: The paper solves a COLT 2014 open problem. The investigated problem is about presenting a convergence rate of the factorized AdaBoost.MH algorithm, which is based on AdaBoost.MH and aims to boost weak classifiers to a strong classifier in the multiclass setting. The paper shows two lower bounds (one depends on... | Rebuttal 1:
Rebuttal: Thank you for your recognition. We are glad you like our paper.
**For the first weakness:** As you say, complicated proofs are not neccessarily better. We believe that it is great to solve an open problem through a easy method.
**For the second weakness:** Thank you for your reminder. The main i... | Summary: This paper resolves an open problem pointed out by [7]. The open problem involves providing a lower bound, which is independent of $n$, for the coefficient $w'_{\Sigma}$ of the weighted multi-class exponential margin-based error in the factorized version of ADABOOST.MH. This result demonstrates that if the $\d... | Rebuttal 1:
Rebuttal: Thank you for your suggestions. We now provide explanations about the issues in weaknesses and questions.
### **For the weakness: significance of the work.**
The main contribution of our work is solving an open problem in [1] and providing a convergence rate for the factorized AdaBoost.MH algori... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Balázs presented a factorized version of ADABOOST.MH and empirically show that this extension achieves promising results. Balázs further raises the convergence property of factorized ADABOOST.MH as an open problem. This submission addresses this open problem and presents an elegant proof.
Strengths: This subm... | Rebuttal 1:
Rebuttal: Thank you for your recognition. We are glad you like our paper. Next, we answer your questions.
1. **Why there is the absolute value in the equations between lines 217 -218?** Answer: Because we are handling the $\ell_ 1$-norm of a vector, and the $\ell_ 1$-norm of a vector is the sum of the abso... | null | null | null | null | null | null |
Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View | Accept (poster) | Summary: The authors applied ECOCs to DNNs to bolster the model’s resilience to weight errors. Most significantly, they have derived a perturbation bound through the utilization of the neural tangent kernel. According to their mathematical analysis, some principles of designing the ECOCs were revealed, leading to the c... | Rebuttal 1:
Rebuttal: We are very thankful for your effort to provide constructive comments and support this work. Our responses to your comments are summarized below.
### **Response to questions**
**Q1). Is there a parallel line of research analyzing the model’s robustness with one-hot/softmax outputs through the len... | Summary: The paper deals with the use of error-correcting output codes (ECOCs) for multi-class classification problem. The standard solution is to use one-hot code. Existing literature shows that there are codes which are better than one-hot code. At the same time there is a lack of theory and explanations of this phen... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your feedback and provide our responses below:
**A1).** We will rephrase in the revised manuscript.
**A2).** Recall that $f(\cdot; \theta)$ is the NN with weights $\theta$. Let $\mathcal{A} (\mathcal{E}, f, \mathcal{D};u)$ be a tra... | Summary: This paper studies theoretical foundations of Error correcting output code for multi-class classification by means of coding theory and NTK. NTK is employed to alter the decoding metric from l2 to Mahalanobis norm and based on that a few code construction methods are proposed.
Strengths: The NTK sees ... | Rebuttal 1:
Rebuttal: Thanks for taking your time to review and support our work. We appreciate your constructive feedback and provide our responses below:
### **Response to weakness 1**
**Q1). All parameters need to be defined in the main text. In (6), $\mathcal{E}([C])$ and in (8) $\bar{\sigma}$
are not defined. -... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game | Accept (poster) | Summary: The Stackelberg Prediction Game (SPG) models with the least squares loss variant (SPG-LS) gaining attention. The spherically constrained least squares (SCLS) method is the latest state-of-the-art method for solving the SPG-LS problem. The authors address the lack of theoretical error analysis for the SCLS meth... | Rebuttal 1:
Rebuttal: ## Answer to Reviewer XvKF
Dear Reviewer XvKF,
Thank you for your job in reviewing our paper. We are very sorry for the inconvenience caused by our presentations. To this end, following your comments, we correct our work in the revision. All the references appear in our main paper.
In regards t... | Summary: The paper investigates the estimation error of the learner obtained through the SCLS method proposed in [3] in comparison to the actual learner. It redefines the estimation error of the SCLS method as a Primary Optimization (PO) problem and applies the Convex Gaussian min-max theorem (CGMT) to convert the PO p... | Rebuttal 1:
Rebuttal: Dear Reviewer PFXk, Thank you for the detailed and thorough review. All the references can be seen in Author Rebuttal by Authors.
### For Weaknesses:
__W1:__ The SCLS method [3] is the latest advanced technique for solving the widely applicable SPG-LS problem, winning the ICML 2022 Outstanding P... | Summary: The spherically constrained least squares reformulation method proposed by Jiali et al. has shown superior performance in addressing the issues of the Stackelberg prediction game. This paper aims to analyze the error between the estimators and the ground truth. The main theory shows that the estimation error a... | Rebuttal 1:
Rebuttal: ## Answer to Reviewer btrD
Dear Reviewer btrD,
Thank you for your job in reviewing our paper. We are very sorry for the inconvenience caused by our presentations. We extend our heartfelt gratitude for your patience and meticulous guidance. Your insightful comments is valuable for us and we appr... | Summary: The spherically constrained least squares reformulation (SCLS) method proposed in paper [3] is the state-of-the-art method for solving the Stackelberg prediction game with least squares loss (SPG-LS), and the paper [3] has won the ICML 2022 Outstanding Paper Award. This paper further enhances the theoretical f... | Rebuttal 1:
Rebuttal: ## Answer to Reviewer rMtE
Dear Reviewer rMtE,
Thank you for your job in reviewing our paper. We are very sorry for the inconvenience caused by our presentations. To this end, following your comments, we correct our work in the revision.
In regards to the weaknesses:
__Weakness 1.__ This paper... | Rebuttal 1:
Rebuttal: ## The references mentioned in the Rebuttal to Reviewer PFXk.
__For Weakness 1.__
[B1] Estimating the Error of Randomized Newton Methods: A Bootstrap Approach. ICML 2020
[B2] $\ell _{1, p}$-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods. ICML 2015
[B3] ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CALVIN: Improved Contextual Video Captioning via Instruction Tuning | Accept (poster) | Summary: The paper focuses on captioning movie scenes, which unlike typical captioning tasks, videos from movies can be captioned in a way that tells the story of the movie. For example, captions from movie scenes should convey how the characters felt, and not just the detail in the image. To be able to caption in thi... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback, GsDL. We're happy to answer your questions regarding our paper's key contributions, model weight release, and how to adapt the model to new movies.
**[Main Contribution of the paper]** While the model weights are important (and we are working to release them)... | Summary: This paper introduces a specialized video LLM named CALVIN, which leverages previous movie context to generate contextual scene descriptions. The authors achieve this by training their model on both image QA tasks and video captioning tasks within a unified framework. Experiments demonstrate that with mixed tr... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback, Y2oh, we're happy to answer your questions regarding a better description of the architectural differences between CALVIN and VideoLLaMA, and regarding further dataset ablations.
**[Clarification of Differences to VideoLLaMA and other recent video architectur... | Summary: This paper addresses the task of contextual video captioning, with a particular application emphasis on settings for film and tv, where audio descriptions can be useful for making these mediums more broadly accessible. Standard vision-language models are often verbose (problematic since audio descriptions must... | Rebuttal 1:
Rebuttal: Dear Reviewer suoE, thank you for your detailed review. We're glad that you found our contextual captioning model insightful. We do think that our thorough analysis of all parts of the video LLM pipeline, from improved data cleaning and handling, to clearly ablated training recipes and architectur... | Summary: Note: Raised score by 1 point after reading reviews, responses and the concerns addressed in the rebuttal phase.
----
This work introduces a video LLM model that can describe movie scenes in context incorporating names of characters and generate short contextual descriptions. They train a model on data from i... | Rebuttal 1:
Rebuttal: Dear Reviewer jqA2, thank you for your positive review of our work, and your interest in our modeling strategy. There were a few questions raised regarding dataset choices and evaluation of API models that we're happy to answer below:
**[Evaluation of the CMD dataset]**
CMD is a classic video dat... | Rebuttal 1:
Rebuttal: We would like to thank all four reviewers for their highly constructive feedback! We very much appreciate their assessment that our paper has “**thorough ablations**”[jqA2,suoE,Y2oh,GsDL], “**good performance**”[jqA2,suoE,Y2oh,GsDL], “well motivated”[jqA2], “important…novel problem”[suoE, GsDL], “... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy | Accept (poster) | Summary: The paper introduces an LLM agent that can solve the game of Press Diplomacy only by self-play, without fine-tuning or regularizing on human data. The method simplifies the previous architecture from the PIKL/CICERO papers by FAIR (https://openreview.net/forum?id=F61FwJTZhb) by avoiding using reinforcement lea... | Rebuttal 1:
Rebuttal: > Well, the paper benefits from the improvement of LLMs over time, as compared with GPT2 (used by CICERO). The alignment to human data is not needed as now the LLMs have the alignment incorporated by the vendor. Same for the planning step, the LLMs intrinsically got better at planning so now we ca... | Summary: This paper focuses on diplomatic activities using LLM-based societal agents without relying on human data. It introduces a new paradigm for AI diplomacy agents that can improve through self-play and experience collection. The new agent, Richelieu, achieves state-of-the-art performance compared to current metho... | Rebuttal 1:
Rebuttal: > There are some typos (line 61, line 69), and certain citations (line 80, line 237, line 276) are not properly functioning.
**A:** We will fix them in the revision.
> Which base model is utilized in the experiment for Figure 4 and Table 1?
**A:** We use GPT 4 as the base model for our agent i... | Summary: The paper presents “Richelieu,” a self-evolving large language model (LLM)-based agent designed for the game of Diplomacy. Richelieu integrates strategic planning, social reasoning, and memory reflection to handle complex multi-agent environments without relying on domain-specific human data. The model self-ev... | Rebuttal 1:
Rebuttal: > Lack of in-depth analysis of the middle process. If the LLM can accurately model relationship and inferring intention. The author could provide more analysis or examples of these modules to prove the effectiveness of LLM on this task.
**A**: The LLM can accurately model relationships and inferr... | Summary: This paper propose a new framework for LLM-based agents to play diplomacy games and improve themselves. The proposed framework, Richelieu, have several components and many abilities. The authors perform good experiments and ablation study to show how the framework.
Strengths: - The framework have several core... | Rebuttal 1:
Rebuttal: > What does "evolve" mean in the article? Does it only refer to the storage of memory modules? If so, it seems that the model itself has not been updated.
**A**: "evolve" means the AI agent's capability and strategy are autonomously enhanced over time without direct human supervision. Beyond memo... | Rebuttal 1:
Rebuttal: Our rebuttal includes a one-page PDF and the following four rebuttals for each Official Review. The PDF contains an example. We show two cases with similar states: the first shows decisions and negotiations made without self-play, and the second shows those made after self-play. This example shows... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Slot-VLM: Object-Event Slots for Video-Language Modeling | Accept (poster) | Summary: This paper proposes to use object-centric slot representations as visual representations in vision language models. Specifically, it adopts the popular video LLM paradigm and concatenates a pre-trained visual encoder with a pre-trained LLM. It uses a slot attention method to group visual features into object s... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback regarding the idea, sufficiency of experiments, and the surprisingly good performance. We greatly appreciate your insights and suggestions and will thoroughly incorporate them into our revised manuscript. Below, we provide detailed responses.
**Q1**:... | Summary: This paper aims to learn a higher level of abstract representation as the input tokens to LLM. The paper proposes a dual-branch (object- and event-centric) to extract the concepts and uses the three-stage training paradigm. The proposed Slot-VLM is evaluated on three VQA datasets and has shown state-of-the-art... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and helpful suggestions. We greatly appreciate your recognition of the interestingness and cruciality of the idea, strong performance, meaningful and comprehensive ablation study, and comprehensive reproducibility details. We have carefully considered... | Summary: This paper introduces a new framework called Slot-VLM that aims to aggregate spatial and temporal relationships between frames for more effective video understanding with Large Language Models (LLM). The Slot-VLM approach aims to generate video tokens that are semantically disentangled for better alignment wit... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive suggestions, and positive feedback on the novelty and interestingness of our proposed concept, the ability to generate interpretable attention maps, the comprehensive comparisons, and the clarity of presentation. The detailed responses are provided below.
... | Summary: This paper proposed Solt-VLM, which aims to generate a small set of semantically decoupled video tokens to comply with LLM for effective video reasoning. It design a dual-branch Object-Event Slots module to learn object-centric slots and event-centric slots to jointly capture the spatial object details and tem... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our insights into spatial-temporal modeling in MLLM, the main motivation, and the effectiveness of method, as well as the quality of our writing. We have carefully considered your valuable comments and suggestions and are committed to incorporating... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
Thank you very much for your great efforts and insightful feedback on our paper. **We are grateful for your positive feedback on our motivation/insights (Reviewer jNDE, KJVB), idea novelty and interestingness (Reviewer gzPw, wQQx, KJVB), comprehensive experiments an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks | Accept (poster) | Summary: This paper identifies and attempts to solve the problem of unstable activation boundaries in ReLU neural networks. The authors define a relu unit in terms of its activation boundary which is the set of inputs that cause the preactivation to be zero. The point on this hyperplane closest to the origin (termed a ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments on our paper! We will address your comments point by point below.
> It would be useful to demonstrate that other normalization solutions suffer from similar problems (like Layernorm etc), perhaps in the Appendix.
Thank you for your suggestion. In the camera... | Summary: This paper introduces a novel approach for analyzing the training dynamics of ReLU networks by examining the characteristic activation boundaries of individual ReLU neurons. The authors figure out the instability in common neural network parameterization and normalization during stochastic optimization. To add... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper! We will address your comments point by point below.
> Discuss GmP on other activation functions.
Our characteristic activation analysis is a very general idea that can be applied to a broader family of activation functions beyond ReLU (e.g., Lea... | Summary: Summary.
Authors consider training neural nets with Adam after a change of coordinate to spherical coordinate system. They show that in spherical coordinate system the direction of the half spaces which specifies the activeness of the Relus in stable with respect to small changes in the angles, hence the opti... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We believe that your major concern regarding Theorem 3.7 is due to a misunderstanding of our notation. Below, we first address your major concern and then respond to your other comments point by point.
> Clarification of the argument of Theorem 3.7
We be... | Summary: In standard neural networks each neuron, and activation function g performs the following operation on the input $x\in\mathbb{R}^{n}$:
$$z=g\left(w^{t}x+b\right)$$
When usually g in the ReLU activation.
In this work the authors identify a critical instability in many common NN settings, which theoretically d... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our work! We are encouraged by your positive comments on the originality, quality, clarity, and significance of our paper. Below, we address your concerns point by point.
> Explain the notations $\mathcal{B}$ and $\boldsymbol{\phi}$ in different contexts.
... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable and insightful comments on our paper and for helping us improve this work. We appreciate that three reviewers support the acceptance of our paper and highlight the novelty, quality, clarity and significance of our proposed characteristic activation analysi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation | Accept (poster) | Summary: This paper proposes a novel architecture for denoising diffusion probabilistic models (DDPMs) that enables simultaneous unsupervised image generation and segmentation. The key idea is to constrain the denoising network with a structured bottleneck that factorizes the image into regions, which are then denoised... | Rebuttal 1:
Rebuttal: # To Reviewer FyWe
**Q: Limited theoretical analysis.**
A: Our system could potentially learn to assign the entire image to a single mask, leaving all other masks empty. In doing so, it would essentially fall back to being equivalent to a standard DDPM UNet architecture. All synthesis would occu... | Summary: The authors propose a model for simultaneous unsupervised semantic segmentation and image generation based on denoising diffusion probabilistic models. The methodology alters the architecture of a typical DDPM by conditioning the decoder on the outputs of a mask generator.
Strengths: In general, the paper is ... | Rebuttal 1:
Rebuttal: # To Reviewer vzUs
**Q: The abstract lacks context/motivation. The conclusions and experiments lack discussions of the limitations of the proposed approach.**
A: In the current abstract, we provide a clear introduction to the challenges and motivations driving our research. The abstract begins b... | Summary: The paper introduces a novel neural network architecture capable of simultaneous image generation and segmentation in an unsupervised manner, eliminating the need for pre-labeled data. The core concept involves training the network to dissect an image, clean individual sections, and reassemble them. Notably, t... | Rebuttal 1:
Rebuttal: # To Reviewer FkB4
**Q: Limited discussion of related work; How does the proposed method differ from the mentioned related works? What are the key advantages it offers?**
A: Thanks for the suggestion. We will incorporate this discussion into our related work section to provide a clear picture of ... | Summary: The paper proposed a structural modification of a DDPM which causes it to learn a decomposition of images into regions. This factorization enables unsupervised segmentation and simultaneously improves the quality of the generated images. The method is evaluated on various datasets, demonstrating its effectiven... | Rebuttal 1:
Rebuttal: # To Reviewer 9WST
**Q: Experiments only cover 2-3 class scenarios.**
A: Our work is at the stage of a new architectural design for diffusion-based segmentation and generation, with 2 or 3 class segmentation results demonstrating improvements across multiple datasets, scaling up to ImageNet.
Tr... | Rebuttal 1:
Rebuttal: # General Reply
We thank the reviewers and answer specific questions in individual responses to each review below. We provide some general clarifications here, as well as specific clarifications below.
Our goal is both image segmentation and image generation, learned simultaneously and in an ent... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Harnessing Multiple Correlated Networks for Exact Community Recovery | Accept (poster) | Summary: The paper studies the problem of exact community detection in correlated stochastic block models (with two symmetric communities). More precisely, a graph is sampled from an SBM with parameters p and q and each edge in the graph is then kept with probability q. This downsampling is performed K times independen... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
We fully agree with the reviewer that the main open problem that our work leaves open is to develop efficient algorithms for the setting studied in our paper (whenever this is possible). The quest for efficient algorithms has been a major driving force beh... | Summary: Given K correlated SBMs, the authors derive information-theoretic conditions for (i) the exact recovery of the community structure and (ii) the perfect recovery of the planted alignment $\pi^*$.
Strengths: The paper is well-written, and I enjoyed reading it. It generalizes [37] and [18] to multiple (K \ge 3) ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
We agree with the reviewer that it is natural to consider more general settings, such as more than two communities, as well as unbalanced communities. We suspect that our methods can be extended to these settings as well. However, in the present paper we d... | Summary: The paper studies the problem of exact community recovery from multiple ($K$) correlated graphs in 2-community balanced symmetric SBM. Prior work of [Gaudio, Ra\'cz, and Sridhar 2022] for $K=2$ case. The paper generalizes their result for any $K$ (constant) number of graphs. In particular, the main result of t... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
Weak recovery in the constant average degree regime using correlated graphs is indeed a fascinating direction for future work (as already suggested in [18]). We suspect that the $k$-core analysis still gives something nontrivial in the large but constant a... | Summary: Theoretical work showing conditions for exact community recovery in $K$ correlated $2$-community SBMs where node labels are not maintained between networks. The work extends previous work for $K=2$ which introduces new challenges and proof mechanisms to allow for $K \ge 3$ networks. Theorems 1 and 2 provide ne... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
Q1 (different subsampling probabilities): This is a very natural question, and our developed theory can fully handle this. We simply chose to keep this part of the model simple, since there are already several parameters involved in the model.
Let us ill... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for the careful reviews and many helpful comments!
We respond to each review separately in its own rebuttal. In this "global" response, we take the opportunity to discuss something that was brought up by multiple reviewers: the threshold for exact graph matching given $K$... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data | Accept (poster) | Summary: Motivated by the need to censor dangerous knowledge in an LLM training corpus, the paper proposes to study LLMs' ability to infer explicit information when finetuned solely on implicit evidence, which is named inductive out-of-context reasoning. To this end, the paper introduces 5 tasks that simulate this scen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and feedback. Here, we respond to the questions brought up by the reviewer:
**W1:** *Opaqueness of OpenAI API fine-tuning.*
**Response:**
We agree that the focus on OpenAI’s fine-tuning API is a limitation when it comes to transparency of methods... | Summary: This paper study inductive out-of-context reasoning, which is one kind of generalization where LLMs may infer latent information by aggregating training data and apply the latent conclusions to the downstream tasks without the ICL.
Strengths: 1. This study focuses on a possible risk inside the LLMs that LLMs ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Here, we respond to the weaknesses and questions brought up by the reviewer:
**W1:** *paper is hard to understand without looking at appendix*
**Response:**
Thank you for the feedback. We opted for task diversity rather than going into detail for one tas... | Summary: Motivated by safety concerns, this paper studies if an LLM can infer a concept or fact without being trained on data that explicitly contains this fact and without using in-context learning. The paper denotes this capability as OOCR (inductive out-of-context reasoning), and constructs 5 different tasks to eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Here, we respond to the weaknesses and questions brought up by the reviewer:
**W1:** *LLMs doing regression has been studied in other works*
**Response:**
We want to clarify that we are not testing whether models can perform regression. While we train m... | Summary: This paper focuses on answering the question "Could an LLM infer the knowledge by piecing together these hints, i.e., connect the dots".
To evaluate the capability of inductive out-of-context reasoning (OOCR), it proposed five suits of experiments in Locations, Coins, Functions, Mixture of Functions, and Parit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive feedback. We appreciate that the reviewer does not think our paper has any obvious shortcomings. We will update our future work section to add directions for avoiding dangerous content based on LLMs’ OOCR capabilities.
Our responses to the... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback from all reviewers and the time and effort they have spent to help improve our paper. We are grateful that reviewers found our paper to be "clearly motivated" (ujQc), with "clear examples" (ujQc), "well-written" (oG6f, wmX9), addressing a "fundamen... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work studies whether language model can infer the verbalize the latent information in its training / finetuning dataset, a task named inductive out-of-context reasoning (OOCR). The authors motivate the study of this task from a safety perspective: even certain harmful content is removed from the training ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Here is our response to the weaknesses pointed out by the reviewer.
**W1:** *Lack of realistic tasks*
**Response:**
The idea behind our methodology was to design diverse tasks that allow for studying OOCR capabilities of relevant LLMs like GPT-4 in a con... | null | null | null | null | null | null |
Learning Place Cell Representations and Context-Dependent Remapping | Accept (poster) | Summary: This paper shows that tuning profiles similar to those of hippocampal place cells can emerge in neural networks trained to minimize a relatively minimalistic spatial encoding objective. In both a feedforward and recurrent neural network, the authors show that the trained network's units are tuned to one or a f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful comments.
Thank you for your perspective on the title. As for the term conjunctive, we did initially focus on spatially bounded contexts, in addition to global ones. However, we ended up limiting the scope of this paper slightly, and we will... | Summary: This paper proposes a new model of hippocampus neurons by devising a new objective function that enforces particular distances between latent codes associated with different position inputs. A Gaussian distance weighting function is proposed. Experiments show that the resulting model produces hippocampus-like ... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their insightful feedback. Below, we address some of the concerns and questions asked.
We understand your concern about the biological plausibility of using Cartesian coordinates. It is true that in the FF case we use explicit position information. However, we ... | Summary: This paper studies the emergence of “conjunctive representations” in the context of place cell representations. The authors investigate how their proposed similarity-based objective function produces context-dependent place cell representations that exhibit remapping behaviors across contexts. The proposed obj... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their well-structured and insightful feedback. In the following, we want to address some of the concerns and answer the questions.
Mentioned weaknesses
1. We agree that especially the use of explicit cartesian coordinates is biologically implausible. We just wa... | Summary: The paper explores neural network models that mimic hippocampal place cells by learning spatial and contextual representations through a similarity-based objective function. It demonstrates how both feedforward and recurrent neural networks can encode and remap these representations in response to context chan... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their well-structured and fair feedback. In the following, we want to address some of the concerns and answer the questions that arose.
Regarding the listed weaknesses we have a few comments that we hope might clarify a few things and answer some of the questio... | Rebuttal 1:
Rebuttal: We would like to sincerely thank for their positive and knowledgable feedback. We attached a PDF including supplementary figures to address some of your major concerns. Those figures are referenced in our individual rebuttals. Notably, we have included ratemaps of an RNN model that has been traine... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: How do place cell responses to location and context emerge? The authors propose that place cell response emerge from a similarity-based object function that encourages places that are closer in space to be encoded as more similar representations. Specifically, the authors find that their model learns place-lik... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their well-structured and insightful feedback. In the following, we want to address some of the concerns and answer the questions.
We absolutely understand the concern about the access to euclidean position in the feedforward model. Here, the RNN model offers a... | null | null | null | null | null | null |
Group and Shuffle: Efficient Structured Orthogonal Parametrization | Accept (poster) | Summary: In this paper, the authors proposed a new structured matrices, namely GS-matrices. It unifies and generalizes structured classes from previous works. Compared to the previous BOFT, it further improves the parameters and training efficiency. Experiments on language processing, text-image generation, and image c... | Rebuttal 1:
Rebuttal: **Training times and double GSOFT.** Thank you for raising these questions. We have fixed these issues in the top-level comment.
**CLIP scores.** Thank you for pointing out this observation. Typically, the more parameters are trained the more easily the model overfits. This results in higher ima... | Summary: This paper follows the orthogonal fine-tuning paradigm that uses orthogonal matrices for adapting the weights of a pretrained model. It introduces a class of structure matrices, defined as GS-matrices, that can well be used to conduct orthogonal matrix. Based on GS-matrices, It proposes structured orthogonal p... | Rebuttal 1:
Rebuttal: **W1.** Thank you for your comments. We added compute times and comparison to the standard GSOFT for subject-driven generation (see the top-level comment). In the NLP tasks, we are also faster or on par with the BOFT approach. Compared to LoRa we are several times slower, but observe a slight qual... | Summary: This paper introduces a new way of constructing orthogonal parametrization for efficient model fine-tuning. The development of the proposed method is motivated by: naive orthogonal fine-tuning, which can be too restrictive due to how it constructs the block-diagonal orthogonal matrix; and improved methods with... | Rebuttal 1:
Rebuttal: **Small experiment scale.** We do understand your concerns regarding the scale of experiments.
For diffusion experiments, the few-shot setting is one of the most challenging tasks for fine-tuning. On the one hand, the model should carefully capture the target concept, but on the other hand, it sh... | Summary: This paper proposes a new parameterization for orthogonal matrices and applies this parameterization to orthogonal finetuning of foundation models. The new orthogonal parameterization is interesting and useful. The experimental results show some improvement over the baselines.
Strengths: - The study of parame... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. First of all, we add a source code for our experiments (see the top-level comment). Regarding the Monarch matrices, the key drawback in the Monarch representation for us is that if one block-diagonal factor has many small blocks, then the other one has ... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our article.
We appreciate your feedback and the constructive remarks you have provided.
Before giving detailed answers to the raised questions, we briefly reiterate the main contribution of our paper.
We propose a new class of matrices, denoted... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning with Fitzpatrick Losses | Accept (poster) | Summary: The paper proposes the Fitzpatrick loss, a tighter version of the Fenchel-Young loss. Fitzpatrick losses are defined based on the monotone operator theory. In particular, the Fitzpatrick function satisfies a tighter inequality than the Fenchel-Young inequality, leading to the tighter loss function. The authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive and constructive feedback.
> A special form of target-dependent regularizers is used for constructing the cost-sensitive Fenchel-Young loss (Blondel et al. 2020, Section 3.4). Does the Fitzpatrick loss have any relation with the cost-sensitive Fenchel-Y... | Summary: This article presents a refreshing take on losses used in ML, with solid motivational examples and a several of interesting and beautiful results in pure convex analysis.
Strengths: The Fitzpatrick function is a well-known tool for achieving very pure-theoretical results like Minty's theorem in convex analysi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive and detailed constructive feedback.
> Citations appear to be out of numerical order when they are cited
This is because the references are sorted alphabetically by author name.
> Line 30: In what sense is "proper" meant? All losses I'm aware of are pr... | Summary: This paper proposes a class of loss functions called Fitzpatrick loss, based on the Fitzpatrick function known in maximal monotone operator theory. It can be shown that building a loss function with the proposed method, can give a convex loss function that lower bounds the Fenchel-Young loss under the same cho... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and for the feedback.
> Although it is nice to also provide the experiments in the paper, I found that the experiment failed to motivate the usefulness of Fitzpatrick loss family in the sense that the performance is not preferable over... | Summary: The paper describes an alternative approach to constructing loss functions. The authors propose a Fitzpatrick loss and compare it to Fenchel-Young loss and Bregman divergence. The work enumerates the basic properties of the introduced loss and compares it to existing loss functions by numerical simulations.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and for the feedback.
> I think the paper needs more thorough numerical evaluation due to the format of the conference.
Our paper is first and foremost a theoretical contribution:
- it introduces a new family of losses that lower-bo... | Rebuttal 1:
Rebuttal: We thank the reviewers for the very positive and constructive feedback, as well as the ACs for their editorial work. We summarize below our main replies to the reviewers (see reply to each reviewer for more details).
* All comments have been addressed (minor typos, some assumptions that were in t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a family of losses based on the notion of a Fitzpatrick function, which takes the role of composable subdifferentials in Fenchel-Young losses. The resulting family of losses is parallel to Fenchel-Young losses in the sense that each Fenchel-Young loss has a shared link function with a certai... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and for the feedback.
> The empirical utility of the developed losses in not entirely clear, since they do not offer a significant advantage over standard losses, and in some cases (e.g. Fitzpatrick sigmoid) may be more computationally... | null | null | null | null | null | null |
Analytically deriving Partial Information Decomposition for affine systems of stable and convolution-closed distributions | Accept (poster) | Summary: This paper studies analytical computing of Partial Information Decomposition (PID) when the underlying distribution is stable distribution and convolution closed distribution. In particular, the paper considers BROJA-PID framework consisting of minimization problem of conditional mutual information over a set ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
While we agree that the assumption of linearity can be strong, this assumption has been made in previous works, e.g., Venkatesh et al. NeurIPS’23 uses a Gaussian assumption with linear dependence on M to calculate the PID of experimental neur... | Summary: The paper focuses on analytically expression of the BROJA-PID [12, 33]. By considering the stable distribution family, the authors are able to generalize the previous Gaussian PID result [17] (one of the uniqueness always vanishes for BROJA-PID) to a wider class of distributions. An upper bound for convolution... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive endorsement of our work. We aim to improve our work based on the suggestions made by the reviewer. The answers to the specific queries of the reviewer are provided below:
**Answer to Q1**: The definition we implicitly adopted for “analytically solving” equ... | Summary: The paper provides an analytical way of evaluating Partial Information Decomposition (PID) of the BROJA type (BROJA-PID). PID is a framework used in neuroscience and other fields to analyze how two sources of information interact to affect a target. The primary challenge addressed by the paper is the difficult... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of our work. We have provided the replies to the queries raised by the reviewer below:
**Reply to Q1**: Our theoretical results can be applied to empirical data by assuming that the data can be reasonably modeled by one of the distributions discusse... | Summary: This paper provides analytical solutions for the Partial Information Decomposition (PID) (specifically, the BROJA PID) for a broad class of continuous distributions. The core proposition of this work lies on the relationship between stable distributions, Markov chains, and the minimisation conditions for Black... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s positive review of our work and their constructive criticism which should improve the overall quality of our manuscript.
We like the reviewer’s suggestion to show analytical PID values/expressions to illustrate our theoretical results. Inspired by this suggestio... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful evaluation of our manuscript. We are grateful for the instructive comments and suggestions provided by all the reviewers that would invariably improve our work. Some common remarks by reviewer Ffcm and reviewer vKRJ regarding the presentation of our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ContextCite: Attributing Model Generation to Context | Accept (poster) | Summary: This paper proposes a simple and effective approach for attributing LLM generation to the sources in the context, where a set of random ablations and their corresponding effects in the model's probability of generating the response are modeled with a (sparse) linear relationship. The method archives impressive... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address each question individually below.
**Effect of number of context sources (and sparsity) on sample complexity.**
We agree with the reviewer that computational cost is a significant limitation of ContextCite. However, we would like to point out t... | Summary: The current paper introduces the task of context attribution, which aims at attributing a generated response of an auto-regressive LM back to the sentences in the input contexts. Target at this task, ContextCite, was proposed to predict which piece of context changes the probability of the generated response m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address each question individually below.
**Token-level context attribution.**
Our task actually does consider attributing arbitrary selections from the response (including individual tokens). We discuss this in Section 2.3. When we evaluate ContextCi... | Summary: In an attempt to understand how language models leverage context information in its generation, this work studies contributive context attribution. Following the recent trend in attribution research, authors propose to use (sparse) linear models to learn the importance of each unit/sentence in the context, and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address each question individually below.
**On technical novelty and connections to data attribution.**
We agree with the reviewer that our work leverages the data attribution literature (e.g., datamodels, LDS); we acknowledge and discuss these connec... | Summary: The authors of 17808 formalizes the problem of context attribution for language models. That is, identifying which parts of the input context caused a model to generate a particular output. The authors propose ContextCite, a method that learns a sparse linear surrogate model to approximate how ablating differe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address each question individually below.
**Justification for linear surrogate modeling.**
Our justification for using linear surrogate models for context attribution is purely empirical. A priori, we do not see any theoretical reason why linearity is... | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful feedback, which we think have highlighted areas where we could have been clearer. We have responded to each reviewer individually in detail, and we use this comment to highlight the additional experiments we have conducted in our appendices as well as in re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Amortized Active Causal Induction with Deep Reinforcement Learning | Accept (poster) | Summary: The authors propose CAASL, which amortizes an intervention policy in the setting of causal structure learning. They apply this algorithm in the setting of a synthetic environment and a gene expression simulator. They use AVICI as a reward model to estimate an approximate posterior over the graph’s adjacency ma... | Rebuttal 1:
Rebuttal: Thanks a lot for the detailed feedback. We answer your questions and concerns below:
- "preferred to see how CAASL links up with AVICI, the simulator, etc": Thanks a lot for the great suggestion. For the camera-ready version, which provides an additional content page, we will include another figu... | Summary: The paper proposes to use RL to train policies that can adaptively select interventions/experiments for obtaining data to do active causal structure learning. The approach amortizes policy training by training the policy on synthetically generated data, an approach that has shown some promise in the amortized ... | Rebuttal 1:
Rebuttal: Thanks a lot for the detailed feedback. We address your questions and concerns below:
- “Section 5.2… More information needs to be given to interpret the results”: In Appendix, Fig 14-18 presents results with regards to metrics other than returns. We argue that returns is an interpretable metric,... | Summary: **Update after rebuttal:**
I personally think that the authors have sufficiently addressed all criticism. Adding an extended discussion regarding the strong performance of a random baseline in many domains is a good idea and would situate the work even better. I remain in favor of accepting the paper, and have... | Rebuttal 1:
Rebuttal: Thanks a lot for the very positive feedback of our work. We provide detailed response to your questions below:
- “Sensitivity to AVICI”: That’s a good point. In our work, we use a pre trained AVICI which has been trained on datasets drawn from wide range of SCM and noise distributions. So a relat... | Summary: The authors propose an intervention design method, Causal Amortized Structure Learning, **CAASL**, which works by using a transformer to directly predict the next intervention to perform given a simulator of the environment (in which the intervention is to be performed) using the Soft Actor Critic (SAC) policy... | Rebuttal 1:
Rebuttal: Thanks a lot for your detailed and very positive review of our work. We provide answers and clarifications to your questions below:
- "How dense/sparse can $A$ be?" For training the policy, we set the density of graphs to 3 edges on average per node. Amortization can also be achieved across diffe... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the detailed feedback regarding our work. We are delighted to find that in general the reviewers find our work tackles an important problem (rMit), well-written (ydzf, rMit, EZYL), novel and interesting combination of existing ideas (o26x, EZYL) and provides clear em... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discrete Modeling via Boundary Conditional Diffusion Processes | Accept (poster) | Summary: The authors propose a discrete diffusion model that operates in the embedding space of vectors that represent discrete tokens. They propose a method that takes into account the fact that whole regions of the space will decode to the same discrete token because during decoding, the closest embedding vector is s... | Rebuttal 1:
Rebuttal: We sincerely appreciate your meticulous review and the valuable comments. Please kindly find our response below.
**Q1: The rescaled vector field and probability path**
*Due to the character limit, this is only an outline. Please refer to comments for more details.*
1. Our neural network predict... | Summary: In this study, the authors propose a framework to address the discrepancy between discrete data and continuous modeling in diffusion processes. The approach involves a two-step forward process that first estimates the boundary as a prior distribution, then rescales the forward trajectory to build a boundary co... | Rebuttal 1:
Rebuttal: We sincerely appreciate your meticulous review and the valuable comments. Please kindly find our response below.
**Q1: Why can our framework enhance performance**
1) **From the perspective of Motivation**
As illustrated in Introduction and Figure 1, we find that the continuous diffusion process... | Summary: A discrepancy exists when using continuous diffusion models to model discrete data, which has not been sufficiently addressed in past methods. This paper redesigns the forward and backward processes of the diffusion model to eliminate this issue. Experiments on translation tasks and CIFAR-10 effectively valida... | Rebuttal 1:
Rebuttal: We sincerely appreciate your meticulous review and the valuable comments. Please kindly find our response below.
**Q1: The embedding layer**
1) We want to clarify that the embedding layer we use is actually trainable, as illustrated in Lines 200 and 208. The $\theta$ is only used to denote the d... | Summary: The paper discusses a novel approach to discrete modeling using boundary conditions and diffusion processes. The primary contributions include:
- Development of a framework for discrete image generation that incorporates binary coding and pixel embedding.
- Introduction of an intermediate state to illustrate t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your meticulous review and the valuable comments. Please kindly find our response below.
**Q: Expanding datasets for images and languages**
1) For both language and image tasks, our framework is constructed on our baselines, Difformer and BitDiffusion, with the same confi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Right this way: Can VLMs Guide Us to See More to Answer Questions? | Accept (poster) | Summary: VLMs offer an interesting opportunity to describe their uncertainty if the images given to them are of poor quality. This paper explores a framework for estimating whether a VLM can identify if its ability to correctly answer a visual question would improve were it given a better picture. The paper introduces ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the unique contribution of our study and the effectiveness of our training method. We believe these advancements highlight our work's potential to drive further research.
We are glad to discuss the following points:
***W1. It would be interesting to see other method Bey... | Summary: - The paper proposes a recourse for unanswerable visual questions — rather than simply abstain, can a VLM indicate how to adjust an image so that the question becomes answerable?
- The authors introduce the Directional Guidance VQA task and a dataset for the task.
- The authors propose a data generation pipeli... | Rebuttal 1:
Rebuttal: We are grateful for your recognition of the contribution and potential impact of our proposed study, and that's exactly what we are targeting. We also expect to facilitate many meaningful real-world applications such as AI assistants for visually impaired individuals.
We appreciate the opportunit... | Summary: This paper evaluates and expands the self-reflection capabilities of multiple MLLMs, so that MLLMs can actively evaluate the adequacy of given query information and give suggestions on how to find more reliable information. This paper proposes a hierarchical cognitive process pattern theory and creates a manua... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the significance of active reflection and evaluation methods. We would like to highlight that our main contribution is not only improving response quality but also providing guidance to acquire more information.
We are glad to discuss the following points:
***W1. The... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable feedback. We are encouraged that the reviewers recognize our contribution in threefold:
- This work identifies a meaningful problem with real-world impact: "Can VLMs guide us in acquiring more information beyond simply abstaining?" (Reviewer *Eiq... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Your LiDAR Placement Optimized for 3D Scene Understanding? | Accept (spotlight) | Summary: This paper proposes Place3D, a full-cycle pipeline that encompasses LiDAR placement optimization, data generation, and downstream evaluations. The framework makes three appealing contributions. 1) To identify the most effective configurations for multi-LiDAR systems, this paper introduces a Surrogate Metric of... | Rebuttal 1:
Rebuttal: **Dear Reviewer `iXBb`,**
Thanks for devoting time to this review and providing valuable comments.
---
>Q: The representation may be a little confusing. The M-SOG and the optimization method, under my understanding, are task-independent. However, there are two optimized LiDAR placements related ... | Summary: The paper targets an overlooked but important aspect of 3D scene understanding -- the influence of LiDAR sensor placements for the success of 3D perception tasks, such as 3D detection and 3D segmentation.
The authors introduce Place3D, a comprehensive pipeline for optimizing LiDAR placement in autonomous driv... | Rebuttal 1:
Rebuttal: **Dear Reviewer `PfQe`,**
Thanks for devoting time to this review and providing valuable comments.
---
>Q: In the experiments, although the baseline LiDAR placement methods are based on the existing configuration of current autonomous vehicle systems (Fig 7), it is better to compare the propose... | Summary: The authors present a very interesting study of optimal lidar placement on autonomous vehicles. They introduce a novel measure of the quality of lidar placement called M-SOG, and also discuss an optimization approach to find the best placement. They measure the impact of the placement on the important vision t... | Rebuttal 1:
Rebuttal: **Dear Reviewer `DpmT`,**
Thanks for devoting time to this review and providing valuable comments.
---
>Q: Writing can be improved.
A: Thanks for your comment. Due to limited space for response, for minor modifications (e.g. notations), please refer to the above Author Rebuttal section. For spe... | Summary: This paper proposes a framework called Place3D to investigate the placement of multiple LiDAR sensors for semantic segmentation and object detection task under various weather conditions. Place3D mainly introduce a Surrogate Metric of Semantic Occupancy Grids (M-SOG) to evaluate the perception performance wit... | Rebuttal 1:
Rebuttal: **Dear Reviewer `8eRR`,**
Thanks for devoting time to this review and providing valuable comments.
---
>Q: Although the proposed method investigates LiDAR placement for more tasks under more weather, the core formulation is similar to that in [34] as cited in the paper.
A: Thank you for the in... | Rebuttal 1:
Rebuttal: **Dear Reviewers, Area Chairs, and Program Chairs,**
We sincerely thank the reviewers, ACs, and PCs for the time and efforts devoted during this review.
We especially appreciate our reviewers for offering valuable comments, providing positive feedback, and drawing insightful suggestions.
---
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-Shot Reinforcement Learning from Low Quality Data | Accept (poster) | Summary: The work investigates methods for zero-shot reinforcement learning (RL) that can be trained on small, homogeneous datasets. This research is driven by the need to make zero-shot RL practical when large, heterogeneous datasets are unavailable. The authors identify the limitations of existing methods that overes... | Rebuttal 1:
Rebuttal: Hi Reviewer ZxeR. See our responses to your comments below. We hope we can work with you to address any misunderstandings. Thank you in advance for your cooperation.
**W1: The problem definition.** **I do not think the settings of this work is so-called Zero-shot RL since the work introduces anot... | Summary: This work addresses zero-shot reinforcement learning (RL), focusing on training agents to perform tasks without explicit rewards during pre-training. The authors investigate the performance degradation that occurs with small, homogeneous datasets and propose conservative zero-shot RL algorithms inspired by sin... | Rebuttal 1:
Rebuttal: Hi Reviewer ZC95. Thanks very much for engaging with our paper and for the positive comments. See our response to your questions below.
**Q1: Could the authors provide a more explicit definition of "low-quality data"?**
To the best of our knowledge, there isn’t one metric that formalises the qua... | Summary: This paper identifies an overestimation problem of zero-shot RL algorithms, particularly in low data or low data quality settings. To address this, they propose to use a value conservative method to mitigate the overestimation (from CQL). They showcase that this effectively allows for reducing the overestimati... | Rebuttal 1:
Rebuttal: Hi Reviewer 5D6P. Thanks very much for engaging with our paper and for the positive comments. See our response to your questions below.
**Q1: Provide a bit more background and intuition about zero-shot RL**.
Thanks for the pointer. We’ll add the following text to help intuition after the formal ... | Summary: This work proposes modifications to Forward-Backward representations method. The modifications are aimed to improve the method's robustness to dataset quality. The vanilla FB suffers when the dataset does not cover the state space well, and overestimates the quality of actions. The authors show this with a sim... | Rebuttal 1:
Rebuttal: Hi Reviewer qeSX. Thanks very much for engaging with our paper and for the positive comments. See our response to your question below.
**Q1: How do the proposed modifications affect the performance of the model when the dataset is of good quality? Does it hurt the performance?**
We asked oursel... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans | Accept (poster) | Summary: This paper identifies a pioneering privacy threat in Federated Learning (FL) introduced by popular diffusion models, called DataStealing. The researchers show that an attacker can use Combinatorial Triggers (ComboTs) to steal private data from a client in vanilla FL, even with strict data management protocols ... | Rebuttal 1:
Rebuttal: Thank you for appreciating our contributions. We address your concerns below:
**Response to Q1:**
We appreciate your suggestion to include more quantitative analysis on defending against malicious updates. To enhance understanding, we analyze the distance of Krum and Multi-Krum across different ... | Summary: The paper introduces a novel attack method named DataStealing, which exploits vulnerabilities in diffusion models trained in Federated Learning (FL) systems. The authors highlight that diffusion models, despite their advanced capabilities in data generation, present new privacy threats when integrated with FL.... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and high appreciation of the innovation and experiments in our work. We address your concerns below:
**Response to W1:**
Thank you for your suggestion. Here is our discussion on the defense mechanisms:
- According to Table 1 and Fig.3 in the main paper, Mu... | Summary: The paper titled "DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans" investigates a novel privacy vulnerability in federated learning (FL) when training diffusion models. The authors introduce a new attack methodology named DataStealing, which leverages multiple Trojans... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and appreciation of the novelty and experiments of our work. We address your concerns below:
**Response to "Discussion on Defense Mechanisms"**:
Thank you for your suggestion. Here is our discussion on the defense mechanisms:
- According to Table 1 and Fig.3 i... | Summary: This paper studies the privacy risks of diffusion models in federated learning (FL) through the lens of data stealing attacks. The authors propose the ComboTs method to target a large amount of images. To further defeat advanced distance-based FL defenses, the authors propose the AdaSCP attack method which ada... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We address your concerns below:
**Response to Q1:**
1) **Access to data does not mean it can be stolen.**
- Federated Learning (FL) aims to protect data privacy by only sending model updates to the central server. In the FL setting, private data **canno... | Rebuttal 1:
Rebuttal: First of all, we would like to thank you for your time, constructive critiques, and valuable suggestions, which have greatly helped us improve our work. We are also grateful that the reviewers unanimously regard our work as novel and convincing. Below, we respond to the suggestions regarding exper... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations | Accept (poster) | Summary: The article studies classical stochastic control in the continuous d-dimensional SDE setting (with and without jumps). The authors suggest to compute the gradients of the value function (expected total reward in the non-discounted finite time regime), which can be applied (in a model-based setting) to policy g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We hope that the following response will help to address some of the concerns.
1. **Applicability and interest to the ML community:** Recently, there has been significant interest in approximating continuous-time optimal control using neural net... | Summary: The paper proposes a novel algorithm for the gradient of machine learning objectives that are based on SDE paths, w.r.t. parameters. The method is scalable for big parameters, as the computational complexity of the estimation is not related to the number of parameters. Empirical results show the scalability ad... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions. We hope that the following response can address some of your concerns:
1. **Limitations induced by the assumptions on the smoothness:**
In the "Author Rebuttal" section, we address concerns regarding the clear presentation of the limitatio... | Summary: This paper formulates an efficient, unbiarsed, and finite variance gradient estimator for an objective function that looks like the stochastic optimal control cost function (it is ubiquitous across various applications). The problem concerns overparametrized SDEs (the paramete dimension n r is of a much higher... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions. We organize our responses as follows
1. **Limitations induced by the assumptions on the smoothness:** Thanks for the suggestion of including a separate discussion of limitations to avoid misunderstanding. In the "Author Rebuttal" section, we a... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and questions. Your feedback provided valuable suggestions and directions for improvement. Below is a brief summary highlighting the main enhancements and changes we will be making.
**Limitation and Generalization**
We will add the following c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models | Accept (poster) | Summary: The paper explores the novel use of large language models (LLMs) as gradient priors in a zero-shot setting. The primary focus is on lossless gradient compression, which is essential for distributed learning applications. The paper introduces LM-GC, a method that integrates pre-trained LLMs with arithmetic codi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and detailed feedback. We are also encouraged by the recognition of our novelty and creativity. We now address the comments and share our vision for further efficiency improvement below.
----
***Computation overhead and potential mitigation***
We have discuss... | Summary: This is an interesting paper that tries to solve an important problem using large language models. Authors propose to use LLMs to compress the gradients with no number representation loss. They explain their method clearly by marrying LLMs with arithmetic coding.
Strengths: This paper opens new views to LLMs ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive feedback. We especially appreciate the recognition of our novelty and our efforts to address this timely question. We are pleased to share additional insights below.
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***Theoretical properties***
Thanks for the suggestion. We agree that t... | Summary: The paper proposes LLMs as compressors for gradients of a neural network. The target usecase is a distributed learning setting, where the gradient updates need to be compressed before being shared.
The gradients are converted to hexadecimal representation and the LLM's outputs are used in an arithmetic coder ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful feedback. We hope our response addresses the concerns and conveys the broader vision of our work.
We kindly emphasize that our primary contribution is demonstrating **the potential of pre-trained zero-shot large language models (LLMs) as effecti... | Summary: This paper studies the potential of LLMs to act as gradient priors in zero-shot settings. The property is evaluated on lossless gradient compression. The proposed method is able to surpass existing compression methods and improve compression rate by 10% to 21% across various datasets and architectures.
Streng... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback and are encouraged that our work was found interesting. We will now address the comments and share further vision of our work.
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***The benefit of using LLMs over traditional approaches.***
We highlight that traditional baselines struggle to c... | Rebuttal 1:
Rebuttal: # General Response
\
We thank all reviewers for their time and constructive feedback. It is encouraging to hear that our work has been found both fun and interesting (R-Br5V) as well as creative (R-UCGG). We particularly appreciate the recognition from all reviewers of the novelty and effectivenes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Particle Approximation Error for Mean Field Neural Networks | Accept (poster) | Summary: The manuscript investigates the mean-field Langevin dynamics (MFLD) and improves the particle approximation error by removing the dependency on the log-Sobolev inequality (LSI) constant. This is of relevance, as the LSI constant in general might deteriorate with the regularization coefficient, limiting the imp... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and useful suggestions.
**The Authors do not really motivate why one should expect that the particles approximation does not depend on the LSI constant.**
The particle approximation error is due to the nonlinearity of $F_0(\mu)$ with respect to $\mu$. In fact,... | Summary: The paper presents an improved finite particle approximation error bound for mean-field Langevin dynamics. The main result establishes a $O(1/N)$ gap between the original and $N$-particle objective which is independent of the LSI constant and improves upon the existing $O(\lambda/\alpha N)$ bound. The is appli... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and helpful comments.
**Besides Theorem 1, the remaining results seem to be a straightforward application. The convergence rate $\exp(-\alpha \lambda\eta k)$ and error bound $\eta/\alpha \lambda$ still suffer from the LSI constan.**
As the reviewer pointed out... | Summary: This paper provides an improved bound on the particle approximation error of MFLD under conditions of a log-Sobolev inequality (and a couple of extra regularity conditions). This bound improves on previous bounds by removing the dependence of the bound on the LSI constant. The authors then apply their bound to... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and thoughtful comments.
**About bounded activations**
In fact, the boundedness of the activation function is commonly assumed in the literature. Hence, it is not a critical limitation. That being said, we can relax the boundedness for each factor. For instanc... | Summary: This work proves a novel particle approximation error for the continuous- and discrete-time mean-field Langevin dynamics (MFLD), which removes the dependency on the log-Sobolev inequality (LSI) constant in the number of particles, leading to a potential exponential in inverse-temperature improvement in the num... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and feedback.
**Verification of Assumption 4 and Lemma 22 of [Kook et al., 2024]**
Thank you for bringing up this point. Indeed, the authors of [Kook et al., 2024] updated their manuscript due to an error in Lemma 22 of the previous version. As a result, a new ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLIPAway: Harmonizing focused embeddings for removing objects via diffusion models | Accept (poster) | Summary: This work addresses the object removal problem with a simple and effective embedding arithmetic strategy. This idea is implemented by combining the object semantic understanding ability from alpha-clip and the generative ability from the text-to-image diffusion models. One good property is that the proposed me... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback which help us improve the paper further.
**Applicability to Higher Resolution Images**\
We appreciate the reviewer's insight regarding the applicability of our method to higher resolution images. As noted, the current limitation is ... | Summary: The paper identifies a common limitation in recent diffusion model-based inpainting methods: unintended hallucinations of the removed object. To address the problem, the paper introduces CLIPAway, a plug-and-play module that does not rely on any specific training. Using vector arithmetic, it successfully obtai... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback.
**Eliminating Shadows**\
We appreciate the reviewer’s concern about shadows in the inpainted images. Since our method uses SD-Inpaint as its backbone, inpainting is performed only on the masked regions, which ensures that only the ... | Summary: This paper proposes an approach that aims to tackle object removal in stable diffusion models. The paper utilizes AlphaCLIP embedding (that are trained with an additional alpha layer mask to enable incorporation of regions of interest), and techniques such as IP-Adapter that decouples the diffusion unet cross... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback.
We appreciate the reviewer's recognition of our method's ability to leverage existing work effectively and its superior performance in removing objects smoothly compared to competitors. To address the concern regarding the qualitat... | Summary: This paper addresses a commonly observed problem when using pre-trained diffusion models for object removal: in standard inpainting setups, these models often add similar objects in place of the ones to be removed instead of extending the background to the masked area. To address this problem, the authors use ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed review.
We appreciate the reviewer's feedback regarding the simplicity of our method. We share the same preference for simplicity over complex solutions. While our solution might seem obvious now, previous research attempted to address object... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their valuable feedback. We have responded to each reviewer's questions in the rebuttal sections, and attached a PDF file with figures for our additional results.
Pdf: /pdf/68d4b94941cfc35765580f003f2c0f9819569eff.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning | Accept (poster) | Summary: The paper proposes a new diffusion-based training loss for offline reinforcement learning, in which the empirical actions from the offline datasets are replaced with the actions sampled from a parameterized policy. The paper calls this new loss “trust region loss” and uses it as one of the objectives to minimi... | Rebuttal 1:
Rebuttal: Thank you for your review. We believe there may have been some misunderstandings regarding our paper. We will strive to address your questions thoroughly and hope you will consider re-evaluating our work.
### Similarities
Firstly, we have discussed some differences between our method and SRPO in... | Summary: This paper introduces Diffusion Trusted Q-Learning (DTQL) for offline reinforcement learning. DTQL employs a dual-policy representation, a diffusion policy trained by behaviour cloning, and a one-step Gaussian policy trained by RL and distilling the diffusion model. Specifically, DTQL introduces a diffusion tr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our method's novelty, motivation, and empirical performance. Thank you for the careful review.
### Responses to Weakness 1.
Thank you for the good suggestion. You are absolutely right that some modes are also missed by $\mathcal{L}_{\text{KL}}$. We wi... | Summary: The paper introduces Diffusion Trusted Q-Learning (DTQL), a novel approach for offline reinforcement learning that enhances performance and efficiency. DTQL utilizes a dual policy framework to bridge the gap between diffusion policies and one-step policies, improving expressiveness without the need for iterati... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate the time and effort you have dedicated to evaluating our work. It seems there may have been some misunderstandings regarding the main points of our paper. We will do our best to clarify these issues and hope you will consider re-evaluating our work.
### Re... | Summary: This paper introduces DTQL, a novel offline reinforcement learning (offline RL) method. With a newly introduced diffusion trust region loss, DTQL constrained policy update within a predefined trust region near a diffusion policy trained by behavior cloning (BC). Through the empirical experiments, DTQL demonstr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of novelty, presentation and performance improvement of our paper. Below, we provide detailed clarifications and answers to solve the remaining concerns.
### Responses to Weakness 1.
We first want to emphasize that one major contribution of DTQL is for sp... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. We believe some aspects of our paper may have been misunderstood, particularly by **Reviewer Px6z regarding the goal, contribution, and logic of our paper**, and by **Reviewer BozX who questioned the differences with SRPO**. Below, we focus on ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sample-Efficient Private Learning of Mixtures of Gaussians | Accept (spotlight) | Summary: The paper studies the important problem of private learning of the Gaussian mixture model to estimate the underlying distribution within a desired total variation distance. By combining different techniques, the authors succeed in deriving bounds that are of quadratic dimension, thus significantly improving th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback. In the following we answer the raised questions.
1. To clarify this further, the goal of density estimation is to learn the overall distribution’s PDF (up to low total variation distance), whereas in parameter estimation we want to ... | Summary: This paper investigates the sample complexity of privately learning mixtures of Gaussians. The authors achieve a sample complexity of approximately $O(kd^2+k^{1.5}d^{1.75}+k^2d)\log R$ where $R$ is an upper bound on the condition number of the covariance matrix and the norm of the mean. This result improves up... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback. We would like to emphasize that our bound does not depend on the condition number of the covariance matrix nor on the magnitude of the mean, i.e., our sample complexity has no dependence on $\log R$. (Otherwise, proving an upper boun... | Summary: This works focuses on the task of density estimation of a mixture of Gaussians under the restriction of differential privacy. Unlike parameter estimation, density estimation does not require accurately estimating the mixture's parameters, but instead bounds the total variation distance between estimated and tr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback. We are happy to add some description that puts everything together and explain where each of the terms come from, as suggested by the reviewer. In the following, we explain the terms in the sample complexity that the reviewer asked a... | Summary: The paper studies the problem of learning a mixture of $k$ $d$-dimensional Gaussians using a differentially private mechanism with respect to the samples. It provides an improved sample complexity which has asymptotically optimal dependence on the dimension $d$ for small $k$, the lower bound is also given by t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback. We now address some of the issues/questions raised by the reviewer.
Note that the algorithm for the univariate case (Section F) is completely different from the multivariate case (Section E). The algorithm in the univariate case req... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their helpful feedback.
We apologize for the difficulty in reading the paper. We will make changes as suggested by the reviewers to improve the readability of the paper, such as adding a table of results, adding some additional technical description to the main b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding | Accept (poster) | Summary: This paper present an innovative, pioneering framework for Continual Test-Time Adaptation in multi-task point cloud understanding, enhancing the model’s transferability towards the continually changing target domain.
Strengths: Introducing CTTA into a multi-task 3D vision setting is practical and realistic. T... | Rebuttal 1:
Rebuttal: We greatly appreciate your detailed review. We're glad to see your kind recognition of the innovation of our pioneering framework for Continual Test-Time Adaptation in multi-task point cloud understanding.
In the following, we will address each of your concerns:
**Q1: Typo.**
Thanks. We will ca... | Summary: The paper introduces an innovative and unified framework for Continual Test-Time Adaption in multi-task point cloud understanding, which includes reconstruction, denoising and registration. The framework integrates with three new modules for different purposes, i.e., automatic prototype mixture for preventing ... | Rebuttal 1:
Rebuttal: We deeply appreciate your thorough review. We are pleased to see your kind recognition of the innovation of our framework for Continual Test-Time Adaptation in multi-task point cloud understanding and our three new modules. Additionally, we appreciate your acknowledgment of the effectiveness demo... | Summary: This paper introduces a novel framework designed to enhance model transferability in continually changing target domains for multi-task point cloud understanding. The framework, termed PCoTTA, includes three key components: Automatic Prototype Mixture (APM), Gaussian Splatted Feature Shifting (GSFS), and Contr... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough review and valuable feedback. We are pleased that you recognized the novelty and practical application of our PCoTTA framework for continual test-time adaptation in multi-task point cloud understanding. We appreciate your acknowledgment of our new benchmark aime... | Summary: This paper presents a new point cloud benchmark for Continual Test-Time Adaptation (CTTA) and compiles relevant 3D datasets. Additionally, this paper devises three innovative modules for PCoTTA, including automatic prototype mixture (APM), Gaussian splatted feature shifting (GSFS), and contrastive prototype re... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your review. We are pleased that you recognized the significance of our compiled point cloud CTTA dataset and the novelty of our three modules to address catastrophic forgetting and error accumulation. Additionally, we are glad that the clarity of... | Rebuttal 1:
Rebuttal: We would like to thank the AC and all reviewers for their efforts and time in reviewing our paper. We appreciate their constructive and valuable comments. We are pleased to see reviewers’ acknowledgement of the significance of our compiled point cloud CTTA dataset or new benchmark (Reviewer SkYG, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline Multitask Representation Learning for Reinforcement Learning | Accept (poster) | Summary: This paper investigates how representation learning in offline multitask low-rank RL can improve sample complexity when using the learned representations in downstream reward-free RL, offline and online RL settings.
The paper assumes that the new task shares the same representation as the upstream tasks makin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of the reviewer's points.
Thank you so much for your suggestion on how to improve the readability for a broader audience. We will definitely add more i... | Summary: This paper introduces the Multitask Offline Representation Learning (MORL) algorithm, which aims to enhance sample efficiency in offline multitask reinforcement learning (RL). By learning a shared representation from pre-collected datasets of different tasks, modeled by low-rank Markov Decision Processes (MDPs... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of the reviewer's points.
* As discussed in Additional Related Work in Appendix A under **Offline Data Sharing in RL**, there have been numerous empir... | Summary: This paper provides a theoretical analysis of representation learning in Multi-Task Offline RL.
Specifically they consider a setting in which the transition kernels have a low-rank decomposition $P(s'|s,a)=\langle \phi(s'),\mu(s,a)\rangle$, such that $\mu(s,a)$ depends on the task but $\phi(s')$ is shared by a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of the reviewer's points.
* Thanks for your suggestion regarding [89]. A preprint version of our results appeared publicly around at the same time of t... | Summary: This paper proposes a representation learning algorithm for offline multitask reinforcement learning. The proposed algorithm, MORL, is designed for offline multitask RL in low-rank MDPs. The learned representation is examined in downstream RL in reward-free, offline, and online scenarios.
Strengths: 1. Detail... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of the reviewer's points.
### Points raised as weaknesses
1. To our knowledge, there is only one other concurrent offline multitask RL theory work [89... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffGS: Functional Gaussian Splatting Diffusion | Accept (poster) | Summary: This paper presents a method to turn the representation of the gaussian splatting as a point cloud into a VAE representation. With that representation it is shown how to perform several tasks such as unconditional generation, conditional generation, and point-to-gaussian generation, all of which are done usin... | Rebuttal 1:
Rebuttal: It is with great appreciation that we acknowledge the insightful feedback from Reviewer TmLe. We have addressed the questions and comments with careful consideration, and we encourage continued conversation to ensure the robustness of our findings.
**Q1: Motivation, Literature, and Experimental E... | Summary: This paper proposes DiffGS, a novel diffusion-based generative model that can generate 3D Gaussian Splatting (3DGS) representing an object. As 3DGS is naturally discrete and unstructured, it is not practical to directly diffuse the 3DGS representation. Thus, the authors propose to reconstruct the 3DGS with str... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to Reviewer cAhv for their comprehensive review and valuable suggestions. In response to the concerns raised, we have offered detailed explanations in the subsequent section, and we are eager to engage in further dialogue.
**Q1: Incomplete Shapes in 3DGS**
It is inde... | Summary: The paper is about generating 3D objects by using gaussian splatting. The method converts the points (with gaussian properties) to continuous fields. With this idea, the irregular structured data can be easily processed by neural networks.
Strengths: The idea is interesting and novel. Usually we need many poi... | Rebuttal 1:
Rebuttal: We are truly grateful for the insightful review provided by Reviewer 6WmW. Your thorough analysis and constructive feedback have significantly enhanced the quality of our work.
**Q1: Visual Quality and Evaluation Protocol**
We justify that DiffGS achieves SOTA performance in terms of both numeri... | Summary: This paper proposes a new generative model for Gaussian primitive generation based on latent diffusion models. In detail, the method disentangles Gaussian Splatting generation into three functions, i.e., Gaussian probabilities, colors, and transforms. The method can achieve unconditional and conditional genera... | Rebuttal 1:
Rebuttal: We are truly grateful for the comprehensive feedback and time that reviewer tnRu dedicated to evaluating our work. Below, we provide responses to each of your questions. We look forward to your further comments on our responses.
**Q1: Benefits of Structural Triplanes in 3D Gaussian Splatting**
T... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their invaluable feedback and the time they spent evaluating our work. We are delighted that the reviewers recognized the representation and importance of our paper. We respond to each reviewer individually, providing comprehensive analyses, visualizations... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This article first transforms 3DGS into a regular representation: the triplane structure, to separately model the location probability, color, and other attributes of each Gaussian. This representation can then be used to train a generative model through a VAE and LDM.
To sample Gaussian positions from the tri... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer 3Kid for the thoughtful feedback and time invested in evaluating our work. We address each question below.
**Q1: Dataset Size and Scalability**
We justify that data fitting is a common step for most generative models. For example, DiffRF requires voxelized ra... | Summary: The paper introduces DiffGS, a text/image-to-3D generative model with 3D Gaussian splatting as its output representation. The model generates Gaussians from text/image condition by CLIP-augmented latent diffusion model (LDM), whose output is a latent vector that can be decoded into a triplane representation. D... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer p5pi for the thorough review and valuable feedback. We have addressed each point in our response below.
**Q1: Comparison with other recent 3D generative models**
We refer the reviewer to the "Global-Q1: Evaluation Against Other SOTA 3D Generative Models" section of th... | null | null | null | null |
Fast Rates for Bandit PAC Multiclass Classification | Accept (poster) | Summary: This manuscript deals with Multiclass (K labels) PAC Classification a partial monitoring scheme, as introduced by Daniely et al. ('11). The complexity for $(\epsilon,\delta)$-PAC of a naive uniform sampling algorithm would be $K/\epsilon^2 \log (|\mathcal{H}| / \delta) \big)$ where $\mathcal{H}$ is a finite fa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. Please see our comment below to the specified questions.
* “Line 177: What is J_{x,y}?”
This is a typo and should be replaced by $r_{x,y}$, this will be fixed in the final version. Thanks for catching!
* “Could the procedure allows for a better de... | Summary: The authors present a novel algorithm to solve the epislon-delta PAC bandit multi-class classification problem.
For a finite hypothesis class, they give an algorithm with sample complexity O(poly(K) + 1/epsilon^2) that improves on the O(K/epsilon^2). They also similarly show that for a possibly infinite clas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. Please see our comment below to the specified weaknesses and questions.
* “...This means that if K is relative large, you mind end up doing worse than the previous bounds which limits the scope of impact of this work.”
You are correct in that our cu... | Summary: This paper studies bandit multiclass classification in the agnostic PAC setting. For both finite and infinite hypothesis classes, they provide efficient learning algorithms (assuming access to a weighted ERM oracle) whose sample complexity significantly improves upon the previous best known rate of $\frac{K}{\... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. Please see our comment below to the specified questions.
* “What are the known lower bounds (if any) in the bandit agnostic PAC setting? Is a poly factor of 𝐾 unavoidable?”
We can prove a simple lower bound of $K / \epsilon + 1 / \epsilon^2$ for ba... | Summary: This paper studies the problem of multiclass classification with bandit feedback, where one only receives information on whether the prediction is correct or incorrect without revealing the actual label. This can be viewed as a very specific case of the well-known contextual multi-armed bandits, where the cost... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. Please see our comment below to the specified weaknesses and questions.
* “...Can this be improved? Perhaps with a computationally inefficient algorithm?”
We believe that the sample complexity (specifically, the polynomial dependence on $K$) could b... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper considers the problem of bandit multi class classification, where the learner only receives the true label if their prediction was correct. That is at time $t$ the learner receives a training sample $x_t$ with unknown label $y_t$. They then predict one of $K$ labels and are told if their prediction ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. Please see our comment below to the specified weaknesses and questions.
* “...it would be nice for the authors to discuss how future works could reduce the degree of the poly dependence upon $K$.”
We believe that the polynomial dependence on $K$ cou... | null | null | null | null | null | null |
Energy-based Epistemic Uncertainty for Graph Neural Networks | Accept (spotlight) | Summary: This paper explores the challenges associated with quantifying uncertainty in Graph Neural Networks (GNNs), particularly in domains involving interconnected data such as graphs. The authors propose a novel method called GEBM, which aggregates energy at various structural levels. This approach enhances predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and are happy that they like our paper overall.
**Weakness 1, Graph-Level Tasks**: Indeed, transferring GEBM to graph-level tasks is an interesting avenue: The energies proposed by our framework are designed with a node-level objective in mind. If s... | Summary: The paper defines an integrable (regularized) energy function to capture epistemic uncertainty via energy a pretrained model. The energy function is a function of the logits so the method is a post-hoc model agnostic. The authors define a diffusion-based hierarchical energy propagation (structure agnostic + lo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their in-depth review and interesting questions and are happy to see that they find our paper strong.
**Weakness 1 & Question 1, Homophily Assumption**: We assume homophily throughout our work. We agree that is benificial to explicitly state this assumption early on and ... | Summary: This paper introduces a method for post-hoc epistemic uncertainty estimation in logit-based Graph Neural Networks (GNNs) by aggregating energy scores at different levels, including node, local, and group levels. Extensive experiments show the effectiveness of the proposed framework.
Strengths: 1. The paper ri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very thorough review and specific pointers for improvements. We are happy that the reviewer likes our rigorous evaluation and structure.
**Weakness 1, Ablations on Diffusion**: We ablate $t$ and $\alpha$ and the diffusion operator in Figure 1 (global response) and ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for the time spent providing valuable feedback. We added all experiments and ablations provided in the pdf to the final version of our manuscript. We briefly want to summarise the most relevant additions to the paper. In the individual rebuttals, we propose word-by-word ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Linear Time Approximation Algorithm for Column Subset Selection with Local Search | Accept (poster) | Summary: This paper considers the Column Subset Selection (CSS) problem. In this problem the input is an arbitrary $A\in \mathbb{R}^{n\times d}$ and a positive integer $k$ ($k$ is thought to be much smaller than $\min\{n,d\}$). The goal is to output a subset of $k$ columns of $A$ denoted by $S\in \mathbb{R}^{n\times k}... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1. Can the authors please share the experimental details and parameter settings that are used for implementing the algorithms.**
Response: We thank the reviewer for rai... | Summary: This paper proposes a new algorithm for the column subset selection problem which combines a local search-type strategy with adaptive sampling techniques to obtain an algorithm running in time linear in $nd$ (i.e. the size of the input matrix) for constant $k$. The resulting solution selects exactly $k$ column... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1. For the problem of row sampling for $\ell_p$ subspace approximation, obtaining tight bounds on the number of rows required for a (1+eps) approximation is an open prob... | Summary: The Column Subset Selection (CSS) problem aims to select a sub-matrix with $k$ columns from a matrix to minimize the residual error. Previous algorithms often have non-linear running times or higher approximation ratios. This paper proposes a local search-based algorithm with linear running time, utilizing a t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Weakness 1. Line 41: What is UG?**
Response: We thank the reviewer for this question. ``UG-hard" refers to problems that are as hard as the Unique Games problem, based on the Un... | Summary: This paper studies column subset selection. Given a matrix A n*d, how to select a matrix A_S of k columns, which preserves the substance in A? That is, reconstruction error ||A - (A_S A_S#) A||_F should be minimized, where A_S# is the psuedo-inverse of A_S. There are many approximation algorithms for this prob... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1. Are there practical use-cases of the CSS (where the interpretabiltiy is crucial? as opposed to SVD). In typical use-cases, do we sample rows (from n like a coreset) o... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the positive ratings and thoughtful comments.
Pdf: /pdf/7f6a983bc81e6da7d1f27022187220b45803f926.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses the classical CSS problem, and describes significant
improvements over the current state of the art.
The main idea is to run the randomized
adaptive sampling algorithm for selecting k columns,
and follow it by several iterations of swapping (local search),
that further increase the accuracy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1. Why is there no comparison with the QRP (QR with Pivoting) method?**
Response: The main reason that QRP is not compared with our CSS method is because they select co... | null | null | null | null | null | null |
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers | Accept (poster) | Summary: This manuscript proposed a layer-wise post-training quantization (PTQ) algorithm called aespa aiming at quantizing large transformer models, and refined quantization objectives for the attention layer which can accelerate the quantization process by pre-computation.
The authors gave clearly descriptions for th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive evaluation and invaluable comments on our work. Our point-to-point responses are as follows.
**1. Consideration of the cross-layer dependencies within the full model (Weakness 1)**
- We appreciate the reviewer's invaluable comments. By considering... | Summary: The paper introduces 'aespa,' a novel post-training quantization (PTQ) algorithm designed to balance accuracy and efficiency in quantizing hyper-scale Transformer models. This method quantizes layer-wise for efficiency and employs attention-wise reconstruction to account for cross-layer dependencies. Extensive... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's acknowledgment of our work and constructive suggestions. Our point-to-point responses are as follows.
**1. Further experiments on LLMs such as LLaMA2 and LLaMA3 (Weakness 1)**
- We appreciate the reviewer's constructive suggestion. Table I (see the PDF att... | Summary: As a cost-effective alternative, learning-free PTQ schemes have been proposed for LLMs. Still, the performance is somewhat limited because they cannot consider inter-layer dependency within the attention module, a significant feature of Transformers. This paper propose a PTQ algorithm that balances accuracy an... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's invaluable comments and constructive suggestions.
Our point-to-point responses are as follows.
**1. Results for OmniQuant (Weakness 1)**
- We appreciate the reviewer's invaluable comment and careful reading. We mention that we used the official GitHub code w... | Summary: This paper propose a new quantization strategy that balances accuracy and efficiency that reconstruct the attention output to consider cross-layer dependency. To accelerate the quantization process, this paper proposes refined quantization objectives for the attention module. This approach gets better results ... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's positive evaluation and invaluable comments on our work.
Our point-to-point responses are as follows.
**1. Redundant formula derivation (Weakness 1)**
- We appreciate the reviewer's comment. In the final version, we will simplify the technical derivations and... | Rebuttal 1:
Rebuttal: We are truly thankful for invaluable comments provided by reviewers. During the rebuttal, we have made our best effort to address all the comments raised by reviewers. In this global response, we summarize our main contribution and emphasize essentiality of our work.
**<Main contribution>**
- ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a post-training quantization approach for transformers. The proposed algorithm finds a better balance between accuracy and efficiency. The method is to perform quantization in a layer-wise way for efficiency and the optimization objective is to maintain the reconstruction of the quantized m... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's invaluable comments.
**1. Novelty (Question 1)**
- Our contribution does NOT lie in the idea of reducing reconstruction error. Our primary contribution is to mitigate the computational overhead of the conventional block-wise reconstruction error minimization ... | null | null | null | null | null | null |
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars | Accept (poster) | Summary: The paper proposes EASE, an exemplar selection algorithm for in-context learning of large language models (LLMs). It takes into account the order of the exemplars and offers the possibility to jointly optimize the instruction and the exemplars. A neural network is iteratively trained on the embeddings of the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. We are glad to know that our paper has provided insights into the impact of exemplar selection through a wide range of baseline comparisons.
We would like to address the specific concerns and questions raised by the reviewer below:
>... | Summary: The paper introduces EASE (Efficient ordering-aware Automated Selection of Exemplars), a new approach to boost in-context learning (ICL) in large language models (LLMs). EASE optimizes the selection and ordering of input-label exemplars without needing model fine-tuning or test-time retrieval. EASE trains a ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and acknowledging that we address a significant problem with a well-founded and novel method.
We would like to address your specific questions below:
>[W1] Similar to [1]
In EASE, we face the challenge of an exploding permutational s... | Summary: The paper introduces EASE, a method for optimizing ICL in LLMs by selecting and ordering exemplars efficiently. Unlike retrieval-based methods that incur extra computation and privacy risks, EASE uses a neural bandit algorithm and optimal transport techniques to find high-quality ordered exemplars without test... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper. We are glad to hear that our approach is unique, the empirical evaluations are extensive on comprehensive baselines, and our explanations are clear.
For the specific concerns and questions raised, we will address them below:
> [W1] O... | Summary: The authors propose EASE, a method for optimizing the selection of few-shot examples for prompting black-box LLMs. EASE is an iterative algorithm that combines NeuralUCB and Optimal Transport. In iteration t, EASE trains a neural network to map embeddings of strings (of few-shot examples) to their average scor... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and acknowledging the strong results, valuable insights, and possibility of future extension.
We will address your concerns below:
> [W1] About "off-the-shelf embeddings"
We justify in the paper that off-the-shelf encoders are "commo... | Rebuttal 1:
Rebuttal: # Global response
We sincerely appreciate the efforts of all our dedicated reviewers. The constructive feedback in the reviews significantly enhanced the quality of our paper. We are very grateful!
In response to the specific questions raised by the reviewers and to address potential weaknesses,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consensus Learning with Deep Sets for Essential Matrix Estimation | Accept (poster) | Summary: This work proposes a simpler yet effective network architecture based on Deep Sets for the estimation of essential matrices.
The method is based on three key properties:
1. A two-stage noise-aware training scheme first uses noise-free inlier matches for training, and then uses original matches and full loss ... | Rebuttal 1:
Rebuttal: We thank the reviewer for these comments.
Q1. UNet:
Our network obtains as input an unordered set of point matches of apriori unknown cardinality. Such input is naturally suitable for DeepSets and other permutation equivariant architectures. Standard convolutional and the UNet architecture canno... | Summary: The paper proposes a deepsets based architecture for essential matrix estimation. The proposed architecture is required to overcome positional nosie, be capable of handling outlier matches which comprise a significant portion of the input data, generalize to unseen image pairs and be capable of handling arbitr... | Rebuttal 1:
Rebuttal: We thank the reviewer for these comments.
Q1.Set transformer ablation:
We tested the SetTransformer in a quick synthetic experiment. We trained both the SetTransformer and our DeepSet model on noise-free data. While our model achieves a highly accurate pose estimation of 86.52% mAP5 (due to the ... | Summary: In this work, authors propose NACNet (Noise Aware Consensus network) for the robust essential matrix estimation. For this purpose authors apply DeepSets based architecture that predicts inlier / outlier class as well as inlier displacement error estimation. Authors also propose a two-stage training: (1st) trai... | Rebuttal 1:
Rebuttal: We thank the reviewer for these comments.
W1.Denoising evaluation:
We include a summary box plot (Figure R.1) of the noise distribution before and after our key-point denoising, measured with respect to the ground truth essential matrix. The median of the mean reprojection error (over each pair)... | Summary: The authors propose a method to tackle the traditional computer vision problem of estimating the essential matrix between two camera views of the same scene based on a set of point matches. The method distinguishes between inlier / outlier matches, and explicitly models the displacement noise in the inlier mat... | Rebuttal 1:
Rebuttal: We thank the reviewer for these comments.
W1. Network capacity and complexity:
We address this question in Table R.1. While our model uses more parameters than NCMNet and BCLNet, which use graph attention architectures, it is 4-6 times faster than these methods and consumes less GPU memory at in... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments.
We addressed each of your questions individually. Please note the attached pdf.
Pdf: /pdf/f7741c49934fbcb69e1799bd936ceb843208495b.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Memorize What Matters: Emergent Scene Decomposition from Multitraverse | Accept (spotlight) | Summary: This paper presents a 3D Gaussian mapping framework that is able to convert multitraverse videos from the same region into a environment while segmenting out 2D ephemeral objects. Leveraging the mlutitraverse data, the scene decomposition emerges in an unsupervised manner, as a result of the consensus in backg... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and helpful suggestions. Please find our detailed response below.
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*Q1. Multiple rounds*
We have added one additional round of segmentation and mapping by incorporating the emerged masks into COLMAP to remove transient objects during Gaussian... | Summary: The paper presents a novel approach for self-supervised scene decomposition using multi-traverse camera data, which results in a high-quality static background scene reconstruction via Gaussian Splatting. The method 3D Gaussian Mapping leverages repeated traversals and feature distillation to capture the emerg... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback and insightful suggestions. Below is our detailed response.
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*Q1: Assumptions on Environmental Stability: The method strictly assumes a stable environment without major geometry change under consistent illumination and weather.*
We agree with you that rel... | Summary: The paper proposes a method called 3DGM that performs foreground-background disentangled 3D reconstruction by capturing the consistent parts from multi-traverse videos. 3DGM leverages 3DGS as the scene reconstruction algorithm, using only camera images as input, and achieves decoupled reconstruction of the 3D ... | Rebuttal 1:
Rebuttal: We are grateful for your insightful feedback and suggestions. Please review our detailed response below.
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*Q1. The end-to-end process for 3DGM appears to be quite complex, with multiple stages involved. However, the paper fails to provide a lucid explanation of the nitty-gritty details involve... | null | null | Rebuttal 1:
Rebuttal: ## Global Rebuttal
We sincerely thank all the reviewers for their insightful comments. We appreciate the positive feedback: **fresh and compelling, innovative approach, evaluation section is thorough, promising and innovative advancement in scene decomposition, valuable contribution to the field o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling Transformer Perception by Exploring Input Manifolds | Reject | Summary: The authors propose a mathematically rigorous methodology based on Riemannian geometry for attributing network importance of tokens in a transformer models input space (e.g. image patches, or ~words in the textual domain). The proposed methodology—whilst based on sound theory—translates into an intuitive algor... | Rebuttal 1:
Rebuttal: **[W1] Feature importance comparisons**
We did not perform an in-depth analysis of the feature importance extraction because it was not the main focus of our work. The primary goals of this work are twofold: first, to theoretically model the exploration of a Transformer's input space, and second,... | Summary: This work attempts to find the set of inputs that generate the same neural network predictions. To this end, the authors interpret the layers of the network as transformations of the input manifold. This interpretation is used to defined equivalence classes over the inputs and to define feature importance. Fin... | Rebuttal 1:
Rebuttal: We will address the questions in the same order as presented by the reviewer.
**1. How does this work differ from https://arxiv.org/abs/2104.13289?**
The two works present several differences, we enlist them down below.
- The theoretical framework in our work can be applied for very general sett... | Summary: The authors present a method for exploring equivalence classes in the input space of Transformer models using a solid mathematical theory. By analyzing the Jacobian of the model, the method reconstructs and navigates these classes, offering a powerful tool for understanding Transformer interpretations and enha... | Rebuttal 1:
Rebuttal: The primary goals of this work are twofold: first, to theoretically model the exploration of a Transformer's input space, and second, to implement this theoretical results using SiMEC, SiMExp, and input exploration techniques to be found in Section 3. These objectives are stated in lines 7-8, 12-1... | Summary: This paper develops a novel theoretical framework grounded in Riemannian geometry for analyzing the input space of Transformer models, and introduce two algorithms, SiMEC and SiMExp, which facilitate the exploration and interpretation of equivalence classes within this input space. These methods offer new insi... | Rebuttal 1:
Rebuttal: We acknowledge the simplicity of our experimental methodology. However, the primary goals of this work are twofold: first, to theoretically model the exploration of a Transformer's input space, and second, to implement this theoretical results using SiMEC, SiMExp, and input exploration techniques ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token | Accept (poster) | Summary: This paper proposes xRAG, a method to map the retriever's embeddings into LM's representation space with one token. While both the retriever and LM are fixed, xRAG trains a simple projector to adapt to them by paraphrase pretraining and context-aware instruction tuning. xRAG can significantly outperform non-re... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We greatly appreciate your time and effort in reviewing our paper. Here, we would like to address your concerns point by point:
- **About multiple documents:**
Please refer to our general response about multiple documents in xRAG.
- **The cost of training**
The training cost of... | Summary: The paper on xRAG presents an innovative approach to context compression in retrieval-augmented generation, achieving significant efficiency gains while maintaining performance. The method's compatibility with various language models and preservation of the plug-and-play nature are notable strengths. However, ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Appreciate your time and effort in reviewing our paper. We would like to address your concerns point by point:
- **Added complexity to the system**
In xRAG, we have emphasized throughout our paper that our key design principle is to add minimal complexity to the existing RAG syst... | Summary: This paper proposes xRAG, a context compression method designed specifically for retrieval-augmented generation. xRAG redefines the use of document embeddings in dense retrieval by integrating them as features from the retrieval modality. It achieves an extreme compression rate to only one token. The authors ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We greatly appreciate your time and effort in reviewing our paper. Here, we would like to address your concerns point by point:
- **Out-of-Domain Generalization**
Thank you for bringing up this issue. If we understand your concerns correctly, they can be divided into two parts:
... | Summary: This paper presents xRAG, a context compression method for Retrieval-Augmented Generation (RAG). Their key idea is to treat document embeddings from dense retrieval as features from a retrieval modality, which allows compressing retrieved documents into a single token. Experiments show that their method can ac... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We greatly appreciate your time and effort in reviewing our paper. Here, we would like to address your concerns point by point:
- **Question about why xRAG could compress a document chunk into a single token**
Thank you for this insightful question. We believe this is the core of... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your time and effort in reviewing our paper. We would like to address the concerns regarding the multiple document expansion of xRAG.
Our work represents a pioneering effort in efficient RAG with modality fusion, a research direction that has been acknowle... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Block Transformer: Global-to-Local Language Modeling for Fast Inference | Accept (poster) | Summary: This paper proposes a Block Transformer architecture which adopts hierarchical global-to-local modeling to mitigate the computational cost and KV cache memory of Self Attention. Block Transformer isolate the global modeling with three blocks: Embedder, Block Decoder, and Token Decoder. Embedder encodes block i... | Rebuttal 1:
Rebuttal: We thank you for the comments, and acknowledging the innovation of trading inference efficiency and parameters, the efficiency of Block Transformer, solid experiments, and good paper organization. We address weaknesses below.
.
**W1. Bigger model size**
Despite bigger model size, Block Transfor... | Summary: The authors introduced the Block Transformer architecture to address the self-attention bottleneck. This is achieved by grouping input tokens into fixed-size blocks and applying self-attention at a corser level throughout the model. At the output layer, a token decoder predicts individual tokens from the block... | Rebuttal 1:
Rebuttal: We thank you for the comments, and we are encouraged that you pointed out the trustworthiness of our extensive experiments with modern setup, and easy-to-follow writing. We address weaknesses below.
**W1. Difference between Block Transformer and Funnel Transformers**
**Major difference in key as... | Summary: The paper introduces the Block Transformer architecture, which aims to improve inference speed in autoregressive language models by adopting a hierarchical global-to-local approach. The architecture separates the global context modeling into lower layers and local detailed interactions into upper layers, thus ... | Rebuttal 1:
Rebuttal: We thank you for the comments, and we are encouraged that you pointed out the improved efficiency of our Block Transformers with extensive experiments. The weakness of our paper is discussed as follows.
**W1. Difference between Block Transformer and MEGABYTE.**
We have discussed the main differ... | Summary: This paper introduces Block Transformer, which is a new architecture that adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks brought by applying self-attention on the global context. In detail, Block Transformer mainly includes three different comp... | Rebuttal 1:
Rebuttal: We thank you for the thoughtful feedback. We are encouraged that you found our research direction interesting and throughput improvement significant. The weaknesses of our paper are discussed as follows.
.
**W1. More parameters are needed to achieve the similar perplexity.**
KV cache IO and mem... | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for providing comprehensive and thoughtful feedback on our manuscript. We appreciate your valuable insights into the strengths and areas for improvement of our work
.
# Core Contributions of Our Work
- **Novelty of approach**: the Block Transformer a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs | Accept (poster) | Summary: In this paper, the authors utilize the De Bruijn Graph Neural Networks (DBGNN) to predict centrality in a dynamic graph, thereby trying to address the challenge of large computation workload on time-respecting paths between pairs of nodes. By giving the problem definition, the authors use DBGNN to predict dyna... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and the positive aspects highlighted in your report. We appreciate the time you invested to assess our manuscript!
For a **clarification of our contribution w.r.t. to DBGNN and the use of additional baseline models**, we kindly refer to our aggregate response. W... | Summary: In the article, the authors explore the potential of using TGNN to predict node centrality metrics, such as temporal closeness and temporal betweenness. Despite the straightforward and intuitive approach presented, the issue of predicting temporal node centralities has not been previously addressed within the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed assessment of our work. While we have addressed some aspects in our aggregate response to all reviewers, in the following we answer your specific questions:
Thank you for your suggestion to compare our method against estimation techniques. We have made addit... | Summary: This paper proposes an algorithm using time-aware graph neural networks (DBGNN) to predict temporal path-based centralities in dynamic graphs. Experimental results show that this method outperforms a static Graph Convolutional Network (GCN) and two state-of-the-art time-aware graph learning techniques in predi... | Rebuttal 1:
Rebuttal: Thank you for the insightful review. In the following, we answer your questions (in addition to our aggregate response).
Considering the **first question about scalability**:
The computational complexity of our model is linear in the number of time-respecting paths of length two in the temporal ... | Summary: This work aims to predict temporal node centralities such as temporal betweenness and closeness centralities using a time-aware graph neural network based on higher-order De Bruijn graph models. The empirical experiments on 13 datasets shows that the proposed DBGNN model can predict temporal centralities effec... | Rebuttal 1:
Rebuttal: We first thank reviewer nntR for the detailed and positive assessment of our work!
In our aggregate response, we clarify some of the aspects mentioned in the weaknesses, such as our **contribution over DBGNN or the use of baseline methods**. Importantly, we have performed additional experiments, ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive assessment. We were delighted that reviewer nntR highlights that we address a "novel and important problem" and propose an "approach with significant speed up" and "good performance". zsX2 praises our "innovative approach" and "comprehensive evaluation" th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach | Accept (poster) | Summary: This paper proposed a Federated Learning model that support Bayesian inference. To alleviate potential bias induced from local client data, a regularisation constraint on model-data mutual information is introduced. The authors show that the MCMC inference with the regularisation can be implemented through the... | Rebuttal 1:
Rebuttal: We would like to thank for the comments, and our response can be found in the following.
**Comment 1-The writing is sometimes difficult to follow:**
1) Due to the space limit of the main text, we placed the theoretical proof, more detailed analysis, and Algorithm 1 in the Appendix of our submis... | Summary: The paper proposes an approach to mitigate training failure in the heterogeneous federated learning setup. The approach combines Bayesian perspective of posterior inference on the client side and regularization of mutual information between weights and data in order to reduce the effect of difference in the lo... | Rebuttal 1:
Rebuttal: We would like to thank for the comments, and our response can be found in the following.
**Comment 1-Further clarification on Our Motivation:**
We agree with the reviewer that in centralized learning, posterior inference is proposed to provide a more reliable assessment of model uncertainty than... | Summary: In this paper, the authors introduce a method for federated learning to bypass problems caused by inter-client data heterogeneity.
For this, they introduce a Bayesian approach with information-theoretic regularizer, that will prevent local models from overfitting. Specifically, the authors add a model-data reg... | Rebuttal 1:
Rebuttal: We would like to thank for the comments, and our response can be found in the following.
**Comment 1-Discussion on tightness of the upper bound:**
First, we are constrained in practice to only leverage local data $S_i$ at individual client $i$ under the FL settings. Alternatively, based on the ... | Summary: The paper considers the problem of Bayesian Federated Learning when there’s data heterogeneity and class imbalance across clients and develops a posterior inference approach for model parameters through mutual information regularization of the data and global parameters in local posteriors. This is achieved vi... | Rebuttal 1:
Rebuttal: We would like to thank for the comments, and our response is as follows.
**Comment 1-Further Clarification on Fig.3:** Fig. 3 visualizes uncertainty calibration and convergence, with quantitative results in Tables 2 and 3 highlighting improvements of our FedMDMI.
i) Figs. 3(a) and 3(b) provide a... | Rebuttal 1:
Rebuttal: Thanks for the comments. Below is our response to common cocerns, with new tables in the attached PDF.
**Comment 1-Complexity analysis :** We provide complexity analysis w.r.t. time, storage, and communication of different methods. We further empirically examine the relationship between computati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Visual Scene Understanding: Incremental Scene Graph Generation | Accept (poster) | Summary: This paper proposes a new task, Continual Scene Graph Generation (CSEGG), where SGG models must dynamically update to recognize new objects and relationships. It introduces a benchmark with three learning regimes: relationship incremental, scene incremental, and relationship generalization. In addition, the pa... | Rebuttal 1:
Rebuttal: **[XGhv.Weakness.1 - CSEGG is limited]** We would like to address the concerns regarding the difference of CSEGG compared to open-vocabulary SGG [a] and zero-shot SGG [b].
First, we respectfully disagree with the reviewer that the significance of CSEGG is limited. The problem setting in open-voc... | Summary: The paper introduces the problem set up of continual learning for image scene graph generation. To this end, they reorganize existing SGG datasets to establish this new benchmarks on three learning scenarios. Next, they present a "Replays via Analysis by Synthesis" (RAS) for generate diverse scene structure, ... | Rebuttal 1:
Rebuttal: **[myHM.Weakness.1 - Focus on Image SGG explanation]** We agree with the reviewer that advancing continual learning in Scene Graph Generation (SGG) to include dynamic or video SGG is a natural and beneficial progression, as it more closely aligns with real-world settings.
However, this research ... | Summary: This paper proposes a benchmark and a framework for incremental scene graph generation using continual learning. They curated the benchmark over an existing benchmark SGG dataset, VG. They proposed three learning scenarios such as relationship incremental, scene incremental and generalization on relationship.... | Rebuttal 1:
Rebuttal: **[Y6eG.Weakness.1-Poor paper presentation]** We appreciate the reviewer’s feedback regarding the presentation of our paper. We would like to note that other reviewers did not mention any issues with the clarity or presentation of our work. However, we value your input and it would be great if you... | null | null | Rebuttal 1:
Rebuttal: We appreciate all the reviewers' feedback. We encourage reviewers to refer to the PDF file containing additional figures. To differentiate these new figures in the rebuttal from those in the main text, we have prefixed them with "R" in the rebuttal. For example, Fig R1 corresponds to Fig 1 in the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fisher Flow Matching for Generative Modeling over Discrete Data | Accept (poster) | Summary: The paper aims to build a Flow Matching [1] based generative model for discrete data. The approach models the discrete data as a categorical distribution that resides on the simplex, thereby translating the problem into continuous flows. Equipped with the Fisher-Rao metric, the simplex is identified as a Riema... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and detailed feedback. We focus here on addressing the key clarification points raised in this review while the global response contains new experiments on molecules and language modelling with additional baselines.
## Riemannian OT is of low-novelty
We acknowl... | Summary: This paper proposes a framework that enables flow matching over a d-dimensional simplex, by instantiating a riemannian flow matching algorithm using the Fisher-Rao metric. Some motivation in connecting the Fisher-Rao metric to natural gradient descent and Riemannian optimal transport is used to justify the cho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort assessing our paper. We now address the key points in the review, while additional experiments are included in the global response and rebuttal PDF.
## Circular justifications for the Fisher Rao
We understand that certain aspects of the theory could ... | Summary: The paper proposed a novel flow-based generative model called *Fisher-Flow* for discrete data. The model uses the Fisher metric to deduce a Riemmanian geometric structure of the statistical manifold. The authors also demonstrated the connections to natural gradient descent and optimal transport. Experiments on... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time and effort they spent on reviewing our work. We are appreciative of the fact that the reviewer found our geometric perspective a “novel extension” to flow matching and that this leads to numerically stable training which is supported by better empir... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and constructive comments. We are grateful that the reviewers appreciated the novelty of our geometric perspective to discrete generative modelling (R 11qA) and that it is a natural application of Riemannian flow matching over simplices (R cNL3). We are al... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sparse High Rank Adapters | Accept (poster) | Summary: This work proposes a new PEFT method, SHiRA, which finetunes 1-2% of pretrained model weights. The authors demonstrate that the resulting sparse adapter weights can be combined in multi-adapter settings with less concept loss than LoRA as the sparse adapters are mostly orthogonal. Further, the authors empirica... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and appreciating the strengths of our work. Below we address the concerns:
1: Thanks for pointing out these related works. SFT is an excellent (and concurrent) work that aims to scale sparse finetuning to LLMs using dynamic masks. In contrast, SHiRA... | Summary: This paper presents a PEFT method, which applies gradient masks for downstream adaptation. It claims three main contributions: rapid adapter switching, lower concept loss, and higher rank. Experiments are conducted in the area of LVMs and LLMs. The proposed method presents SOTA performance and adapter switchin... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and appreciating the strengths of our work and its motivation. Below we address the concerns listed in weaknesses section:
1: Thank you for noticing that we have a lot of contributions in our paper. To summarize our main contribution, we highlight t... | Summary: The authors propose a new type of adapter, SHiRA, for parameter-efficient fine-tuning. SHiRA selects a part of parameters for update and thus triggers both rapid adapter switching and multi-adapter fusion, while traditional methods like LoRA can't have it both ways. Experiments based on Stable Diffusion and LL... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and appreciating the strengths of our work. Below we address the concerns listed in weaknesses section:
1. Please refer to response (B) from the common response section. In summary, existing partial finetuning techniques enable gradients for the ent... | Summary: Low Rank Adaptation (LoRA) is a crucial technique for fine-tuning LLMs and LVMs. This paper addresses two limitations of LoRA: 1. inference overhead while enabling rapid adapter switching; 2. concept loss with multiple adapters. The paper proposes Sparse High Rank Adapter (SHiRA), which directly trains a small... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the strength and contributions of our work to the domain of parameter efficient finetuning. We are happy to see that the reviewer finds the effectiveness of SHiRA surprising. In fact, this is precisely our point since LoRA is a well-established PEFT method and... | Rebuttal 1:
Rebuttal: We thank all reviewers for constructive feedback and for appreciating SHiRA’s many strengths. Overall, reviewers found: (1) SHiRA has significant benefits for mobile/edge deployment due to reduced memory and latency for adapter switching (Reviewers K5NY, 2XqP, ZsNM, Xu4N, AaSE); (2) SHiRA addresse... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces Sparse High Rank Adapter (SHiRA), a novel method to address the limitations of Low Rank Adaptation (LoRA) in some settings. SHiRA aims to minimize inference overhead, facilitate rapid adapter switching, and reduce concept loss when using multiple adapters. By training only 1-2% of the base... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and appreciating the strengths of our work. Below we address the concerns listed in weaknesses section:
1) As discussed in our submitted paper, SHiRA is orthogonal to advanced LoRA variants, e.g., DoRA, and can be efficiently combined with them to i... | null | null | null | null | null | null |
MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models | Accept (poster) | Summary: This paper introduces MATES, a method termed "model-aware data selection with data influence models". MATES is designed to optimize data selection for large language model (LLM) pre-training efficiently. The method dynamically considers various influence models during different stages of pre-training, tailored... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We will address your questions/comments below:
**Weakness 1:** Comparison with no continuous pretraining and full training
**Response:** We first want to clarify that we only followed the Pythia architecture but **didn’t use any of its pretrained weights.*... | Summary: This paper introduces model-aware data selection with data influence models (MATES) that selects high-quality data for pre-training large language models (LLMs). MATES addresses the issue that existing data selection methods don’t adapt to evolving data preferences during pre-training. MATES continuously adapt... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We will address your questions/comments below:
**Weakness & Question 1**: Only comparing MATES to other static baseline methods on the Pythia-410M model. For most of the experiments, the authors only compare random selection.
**Response**: We acknowledge t... | Summary: It is important to carefully select data during the pretraining stage, as the pretraining data are often obtained from web crawling and can be extensive and noisy. Existing methods for data selection include heuristic-based approaches, clustering-based techniques, and the use of influence models. However, thes... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We will address your questions/comments below:
**Weakness 1**: The initialization of the data influence model.
**Response**: We only initialize our data influence model with pretrained BERT at 10k steps and continuously fine-tune it at the following steps.... | Summary: The paper proposes a new method “MATES,” which aims to select pretraining data using a reference high-quality data source. MATES uses an estimated influence function to iteratively select the most influential datapoints. Experiments on the Pythia model + dataset show good promise over existing data curation me... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We will address your questions/comments below:
**Weakness 1:** Loose writing and missing context.
**Response:** Thank you for pointing it out! We will carefully revise the paper from the following perspectives:
1. Clarify the experimental setup, as mentio... | Rebuttal 1:
Rebuttal: ## 0 Overview
First, we thank all the reviewers for their great efforts and insightful feedback.
In this post, we summarize positive points from the reviews, clarify the experimental setup, and address the shared questions proposed by the reviewers with additional experiments to support and stre... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable DP-SGD: Shuffling vs. Poisson Subsampling | Accept (poster) | Summary: DP-SGD is one of the must common algorithms currently deployed for performing machine learning tasks while maintaining differential privacy. As the "S" in its name reminds us, the gradient is not computed over the full dataset at each turn, but instead over a subset sampled from it. The commonly used privacy a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments.
> add a comparison of the the various privacy analyses to the upper bounds provided by existing literature on general shuffle model
While the amplification bounds for shuffling such as by Feldman et al. provide upper bounds, we did not conside... | Summary: This paper provides theoretical and empirical analyses of three different DP-SGD minibatch sampling schemes, and also implement an efficient beam pipeline for using Poisson sampling in practice via a truncation-based approach. Theoretically, they derive new lower bounds for the privacy accounting / noise cali... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments.
> quantify the gap between Poisson sampling and Truncated Poisson sampling
While it is likely that our analysis for truncated Poisson sampling is not optimal, it is reasonable enough in practice. The loss due to truncation is very minimal to t... | Summary: The paper focuses on practical implementations of DP training of ML models at scale in the multi-epoch setting. The contribution is two-fold: the paper gives a rigorous analysis for a practical version of Poisson subsampled DP-SGD where the batch size is upper bounded, and proposes lower $(\varepsilon,\delta)$... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments.
> novelty over prior work [Similar to Reviewer vHjL]
While it may seem that our privacy analysis used some standard techniques (discretization, post-processing and PLD accounting), it was a priori unclear if such a simple method would be effec... | Summary: This paper investigates the utility of models trained with DP-SGD based on previous findings on the gap in privacy guarantee between shuffled batch sampling (commonly used in practice) and Poisson subsampling (used in theoretical analysis) for private training. A scalable implementation of Posson subsampling i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments.
> techniques for extending to multi-epoch are straightforward. [Similar to Reviewer 1Uvo]
While it may seem that our privacy analysis used some standard techniques (discretization, post-processing and PLD accounting), it was a priori unclear i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments. In the attached pdf, we plot the noise multiplier $\sigma$ values against the number of epochs for different accounting methods, to answer a comment from Reviewer 9tRV.
Pdf: /pdf/ce8be1d12b9f4c7daf799376c70d37c363510a9d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference | Accept (poster) | Summary: The paper addresses the incompleteness of current sequential covariate adjustment criteria in causal inference. It introduces a sound and complete graphical criterion for sequential covariate adjustment, termed Sequential Adjustment Criterion (SAC), and provides an algorithm for identifying a minimal sequentia... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback.
---
> For example, in Eq. 2, the subscript should be used to distinguish the orders of variables, specially, in the left-hand side of the equation.
In Eq. (2) included in Definition 3, $\mathbf{X}$ and $\mathbf{Y}$ are already defined as $(\mathbf{X}_1,\cdo... | Summary: The paper investigates the problem of identifying total causal effects via sequential covariate adjustments, which generalizes the standard, well-studied covariate adjustment. Unlike the standard static case, where there exists sound and complete graphical identification criterion, for the sequential counterpa... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and detailed comments.
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> For example, in Definition 3 the authors consider H_i and in Eq. (2) they use in the formula h_{j-1}. So, what is the definition of h_{-1} and of h_{0}? Note that according to the definition of H_i, for H_{-1}, the sets X^{(-1)} a... | Summary: To estimate causal effects given observational data, previous works provide graphical criterion that is not complete in the sequential and multi-outcome cases. The following work extends the complete adjustment criterion to these cases. The non-completeness of previous sequential and multi-outcome criterion is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback!
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> The work does seem to be a simple extension of AC to the sequential case.
Our proposed method is an extension of the AC, similar to how the sequential back-door adjustment extends the back-door adjustment. The principle of our me... | Summary: This paper develops a complete and constructive criterion for sequential covariate adjustment. An algorithm is provided to identify a minimal sequential covariate adjustment set, which is efficient by ensuring that no unnecessary vertices are included.
Strengths: - The problem considered is important in causa... | Rebuttal 1:
Rebuttal: Thank you for sharing your thoughts and feedback with us.
> Simulation studies are not provided, though it is fine for a theory-focused paper.
As you noted, our work is theory-oriented, presenting a complete criterion for sequential covariate adjustment and introducing an algorithm for construct... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation | Accept (poster) | Summary: This paper introduces a dual strategy for real-world image restoration: GenIR and DreamClear. GenIR, a novel data curation pipeline, addresses dataset limitations by creating a large-scale dataset of one million high-quality 2K images. DreamClear, a Diffusion Transformer-based model, utilizes generative priors... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the valuable and positive comments on our work. We address the questions and clarify the issues accordingly as described below.
**Q1: Quality assessment of the dataset**
**[Reply]** Thank you for this valuable advice. We calculate the FID value between di... | Summary: The paper introduces a dual strategy to tackle the challenges of image restoration (IR) datasets and the development of high-capacity models for image restoration. This strategy comprises:
GenIR: An innovative data curation pipeline designed to bypass the laborious data crawling process, providing a privacy-sa... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the valuable and positive comments on our work. We address the questions and clarify the issues accordingly as described below.
**Q1: About privacy preservation**
**[Reply]** Thanks for this valuable advice. To minimize the risk of generating images that ... | Summary: The authors propose GenIR, a privacy-safe automated pipeline that generates a large-scale dataset of one million high-quality images for training of the image restoration (IR) models. Additionally, they introduce DreamClear, a IR model that seamlessly integrates degradation priors into diffusion-based IR mode... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the valuable and positive comments on our work. We address the questions and clarify the issues accordingly as described below.
**Q1: About the generated dataset**
**[Reply]** Thanks for your valuable suggestion. The primary goal of the proposed GenIR is ... | Summary: The paper introduces "DreamClear," a high-capacity image restoration model, and "GenIR," an innovative data curation pipeline for image restoration (IR) tasks. DreamClear leverages Diffusion Transformer (DiT) models and generative priors from text-to-image (T2I) diffusion models, combined with multi-modal larg... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the valuable and positive comments on our work. We address the questions and clarify the issues accordingly as described below.
**Q1: About Model Implementation**
**[Reply]** Thanks. We explain the motivation behind our model design as follows:
* Dual Bra... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for their valuable and positive comments on our work. This is a **global response** to reviewers' questions.
**1. Efficiency Comparison**
We provide the efficiency comparison results as follows:
| | | | |
| :-------: | :------: | :------: | :------: ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transfer Learning for Diffusion Models | Accept (poster) | Summary: This paper introduces a new framework, the Transfer Guided Diffusion Process (TGDP), for transferring a pre-trained diffusion model from the source domain to the target domain. They connect the score function for the target domain and the score function of the source domain with a guidance term related to the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We appreciate the time you spent on the paper. Below, we address your concerns and comments.
**Q**: *The authors could discuss what is the Lemma 3.2 in the conditional version. What is the Lamma 3.2 in the conditional version?*
**A**: Than... | Summary: This paper addresses the transfer learning problem for diffusion models, specifically adapting pre-trained diffusion models to downstream datasets, particularly when the data size is small. Traditional parameter-efficient fine-tuning methods often use pre-trained models as parameter initialization, selectively... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We appreciate the time you spent on the paper. Below we address the concerns and comments that you have provided.
**Q**: *The title "Transfer Learning" is too broad. The paper focuses solely on (few-shot and supervised) domain adaptation, w... | Summary: The paper proposes an approach for transfer learning based on score-based generative models and density-ratio estimation. The authors show that in order to transfer a trained diffusion model from a source to a target domain, all that is needed is the expectation of density ratios of source and target domains. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We appreciate the time you spent on the paper. Below we address the concerns and comments that you have provided.
**Q**: *The first experiment on a 2D Gaussian illustrates the performance of the method in comparison to baselines decently, ... | Summary: introduces a novel approach called Transfer Guided Diffusion Process (TGDP) for transferring knowledge from a pre-trained diffusion model in the source domain to a target domain with limited data. The authors present the methodology for transferring knowledge, including the formulation of the guidance network ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We appreciate the time you spent on the paper. Below we address the concerns and comments that you have provided.
**Q**: *The paper primarily validates TGDP on Gaussian mixture simulations and ECG datasets. Its performance on other types of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We appreciate the time you spent on the paper. We summarize the positive feedback that we received as follows:
Motivation and Novelty: The whole framework is innovative and reasonable (9nph); the proposed method for transfer learnin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift | Accept (poster) | Summary: The paper addresses the significant challenge of data heterogeneity and distributed concept drift in federated learning. The authors propose a novel framework, which integrates classifier clustering and feature alignment to improve model performance and collaboration among clients facing different concept drif... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Below we address specific questions:
> W1 & Q1: Computation Overhead: The proposed FedCCFA framework involves additional computational steps to train balanced classifiers [...] exploring more efficient methods to achieve balanced classif... | Summary: This paper explores the impact of distributed concept drift on federated learning (FL), and proposes a novel FL framework, FedCCFA, to adapt to distributed concept drift with data heterogeneity (i.e., label distribution shift). Extensive experiments demonstrated the effectiveness and generality of the proposed... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Below we address specific questions.
> W1: In the introduction, the authors use the distribution of medical images [...] It is recommended that the authors directly use label distribution shift to describe the problem addressed in the paper.
A1: We belie... | Summary: The paper proposes a federated learning framework called FedCCFA, which addresses the challenges posed by distributed concept drift and data heterogeneity. The authors introduce innovative solutions such as classifier clustering and adaptive feature alignment to enhance collaboration and improve model performa... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and kind words to our work. Below we address specific questions:
> W1: I am unable to fully understand the motivation of the paper. Figure 1 does not clearly illustrate how Decoupled Clustering [...] the underlying reasons remain unclear. Why does introducing ... | Summary: This paper proposes to consider two cross-coupled important issues, i.e., federated learning and concept drift. To address such a challenging problem, the FedCCFA framework has been proposed composed of classifier clustering and feature alignment modules. The former is designed to cope with concept drift and e... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and kind words to our work. Below we address specific questions:
> W1: Lack of a framework pipeline to intuitively explain the overall design.
A1: Thank you for your valuable feedback. Please see the attached PDF in the top-level comment for our framework pip... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all reviewers for providing constructive comments that helped us to improve our paper. We are encouraged that reviews think our paper:
- "The idea of letting the global/local models evolve over time is interesting and relevant" (Reviewer U5Wz),
- "A relativ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a solutions based on clustering to group classifiers in the federated learning setting. Such an approach is meant to deal with concept drift in data so as to track and adapt the evolutions of the classifiers of the clients in federated learning. On of the core points is to align features a... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and kind words to our work. Below we address specific questions:
> W1: the partitioning of feature extractor + classifier is relevant and requires to share a common feature extractor. It is not clear to this reviewer whether the feature extractor is fixed or a... | null | null | null | null | null | null |
CogVLM: Visual Expert for Pretrained Language Models | Accept (poster) | Summary: This paper introduce CogVLM, a powerful open-source visual language foundation model. CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. The contributions are summarized:
1. introduce the CogVLM model, which... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough review and insightful feedback. Your comments are invaluable in helping us improve our work. We address each of your points below:
## 1. Parameter efficiency and LoRA consideration
Thank you for this good question. The Visual Expert indeed in... | Summary: The paper introduces CogVLM, an new open-source visual language foundation model. Contrary to the popular method of adapting LLMs by fine-tuning their original weights, CogVLM introduces new weights specifically for processing the visual tokens. Concretely, CogVLM copies all weights of the LLM to form visual e... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough review and insightful feedback. Your comments have been invaluable in helping us improve our work. We address each of your points below:
## 1. Prompting differences between tasks
Thank you for highlighting this important aspect. In our approa... | Summary: This paper aims to add multimodal capability while maintaining the language capability of LLM. The authors thus propose a feature fusion strategy, without sacrificing any performance on NLP tasks. The experimental results on multiple datasets are impressive, which demonstrate the validity of the proposed metho... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough review and insightful feedback. Your comments are invaluable in helping us improve our work. We are pleased to address each of your points below:
## 1. MME Benchmark Results
Thank you for suggesting the inclusion of MME benchmark results. We ... | Summary: This paper proposed a large vision language model, CogVLM, which shows capabilities in various benchmarks. In contrast to popular solutions that fuse the vision and language token in LLM with shared trainable parameters, CogVLM bridges the gap between the frozen pre-trained language models and image encoders b... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough review and constructive feedback on our paper. Your insights are valuable for improving our work. We address each of your points below:
## 1. Increased number of parameters and memory usage
We acknowledge that the Visual Expert introduces add... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding the Transferability of Representations via Task-Relatedness | Accept (poster) | Summary: This paper considers transfer learning in a general cross-domain, cross-task setting. It introduces the concept of "task-relatedness" into measuring whether transfer learning can be successful. Specifically, task-relatedness consists of (1) a reference loss term for measuring the difference in class prior dist... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are delighted that you found our analysis clear and method practically valuable. We respond to your concerns below:
> 1. Limited novelty: (A) the idea ... is not new. (B) the proposed method ... justification.
A: While we agree with the reviewer that dist... | Summary: This paper proposes an analysis that analyzes the transferability of the representations of pre-trained models to downstream tasks in terms of their relatedness to a given reference task. It aims to understand when the knowledge of these pre-trained models can be transferred to obtain high-performing models on... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We respond to your concerns below:
> Compare with [1,2,3,4,5]
We thank the reviewers for pointing us to additional related works. We briefly compare our approach to them here and will include the discussion in the main paper.
[1,3,5]: study the problem of f... | Summary: This paper analyzes transfer learning from the perspective of task relatedness, a model for transforming from a reference task (pretraining task) to the target task is proposed, which consist of prior transform, label transform, and feature transform.
The task relatedness is then measured as the distribution ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you found our work intuitive. We respond to your concerns below:
> The datasets for evaluation and empirical studies seem to be small in scale, for example, MNIST and CIFAR which is small in the image resolution, or Aircraft and DTD which is... | Summary: The paper presents a way of assessing the transferability of representations from a pre-trained model to a target task by assessing the impact of the pre-trained model's representations on a reference task. By "transforming" the reference task into the target task, the paper produces a bound on the training lo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We respond to your concerns below:
> Utility: A) I was ... methods. B) Specifically, ... target task.
A: While Alg. 1 requires labels of the target task, we discuss in Lines 329-347 how to use Alg. 1 for the case when target labels are unavailable. The main ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback and insightful questions. We are encouraged that all the reviewers found the main contribution of the paper of rigorously analyzing transferability to target tasks in a cross-domain cross-task transfer learning setting using task-relatedness n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universal Rates of Empirical Risk Minimization | Accept (poster) | Summary: In this paper, the authors build on earlier work in learning theory that provides an alternative to the classical PAC theory in the setting of realizable binary classification. That earlier work considered *universal* learning rates, where a rate is universal if the data source is fixed across sample sizes (a... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments and insightful suggestions on our paper. Below, we provide detailed responses to your concerns and questions:
1. For the assumption of realizability, as is traditional in the learning theory literature, we first focus on the realizable case to build insight... | Summary: This paper studies the performance of ERM on realizable binary classification problems in the "Universal learning" framework of [1]. Specifically, the original work [1] showed that the optimal universal rate of convergence was in general not achieved by ERM procedures. Nevertheless, characterizing the universa... | Rebuttal 1:
Rebuttal: Thank you for your comments on our paper. We are happy to know that you found the problem and our results interesting.
1. For the weakness, we adopted the same universal learning framework of the work of [Bousquet et al., 2021] as well as some related definitions. However, our theory differs a l... | Summary: The main goal of the proposed work is to understand the learning procedure with a focus on Empirical risk minimization. The authors claim to provide a complete picture of the four possibilities of different learning rates by ERM. The work also introduces many new concepts related to combinatorial dimensions.
... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and positive remarks on our paper.
For the weakness you mentioned, we would like to point out that, in addition to numerous practical works, the interdisciplinary NeurIPS conference also has a rich history of many seminal purely-theoretical works in learnin... | Summary: The work contributes to a recent line of work on universal learning rates, i.e. rates with distribution-dependent constants. It characterizes the universal learning rates for the ERM learning rule (specially, the "worst-case" version thereof), showing a partition into four possible optimal universal ERM rates ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and helpful suggestions on our paper. Below, we provide detailed responses to your comments and questions:
1. For the weakness, we will definitely try to improve the writing and add intuitive explanations in the final version.
2. To answer the first question... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the universal rates for ERM learners in the realizable case. While a complete characterization of universal rates for PAC has been studied, it was previously not clear what are the universal rates for the popular ERM learners. This papers presents a tetrachotomy for the ERM learners. In doin... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback on our paper. Below, we provide detailed responses to your questions:
1. ERM learners are quite different from the designed optimal learners constructed in [Bousquet et al., 2021]. Our analysis reveals a completely different characterization of when ERM lear... | null | null | null | null | null | null |
Diffusion Imitation from Observation | Accept (poster) | Summary: This paper introduces Diffusion Imitation from Observation, an adversarial imitation learning method using a conditional diffusion model as the discriminator for policy learning. DIFO learns a diffusion model on expert and agent state transitions, with an auxiliary binary classification objective to discrimina... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> This method requires online interactions to train the diffusion discriminator and the policy. One popular alternative approach in this setting (online + expert demonstra... | Summary: The paper introduces a novel method named Diffusion Imitation from Observation (DIFO), which integrates diffusion models into the adversarial imitation learning from observation (LfO) framework. Traditional adversarial imitation learning methods often struggle with hyperparameter sensitivity and training stabi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> As shown in Section 5.7, using the diffusion loss alone demonstrates very poor results. The major contributing factor is the BCE loss and the discriminator as a whole.
... | Summary: This paper introduces Diffusion Imitation from Observation (DIFO), a novel approach to Imitation Learning from Observation (ILfO). DIFO innovates by employing a diffusion model as the discriminator, departing from the conventional feed-forward neural network approach. The authors leverage the connection betwee... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> There are other methods [1, 2, 3] that use metrics, i.e. likelihood, ELBO and entropy, from generative model for ILfO. I suggest discussing them in the related works.
W... | Summary: This paper leverages a diffusion model to learn a expert-state transition and additionally a discriminator that can differentiate expert and agent states. The paper conducts experiments on standard RL environments and demonstrate better performance and data efficiency.
Strengths: - The idea is simple and its ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> the paper indicates that a "smoother contour" allows for bringing a learning agent closer to the expert. I was wondering if this property is only useful for specific env... | Rebuttal 1:
Rebuttal: The attached PDF file contains the following content:
- **Two additional baselines (DePO and OT) and the results [Reviewer AzJp, Reviewer Dab5, Reviewer P9FE]**: We additionally include two recent and relevant baselines: Decoupled Policy Optimization (DePO, 2022) [1] and Optimal Transport (OT, 202... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph neural networks and non-commuting operators | Accept (poster) | Summary: This paper considers the tasks of extending GNNs to multiple graphs with a shared node set (and the edges representing different types of relations).
In order to do this in a principled manner, the authors consider the algebra of non-commutative polynomials and provide a detailed analysis of stability to pert... | Rebuttal 1:
Rebuttal: Thank you for your thorough and positive review. See below our comments and answers to your questions.
>Discuss related work ...
We thank the reviewer for pointing this out. We have added all the above references as relevant related work in the Introduction. The new paragraph is in a comment fo... | Summary: This paper introduces a new type of neural network called graph-tuple neural networks (GtNN) that can handle multiple graphs with the same set of nodes and non-commuting graph operators. The authors develop a mathematical theory to show the stability and transferability of GtNNs and derive related bounds, prov... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. We address them below:
>No experiments on real data to prove the transferability and stability gain using their method compared to GNN.
We have included some initial results on using operator networks in real-world data in a new final Secti... | Summary: This paper considers the node-level learning task with several graphs sharing the same vertex set called graph-tuple neural networks (GtNNs). The stability and transferability of GtNNs are studied using properties of non-commuting non-expansive operators. The authors show that all GtNNs are transferable on con... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and positive feedback. We address each of your comments and questions below:
> The tightness of the bounds is only demonstrated through numerical experiments rather than formal theoretical analysis.
Thank you for pointing this out. We have added a theoretic... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough reading and constructive comments. We are encouraged that all three reviewers found the paper interesting. The paper’s main weakness, according to the reviews, was the lack of a more thorough empirical evaluation. To address this, we include two additional... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation | Accept (poster) | Summary: The paper proposes a framework to generate animated surfaces from input videos. It has a static step to generate a base triangle mesh, and textures represented by 3D Gaussians. A subsequent dynamic step changes the positions of the mesh vertices and the gaussians. The framework leverages on Zero123 and SuGaR t... | Rebuttal 1:
Rebuttal: The authors are grateful for the insightful feedback and support from the reviewer. Below we address the mentioned concerns separately.
### **Q1: Video qualitative results.**
**A**: In the first paragraph of appendix, we have provided the link to our anonymous project page, which contains the inp... | Summary: DreamMesh4D is a novel framework for transforming static images into 4D dynamic meshes. It refines object geometry and texture using a Gaussian-mesh hybrid and a geodesic-based deformation graph. A new skinning algorithm combines the best of LBS and DQS for enhanced deformation. The method excels in creating h... | Rebuttal 1:
Rebuttal: The authors are grateful for the valuable and in-depth feedback of the reviewer. Below we address the mentioned concerns separately.
### **Q1: Limited novelty compared to previous methods.**
**A**: Previous methods for monocular video-to-4D generation include Consistent4D, DreamGaussian4D, 4DGen ... | Summary: This paper proposes DreamMesh4D that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D objects from a monocular video. The authors bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices... | Rebuttal 1:
Rebuttal: The authors are grateful for the thoughtful feedback and valuable questions. Thanks for the support for this work! Below we will address the questions and concerns separately.
### **Q1: The difference between the proposed method and SuGaR.**
**A**: While SuGaR proposes a pipeline reconstructing s... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable feedback and agree that:
* we address a challenging problem to generate dynamic objects from a monocular video;
* the method to generate dynamic meshes through a static-to-dynamic optimization process is new, and the losses terms make sense and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback | Accept (poster) | Summary: This paper studies online learning in low-rank MDPs with adversarial losses (having linear structure), and derives a set of regret guarantees:
1. A model-based and inefficient algorithm ensures $T^{2/3}$ regret, assuming the loss feature vector is unknown. The algorithm has a two-stage design: the first stage... | Rebuttal 1:
Rebuttal: Thank you for your support and valuable feedback. We will adopt your writing suggestions in our future version.
Your questions about $poly(A)$ dependence in regrets and the difficulty of adaptive adversaries are explained in **Q1** and **Q3** of the global response. We answer your other questions... | Summary: This paper studies adversarial Low-Rank MDPs with unknown transition and bandit feedback. The authors give three main results, targeting either tighter regret or computational efficiency. The authors show that the linear structure of the reward function is necessary for the case of bandit feedback to achieve r... | Rebuttal 1:
Rebuttal: Thank you for your support and valuable feedback. Your question about $poly(A)$ dependence in regrets is explained in **Q1** of the global response. We answer your other questions below.
**Q1**: *The authors should provide more discussions on the computational complexity of the proposed algorit... | Summary: This work initiates the study on learning adversarial low-rank MDPs with bandit feedback. The authors propose an inefficient algorithm with $T^{2/3}$ expected regret, and an oracle-efficient algorithm with $T^{5/6}$ expected regret. Further, the authors also show an oracle-efficient algorithm with $T^{5/6}$ hi... | Rebuttal 1:
Rebuttal: Thank you for your support and valuable feedback. Your question about why we use log-barrier is explained in **Q2** of the global response. We answer your other questions below.
**Q1**: *The first algorithm is similar to learning adversarial MDPs by reducing to learning adversarial bandits. I w... | Summary: The paper explores regret minimization in low-rank MDPs with adversarial bandit loss and unknown transitions. This means that the transition can be expressed as $P(x' \mid x,a ) = \phi^\star (x,a)^T\mu^\star(x')$ for some unknown $\phi^\star$ and $\mu^\star$ and the loss is also linear in $\phi^\star$. They c... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for your valuable feedback. Your questions about the use of the log-barrier and the difficulty of dealing with an adaptive adversary are explained in **Q2** and **Q3** of the global response. We answer your other questions below.
**Q1**: *In Algorithm 1, mos... | Rebuttal 1:
Rebuttal: ## **Global Response**
We thank all the reviewers for their valuable feedback. We would like to clarify several common concerns here.
**Q1** : *The $poly(A)$ dependence in the regret bound looks not ideal. Is it common in low-rank MDP literature?*
**A**: We would like to point out that poly-dep... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models | Accept (spotlight) | Summary: The paper presents a novel initialization strategy for LoRA finetuning where A and B are initialized according to the top r singular values and the pre-trained weights are initialzed with the remaining components.
The authors show that this decomposition can help finetuning converge faster and behaves favorab... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive review. To answer your questions thoroughly, we have conducted numerous new experiments. We hope these efforts will address your concerns.
**Q1: Need to provide error bars in the results**
**A1**: The original paper includes extensive experiments co... | Summary: The authors present PiSSA, a relatively simple change to the LoRA framework leading to a large amount of demonstrated benefits. PiSSA proceeds by adjusting the initialization step of standard LoRA; rather than freezing the original weight matrix W and learning a low-rank perturbation W' = W + BA, PiSSA instea... | Rebuttal 1:
Rebuttal: Thank you for recognizing the originality, quality, and significance of our article. We also appreciate your suggestions for improving the writing. As we cannot modify the original text during the rebuttal period, we will incorporate your recommendations in the camera-ready version.
**Q1: In the ... | Summary: In this paper, the authors proposed a modified low-rank adaptation (LoRA) method for fine-tuning large pretrained models. Specifically, the proposed method initializes the A and B matrices with singular matrices and freezes the weight matrix to be the residual. Further, the authors provide a quantization step ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of PiSSA as a simple yet effective method, and for your appreciation of the article's quantitative and qualitative analysis. Here are responses to the concerns raised:
**Q1: Provide more insight, for example, comparing the subspace of the gradient of conventional Lo... | Summary: The paper presents a method for creating lora-like fine-tuning adapters for base models. The idea is to initialize W, A and B matrices in LoRA from SVD decomposition. W is consists of less important ranks while A and B consist of the most important ranks. This implies that less important principle components o... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the originality of PiSSA, the quality of our experiments, and our contributions to the community. Here are the answers to your queries:
**Q1: Comparison with advanced LoRA adapter methods, e.g., DoRA**
**A1**: In our paper, we demonstrated PiSSA's effectiveness throug... | Rebuttal 1:
Rebuttal: 1. **Contribution**
1. This paper analyzes the gradient of LoRA, showing that $A$ and $B$ initially have zero gradients and random gradient directions, respectively. This leads to slow convergence and might result in suboptimal local minimum points found.
2. We propose the PiSSA initializa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models | Accept (poster) | Summary: The paper investigates whether speculative sampling is compatible with watermarking for LLMs.
Strengths: S1. This paper is original in the landscape of LLM watermarking.
S2. This paper shows an interesting "no-go" theoretical result (Th. 1).
S3. This paper proposes 2 designs sustaining either the sampling ... | Rebuttal 1:
Rebuttal: We are thrilled to receive such a meticulous and knowledgeable review of our research. It is a privilege to have our paper assessed by a reviewer with such extensive knowledge in the landscape of LLM watermarking. Your acknowledgment of the originality of our work is truly rewarding. In the follow... | Summary: This paper explores the inherent trade-off between watermark strength and speculative sampling efficiency in large language models. A no-go theorem is presented, proving that it is impossible to maintain the highest watermark strength and sampling efficiency simultaneously. This paper also proposes a framework... | Rebuttal 1:
Rebuttal: Thank you for spending time reviewing our work. However, I am afraid that your 4-point rating may be based on incorrect premise.
We would like to clarify a misunderstanding in your comment. You claimed that our paper only used Llama-7b and Llama-68m models. If you read lines 273-278, you will fin... | Summary: This paper studies the trade-offs between sampling efficiency and watermark strength to see if LLMs can generate watermarked output efficiently. It is proven in this work that it is not possible to simultaneously maintain the highest watermark strength and the highest sampling efficiency. Therefore, upon the n... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and recognizing our research as the first study into the relationship between sampling efficiency and watermark strength.
Regarding the experiment section, we agree that it appears to be relatively short in the main paper due to space limit. To provide a more ... | Summary: This paper proposes it is impossible to maintain the highest watermark strength and sampling efficiency simultaneously for content generation by considering integrating an unbiased watermarking method [1] and speculative sampling strategy [2] [3], where they provide rigorous theoretical analysis and empirical ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for recognizing the significance of our work. We are grateful for the opportunity to address your questions.
> Why do the authors mean by "naively applying speculative sampling to a watermarked target distribution may significantly reduce the overlap pro... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Sample-Communication Complexity Trade-off in Federated Q-Learning | Accept (oral) | Summary: This paper addresses the challenge of Federated Q-learning, focusing on the trade-off between sample complexity and communication complexity. Federated Q-learning involves multiple agents collaboratively learning the optimal Q-function for an infinite horizon Markov Decision Process (MDP) with finite state and... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your constructive feedback. We appreciate the time and effort you spent on our paper and the helpful comments you provided. Please find our itemized responses to your questions below.
- _While the main focus of this work is theoretical, the paper could benefi... | Summary: This paper discusses the sample and communication complexity of federated tabular Q-learning. The main contributions can be summarized as follows. First, the paper provides a lower bound on the communication
cost to guarantee a linear speed-up with respect to the number of agents. Then, it proposes a novel F... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your constructive feedback. We appreciate the time and effort you spent on our paper and the helpful comments you provided. Please find our itemized responses to your questions below.
Q1. Can you quantify the benefit of variance reduction in the upper bound? ... | Summary: This paper investigates the sample and communication complexities of federated Q-learning with intermittent central aggregation of the Q-value function.
The authors demonstrate that to achieve any speedup in sample complexity through federated collaboration, the communication complexity must be at least $\Omeg... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your constructive feedback. We appreciate the time and effort you spent on our paper and the helpful comments you provided. Please find our itemized responses to your questions below.
- _Several papers report that a one-shot average is sufficient to achieve l... | null | null | Rebuttal 1:
Rebuttal: Based on the suggestion by Reviewer YcCi, we have performed two empirical studies and included their results in the attached PDF. We refer the reader to the response to Reviewer YcCi for additional details about the experiments and a discussion of the results.
Pdf: /pdf/16ef40bb8152ec5aa49e1a31eaf... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training | Reject | Summary: The paper revisited the problem of label leakage in split learning in the context of fine-tuning large models with parameter-efficient training. Based on modern use cases, they proposed two privacy-preserving protections for gradients and activations during split learning. The proposed methods are evaluated on... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful feedback. We address their concerns and questions below.
>The evaluation part lacks the comparison to existing privacy-preserving solutions over label leakage and some trivial solutions such as directly applying differential privacy, which is easy to ... | Summary: This study addresses the privacy concerns associated with the fine-tuning of Large Language Models (LLMs), focusing on SplitNN. It explores how gradients and activations can leak data, potentially allowing attackers to reconstruct original data sets. In experiments, the proposed method reduces label leakage wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and provide responses to their concerns hereafter.
> The claim that backpropagation is "conditionally linear" is not sufficiently rigorous. The manuscript suggests that $\text{backprop}(x, \theta, g_h{+}z){ + }\text{backprop}(x, \theta, g_h{-}z) = \text{ba... | Summary: This paper addresses privacy leakage during API-based Parameter Efficient Fine-Tuning (PEFT). Their designed P3EFT is a multi-party split learning algorithm that leverages PEFT adjustments to uphold privacy with minimal performance overhead. Their method proves competitive in both multi-party and two-party set... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and answer their questions below.
>Their approach shows limited privacy improvement compared to the scenario Without LoRAs, as indicated in Tables 1, 2, and 3, thereby restricting the overall benefits.
The baseline 'Without LoRAs' represents the best-case... | Summary: This paper proposes an algorithm to preserve the label privacy while achieve good accuracy in the split learning regime. The algorithm is used in parameter-efficient fine-tuning and empirically tested on some language models.
Strengths: The paper clearly presents its motivation and contribution. The modificat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and address their concerns and questions below.
>The main concern is the scalability of this method. For one iteration, the number of backpropagation is m (at least 2), which is too slow even for PEFT. The computation cost of PEFT is 2/3 of full training s... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their detailed feedback.
We are pleased that the reviewers **1WCr** and **S8Y2** concur with our assessment regarding the critical importance of private API-based fine-tuning of large models in the contemporary landscape. We also ap... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis | Accept (poster) | Summary: This paper proposes LE3D, an HDR 3D Gaussian Splatting method with Cone Scatter Initialization, Color MLP, and depth distortion. Specifically, this paper introduces the Cone Scatter Initialization to enrich the estimation of SfM. The Color MLP aims to represent the RAW linear color space. The goal of depth dis... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation, effectiveness, and thoroughness of our experiments on LE3D. We also appreciate your valuable comments! Below, we address the specific points.
1. **Does CSI really matter?** We have done our ablation studies on CSI in our main paper, please refer to Fig. 4... | Summary: The paper LE3D proposes training a 3DGS representation with raw images, instead of preprocessed LDR images. This allows more accurate scene recovery in low-light environments and unlocks applications such as HDR rendering or refocusing. While there has been prior work on training neural scene representations w... | Rebuttal 1:
Rebuttal: Thank you for recognizing our motivation and the structure of our paper, as well as your valuable comments! Below, we address the specific points.
1. **About the related work**: we will add and discuss them in our next version. We will add more discussion for sRGB images (both w/wo multi-exposure... | Summary: LE3D is a novel method for real-time novel view synthesis, HDR rendering, refocusing, and tone-mapping changes from RAW images, especially for nighttime scenes. It addresses the limitations of previous volumetric rendering methods by introducing Cone Scatter Initialization to improve SfM estimatied pointclouds... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our writing and your valuable comments! Below, we address the specific points.
1. **Request for more insights on MLP and SH**: We have analyzed the instability factors of SH during training and provided more statistical data to demonstrate its limited representat... | Summary: The authors aim to leverage 3D Gaussian Splatting with a few additions and changes in order to perform HDR view synthesis. The authors propose that with the addition of cone-scattering to the Structure from Motion initialization, replacement of Spherical Harmonics with a simple MLP for color representation, an... | Rebuttal 1:
Rebuttal: Thank you for recognizing our structural regularization and the thoroughness of our experiments! Below, we address the specific points.
1. **About the typos**: thanks for pointing them out! We will definitely fix them in the next version of our paper.
2. **About the difference between LE3D and c... | Rebuttal 1:
Rebuttal: First and foremost, we would like to express our gratitude to all the reviewers and the Area Chair for their diligent review. We sincerely appreciate your recognition of our writing, your appreciation of the effects shown in our demo video, and your identification of typos and weaknesses in our pa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games | Accept (poster) | Summary: This paper introduces a new variant of fictitious play where agents respond to the last actions of team members. The authors need to improve their academic writing skills largely. The expression between paragraphs and sentences lacks logic and consistency. Moreover, the paper does not list the challenges and g... | Rebuttal 1:
Rebuttal: We disagree with the reviewer’s claim that expressions between paragraphs and sentences lack logic and consistency except possibly for very few instances. We would appreciate it if the reviewer could explicitly refer to any specific examples so that we can address them.
The first two paragraphs o... | Summary: This paper addresses the complex problem of multi-team games by introducing a new variant of fictitious play called Team-FP. The method aims to enable teams of self-interested agents to reach Team-Nash Equilibrium (TNE) in multi-team games, with a particular focus on zero-sum potential team games (ZSPTGs). The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and constructive comments. Firstly, we want to highlight that the equation (4) is inherited from polymatrix games. For example, the equation always holds for two-team games. As discussed in the introduction, the polymatrix structure (or network separable in... | Summary: This paper introduces Team-Fictitious Play (Team-FP) dynamics as a novel approach for teams of self-interested agents to converge to Team-Nash equilibrium in multi-team games. The study focuses on games where multiple teams interact strategically, aiming to maximize their collective utilities. For this purpose... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough reading of our paper and constructive comments. We appreciate the feedback regarding the notation. We have reserved uppercase letters to denote sets; e.g., $A^1$ denotes agent 1’s action set. For the explicit exposition, we underlined the parameters related... | Summary: In this work, the authors introduce a novel variant of virtual play, referred to as **Team-Fictitious Play (Team-FP)**, aimed at assisting self-interested agents within teams to reach **Team Nash Equilibrium (TNE)** in multi-team games. The paper focuses on zero-sum potential team games (ZSPTGs), where teams i... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thorough review and constructive comments. We can gladly include further clarifications on the points raised, which will improve our paper’s accessibility. In the following, we address the reviewer’s questions:
**[How can we visualize the learning dynamic and the solu... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable time and constructive comments.
Based on Reviewer Jh6E’s request, we have illustrated the Team-FP dynamics and the fundamental idea of the proof in Figure 1 of the attached PDF. Furthermore, based on the other comments, we have conducted several new exper... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Neural Contextual Bandit against Adversarial Corruptions | Accept (poster) | Summary: This paper studies the problem of contextual bandits with neural function approximation faced with adversarial corruptions. It proposes an algorithm named R-NeuralUCB, which can improve the robustness of neural contextual bandit training. It provides regret analysis and conducts experiments to show the advant... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for your valuable questions and comments.
Given the page limit of 6000 characters, we will try our best to provide our detailed response in the form of Q\&A. Please also see our manuscript for cited papers. *Thank you!*
**Q1: Overall discussion on ... | Summary: This paper proposes a novel neural contextual bandit algorithm, called R-NeuralUCB, to improve robustness against adversarial reward corruptions. The authors provide regret analysis for R-NeuralUCB under over-parameterized neural network settings, without the commonly adopted arm separateness assumption. The a... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for your valuable questions and comments.
Given the page limit of 6000 characters, we will try our best to provide our detailed response in the form of Q\&A. Please also see our manuscript for cited papers. *Thank you!*
**Q1: Overall discussion o... | Summary: This paper presents R-NeuralUCB, a neural-based network UCB algorithm for robustness under adversarial rewards corruptions in stochastic $K$ multi-armed contextual bandits. Based on NeuralUCB [1], before the arm pulling, R-NeuralUCB additionally optimizes a context-aware gradient descent for each arm by using ... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for your valuable questions and comments.
Given the page limit of 6000 characters, we will try our best to provide our detailed response in the form of Q\&A. Please also see our manuscript for cited papers. *Thank you!*
**Q1: Discussion on computati... | Summary: The paper studied neural contextual bandits under adversarial reward corruptions. The adversary is allowed to perturb the reward after observing the action selected by the bandit player, but is subject to the constraint that the total reward corruption must be bounded by some budget C. Within this attack frame... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for your valuable questions and comments. We will try our best to provide our detailed response in the form of Q\&A. Please also see our manuscript for cited papers. *Thank you!*
**Q1: The regret lower bound in terms of corruption level $C$ under ne... | Rebuttal 1:
Rebuttal: We would like to sincerely thank reviewers for your valuable questions and comments. *Please refer to attached PDF for added experiments.*
Given the page limit of 6000 characters, we will try our best to provide our detailed response. Please also see our manuscript for cited papers. *Thank you!*
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms | Accept (poster) | Summary: This paper proposes a method to upper-bound the generalization gap $G(w)$ (discrepancy between the empirical risk and the theoretical risk for a given parameter $w$ of our model) by tractable _topological_ quantities.
Namely, given a set $W = \{w_\tau,\dots,w_T\}$ (typically, a sequence of iterates for a SGD... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent on the paper and their insightful comments.
We address below the main questions (alongside weaknesses).
**Could you give (for, say, theorem 3.4) a sort of "sketch of proof" and more intuition on "why this should be true"?**
First, we would like to poi... | Summary: Prior work has sought to provably bound the generalization of a neural network based on a complexity measures, eg using some form of evaluation of mutual information between the data and the training path, however such proofs have relied on the topologies from the asymptotic infinite training case and other im... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their insightful comments on our work.
> Some of the benchmarking was more intuitive as the complexity compared to generalization charts as opposed to the Table 1 for instance, I don't know if that could be simplified in some fashion
We believe that Tabl... | Summary: - The authors provided a novel topological-complexity-based uniform generalization error bound, constructed on the $\alpha$-weighted lifetime sums or positive magnitude. This bound shows better correlation with the generalization error compared to existing bounds.
- The authors proposed an implementation schem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review. Below we address all the raised concerns. We hope that the reviewer reconsiders their score in the light of our comments.
Before delving further, we would like to point out that our main focus is to obtain not just theoretical insights but also e... | Summary: The paper makes significant contributions by establishing new theoretical connections between generalization and topological complexity measures, specifically $\alpha$-weighted lifetime sums and positive magnitude. The authors introduce these novel measures and link them to generalization error using innovativ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interesting comments.
> Maybe I get this wrong but in line 113, is $Y\subset X$ or $Y\subset A$?
We thank the reviewer for pointing out this minor typo in the definition of the PH dimension. Indeed, we have replaced $Y\subset X$ by $Y\subset A$ in the final versio... | Rebuttal 1:
Rebuttal: The rebuttal pdf includes figures that are mentioned in some of our answers to the reviewers.
Fig. 1.a analyses the sensitivity to $s$ of the correlation between the generalization error and $\mathrm{PMag}(s\mathcal{W})$.
Fig. 1.b is a preliminary result regarding the comparison of our topologica... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Regret of Linear Ensemble Sampling | Accept (poster) | Summary: This paper proposes a simple ensemble sampling (ES) approach and its frequentist analysis for the linear bandit problem. Specifically, it shows that the proposed algorithm achieves $\tilde{O}(d^{3/2}\sqrt{T})$ regret when the ensemble size $m$ is $\Omega(K \log T)$. This regret bound improves the dependency on... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and particularly recognizing the theoretical value of our work. We are more than happy to address any questions.
**Linear Contextual Bandits:**
Please note that none of the existing linear ensemble sampling literature [15, 20, 8] has discussed linear contextua... | Summary: The paper explores ensemble sampling for linear bandit problems and enhances the existing regret bounds by a factor of $d$, aligning the scaling with respect to $d$ to that of Thompson sampling algorithms. The algorithm is somewhat simpler than the existing work [8] by permitting any policy for selecting the e... | Rebuttal 1:
Rebuttal: We appreciate your overall positive feedback and particularly recognizing the theoretical value of our work. We sincerely hope you take our response into account in reassessing our work.
---
### **Scaling of $K$**
We respectfully believe there is a misunderstanding in your comment. We are more t... | Summary: This paper proposes a neater version of linear ensemble sampling and streamlines the analysis of OFUL-inspired algorithms for linear bandits. The authors proved that this version of linear ensemble sampling has its high-probability regret upper bound the same order as LinTS, i.e., $\tilde{O}(d^{3/2}\sqrt{T})$ ... | Rebuttal 1:
Rebuttal: We appreciate your overall positive feedback and particularly recognizing the theoretical value of our work. We strongly and sincerely believe that the first two points you mentioned as weaknesses are not weaknesses. We sincerely hope you take our response into account in reassessing our work.
--... | Summary: An analysis of Ensemble Sampling in the linear setups with closed-form incremental update and finite action set.
Strengths: Provide a new analysis of linear Ensemble sampling.
Weaknesses: 1. **Lack of Practical Implications for Ensemble Sampling in Complex Settings:**
- The paper does not discuss how Ensembl... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have invested in evaluating our work. However, we believe there are several fundamental misunderstandings in your review that we would like to address.
**The theory-practice gap:**
Variants of ensemble sampling have already been shown to perform effectively i... | Rebuttal 1:
Rebuttal: Although our main contribution is providing a tighter regret bound for linear ensemble sampling, we have performed experiments as per Reviewer QBvS's request. The results are shown in the attachment.
We strongly believe that our work should be evaluated on its theoretical merit. Our improved regr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains | Accept (poster) | Summary: This paper studies federated learning under heterogeneous data domains and introduces a federated prototype learning strategy, denoted as FedPLVM, to mitigate the problem. A dual-level prototype generation method is proposed to address domain variance between hard and easy domains, reducing the communication b... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Here is our response to the mentioned weaknesses:
1. The key point of our dual-level clustering is local prototypes clustering. Different from easy domains, hard domains often exhibit looser clustering, increasing the risk of misclassification, especially for... | Summary: This paper focuses on Federated Prototypes Learning and reveals that existing methods create the same prototypes for different clients, which neglects the distribution diversity. In this work, the authors introduce the variance-aware dual-level prototype clustering and alpha sparsity prototype loss. Various ex... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Here is our response to the mentioned weaknesses:
1. We introduce \alpha-sparsity loss to mitigate the potential risk of feature representation overlapping caused by the dual-level clustered prototypes operation. This loss focuses on maximizing inter-class di... | Summary: This paper aims to investigate the federated prototype learning problem with data heterogeneity. To handle the cross-domain representation variance problem, a new method termed FedPLM is proposed, which includes a dual-level prototype clustering mechanism and an alpha-sparsity prototype loss. Experiments are c... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Here is our response to the mentioned weaknesses:
1. Our design principle aims to ensure our method excels particularly in challenging domains, which often have lower baseline accuracy, such as SVHN. This aligns with our motivation to tackle diverse learning ... | Summary: The paper introduces FedPLVM, a federated learning approach that improves federated prototype learning (FedPL) in heterogeneous data domain setting. Traditional FedPL methods create the same number of prototypes for each client, leading to performance disparities across clients with different data distribution... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Here is our response to the mentioned weaknesses:
1. Firstly, we want to highlight that the main objective of this paper is to develop a global model that works across all domain datasets. This approach aligns with the baseline methods, such as FedProto and F... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their insightful feedback and recognition of our work. We particularly appreciate Reviewer GDmh’s comment on the well-designed of our proposed $\alpha$-sparsity prototype loss, Reviewer dkgn's comment on the novelty of our proposed dual-level prototypes cluster... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The domain gap among multiple clients impedes the generalization of federated learning. To mitigate cross-domain feature representation variance, the authors introduce FedPLVM, which establishes variance-aware dual-level prototypes clustering and employs a novel α-sparsity prototype loss. To verify the effecti... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Here is our response to the mentioned weaknesses:
1. Local prototype clustering is beneficial because it captures essential variance information, not just the average data, which is particularly crucial in hard domains. Easy domains tend to show tight cluster... | null | null | null | null | null | null |
Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models | Accept (poster) | Summary: This paper studies the problem of learning a sparse model under rare events and scale-invariance. They provide optimal subsampling function to handle scale-invariance for a Lasso-regularized model. They finally provide experimental results for their subsampling scheme.
Strengths: * Well-written
* Looking at M... | Rebuttal 1:
Rebuttal: Thank you for your insightful review of our paper.
We would like to point out that rare events data and sparse models are common in
practice, and scale-dependence is a frequent and crucial issue in subsampling
that has not been addressed in existing literature. Following your great
suggestion, we... | Summary: The scale-invariant optimal subsampling function proposed in the paper addresses the challenge of inactive features in rare-events data by overcoming the issue of scale-dependence in exitsing optimal subsampling methods. In the context of variable selection for rare-events data, where distinguishing active and... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive review of our paper. We appreciate your constructive suggestion on providing a detailed discussion on why the A-OS
and L-OS in Section 1 are affected by the scaling of the covariate
$\mathbf{x}$. Yes, we also believe the point would be clarified by the fo... | Summary: This paper introduces an optimal subsampling algorithm designed for imbalanced data with inactive features. The primary algorithm can be summarized in three steps:
1. Train a pilot model using a lasso-penalized objective.
2. Subsample the data based on the pilot model.
3. Train the subsampled data using an ad... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback on our work along with the
insightful question. Please see below for our response.
**Q1. If more computational resources are available, is Lasso always the best
practical choice for pilot estimation?**
**R1.** In general, better pilot estimation lea... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and effort in assessing our
work. Their insightful comments and questions are valuable in helping us improve
the paper. Please find our individual responses to the reviewers' comments and
questions below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games | Accept (poster) | Summary: This work studies first-order methods for equilibrium computation in extensive form games (EFGs) and the effect of the regularization choice (distance-generating function). The key parameter for the performance of these methods is the ratio between the strong convexity modulus and the diameter of the regulariz... | Rebuttal 1:
Rebuttal: Thanks for your feedback!
Q1: “Is there some motivation in preferring a FOM rather than kernelization?”
A1: Given that FOMs have been extensively explored in the literature, they can benefit from the existing techniques. An example of this is the adoption of the COMD approach in our paper, which... | Summary: The paper examines Distance Generating Functions (DGFs), a framework developed for providing online learning and equilibrium-computing first-order methods for extensive-form games (EFGs). Specifically, DGFs were introduced in the literature as a form of regularization tailored to the strategy space of EFGs. Co... | Rebuttal 1:
Rebuttal: Thanks for your feedback!
Q1: “The main takeaway message of the paper might be the lower bound, which establishes that the results of [2] cannot be improved by adopting the DGF approach. If this is the case, highlighting this point would greatly benefit the paper.”
A1: Thanks for the suggestion... | Summary: This paper studies the optimality of diluted entropy functions for online learning in extensive form games. The weighted one diluted entropy is shown to be optimal up to logarithmic factors, for which a new lower bound is also provided.
Strengths: The paper studies a very relevant problem of the optimal choic... | Rebuttal 1:
Rebuttal: Thanks for your feedback!
Q: “Could you provide some hindsight on how are the treeplex, diameter-to-strong-convexity ratio of DilEnt used?”
A: The ratio between diameter and strong convexity of the regularizer on the feasible set (in the case of extensive-form games, the feasible set of every pl... | Summary: The paper offers significant advancements in understanding the efficacy of distance-generating functions (DGFs) for extensive-form games (EFGs), specifically focusing on the optimization and application of first-order methods (FOMs). Central to the study is the exploration of the weight-one dilated entropy (Di... | Rebuttal 1:
Rebuttal: Thanks for your feedback and your praise of our paper!
We address the two weaknesses you mentioned:
- Your claim that our paper does not deal with “partial information” is wrong. The techniques of the paper apply to imperfect-information extensive-form games. You are however right that the paper... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence | Accept (poster) | Summary: The paper presents a novel method, Contextually-spectral based correspondence refinery (CANDY), to address the Dual Noisy Correspondence (DNC) problem in contrastive multi-view clustering (MvC). CANDY utilizes inter-view similarities as context and employs a spectral-based module for denoising correspondence, ... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of our method. We will address your questions one by one.
> ***Question 1**: **How will the choice of the Gaussian kernel parameter σ influence the performance** of CANDY? The sensitivity of the clustering results to this parameter needs to be thoroughly examined... | Summary: The authors delve into the study of contrastive multi-view clustering (MVC) and aim to address the false positive and false negative correspondence issues, collectively referred to as dual noisy correspondence. To tackle this problem, they propose a two-fold solution named CANDY. Firstly, CANDY exploits inter-... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews and suggestions. Below, we will address each of your questions.
> ***Question 1.1**: I have some doubts regarding the results in Figure 4. Firstly, **how is the cross-view similarity matrix arranged**? The current form appears to be arranged by the ground trut... | Summary: This paper addresses a new problem called dual noisy correspondence, which the authors claim is practical and underexplored in the multi-view learning community. Dual noisy correspondence refers to two challenges: 1) false positive correspondences induced by irrelevant multi-view data and 2) false negative cor... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We will address your questions one by one.
> _**Question 1**: The authors claim to address a new problem, dual noisy correspondence (false positive and false negative). However, reviewers noted that similar issues have been explored in works [A] of other fields... | Summary: The manuscript "Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence" addresses the Dual Noisy Correspondence (DNC) issue in contrastive multi-view clustering (MvC), where noise affects both positive and negative data pairs. The authors propose CANDY (Contextually-spectral based correspon... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews and suggestions. We will address your questions one by one.
> ***Question 1**: The method's **applicability to other types of multi-view learning tasks**, such as classification or retrieval, is not explored, limiting its broader impact within the multi-view l... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
We sincerely appreciate your time and effort in reviewing our paper and providing constructive feedback. We thank the reviewers for your recognition of our novelty and contributions.
* This method serves as a plug-and-play module that can be integrated into other contrast... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Why Go Full? Elevating Federated Learning Through Partial Network Updates | Accept (poster) | Summary: The authors observe 'layer mismatch' phenomenon in federated learning, and propose FedPart that uses partial network updates to address the former issue. They show that FedPart outperforms previous methods and also reduced communication and computation overhead.
Strengths: The proposed idea is neat.
The demo... | Rebuttal 1:
Rebuttal: Thank you for the reviewers' comments. Next, we will address each of the issues raised one by one.
**W1 & Q1: What exactly is the update stepsize in Fig1? ... The authors try to use Figure 1(a) to show that the 'update stepsize' increases after each averaging and thus it indicates layer mismatch... | Summary: In this paper, the authors suggest a new approach for the network update step in federated learning. Considering that traditional federated averaging updates and aggregates all parameters at once, leading to a divergence between the global model and the local solution to a client’s specific task, they simply p... | Rebuttal 1:
Rebuttal: Thank you for the reviewers' comments. Here are our responses.
**Q1: In Figure 1, the update step size ... seems extremely sharp ... if the momentum terms ... were properly reset?**
We did reset the optimizer's state before the start of each local round. The relevant code is at line 131 in the f... | Summary: This paper discovered the layer mismatch challenge in federated learning due to the full network update. To mitigate this challenge, the authors proposed the FedPart method. Specifically, the FedPart method would ask the clients do full network update in the beginning communication rounds, and then it would as... | Rebuttal 1:
Rebuttal: Thank you for the reviewers' comments. Below, we address the concerns raised in the "Weakness" section.
**W1: I suggest the author provide more evidence and discussion of the proposed layer mismatch challenge in the paper. In the current version, there is no experiment to validate the proposed ch... | Summary: The paper proposes a new and novel method to partially train networks to achieve better training efficiency but also, in some cases, better performance.
Strengths: Training efficiency is an extremely important and timely topic. Given that FL aims to have massive networks to train upon any efficiency gains are... | Rebuttal 1:
Rebuttal: Thank you for the reviewers' comments. Here are our responses.
**Q1: How would the method perform on IID and non-IID data?**
Thank you for the reviewer's comment. We have conducted additional experiments to enrich our analysis of non-IID data scenarios. We added experiments with an alpha=0.1 set... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning | Accept (poster) | Summary: This paper identifies that the KD-based method which is used to tackle the data heterogeneity becomes more vulnerable under the model poisoning attacks. Moreover, the models unknowingly align benign client models with a poisoned server model in a malicious setting. It is also called attack amplification. To ad... | Rebuttal 1:
Rebuttal: **W1- Two methods:**
We appreciate the reviewer's concern regarding our evaluation of HYDRAFL on only two existing methods and agree that this is a limitation. We chose these two methods as initial test cases to demonstrate the *attack amplification* phenomenon we discovered, to motivate the need... | Summary: This paper investigated the phenomenon termed *attack amplification* in federated learning with Knowledge Distillation (KD) and proposed an FL framework named HYDRA-FL to reduce the impact of poisoning client attacks in FL. An auxiliary classifier is introduced to employ KD loss on the shallower layer and redu... | Rebuttal 1:
Rebuttal: **W1- Sensitivity analysis:**
We agree with the reviewer that sensitivity analysis is crucial. Therefore, in addition to Figure 7 (FedNTD), we explain Table 4 (MOON) in Appendix E.2 since it is an ablation over the different shallow layers, diminishing factors, and attack/no-attack settings. We c... | Summary: This paper first empirically demonstrates the fact that KD algorithms amplify attack effectiveness. Then the authors propose HYDRA-FL as a method for mitigating the attack amplification problem, through a novel loss function template that can be applied to any FL algorithm wherein the local training objective ... | Rebuttal 1:
Rebuttal: **W1- Real-world settings:**
*Real-world use cases and task complexity:*
We agree that real-world dataset analysis would be valuable. While our current choice of datasets allows for direct comparison with previous works, we plan to evaluate more complex models and tasks, such as language and mul... | Summary: This work addresses the challenge of data heterogeneity in Federated Learning (FL) and its impact on global model performance.
The authors demonstrate why KD is susceptible to the issue of poisoning attacks and use these findings as a foundation to propose a novel method HYDRA-FL. Experimental results demonstr... | Rebuttal 1:
Rebuttal: **W1: Unstable performance advantage:**
We thank the reviewer for giving us the opportunity to provide further explanation of this variability.
We start off by saying that we see stronger accuracy gains in high-heterogeneity settings (low alpha), as we show in Tables 1 and 2 (Page 8). The reason ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your detailed, insightful reviews and helpful comments, with scores ranging from 5 to 7. Our efforts to address your questions and comments have significantly improved the paper. We are particularly grateful for the explicit questions on how our work will benefit the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ROIDICE: Offline Return on Investment Maximization for Efficient Decision Making | Accept (poster) | Summary: The article proposes a novel offline algorithm for learning policies optimising Return on Investment (ROI): the ratio between the (discounted) cumulated rewards obtained by a policy, and the (discounted) cumulated costs of the actions taken. The paper focuses specifically on offline RL, and in particular build... | Rebuttal 1:
Rebuttal: Dear Reviewer Yyxa,
We appreciate your feedback and have provided responses to your questions below.
**1. Could you provide a substantive argument of why optimizing ROI could offer a better framework for cost/returns trade-offs?**
Constrained reinforcement learning is a framework that aims to m... | Summary: This paper introduces a novel approach for solving constrained offline reinforcement learning problems. The authors apply the Charnes-Cooper transformation to convert the linear-fractional programming into an equivalent linear programming problem, and draw inspiration from the DICE framework to maximize ROI un... | Rebuttal 1:
Rebuttal: Dear Reviewer T1wG,
We thank the reviewer for the detailed feedback. We address your remarks below.
**1. Important baselines are missing: apart from DICE-based methods, comparisons should include other offline constrained RL methods.**
Our experiment in continuous domains from Section 5.2 inclu... | Summary: This paper provides a fraction linear programming framework for solving offline RL problems for return on investment (ROI). The fraction linear programming can be trasformed to a linear programming and a convex regularizer is used to control the distribution mismatch. The authors provide adequate experimental ... | Rebuttal 1:
Rebuttal: Dear Reviewer 8zrU,
We appreciate your comprehensive comments. Please find the response to your questions below.
**1. Theoretical analysis for the sample complexity in ROI-LP.**
Sample complexity analyses in the LP formulation of RL traditionally estimate a policy's return using a linear combin... | Summary: The paper addresses the problem of maximizing the Return on Investment (ROI) in the context of offline reinforcement learning (RL). The method introduced is ROIDICE, which stands for Return on Investment Decision-making in the Offline Setting. ROIDICE is an offline policy optimization algorithm designed to opt... | Rebuttal 1:
Rebuttal: Dear Reviewer Z94y,
We thank the reviewer for the thorough and constructive comments. We hope we can address your concerns below.
**1. Could you provide details on the computational resources and time consumption for different methods**
We provide details on the resources and runtime to demonst... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and effort in providing constructive feedback and insightful reviews of our paper. We are grateful that the reviewers recognized our paper for presenting a novel offline policy optimization framework that effectively optimizes the trade-off betwe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty | Accept (poster) | Summary: The authors propose group-fair multi-matching algorithms in the presence of uncertainty about the valuations of agent-item pairs. The algorithms fall under two families: Conditional Value at Risk (CVaR), and robust optimization. The types of utilities they consider are also two-fold: normal utilitarian, and eg... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and helpful suggestions.
Please see our replies below.
**Propositions 3.4, 3.3, 3.1 should be stated informally in the main text.**
Thank you for the suggestion. We agree that the equations make it less readable.
We will replace them with a informal statemen... | Summary: This paper considers a new fair allocation problem. In the classical fair allocation problem, each item can only be matched with at most one agent, and the utilities are known prior. This paper considers a variant where each item is required to be matched with some number of agents. Agents can also be matched ... | Rebuttal 1:
Rebuttal: Thank you for your comments!
**To me, the downside is that the running time of the proposed algorithm is high, especially since the algorithm is required to solve LP. This limits the application of algorithms.**
We acknowledge that some of our proposed algorithms have high runtimes. However, we... | Summary: This paper looks at the problem of computing resource-agent matchings under uncertainty in a group setting, where the actual valuations of agent-resource matchings are unknown. When the distribution of the valuation uncertainty is known, they look at stochastic optimization using Conditional Value at Risk (CVa... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! Please see our replies below.
**Compare the uncertainty-aware solutions to other prior approaches at solving these problems?**
We first emphasize that while we present novel solutions, our primary contribution is suggesting new *objectives* for the co... | Summary: The authors study a resource allocation problem where the objective is to optimize for efficiency and fairness under the presence of some uncertainty. The agents are partitioned into groups and the items need to be assigned so as to be fair to the groups. They study two maximization objectives : 1) a weighted ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions!
**Please mention the randomized rounding procedure or at least the properties of the output rounded solution.**
Thank you for the suggestion. We will add more details on the rounding procedure in the camera ready version of the paper. Please see the f... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your detailed and thought-provoking reviews. We have responded to most of your points individually, but a few points were worth addressing globally.
**Algorithm runtime/scaling**
We acknowledge that some of our proposed algorithms have high runtimes. However, ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors of this paper investigate the fair multi-matching problem under uncertainty. Both stochastic and robust optimizations are considered to solve the proposed problem.
Strengths: S1. Fairness is an important and practical concern in resource allocation problems.
S2. The theoretical results of this pa... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We respond to your comments and questions below.
## W1
As mentioned on line 306, we adopt the model used by several major conferences: ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024 [1]. In this model, papers are the agents, reviewers are the items, and the va... | null | null | null | null | null | null |
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning | Accept (poster) | Summary: This paper tackles Goal-conditioned Reinforcement Learning (RL) when used with Temporal Logic Objectives. The benefits of directly considering Deterministic Finite Automatons (DFAs) as the task definition leads to a new class of Compositional DFAs introduced that cover a conjunction of several tasks. These cDF... | Rebuttal 1:
Rebuttal: _Thank you for the time and effort put into your review._
### Comparison to LTL2Action and GCRL-LTL.
We provided an in-depth empirical comparison along with a detailed discussion with LTL2Action in Appendix C.8. We plan to use the extra space afforded by a camera ready to move it into the final v... | Summary: This paper extends the framework of goal-conditioned reinforcement learning to support temporally-extended goals specified as (compositions of) deterministic finite automata (cDFA), avoiding the limitations of state-based goals and the ambiguities of natural language specifications. The authors introduce a gra... | Rebuttal 1:
Rebuttal: _Thank you for the time and effort put into your review._
### Regarding the comment on the labeling function.
We agree with the potential usage of computer vision algorithms to segment and label states of the world from pixels. We think there is a lot of interesting and fruitful future work that ... | Summary: This work focuses on goal-conditioning policies using DFAs. This leverages the ability of DFAs to be composed - in this case the work focuses on conjunction. Two main pieces are introduced in leverging DFAs: 1) a GATv2 model which provides task embeddings from the DFA, 2) pre-training of the GATv2 on Reach Avo... | Rebuttal 1:
Rebuttal: _Thank you for the time and effort put into your review._
### Regarding Clarity of Sec. 3.1.
We appreciate this feedback on clarity so we will include an explanation for the specific graph encoding we use. Essentially, we apply four operations on a DFA to construct a graph encoding:
1. Add inte... | Summary: This paper considers the multi-task setting where each task is a temporal logic task specified by a conjunction of deterministic finite automata (cDFA). To address the sample efficiency and generalisation problems present in this setting, they propose a method for generating good cDFA embeddings which can then... | Rebuttal 1:
Rebuttal: _Thank you for the time and effort put into your review._
### Regarding LTL2Action not being finite
The reviewer is right in the sense that in the LTL2Action paper, the encoder could *mechanically* be applied to standard LTL, but there is no indication that this will work. **The reality is that t... | Rebuttal 1:
Rebuttal: _We'd like to thank the reviewers for their time and efforts.
Below, are common points we'd like to emphasize across all reviews._
## RAD Pretraining + Frozen Embeddings
First, we wanted to highlight what we consider the most important contributions of our work:
1. The RAD pretraining.
1. The fr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IMAGPose: A Unified Conditional Framework for Pose-Guided Person Generation | Accept (poster) | Summary: This paper proposes a diffusion-based pose-guided image generation method. Specifically, given an image and a sequence of poses, this paper aims to generate images that follow the input poses and maintain the appearance of the input image. To capture the texture details of the input image, they propose to comb... | Rebuttal 1:
Rebuttal: Dear Reviewer Cm6N
We thank the reviewer for the positive feedback and valuable comments.
**Q1: Limited novelty. 1） The main contribution of this paper is forming four image latents into a 4-grid join latent, which is very similar to [1]. 2） The design of Sec 3.2 combines the VAE feature with th... | Summary: This paper considers three different pose-guided image generation scenarios from a scene perspective and attempts to cover all scenarios using a unified framework. In my opinion, it is very insightful and inspiring. The proposed IMAGPose framework unifies all scenarios through several ingenious components, nam... | Rebuttal 1:
Rebuttal: Dear Reviewer rV96:
We thank the reviewer for their detailed feedback and encouraging comments.
**Q1: Comparison with Reference UNet [1]**
**Response:** Please refer to the **shared response** on "Differences with technologies like Animate Anyone." We have added and discussed these differences.... | Summary: This paper thoroughly analyzes and considers the application scenarios of pose-guided person image synthesis from the perspective of real-world significance. Author introduces previously unconsidered but intriguing scenarios and proposes the IMAGPose framework to unify different tasks. Comprehensive experime... | Rebuttal 1:
Rebuttal: Dear Reviewer QBtt:
Thank you for your review and insightful comments. We address your questions as follows.
**Q1: The IMAGPose framework heavily relies on the detection results from
OpenPose. I am curious about how the performance of IMAGPose would be
affected if OpenPose produces poor results.... | Summary: The paper introduces IMAGPose, a unified conditional framework designed to overcome the limitations of existing diffusion models in pose-guided person image generation. Traditional models primarily focus on generating a target image from a single source image and a target pose. IMAGPose extends this capability... | Rebuttal 1:
Rebuttal: Dear Reviewer 1dhK:
Thank you very much for your support and constructive suggestions.We are glad to see the positive assessment of our paper and appreciate the detailed feedback.
**Q1&Q4&Q7: (1) Computational Requirements and Resource Constraints？(2) Impact on Practical Usability，such as high-... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their helpful feedback and insightful comments.
We are glad that the reviewers find our paper “ *highly insightful* ” (**QBtt**), “ *clear and well-motivated* ” (**QBtt**), and “ *simple yet ingenious* ” (**rV96**), “ *enlightening* ” (**rV96**) and “ *we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Separations in the Representational Capabilities of Transformers and Recurrent Architectures | Accept (poster) | Summary: This paper demonstrates theoretical separations in the representational abilities of Transformer and Recurrent Architectures on selected synthetic tasks, including index lookup, nearest neighbor, recognizing bounded Dyck languages, and string equality. The class of recurrent architectures examined includes pop... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and time.
Responses to the individual comments below.
> “The experimental part is fairly brief. The analysis of computational or statistical learning complexity is left to future work.”
We respectfully disagree with this assessment. The central claims of... | Summary: This works studies the differences between Transformers and recurrent models with respect to 4 tasks: index lookup, associative recall, string equality and Bounded Dyck languages. The authors prove that for index lookup and nearest neighbor recall, there exists a 1-layer Transformer that needs poly-logarithmic... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and time.
> “The analysis is specific to synthetic tasks with 1-layer recurrent models and Transformers …”
> “How do we extend these results to deeper networks?”
We wish to clarify what may be a misunderstanding here – we should have been more explicit abo... | Summary: This paper analyzes the differences in terms of representations between Transformers and recurrent architectures. They highlight multiple cases: a) a setting where 1-layer Transformer can represent the task with a log number of parameters but not RNNs (index lookups) b) a case where RNNs can represent the task... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and time.
Responses to the individual comments below.
> “I believe that the authors could further improve the presentation of their results. In particular, it is not clear at the beginning why they choose the tasks they propose and it sounds a bit like a "c... | Summary: In this paper, the authors study the representational separation results about two widely used classes of language models: transformers and RNNs. For a set of practically well-motivated tasks, they establish lower and upper bounds for attention based one-layer (and some for two) transformers and arbitrary RNNs... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and time.
Responses to the two points raised in your review below.
> “In particular, for the index lookup task, it makes sense that the only way RNNs can retrieve the symbol $s_p$ at an unknown location revealed at the end is only via storing all the past i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful feedback and their time. We are encouraged to see that they found our results interesting (Rev *LT9S, FtyK, BJPt*), well-motivated (Rev *LT9S, BJPt*), and to be of value to the community (Rev *FtyK*). We are further pleased to see that they found ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Open-Book Neural Algorithmic Reasoning | Accept (poster) | Summary: This paper proposed an open-book learning framework that allows networks to utilize the entire training dataset during reasoning, significantly enhancing performance on the CLRS Algorithmic Reasoning Benchmark and revealing intrinsic connections between different tasks through an attention-based mechanism.
St... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments. The following addresses the questions and concerns one by one.
**W1:** Introducing the additional memory seems to have a large storage overhead if the training set is large.
**A:** The proposed open-book framework is flexible enough to adapt to di... | Summary: This paper presents open book Neural Algorithmic Reasoning (NAR). The central claim the authors investigate is whether open book reasoning -- allowing a model to query information from its training set relevant to the current query -- can be unified with existing NAR architectures. In doing so, the authors pre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. The following addresses the questions and concerns one by one.
**W1:** Concerns about the OOD performance.
**A:** The results presented in the paper are exactly the out-of-distribution performances of the proposed framework. As pointed out in Lin... | Summary: The paper proposes a method to use the training dataset more explicitly during test time inference to improve performance. This is done with a dataset encoder module plus another processor module named open book processor. The authors validate their method in the single and multi-task set-up with good results.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. This paper explores a new paradigm in neural algorithmic reasoning (NAR). We propose an open-book framework and provide a concrete implementation to demonstrate that open-book information does have the potential to enhance neural network reasoni... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for the helpful and constructive comments. We greatly appreciate the time and effort you put into this review, and we will incorporate your suggestions into the final version. The paper explores a new paradigm in neural algorithmic reasoning. We propose an open-book framewor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active Perception for Grasp Detection via Neural Graspness Field | Accept (poster) | Summary: This paper proposes an next-best-view planning method for grasp perception. The authors use neural field to model the grasp distribution of a scene, which is learned from the graspness detection result of different views. Then the NBV is designed as the view with the largest inconsistency between the rendered ... | Rebuttal 1:
Rebuttal: **Response to Reviewer DbvE**
Thanks for your valuable feedback. We understand your concerns about the goal of active grasp detection, the performance gain of our method and some other details. We address your concerns below:
**Q1: Meaning of NBV for grasp perception.**
While finding one feasib... | Summary: This paper studies active perception for robotic grasp detection. It proposes an active grasp detection framework based on the Neural Graspness Field (NGF), which incrementally models the scene and facilitates next-best-view planning. For next-best-view planning, it aims to reduce the uncertainty of the NGF th... | Rebuttal 1:
Rebuttal: #### **Response to Reviewer 4AgP**
Thanks for your valuable feedback and we address your concerns below:
**Q1: Video recording of real-world experiments.**
Thanks for your suggestion. In **Figure 3** of the rebuttal PDF, we provide keyframe screenshots of one execution, including the active per... | Summary: The paper introduces a novel framework utilizing a Neural Graspness Field (NGF) in conjunction with a pre-trained graspness prediction network to enhance active grasp detection. It applies online training to the NGF upon encountering a new scene view, producing RGB, depth, and graspness maps. The method involv... | Rebuttal 1:
Rebuttal: #### **Response to Reviewer peVt**
Thanks for your valuable feedback. We understand your concerns regarding the computation of information gain with NGP and the other issues you have raised. We address your concerns below:
**Q1: Incremental training approach of the NGF.**
In the initial stage o... | Summary: This work proposes an active perception method for grasp detection composed of two parts: neural graspness field mapping and next-best-view planning with a graspness inconsistency-guided strategy. And a corresponding inference strategy is also proposed by decoding the graspness score from NGF to generate grasp... | Rebuttal 1:
Rebuttal: **Response to Reviewer DQft**
Thanks for your valuable feedback. We appreciate your acknowledgment of our active perception method based on neural graspness field and the performance improvements achieved. We address your concerns below:
**Q1: The relative weak improvement on novel set.**
In ou... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful feedback on our submission. We are grateful for their acknowledgment of our paper's contribution to active perception in robotic grasping. To address the questions raised regarding our method design and experimental work, we have provided compr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting | Accept (poster) | Summary: The proposed method extends the idea of GaussianFlow to explicitly decouple camera motion and object motion from optical flow on input monocular video. Specifically, the iterative camera pose refinement further boosts the rendering quality and performance on various datasets. Extensive qualitative and quantita... | Rebuttal 1:
Rebuttal: # Section 4 Response to Reviewer 2xXk
We thank the reviewer for the constructive assessment of our work. In the subsequent sections, we respond to each concern in detail. Please feel free to use the discussion period if you have any additional questions.
## 4.1 Questions
### 4.1.1 Failure case in ... | Summary: This paper proposes using off-the-shelf 2D optical flow to supervise the deformation field for 3D Gaussian Splatting (3DGS) in dynamic scenes. The optical flow is decomposed into camera flow and motion flow. The 3DGS flow is projected into 2D to match the estimated flow. Camera pose and Gaussian parameters are... | Rebuttal 1:
Rebuttal: # Section 3 Response to Reviewer pL6e
We thank the reviewer for the constructive assessment of our work. In the subsequent sections, we respond to each concern in detail. Please feel free to use the discussion period if you have any additional questions.
## 3.1 Weaknesses
### 3.1.1 Novelty
We appr... | Summary: The paper proposed MotionGS a novel deformable 3D Gaussian splatting approach. The approach initializes camera poses and 3D Gaussians based on an analytic structure-from-motion method as 3DGS. In addition an optical flow network is used to compute optical flow between neighboring frames. Given the initial dept... | Rebuttal 1:
Rebuttal: # Section 2 Response to Reviewer 4hTv
We thank the reviewer for the constructive assessment of our work. In the subsequent sections, we respond to each concern in detail. Please feel free to use the discussion period if you have any additional questions.
## 2.1 Weaknesses
### 2.1.1 Limiting assump... | Summary: This paper presents a novel approach to dynamic scene reconstruction by incorporating explicit motion priors into 3D Gaussian Splatting (3DGS). The proposed framework, MotionGS, introduces an optical flow decoupling module that separates camera flow and motion flow, which respectively correspond to camera move... | Rebuttal 1:
Rebuttal: # Section 1 Response to Reviewer rwSL
We thank the reviewer for the constructive assessment of our work. In the subsequent sections, we respond to each concern in detail. Please feel free to use the discussion period if you have any additional questions.
## 1.1 Weaknesses
### 1.1.1 Time, memory, a... | Rebuttal 1:
Rebuttal: # Section 0: Response to all reviewers
We would like to extend our heartfelt gratitude to all the reviewers for their thorough evaluation and constructive feedback on our work. Their insights have been invaluable in refining and enhancing the quality of our research. Below, we provide additional e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Policy Optimization for Robust Average Reward MDPs | Accept (poster) | Summary: The authors introduce study a policy gradient algorithm for solving unichain average reward robust MDPs. They show a linear convergence rate for increasing step sizes and a $O(1/k)$ convergence rate for fixed step size, where $k$ is the number of iterations of the algorithms.
Strengths: This is a good paper. ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the helpful and insightful feedback. Below we provide point-to-point responses to the weaknesses and questions.
**W1. Present assumption 2.1 earlier.** In the revision, we will introduce the unichain assumption earlier in abstract and emphasize that our contributions ar... | Summary: The paper studies gradient-based methods for robust average reward MDPs. The paper first derives a sub-gradient for the robust average reward (which is nonsmooth), and then uses it to define a mirror descent algorithm. They prove a few structural properties of the setting and then use them to provide a converg... | Rebuttal 1:
Rebuttal: **W1. Listed in the question section.**
Please refer to the response of Q1-Q6.
**Q1. Unichain assumption should be placed earlier.**
We thank the reviewer for this comment. In the revision, we will introduce the unichain assumption earlier in the paper and emphasize that our contributions are ba... | Summary: The authors present a gradient-based algorithm for average reward robust MDP (finite MDP). This setting has been studied in prior works, however, this paper proposes a policy optimization-based algorithm which is not yet done. They do a theoretical analysis of this setting and show linear convergence (by incre... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the helpful and insightful feedback. Below we provide point-to-point responses to the weaknesses and questions.
**W1. Didn't mention the assumptions for the lemma/theorem statements.**
We thank the reviewer for this comment. In the revision, we will rephrase the stateme... | Summary: The authors consider the mirror descent algorithm in the context of robust average cost MDPs. The consider $(s,a)$-rectangular uncertainty sets across a general distance metric. The Bregman divergence chosen for the purpose of analysis is the Euclidean 2-norm distance. The authors leverage on a prior result pr... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the helpful and insightful feedback. Below we provide point-to-point responses to the weaknesses and questions.
**W1. Related works on robust average cost MDPs.**
We thank the reviewer for pointing this out. In the revision, we modify our statement as 'We characterized ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Discrete Latent Variable Structures with Tensor Rank Conditions | Accept (poster) | Summary: This study develops new methods for identifying causal structures in discrete latent variable models using rank constrain. By leveraging this nontrivial algebraic property, the authors propose criteria and algorithms for discovering hidden causal relationships. They validate their approach through simulations.... | Rebuttal 1:
Rebuttal: We appreciate your careful review and suggestions and would like to thank you for your positive assessment of our work.
>Q1: I wonder if some assumptions are a bit strong such as the faithfulness assumption and the three pure child assumption and I wonder how they play into practical scenarios.
... | Summary: This paper aims to learn latent causal models with discrete random variables. To this end, a tensor rank condition on contingency tables of observed variables is used. Specifically, the paper establishes connections between the d-separation of the observed variables and the “tensor rank” of the said random var... | Rebuttal 1:
Rebuttal: Thank you for your insightful and valuable questions and for spending the time and effort on this review. We will respond to these issues point by point.
>Q1: Allowing edges between observed variables: ... I think you can be more open about it, e.g., what are the challenges, what are the missing ... | Summary: This paper studies the problem of learning causal structures among latent variables from discrete observational data. The author presents a tool, termed the tensor rank condition, to establish the connection between rank constraints of the probability tensor and d-separation relations in the causal graph. The ... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and suggestions and thank you for your positive assessment of our work.
>Q1: For the sufficient observation assumption, it seems that the cardinality of the observed variable support can be equal to the cardinality of the latent support, as discussed in Remark... | Summary: This paper studies the problem of learning latent variable structure in the discrete LSM measurement model. My understanding is that the paper operates under the following assumptions:
1) All latent variables are discrete
2) There are no connections between observed variables (i.e. observed variables are inde... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We will respond to these issues point by point.
> Q1: [1] proposes a structure learning algorithm for measurement model with discrete latent variables, under what seems to be a weaker set of assumptions. In particular [1] does not require each latent... | Rebuttal 1:
Rebuttal: **General Response**
We thank the reviewers for their efforts in reviewing our manuscript and for the insightful comments and suggestions. Please see below for our general response.
**Sufficient Condition: Three-Pure Children Assumption.**
To ensure the identifiability of latent variables, the p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation | Accept (poster) | Summary: The paper studies the problem of selection bias due to hidden confounding in recommendation systems. Previous methods struggle with real-world application due to reliance on strong assumptions or unavailable RCT data. The proposed solution, MetaDebias, leverages heterogeneous observational data, which is more ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. Below, we hope to address your concerns and questions to improve the clarity and readability of our paper.
> **[W1] I found the use of the term heterogeneous to describe the observational data ... | Summary: The paper addresses the issue of selection bias in recommender systems, particularly when hidden confounding factors are present. The authors propose a new approach using heterogeneous observational data, where some data is affected by hidden confounding and some is not. The proposed MetaDebias is a meta-learn... | Rebuttal 1:
Rebuttal: We sincerely appreciate your approval of the idea and the novelty of this work, and thank you for the helpful suggestions. Below, we hope to address your concerns and questions to improve the clarity and readability of our paper.
> **[W1] In the comparative experiments shown in Table 1, the impro... | Summary: This paper proposes to use heterogeneous observational data to address hidden confounding in recommender system.
Strengths: + Addressing selection bias in recommender system is very important.
+ If the assumption holds, i.e., confounder missing mechanism follows the user attribute missing mechanism, I would s... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. Below, we hope to address your concerns and questions to improve the clarity and readability of our paper.
> **[W1] It seems to me that using missing feature mechanism to estimate the missing c... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We sincerely thank all reviewers and AC for your great effort and constructive comments on our manuscript. During the rebuttal period, we have been focusing on these beneficial suggestions from the reviewers and doing our best to add several experiments.
As reviewers high... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation | Accept (poster) | Summary: The paper studies a class of convex resource allocation problems, in which the utilities and constraints are private (and bounded).
The paper proposes a simple algorithm that applies mirror descent to the dual problem (while the update of the primal variables is assumed to be exact).
The main technical result ... | Rebuttal 1:
Rebuttal: Dear Reviewer YdYP,
Thank you very much for your thorough reading and for providing insightful comments. We will respond to your concerns below one by one. We are more than happy to answer follow-up questions.
**Weaknesses**
- **Response:** Thank you for this sharp observation. Yes, you are ri... | Summary: The paper addresses the allocation problem under Joint Differential Privacy within the framework of a convex consumption function, a concave utility function, and a convex 'personal' domain.
The contributions of this work are threefold. Firstly, it derives results similar to those in previous research, but no... | Rebuttal 1:
Rebuttal: Dear Reviewer 8BYy,
We want to express our gratitude for your time in reviewing our work and the valuable comment you sent to us. Below, we reply to your concerns one by one. Feel free to let us know any follow-up questions.
**Weaknesses**
**Response:** Thank you very much for the suggestions... | Summary: The submission studies jointly differentially private algorithms for resource allocation problems, which are a broad generalization of packing linear programs. The work addresses this challenge by considering a primal-dual formulation of the problem, and running a noisy mirror descent algorithm on the dual. St... | Rebuttal 1:
Rebuttal: Dear Reviewer kLe8,
We sincerely thank you for reviewing our work and for your valuable feedback. Below, we respond to the concerns you raised one by one. Please feel free to let us know if you have any further follow-up questions. We are more than happy to take them.
**Weaknesses**
1. **Respon... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers kLe8, 8BYy, YdYP,
We sincerely thank you for your time in reviewing our work and in providing valuable feedback. We would like to initiate a global response to a common concern raised by all of you:
- Given that upper bounds are for an algorithm that can violate constraints, but t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks | Accept (poster) | Summary: The paper discusses the role of initial membrane potential (IMP) of neurons in spiking neural networks (SNNs). The authors found that IMP has a significant impact on the firing patterns of LIF neurons. Then, they propose a learnable IMP mechanism to improve the performance of SNNs. Additionally, the paper intr... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have carefully studied your comments and argue that your concerns can be addressed.
> Weakness 1: One major concern is about whether learnable IMP can be implemented in neuromorphic chips since the IMP is a float number. To my experience, I do not belie... | Summary: This paper investigates how the initial membrane potential affects the neuronal spike pattern and the model performace. The evolve of initial membrane potential would generate novel firing pattern and furthermore change the SNN output. Thus, by making the initial membrane potential a trainable parameter, the S... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our research direction and the innovative approach we have employed.
> Weakness 1: In this paper, all experiments and illustrations are 4 time steps SNN. It remains non-clear that in the long time steps task, how much could the initial membrane potential influence th... | Summary: This paper analyzes the dynamics of membrane potential and proposes to improve performance by correcting the initial membrane potential to learnable parameters. This article proposes to use only the output of the last timestep as the classification feature during inference. In general, this paper proposes a si... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We first list your advice and questions, then give our detailed answers.
> Weakness 1: If the residual technique is altered, such as MS-ResNet, the residual connection is calculated using the membrane potential. In this instance, will a better initializatio... | Summary: presents a novel approach to understanding and modeling the dynamics of spiking neural networks
Strengths: offering new insights into SNN modeling and potential applications in various domains.
theoretical framework for SNN dynamics is novel and addresses existing limitations in the field.
Weaknesses: Some o... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our article and providing constructive feedback. We’d like to reply to your concerns in detail.
> Weakness 1: Some of the assumptions in the theoretical framework could be more explicitly stated and justified.
**Answer:** We will provide a clearer explanat... | Rebuttal 1:
Rebuttal: Thanks for all reviewers' valuable comments. We are encouraged that reviewers recognize the effectiveness of setting learnable initial membrane potential states (IMP) for spiking neurons, and consider the idea of using the output of the last time step for supervised learning and output representat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS | Accept (spotlight) | Summary: LightGaussian introduces a three-stage technique to efficiently reduce the number of Gaussian primitives. In the first stage, redundant Gaussian primitives are pruned based on global significance, rather than opacity. The second stage involves SH distillation, which utilizes data augmentation from synthesized ... | Rebuttal 1:
Rebuttal: **[W1]: Analysis over other methods? Why is FPS missing for Compressed 3D-GS?**
Under a fair experimental setting, we observed that LightGaussian outperforms Compact 3DGS and Compressed 3DGS in 4 out of all 5 metrics on the MipNeRF360 dataset while also running fastest on Tank and Temples datase... | Summary: The paper focuses on compressing 3D Gaussian Splatting (3D-GS) models by mainly focusing on reducing the number of Gaussians and compressing the feature size of Gaussians. With three key steps: gaussian pruning and recovery, spherical harmonics distillation, and vector quantization, the paper achieves ~15x red... | Rebuttal 1:
Rebuttal: **[W1] Vector quantization step lacks novelty**
LightGaussian is motivated to design a holistic pipeline that effectively reduces the redundancy in the optimized Gaussians (NxC) for both the primitive count (N) and feature dimension (C). A large number of points will additionally result in slow ... | Summary: In this paper, the authors delivered a compact 3D Gaussian representation, i.e., LightGaussian for novel view synthesis. There are three technical contributions. Firstly, the authors present Gaussian Pruning and Recovery that measure the significance of each Gaussian to the view quality and then prune 3D Gauss... | Rebuttal 1:
Rebuttal: **[W1] Rephrase the motivation.**
We are motivated by the observation that the efficient point-based representation, 3D-GS, and its many follow-ups perform poorly in model size because they have to store each of the N (usually millions) points. A large number of points typically results in slow ... | Summary: This manuscript presents a pipeline to drastically reduce the size of pretrained 3D Gaussian splatting models in a way that preserves novel view image fidelity and increases rendering speed. This pipeline consists of three parts: 1) pruning based on an introduced global significance score followed by fine-tuni... | Rebuttal 1:
Rebuttal: **[W1 & W2] Does the score need to account for the exponential drop in contribution to the pixel color? Is the score for each Gaussian computed using multiple cameras or a single camera?**
We thank you for the insightful suggestions. With considering the “exponential drop” in Eq.3, we found the ... | Rebuttal 1:
Rebuttal: **General Response: Rendering speed of Compressed 3D-GS is missing, and a fair comparison?**
The reason for omitting the FPS of Compressed 3D-GS [34] in the main draft is because we reiterated the metrics from their original paper, which does not provide FPS metrics.
We also respectfully poin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures | Accept (poster) | Summary: The authors propose the MixEval to match the real-world human queries with existed benchmarks. MixEval is a two-stage benchmark reconstruction pipeline consisting of (1) wild query detection, and (2) grounding existing benchmarks in the mined queries. The authors match each crawled web user query with its most... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding this work solid and effective! Below are our responses to the concerns:
## Concern 1
> In Section 3.2, the authors use dot product to match the query with the original benchmark, but do not explain how to use new queries in the test process and whether the querie... | Summary: The paper reconstructs a new benchmark named MixEval by matching queries collected from the internet with existing benchmarks. This new benchmark aligns with the distribution of human preferences, reflecting the real distribution of queries on the internet. Additionally, considering the overlap and difficulty ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the comprehensive experiments, analysis, and insights of this work. Meanwhile, we understand the reviewer's concerns, which are also very important to us. Below we clarify:
## Concern 1
> Given that alignment with Arena Elo is used to measure the degree of a... | Summary: The paper introduces MixEval, a new benchmarking framework designed to overcome the limitations of traditional benchmarks and LLM-as-judge methods for evaluating LLMs. By leveraging web-mined user queries and matching them with existing benchmark queries, MixEval aims to offer a fast, efficient, and dynamic ev... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing MixEval as a timely work to the community! We are also grateful for your acknowledgment of the novelty, efficiency, thoroughness, and clarity of our work. Below are our responses to the concerns:
## Concern 1 & 3
> Pipeline Brittleness: a. Web User Query Dete... | Summary: This paper introduces MixEval, a new approach/benchmark to evaluate LLMs effectively in real-world scenarios. Traditional benchmarks often miss the comprehensiveness and subtlety of actual user queries, while existing methods like LLM-as-judge benchmarks are difficult to scale up. MixEval addresses these issue... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding this work novel, effective, comprehensive, and solid! Below are our responses to the concerns:
# Major Issue:
## Concern 1
> MixEval dynamically updates by mixing popular benchmarks (e.g., MMLU, BoolQ, GSM8K), which may not mitigate contamination. Most of these ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback. We identify that the main concern among reviewers is about the contamination of MixEval. **Therefore, we conduct additional contamination analysis and provide the general response here.**
# High-Level Takeaways
1. **Low Natural Contaminatio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers | Accept (poster) | Summary: This study investigates the influence of architecture on pre-trained language models' base capabilities. It reveals that the contribution ratio of Multi-Head Attention to pre-trained language modeling affects base capabilities. FFN-Wider Transformers reduce this ratio, leading to a decline in base capabilities... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and valuable feedback!
We answer your questions below.
***
**Q1:** How is the pre-training performance of the models?
**A1:** For the BERT and GPT experiments, the results are primarily presented in Tables 1 and 2, with a textual description of the pre-trainin... | Summary: The paper studies the contribution raion of FFN and MHA layer in transformers and its effect on out-of-distribution performance. It finds that the wider FFN layer will increase its contribution ratio and lower the OOD performance. Lastly, the paper proposes a new architecture that moves part of the FFN layer t... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and valuable feedback!
We answer your questions below.
***
**Q1:** The paper does not give a convincing explanation of why the study aligns pre-trained performance with different parameter scales. A larger scale model may not fully converge when it has a simila... | Summary: This paper examines how the architecture of a transformer model influences its base capabilities, such as out-of-distribution tasks, transfer learning, and few-shot learning. Specifically, it explores the effects of replacing the feed-forward network (FFN) with a wider FFN (FFN-wide) in various parts of the ar... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and valuable feedback!
We answer your questions below.
***
**Q1:** since the training loss is lower for MoE with CEA compared to vanilla MoE, it is hard to determine whether the improvements are due to the architecture's inductive bias or just the lower trainin... | Summary: The paper examines the influence of architecture on the base capabilities (OOD, transfer learning, few-short learning) of large language models. The main focus is on FFN-Wider transformers and understanding why they have poorer base capabilities compared to vanilla transformers. The contibution ratio of multih... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and valuable feedback!
We answer your questions below.
***
**Q1:** Some choices of parameters such as chosing intermediate dimension to 32d instead of say 8 or 16d can be better explained.
**A1:** We agree with your opinion that attempting more width would enh... | Rebuttal 1:
Rebuttal: Here is a PDF attachment containing the figures and tables referenced in the detailed responses below.
Pdf: /pdf/55d9011d221d8b62d147838c959f2a2348f24ab4.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Alignment for Honesty | Accept (poster) | Summary: This paper focuses on the task of honesty alignment. The authors first explore the task formulation of the alignment for honesty problem, then develop a series of evaluation metrics based on the change of response type to qualify the honesty of a model. They then propose a collection of training methods to imp... | Rebuttal 1:
Rebuttal: # Response to Weakness 1
Thank you for your feedback. As you kindly pointed out, the supervised fine-tuning methods are indeed only part of this paper. The primary contribution of our work is the development of a comprehensive and feasible framework for "alignment for honesty": this includes estab... | Summary: This paper targets honesty as an important dimension of alignment. The work posits that an honest model should respond candidly when it possesses knowledge and humbly acknowledge its limitations when it does not. Given the difficulty in explicitly delineating the boundaries of a model's knowledge, the paper ap... | Rebuttal 1:
Rebuttal: # Response to Weakness 1 and Question 1
> ... The paper only explores the impact of one hyperparameter in Section D.5.1. It is believed that further exploration of related hyperparameters could be quite interesting.
>
> Is there further investigation and case study of the relation between the hyp... | Summary: The paper "Alignment for Honesty" addresses the critical challenge of ensuring that large language models (LLMs) consistently produce truthful outputs. The authors propose several techniques to enhance truthfulness, including training on curated datasets, using reinforcement learning from human feedback (RLHF)... | Rebuttal 1:
Rebuttal: # Response to Question 1
> While the article emphasizes the importance of honesty, it does not seem to discuss in detail how to *maintain the model's helpfulness while improving honesty*. Could this lead to the model being overly cautious in practical applications and unable to provide useful info... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable and insightful feedback. We hope our responses have addressed your concerns, but please let us know if you have any further questions or require additional clarifications.
The attached PDF includes the experimental results in response to Reviewer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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