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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Principled Bayesian Optimization in Collaboration with Human Experts | Accept (spotlight) | Summary: This submission discusses the application of human's expertise knowledge into Bayesian Optimization algorithm. A principled approach, COBOL, was proposed which provides two novel guarantees: handover guarantee that ensures queries for expert's label diminishes to zero with iterations; and no-harm guarantee tha... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and helpful suggestions. The following are our detailed responses.
# Response to the concern regarding the no-harm guarantee and $\eta$.
The suggestion for an adaptive trust weight $\eta$ is interesting and could offer better resilience to adversit... | Summary: The paper introduces a Bayesian optimization algorithm designed to include human expert knowledge. Expert input comes in the form of simple accept/reject feedback (as in "this experiment is / is not worth doing"). To incorporate feedback two models are maintained, (1) a standard GP model of the objective, an... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and the helpful suggestions. The following are our detailed responses.
## P1
> does assumption 2.3 really need a justification? This is a standard assumption.
We do some justification because it is also an important assumption and we want to diffe... | Summary: The paper proposes to use human rejection as a feedback in human-AI collaborated Bayesian optimization. The proposed solution uses constrained optimization to explore the regions humans think might be beneficial. The authors offered guarantee that 1) the proposed algorithm has the same regret as the vanilla BO... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. The following are our responses.
# Insufficient theoretical results
We agree that acceleration through expert knowledge is central to our contribution. However, we do not believe that "theoretical" acceleration is essential for the completeness of our work. Our... | Summary: This paper introduces a robust Bayesian optimization algorithm that incorporates human expert knowledge to accelerate the optimization process. The key theoretic contributions include a handover guarantee, ensuring the number of expert labels required decreases over time, and a no-harm guarantee, ensuring the ... | Rebuttal 1:
Rebuttal: Thank you very much for the positive feedback and the helpful comments. The following are our detailed responses.
# Point-by-Point Responses to Weaknesses
1. To avoid any confusion, we have changed it to 'expert function' or just 'function' instead.
2. We have added the notations with explanat... | Rebuttal 1:
Rebuttal: # Global response and added experiments
The authors would like to thank all reviewers for their effort. As per the reviewers' requests, we have added five additional plots to the rebuttal PDF and incorporated these figures into our manuscript.
- **Fig. R1**: Nonstationary Human Accuracy (**dzPz,... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper studies the integration of human expert knowledge into Bayesian Optimization (BO) processes through binary accept/reject recommendations. The authors introduce an approach that ensures two key guarantees: Handover Guarantee: The approach proves a sublinear bound on the cumulative number of binary lab... | Rebuttal 1:
Title: Responses to the review
Comment: Thank you very much for the positive feedback and the helpful comments. The following are our detailed responses.
# On 'Limited Generalizability'
In fact, our rebuttal experiments (Figures R2, R3 in the global response) demonstrated that our approach can be extende... | null | null | null | null | null | null |
Tackling Uncertain Correspondences for Multi-Modal Entity Alignment | Accept (poster) | Summary: This paper aims to address the uncertainty in entity alignment within multimodal knowledge graphs (MMKGs). The authors design a MKE module to handle relations, attributes, and visual knowledge, enhancing attribute alignment and filtering through large language models and contextual learning. To address the iss... | Rebuttal 1:
Rebuttal: Many thanks for your valuable comments. We appreciate your recognition of the soundness of our method, the clarity of the writing, and the SOTA performance. In response to your concerns, we would like to address the following points:
- **[W1: Problem description & Challenges]**:
As describe... | Summary: This paper proposed a novel method for tackling uncertain correspondences in multi-modal entity alignment, called TMEA. The approach addressed challenges such as weak inter-modal associations, description diversity, and modality absence that hinder effective entity similarity exploration. TMEA employd alignmen... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We appreciate your recognition of our method's novelty, effectiveness, and the sufficiency of our experiments. In response to your concerns, we would like to address the following points:
- **[W1: Reproducibility & Complexity]**:
To ensure reproduc... | Summary: This paper addresses the task of multi-modal entity alignment for integrating MMKGs. Existing efforts mostly focus on capturing entity features via diverse modality encoders or fusion methods but face issues with uncertain correspondences. To overcome these challenges, the authors propose a novel method called... | Rebuttal 1:
Rebuttal: Many thanks for your valuable feedback on our paper. We appreciate your recognition of
the novelty of our research problem and methodology, as well as the robustness of our experiments. In response to your concerns, we would like to address the following points:
- **[W1 & Q1: Summary of contribu... | Summary: This paper addresses the task of aligning entities across multi-modal knowledge graphs (MMKGs). The authors propose a novel method called TMEA to tackle the challenges of uncertain correspondences between inter-modal or intra-modal cues of entities. The TMEA method consists of several key components: 1. Multi-... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback on our paper. We appreciate your recognition of the significance of our research problem, extensive experiments, and clear presentation of the methodology. In response to your concerns, we would like to address the following points:
- **[W1: Module c... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Full-Atom Peptide Design with Geometric Latent Diffusion | Accept (poster) | Summary: The paper presents a new diffusion model for generating peptide binders given protein pockets. It also presents a new benchmark dataset, created by selecting examples from PDB and ensuring sequence dissimilarity between training and test data.
Strengths: Thank you for constructing a benchmark dataset for this... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful reviews!
> W1: In the intro/abs, please state clearly that PepGLAD needs to be given the binding site.
Thanks for the suggestion! We will state it clearly in the revision.
> W2: Line 24 is hard to understand: ‘The key of peptide design …’.
Sorry for the confusion. Fi... | Summary: This paper explores the structure-based peptide design problem. A benchmark on this task and a powerful diffusion-based model for full-atom peptide design, named PepGLAD, are proposed. PepGLAD explores the geometric latent diffusion, where the sequence and the full-atom structure are jointly encoded by a vari... | Rebuttal 1:
Rebuttal: Thanks for your insightful and constructive comments!
> W1: This paper failed to further explore the explicit interaction modeling in geometric latent space.
We apologize for not making this point clear sufficiently.
Our model inputs the pocket as a condition for diffusion. During denoising, we... | Summary: This paper introduces a novel latent diffusion model, Peptide design with Geometric Latent Diffusion (PepGLAD), for the task of peptide design. The authors propose an affine transformation to project the raw Euclidean space into a standardized one, ensuring physical symmetry. Overall, I think it is a good pape... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments!
> Q1: What are the key differentiating factors of PepGLAD compared to GeoLDM [1]? While I recognize the shift from latent molecular generation to full-atom peptide generation, are there any specific model design elements (apart from the affine transformation... | Summary: The authors proposed PepGLAD, a latent diffusion model for full-atom peptide design. This work mainly addressed two challenges for peptide design, (1) full-atom modeling, and (2) diverse binding geometry. Specifically, a variational auto-encoder was trained to learn latent representations of protein-peptide in... | Rebuttal 1:
Rebuttal: Thanks for your valuable efforts and comments!
> W1. Dataset is small. Data augmentation attempts do not seem to help. Although sequence-based clustering has been utilized to prevent data leakage, training/testing VAE and diffusion model on 6105/93 data points make the results less convincing.
T... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective | Accept (poster) | Summary: The authors build on the work of Larsen et. al. [1] to estimate the fundamental limit of pruning i.e the smallest achievable density of a network by pruning. This is done by estimating the statistical dimension of the neural network and leveraging convex geometry.
Similar to Larsen et. al. [1] the authors pro... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and constructive comments.
**Weakness 1: The analysis of Figure 4 is unclear to me. I cannot follow the interpretation of the provided plots to infer that iterative pruning can be beneficial. The provided plots suggest that removing a large fraction of parameter... | Summary: This paper leverages the framework of statistical dimension in convex geometry to characterize the sharp phase transition point, i.e., the fundamental limit of the pruning ratio. Two key factors are found to be important for pruning, weight magnitude and network flatness. The flatter the loss landscape or the ... | Rebuttal 1:
Rebuttal: **Weakness 1: One thing that sounds strange to me is that one conclusion is that "The smaller the network flatness (defined as the trace of the Hessian matrix), the more we can prune the network", which is contrary to the previous common belief, i.e., the flatter the network's landscape, the easie... | Summary: The paper investigates the theoretical limits of how sparse a network can be pruned without sacrificing performance. The authors formulate pruning as a convex geometry problem. By imposing sparsity constraints on the loss function, they show that the pruning ratio can be bounded by the width of the loss landsc... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! The concerns and questions in the review are addressed as follows.
**Weakness 1: Limited technical novelty: applying convex geometry to attain similar results (e.g. bounds dependent on the Gaussian width) has been explored in prior works, some of which are c... | Summary: This paper tries to answer the question of how sparse can a deep neural network be pruned without increasing the loss function. The authors employ high-dimensional geometry tools such as statistical dimension, Gaussian width, and the Approximate Kinematic Formula to derive the lower bound and upper bound of th... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful feedback. Below are our detailed responses to your concerns:
**Weakness 1: The derived upper bound and lower bound on the pruning ratio depend on the new weights of the pruned networks. This is strange to me, meaning that the limit of the pruning ratio v... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference | Accept (poster) | Summary: This paper focuses on cross-domain Federated Graph Learning, where graph data stored in clients exists negative domain structural shifts. Authors observe the presence of spectral biases as a reflection of structural shifts. Thereafter, this work proposes the Generic Spectral Knowledge Sharing (GSKS), which all... | Rebuttal 1:
Rebuttal: Dear Reviewer cuMU:
Thank you for your thorough review and the kind words about our well-motivated study and handling of structural heterogeneity. We sincerely appreciate your time and effort. We hope that our responses below will address your concerns.
### Weakness
**W1: Lacking comprehensive ... | Summary: The paper introduces a novel method FedSSP designed to address the current limitations of personalized Federated Graph Learning methods. The author highlighten that existing methods fail to deal with domain structural shift and ignore the uniqueness of datasets in cross-dataset scenarios. To address the limita... | Rebuttal 1:
Rebuttal: Dear Reviewer 3kee:
We deeply appreciate your positive feedback regarding the innovative aspects of our methods and the comprehensiveness of our experiments. Thank you for your time and effort in reviewing our paper. We hope that our responses below will address your concerns and further affirm t... | Summary: This work proposed a personalized federated graph learning framework for federated graph classification. The framework includes strategies for sharing generic knowledge and satisfying personalized preferences. The authors evaluated six different cross-dataset and cross-domain settings and showed good performan... | Rebuttal 1:
Rebuttal: Dear Reviewer jSYo:
We sincerely appreciate your time and effort in reviewing our paper, and we are grateful for your positive feedback on our writing and the effectiveness of our methodology. We hope that our responses below will address your concerns and lead to an updated score.
### Weakness
... | Summary: FedSSP tackles structural heterogeneity well in personalized Federated Graph Learning. When it comes to cross-domain scenarios, the structural heterogeneity becomes more negative than usual. It is crucial to mitigate the impact of domain structural shifts. FedSSP proposes two strategies to address these challe... | Rebuttal 1:
Rebuttal: Dear Reviewer LD7P:
Thank you for your encouraging comments on the coordination of our methods and the overall significance of our work. We hope that our responses below will address your concerns and reinforce your positive evaluation.
### Weakness
**W1: The client scale in the experiments is ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair Online Bilateral Trade | Accept (poster) | Summary: This paper addressed the problem of fair bilateral trade: at each round $t\leq T$ a buyer and a seller, with respective valuations $B_t$ and $S_t$, want to trade a good. The agent acts as a facilitator for the trade, by posting a common price $p_t$. The trade happens if $p_t \leq B_t$ and $p_t \geq S_t$. Impo... | Rebuttal 1:
Rebuttal: - Adding a summary table
Great idea! We are happy to do it.
- Fair dynamic pricing
The few existing works on fair dynamic pricing study notions of fairness that are orthogonal to what ours would be when translated to dynamic pricing.
For example, see Xu et al., Doubly Fair Dynamic Pricing, an... | Summary: This paper studies the fair online bilateral trade problem, where a platform posts prices for one item at a time. At each time point, a (buyer, seller) pair arrives, each with private valuations. A trade occurs if the posted price is between the buyer and seller valuations. In this paper, the goal is to maximi... | Rebuttal 1:
Rebuttal: - Weighted fair gain from trade objective
A possible weighted generalization of the fair gain from trade could be
$WFGFT(p,s,b) = \min( w \cdot (p-s)^+$, $(b - p)^+ )$
for a fixed constant $w \ge 1$ and where we recall that $p$ is the posted price, $s$ is the seller valuation and $b$ is the bu... | Summary: The paper considers a fair version of online bilateral trade problem to minimize fair GFT regret w.r.t. optimal fixed price in hindsight.
The paper is comprehensive in studying both upper/lower bounds in various settings.
Strengths: - The problem setup is pretty interesting, with a good motivation to the prac... | Rebuttal 1:
Rebuttal: - Weakness 1
We are not sure we understand what the reviewer meant here.
We are happy to provide clarifications if the reviewer needs some.
- Valuations not in $[0,1]$
Extending beyond $[0,1]$ works as in the bandit literature: If $[0,1]$ is replaced by $[0,m]$ (for some $m$), the same results... | Summary: The paper focuses on the online bilateral trade problem, in which at each round a buyer and a seller with private valuations for an item arrive, and the platform has to post prices for the item being traded. In this paper the objective of the platform is that of maximizing the cumulative “fair gain from trade”... | Rebuttal 1:
Rebuttal: - Additional challenges here compared to Cesa-Bianchi et al., EC '21
A first high-level observation is that the pairs "assumption"/"regret rate" differ between our setting (Fair Bilateral Trade) and that of Cesa-Bianchi et al. [10] ("regular" Bilateral Trade), suggesting that different ideas will... | Rebuttal 1:
Rebuttal: We thank the four reviewers for the time spent reading our work and for sharing their comments.
We will update the submission in light of the feedback.
In particular, we will further highlight our technical contributions and the economic relevance of our results, we will provide additional discus... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Score Distillation as a Bridge Between Image Distributions | Accept (poster) | Summary: The paper analysis score distillation methods in a single framework and hypotheses about two possible error sources - the single step ODE solver approximation and the mismatch between the assumed and true source image distribution. It then proceeds to tackle the first of them using a custom negative prompt des... | Rebuttal 1:
Rebuttal: > 1. The formulas of DDS and the proposed method are the same up to the source prompt.
A small yet fundamental difference is that DDS is a specialized method for editing that computes the source distribution direction based on a reference image instead of the current optimized image. That is, DDS... | Summary: This paper proposes interpreting score distillation sampling (SDS), a widely used method for generating 3D, 4D, and vector graphics, through the lens of the Schrödinger Bridge (SB) problem. According to the paper, SDS is a linear approximation of the optimal path moving from the current distribution to the tar... | Rebuttal 1:
Rebuttal: > 1. The solution in section 2.4 was quite naive and heuristic. A major drawback is whether other models (MVDream, SDXL, PixArt, SD3, etc.) can understand the descriptions "oversaturated, smooth,…" well.
Although using negative prompt is a common practice in text-based diffusion models, how to u... | Summary: This paper revisits the application of Score Distillation Sampling (SDS) for tasks with limited data availability by proposing a new interpretation based on Schrödinger Bridges for optimal-cost transport between distributions. The paper highlights that existing SDS methods produce artifacts due to linear appro... | Rebuttal 1:
Rebuttal: > 1. Some technical parts lack enough rationales, such as the negative prompt and two-stage optimization process. How do you choose these negative prompts?
We propose a two-stage optimization process motivated by our SB framework. To reduce the effect of the distribution mismatch error, we aim t... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful feedback. We propose an optimal transport view to understand score distillation, which reviewers “really like” (Cgf2), and find “novel” (sBp4). We provide illustrations and experiments under this single framework, which reviewer XvCb finds “quite interes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective | Accept (poster) | Summary: The paper proposes a new framework that revisits SHGL from a spectral clustering perspective, incorporating rank and dual consistency constraints. This approach uses a rank-constrained spectral clustering method to refine the affinity matrix and remove noise, while also integrating node-level and cluster-level... | Rebuttal 1:
Rebuttal: Thanks for the positive comments on the novelty, theoretical analysis, and experimental results of our method. We are so encouraged and will try our best to address the concerns one by one.
> **Q1.** Unclear why three challenges are challenging to address and the logical relationships between the... | Summary: This work deals with the problem of self-supervised representation learning in heterogeneous graphs. First, a spectral-clustering based objective is presented to unify the objectives of existing methods. Second, a novel self-supervised method is proposed that tries to capture both the cluster information and n... | Rebuttal 1:
Rebuttal: Thanks for the positive comments on the novelty, theoretical analysis, and experimental results of our method. We are so encouraged and will try our best to address the concerns one by one.
> **Q1.** Presented theorems are often unclear or imprecise. For example,
> **Q1-a**. In Theorem 2.6, no d... | Summary: This paper proposes a theory-backed method for Self-Supervised Heterogeneous Graph Learning (SHGL) based on spectral clustering and incorporates rank constraint and node/cluster consistency regularizers to generate better embeddings. In specific, the authors start by showing that existing algorithms divide the... | Rebuttal 1:
Rebuttal: Thanks for the positive comments on our theoretical and experimental results. We are so encouraged and will try our best to address the concerns one by one.
> **Q1.** Some undefined notations, e.g., the dimensions of $g_\phi$ and $p_\varphi$.
**A1**. We employ $g_{\phi}\in\mathbb{R}^{f\times d_... | Summary: Overall, this paper makes the first attempt to theoretically revisit previous SHGL methods from the spectral clustering perspective in a unified manner. Specifically, this paper revisits SHGL from the spectral clustering and introducing a novel framework enhanced by rank and dual consistency constraints. Speci... | Rebuttal 1:
Rebuttal: Thanks for the positive comments on the novelty, theoretical analysis, and experimental results of our method. We are so encouraged and will try our best to address the concerns one by one.
> **Q1.** Any real examples to illustrate the noise in meta-path-based graphs and adaptive graph structures... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful and constructive comments. Due to space limitations in the rebuttal, we listed tables and figures in the uploaded PDF file. All modifications will be found in the final version. Our key responses are summarized as follows:
**> Clarific... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network | Accept (spotlight) | Summary: This paper posits that real-world image dehazing is particularly challenging due to the intricacies of accurately modeling haze distributions and the limited availability of paired real-world data. Traditional and deep learning-based methods struggle to address the complexities of real haze, often resulting in... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. If not specifically stated, all experiments are conducted on the real-world image dehazing task with *RTTS* for space limitation.
**W1 & W2: Typo and Misuse of symbols.**
We apologize for the errors in our writing and appreciate you pointing out these mistakes i... | Summary: This paper focuses on challenging real-world image dehazing problem. The authors develop their network based on unfolding network, while leveraging cooperative proximal mapping modules to facilitate the estimation of transmission map and image content. In addition, the authors propose an interesting teacher ne... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. If not specifically stated, all experiments are conducted on the real-world image dehazing task with *RTTS* for space limitation.
**W1: Unclear derivation of equation**
For space limitation, we only show the detailed derivation of Eq. (7) and the Eq. (10) can be... | Summary: The paper aims for real-world image dehazing, where the paper tries to handle the difficulties of modeling real haze distributions and the scarcity of real data. For modeling haze, the paper proposes to jointly model atmospheric scattering and image scenes by a cooperative unfolding network. To handle the scar... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. If not specifically stated, all experiments are conducted on the real-world image dehazing task with RTTS for space limitation.
**W1: Contribution**
Our work goes beyond simply combining existing methods. We introduce the Cooperative Unfolding Network (CORUN), ... | Summary: This paper focuses on Real Image dehazing. They propose a cooperative unfolding network (CORUN) to integrate physical knowledge for image dehazing. The proposed CORUN exploits the complementary information between components in Atmospheric Scattering Model. Besides, due to the lack of real paired data, this pa... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. If not stated, all experiments are conducted on the RTTS dataset.
```
*Experiment results are placed in the attached PDF for space limitations*
```
**W1: Writing and formula**
- The redundant comma will be removed.
- The simplified $P$ in eq.(2) differs from th... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to all the reviewers (**R1**-**Udmv**, **R2**-**ivCJ**, **R3**-**h5fd**, and **R4**-**wF18**) for their insightful and considerate reviews, which help us to emphasize the contributions of our approach. We are pleased to hear that the reviewers approved the novelty o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unleashing the power of novel conditional generative approaches for new materials discovery | Reject | Summary: This paper presents a framework for crystal structure generation, focusing on polymorphs. The framework utilizes matrix representation of crystals and various generative models used in vision tasks, with specially designed similarity metrics and loss function. It is tested on (1) modification of given structur... | Rebuttal 1:
Rebuttal: Thank you for your attentive feedback. We appreciate your thorough review. Here are our responses to your concerns:
Regarding the Objectives and Methodology:
The primary objective of this work is to propose models capable of generating stable crystal structures, aligning with the existing researc... | Summary: The authors studied the use of diffusion and flow matching approaches for the generation of crystalline materials. The authors trained UNet models on polymorphs in the AFLOW database (which has a series of DFT-computed properties for these materials) using either simple R3 regression, diffusion/flow matching. ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your thorough review. Here are our responses to your concerns:
We employed a network architecture not previously used for this task and did not utilize standard invariances/equivalences, but this deviation from standard practices is justified by... | Summary: The paper addresses the inverse problem of generating crystal structures based on given properties, thereby avoiding the need for extensive computational resources typically required in traditional methods. The authors utilized the AFLOW materials database, selecting unstable and stable series of structures fo... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on our paper. We appreciate your insights and have addressed your concerns below:
We did compare the AFLOW database with the Materials Project in the critique of the GNOMe paper(Introduction section), noting that the Materials Project has a much smaller datase... | Summary: The paper deals with an important application of generative models for science: generation of crystalline structures. However, I have some serious concerns. First, scope. While comparing different methods for the same objective is informative, I am not so sure what is the purpose here. Having so many different... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper. We appreciate the time you have taken to provide a thorough review. Below, we address each of your concerns:
Firstly, scope. The primary aim of our paper is to create a comprehensive comparison of different generative approaches in the field of m... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation | Accept (poster) | Summary: This paper proposes FedLPA, a novel one-shot federated learning method that uses layer-wise posterior aggregation. It aggregates local models to obtain a more accurate global model without requiring extra datasets or exposing private label information. The key innovation is using layer-wise Laplace approximati... | Rebuttal 1:
Rebuttal: Dear Reviewer 4qUr, thanks for your comments, which helped us improve our paper. The answers to all your questions are as follows:
> **Q1**: The method relies on multiple layers of approximation - empirical Fisher to approximate the Hessian, block-diagonal Fisher instead of full, and approximati... | Summary: The paper "FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation" introduces FedLPA, a novel one-shot federated learning method that addresses challenges associated with high statistical heterogeneity in non-identical data distributions. The framework uses layer-wise posterior aggregation b... | Rebuttal 1:
Rebuttal: Dear Reviewer XECp, thanks for your comments, which helped us improve our paper. The answers to all your questions are as follows:
> **Q1**: **Implementation Complexity**: The use of layer-wise posterior aggregation and the empirical Fisher information matrix introduces significant complexity. ... | Summary: This paper proposes a one-shot Federated Learning (FL) method, denoted as FedLPA, to address heterogeneous data distribution among clients. FedLPA does not demand auxiliary datasets or private label information during aggregation on the server side. To achieve this, FedLPA infers the posteriors by leveraging t... | Rebuttal 1:
Rebuttal: Dear Reviewer F77H, thanks for your comments, which helped us improve our paper. The answers to all your questions are as follows:
> **Q1**: There are some typo problems.
**Answer**: Thanks for pointing that out and thanks for your efforts in reviewing our paper. We found the following typos an... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Understanding Evolving Patterns in Sequential Data | Accept (spotlight) | Summary: This paper proposes a novel metric, Evolving Rate, using mutual information to measure the existence of the evolving patterns in sequential data. For the scenarios in which data samples across disparate time steps are not aligned, the paper proposes to build the correspondence between snapshots using optimal t... | Rebuttal 1:
Rebuttal: Responses to Weakness
1. (Cost function) The cost function of Optimal Transport (OT) is defined in Eq (7). In the literature [1-2], OT typically uses a distance metric between two samples, each sampled from different marginal distributions, as the cost function. For instance, $z^s$ and $z^t$ repre... | Summary: The article introduces a groundbreaking technique for measuring changes in sequential data, marking a notable advancement in the realm of machine learning. The authors bring forth the Evolving Rate (EvoRate) and its advanced iteration, EvoRate$_\mathcal{W}$, as effective tools for evaluating the temporal dynam... | Rebuttal 1:
Rebuttal: Responses to Weakness:
1. (scalability & training an auto-encoder) We have added experiments on the scalability of the method with different dimensions in the encoding space using the Video Prediction dataset KITTI.
Encoding Dim|128| 256 |512| 1024
|-------------------------|-----------... | Summary: This paper introduces EvoRate, a novel metric designed to quantify the evolving patterns in sequential data. The authors propose leveraging mutual information (MI) to measure the temporal dependencies between data points in a sequence. The paper addresses a significant challenge in machine learning: identifyin... | Rebuttal 1:
Rebuttal: 1. (EDG setting) We thank you for pointing it out and will include an illustration of the Evolving Domain Generalization (EDG) setup. A brief explanation of EDG can be found in lines 328-330. More specifically, The EDG tasks setup involves using the training dataset $D_S = \\{ \\{ x_{t,i}, y_{i,t}... | Summary: This paper aims to identify the evolving pattern in sequential data. In addition, given evolving pattern may present in the sequential data, this paper would like to introduce a technique that can identify the best temporal order and features for learning the sequential data.
To address that, this paper propos... | Rebuttal 1:
Rebuttal: - W1 & Limitation (motivation is not very clear) - **Our motivations and contributions**: Our work's contribution is three-fold:
- It is theoretically motivated by the use of MI to estimate evolving patterns.
- It applies a specially designed similarity critic that considers the autoregressiv... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable comments on our work.
We have received four reviews with ratings of 3, 6, 7, and 7.
We are pleased that the reviewers have good impressions of our work, including:
- Addressing an interesting and important problem (8iST, rYZr, V8vA);
-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NVRC: Neural Video Representation Compression | Accept (poster) | Summary: This paper proposes a INR-based video compression framework, Neural Video Representation Compression (NVRC), targeting compression of the implicit representation.
Based on the proposed novel entropy coding and quantization models, NVRC is able to optimize an INR-based codec in a fully end-to-end manner.
Th... | Rebuttal 1:
Rebuttal: ***Q: Evaluation on more datasets***
A: Thank you for this suggestion. We agree that evaluation on more datasets is important, and we have included additional results (MCL-JCV and JVET CTC Class B, RGB setting) in the rebuttal results (see the attached pdf). Here we used the JVET CTC Class B rath... | Summary: This paper focuses on the implicit neural representation for video compression, where several modifications are applied for the coding of feature grid and network layer. An enhanced training pipeline is also applied.
Strengths: 1. The claimed performance over traditional codec and VAE-based codec is impressiv... | Rebuttal 1:
Rebuttal: ***Q: The difference between NVRC and [13]/[Guo2023]****
A: We thank the review for highlighting this point. We agree that there are multiple INR-based codecs that focus on joint rate-distortion optimization, and will describe these in the revised paper.
When we refer to a "fully end-to-end opti... | Summary: This paper describes the INR-based video codec NVRC. NVRC is optimized E2E and includes a quantized model which is critical for device reproducibility (though this is not discussed in the paper). The performance on UVG is good and significantly better than VVC VTM 20.0. Benchmarks are given for an NVIDIA 4090 ... | Rebuttal 1:
Rebuttal: ***Q: For real-time scenarios***
A: We agree with the reviewer that the proposed method is not yet appropriate for real-time applications. The relatively long encoding time is one of the main limitations of this AND other INR-based compression methods. We will mention this in the paper as an impo... | Summary: - This paper proposes an INR-based video codec, NVRC, which aims to improve the rate efficiency by encoding parameters hierarchically. Experimental results of the proposed method have been shown in RD performance on the UVG dataset.
Strengths: - Experiments show good RD performance compared to recent INR-base... | Rebuttal 1:
Rebuttal: ***Q: Showing coding gain for each contribution on Page 2.***
A: Thank you for your suggestion. In the original paper, we presented these figures in the ablation study, including (contribution 2) the use of different quantization setting and entropy models (V1/V2/V3 for entropy models and V5 for ... | Rebuttal 1:
Rebuttal: Thank you the reviewers for thorough feedback to our submission. We will address the concerns individually.
Pdf: /pdf/ffe9b2ece59bb0241872a29602883e6f600808fe.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model | Accept (poster) | Summary: This paper introduces a simple yet effective VL tracking framework based on a multimodal large language model, called ChatTracker. The main idea is to utilize the rich world knowledge in multimodal large language model to generate high quality language descriptions and improve tracking performance. Specificall... | Rebuttal 1:
Rebuttal: ***Q1: Line 127 mentions only the initial bounding box and search frame as inputs and does not mention template frames as inputs. However, the equation in line 128 writes template as input.***
Thanks for your careful reading.
In the equation $P^{t}_{VT} = \mathcal{F}_V$$_T$$(I^{t};I^{1}, G) $ ,... | Summary: This paper proposes ChatTracker, a novel framework that leverages MLLMs for visual object tracking. The Reflection-based Prompt Optimization (RPO) module can narrow the knowledge gap between the VL tracker and the MLLM. ChatTracker also achieves SoTA performance on several tracking datasets.
Strengths: 1. Thi... | Rebuttal 1:
Rebuttal: ***Q1: SOTA trackers are missing in Table 1, such as OVLM[1], MMTrack[2].***
Thank you for your valuable suggestion. We will include comparisons with OVLM and MMTrack in the revised manuscript. Additionally, we have found that compared to their variants with highest performance, our ChatTracker d... | Summary: The paper proposes a novel Multimodal Large Language Model framework to improve the vision-language visual tracking performance.
By introducing the reflection-based prompt optimization module, the tracking prompt can be iteratively refined via tracking feedback.
The proposed method shows state-of-the-art resul... | Rebuttal 1:
Rebuttal: ***Q1: The paper should illustrate the number of chat iterations in the reflection-based prompt optimization module and their performance effect. The example in Figure 5 shows that the module needs 2 iterations to get an accepted prompt but the overall analysis should be considered, especially ... | Summary: The paper proposes a new Visual-Language (VL) tracking framework called ChatTracker, that integrates MLLMs into VL tracking through iterative refinement of text prompts for VL trackers. The text prompts optimized using the proposed Reflection-based Prompt Optimization (RPO) module are more accurate than manual... | Rebuttal 1:
Rebuttal: ***Q1. The potential limitations mentioned in the weaknesses should be addressed or discussed in the paper. Have the authors done any experiments where foreground/background prompts were updated while the video is being processed? What were the insights?***
Thanks for pointing this out. Tempora... | Rebuttal 1:
Rebuttal: Dear All Reviewers,
Thank you again for taking the time to review our work and for providing us with valuable feedback.
We are excited that you found our results impressive (gPPX, z5a5, Bwy6) and our experiments well-designed (gPPX, aCWh, Bwy6), appreciate the innovative uses of multimodal large... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforcing LLM Agents via Policy Optimization with Action Decomposition | Accept (poster) | Summary: The paper proposes Bellman backup with Action Decomposition (BAD) and its realization on PPO (POAD), which aims to train with token-level policy for more fine-grained credit assignment on the crucial part of the language response made by the language agent. To address the issue of distorted Q-value by the intr... | Rebuttal 1:
Rebuttal: ### We thank Reviewer 9Wzo for his/her constructive comments that will surely turn our paper into a better shape.
> **Q1** Could the author explain intuitively why the benefit of reduced action space outweighs the problem of extended episode length with sparse reward?
**A1** Thank you for your t... | Summary: The paper proposes a novel approach to optimizing language agents in reinforcement learning (RL) environments, addressing the challenges of limited environmental knowledge and vast action spaces. Traditional methods like GLAM and TWOSOME optimize language actions as whole units, leading to inefficiencies in cr... | Rebuttal 1:
Rebuttal: ### We thank Reviewer 671a for his/her constructive comments that will surely turn our paper into a better shape.
> **Q1** BAD has a significant correlation with some existing token-level optimization methods [2][3] and hierarchical optimization methods [1]. Can the authors make a more in-depth c... | Summary: The paper investigates LLM agents: RL agents where sampling actions from a policy means sampling a sequence of tokens mapping to an action from a (suitably conditioned) large language model. Authors notice a problem with previous implementations of this idea: since actions are typically described as a sequence... | Rebuttal 1:
Rebuttal: ### We thank Reviewer JxkT for his/her constructive comments that will surely turn our paper into a better shape.
> **Q1** The unclear formalism and notation errors.
**A1** We apologize for the confusion caused to the reviewers and readers. Indeed, we recognize that the use of POMDP in our formu... | Summary: This paper introduces Policy Optimization with Action Decomposition (POAD), a novel method for reinforcing language agents by optimizing at the token level rather than the action level. The authors derive a theoretical framework called Bellman backup with Action Decomposition (BAD) to address discrepancies bet... | Rebuttal 1:
Rebuttal: ### We thank Reviewer H4uQ for his/her constructive comments that will surely turn our paper into a better shape.
> **Q1** More baselines should be included for comprehensive analysis.
**A1** We appreciate the feedback from H4uQ and also extend our gratitude to Reviewers 671a and 9Wzo for recomm... | Rebuttal 1:
Rebuttal: # Meta Responses
We are delighted to receive positive feedback from all the reviewers, and thank the reviewers for their valuable suggestions.
## Answers to some common questions and New results (see the PDF attached here)
For convenience, we provide detailed answers to some of the common question... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Communication Bounds for the Distributed Experts Problem | Accept (poster) | Summary: The paper considers a distributed variant of the classical problem of learning with experts, where the cost of each expert needs to be aggregated across different servers. Based on three different aggregation models, i.e., sum, maximum and $\ell\_p$ norm, the authors propose three different algorithm based on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and address the main concerns below.
**Q1. The coordinator assumption**
We agree with the reviewer that in some scenarios there does not exist a coordinator to coordinate the communications among downstream servers. The motivation for the coo... | Summary: The paper investigates the experts' problem in a distributed context, where the costs associated with experts are distributed across multiple servers. The authors present results for two communication models: the message-passing model and the blackboard model. They explore two aggregation functions: the sum an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and address their main concerns below.
**Q1. Comparison with Kanade et al.**
We thank the reviewer for the suggestion. We will move the comparison with Kanade et al. to the main text in a revision, as we indeed consider it very relevant to our work.
**Q2. ... | Summary: I reviewed this paper a few years back a few times and declined to review this paper for a while to be fair to the authors. I found the paper’s quality has not improved much (as opposed to the average quality improvement for many recycled theory papers) so I can only give somewhere between borderline and accep... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and address their main concerns below.
**Q1. Presentation**
We thank the reviewer for pointing out the issues in our presentation.
The problem is indeed an experts problem as we assume each server can observe the full cost vector. The reason we introduced... | Summary: This paper studies the classical experts setting in a new communication-focused model that is motivated by evaluating models when the data points are stored across many different servers. At a high level, the model is the following. There are $n$ experts (think of them as different models). There are $s$ se... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and address their main concerns below.
**Q1. Motivation for the Broadcast Model**
We thank the reviewer for acknowledging the setup for the message-passing model. For the broadcast model, there are fewer scenarios for the distributed experts problem than in... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning | Accept (poster) | Summary: The paper introduces Adversarial Meta-Tuning (AMT) to enhance the robust generalization of pre-trained models for out-of-domain few-shot learning by constructing a robust LoRAPool through meta-tuning Low-rank Adapters (LoRAs). The approach significantly outperforms previous methods in both clean and adversaria... | Rebuttal 1:
Rebuttal: We extend our appreciation for your constructive feedback on our manuscript. Below, we address each of your points comprehensively. Should there be any additional queries or clarifications needed, please feel free to let us know.
#### Q1. Adversarial robustness evaluation of unseen attacks
> - We... | Summary: This paper proposes to tackle the problem of improving the generalization of pre-trained models to data drawn from a different distribution than the training data. To achieve this, the authors propose using meta-learning to train the models. Since they consider a single source domain, they adversarially genera... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments on our paper. Please kindly find our response to your comments below. We hope that our response satisfactorily addresses the issues you raised. Please feel free to let us know if you have any additional concerns or questions.
#### Q1. More discussions conc... | Summary: This paper deals with how to effective adapt a pretrained model to cross-domain few-shot learning task. It focus on both adversarial robustness and clean accuracy of trained model. To realize the goal, it utilizes adversarially trained LoRA to adapt the pretrained model. Specifically, it utilizes SAM to deter... | Rebuttal 1:
Rebuttal: Thank you sincerely for your thoughtful feedback on our work. Below, we have provided a detailed explanation for your concerns as follows. Please do not hesitate to let us know if you have any further questions.
#### Q1. Technical contributions concerning adversarial singular value and vector per... | Summary: The paper proposes a method for training loras for vision transformer models such that the model easily adapts to an unseen few shot classification task. The goal is to have these loras robust to adversarial noise.
Strengths: The experiments seem comprehensive and show impressive performance. The idea of pert... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing valuable feedback. We detail our response below point by point. Please kindly let us know whether you have any further concerns.
#### Q1. Instances of novel and adversarial environments
> We appreciate the reviewer's great feedback and agree that disc... | Rebuttal 1:
Rebuttal: # Summary of changes
We extend our sincere thanks to the reviewers for their constructive feedback. We have summarized additional experiments and clarification made during the rebuttal period as follows.
**Clarification:**
1. Illustrated our technical contributions concerning adversarial singul... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks | Accept (poster) | Summary: The paper tries to address the critical issue of biases in blackbox feature extractors used for image classification tasks. These biases can impact the performance of models when adapted for downstream tasks. The authors investigate existing debiasing techniques and propose a novel method using a clustering-ba... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We address the concerns below:
**Deeper analysis of the approach.**
We refer the reviewer to the ArcFace paper [6] for details on the margin loss. Here, we show how the adaptive nature of the margin loss aids in the learning of the bias-conflicti... | Summary: In this paper, the authors propose a simple method with a clustering-based adaptive margin loss for debiasing blackbox pretrained models. Whereas prior works have explored settings where pretrained models are tunable, the authors instead explore a more constrained and realistic setting, where a black-box netwo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We address the concerns below:
**Clarifications about Table 1:** Table 1 is solely meant for analyzing the nature of the different feature encoders, wherein we can see that different feature encoders may have different levels of awareness of the ... | Summary: This work explores a problem setting where one wants to train a classifier on top of a large and frozen pre-trained model, while avoiding bias and improving fairness. It proposes a computationally efficient methodology to accomplish this objective, centered around a novel loss function, the Adaptive Margin Los... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and feedback and address the specific concerns below.
**Effects of starting from weight decay $\lambda=0$ in Figure 2 of main paper**: We have shown the effects starting from weight decay = 0 on the different group accuracies for Waterbirds, CelebA... | Summary: The papers address the debiasing problem using pretrained but frozen feature extractors on downstream applications. Then, they propose a clustering-based method that relies on bias-amplified training through cross-entropy loss. After training, they cluster the biased features and mitigate the biases using the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions and appreciating the comprehensive experiments. We address the concerns below:
**Assumption on alignment of bias of the downstream dataset with that in the pretrained model**. Please see the general responses.
**Table 8: Scores of ViT-B on ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments, questions, and suggestions. We are pleased that our work has been appreciated and positively rated. The reviewers recognized the significance and under-researched nature of our problem statement (Reviewer *wKPv*), noting its growing importa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Variance estimation in compound decision theory under boundedness | Accept (poster) | Summary: This submission studies the variance estimation problem for Gaussians under the compound decision/empirical Bayes settings, assuming bounded means and variance. The main results are up-to-constants matching upper and lower bounds on the estimation rate, for mean squared error. For the upper bound, the proposed... | Rebuttal 1:
Rebuttal: Thank you for your very helpful comments and thorough feedback! Our responses below address the points raised in your review.
[Weakness 1] We agree the title can be revised for clarity. In the revision, we will revise the title to ``Variance estimation in compound decision theory under boundedne... | Summary: This paper gives a sharp minimax rate of the variance estimation under mild assumption on a Gaussian-based model.
Strengths: The theoretical results are solid and complete, and the related reference are discussed and linked with their own work.
Weaknesses: The sign in equation (5) and also in line 94 in not ... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Our response to your review is below.
[Weakness 1] We have conducted an experiment in the attached pdf, and the details are in the global response to all reviewers. Yes, the symbols $\asymp$ and $\lesssim$ mean ``up to universal constants''. The notation we use is def... | Summary: This paper studies the problem of variance estimation in compound decision setting, with the assumption that means are bounded. Main contributions:
- The authors prove the minimax rate of variance estimation in the setting of the paper, with the proof of the lower bound and a proposed estimator achieving the ... | Rebuttal 1:
Rebuttal: Thank you for your comments! Below, we address some of the points raised in your review.
[Weakness 1] We have conducted an experiment in the attached pdf, and the details are in the global response to all reviewers.
[Question 1] Most other variance estimation procedures in the literature (beyon... | Summary: The paper studies the variance estimation of the normal means model and establishes the minimax squared error in terms of $n$, the number of observations. The results assume a bounded parameter space, where the absolute means and variance are at most 1 and $L^2$ (a large hidden constant), respectively. The est... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback! Our responses below address the points raised in your review.
[Weakness 1] Please see the global response to all reviewers regarding the dependence of the rate on $L$.
[Weakness 2 and Question 2] We completely agree with your comment that Gaussianity is cri... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their helpful comments. Below, we address those points which were raised by multiple reviewers.
Two reviewers asked about the dependence of $L$ in the minimax rate. Thank you both for the comment; it is well-received. We would like to make the case that $L \asymp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction | Accept (poster) | Summary: 1. The paper introduces a novel self-supervised method for reconstructing high-quality image sequences from sparse binary quanta image data.
2. The paper mainly adapt a self supervised denoising algorithm called GAP. Instead of directly adopting the GAP method, the authors extended it to spatiotemporal struct... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments. Here, we answer the reviewer’s questions one by one:
1. **In Equation 1, there is a confusion between x_{in} and x_{inp}. Moreover, the right side of Equation 1 seems to have overlooked x_{tar}.**\
Thank you for pointing out the mistake. We will c... | Summary: This paper presents a self-supervised method for reconstructing high-quality video from sparse binary quanta image data produced by single-photon avalanche diode (SPAD) arrays. The authors propose a novel masking strategy to handle the binary nature of the data and extend their method to 3D to leverage spatiot... | Rebuttal 1:
Rebuttal: Thank the reviewer for the comments. We noticed several factual errors in this review:
1. **L1: "SPAD" is not defined on first use.**\
SPAD is defined in L1 on first use.
2. **L22: "QBP" is not defined on first use.**\
QBP is defined in L21-22 on first use.
3. **L205: More information on the iPho... | Summary: The paper proposes to extend the Generative Accumulation of Photons that was proposed for Poisson noise to 1 bit Quanta image sensors.
Strengths: The proposed method is novel, and mathematically interesting. The authors have put in a lot of effort to fit GAP for the problem of 1-bit QIS reconstruction.
Weakn... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and we want to answer the reviewer’s questions one by one:
1. **The proposed method does not seem to be performing better than a supervised method. It is not clear what is the supervised method that was used in the comparisons.**\
We apologize for ... | Summary: This paper introduces a method to reconstruct/denoise high-resolution high-frame-rate videos captured by 1-bit quanta imaging sensors (e.g., SPAD arrays) without heavy spatio-temporal binning. The paper also captures and will release a new SPAD dataset.
The proposed method is loosely based on Generative Accum... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and we want to answer the reviewer’s questions one by one. For conciseness, we grouped similar and relevant questions and comments.
1. **Is the Poisson distribution valid in the photon-starved regime (fewer than one photon per pixel on average)? Ho... | Rebuttal 1:
Rebuttal: We have addressed each reviewer's comments and questions in detail in reviewer-specific rebuttals underneath each review.
A new figure demonstrating the photon splitting process is presented in the attached PDF. The figure also more clearly indicates the artifact resolved by the masked loss. This... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators | Accept (poster) | Summary: The authors proposes a Bayesian policy reuse-based framework referred to as CBPR, which allows for a collaborative artificial agent to adaptively select optimal collaborative policies from multiple policy networks. They did so by extending intra-episode belief to collaborative scenarios and incorporating this ... | Rebuttal 1:
Rebuttal: We are grateful for the time and effort that reviewer QDdz has invested in reviewing our paper, and we appreciate the recognition of the main advantages of our method. Additionally, thank you for your insightful comments regarding Theorem 1 and its proof.
**Clarification on the Relationship Betw... | Summary: This work explores how to address the challenges of non-stationary human behavior in human-AI collaboration. The authors propose a Bayesian framework that adaptively selects optimal models during training episodes to capture the underlying consistent human behavior in solving meta-tasks. Theoretical analysis s... | Rebuttal 1:
Rebuttal: We really thank you for your valuable comments on improving our work. We sincerely hope the reviewer can raise the assessment score of our paper if the following responses have successfully addressed the concerns.
> Q1: The uncertainty of σ?
The human-AI collaboration problem we address is inher... | Summary: This paper introduces Collaborative Bayesian Policy Reuse (CBPR), a framework that addresses the challenge of collaborating with non-stationary human behavior by adaptively selecting optimal collaborative policies based on the current meta-task. CBPR identifies meta-tasks underlying human decision-making and t... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewer 3rF3 has invested in reviewing our paper. For the Weakness1, Weakness2, Q1 and Q3 you mentioned, we have added experiments to support our insight. We apologize for the confusion caused by Figure 2. We have simplified it in the rebuttal PDF to make the framewor... | null | null | Rebuttal 1:
Rebuttal: We thank the time and effort reviewers have invested in reviewing our work. We have provided detailed explanations and clarifications to address your concerns regarding problem definition, experiments and discussions.
During this stage, we have supplemented the experiments to address reviewers' ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Periodic agent-state based Q-learning for POMDPs | Accept (poster) | Summary: The paper proposes a new RL algorithm for POMDPs. The standard approach to convert the POMDP in a belief MDP is not possible in the RL setting, when there is no knowledge about the system model. The alternative is to use an agent-state which is a function of the observation history. Standard RL algorithms can ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback.
### **1. Considering other non-stationary approaches**
As we mention in the related work section, there are some other approaches to non-stationarity that have been taken in the literature e.g., continual learning and hierarchical learning including option... | Summary: This work proposes a type of non-stationary policy for POMDPs that is periodic. The authors argue that typical agent states in partially observable RL do not satisfy the Markov property and illustrate why introducing non-stationarity can improve the optimal policy within the policy class (vs considering only s... | Rebuttal 1:
Rebuttal: Thank you for your comments and the positive endorsement. We address your questions and concerns below.
### **1. Could you please comment on the relevance of the insights towards larger POMDPs, like those common in deep RL?**
The current state of the art $Q$-learning based deep RL algorithms for... | Summary: This paper presents the problem of learning a policy in a partially observable environment in a model-free way. The authors address this problem by proposing to learn a non-stationary periodic policy that can outperform stationary ones. This aspect is motivated by the fact that the policy can be constructed ei... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We address your questions and concerns.
### **1. Monotonically increasing performance of periodic policies**
We illustrate this via an example. Let $Π_L$ denote the set of all agent-state based policies with period $L$. Consider a policy $(π_1, π_2, π_1, π_... | Summary: The paper introduces PASQL (Periodic Agent-State Based Q-Learning), a novel reinforcement learning approach tailored for Partially Observable Markov Decision Processes (POMDPs). Traditional methods often rely on transforming POMDPs into fully observable MDPs by employing belief states. However, belief states r... | Rebuttal 1:
Rebuttal: Thank you for the positive endorsement. We address your questions and concerns.
### **1. How does PASQL scale with the dimensionality of the state and action spaces in practical scenarios?**
In the paper, we have focused on the tabular setting to analyze the simplest form of the algorithm. In a ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the comments and feedback. We address some of the common issues raised by the reviewers below.
### **1. Practical implementation of the algorithm**
We want to clarify certain points regarding the practical implementation of the algorithm.
- In terms of implementation... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation | Accept (poster) | Summary: This paper presents a new framework for zero-shot object navigation. Unlike previous methods that only provide objects in close proximity, this paper constructs a scene graph that captures the relationships between objects, groups, and rooms.
This scene graph allows for a more comprehensive understanding of t... | Rebuttal 1:
Rebuttal: **1. Discussion of scene-graph-based navigation**
We thank the reviewer for the constructive advice. We will add more literature on scene graph-based navigation in the final version of paper.
3D scene graph is widely utilized in various embodied tasks, such as grounding [1] and task planning [2]... | Summary: The paper proposes a 3D scene graph prompting strategy and designs a hierarchical chain-of-thought prompt for improving LLM-based zero-shot object navigation. The 3D scene graph is incrementally updated and pruned to reduce the computational complexity. A re-perception mechanism is also introduced to correct t... | Rebuttal 1:
Rebuttal: **1. Error in the prediction of relationship and distance**
We measure the accuracy of relationship and distance prediction on episodes of MP3D validation set.
For relationship prediction, we conduct human study to annotate the correctness of each relationship predicted by SG-Nav. For each episo... | Summary: This paper propose to use 3D Scene Graph Prompt in LLM-based Zero-shot Object Navigation, which fully use information of whole scene and is explainable. Also it propose prune-based method to accelerate the construct of graph. Enough expriments show the superority of the method and the effectiveness of each mod... | Rebuttal 1:
Rebuttal: **1. Inference latency of SG-Nav**
In each step of navigation, the time cost of SG-Nav can be divided into perception, graph construction and reasoning, which takes 0.3s, 1.3s, 0.14s on average, accounting for 17.3%, 74.8% and 7.9%. The edge pruning belongs to graph construction, which only takes... | Summary: This paper introduces a zero-shot object navigation framework using a 3D scene graph to represent the environment. It employs a hierarchical chain-of-thought prompt to LLMs for goal location navigation and includes a re-perception mechanism to correct errors. Experiments are conducted on MP3D, HM3D, and RoboTH... | Rebuttal 1:
Rebuttal: **1. About open-vocabulary setting, is it training-free**
Our method is training-free and open-vocabulary. Since most objects occur in the scene belong to some common categories, we can pre-define the relationship among these categories and save them to a dictionary, which accelerates inference s... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable and constructive comments. We provide detailed answers to these questions and will revise the paper accordingly.
Pdf: /pdf/6ab6cb986782b821085d5f8db742971928c99131.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Embeddings Rank: Aligning 3D latent dynamics with movements | Accept (poster) | Summary: The authors propose a novel method for reducing the dimensionality of neural dynamics and aligning them with movements. This method is compared with six existing methods across different brain regions. Experiments demonstrate that all movement parameters can be accurately decoded from neural dynamics using thi... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback from R#3. We thank R#3 for recognizing the contribution of our work.
**Strengths** Extensive comparison to prior works\
Thanks!
**Weaknesses1** Unclear methodology\
We briefly described our method in the global rebuttal with an intuitive diagram of our mod... | Summary: The paper introduces a novel dimensionality reduction method called Neural Embedding Ranks (NER) for aligning neural dynamics with movement in brain-computer interfaces. NER uses a ranking loss to embed neural dynamics into a 3D latent space that aligns with continuous movement parameters. The authors apply NE... | Rebuttal 1:
Rebuttal: We appreciate R#2 for recognizing the contribution and strength of our work.
**Weaknesses1** Limited explanation of method\
Agree. We will add a detailed explanation (see below) later.
**W2** Application of existing RNC method\
We came up with the idea of modifying the loss in the CEBRA paper (... | Summary: The authors propose a dimensionality reduction method, specifically to learn latent neural dynamics in a 3-dimensional space. The authors perform experiments that test the transfer of their model from one hemisphere to another, from one year to another, from one brain region to another, and how well dimensiona... | Rebuttal 1:
Rebuttal: We appreciate the detailed and constructive feedback from R#1. We thank R#1 for recognizing the contribution of our work. R#1's concern about the technical flaws in evaluating our models against CEBRA and pi-VAE is valid and deserves detailed explanations.
**Strengths** Generalization across two ... | null | null | Rebuttal 1:
Rebuttal: We appreciate the Area Chair for handling our manuscript and the constructive feedback from Reviewer VFtX, f3GE, and rR7L (R#1, R#2, and R#3). We were encouraged that all three reviewers recognized our paper's contribution (3/3/3). We hope our general rebuttal letter, along with the attached 7 Reb... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting | Accept (poster) | Summary: The manuscript presents a novel approach to High Dynamic Range (HDR) novel view synthesis (NVS) by proposing a new framework called High Dynamic Range Gaussian Splatting (HDR-GS). The proposed HDR-GS framework addresses the limitations of existing HDR NVS methods, which are primarily based on Neural Radiance F... | Rebuttal 1:
Rebuttal:
## Response to Reviewer VwS8
`Q-1:` Highlight of innovations and explanations of model design and theoretical underpinnings
`A-1:` We propose the first 3DGS-based framework with 1.91 dB improvements and 1000x inference speed for HDR novel view synthesis (NVS). Firstly, we find that... | Summary: This paper proposes a 3D Gaussian Splatting-based method, HDR-GS, for the high dynamic range novel view synthesis. To efficiently perform this task, a new Dual Dynamic Range Gaussian point cloud model is presented (in Section 3.1). This point cloud model has more attributes including the HDR color, exposure ti... | Rebuttal 1:
Rebuttal:
## Response to Reviewer tLXM
`Q-1:` Analysis of logarithmic tone-mapper vs. linear tone-mapper
`A-1:` Thanks for providing a mathematical explanation of the logarithmic tone-mapping operation. We will add this analysis in the revision with acknowledgment.
`Q-2:` Why usin... | Summary: This paper introduces HDR-GS, a framework designed for efficient rendering of high dynamic range (HDR) novel views. HDR-GS leverages a Dual Dynamic Range (DDR) Gaussian point cloud model that utilizes spherical harmonics for HDR color fitting and an MLP-based tone-mapper for low dynamic range (LDR) color rende... | Rebuttal 1:
Rebuttal:
## Response to Reviewer E3vH
`Q-1:` Questions about the training data
`A-1:` (i) Actually, our method only requires an LDR image with a single exposure time $\in$ {$t_1, t_3, t_5$} at each view to train in Eq.(15).
(ii) **As claimed in Lines 198 - 200, we do not use HDR images for... | null | null | Rebuttal 1:
Rebuttal:
## General Response to All Reviewers
Thanks for your time and valuable comments. We really appreciate you for recognizing our framework soundness (`E3vH`,`tLXM`, and `VwS8`), method novelty (`tLXM` and `VwS8`), outstanding performance (`E3vH`,`tLXM`, and `VwS8`), and good writing ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents | Accept (poster) | Summary: This paper proposes a new backdoor attack against Reinforcement Learning, termed SleeperNets. SleeperNets adopted dynamic reward poisoning to overcome the insufficiency of static reward poisoning proposed in previous works. The author provided a theoretical analysis of the advantages of dynamic adversarial poi... | Rebuttal 1:
Rebuttal: **Overview:** We thank the reviewer for their strongly positive assessment of our paper. We appreciate them for acknowledging the many theoretical and technical contributions of our paper such as showing “the drawback of static reward poisoning”, providing “a theoretical analysis of the advantages... | Summary: The SleepNets paper considers a new ("outer loop") threat model, more powerful than those typically considered in adversarial RL settings.
The authors consider a stealthy attacker, aiming to both be successful (essentially tricking the learner into believing the underlying MDP is instead one of the attacker's ... | Rebuttal 1:
Rebuttal: **Overview:** We thank the reviewer for their positive assessment of our paper and for their insightful questions. The reviewer’s main concern was the feasibility of the outer loop threat model. This is something we’ve taken much time to consider over the course of writing our paper, thus we will ... | Summary: The authors introduce a novel framework for backdoor poisoning RL agents, SleeperNets. SleeperNets assumes that adversaries can inject adversarial perturbations into the agent's observations during policy training within some total budget. Unlike in prior frameworks, the adversary implements its attacks post-h... | Rebuttal 1:
Rebuttal: **Overview:** We thank the reviewer for their positive assessment of our work in noting the sensibility of our threat model the insight brought by our theoretical analysis. The reviewer also brought with them much insight in the form of constructive questions and citations. We thank the reviewer f... | null | null | Rebuttal 1:
Rebuttal: We would like to first extend our thanks to the reviewers for their time in reading our paper, evaluating its merits, and highlighting its novel contributions. We hope that our extensive responses have sufficiently answered all the reviewers’ questions, but openly invite any further questions or c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off | Accept (poster) | Summary: This paper proposes a watermark technique called WaterMax to distinguish LLM-generated texts and human-written texts. WaterMax starts with the watermark detector and asks LLMs to generate a group of candidates from which the one with the lowest p-value determined by that detector is selected as the final outpu... | Rebuttal 1:
Rebuttal: > Q1: "Is it possible to include low-entropy tasks such as code generation to test if the detector can still properly function? Several works [1,2] can be refered to.""
We thank the reviewer for the references. At the detection side, these works weight the token value depending on an estimated en... | Summary: The authors propose a method for watermarking language models through the use of rejection sampling. By sampling and discarding "chunks" from the model until the p-value returned by an (arbitrary) detection rule is sufficiently low, the proposed method simultaneously preserves output text quality while achievi... | Rebuttal 1:
Rebuttal: > W1: "A previous LLM watermarking work, "SemStamp" [1], is similarly ... Adding experimental comparisons to SemStamp ... Otherwise, the authors should probably cite it."
We thank the reviewer for pointing us to SemStamp. We acknowledge that there exists some proximity to our work in the sense th... | Summary: The paper presents a novel watermarking scheme for large language models (LLMs). The proposed WaterMax scheme aims to achieve high detectability while maintaining the quality of the generated text, without modifying the LLM's weights, logits, temperature, or sampling technique. WaterMax balances robustness and... | Rebuttal 1:
Rebuttal: > W4: "The scheme requires careful tuning of parameters ... This adds complexity to its implementation."
The tuning of our algorithm is **less complex** than the tuning of KGW. The two main parameters of WaterMax are the number of chunk $N$ and the number of drafts per chunk $n$:
- Detectability/... | Summary: The work proposes a new LLM watermarking scheme called WaterMax, that does not modify the distribution or the sampling procedure but uses rejection sampling on multiple generated segments of tokens to cause a high watermark score. A theorem is given to characterize the detector power under attack, given certai... | Rebuttal 1:
Rebuttal: > Q1: "*Is GPT4 unreliable for the task of judging text quality? Can you provide empirical evidence?*"
First of all, using closed-source models available through API is against our ethics because it prevents reproducibility: GPT-4 is not free; we do not fully control the prompts; it will be no l... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive comments which help us improving our submission.
**The reviewers globally find that the limitations on the complexity is not enough outlined.**
The submission already accounted that:
- The main point of WaterMax is to strike the trade-off detecta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning | Accept (poster) | Summary: This paper provides an interesting approach HiCS-FL to investigate the client sampling problem in federated learning, especially for the non-iid setting. This paper estimates the clients' data statistical heterogeneity (label distributions) via the client-updated gradients of the output layer's weights. By usi... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable comments. Please find our responses below.
**Q4.1**
A potential privacy issue may be raised since it needs to access the individual client update (i.e., gradient) information. More client's data information may be leaked from the gradient inversion attacks.
*... | Summary: The paper address data heterogeneity by clustering clients via the gradients of the output layer to distinguish between clients with balanced from those with imbalanced data. HiCS-FL assigns different importance to the clusters according to their average estimated data heterogeneity. The paper found that there... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable comments. Please find our responses below.
**Q3.1**
The approach of clustering to address heterogeneity might be practical as there are simpler methods that can achieve the same goal. The experiments were done on a small set of clients, 50. As the number of cl... | Summary: The paper addresses the challenges posed by non-IID data in FL systems, particularly under communication constraints where only a small fraction of clients can participate in each training round. It introduces HiCS-FL, a novel client selection method. HiCS-FL estimates the statistical heterogeneity of a client... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. Our answers addressing unique questions are below; responses to concerns raised by multiple reviewers are in the Author Rebuttal block for sake of brevity. Note that **Tables 4-5** can be found in the **pdf** file at the bottom of the **Author Reb... | Summary: The authors propose a novel client selection method to address federated learning scenarios where clients exhibit varying degrees of data imbalance. The authors estimate the label distribution entropy of clients on the server side using the gradient of the output layer's bias. Based on this estimation, they cl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the informative comments and valuable questions. Our answers addressing unique questions are below; responses to concerns raised by multiple reviewers are in the Author Rebuttal block for sake of brevity. Note that **Tables 1-3** can be found in the **pdf** ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and valuable comments. We have attempted to address all the points they raised. Excerpts from their most significant/repeated questions, followed by our responses, are below. Please note that the **tables** reporting new experimental results are ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors | Accept (poster) | Summary: The paper presents a method for learning neural signed distance functions (SDFs) from noisy point clouds. This approach integrates the advantages of data-driven and overfitting-based methods to enhance generalization, accuracy, and inference speed. They employ statistical reasoning within local regions to refi... | Rebuttal 1:
Rebuttal: 1. Typo
We will correct these typos and proofread the paper more carefully.
2. Impact on Performance by the Prior
Since we use ground truth signed distances and a mature neural network to learn an implicit function as a prior, the network usually converges quite well. Imperfect priors should no... | Summary: The presented work tackles the problem of reconstructing a shape from a noisy point cloud (PC) into an implicit representation, using the signed distance function (SDF). Recent methods are categorised into i) data-driven approaches that learn a shape prior with a dataset of training shapes, with poor generaliz... | Rebuttal 1:
Rebuttal: 1. Contribution and Novelty
Our novelty is not only the loss but also the way of generalizing the prior. Please see G1-G3 in our rebuttal above.
2. Why Local Patches Work Better
Please read “G4. Why Local Patches Work Better” in our rebuttal above for the analysis.
3. Writing
We will follow y... | Summary: The authors propose an implicit surface reconstruction method from point clouds that uses a learned prior to initialize the optimization of a neural SDF. First, a neural SDF generator based on DeepSDF [66] is trained on a dataset of shapes to learn a prior over shapes. Given a point cloud, both the shape code ... | Rebuttal 1:
Rebuttal: 1. Contribution and Novelty
Our method is not a simple combination of DeepSDF and noise2noise. Please see G1-G3 in our rebuttal above.
2. Why Local Patches Work Better
Please read “G4. Why Local Patches Work Better” in our rebuttal above for the analysis.
3. We will add references you mentione... | Summary: The paper proposes 3D shape reconstruction given a noisy point cloud using a test-time optimization approach. The paper proposes a new loss function that can work directly on noisy point clouds without the need of ground truth normals or SDF. The approach involves learning a network (DeepSDF) to predict SDFs a... | Rebuttal 1:
Rebuttal: 1. Why pulling works
Pulling queries towards surface points has been widely used to estimate a signed distance function from a point cloud. We can use a neural network to infer the SDF by pulling randomly sampled queries to their nearest surface points using the predicted signed distances and gra... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' valuable comments, which highlighted our simple and interesting method (AdmT, mU7t), strong performance and extensive experiments (fwt4, mU7t, 76DM), and the broad significance and usefulness of our work (mU7t, AdmT).
G1. Our Novelty
Our novelty lies in how to combi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transferring disentangled representations: bridging the gap between synthetic and real images | Accept (poster) | Summary: The work is situated in the area of Disentangled Representation Learning. The authors propose a new intervention-based metric (OMES) to assess the degree of disentanglement in different models. They perform extensive and comprehensive experiments validating and comparing OMES with other metrics. They thoroughl... | Rebuttal 1:
Rebuttal: We thank the reviewer for considering our work and for their valuable comments and insights.
* > I found the results section in 3.3 quite hard to read ... promote readability.
We agree with the reviewer that, mainly due to space limitations, the experimental section is very compact. We will at... | Summary: This paper conducts an empirical investigation into the problem of transferring disentangled representations from synthetic data to real-world data (syn2syn, syn2real, real2real). They start with well-defined research questions and perform the investigation on the feasibility and effectiveness of transfer lear... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable insights provided with their comment. In the response, we will follow the structure of the bullet list in the review.
* > The presentation of the proposed metric has poor readability... at least or rephrase the contents.
We agree with the reviewer that th... | Summary: This paper proposes a novel classifier-free metric for quantitatively measuring disentanglement and investigates transferring disentangled representations from a synthetic dataset with ground truth factors of variation (FoV) to a target real-world dataset. The authors introduce OMES, a novel intervention-based... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort spent in providing valuable feedback on our work. In the response, we will follow the paragraph listing structure of the review.
* > The implications on transferring disentanglement from synthetic datasets ... and potential source datasets.
Please refe... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their efforts in reading our paper and also for providing valuable insights and new interpretations for our work. There is a general agreement on the effectiveness of the new metric and the extensive assessment with a thorough experimental analysis.
In this common respo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters | Accept (poster) | Summary: This paper mainly focuses on the quantization problem of large language models and the problem of low-rank decomposition solvers, and proposes a method for quantizing large language models for multiple tasks and integrating multiple low-rank decomposition solvers. The article first analyzes that the current ma... | Rebuttal 1:
Rebuttal: ## Q1
It refers to the $q$-th parameter, i.e., $w_q$ denotes the $q$-th element of $\mathbf{W}$ after it is flattened.
## Q2
In Appendix A.2, we implement MLGPTQ using **Cholesky decomposition** to increase speed and computational stability, similar to GPTQ's implementation (https://github.com/Au... | Summary: The paper addresses the need for efficient fine-tuning and deployment of large language models (LLMs) in multi-task scenarios, which has been largely overlooked in favor of single-task scenarios. Existing quantization methods, such as GPTQ and AWQ, and parameter-efficient fine-tuning techniques like LoRA, are ... | Rebuttal 1:
Rebuttal: ## Editorial Comments
**Undefined Acronyms**
There are three undefined acronyms in our abstract:
- **LoRA**, short for Low-Rank Adaptation, is one of the most widely used parameter-efficient fine-tuning techniques for LLMs.
- **GPTQ** and **AWQ** are state-of-the-art quantization algorithms for ... | Summary: This paper introduces LoRA-Inlaid, an innovative and efficient system for quantizing and serving Large Language Models (LLMs) in multi-task environments. By utilizing the Multi-LoRA GPTQ (MLGPTQ) algorithm, LoRA-Inlaid facilitates sharing a unified quantized model across various LoRA adapters, significantly re... | Rebuttal 1:
Rebuttal: ## W2 & Q4 (first half)
Since divergent metrics (SacreBLEU and ROUGE-1) are used for different tasks, Fig 5 shows the relative performance to align the y-axis. We have presented the detailed results in _Table A_ of the one-page PDF, showing that MLGPTQ consistently outperforms the baselines.
## Q1... | null | null | Rebuttal 1:
Rebuttal: # Global Responses
We are grateful to all reviewers for the careful reviews. We provide _Global Responses_ to common questions, followed by individual responses. Please refer to the attached one-page PDF for related figures and tables.
## Details of the Incremental Quantization and Multi-task ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Blind Image Restoration via Fast Diffusion Inversion | Accept (poster) | Summary: The paper introduces a blind image restoration method based on DDIM, which iteratively optimizes the initial noises and the degradation model parameters through the restoration loss of reconstructing the degraded image. As a result, the restored image remains on the data manifold of the pretrained diffusion mo... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see the detailed response below.
### Q1. Further distinction from existing methods based on inversion
First, we point that there is a a fundamental difference between *inverting a clean image* (mostly useful for applications like image editing), where we aim f... | Summary: The authors propose a novel approach to solving image restoration problems using diffusion models, termed BIRD.
Unlike previous approaches, BIRD alternates between optimizing a parameterized forward operator and the initial latent variable of a DDIM to address various IR problems in a blind manner (i.e., with... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see the detailed response below.
### Q2. Specific areas needing attention
>The phrase "better neural network architecture choices" on lines 21-22 should be made more specific to avoid ambiguity.
thanks. We mean that better NN architectures (for example, Tran... | Summary: This paper proposed new blind image restoration (BIR) method by exploring the image prior induced by diffusion model (DM). Different from the existing DM-based methods, this work presents a diffusion inversion technique, such that the estimated image can be constrained to lie in the image manifold learned by t... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see the detailed response below.
### Q1. Add more comparisons [3-7]
| Method| Zero-shot?| Task-agnostic? | Code available? | |
|---|---|---|---|---|
| BIRD | *Yes* | *Yes* | N.A | |
| [3] | Yes | No | Yes | |
| [4] | Yes | No | Yes | ... | Summary: The paper presents a method to accelerate blind image reconstruction by leveraging pre-trained diffusion models. The authors suggest a strategy that simultaneously optimizes the degradation model parameters and the restored image, thereby improving the reconstruction process’s efficiency.
Furthermore, the aut... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please see the detailed response below.
### Q1. Theoretical Discussion and Experimental Comparison to [3-5]
| Method| Zero-shot?| Task-agnostic? | Code available? | |
|---|---|---|---|---|
| BIRD | *Yes* | *Yes* | N.A | |
| [1] | No | No | Yes | ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and feedback. We attached a pdf containing some additional results based on their comments and suggestions.
Pdf: /pdf/d7ccbe6f8498b97f3ab8890ed32a366d8125795d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families | Accept (poster) | Summary: This paper focuses on the theoretical study of variational inference using alpha-divergences using exponential family. This is an important problem in the field of variational inference. Specifically, this work proves a geometric convergence rate for the algorithm proposed in [9]. Moreover, this paper also pro... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and honest feedback. Below, we provide detailed responses to your second question. The two weaknesses you pointed out are adressed in our global rebuttal. We hope that our responses will clarify the issues you raised.
> What is the technical difficulty to derive... | Summary: This procedure studies the asymptotic properties of a variational algorithm that ensures a monotonic decrease in the alpha-divergence. Specifically the authors investigate behavior in the setting where the variational distribution belongs to the exponential family of distributions. In this setting, and when ... | Rebuttal 1:
Rebuttal: Thank you for your meticulous review and kind feedback. Below, we provide detailed responses to each of your questions, aiming to further clarify our work and address any remaining uncertainties.
> *Diverse remarks on notation* + L196 : Notation $\circ$ is undefined (is this a composition?)
Yes,... | Summary: The paper studies the converge properties of an optimization algorithm, used to minimize the alpha divergence when the variational approximation belongs to the family of exponential distributions. The optimizer is a modification of an existing method and its performance is competitive with existing methods (wi... | Rebuttal 1:
Rebuttal: We are grateful for your careful reading and constructive comments on our submission. Below, we respond to each of your questions, hoping to clarify any uncertainties.
> The paper lacks clarity and is difficult to read.
We are sorry to hear that despite our best efforts to make the paper and the... | Summary: This paper proposes the asymptotic analysis for both exact and empirical alpha-divergence minimization algorithms, especially in the case of infinite number of iterations. The paper mainly focuses on the exponential family setting, and provides geometric convergence analysis of exact minimization algorithms us... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive review of our paper. We greatly appreciate the time and effort you have dedicated to providing feedback. We are committed to addressing the concerns raised and improving our work. Below, we respond to each of your points in detail.
> The basis of the t... | Rebuttal 1:
Rebuttal: We deeply thank the reviewers for their careful and detailed reviews of our manuscript. We are grateful for their constructive feedback and for offering us an opportunity to improve our work. Below, we provide responses to some points that have been raised by multiple reviewers. We hope that this ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper explores alpha-divergence Variational Inference (VI) from a theoretical perspective, and in particular the monotonic alpha-divergence minimization algorithm. It includes an asymptotic analysis of the algorithm applicable to exponential families, establishing conditions that ensure convergence to a l... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and encouraging feedback on our submission. We provide detailed responses to each of your questions below, hoping to clarify any uncertainties you may have had reading our paper.
> I am unsure why conditions (H4) and (C0') are considered realistic and sensible.... | null | null | null | null | null | null |
BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens | Accept (poster) | Summary: This paper presents BiScope, a new algorithm for AI-generated text detection, that leverages logits from a tect expansion task to detect AI-generated text. The algorithm proceeds in three steps:
1. Given a candidate input text T, break it into two segments (seg1, seg2, such that seg1 + seg2 = T). A "text comp... | Rebuttal 1:
Rebuttal: Thanks for your valuable review and suggestions. Here are our point-by-point responses:
**W1**: Regarding the OOD evaluation, previous studies [1, 5, 7, 8] shifted either the data (cross-dataset) or the generative models (cross-model).. We strictly follow their settings in our OOD evaluation. Our... | Summary: The paper describes work on detecting machine-generated texts using a proposed method called BiScope which exploits a model’s states by considering both the preceding token information and the next token information via an bi-directional cross-entropy loss calculation method. The proposed BiScope method does n... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and suggestions. Here are our point-by-point responses:
**W1**: Thank you for pointing out the problem. We generated our datasets using five of the latest commercial LLMs, following the generation methods outlined in previous studies [1, 7, 8]. Due to the page limit o... | Summary: This paper proposes extracting various features from predictive distributions of surrogate LLMs to detect LLM-generated text. Relative to prior work, the main novelty appears to be the use of bi-directional cross-entropy losses to extract features. These features are then fed into a traditional supervised clas... | Rebuttal 1:
Rebuttal: Thanks for your insightful review. Here are our point-by-point responses:
**W1**: We have included three more baselines in the comparison in Table 6 (in the submitted PDF file): Binoculars [5], GhostBuster [1], and OpenAI Detector [6]. The results show that BiScope outperforms all three baselines... | Summary: This paper develops an AI-generated text detection method called BiScope. The key idea is to formulate the detection task as a guided text completion task. The generated text and the original text are used to calculate two types of cross-entropy losses, which are used to extract features for classification.
S... | Rebuttal 1:
Rebuttal: Thanks for your insightful review. Here are our detailed point-by-point feedbacks for your questions:
**W1**: As mentioned in Section 3.3., the summary generation is not necessary. We have two designs for generating the completion prompt: one with a summary and the other without. The latter has a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for your thoughtful comments! We are glad that the reviewers found our paper “tackles an important problem” with a novel idea. We also thank you for your appreciation of our dataset contribution, method’s robustness, and paper presentation.
To further address your conce... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unified Guidance for Geometry-Conditioned Molecular Generation | Accept (poster) | Summary: The paper introduces UniGuide, a unified framework for geometry-conditioned molecular generation using unconditional diffusion models. UniGuide is designed to address the adaptability issues in current molecular diffusion models by providing a general training-free approach via a condition map that transforms ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful feedback and are pleased with the positive comments on UniGuide's novelty and effectiveness for various drug discovery tasks. We would like to clarify the remaining questions and concerns in the following.
> The contribution and the generaliza... | Summary: This paper proposed a training-free framework for guided diffusions in unconditional molecular generation. UniGuide applies to a wide range of design tasks such as SBDD, FBDD and LBDD, unified by the proposed condition map, which projects from the product space of conditional input that lies in general geometr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback regarding our manuscript, particularly regarding the presentation and interpretation of UniGuide. We believe we can address these concerns effectively, as detailed in the following answers.
> Only highlight the best diffusion-based ap... | Summary: This paper proposed a method named UniGuide for conditional molecular generation with unconditional diffusion models, without the need of additional training and parameters. The proposed framework is an extension of self-guided diffusion models on the conditional molecular generation task. The authors designed... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and will address their concerns below.
> Superiority of UniGuide and insufficient experimental support
UniGuide's appeal builds upon multiple aspects:
- No Training: UniGuide is based on guidance, and as such, it **does not require any... | Summary: The paper introduces UniGuide which is a general framework for conducting conditioning over the unconditional molecule diffusion models during inference. To achieve this, Uniguide introduced a concept called a condition map for different applications. With a condition map,it could control the score function by... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable feedback and will address the raised concerns in our answer below.
> However, the paper's contribution is hard to evaluate. Though the author claims a new general framework for Uniguide, the form takes exactly as a gradient guidance ... | Rebuttal 1:
Rebuttal: We are pleased with the positive feedback on our work, particularly **noting its motivation and its broad and flexible applicability across various drug discovery tasks**.
We incorporated clarifications and additions to our evaluations - please refer to the attached PDF and individual re... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a framework for geometric guidance of diffusion models to enable flexible generation for protein and small molecule tasks. Their method is based on self-guidance from geometric conditions. They propose a condition map to map geometric conditions to the latent condition space to guide diffus... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and positive evaluation of our method! We provide detailed answers in the following.
> Additional details on training required for UniGuide’s generality
- We highlight that **UniGuide does not require extra training** as it controls the generation o... | null | null | null | null | null | null |
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning | Accept (poster) | Summary: This paper analyzes the convergence of scaffnew in the quadratic setup and achieves a linear speedup in the number of clients. It is not attained by the original paper of Scaffnew. Additionaly, the author find an application of the federated quadratic problem -- Federated
Linear Stochastic Approximation and T... | Rebuttal 1:
Rebuttal: **The achieved speedup holds only for quadratic setup and the application of the quadratic loss function is quite limited.**
We emphasize that our analysis holds for the general setting of linear stochastic approximation, which encompasses minimization of quadratic functions, but also works for ot... | Summary: This paper provides a non-asymptotic analysis of Federated Linear Stochastic Approximation. The authors provide (biased and unbiased) finite-time MSE bounds for general LSA and TD learning under the assumption that the noise is i.i.d.. For Markovian noise, only an unbiased MSE bound is provided. Most important... | Rebuttal 1:
Rebuttal: **Is it possible for the authors provide a table similar to Table 1 in order to illustrate and compare the different outcomes between the three different scenarios analyzed by the paper (i.r. the i.i.d. , Markov and TD setting)?**
Thank you for the suggestion, which will greatly help to improve th... | Summary: This paper first analyzed the performance of federated linear stochastic approximation or FedLSA algorithm. Second, it proposed a new algorithm called stochastic controlled averaging for Federated LAS or SCAFFLSA and analyzed its performance. The key idea of SCAFFLSA is to use a control variable to mitigate th... | Rebuttal 1:
Rebuttal: **The experiments are relatively weak. It will be more beneficial if the authors could provide more applications of the proposed algorithm to other problems, especially for federated TD learning.**
The Garnet problems used in our experiments are common in the TD literature and serve the purpose of... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough feedback. We are pleased that reviewers deemed our contributions as new ("new analytical framework", "new algorithm", reviewer WekN; and "the ideas of the paper are new", reviewer bpaa), and that our analysis technique is the first to show that Scaffnew ha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism | Accept (poster) | Summary: This paper proposes a principled AL paradigm to alleviate the annotation hurdle of 3D molecular graphs. It introduces a novel diversity component for 3D molecular graphs, which is provably at least as expressive as the GWL test. Furthermore, the authors develop an effective and efficient pipeline to compute un... | Rebuttal 1:
Rebuttal: > W1: lack of necessary explanations
Thank you for your comments.
For "USR", the primary concept involves using statistical moments to approximate the geometry of the molecules, capturing essential features of their shapes. Detailed explanations of this method are provided in Section 2.1.3 (line... | Summary: This paper introduces a principled active learning (AL) paradigm tailored for molecular learning. The proposed AL approach aims to alleviate the hurdle of human annotation by automatically querying labels for the most informative samples. The authors treat molecules as 3D molecular graphs and they introduce a ... | Rebuttal 1:
Rebuttal: > W1: Overly Strong Claims
Thank you for your comments. We will use more precise language and include additional discussions in the paper to clarify our claims. We will address your concerns in the Questions section.
> W2: Need for More Detailed Experimental Insights
In the current version of o... | Summary: The paper proposes an active learning scheme for molecular property prediction using uncertainty estimates from Dropout Monte Carlo and diversity metrics. In each active learning iteration, molecules are selected by maximizing uncertainty and diversity in the batch by solving a quadratic programming problem. T... | Rebuttal 1:
Rebuttal: > W1: Many of the claims in the paper are too bold, neglecting several important related works in the field.
Thank you for your comments. We will modify the language in the paper to clarify our intentions.
The first two active learning (AL) papers do not specifically consider 3D geometry informa... | Summary: This paper describes a way of sampling 3D graphs for active learning on
molecules, leveraging isometries.
Strengths: I think this work is interesting, and potentially useful.
Clearly some kind of active learning would have useful use cases.
Also, the use of isometries could also be relevant, depending on the ... | Rebuttal 1:
Rebuttal: >W1: about actual atoms
Our framework contains two components for selecting important molecules: diversity and uncertainty. **We do consider the actual atoms in the uncertainty part, as the atom types are embedded into node features.**
Our diversity component focuses on molecular geometries, **... | Rebuttal 1:
Rebuttal: We thank the reviewers for their invaluable comments and suggestions. In this global response, we would like to clarify a few points and present new results based on the feedback received.
### Clarification
To start our rebuttal, we would like to clarify a few points about our method.
> Our model... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Classification Done Right for Vision-Language Pre-Training | Accept (poster) | Summary: The paper proposes a simple alternative to CLIP-style pretraining that doesn't require a text encoder and can be done using only a text tokenizer. The goal is to provide a simpler yet more efficient alternative to vision-language model (VLM) pretraining, which is known to be very expensive. Additionally, the a... | Rebuttal 1:
Rebuttal: > **W1**. Other popular few-shot and zero-shot classification benchmarks.
**A1**. Thanks for the advice. We have added more evaluation benchmarks, including 10-shot classification on ImageNet-1k, pets and cars, and zero-shot classification on 8 more datasets.
**10-shot classification**
We fol... | Summary: This paper proposes a simple classification-based vision-language pretraining method. The proposed SuperClass approach directly uses an off-the-shelf subword-level tokenizer to obtain the classification labels from raw text, without requiring any preprocessing. Then, the vision encoder is trained by optimizing... | Rebuttal 1:
Rebuttal: > **W1**: The robustness of the proposed method to different model types remains unclear.
**A1.** Thank you for the valuable advice. To evaluate the robustness of our proposed method across different model types, we selected two representative convolution-based networks: ResNet50 and ConvNext-Ti... | Summary: This paper explores a new direction to pretrain vision backbones using large scale image-text pairs for learning visual representations which are suitable to various downstream tasks. More specifically, this work proposes a classification based objective function as an effective alternative to CLIP's standard ... | Rebuttal 1:
Rebuttal: > **W1**: Comparisons with other related SOTA works
**A1**. Thanks for the advice. We have added more SOTA methods for comparison, including self-supervised learning methods (MoCov3, Dinov1&v2, MAE, BEiT, CAE) and weakly-supervised methods (CatLIP, Cappa). Additionally, we have evaluated more dow... | Summary: This paper introduces a multi-label classification pre-training style for visual image encoder pre-training.
Strengths: The proposed method is straightforward.
Weaknesses: - The zero-shot capacity of such a multi-label pre-trained model is not well demonstrated.
- The paper lacks a comprehensive comparision ... | Rebuttal 1:
Rebuttal: >**Q1** Zero-shot capacity.
**A1.** We thank the reviewer for triggering the discussion on zero-shot abilities of CLIP and our model. Honestly, our model does not come with trivial zero-shot image-text retrieval usage. However, we can enable this behavior by levering LiT [a], which learns a text ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization | Accept (poster) | Summary: This paper proposes the Multi-Task Prompt Decision Transformer (MPDT) algorithm for zero-shot multi-task offline reinforcement learning (RL). Leveraging a pre-trained language model (PLM) with prompt tuning, the MPDT innovatively decomposes multi-task prompts into task-specific and cross-task components. It al... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review of our work. We'll answer your questions one by one in the following, including some misunderstandings and some essential academic questions worth exploring.
**W1: About the novelty of the paper**
Please check Author Rebuttal AR1.
**W2: About the p... | Summary: Multi-task learning is a critical pursuit in decision-making, and Decision Transformer (DT) is a popular framework in solving various decision-making problems. The author observe the suboptimal performance of prior work utilizing DT for multi-task learning, and propose a new method, Multi-Task Prompt Decision ... | Rebuttal 1:
Rebuttal: Thanks for the careful review of our work.
**W1: How can the authors guarantee that they use the same amount of information in the test set for all baselines?**
In methods not involving prompts, we indeed fine-tuned these methods on the test set. To highlight the superiority of MPDT as much as... | Summary: This paper proposes a new method called Multi-Task Prompt Decision Transformer (MPDT) for efficient generalization to unseen tasks in offline reinforcement learning. MPDT involves two stages. First, the multitask training phase: MPDT is initialized with parameters from a pertained LM, which is GPT2. It decompo... | Rebuttal 1:
Rebuttal: Thanks for reviewing our work attentively. We will answer the reviewer's questions one by one in the following.
**W1: The paper appears to be hastily written and the presentation is hard to follow**
We will revise all unclear and erroneous statements highlighted by the reviewer further to impr... | Summary: This paper proposes a novel Multi-Task Prompt Decision Transformer (MPDT), which leverages pre-trained language models as the initialization and adopts test-time adaptation. This approach achieves efficient generalization to unseen tasks through the prior knowledge from the pre-trained language model and by de... | Rebuttal 1:
Rebuttal: Thanks for reviewing our work attentively. We'll answer your questions one by one in the following. Considering the word limitation, we combine Weaknesses and questions with similar meanings to answer.
**W1: why the language model could contribute to the RL tasks?**
In Section 4.1, the Initiali... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful feedback. Here, we address three main comments: innovation (AR1), the collection method of unlabeled data for TTA (AR2), and the purpose of using a pre-trained embedding layer (AR3). AR4 is the list of hyperparameters used during training and testing. T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching | Accept (poster) | Summary: The authors propose a novel framework to achieve strong domain generalization from synthetic disparity+semantic labels datasets. To achieve this goal, the framework employs object-aware dual-level contrastive learning (HODC) to guide the backbone network toward robust and general feature extraction: these gene... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and helpful suggestions. We would like to make the following response to your questions:
> Q1: The method requires semantic labels to achieve SOTA performance: it is true that the proposal is effective even without the object prior, however, it is necessary to ac... | Summary: The authors propose an additional training objective for image stereo matching methods in order to improve generalization from synthetic training data to real test images. It consists in a contrastive loss pushing image features aggregated according to some superpixels in one image to be similar to correspondi... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and helpful suggestions. We would like to make the following response to your questions:
> Q1: the smooth L1 loss should be mathematically defined or a reference should at least be provided.
A1: Thank you for pointing this out. We will add a reference to the smo... | Summary: This paper proposes a new framework for domain generalized stereo matching termed effective hierarchical object-aware dual-level contrastive learning (HODC). HODC improves the domain generalization ability of stereo matching by encouraging region-level information in extracted features. HODC can be easily inte... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and helpful suggestions. We would like to address your concerns as follows:
> Q1: The qualitative comparison in the article is insufficient. Considering that the generalization capability of stereo networks on public datasets is already quite good, the improvemen... | Summary: This work proposed the hierarchical object-aware dual-level contrastive learning (HODC) framework for stereo matching. Their major technical contribution is a dual-level contrastive loss, which matches object features between intra- and inter-scale regions. Applying the proposed loss and only trained synthetic... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and helpful suggestions. We would like to make the following response to your questions:
> Q1: As mentioned, it is not a new direction to explore the semantic and structural information in the stereo matching task (lines 43-45). Although the previous works took a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for providing positive and insightful feedback. We are encouraged by the reviewers' appreciation that the paper is well-written, easy to follow and pleasure to read (Reviewer jWZr, NWJx, uUe2, ApGF), the ideas are novel yet easy to implement (Reviewer jWZr, NWJx), the re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models | Accept (poster) | Summary: This paper proposes Safe LoRA to defend against the harmful finetuning issue for LLMs. The core idea of Safe Lora is to project the harmful gradient update to the subspace constructed by the alignment update. To guarantee utility performance, the authors propose to use cosine similarity to determine whether t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for offering detailed reviews on$\textsf{Safe LoRA}$. We appreciated the comment saying that “The paper is well-written and concise enough, … the potential audience of this paper will be large given its simplicity.” and shared a common perspective as the reviewer, ... | Summary: This paper studies the problem that finetuning may compromise safety, as observed in the previous work. The author proposes Safe LoRA, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, reducing the saf... | Rebuttal 1:
Rebuttal: Thanks for your genuine appreciation of the clarity and precious reviews on our work! We are delighted to receive a comment denoting that “The idea of $\textsf{Safe LoRA}$ is simple yet effective, making it practical for mitigating safety risks in fine-tuning LLMs.” Please see the response below a... | Summary: The paper proposed a post-hoc fine-tuning projection method which utilizes the aligned and unaligned weights of the model to compute the projection matrix. The method is simple (one-liner patch) and training-free. Extensive experiments showed the effectiveness of the proposed method.
Strengths: 1. The paper ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the work and stating that “The motivation is sound which tries to address the problem of decreased safety after fine-tuning”. We will address and justify some concerns raised by the reviewer in the comment below.
**Insights for $\textsf{Safe LoRA}$**
... | Summary: This paper propose a novel training-free method, Safe LoRA, to project the original LoRA to the sadety-aligned subspace. The experimental results illustrate that the proposed method can preserve the utility of downstream task and the safety of LLM output.
Strengths: Strength:
1. This paper focused on an imp... | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewer for the comprehensive comments concerning alignment of LLMs. We are delighted to receive the positive feedback that “The proposed method is also very easy to follow…” Please see our point-to-point response to your comments below.
**On the explanation ... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewers for their generous advice on $\textsf{Safe LoRA}$ as it will boost our understanding on the realignment issue of LLM fine-tuning. Due to character limits, we will be answering some common concerns in the general response below.
**Application Scen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AdaFace: A Versatile Face Encoder for Zero-Shot Diffusion Model Personalization | Reject | Summary: This work presents a zero-shot face-generation method based on diffusion models. The proposed method first extracts face features using Face2Vec and trains a network to map these features into the textual space (i.e., the prompt embedding space for diffusion’s text condition). The main difference from existing... | Rebuttal 1:
Rebuttal: Thank you reviewer jpRS for your thorough and detailed review of our paper. We appreciate the time and effort you have invested in providing your critical feedback. While some of your comments are indeed challenging, we believe they are valuable for improving the quality and rigor of our work.
1\... | Summary: This paper proposes AdaFace, a face encoder that maps facial features from the image space to the text space through the AdaFace Prompt Inverter, utilizing the structure and pre-trained weights of the CLIP text encoder for initialization. During the face distillation phase, AdaFace employs random Gaussian face... | Rebuttal 1:
Rebuttal: Thanks reviewer 6DX2 for your constructive feedback. Your comments are valuable in improving the quality and clarity of our work.
Responses to weaknesses:
1\. **The performance issue of the Huggingface online demo**.
Thank you for trying out our demo. We would like to clarify that our first mo... | Summary: This paper proposes AdaFace, a method for personalizing text-to-image diffusion models for human faces. At its core, it learns a prompt inverter that maps face embeddings from a pretrained face encoder to the text embedding space of diffusion prompts. It leverages various components including face distillation... | Rebuttal 1:
Rebuttal: Thank you reviewer Uvbt for your favorable evaluations. Your comments are valuable in improving the quality and clarity of our work.
1. **Limited examples**. We have added more examples of Paris Olypics Atheletes in the attached PDF file.
2. **Ablation studies of proposed components**. Due to li... | Summary: This paper proposes AdaFace, a test-time-tuning-free method for personalized text-to-face-image generation. Previous methods involving face features in the feature space of a face encoder, which is not flexibly composable with natural language for personalized generation. Thus, this paper proposes to map the f... | Rebuttal 1:
Rebuttal: Thanks reviewer jmG6 for your constructive feedback. Your insights have been incredibly valuable in improving the quality and clarity of our work.
Responses to weaknesses:
1. **In terms of novelty**.
* While ELITE is the first to propose a text-space embedding method for personalization, it ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their high-quality feedback and insightful comments. We will incorporate these suggestions into a future version of our paper. In particular, we appreciate your recognition of the novelty of our method, especially its seamless integration with video generation pipeli... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mixture of Nested Experts: Adaptive Processing of Visual Tokens | Accept (poster) | Summary: The paper builds on the Mixture-of-Experts paradigm for vision transformer, and adds a hierarchical aspect to it, yielding a Mixture of Nested Experts (**MoNE**). More specifically, experts are defined as subsets of nested channels in the Feed Forward Networks, such that each expert has a different compute cos... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We appreciate the recognition of MoNE to have hardware-efficient routing design, the use of compute-aware experts to optimize the accuracy/efficiency trade-off, and the thorough ablation study demonstrating the impact of router placement. Below ... | Summary: This paper present a method to select nested portions of a transformer network, using a MoE router-expert assignment method where each expert is a progressively larger slicing of a single underlying model. A capacity budget determines how many tokens can go to each expert, while a router network scores expert... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and glad to see that the reviewer found our work to be well-written, interesting and effective with comprehensive eval.
**Projections and compute save. Projection from D/m to D for (QK)V.**
In a Transformer model, there are total 8 primary linear p... | Summary: This paper tried to use Matryoshka mechanism to assign tokens to different experts.
Strengths: 1. Seems like the proposed approach can learn some effective components in images, shown in visualizations .
2. The empirical performance is good compared to mavit.
Weaknesses: 1. Why there is not comparison with F... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback and are pleased they found our method produces informative visualizations and better results than MatViT. We'll address their questions below.
**Comparison with Single-Scale FF and Fine-tuning with Same Compute**
We appreciate the reviewer's sugges... | Summary: This paper introduces the concept of Mixture of Nested Experts (MoNE), which utilizes a nested architecture to process visual tokens more efficiently in visual media like images and videos. MoNE aims to leverage redundancy in data, choosing experts in a priority order to process visual tokens, thereby achievin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We are glad to hear that the reviewer found the core idea of the work – nested structures to exploit information redundancy – to be well motivated and interesting, the experimental results to be promising and the paper to be well-written. Below we a... | Rebuttal 1:
Rebuttal: **Latency/Throughput**
We present the latency/throughput gains of MoNE compared to baselines here, in addition to the FLOP gains mentioned in the paper. In the table below, we show absolute wall clock times and throughput for MoNE compared to a baseline ViViT model, on a single V100 GPU, achievin... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes the Mixture of Nested Experts (MoNE) framework. MoNE is built on top of the MatFormer architecture which utilizes a nested architecture where smaller, less computationally expensive sub-models are nested within larger models. Similar to MatFromer, MoNE uses structured slices of the model we... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful review. We are glad to hear that the reviewer found the paper to be well-written, the MoNE framework to be intuitive, and compelling experimental results. Below we answer some of the questions raised by the reviewer.
**Dynamic Routing vs Mat... | null | null | null | null | null | null |
Structured flexibility in recurrent neural networks via neuromodulation | Accept (poster) | Summary: The paper proposes an RNN architecture that include synaptic modulation, motivated by neuromodulatory factors in the brain. In essence, the paper shows it is possible to linearly influence the connectivity matrix of a low-rank RNN by scaling it with the output of smaller RNN, the latter nominally describing ne... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful comments. We have addressed some shared concerns regarding performance comparisons to LSTMs and vanilla RNNs in the general rebuttal. To respond to your comments regarding weaknesses of the paper:
In general, we don’t believe the NM-RNN will outperform the LST... | Summary: The authors introduce and implement a novel biologically-inspired variant of standard recurrent neural networks (RNNs), which they evaluate on a number of tasks that require dynamics to generalize across task conditions (Measure-Wait-Go), switch between tasks/task contexts (four-task set from Duncker et al.), ... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough comments. Indeed, our motivation was to create a model somewhere between RNNs and biologically-accurate biophysical models. Specifically, we identified neuromodulation as a feature of biological networks that is not often modeled in RNNs. The goal of this pape... | Summary: This work studies the effects of synaptic gain scaling, a neuromodulatory mechanism, on the performance of task-trained low-rank RNNs – which have been used to understand the dynamics and other finer details of neural computation. Specifically, it introduces a simple time-varying neuromodulatory mechanism impl... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback. We have addressed some of your comments in the general rebuttal, in particular the second point under Weaknesses (regarding performance comparison to LSTMs and vanilla RNNs). We would also like to respond to the additional weaknesses noted.
While we current... | Summary: This work mimic the synaptic plasticity observed in brain which is driven by neuromodulators to develop neuro-inspired artificial neural networks. It proposes the neuromodulated NM-RNN, it has a neuromodulatory subnetwork that outputs a low-dim output that will scale the synaptic weights of low-rank RNN. It ha... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback. We have addressed some of your comments from the Weaknesses section in the general rebuttal (in particular, concerns about Evaluation and Baselines). We would also like to clarify why we did not initially provide an LSTM baseline for the Measure-Wait-Go and m... | Rebuttal 1:
Rebuttal: We would first like to thank all of the reviewers for their insightful and thorough comments on our paper. We have carefully considered your feedback and appreciate your help in both strengthening our current paper’s claims and providing future directions for us to consider.
In this general rebut... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation | Accept (poster) | Summary: This paper proposes a leveraged matrix estimation method for low-rank policy evaluation and extends it to policy iteration as a model-free learning algorithm. The main idea is to separate the Q matrix estimation into two phases. The first step is to use half of the sample budget to estimate the leverage scores... | Rebuttal 1:
Rebuttal: Thank you for your review! Please find our responses below.
**A. Comparison with other matrix estimation methods.**
As mentioned in Section B.1 of our general rebuttal, our work is based on recent progresses in analysis of matrix completion methods providing entrywise guarantees [1,8,9]. It remai... | Summary: This paper considers the reinforcement learning problem with low-rank latent structure. The objective of this problem is of learning an $\epsilon$-optimal policy in a tabular setting. For this problem, they devised LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy ... | Rebuttal 1:
Rebuttal: Thank you for your careful and insightful review! Please find our responses below.
**A. Our low-rank matrix estimation method is novel.**
Thank you for raising concerns about the novelty of our matrix estimation method. We believe this is an important point to clarify, so we have included a discu... | Summary: The works present a policy iteration algorithm that relies on supposedly low rank structure of value function (Q-function)
The proposed work first estimates the low-rank matrix of a given policy before performing policy iteration step. The Leveraged Matrix Estimation (LME) algorithm learns the Q-matrix for a g... | Rebuttal 1:
Rebuttal: Thank you for your review! Please find our responses below.
**A. Appearance of low-rank $Q$ matrices in the real world problems.**
Low-rank $Q$ matrices have been observed practically, as shown in Section 4.1 in [43] for a wide range of environments (Atari games). Motivated by this observation, f... | Summary: In this paper, the authors consider the problem of learning an $\epsilon$-optimal policy in systems with low-rank latent structures and is order optimal under weaker conditions. The proposed algorithm iterates between exploitation (policy evaluation) and exploration (policy improvement). For policy evaluation,... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and positive feedback! Please find our responses below.
**A. We do not impose any constraints on the rank/spikiness of the original matrix.** According to Theorem 1, we need number of samples $T = \widetilde{\Omega} \left( \frac{(S+A)}{(1-\gamma)^3} \frac{r_{\ma... | Rebuttal 1:
Rebuttal: Thank you all for your efforts in reviewing our paper. We feel that some of the paper contributions might have been overlooked and we take the freedom to highlight them below.
**A. Our matrix completion scheme is novel.**
We want to emphasize that our matrix completion method is novel and not me... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework | Reject | Summary: Proposed a graph based learnable multi-agent framework. The framework consists of multiple stages : Forwarding: Election (K: Answer agents; R: Reviewer) -> Review -> K Discuss till a final conclusion is reached. Proposed a mechanism to learn the graph connections dynamically.
The major Contributions Introduc... | Rebuttal 1:
Rebuttal: **Q1. Table 1 involves a comparison between open/closed source single LVLMs with smileGeo-single. However, smileGeo appears to primarily focus on a multi-agent framework, without introducing any new single LVLM architectures.**
Thank you for your comments. In fact, Table 1 compares the results di... | Summary: This works proposes a new visual geo-localization framework with multiple LVLM (Large Vision Language Model) agents. The agents communicate with each other to estimate the geo-location of the input image. A dynamic learning strategy is proposed to optimize the communication patterns among agents to improve eff... | Rebuttal 1:
Rebuttal: **Q1. The authors could make the geo-localization setting more clear in the introduction, for example, the paper focuses on worldwide city-level geo-localization. There are lots of different settings for geo-localization problem and this could be confusing for some researchers.**
Thank you for yo... | Summary: The paper introduces smileGeo, a novel framework for visual geo-localization, which involves identifying the geographic location of an image. The authors argue that while Large Vision-Language Models (LVLMs) show promise in this area, their individual performance is limited. SmileGeo leverages the concept of "... | Rebuttal 1:
Rebuttal: **Q1.The paper only seems to tackle the problem of geolocalizing landmark images. While this is a challenging problem, the current literature [1-3] has already tried to address the problem of geolocalizing arbitrary ground-level images. The latter problem requires learning sophisticated geographic... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A probability contrastive learning framework for 3D molecular representation learning | Accept (poster) | Summary: To address the problem of potential false positives and false negatives in contrastive learning of molecules, this paper proposed a learnable weighted contrastive learning approach for molecular representation learning. The effectiveness of the proposed method is tested on the MoleculeNet and QM9 datasets.
St... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the promising potential of our application.
W1: There are existing works about the false labels in contrastive learning [1,2] and weighted contrastive learning [3,4,5]. None of these are discussed in the related works while they are quite r... | Summary: This paper introduces a probability-based contrastive learning framework. It regards learnable weights as variables with different distributions and automatically identify and mitigate false positive and negative pairs via Bayesian modeling. The author verify the effectiveness of their method in 13 out of 15 p... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the presentation and novelty of our work.
Weaknesses:
(1) Algorithm A in lines 199-216 is filled with text. Can the author present the algorithm more elegantly?
A1:We appreciate the suggestion. We will revise the manuscript by converting ... | Summary: This paper proposes a probability-based contrastive learning framework for 3D molecular representation learning. It addresses the issue of false positive and negative pairs in existing methods. Experiments show its effectiveness, outperforming other baselines. The approach has wide applicability and can boost ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the innovation of our work.
W1: Eq.3 is supposed to be $e^{w_i^+s_i}$, not $w_i^+s_i$. Or am I misunderstanding?
This might be a misunderstanding, Equation 3 is correct as written. This equation is part of our Bayesian augmentation method,... | Summary: Commonly used data augmentation methods may produce false positives or negatives in learning molecular representations. This study proposes a novel probability-based contrastive learning framework to tackle false positive and negative pairs in molecular representation learning. Bayesian modeling is used to lea... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the presentation and novelty of our work.
W1: Why not design the probability distribution based on the similarity score distribution, a simpler approach?
We agree that using a similarity thresholding is a simpler approach. However, as the ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We are happy that the reviewers find our work innovative and promising in general. We notice there are also some questions and concerns from several perspectives. A common issue raised by the reviewers is the comparison against directly using sim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training for Stable Explanation for Free | Accept (poster) | Summary: The paper proposes a novel metric for assessing the stability of explanations in machine learning models, which is crucial for their trustworthiness. The authors introduce a method called R2ET (Robust Ranking Explanation via Thickness) designed to train models to generate stable explanations efficiently and ef... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
> To Weakness 1: The paper’s discussion of explanation robustness is focused on adversarial robustness. However, the explanation robustness can also be affected by other factors, such as distributional shifts [1, 2]. It would be beneficial to dis... | Summary: The paper aims at a robust explanation of predictive models. A new concept called “feature ranking robustness” is proposed and a corresponding objective function called “explanation thickness” and an optimization algorithm R2ET is designed to increase the thickness during model training time. Theoretical analy... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
>To Weakness 1: The thickness concept has been used for prediction robustness.
Their fundamental meanings differ: the thickness for prediction robustness measures the distance between two decision boundaries, while explanation thickness qualifie... | Summary: The paper describes a regularizer to add to a loss function to encourage the resulting model to
have input attributions robust to ranking changes in its top k features. That is, for an input x,
the input attributions (a score for each input) for this input will be similarly ordered (at least
in the top k scori... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
Due to the character limitation of rebuttal, we have to make the response concise and simple. Feel free to discuss them if more questions/concerns.
> To W1:
We agree that P@k may not be the “best” and “direct” metric for capturing ranking stabi... | Summary: In this work, the authors study explanation robustness particularly for saliency-based explanations based on gradient information. They propose to use a robustness metric based on the saliency ranking of features. The central benefits claimed (taken from the introduction_ for this approach are:
* Relying on $... | Rebuttal 1:
Rebuttal: > To Weakness 1: The authors first motivation is that explanations within a small L-p norm can flip all of the rankings and have significantly different "top" features ...
1. **There is a misunderstanding of what the L-p norm is measuring** and let us clarify that. The “small L-p norm” is **not u... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning | Accept (poster) | Summary: This paper integrates a Large Language Model (LLM) within a Sequential Monte Carlo (SMC) algorithm, where the LLM functions as both the proposal distribution (revising existing particles) and the evaluator (assessing each hypothesis in light of new data). The authors applied this method to two cognitive psycho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback!
> Concern 1: The main weakness of the work is that both the enhancement in problem-solving capability and the correspondence with human data are not convincing. It is unclear which aspects of the pipeline benefit from the inclusion of the LLM and ... | Summary: The authors tackle the problem of learning natural language hypotheses and collecting new experiments to enable hypothesis refinement. They propose a system that integrates LLMs and SMC/BOED to solve this problem; crucially, this method goes beyond prior work on inductive reasoning by allowing for experimentat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below we address concerns and questions.
> Concern 1: I'm not sure if they're the most compelling demonstrations of the value of natural language hypotheses/they might not fully utilize the power of natural language hypotheses. For example, it seem... | Summary: The paper addresses the problem inferring rules and designing experiments based on them. To do so, 1) they propose representing rules in natural language, generated with LLMs. 2) Using Monte-Carlo algorithms to score them 3) Revising these rules and proposing new experiments with LLMs. They instantiate the pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below, we address concerns raised by the reviewer.
> Concern 1: I think the description of the baselines could be made clearer.
Please see our global response where we provide more details of the baselines. They will be included in our revised pap... | Summary: This paper describes a model of online construction of rules that explain data, where (a) hypotheses are expressed in natural language, and (b) they are updated by proposing experiments (inputs to the ground-truth rule). The method consists of an extension of SMC, where experiments and hypotheses revision are ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below, we address concerns and questions raised by the reviewer.
> Concern 1: While the domains used here are interesting for a first study, they are still relatively simple.
We fully acknowledge this concern.
Our work evaluates the models on tr... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed feedback. We have posted responses to each reviewer's individual comments. Here, we address some common concerns raised by the reviewers.
> Common concern 1: Want to see qualitative differences, i.e., actual hypotheses proposed by the models
Below a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests | Accept (spotlight) | Summary: The paper looks at how no-regret learning algorithms behave in harmonic games, which model situations where players have conflicting interests. This is different from the often-studied potential games where interests are shared. The authors show that in continuous time, FTRL dynamics are Poincaré recurrent and... | Rebuttal 1:
Rebuttal: Dear Reviewer aEEW,
Thank you for your strong positive evaluation and encouraging remarks! We reply to your questions and comments below:
> Empirical results beyond the matching pennies example could strengthen the work.
Duly noted. We have included a pdf in our global rebuttal with additional ... | Summary: The paper studies the behavior of no-regret dynamics, and in particular follow the regularized leader (FTRL), in harmonic games--the strategic counterpart of potential games. They establish the following main results: i) the continuous-time version of FTRL is Poincare recurrent; ii) an extrapolated version of ... | Rebuttal 1:
Rebuttal: Dear Reviewer sMJW,
Thank you for your positive evaluation and your input! We reply to your questions and remarks below:
> The fact that FTRL is Poincare recurrent is perhaps not that surprising conceptually given the recent paper of Legacci et al. which shows that for the special case of replic... | Summary: This paper studies multi-agent no-regret learning dynamics in general harmonic games, which is the strategic complement of potential games. The paper's main contributions are the convergence properties of the family of "Follow-the-regularized-leader" (FTRL) algorithms and its variants in general harmonic games... | Rebuttal 1:
Rebuttal: Dear Reviewer gj66,
Thank you for your strong positive evaluation and encouraging remarks! We reply to your questions and comments below:
> Theorem 3/4: $m_i$ is used to choose the step size but has not been defined? The definition is in the appendix but a pointer should be given in the main bod... | Summary: The contributions of this paper are two-fold:
i) They prove that continuous FTRL dynamics for general harmonic games are Poincaré recurrent and hence do not converge. Their result generalizes the original result of
Mertikopoulos, P., Papadimitriou, C. H., and Piliouras, G., 'Cycles in adversarial regularize... | Rebuttal 1:
Rebuttal: Dear Reviewer 1Wrk,
Thank you for your strong positive evaluation and encouraging remarks! We reply to your questions and comments below:
> Just some missing citations on no-regret learning for games [suggestions follow]
Thanks for the pointers, we were aware of some but not all of the referenc... | Rebuttal 1:
Rebuttal: Dear reviewers, dear AC,
We are grateful for your time, comments, and positive evaluation!
To streamline the discussion phase, we replied to each of your questions and comments in a separate rebuttal below, and we will integrate all applicable points in the next revision opportunity. To bett... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data | Accept (poster) | Summary: The paper proposes an attention based early-fusion method for multi-modal learning. Specifically the paper uses a perceiver style iterative updates to a latent vector using a fusion layer. The fusion layer sequentially updates the latent vector using cross-attention with each of the modalities. The approach s... | Rebuttal 1:
Rebuttal: ### **Comments on weaknesses:**
**Usefulness of iterative fusion:**
In short, having multiple fusion layers (i.e., iterations) helps to prevent overfitting. During development, we found that the modality-specific updates only learn how to operate on the latent embedding $S$ directly after the p... | Summary: The authors present HEALNet as an early fusion approach for integrating different data modalities. HEALNet utilizes an end-to-end training process with an additive method for combining modalities, rather than handling them in parallel. This strategy enables HEALNet to scale and adapt effectively to datasets of... | Rebuttal 1:
Rebuttal: ### **Comments on weaknesses**
- **“methodology is not clearly written and difficult to understand”**: We would like to point to the step-by-step methodology, which is discussed in detail in Section 3, illustrated in Figure 1, and presented as pseudocode in Appendix A. We believe this to be a ver... | Summary: This paper presents HEALNet, a method for end-to-end multimodal fusion of mixed-type biomedical data. In contrast to methods like feature concatenation or Kronecker product fusion, HEALNet employs an iterative cross-attention structure that operates on the raw input modalities, representing a hybrid between ea... | Rebuttal 1:
Rebuttal: ## **Responses to Weaknesses and Questions**
**Hyperparameter optimization:** We would like to clarify that we ran the Bayesian Hyperparameter optimisation for all baselines. The shared parameters in Table 5 were ran equally for all baselines from Table 1. We acknowledge that Table 5 currently d... | Summary: The authors present a multi-modal fusion architecture, named HEALNet, which aims to preserve modality-specific structural information and capture cross-modal interactions in a shared latent space. HEALNet enables intuitive model inspection by learning directly from raw data inputs instead of opaque embeddings.... | Rebuttal 1:
Rebuttal: ## **Comments on Weaknesses**
**Clarification on Novelty:** As you rightly point out, the general idea of iterative cross-attention with latent state passing has been previously suggested, which we acknowledge as a starting point for HEALNet’s architecture (L89, L113, L151). While other iterative... | Rebuttal 1:
Rebuttal: ### **General comment**
We would like to thank all reviewers for their time and insightful comments. We are encouraged that you acknowledge HEALNet as having an excellent structure and logical flow, sound experiments, and an interesting methodological contribution, as well as being clear, thoroug... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Local and Adaptive Mirror Descents in Extensive-Form Games | Accept (poster) | Summary: The submission considers the problem of learning epsilon-Nash equilibria from trajectory feedback in zero-sum extensive-form games. The submission focuses on developing that avoids importance sampling over action sequences.
Strengths: The submission is well-written, and the problem and solution are reasonable... | Rebuttal 1:
Rebuttal: We thank Reviewer HP7A for the overall positive review and the interesting references. We would like to answer the remarks and the question below.
> My main criticism of the submission is that it purports its motivation to be motivated by solving large games. However, using a fixed sampling is un... | Summary: The paper studies the extensive-form game under the fixed sampling policy framework. It proposes the algorithm based on online mirror descent. The paper gives near-optimal regret bounds for the proposed algorithm, under different learning rate settings. The algorithm is justified with experiments.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank Reviewer yah9 for the overall positive review. We would like to answer the points made below.
> The framework itself is still confusing. While the authors made some discussion of the framework in section 2.2, the advantage of fixed sampling policy seems not reflected in the paper's theor... | Summary: I reviewed an earlier version of this paper. While I have some additional comments, my review is largely similar to my previous review, since the paper has only a few minor edits relative to the previous version, as far as I can ascertain.
The paper introduces algorithms designed to approximate Nash equilibri... | Rebuttal 1:
Rebuttal: We first thank Reviewer 1VRY for taking the time to review our submission and for suggesting improvements. We address them below.
> The paper could delineate its contributions better. As noted by most reviewers in a previous submission, the similarity to the regret circuit decomposition of CFR is... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom | Accept (spotlight) | Summary: The paper addresses the problem of learning with noisy labels by considering a model where instance-dependent confusion matrices occur occasionally across the samples, and the rest of data share a common nominal confusion matrix. The paper claims two main contributions: (1) showing that a single confusion matr... | Rebuttal 1:
Rebuttal: __[Regarding Large-Scale Datasets]__ We agree with the reviewer on this point. In the current manuscript, we have tested our methods against baselines on a dataset of size 50,000 samples in both machine annotations and real datasets. We did not use larger data size due to resource limitations and ... | Summary: The paper investigates the challenge of instance-dependent noisy labels, which are modeled using an instance-dependent confusion matrix reflecting annotator errors. Traditional approaches assume a consistent confusion matrix across instances, which simplifies the problem but is unrealistic. This study models t... | Rebuttal 1:
Rebuttal: __[Regarding $S \to \infty$ in Theorem 3.6]__ We agree that an analysis with finite $S$ is more desirable (as we did in Theorem 3.5). Nonetheless, we argue that even with infinite $S$, the result in Theorem 3.6 is meaningful and nontrivial. Let us explain.
First, the aggregated noise will not disa... | Summary: The paper extends further estimation of transition matrix in label noise learning. In previous studies, label noise is often assumed to be class-dependent. Hence, one can use the noisy label data and apply loss correction to learn a good classifier. The paper extends from such a modelling approach by consideri... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments.
__[Regarding the Lack of Public Multi-rater Dataset]__ We agree. It would benefit the community if more public multi-rater datasets are available. We are working on creating such data using Amazon Mechanical Turk (AMT), but the size of data is still limited... | Summary: This paper studied the identifiability problem of instance-dependent label noise with multiple annotators. To achieve the identifiability, this work first claimed a fact that only a proportion of all instances may have a labeling difficulty that significantly deviates from the general population. Then, it conn... | Rebuttal 1:
Rebuttal: __[Regarding Impact of the Number of Annotators]__ In Figure 3 of the supplementary section H.1, we presented experiments showing the impact of the number of annotators for outlier identification. The results indicate that increasing the number of annotators improves the detection of instance-depe... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their attentive reading and valuable comments/suggestions. We have replied to each reviewer in their corresponding sections. Here we present a summary of major comments and our responses accordingly.
__[Reviewer okUb]__
Reviewer okUb suggested investigati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language | Accept (poster) | Summary: This paper focuses on the setting where the goal is to use LLMs to make numerical predictions, as these models can be grounded by the provision of side-information (given in text) as well as other information that may be learned during their pretraining.
They propose LLM Processes, an approach to apply LLMs ... | Rebuttal 1:
Rebuttal: Thanks for taking the time to review our paper and for the positive words about our contributions. We address your questions below.
Figure 9 is intended to demonstrate how predictions from an LLMP can be influenced by conditioning on various scenarios communicated via text prompts to the model. T... | Summary: This paper proposes using LLMs to model joint distributions over numerical outputs while conditioning on potentially multiple covariates per data point. This is achieved through tokenizing a series of input and output pairs and then decoding corresponding outputs for another input. Outputs for numerical values... | Rebuttal 1:
Rebuttal: Thanks for taking the time to review our paper and for the positive words about our contributions. We address your questions below.
**The main weakness in the approach that I potentially see is the runtime. From what I could tell, there is no runtime results in either the main paper or the append... | Summary: This paper investigates the regression problem in large language models via in-context learning. They evaluate a variety of regression tasks such as for-casting and time series prediction, multi-dimensional regression, and more. They look into prompt engineering exploiting both numerical examples and their tex... | Rebuttal 1:
Rebuttal: Thanks for taking the time to review our paper and posing insightful questions. Answers to your questions are below:
**If I correctly understand the experimetnal part reports resutls on both training and only prompting. In this case make these two paradigms more clear in the organization of the e... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the time and effort they put into reading and commenting on our paper. Our work has presented a new zero-shot approach for generating probabilistic predictions with LLMs using plain language to augment numerical data. Reviewers believe that our work is both important and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization | Accept (poster) | Summary: This paper reveals a new observation in multi-view representation learning, that is, the performance of DCCA-based methods will gradually decrease as training progresses. The authors explore the possible reasons from the rank of weights and conclude that the Correlation Invariant Property is the key to prevent... | Rebuttal 1:
Rebuttal: We appreciate your comments and feedback. In addition to the general response, we address your itemized concerns here.
>In fact, the authors call this degradation phenomenon model collapse, which is not very accurate. Collapse should be very extreme. The performance degradation is a bit like an o... | Summary: The authors propose NR-DCCA, a novel approach of Deep Canonical Correlation Analysis(DCCA) equipping with noise regularization, in order to prevent DCCA-based methods from model collapse in the multi-view representation learning(MVRL) task. First, the authors analyze the difference between Linear CCA and DCCA,... | Rebuttal 1:
Rebuttal: We appreciate your comments and feedback. In addition to the general response, we address your itemized concerns here.
>However, as I know the relation between weight matrices’full-rank property and the overfitting phenomenon is still underexplored. The authors do not provide enough proofs to sup... | Summary: The paper proposes noise regularization term to prevent collapse issues found in deep canonical correlation analysis (DCCA) methods. The term makes DCCA to behave in a way similar to linear CCA, which is robust to collapse by definition, thereby making DCC with the regularization robust against collapse issue.... | Rebuttal 1:
Rebuttal: We appreciate your comments and feedback. In addition to the general response, we address your itemized concerns here.
>Readability. weak1
We apologize for not specifying the structure of the MLP in the paper. All our MLPs use the Leaky ReLu activation function. The first linear layer is feature\... | Summary: This work focuses on multi-view learning. Specifically, it studies the deep canonical correlation analysis and its variants. This study observes the issue of model collapse and proposes a regularization learning strategy to release the problem, then solve the early stop challenging.
Strengths: 1. Multi-view l... | Rebuttal 1:
Rebuttal: We appreciate your comments and feedback. In addition to the general response, we address your itemized concerns here.
>Adding regularization has been fully explored in different machine learning scenarios. To this end, the proposed method is lack of research novelty, which may diminish the paper... | Rebuttal 1:
Rebuttal: We thank all reviewers for their questions and constructive feedback. In the general response, we respond to the five core issues :
**More DCCA methods**, **Larger MVRL experiments**, **Effects of MLP structures**, and **New metric of weight redundancy and Contributions.** The image quality m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Initializing Services in Interactive ML Systems for Diverse Users | Accept (poster) | Summary: The paper introduces a novel method for initializing machine learning services tailored to diverse user preferences. The work addresses the challenges of non-convex optimization and lack of pre-existing user preference data before running a service; the authors propose a randomized algorithm that adaptively se... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review of our paper. We appreciate your positive comments on both the theoretical and experimental study, as well as finding our paper well-written. Below, we hope to address your concerns regarding the computational complexity of our algorithm:
We want ... | Summary: This paper introduces a novel method for initializing services in interactive machine learning (ML) systems tailored to diverse user preferences. The focus is on scenarios where multiple models or services are deployed, allowing users to choose the one that minimizes their personal losses. The authors highligh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review, and for finding our tight bounds on a novel setup to be a significant contribution, as well highlighting the practical impacts of our fairness considerations. Below, we hope to address some of the reviewer’s questions:
**Question 1 (Performance of ... | Summary: 1) This paper introduces a new algorithm to efficiently initialize a system providing K services to N users (K << N) where user preferences are unknown beforehand and the system iteratively learns about user preferences as the services are recommended, example : netflix movie recommendations.
2) The proposed... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review, and for finding our problem setup novel with the potential to encourage future research in this direction. We appreciate the thoughtful questions the reviewer asked, and believe that our findings and explanations to these questions will further stre... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed reviews. We deeply appreciate the questions and insights the reviewer's gave in their reviews. We attach below a set of empirical studies to hopefully answer the questions the reviewers had. Please let us know if we can answer any more questions. Thank... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting | Accept (poster) | Summary: The authors study an important problem of time series forecasting considering the increasing concerns of privacy and copyright. The paper proposes a novel LLM-empowered federated time series forecasting model with three main components, i.e., modality alignment, prompt adaption, and personalized strategy.
Str... | Rebuttal 1:
Rebuttal: We show great gratitude to you for approving of the quality of our paper. We have make the detailed response to thoroughly release your concerns.
> W1 & Q1 & Q2: Concerns on the applicability in real world.
We are sorry for not covering the deployment of Time-FFM in the manuscript. Since we make... | Summary: This paper proposed a federated foundation model for time series forecasting. This foundation model is trained in a distributed setting, with global shared parameters in a server, and domain / dataset specific parameters in clients. This allows the authors to personalize their predictions to local domain-speci... | Rebuttal 1:
Rebuttal: We are deeply grateful for the insightful review you have provided for our manuscript. We have made the following response.
> W1 & W3: Missing literature on **(1)** time series foundation models, **(2)** deep learning methods, and **(3)** federated learning methods.
We show great appreciation th... | Summary: This paper introduces TIME-FFM, which is a Federated Foundation Model for Time Series Forecasting. TIME-FFM is comprised of (1) Modality Alignment which aligns time series patches with text tokens; (2) Prompt Adaption which learns the text prompts for an input time series; (3) LM backbone; (4) Prediction Head ... | Rebuttal 1:
Rebuttal: We express our gratitude to you for providing constructive feedback on our paper. We have addressed the specific concerns as detailed below.
> W1: The overall novelty is limited. Modality Alignment, Prompt Adaption and different prediction head has been explored by previous methods.
Thanks for y... | Summary: The paper introduces TIME-FFM, a federated foundation model aimed at addressing the challenges of time series forecasting due to data scarcity and privacy concerns. The approach involves transforming time series data into text tokens, leveraging pretrained language models (LMs) for analysis, and using a person... | Rebuttal 1:
Rebuttal: We express our sincere thanks for the detailed and thoughtful review of our manuscript and for the encouraging appraisal of our work. We have addressed the specific concerns and respond to the constructive recommendations detailedly as follows.
> **W1. Algorithm Description**: On page 6, the desc... | Rebuttal 1:
Rebuttal: We commerce by thanking the four reviewers for their thoughtful and constructive comments. We are really encouraged to see that the reviewers appreciate some positive aspects of our paper, such as technical quality (**Reviewer LqRs, PDR4, uvxC, and 7qPS**) and presentation skills (**Reviewer LqRs,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Schur Nets: exploiting local structure for equivariance in higher order graph neural networks | Accept (poster) | Summary: This paper proposes Schur layers. Schur layers are meant to be used in higher-order MPNNs and are based on respecting local automorphism equivariance, however without the need for explicitly computing all automorphisms. In experiments, this method achieves state-of-the-art results on ZINC.
Strengths: - **(S1 ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments on the paper.
QUESTION 1. What is the intuition behind Lemma 1- theorem 3? The theory of equivariant neural nets
usually proceeds by considering the action of a group on the space that the output of a given layer of the
network lives in, and ... | Summary: This paper introduces Schur layers, a novel approach in graph neural networks (GNNs) that enhances expressive power by leveraging spectral graph theory. Traditional GNNs struggle with capturing complex local graph structures due to their reliance on full permutation equivariance, which is overly restrictive an... | Rebuttal 1:
Rebuttal: QUESTION1 In short: it is not the Schur layers themselves that make this architecture more expensive than
classical GNNs, but rather the higher order message passing itself (like in P-tensors).
The natural thing to compare Schur-layers to are the vanilla "linmaps" operations.
In the case of a s... | Summary: This paper introduces Schur Net, an architecture designed to attain subgraph equivariance without fully determining the automorphism group. Utilizing spectral graph theory, Schur layers incorporate equivariant side-information from local structures to improve expressiveness. The authors have confirmed Schur Ne... | Rebuttal 1:
Rebuttal: Thank you for your review and several suggestions that are very much on point.
QUESTION 1. Yes, in our experiments we used for order Schur layers, which just use equation 8. What we like about our approach is that it bypasses all the group theory and reduces to something so simple. The rest of th... | Summary: This paper proposes to define permutation equivariant functions on subgraphs via spectral theory as opposed to the more traditional 'equivalence classes' of permutations [Maron et al.] The paper lays out the theory behind producing equivariant maps via the eigen decomposition of the subgraph's Laplacian and th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and questions.
QUESTION 1. We experimented with various architectures, but in the final experiments we just used subgraph-layers corresponding
to cycles, edges and vertices. The cycles were of size 5 and 6 corresponding to the aromatic rings
that commonly... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading of our paper and thoughtful comments. Points raised by individual reviewers are addressed in the individual responses, here we would like to make some general comments.
- First of all, we stress that our paper has two separate aims: 1. Making a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations | Accept (oral) | Summary: In this paper, the authors propose a novel Pfafifian-based anti-symmetrization method to represent the generalized wavefunction in quantum chemistry. Unlike the traditional Slater determinant-based anti-symmetrization, the Pfaffian-based method offers greater flexibility in selecting the number of orbitals. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their invaluable feedback and hope to address their concerns. Firstly, we would like to highlight the broad range of new experimental evidence we present in the general comment. The following details how the experiments relate to the reviewer's concerns.
**Ablation studi... | Summary: This paper proposes a new ansatz (Neural Phaffian) for parameterizing wave functions. The new ansatz improves the expressive power by making it possible to increase the number of orbitals. It is also beneficial to tasks like generalization between different systems. The effectiveness is demonstrated with plent... | Rebuttal 1:
Rebuttal: We are delighted by the reviewer's positive feedback and want to address the remaining concerns. Firstly, we would like to highlight the broad range of new experimental evidence we present in the general comment. The following details how the experiments relate to the reviewer's concerns.
**Time ... | Summary: In this paper, the authors propose using Pfaffians instead of Slater determinants to learn a generalized neural wave functions so that the permutation antisymmetry enforcement can be better addressed. Empirical study shows one single proposed model can generalize to various systems (second-row elements) with c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our manuscript and would like to take the opportunity to address the few concerns that were raised. Firstly, we would like to highlight the broad range of new experimental evidence in the general comment. The following details how the experiment... | Summary: This paper proposes NeurPf, a novel approach that replaces the standard determinant structure with a Pfaffian-based structure that allows systems of varying sizes to be represented with a single neural wave function. The key idea of the new ansatz is that given a large enough skew-symmetric matrix $A$, we have... | Rebuttal 1:
Rebuttal: We thank the reviewer for their invaluable feedback and suggestions. We hope to address their concerns. Firstly, we would like to highlight the broad range of new experimental evidence we present in the general comment. The following details how the experiments relate to the reviewer's comments.
... | Rebuttal 1:
Rebuttal: We thank all reviewers for their invaluable feedback and great suggestions for additional experimental evaluation. We enriched our work with several ablation studies, which we present in the attached PDF. We will add all results to the manuscript.
**Fig 1: TinyMol baselines**\
In addition to the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Score Matching Suitable for Estimating Point Processes? | Accept (poster) | Summary: EDIT: I have changed my score from a "weak accept" to an "accept."
The paper studies the potential use of score matching (SM) to do inference
with the model of interest is a Poisson point process or Hawkes
processes. Because using SM in practice requires modifying the objective
with integration by parts, the ... | Rebuttal 1:
Rebuttal: Thank you so much for reviewing our paper. We answer your questions below.
> The argument to choose their weighting function $h$ is definitely better than simply picking one. That being said, it still not clear how close to optimal their choice of h is given they are only optimizing one term in th... | Summary: This paper studies the use of score matching for estimation in point process models, and proves the incompleteness in the original score matching estimators due to the bounded support. Weighted score matching is use to address the issue and theoretical results are establish ed for the consistency and optimalit... | Rebuttal 1:
Rebuttal: Thank you so much for reviewing our paper. We answer your questions below.
> Denoising score matching for point processes is not considered.
We thank the reviewer for pointing this out. Indeed, denoising score matching (DSM) is not considered in our paper because we mainly focus on correcting th... | Summary: The paper considers the problem of utilizing score-matching approaches for point processes. The main motivations for the paper are the Poisson and Hawkes processes. Both of these model the repeated occurrences of events over a finite time interval $[0, T]$. The Poisson process models the occurrence of an event... | Rebuttal 1:
Rebuttal: Thank you so much for reviewing our paper. We answer your questions below.
> For example, there are several assumptions on the weight function in Equation 9 but it is not clear which properties are used in the proof of Proposition 3.3. For instance, Line 400 features an explicit expression for $h... | null | null | Rebuttal 1:
Rebuttal: We express our sincere appreciation to all reviewers for their time, effort, and insightful feedback. We are encouraged by their recognition of the significance of our work in identifying the incompleteness of the original score matching for point processes, proposing the weighted score matching m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Finding good policies in average-reward Markov Decision Processes without prior knowledge | Accept (poster) | Summary: The goal of the paper is to learn near-optimal policies in average reward MDPs without prior knowledge of complexity parameters, contrasting extensive prior work which requires knowledge of the values complexity parameters. They rule out the possibility of easily removing this knowledge requirement through est... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for their very constructive feedback on our paper.
While the algorithms we present are combinations of previously known methods, the main point of interest of this paper is that $H$ is not the right measure of complexity due to the impossibility of estimating it an... | Summary: This paper studies the problem of learning a good policy in averaged-reward MDP with finite diameter. Given previous work on the problem assuming knowledge on the optimal bias span $H$, the authors try to remove the prior knowledge and propose diameter-dependent sample complexity without any prior knowledge. I... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful feedback on our paper.
Our main contribution in this paper is that H is not the right complexity measure for best policy identification in average reward MDPs. Once that point is made, we then turn to other measures and provide an algorithm that shows that ... | Summary: This paper addresses average-reward Markov Decision Processes (MDPs). In the context of the generative model, existing literature presents an $\epsilon$-optimal policy with a sample complexity of $O(SAD/\epsilon^2)$. However, this approach requires prior knowledge of an upper bound on the optimal bias span $H$... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback on our paper.
Our main point in this paper is that H is not the right complexity measure for best policy identification in average reward MDPs. Once that point is made, we then turn to other measures and provide an algorithm that shows that a boun... | Summary: The problem of identifying near optimal policies with high probability, either in the generative, or in the online setting, is considered when the state-action space is finite, and the criteria to compare policies is how much reward they collect on the average in the long term ("PAC setting"). Algorithms are c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback on our paper.
It is true that we do not relate the PAC setting to most other settings, but we do point out that the results in the cumulative regret setting are unapplicable here, which is quite different from the finite horizon or discounted mo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Theoretical Analysis of Weak-to-Strong Generalization | Accept (poster) | Summary: The paper provides a theoretical analysis of weak-to-strong generalization, a phenomenon where strong models can learn from weak supervisors and outperform them [[Burns et al 2023](https://arxiv.org/abs/2312.09390)].
The authors make precise assumptions about the nature of the strong student model family and ... | Rebuttal 1:
Rebuttal: > __For example, can the bound predict the result in [Burns et al 2023] that weak-to-strong generalization doesn't work well on the reward modeling task?__
Great question! Our analysis is limited to classification for now, since we essentially assume that the class-conditional sets $\mathcal{X}_i... | Summary: The paper provides a theoretical explanation for weak-to-strong generalization. A weak model produces pseudolabels that can have errors and may not cover the entire input space. The paper argues current weak supervision theory fails to explain how and when the strong model can correct the psuedolabels (pseudol... | Rebuttal 1:
Rebuttal: > __Is it possible to provide a final result conditioned on the entire covered set?__
Yes, we just left it out for space and because getting a combined bound with a simple functional form requires more definitions (max/min of the weak label errors $\alpha_i$, the minimum expansion parameter acros... | Summary: This paper proposes a theoretical framework to interpret the weak-to-strong generalization phenomenon. It shows that strong student models trained on noisy labels from weak teacher models can outperform the weak teacher models, correcting their errors and generalizing well to examples where the weak teacher mo... | Rebuttal 1:
Rebuttal: > __Adding comparisons with recent concurrent works ([1, 2, 3]) would be beneficial.__
Thanks for pointing these out! First, we just want to note that all 3 of these works appeared on arXiv after the NeurIPS submission deadline, so we were not aware of them at the time of submission.
Broadly, [... | null | null | Rebuttal 1:
Rebuttal: Overall comments:
--
Thanks to all the reviewers for their time, effort, and helpful feedback. We are encouraged that all the reviewers found our work technically sound, novel, and potentially impactful. We have replied to individual points below. We hope the reviewers will consider raising thei... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation | Accept (poster) | Summary: The authors of this paper focus on the task of label distribution shift in the online setting without true labels. Following the importance of improving feature extractors even during test-time found in the current literature, they propose to update the feature extractor online with unlabeled instances during ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments! Here are our responses.
**Motivation and its justification**. We would like to answer your questions in this point separately.
- “Why is improving feature extractors helpful for only label shift?” In fact, updating feature extractor can hel... | Summary: This paper addresses the problem of online label shift and proposes a novel algorithm that exploits feature representation learning to enhance performance. Inspired by test-time training literature, the proposed method uses self-supervised learning to refine the feature extraction process. The algorithm comes ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments! Here are our responses.
**The storage of previous historical data**. This is actually an insightful point! We acknowledge this can bring additional privacy concerns, depending on the practical scenarios, and we would like to discuss this in... | Summary: This paper addresses the online label shift (OLS) adaptation problem, which involves continually adapting a pretrained offline model to test data with various and evolving label distribution shifts. The proposed method integrates existing self-supervised learning (SSL) techniques into current OLS methods, base... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for providing some useful comments about data privacy concerns and some typos. However, we respectfully disagree with the criticism on novelty, paper structure and the experiment set-up (data split and baseline performance). We reply to the weaknesses and questi... | Summary: This paper introduces a novel method for addressing label shifts in an online setting, where data distributions change over time, and obtaining timely labels is challenging. Unlike traditional approaches, this paper explores enhancing feature representations using unlabeled data during test time. The proposed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments! Here are our responses.
**Discussion with concept drift.** Yes, you are right that the (online) generalized label shift problem is a sub-field of concept drift and has its particular assumption. According to the definition of generalized la... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses the problem of online (generalized) label shift, where label information is unavailable during testing, and the label distributions change over time. The main contribution of this work is the proposal of a unified framework that integrates feature learning into the online learning process,... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: We would like to thank the reviewer for these meaningful questions! Here are our responses.
**Theoretical insights of SSL and the choice of gradient step size**. We have to admit that the theoretical study for feature learning is generally hard; Instead, the analysis for... | null | null | null | null | null | null |
Approximating mutual information of high-dimensional variables using learned representations | Accept (spotlight) | Summary: This paper explores the idea that underlying low-dimensional structure in high-dimensional data can be exploited to approximate mutual information (MI) efficiently and with a reasonable number of samples. The approach learns a low-dimensional embedding of high-dimensional data using a neural network architect... | Rebuttal 1:
Rebuttal: Thank you for this thoughtful review. Below we address specific points:
***“\[...\] the experiments fail to provide comparison to Sliced MI or related methods.”***
We appreciate the reviewer’s insight that sliced MI is likely to capture dependence well in high dimensions, particularly for Gaussi... | Summary: The focus of the paper is on approximating mutual information (MI) between multidimensional variables. This problem is challenging as the approximation of the MI suffers from the curse of dimensionality. The authors propose a method that approximates the MI via an embedding in a lower dimensional space. They t... | Rebuttal 1:
Rebuttal: Thank you for this review. Below we address specific questions and concerns.
***“I would advise reporting the standard deviation in all tables (error bar in figures).”***
Thank you for raising this. We apologize for omissions and will include s.d. where applicable in our revision. We have includ... | Summary: This paper proposes latent MI (LMI), a method for estimating the mutual information (MI) between two high-dimensional multivariate random variables. For that, the technique uses the non-parametric MI estimator from [KSG04] on lower-dimensional latent representations that are learned by neural networks such tha... | Rebuttal 1:
Rebuttal: Thank you for this insightful review.
***“Overall, the proposed method consists in applying an existing estimator to pre-processed input variables, in the form of latent representations. It seems to lack a joint design, which results in two additive and independent sources of error (one from the... | Summary: This paper introduces an approach to approximate mutual information (MI), by applying a nonparametric MI estimator to the learned representations. The representations are learned by minimizing a weighted sum of the reconstruction loss and the prediction loss. The authors conducted experiments on both synthetic... | Rebuttal 1:
Rebuttal: Thank you for this thoughtful review. You raise one major point of concern:
“***\[...\] the learned representations might not capture the dependence between $X$ and $Y$, making the LMI fail.***”
We agree that there are situations where LMI will fail (as is broadly true for all MI estimators \[2\... | Rebuttal 1:
Rebuttal: We deeply appreciate the thoughtful feedback shared by the reviewers. In our responses to reviewers, we share some additional analyses, discussions, and clarifications (companion figures to responses are included in the attached .pdf). Overall, we think we have managed to address all of the concer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems | Accept (poster) | Summary: This work introduces a patch-based diffusion modeling approach to efficiently learn image priors that can be used to solve inverse problems. Particularly the model maintains memory and data efficiency due to the patch-based operating scheme. Experiments are demonstrated in both natural and medical image domain... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Comment: Details regarding calculations of PSNR and SSIM are missing**
The PSNR and SSIM of RGB images are calculated in the RGB domain. Data preprocessing consisting of dividing all the RGB values by 255 was done first, so all the reconstr... | Summary: In this work, the authors propose a novel method for learning efficient data priors for entire images by training diffusion models only on image patches. During inference the authors introduce a patch-based position-aware diffusion inverse solver, which obtains the score function for the whole image through sc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Comment: Additional evaluations should be conducted against other inverse problems, sensitivity analysis should be included with different forward operators**
We conducted more experiments with different forward models: namely 60 view paral... | Summary: This paper proposes a patch-based diffusion model for inverse problems, such as CT reconstruction and natural image deblurring. This method divides images into patches, reducing the size of the input data fed into the network, thereby decreasing memory consumption.
Strengths: This method provides a feasible a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Comment: Innovation is limited**
The main innovation of our method is a method to formulate a diffusion based image prior from solely the patches of the image. Diffusion models are known for requiring a large amount of memory for training a... | Summary: This manuscript discusses diffusion models for inverse problems. The authors discuss using image patches of the image to improve computational bottlenecks and overcoming the lack of sufficient data in training appropriate surrogate neural network priors for the inversion task. The authors discuss details of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Comment: Work is partly incremental, heavily relies on cited publications**
The papers [12] and [18] apply diffusion models to solve 3D reconstruction problems, whereas the proposed method performs experiments on 2D reconstruction problems ... | Rebuttal 1:
Rebuttal: We would like to sincerely thank all the reviewers for the valuable comments and constructive feedback on our paper. We provide point-by-point responses to address each reviewer’s comments and highlight our response to some key questions and additional experiments and results as below:
**More ba... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors present an approach for tile-based training and prediction of diffusion models applied for inverse problem posterior sampling.
The core idea is that training is done with random patches and during generation the authors use a differently shifted non-overlapping tiling grid for each iteration of the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Comment: Tiling scheme for unets can be implemented with overlapping patches**
We implemented the method provided in the above reference [1] while using the same trained network, hyperparameters, and DPS for inverse problem solving. The net... | null | null | null | null | null | null |
Learning Group Actions on Latent Representations | Accept (poster) | Summary: Learning group actions on latent representations
Abstract
The work’s contributions are clear from the abstract.
Introduction
Lines 13-18: Group actions are explained simply and intuitively but the less familiar reader may benefit from a basic example beyond reference to geometric transformations, such as so... | Rebuttal 1:
Rebuttal: Thank you for your very detailed and insightful review. We appreciate the thorough understanding you demonstrated and the thoughtful feedback you provided. We are grateful for the time and effort you invested. We will revise the unclear sentences and captions the reviewer kindly pointed out.
As f... | Summary: This paper introduces an approach to modelling group actions using autoencoders, particualrly by learning these actions in the latent rather than observed data space. The authors argue that this allows modelling a broader range of real-world scenarios, and does not require particular layers - however, it does ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the time and effort you have dedicated to reviewing our paper. We will address the weaknesses and questions you raised below.
**Weakness**
- We agree that requiring ground truth is a limitation, as we discuss in the paper. However, we would also... | Summary: This paper focus on learning group actions in latent space instead of data space. The group action takes effects between the encoder and decoder in an auto-encoder structure. Several tasks with different group actions are evaluated in the experiments.
Strengths: 1. The paper is well organized and the core ide... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the time and effort you have dedicated to reviewing our paper. We will address the weaknesses and questions you raised below.
**Weakness**
1. While there are some works that explore models with group actions in latent representations, the novelt... | Summary: The paper introduces a new method to learn group structure such as SO(3) rotations on data by representing the group actions in a latent space. The authors propose an autoencoder-based method to represent this latent space, and demonstrate how it can represent equivariance to latent factors.
Strengths: The pa... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the time and effort you have dedicated to reviewing our paper. We will address the weaknesses and questions you raised below.
**Weakness**
- We note that we actually used multiple architectures in the experiments, sometimes with "plain" CNNs (MN... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback provided by all the reviewers. We are grateful for the time and effort you dedicated to our paper. To provide further clarity, we have included a PDF with additional samples. Please refer to our individual responses for detailed information on each review. We lo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Representations for Hierarchies with Minimal Support | Accept (poster) | Summary: This paper develops a framework to identify a subset of entries required to uniquely distinguish a graph among all transitively-closed DAGs. It achieves robust performance on synthetic hierarchies and a larger real-world taxonomy.
Strengths: S1: A framework is proposed for detecting a sufficient smallest subs... | Rebuttal 1:
Rebuttal: Thank you for your review and questions.
> W1: The end-to-end efficiency is not clearly evaluated in this work, making it a bit unclear to assess the significance of the work in practice. For example, the proposed technique helps learn representations with minimal entries supported during trainin... | Summary: This paper proposes to distinguish a directed graph (digraph) among all transitivity-closed DAGs by finding minimal signed directed graph (sidigraph).
This paper exploits this idea to propose a more efficient algorithm for node embedding models.
Strengths: - Theoretical supports in Sec. 3 for the sidigraph ob... | Rebuttal 1:
Rebuttal: Thank you very much for your questions and comments.
> It is not clear regarding prop 2 if the distinguishing serigraph H' is "lighter" than the transitive reducted G'.
>
Please note that while $G^{\prime}$ is a digraph with only positive edges specified, $H^{\prime}$ is a sidigraph with positi... | Summary: This paper proposes a novel framework for identifying a minimal subset of entries in the adjacency matrix that uniquely distinguishes a directed acyclic graph (DAG) among all transitively-closed DAGs. The authors provide a provably optimal algorithm for computing this minimal set. They then leverage these insi... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and questions!
> While the paper demonstrates the effectiveness of transitivity bias and hierarchy-aware negative sampling for DAGs, the authors could discuss whether these ideas extend to learning representations of other graph families characterized by diff... | Summary: Authors propose an algorithm to identify a subset of entries required to uniquely distinguish a graph among all transitively-closed DAGs, based on the theoretical analysis of transitive reduction of the associated signed digraph. These newly identified subsets are leveraged to learn node embeddings via contras... | Rebuttal 1:
Rebuttal: Thank you for your review and insightful questions!
> 1. As expressed by authors the range of applications relating to their study remains significantly narrow even if interesting. It could be of interest to compare their approach on the studied datasets with methods enforcing transitivity while ... | Rebuttal 1:
Rebuttal: We are sharing a pdf with plots for GT-Box with negative ratio $k=128$ (analogous to Figure 7, which is for $k=4$).
Pdf: /pdf/54ceb014439bc5d1d5035bce119baabc81ca575a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses the challenge of training node embedding models on large directed graphs (digraphs) where it is impractical to observe all entries of the adjacency matrix during training, necessitating sampling methods. Recognizing that many entries remain unobserved in very large digraphs, the authors de... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and questions!
> 1. The effciency and robustness of the proposed method is verified via experiments on various types of hierarchies, including Balanced tree, nCPR, Price, and MeSH. However, it could be of more intuitive if the generic motivation of pursuing h... | null | null | null | null | null | null |
SCOREQ: Speech Quality Assessment with Contrastive Regression | Accept (poster) | Summary: The paper proposed a reference-free speech quality prediction framework. This work is based on NOMAD and uses triplet loss for contrastive regression to mitigate the generalization problem of reference-free speech quality metrics.
The author conduct experiments on various datasets to show the L2 loss failed t... | Rebuttal 1:
Rebuttal: *In lines 165-167, the authors state "Our experiments show that the adaptive margin’s contribution is minimal,
while significant improvement is achieved through our batch-all strategy", but there is no ablation study about
adaptive margin and batch-all strategy.*
Thank you for highlighting the ... | Summary: The paper proposes a triplet loss function for contrastive regression for MOS prediction, which helps the model learn more about the relative rank of speeches.
Strengths: 1. The author proposes to solve an important problem in the field of MOS prediction that the L2_loss lacks the awareness of rank.
2. The pr... | Rebuttal 1:
Rebuttal: **1**. *"Why the distance is the absolute values. If the anchor is 2, then the pair of (0.5, 4) is the same as
(3.5, 4) since the distance is both (1.5^2, 2^2)? In this way, how can we know that the quality of a
sample is better or worse than the anchor?"*
Our approach distinguishes between tra... | Summary: The paper presents SCOREQ, a novel approach for speech quality prediction using a triplet loss function for contrastive regression. SCOREQ addresses domain generalization issues in current no-reference speech quality metrics by incorporating Mean Opinion Score (MOS) labels and improving training efficiency. Un... | Rebuttal 1:
Rebuttal: *"Correlation with Human Judgments: Could you provide more detailed analysis on how well the
SCOREQ framework correlates with human judgments on both a sample and distribution level?
Specifically, it would be valuable to see if the framework can capture variances in human perception
on a popula... | Summary: This paper proposes a novel speech quality assessment method based on contrastive regression. Many speech quality assessment predictors are obtained based on supervised training by estimating an MOS value given an audio. However, it is well-known that these supervised training methods have serious over-fitting... | Rebuttal 1:
Rebuttal: *"The explanations (especially the introduction part) are twisted. Section 1 uses Figure 4 as an example,
but it is very difficult to understand since we do not have explanations about the related terminologies and
backgrounds in Figure 4—Ditto for line 38 about Figure 1 (a)."*
Figure 4 shows... | Rebuttal 1:
Rebuttal: **Rebuttal**
We thank all the reviewers for their helpful feedback. Three common questions are raised by reviewers **2CaS** and **XmpT**: diversity of test sets, applicability of our method in other tasks, and memory constraints. We address these concerns here.
The remaining questions and concer... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper "SCOREQ: Speech Quality Assessment with Contrastive Regression" tackles the challenges of Out-of-Distribution (ODS) and Out-of-Domain (ODM) problems by introducing a novel approach using a triplet loss function designed for contrastive regression.
### Handling ODS and ODM Problems (Domain Generaliza... | Rebuttal 1:
Rebuttal: *1. "It is mentioned that the SCOREQ method could be applicable to other regression-based tasks.
Can you provide more details or preliminary results on how SCOREQ performs in other domains?"*
See our answer in global rebuttal: **Domain-Specific Evaluation**
----
*2. "How does the computational... | null | null | null | null | null | null |
Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach | Accept (poster) | Summary: This work studies adversarial RL and policy stability from the perspective of the Lyapunov spectrum. A regularization term is introduced to encourage more robust policies.
Strengths: - A novel idea being introduced from classical literature and implemented in the deep RL setting
- The regularization introduce... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our work. We are delighted that you are interested in our paper! The question you raised is very insightful, and we would like to share some opinions about it.
### Weaknesses
- **The correlation between Figure 2 and 3 is not immediately clear to me... | Summary: Deep RL methods are usually lack of robustness in control tasks whose dynamics are chaotic, thereby having positive maximal Lyapunov exponents (MLEs). This paper proposes an approach that improves the stability of trained deep RL controller through MLE regularization.
Strengths: * The problem studied in this ... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful review. We value your insightful questions, and you can find our response below.
### Weaknesses
- **This paper dedicates many pages to the chaotic phenomena in RL (Sections 3 and 4), which have been addressed in previous works as introduced in Sections 1 and... | Summary: To address the issue of stability of deep reinforcement learning, the authors first gauge the chaotic behavior of various state-of-the-art deep reinforcement learning policies in continuous control environments, and quantify the stability of those policies with significant impact of their applicability to real... | Rebuttal 1:
Rebuttal: We greatly appreciate your time and effort in reviewing our work. Your questions are very insightful, and we would like to offer our thoughts on it.
### Weaknesses
- **The proposed method on maximal Lyapunov exponent regularization needs to be further explored and studied, both in terms of the th... | null | null | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for taking the time to review our paper and providing valuable feedback. Your comments have been insightful and have certainly contributed to the refinement of our work. We appreciate the effort you have put into the review process. After... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Exploration for Data-Efficient General Value Function Evaluations | Accept (poster) | Summary: This paper presents a novel method named GVFExplorer for efficiently evaluating multiple Value Functions with different policy (GVFs) in parallel using off-policy methods. It adaptively learns a single behavior policy that minimizes the total variance in return across GVFs, thus reducing required environmental... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and valuable experimental suggestions, which we have now incorporated into our rebuttal. Based on your input, we have also **added results in Mujoco in the main comment (refer Fig 1 in PDF)**. We appreciate your recognition of the novelty of adaptively learning... | Summary: This paper propose a new algorithm to solve the general value function evaluations problem. In essence, GVFs can be seen as high dimensional value functions. The authors propose a temporal difference learning algorithm that minimizes the overall variance in the return distribution, in the hope to improve the b... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and positive feedback on our algorithm's rigorous derivation and analysis. Based on your suggestion, we have now **added Mujoco experiments result in the main comment**. We respond to each query below.
1. **“Why did we choose to minimize variance of return as our... | Summary: This paper presents a new algorithm for collecting data needed to learn multiple GVFs in parallel. By focusing data collection on high-variance (s,a) pairs, an agent is able to collect data that will reduce the variance of estimated GVFs. The authors contribute a sort of contraction-mapping proof that using th... | Rebuttal 1:
Title: Author's Response to Reviewer pomH
Comment: Thank you reviewer for your thoughtful and constructive feedback. We provide a detailed response to the asked questions below.
1. **“Clarification regarding Theorem 4.2”**
We appreciate your careful attention, you are correct that Theorem 4.2 demonstrates... | null | null | Rebuttal 1:
Rebuttal: Thank you for the valuable and constructive feedback. We are encouraged by your recognition of the *novelty of the problem* and acknowledgment of our *algorithm derivations as systematic and rigorous*.
Based on your feedback, we have extended our experimental results in the **Mujoco environments ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage | Accept (poster) | Summary: This paper focuses on the optimization and learning methods for ``online'' RLHF and contrastive offline methods (DPO, IPO). The authors aim to understand the separation between these two type of methods, where they are different in terms of whether new responses can be sampled or not. The authors state that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and we address the reviewer's comment below:
> Discussion on online data.
Thank you for pointing out the subtlety of the terminologies. We agree that HyPO indeed does not query any additional information from $r^\ast$, which is different from t... | Summary: This work considers the statistical separation between contrastive algorithms (DPO and IPO) and RLHF. It proves that DPO/IPO requires global convergence assumption which is in general a very strong assumption while on the other hand RLHF only requires local coverage. This separation stems from the explicit KL ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the postive feedback and we hope our rebuttal can be helpful to further demonstrate our contribution:
> Second sentence of L562 should be appended to L231 to make the proof sketch immediately clear. Theorem E.1 could be relocated to Section 5 to be more self-contained.
... | Summary: The paper focuses on the paradigm of fine-tuning large language models (LLMs) using human preference data. It delves into two primary techniques: online reinforcement learning (RL) and offline contrastive methods. The authors challenge the previous notion of these techniques being equivalent by conducting a th... | Rebuttal 1:
Rebuttal: We thank reviewer for the helpful comments and we address them below:
> Insufficient experiments.
We agree that experimenting on larger model sizes and additional dataset is important to demonstrate the effectiveness of HyPO and validate our theory. In the supplementary material for the rebutta... | Summary: This paper studies learning from human preference feedback for aligning large language models (LLMs). Many existing works share the same observation that offline alignment methods such as DPO underperforms their online counterpart such as PPO. But this phenomenon has not been well understood. This paper studie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and we address the reviewer's comments below:
> Computational cost analysis.
We thank the reviewer for pointing this out. Indeed the computational analysis is important in comparing finetuning methods. We had a short discussion at the end of A... | Rebuttal 1:
Rebuttal: ### General responses
We thank all reviewers for their positive and constructive feedback. In the general response we provide some additional empirical results, which will be incorperated in our final version of the paper.
1. **Large-scale experiments:**
In response to Reviewer ewpX’s suggestio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Animate3D: Animating Any 3D Model with Multi-view Video Diffusion | Accept (poster) | Summary: This paper introduces a framework for 4D generation to animate static 3D models, consisting of two components: MV-VDM, a multi-view video generation model, and a framework combining reconstruction and 4D score distillation sampling (4D-SDS). A spatiotemporal attention module enhances consistency, using multi-v... | Rebuttal 1:
Rebuttal: **W1: Pipeline without significant innovation.**
Thanks. Our work enjoys good novelty in both task formulation and solution pipeline.
Firstly, we redefine the concept of 4D generation by introducing a novel task: animating any off-the-shelf 3D objects. This innovative task holds significant relev... | Summary: This paper proposes Animate3D, a 4D generation framework that consists of a multi-view video diffusion model (MV-VDM) followed by 4D Gaussian Splatting optimization, as well as a dataset of 38K animated 3D objects (MV-Video) that is used to provide spatiotemporal supervision to train the model. Different from ... | Rebuttal 1:
Rebuttal: **W1: Data copyrights and licensing**
We confirm that all models downloaded from Sketchfab have a distributable Creative Commons license and were obtained using Sketchfab’s public API. Besides, models marked as ``NoAI'' and restricted due to objectionable or adult thematic content were excluded f... | Summary: This work presents Animate3D, a framework for animating static 3D models. The core idea involves two main components:
1. A multi-view video diffusion model (MV-VDM) conditioned on multi-view renderings of the static 3D object, trained on a large-scale multi-view video dataset (MV-Video).
2. A framework that co... | Rebuttal 1:
Rebuttal: **W1: Missing Reference: Animate124**
Sorry for missing Animate124, which is a great pioneering work. Comparison is in the global response (1. Comparison methods), required by other reviewers. Reference will be added in our revised paper.
**W2: More comparisons with 4DGen, TC4D and original 4Df... | Summary: This paper proposes an animation method that animates a 3D model in a 4D one. A Multi-View image conditioned multi-view Video Diffusion Model (MV-VDM) is presented to generate multi-view videos from multi-view renderings of a static 3D object. The MV-VDM is leveraged to train the 4D Gaussian Splatting (4DGS... | Rebuttal 1:
Rebuttal: **W1: (1) Issues about unfair comparison: 4Dfy and DG4D do not leverage multi-view images; (2) 4Dfy is based on nerf instead of 4DGS; (3) Add comparison with AYG and Animate124.**
**(1)**: We proposed a new task of animating any off-the-shelf 3D object, and there is **no previous work specially d... | Rebuttal 1:
Rebuttal: We thank for the reviewers' appreciation of our work, as they give positive comments of "problem setting is interesting, well-motivated and straightforward" (R2, R4), "achieve state-of-the-art performance of 4D generation" (R3, R5), "the large-scale 4D dataset could have significant influence on t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper focuses on animating 3D objects with multi-view diffusion models. To improve spatial and temporal consistency, this work builds a large-scale multi-view video dataset, MV-Video and designs an attention module to enhance spatial and temporal consistency by integrating 3D and video diffusion models. T... | Rebuttal 1:
Rebuttal: **W1: Inappropriate title: 3D objects instead of 3D models**
Thanks for the advice, we will consider revising it in the revised version.
**W2: Effectiveness of the proposed dataset in video or 3D diffusion models**
Given limited time, we only finetune SVD on a subset (20\%) of our dataset, and ... | null | null | null | null | null | null |
DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus | Accept (poster) | Summary: DoGaussian incorporates the 'divide-and-conquer' approach and introduces the ADMM algorithm into the 3DGS training process for large-scale 3D reconstruction tasks, reducing training time by 6+ times compared to the original 3DGS. Specifically, DoGaussian first splits the scenes into K local blocks of similar s... | Rebuttal 1:
Rebuttal: - ***Q1: Noticeable artifacts in the teaser, particularly in the picture located in the bottom right corner.***
- **A1:** From Fig.3 in the attached PDF, we can observe that the artifacts in the teaser appear to be near the scene boundary, which is a common problem for 3DGS-based methods and not a... | Summary: This paper proposes a distributed training strategy for 3dgs in large-scale scenes. The scene is evenly splitted into K blocks, but also maintain the global scene representation. Then the optimization of the scene is transferred to a condition optimization solved by the classic ADMM. The results demonstrate ... | Rebuttal 1:
Rebuttal: - ***Q1: applicability on autonomous driving scenes***
- **A1:** See **A4** in the common questions.
- ***Q2: It would be better to modify some representations in the paper***
- **A2:** Thanks for the suggestions. We revised the paper accordingly as suggested:
***(a)*** line 82, we revised it to ... | Summary: This paper introduces traditional ADMM to Gaussian Spaltting and achieves distributed Gaussian Splatting training. The proposed distributed approach reduces training time and guarantes training convergence and stability. Experiments demonstrates both effectivenss and efficiency of this method.
Strengths: 1. T... | Rebuttal 1:
Rebuttal: - **Q1: More experiments are needed to prove the superiority of the consensus step. More qualitative and quantitative comparisons with VastGaussian in areas where blocks overlap.**
- **A1:** We include more qualitative results in Fig.2 in the attached PDF to show the importance of the consensus st... | Summary: The paper presents DoGaussian, a novel method for accelerating the training of 3D Gaussian Splatting models for large-scale 3D scene reconstruction. It introduces a distributed training approach using scene decomposition and the ADMM, resulting in a 6x faster training time while maintaining high-quality render... | Rebuttal 1:
Rebuttal: - ***Q1: Provide more qualitative results in the rebuttal (compared with the original 3DGS and VastGaussian).***
- **A1:** We include more qualitative results in Fig.1 in the attached PDF.
- ***Q2: More baselines should be included, such as Fed3DGS.***
- **A2:** We include the results of Fed3DGS ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their detailed comments and suggestions. Some **common questions are answered below**, and **more qualitative results** are provided in the attached PDF.
- ***Q1 How much GPU memory is needed for the master node? Will its memory consumption be linearly increase... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ESPACE: Dimensionality Reduction of Activations for Model Compression | Accept (poster) | Summary: The proposed method applies a PCA-inspired method to the activations of LLMs for model compression.
Strengths: - While most papers focus on quantization, pruning, or weight decomposition, the proposed approach goes in an interesting direction.
- The paper is easy to follow and the proposed method is simple.
-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback provided, and for recommending acceptance of our paper. We also appreciate the reviewer underscoring the novelty, clarity, relevance, technicality, and substantiveness of our work. Here we provide answers to the concerns and questions raised:
* Response to We... | Summary: The paper introduces ESPACE (Eigen Static Principal Activation Component Estimation), a technique for compressing large language models (LLMs) by focusing on the dimensionality reduction of activation tensors rather than the traditional weight-centric tensor decomposition. The method involves projecting activa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback provided, and for recommending acceptance of our paper. We also appreciate the positive comments highlighting the novelty of our approach, its solid theoretical foundation, and the promising empirical results. Here we provide answers to the concerns and quest... | Summary: The paper introduces a novel technique for compressing large language models (LLMs) by reducing the dimensionality of activation tensors. The ESPACE method differs from traditional weight-centric compression approaches by focusing on activation tensors instead. ESPACE projects these activations onto a pre-cali... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback provided, and for recommending acceptance of our paper. We also appreciate the positive comments provided with respect to the novelty, technicality, and clarity of our work. Here we provide answers to the concerns and questions raised:
* Response to Weakness ... | Summary: This paper introduces ESPACE, a method that reduces activation in models via tensor decomposition, thereby aiding in the reduction of model size and GEMM latency.
Strengths: - The paper is easy to follow;
- The method is simple but efficient.
Weaknesses: - Wikitext-103 is a specific type of language dataset ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback provided. We provide detailed answers to the reviewer below:
* Response to weaknesses #1 and #4 on Wikitext being an insufficient benchmark for empirical results and the need for a more comprehensive set of validation metrics.
* * The reviewer is correct in... | Rebuttal 1:
Rebuttal: Dear reviewers,
We would like to thank you for the useful feedback to our work. We’ve addressed every concern and question raised in the individual responses. In this common response we would like to emphasize a few points.
First, it seems that some parts of the paper were missed by some reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization | Accept (poster) | Summary: The paper introduced a new parameter-free algorithm based on forward-backward splitting technique and variable metric for decentralized learning problems for convex locally smooth functions. Convergence guarantee with favorable rate and analysis are provided.
Strengths: 1. The paper proposed the first paramet... | Rebuttal 1:
Rebuttal: We thank the Referee for reviewing our work and the positive assessment on the novelty of the paper. Our reply to her/his comments/questions follows.
1. **"The proposed algorithm is complicated":** The proposed algorithm has comparable communication cost (step S.1 and S.2) and computational co... | Summary: This paper proposed a parameter-free method for decentralized learning and showed that the method converges to the optimal solution linearly without hyperparameter tuning.
Strengths: This paper proposed a novel decentralized method and the convergence rate is analyzed under the general setting.
Weaknesses: 1... | Rebuttal 1:
Rebuttal: We thank the Referee for her/his comments, which will help us to clarify some parts of the paper as well as improve the revised version. Our detailed reply follows.
1. **The reviewer thinks that the results in Corollary 4.1 require hyperparameter tuning"**: We apologize for this misunderstanding... | Summary: This paper studies adaptive parameter determination in decentralized optimization. It is a meaningful and interesting topic to investigate. They propose a decentralized method to solve consensus optimization, develop an adaptive parameter strategy, and show the linear convergence of their algorithm.
Strengths... | Rebuttal 1:
Rebuttal: We thank the Referee for the review. We kindly disagree with her/his assessment, which is an oversimplification and trivialization of our contributions, missing the challenges our work addresses. Details follow.
1)**About the global min-consensus:** The Referee's sole concern is the presence of ... | Summary: This paper introduces a new algorithm for decentralized optimization. The main advantage over previous work in this domain is that it allows adaptive stepsize selection (via backtracking) that is independent of the properties of the functions being minimized. The analysis of the algorithm recovers the linear... | Rebuttal 1:
Rebuttal: We thank the Referee for reviewing our paper and her/his positive assessment. We are glad that she/he recognized the major novelty of the proposed approach, i.e., the novel operator splitting technique that naturally leads to local stepsize adaptation.
Our reply to her/his questions follows.
1... | Rebuttal 1:
Rebuttal: We thanks all the Referees for reviewing our paper and their feedback and suggestions. We did our best to address any concern in the individual reply, which we refer to for details. Below we only discuss the new added experiments.
A common request from multiple Referees has been expanding the ex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction | Accept (poster) | Summary: The paper explores a method for estimating the transition matrix from multiple sequence alignments (MSAs). It utilizes a phylogenetic tree model, which is parameterized by the transition matrix and site rates. To estimate the transition matrix via maximum likelihood, an alternating optimization method is emplo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Please find our response below:
> The idea of simplifying the trees by partitioning the MSA into pairs of similar sequences is an approximate representation of the cherries in the tree. Since the partitioning algorithm is a greedy one, it may overlook the ful... | Summary: The paper proposed a fast method for phylogenetic estimation from MSA called FastCherries, this method significantly speeds up the computational process with high accuracy. This method was demonstrated to be orders of magnitude faster than existing methods while achieving similar statistical efficiency. Furthe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We respond below:
> The paper lacks a comparison to other methods in terms of computational resources, such as memory usage. Could the author provide some results?
Our method uses linear space; the logarithmic factors in the computational runtime come from d... | Summary: The authors devise a fast and accurate phylogenetic inference algorithm based on CherryML. Their scalability allows them to fit flexible models to large protein families, prohibitively expensive for previous methods. As an example, they estimate site-specific substitution rates and use these rates to predict t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Please see our response below:
> The authors state "The first probabilistic model of protein evolution was proposed by Whelan and Goldman". It would strengthen the paper to better cite the models that Whelan and Goldman were inspired by.
Thank you. We will ... | Summary: This paper introduces a new method for estimating amino acid substitution rate matrices from multiple sequence alignments, speeding up computation by orders of magnitude. The method, called SiteRM, outperforms traditional methods and large protein language models in variant effect prediction, showing its speed... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We respond below:
> while the paper demonstrates the effectiveness of SiteRM in variant effect prediction, further exploration of its applicability to other evolutionary biology tasks or datasets could further understand its capabilities and limitations.
We ag... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to carefully read our manuscript and provide thoughtful feedback. Different reviewers have asked different interesting questions, to which we have replied individually; we will also clarify them in the final version of our paper. The only major critic... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning the Infinitesimal Generator of Stochastic Diffusion Processes | Accept (poster) | Summary: This paper proposes a relevant and sound approach for learning self-adjoint SDE generators via operator learning techniques.
The paper includes a compactification, a novel prior knowledge inclusion, and first-of-its-kind statistical learning guarantees that extend the known ones from discrete Markov processes... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
1. Thank you for emphasizing the realistic setting of imperfect partial knowledge. This motivated us to _... | Summary: The paper considers a time-homogeneous Stochastic Differential Equation (SDE) with known diffusion part and known or unknown drift. The problem is to find properties of this equation from known data, in particular, to find a (low-rank) representation of its infinitesimal generator (IG). For this purpose, the r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
### Major:
- Indeed, the topic of learning IG of a stochastic process with kernel-based methods, and, i... | Summary: In this paper, the authors consider the problem of learning the infinitesimal generator a Stochastic Diffusion Process (SDP). Compared to existing approaches such as [1] they tackle the unbounded nature of the generator by introducing a novel statistical framework which is based on the Dirichlet form associate... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
__Main contributions and broader impact.__ In the general reply we tried our best to provide more detail... | Summary: This paper discusses learning the generator of stochastic diffusion processes in reproducing kernel Hilbert space. In particular,
section 2: background on the generator, Dirichlet form, energy, learning in RKHS, empirical risk in the Hilbert-Schmidt norm
section 3: introduce an energy-based risk functional for... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
Thank you for pointing this out. Indeed, clearer motivation beyond the Dirichlet form should help the re... | Rebuttal 1:
Rebuttal: We wish to thank all reviewers for their insightful evaluation of our paper. We appreciate all their comments and remarks, which we will incorporate in our revision. Before addressing each review in detail, we would like to point out some general remarks that apply to all of them.
## __Assumpti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation | Accept (poster) | Summary: This paper proposes OmniTokenizer, a tokenizer that can be used to tokenize both image and video data. To train the OmniTokenizer, they devise an architecture that decouples spatial and temporal axis, offering efficiency. Next, they perform a progressive training scheme that initializes from image-only trainin... | Rebuttal 1:
Rebuttal: 1. My main weakness point with the paper is the lack of comparisons to the relevant baselines. I think MagVITv2 is the closest baseline to your method, but there is no in-depth comparison to it. I can see in Table 4 that MagVITv2 was reported in NAR setting while yours is in AR setting. I think it... | Summary: This paper proposes OmniTokenizer, a transformer-based visual tokenizer model that processes both image and video input and achieves state-of-the-art reconstruction quality. OmniTokenizer's core designs are a decoupled spatial-temporal attention mechanism and a progressive training schedule. Two OmniTokenizers... | Rebuttal 1:
Rebuttal: 1. Although there are extensive quantitative results on image/video reconstruction and generation, the qualitative comparisons are insufficient. Much more video reconstruction/generation comparisons can be provided in the supplementary materials to better demonstrate the effectiveness of OmniToken... | Summary: The paper introduces OmniTokenizer, a transformer-based tokenizer designed for both image and video tokenization within a unified framework. This tokenizer employs a spatial-temporal decoupled architecture, using window attention for spatial and causal attention for temporal modeling. The approach leverages a ... | Rebuttal 1:
Rebuttal: 1. The model does not compare with magvitv2 thoroughly, which should be a strong baseline in terms of reconstruction and generation.
=>Answer: Please refer to question 2 of our global rebuttal.
2. The architecture for single frame mostly follows ViT-VQGAN and the improvement comes from... | Summary: The paper introduces OmniTokenizer, a transformer-based image-video tokenizer designed for visual generation tasks. It adopts a spatial-temporal decoupled architecture, integrating window attention for spatial modeling and causal attention for temporal dynamics, allowing it to process both image and video data... | Rebuttal 1:
Rebuttal: 1. The novelty is limited.
=>Answer: Please refer to question 1 of our global rebuttal.
2. The spatial-temporal decoupled architecture is a key aspect of OmniTokenizer. A deeper dive into the role and impact of different attention mechanisms on the model's performance could offer more clari... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable comments. We are happy the reviewers think the progressive training strategy is **intuitive** [Review pXcq, Reviewer ZuXX] and **effective** [Review hbMY, Reviewer ZuXX]. Below we respond to the common concerns of reviewers.
1. Novelty of th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees | Accept (poster) | Summary: This paper considers solving FDEs (Functional Differential Equations) using neural networks. The difficulty of solving FDEs compared to PDEs (Partial Differential Equations) lies in the fact that the input space is infinite-dimensional. In this case, the author employs Cylindrical Approximation to reduce the i... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 33cu for their insightful comments and recognition of our paper's strength.
Thank you for pointing out the related papers; we enjoyed reading them and will include them in the Related Work section.
Please note that, just in case, their focus is functional approximation, not ... | Summary: The power of PINNs is leveraged to solved high-dimensional PDEs which are obtained from FDEs through the cylindrical approximation. FDEs are computationally expensive to learn, and PDEs are more well-studied in the context of learning. This is a novel work in making FDEs more accessible for computation since t... | Rebuttal 1:
Rebuttal: We greatly appreciate Reviewer LXGL for their time and recognition of our paper's strength.
> The authors presented their weaknesses/limitations in a section. They could try to expand their suites of experiments to the applications outlined in the appendix e.g. Navier-Stokes modeling, etc.
This ... | Summary: In this paper, they used cylindrical approximation to transform the functional differential equation (FDE) into a higher-dimensional PDE in order to solve it using PINN.
Strengths: This is a relatively new and intriguing topic in the field of functional differential equations (FDEs), aiming to solve them usin... | Rebuttal 1:
Rebuttal: We greatly appreciate Reviewer 5Xf5 for their time and invaluable comments.
We will incorporate all the suggestions into our manuscript.
> I'm curious about the range of $\boldsymbol{a}$ in the experiments.
It is provided in lines 1456-1458.
> Aside from FTE and BHE, I'm curious if there are mo... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Retrieval: End-to-End Information Retrieval with One Large Language Model | Accept (poster) | Summary: This paper introduces Self-Retrieval, an end-to-end IR system driven entirely by a single LLM. This model integrates all essential IR functions—indexing, retrieval, and reranking—into the LLM's architecture. By internalizing the retrieval corpus through self-supervised learning, the model transforms the retrie... | Rebuttal 1:
Rebuttal: Thanks for your time and insightful comment. We would like to address your questions in turn.
### About NQ@40k in our experiments
Currently, the most common and widely used retrieval method is dense retrieval, which primarily focuses on passage-level retrieval ([2, 3]). Consequently, we constructe... | Summary: This paper proposes Self-Retrieval, an LM that retrieve, rerank passages, and generate answers using a single model. For the retrieval task, it adopts generative retrieval and directly generates passage text. For reranking, it utilizes the generation probability as the relevance score. For answer generation, i... | Rebuttal 1:
Rebuttal: Thank you for your suggestions on our work. Here is our response to your concerns.
### Analyze the latency
We compared the efficiency of Self-Retrieval with SEAL in the General Response Table 1. Our generation process has the following characteristics:
1. Early Stop Mechanism: Our method includ... | Summary: This paper introduces Self-Retrieval, a new generative retrieval architecture. Self-Retrieval first memorizes the corpus into LLM's parametric knowledge using self-supervised training. Given a query, it generates the target document with constrained decoding, then re-assess document by decoding if the documen... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We appreciate your feedback and will address each of your points in turn.
### Experiments on non-wikipedia datasets
Please refer to the General Response.
### Experiments on untitled documents
With untitled documents or titles of poor quality, Self-Retriev... | Summary: The paper proposes an approach of self-retrieval, which uses the probability of generation of the passage as the ranking criterion. To limit the generation to the existing passages, a trie-structure is used, forcing the generation to produce the existing passages.
The experiments compared the method with sever... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments! We are very glad to address your concerns one by one.
### Details of Trie
The trie is pre-built based on the corpus before retrieval. Most documents/passages only share a small common prefix. The LLM stops generating once it has produced enough tokens to determin... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful comments and valuable suggestions. In our responses, we provided details of the Trie structure to Reviewer xB99, additional experiments on untitled documents and retrieval + reranker baselines to Reviewer g3kT, along with clarifications regardi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users | Accept (poster) | Summary: In this paper, the authors propose a new framework called Automatic Red-Teaming (ART) designed to identify safety risks in text-to-image models. The framework leverages both vision language models (VLMs) and large language models (LLMs) to establish connections between unsafe generations and their prompts. ART... | Rebuttal 1:
Rebuttal: **1. The ART framework is complex, involving multiple stages of fine-tuning and iterative interactions, which might be challenging to implement and reproduce. The framework heavily relies on pre-trained models, which might not be accessible or practical for all researchers or developers.**
**A:**... | Summary: This work proposes an automatic red-teaming frame to evaluate the safety of generated images for text-to-image models. The proposed method adopt a multi-agent framework. It consists of a LLM as Writer Model, a VLM as Guide Model, a set of toxic text and image detectors as Judge Models. The safe prompts likely ... | Rebuttal 1:
Rebuttal: **1. The proposed evaluation heavily depends on the effectiveness of two sets of detectors. Have authors done any verification on their accuracy? For example, use the human-inspected Adversarial Nibbler to do a sanity check.**
**A:** Thanks for your suggestions. We would like to clarify that the ... | Summary: This paper introduces a novel Automatic Red-Teaming framework to evaluate the safety of text-to-image models systematically which also investigate the benign prompts in addition to adversarial prompts. It shows that current text-to-image models are toxic in fact. This paper also introduces three large datasets... | Rebuttal 1:
Rebuttal: **1. The implementation of ART involves multiple components, including language models and vision language models, which may make the inference slow. How long does ART take? Is the running time much slower or similar to that of previous methods?**
**A:** As we stated in Appendix L, the time cost ... | Summary: The paper proposes a safety evaluation framework for text-to-image models. This is motivated by protecting benign users from unintentional harmful content generated by these models. In particular, the method combines vision language models and LLMs to identify and mitigate unsafe generations that are likely tr... | Rebuttal 1:
Rebuttal: **1. The proposed method consists of multiple pretrained large models and their interactions, which may complicate its implementation. Simplifying the framework or providing more detailed implementation guidelines could help.**
**A:** Thanks for your valuable suggestions. Our method primarily use... | Rebuttal 1:
Rebuttal: Thanks for all your comments and valuable suggestions. We attach the results on Midjourney in the pdf file.
Pdf: /pdf/98683c4fbc19fee516639c8f0a7ac3306e545bf9.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling | Accept (poster) | Summary: This paper proposes a locally constrained compact point cloud model (LCM), which consists of a locally constrained compact encoder and a locally constrained decoder based on Mamba. The encoder replaces the self-attention layer with a local aggregation layer, thus achieving a perfect balance between performance... | Rebuttal 1:
Rebuttal: ### **@Q1 - Figure quality and font size.**
Thank you for your feedback regarding the quality of the figure and formatting in our paper. We appreciate your attention to these details, as they are crucial for a clear presentation. We acknowledge that the fonts in Figures 4 and 5 are too small and ... | Summary: This paper first proposes a locally constrained compact encoder, which leverages static local geometric constraints to aggregate the most relevant information for each patch token, achieving an elegant balance between performance and efficiency. Moreover, this paper also proposes a locally constrained Mamba-ba... | Rebuttal 1:
Rebuttal: ### **@Q1 - Comparison with PointGPT-L.**
Thank you for your question. We understand the importance of comparing our method with all relevant benchmarks, including PointGPT-L, to provide a comprehensive evaluation.
PointGPT[1], a point cloud pretraining approach published at NeurIPS 2023, propos... | Summary: To address the issues of quadratic complexity and constrained decoders in existing masked point modeling methods based on Transformers, this paper proposes a locally constrained compact point cloud model. First, to tackle the complexity problem, the paper presents an observational experiment with top-K attenti... | Rebuttal 1:
Rebuttal: ### **@Q1 - The computational cost of KNN.**
Thank you for your question and for highlighting an important aspect of our model's design.
In fact, the computational cost of KNN in LCM is very low. While Figure 5 may give the impression that KNN is performed at each layer, our method actually req... | Summary: This paper proposes LCM, a locally constrained compact point cloud model, to improve the efficiency and performance of point cloud processing tasks. It consists of a locally constrained compact encoder and a locally constrained Mamba-based decoder. A locally constrained compact encoder utilizes the proposed lo... | Rebuttal 1:
Rebuttal: ### **@Q1 - Static Importance Perception & Long-Range Dependency Modeling & Limitations.**
Thank you for your insightful question. Our current model does have limitations in handling dynamic importance perception and long-range dependency modeling. Our design prioritizes efficiency, which can be ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and their thoughtful comments and questions. We are pleased to find that:
* All reviewers unanimously appreciated the novelty and effectiveness of our work, recognizing it as a creative solution that revolutionizes the point cloud self-supervise... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Embedding Dimension of Contrastive Learning and $k$-Nearest Neighbors | Accept (poster) | Summary: This paper establishes various asymptotic upper (and some lower) bounds on the dimensionality required so that there is an embedding satisfying various types of ordinal constraints on the pairwise distances. The constraints are either triplet constraints or k-nearest neighbor relations. The paper considers dif... | Rebuttal 1:
Rebuttal: We thank the reviewer for your thoughtful, detailed, and precise comments. Please see our reply below:
### W1 Missing conclusion / future work section
Thanks, please see the global response above for a proposed conclusion / future work section.
### W2 Exact $d=\\sqrt{m}$ in experiments
Thank y... | Summary: This paper discusses the number of compressed dimensions that satisfy the triplet or kNN constraints. In particular, theoretical results are derived for various distance measures as well as $L_2$.
Strengths: The paper obtains intuitive and useful results. To this end, it introduces the concept of arboricity a... | Rebuttal 1:
Title: Rebuttal
Comment: Thank you for your comments.
> ... Whether the embedding is actually obtained ...
> To compute $F$, what computation do we really need?
Yes, all embedding constructions are explicit. The construction for $\\ell_2$ is in Section 2, and the construction for k-NN embeddings is in Sec... | Summary: The paper "Embedding Dimension of Contrastive Learning and k-Nearest Neighbors" investigates the minimum embedding dimension required for representing datasets labeled with distance relationships in l_p-spaces, focusing on contrastive learning and k-Nearest Neighbor (k-NN) settings. The main findings suggest t... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive feedback, thoughtful review and the suggestions.
### Complexity of Proofs
Thanks a lot for pointing this out, we’ve substantially updated the exposition by adding illustrations of the key concepts (you can find some examples in the *global response* to all reviewe... | Summary: Paper studies the embedding dimension of contrastive learning and kNN problem.
In the first, we are given n points along with some constraints of the form (x,y, z1, z2, .., zm) which mean that x is closer to y and far from z1 z2 zm. Indeed, y is said to be positive label for x and z1, z2, zm are negative exma... | Rebuttal 1:
Rebuttal: Thanks a lot for your careful review. We’ve updated the experimental section to provide a more precise validation of the theoretical bounds (please the global response posted to all the reviewers above).
Regarding the downstream applications of our embeddings, the answer to this question is two-f... | Rebuttal 1:
Rebuttal: ## Additional Figures
Based on the reviews, we decided to add figures to clarify the proofs. Please see the attached PDF.
## Conclusion
As suggested by the reviewers, we will use the extra space of the final version to include the following discussion of the future work and to reiterate our fin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning from Snapshots of Discrete and Continuous Data Streams | Accept (poster) | Summary: This paper considers the problem of online learning in the setting of discrete and continous data streams. It first introduces two novel learning frameworks: the update-and-deploy setting and the blind-prediction setting. The update-and-deploy setting allows a learning algorithm to discretely query a data stre... | Rebuttal 1:
Rebuttal: Thank you for your positive comments towards the contributions made by our two learning frameworks and algorithms!
### Weaknesses
**Comment 1**: This paper is primarily a learning-theoretic paper so it's focused on establishing a concrete theory in terms of mathematical statements and proofs. Sin... | Summary: The paper studies the online learning problem where the algorithms are receiving a stream of $(X_i, Y_i)$ in which $X_i$ is an instance and $Y_i$ is the corresponding label, and in each time point $i$, the algorithm is required to make a prediction $\hat{Y}_i$, which is based on the historical data and the cu... | Rebuttal 1:
Rebuttal: Thank you for your comments regarding the strength of our paper!
### Weaknesses:
**Comment 1**: Thank you for this comment. After a revisit to Section 1.3, many of the terms such as $MB_{\mathcal{P}(H)}$ and $LD(H)$ were not properly defined. We will fix this revision to make sure that the terms ... | Summary: This paper studies mistake bounds for discrete and continuous labelled data streams in two different coupling settings between labeller and learner (update-and-deploy and blind-prediction)
Strengths: The paper is a theory paper that characterizes the learnability of pattern classes. The proof in the main pape... | Rebuttal 1:
Rebuttal: Thank you for your comments about our paper's proofs! We are glad that it was highly accessible!
### Weaknesses:
**Comment 1**: Thank you for this feedback. We agree that some structural changes can be made to the paper to make it more readable. The main highlights of this paper are to show that ... | Summary: This paper introduces a novel learning-theoretic framework for understanding online learning from continuous and discrete data streams through selective querying. The authors propose two settings: the update-and-deploy setting, where a learner updates a predictor based on queried data, and the blind-prediction... | Rebuttal 1:
Rebuttal: Thank you for your comments on the novelty and appeal of our work!
### Weaknesses
**Comment 1**: Thank you for the feedback! We agree that presenting the theoretical framework and giving a tangible construction by referring to the specific examples mentioned in the introduction will make it much ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation | Accept (poster) | Summary: The authors introduce PhyloGen, a new method that uses a pre-trained genomic language model to generate phylogenetic trees without relying on evolutionary models or aligned sequences. PhyloGen treats phylogenetic inference as a conditionally constrained tree structure generation problem, jointly optimizing tre... | Rebuttal 1:
Rebuttal: **Weakness:**
**W1: Table 5 Clarification:** Tab. 5 presents results for the DS1 dataset, not an average across eight datasets. The impact of removing KL or S has already been shown in this table.
**Figure 6 Explanation:** Fig. 6 shows ablation results for the DS1 dataset, chosen for its represe... | Summary: The paper propose phylogenetic tree inference by modeling it as a problem of conditional-constrained tree structure generation. Its goal is to jointly generate and optimize the tree topology and branch lengths. By mapping species sequences into a continuous geometric space, PhyloGen performs end-to-end variati... | Rebuttal 1:
Rebuttal: **Weakness: Formulation Clarify:**
Thank you for your valuable feedback. Regarding the introduction of the R term in Eq. 7, we state the following:
**Introduction of R:** R represents the posterior probability in the second part of the variational network. Its inclusion aims to enhance model ex... | Summary: The authors propose a new method, PhyloGEN, for phylogenetic inference. The method is able to perform end-to-end variational inference in order to jointly optimize the tree topology and the branch lengths. To achieve this, the authors propose using a pre-trained genomic language model to extract genome embeddi... | Rebuttal 1:
Rebuttal: **Weaknesses：**
**W1: Clarity in Methods:**
We acknowledge the reviewers' concerns about the presentation of the methods section. In response, we have made a thorough revision:
1. **$f_i$** enriches the node feature $i$ by integrating contributions from its children and its inherent traits. In ... | Summary: This paper presents PhyloGen, a novel approach for phylogenetic tree inference using pre-trained genomic language models and graph structure generation. PhyloGen aims to jointly optimize tree topology and branch lengths without relying on evolutionary models or equal-length sequence constraints. The method dem... | Rebuttal 1:
Rebuttal: **Weakness:**
**W1: Limitations:**
Please see the General Response.
**W2: Comparison with Recent Methods:**
We appreciate the reviewer's concern regarding the comparison with recent methods. We believe that our manuscript already includes comparisons with several of the latest approaches, spe... | Rebuttal 1:
Rebuttal: **General Response:**
We are very grateful to the four reviewers for their insightful comments, which have significantly improved the quality and clarity of our manuscript.
**Summary:**
We are pleased that the reviewers recognized the highlights of our work, including the novel framework combi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Architecture of Decentralized Expert System for Early Alzheimer's Prediction Enhanced by Data Anomaly Detection | Reject | Summary: This work introduces a novel approach to diagnosing Alzheimer's Disease using a decentralized expert system. This system leverages blockchain technology and Federated Learning to enhance data privacy and manage large volumes of MRI data effectively. The key innovation lies in integrating these technologies to ... | Rebuttal 1:
Rebuttal: - While the system shows promising results, the article does not provide extensive comparative data against traditional centralized systems or other decentralized approaches, which could validate its superiority more robustly. This work lacks of comparative performance data.
- Response: The curr... | Summary: The authors assume that applying blockchain platforms to combine datasets for Alzheimer’s Disease and then using federated learning for multi-centralized training can improve diagnostic performance. However, the manuscript lacks technical details and experimental evidence. All descriptions are conceptual, maki... | Rebuttal 1:
Rebuttal: - What is your main contribution, a model, a framework or just a proposal?
- Response: Our main contribution is the development of a framework. This framework outlines a decentralized expert system architecture that integrates advanced technologies such as blockchain and federated learning for ear... | Summary: The paper presents a decentralized expert system designed to predict early-stage Alzheimer's Disease using AI-driven MRI analysis. The system leverages blockchain technology and Federated Learning to ensure data privacy and security while performing anomaly detection on patient-submitted data. The architecture... | Rebuttal 1:
Rebuttal: - First and perhaps the most important aspect is that the paper fails to present the real-world challenges associated with the adoption of such decentralized approaches, especially as it pertains to patients engaging with blockchain wallets and data submission interfaces.
- Response: The current ... | Summary: This paper introduces an innovative decentralized expert system designed for early prediction of Alzheimer's Disease (AD), leveraging blockchain technology and Federated Learning. Traditional diagnostic methods often result in delays and imprecision, particularly in early-stage AD detection, while centralized ... | Rebuttal 1:
Rebuttal: - The integration of blockchain and Federated Learning introduces significant computational complexity and potential delays due to off-chain processing and communication overhead. What specific measures are in place to mitigate the computational complexity and communication overhead in the decentr... | Rebuttal 1:
Rebuttal: Our paper aims to extend the current federated learning research, specifically in the context of healthcare data analysis, AD progression monitoring, privacy preservation, and practical deployment. The list of the major points and detailed comparison not only highlights the novelty of our work but... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Preference-based Pure Exploration | Accept (poster) | Summary: This work focuses on the setting of multi-armed bandits with vectorized rewards and studies the identification of the Pareto Optimal arms with a fixed confidence. Compared with existing works, this work considers a more general preference definition (i.e., induced by a preference cone) or targets at finding th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing and pointing out several notational issues to improve the manuscript. We have done a proofreading of the paper and fixed the typos and issues. We address the other concerns here.
1. **Novelty with respect to Track and Stop:** As pointed out by re... | Summary: This paper generalized the strack-and-stop style of best arm identification analysis and algorithm design to the setting of vector-valued bandit problems where the pareto frontier must be found. Novel upper and lower bounds are proposed as well as a convex relaxation of the lower bound that produces an impleme... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the strengths and novelty of the contributions along with pertinent questions regarding the computational approach. We respond to them here.
1. **Cost of Convex Relaxation:** In the setting with Gaussian bandits, as we show Theorem~2 in the paper, nothing i... | Summary: The paper considers a generalization of the fixed confidence best arm identification. Specifically, in this setting we have a collection of arms each having a mean vector associated with it. Further, we have an ordering that establishes a preferences over the vectors. The objective is to identify the Pareto fr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for pointing out several avenues for improving our work. We address the concerns below.
**Editorial edits:** We have revised our manuscript to rectify all the errors and typos as mentioned by the reviewer.
**Identification of the Pareto Front:** This paper is ... | Summary: This paper studies the preference-based pure exploration problem for bandits with vector-valued rewards and a set of preferences imposed over them. The objective is to identify the most preferred policy over a set of arms according to the preferences induced on the reward vectors by an ordering cone C. The tec... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for appreciating the strength of our contributions. We have rectified the errors, typos, and notational issues in our revised manuscript.
---
Rebuttal Comment 1.1:
Comment: Many thanks for the response. I like to maintain my score. | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for providing several valuable comment to improve the presentation and writing of our manuscript. We have incorporated those comments and are uploading a revised version here.
Pdf: /pdf/cb8363b0cdb90fdd4c2d421cece4027034c61146.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training | Accept (poster) | Summary: The paper introduces the Multi-Agent Sparse Training (MAST) framework to address computational overhead in Multi-agent Reinforcement Learning (MARL) by enhancing value learning through the Soft Mellowmax Operator with a hybrid TD-(λ) schema and a dual replay buffer mechanism. MAST achieves significant reductio... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! Please find our responses to your comments below. We will be happy to answer any further questions you may have.
### Weaknesses
> **W1**: This work focuses only on one benchmark (StarCraft II), applying it to other benchmarks can give us a ... | Summary: The paper presents a significant advancement in the field of MARL by introducing the MAST framework which aims at improving the Reliability of Training Targets and Improving the Rationality of Sample Distribution. Overall this paper is well-written and easy to follow, on a very interesting research direction w... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! Please find our responses to your comments below. We will be happy to answer any further questions you may have.
---
### Weaknesses
> **W1**: An ablation study would be good to tell to how much extent the 2 designs are contributing to th... | Summary: This paper introduces dynamic sparse training (DST) to the Deep Multi-Agent Reinforcement Learning (MARL) settings for the first time in the literature. Furthermore, it shows that applying directly DST algorithms to MARL does not lead to optimal results. Consequently, it proposes a new framework named Multi-A... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! Please find our responses to your comments below. We will be happy to answer any further questions you may have.
---
### Questions
> **Q1**: While the theoretical reduction in terms of computational resources is impressive, can you commen... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning | Accept (poster) | Summary: This paper proposes to solve parametric PDEs with the introduction of context parameters. The low-rank design allows for rapid adaptation to unseen conditions, and the experiments show comparable or slightly better performance of the method than baselines.
Strengths: 1. The utilization of context parameters f... | Rebuttal 1:
Rebuttal: We're thankful for the reviewer's feedback and have addressed the raised concerns below.
### The main architecture of the proposed model is similar to LoRA.
The paper primarily focuses on the general adaptation framework rather than the LoRA implementation. The key points are:
- Classical ERM t... | Summary: This paper focuses on the PDE solver generalization and proposes the GEPS model based on a low-rank-based meta-learning strategy. Specifically, the authors config the PDE solver as a low-rank framework, where different environments correspond to domain-specific diagonal matrices. During adaption, only the diag... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and recommendations to improve the paper. We have addressed the raised concerns below, including additional experiments as suggested.
### Utilizing shared backbone and domain-specific weights for multi-environment or multi-task learning is a widely... | Summary: In this paper, we propose a meta-learning method called GEPS, which utilizes an adaptation approach to generalize a PDE solver to unseen environments. This method demonstrates better generalization compared to classical ERM approaches.
Strengths: This model can adapt to a new environment \( f^e \) in one shot... | Rebuttal 1:
Rebuttal: We're thankful for the reviewer's helpful feedback and have addressed the raised concerns below.
### Section 3.2 provides the motivation for the proposed method, but I am curious about how this motivation relates to the methods proposed in Sections 3.3.2 and 3.3.3:
The main messages are:
- Clas... | Summary: The authors propose GEPS, a method for meta-learning neural solvers for parametric PDEs. Similarly to gradient-based meta-learning methods such as MAML, the authors formulate the problem as a two-step optimization problem: the goal of the first step is to learn a model that's able to easily adapt to new enviro... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and have addressed the raised concerns below.
### More experiments investigating the physics-aware component of the model would be illuminating:
For the hybrid physics-ML setting, we make two assumptions:
- the physics is only partly known and sha... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their comments and suggestions. We are encouraged that they found our method clear and well-written (Reviewer 3TLD, Reviewer m65H). We particularly appreciate that you found we tackle an important challenge (Reviewer m65H) and provide a straightforward yet strong and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Skill-aware Mutual Information Optimisation for Zero-shot Generalisation in Reinforcement Learning | Accept (poster) | Summary: Proposes a new contrastive learning objective for use in meta-RL with contextual policies. The new objective incorporates a notion of skills into the mutual information estimate. Theoretical and empirical evidence in support of the superiority of the proposed method is presented.
Strengths: - The paper is cle... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer's professional review work on our paper. We would like to express our sincere appreciation for the reviewer's recognition of our work, especially regarding the writing and the motivation behind our work. We also greatly appreciate the acknowledgement of our th... | Summary: This paper introduces Skill-aware Mutual Information (SaMI) and Skill-aware Noise Contrastive Estimation (SaNCE) to enhance zero-shot generalization in reinforcement learning (RL). The authors address the challenges faced by Meta-Reinforcement Learning (Meta-RL) agents in tasks requiring different optimal skil... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback, especially your comments on the scalability of the method and variability in skill definitions. We are also grateful for the reviewer's recognition of the novelty and contributions of our work, as well as the acknowledgement of our empirical validation and pr... | Summary: Learning generalizable skills across different tasks is desirable in Reinforcement Learning. Some methods embed task information into a context latent space which is then used to train policies. This paper proposes a new objective Skill-aware Mutual Information (SaMI) that incentivizes the distinction of conte... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work. We fully agree with the reviewer that generalising RL agents across tasks is an important topic that will enhance the field of RL, and we are very grateful for your positive feedback on it. We appreciate your acknowledgement of our presentation... | Summary: The paper proposes an alternative approach to infoNCE for learning contrastive task representations by using the structure of skills in a meta learning setting. In doing so, the algorithm samples more negatives within the same task as the positive (but with lower reward), in a procedure that somewhat resembles... | Rebuttal 1:
Rebuttal: Thank you for the reviewer's feedback and for acknowledging our empirical results and recognising the importance of our research contributions to scaling contrastive learning methods for control. We hope our clarifications, corrections and analysis can convince the reviewer to reconsider their sco... | Rebuttal 1:
Rebuttal: Thank you very much for the reviewer's feedback. We appreciate the time and effort that the reviewers dedicated to providing feedback on our manuscript, and are grateful for the insightful comments and valuable improvements to our paper. We have addressed each of the reviewers' comments individual... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents an interesting way to deal with the "log k curse", that is novel while being sensible. The results seem promising, even though their presentation needs improving, and I believe it can serve as foundation for many future works to build upon. Overall I think the idea and execution are solid, b... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s comments, especially the helpful comments on the presentation of experimental results. We appreciate your recognition of the novelty of our work and the potential of our method. We have made revisions to enhance the clarity and presentation of our experimental results ... | null | null | null | null | null | null |
Achievable Fairness on Your Data With Utility Guarantees | Accept (poster) | Summary: This paper addresses a significant limitation in the fairness literature, which is the use of uniform fairness requirements across diverse datasets. It proposes the YOTO framework to approximate the fairness-accuracy trade-off and reduce computational costs while existing methods typically require multiple mod... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of the quality and originality of our work. We clarify the questions raised below.
> The content in lines 121-126 that illustrates the suboptimal problem, as shown in Figure 1, is highly similar to the content in lines 61-66. This content should ... | Summary: Considering inherent accuracy-fairness trade-off in real-world scenarios with data imbalance and bias, imposing strict fairness constraint could be impractical. To address this, the paper introduces an efficient method to contextualize and accurately estimating fairness-accuracy trade-off curve for each datase... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for acknowledging the novelty and practical utility of our approach and the clarity of our writing. We respond to the questions raised below.
> Assumptions for Statistical Guarantees: The statistical guarantees rely on assumptions that may not hold in all practical... | Summary: This paper proposes a computationally efficient method to estimate the accuracy-fairness trade-off curve with the statistical guarantee given the dataset and model class. Specifically, it first adopts an existing method, You-Only-Train-Once (YOTO), to get the trade-off curve and then proposes a systematic way ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful review and for highlighting the strengths of our work, including its motivation and presentation. Below we address some of the questions raised.
> [...] given the statistical guarantee, can the authors provide a more comprehensive evaluation or ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Critic Reinforcement Learning under Partial Observability | Accept (poster) | Summary: This paper proposes a dual-critic architecture for the asymmetric RL regime where a policy deployable in partially observable settings is trained under full observability. The proposal is meant to be an improved version of Unbiased Asymmetric Actor Critic that, through its two critics, improves learning effici... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We provide the following clarifications in response to your comments.
> Weakness 1: About the insight.
>
> ... the proposed method is somewhat derivative of two other well-known methods. ... there isn't enough insight ...
**In this study, we address the iss... | Summary: The authors propose a methood that uses a weighted dual critic structure for tackling POMDPs. The dual critic structure consists of a critic that receives global state information while the other critic receives only the partially observations of the state. The authors provides some simple yet concrete analyti... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. Below, we provide detailed responses to each point.
> Weakness 1:
>
> ... harsh jump-discontinuity ... numerical instabilities.
We fully understand your concerns. **The numerical instability arises primarily from the variance of $V(h, s)$... | Summary: The paper presents Dual Critic Reinforcement Learning (DCRL), a framework designed to handle partial observability in RL. Traditional RL methods often struggle with high variance and instability when relying on full-state information. DCRL addresses this by integrating two critics: an oracle critic with access... | Rebuttal 1:
Rebuttal: Thank you for your encouraging words and constructive feedback. We appreciate your time reviewing our paper and provide point-by-point responses to your comments below.
> Question 1:
>
> There is a typo in the definition of $R(h,a)$ in line 122.
We greatly appreciate your pointing out the issues... | Summary: In this work, the authors aim to learn policies in the POMDP setting. They make use of two critics - one that has the privileged state information and one that doesn't and uses only history. Creating a dual value function which is a convex combination of these two value functions. They use this dual value func... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments and suggestions. We have revised our paper accordingly. Below, we present detailed responses to each point.
> Weakness 1 and Question 1:
>
> Even assuming that you have the state information during training is a strong assumption. You might not ... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for their insightful and constructive feedback. In response to these helpful comments, we conducted supplementary experiments in the MiniGrid environment (see the attached PDF):
1. **Figure 1**: Ablation studies on different values of $\beta \in \\{1/5, 1/3, 1/2, 2... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a dual critic architecture for learning POMDPs. One critic is the standard critic that uses the history information while the other critic is the unbiased asymmetric critic that uses the history and state information. The authors prove that using both critics reduces variance and propose a ... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments, which have significantly enhanced the quality of our manuscript. Below, we provide a point-by-point response to your feedback.
> Weakness 1:
>
> The primary weakness of this paper is novelty. While I can appreciate that the dual critic architecture reduce... | null | null | null | null | null | null |
Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing | Accept (poster) | Summary: The paper proposes a novel method, "Gaussian Mixture Domain-Indexing" (GMDI), to address domain adaptation with inaccessible domain indices. The technique improves upon prior work by modeling the domain indices prior with a Gaussian Mixture. Empirically, it has been shown that the proposed method achieves stat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and positive comments. The following is our responses to the questions mentioned in the comments.
**1. Computational Overhead: The use of CRP and dynamic mixtures increases computational overhead, which might make the method less practical for large-scale or... | Summary: This paper proposes a Bayesian Domain Adaptation method with Gaussian Mixture Domain-Indexing (GMDI) to address the challenge of inferring domain indices when they are unavailable. Existing methods often assume a single Gaussian prior for domain indices, ignoring the inherent structures among domains. GMDI mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments, the insightful questions, and helpful suggestions. The following are our responses to the questions mentioned in the comments.
**1. GMDI relies on the availability of domain identities but cannot infer them as latent variables. This limits its appl... | Summary: The paper introduces the Gaussian Mixture Domain-Indexing (GMDI) algorithm for domain adaptation when domain indices are unavailable. Unlike traditional methods that use a simple Gaussian prior, GMDI employs a Gaussian Mixture Model adjusted by a Chinese Restaurant Process, enabling adaptive determination of m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and constructive suggestions. The following is our responses to the questions mentioned in the comments.
**1. The learning loss for the proposed model is over too complex, featuring multiple conditional Kullback-Leibler divergences, which might comp... | null | null | Rebuttal 1:
Rebuttal: We thank all the respected reviewers for their detailed comments and believe that all the mentioned issues can be properly addressed in the final version of our paper. The major concerns lie in the ablation and computational cost experiments. We take this opportunity to clarify these issues and pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Humanoid Locomotion as Next Token Prediction | Accept (spotlight) | Summary: This paper proposes to use next-token-prediction as a learning objective and train a casual transformer for humanoid locomotion. Compared to previous RL-based methods, the advantage of this method is to fuse data from different sources, including mocap data, videos, RL controller, and MPC controller. The autho... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! Please find our responses below:
> As shown in Figure 8, the prediction error is correlated to the tracking error. I am curious about how much data the authors need to make these two errors correlated, since from my knowledge, the real world is much mo... | Summary: Rather than training an RL model to learn how to walk, this paper focuses on an SSL based approach towards humanoid locomotion. Using autoregressive prediction of actions and sensor data, they pre-train and deploy zero shot to the real world. The results are strong, performing better than RL at times.
Strengt... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! Please find our responses below:
**Additional details on some experiments.**
Our goal in Figure 5 is to give a qualitative sense and we use one trial for each command. The following table shows a quantitative comparison between our model, RL, and MPC ... | Summary: This paper trained an autoregressive transformer for humanoid robot walking control using four types of data. The data sources included trajectories rolled out by RL Policy and Scripted Method, existing datasets, and human poses from YouTube videos. This work successfully enabled the humanoid robot to walk in ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions! Please find our responses below:
> Claiming that training with video might be overclaiming; in fact, it only uses human poses from the video, which is quite different from training with video.
We pre-process human videos using a pre-trained transforme... | Summary: This work view the robot locomotion control problem as an next token prediction problem. A causal transformer is trained autoregressively on various sources of data. The performance on a full-sized humanoid robot's locomotion indicates that this formulation can be a promising path for complex robotic control p... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions! Please find our responses below:
> In Sec. 3.6 Model inference, the first step might not follow the statement.
Thanks for pointing this out. Indeed, at the first step we have the current observation but not the action from the previous step. We use zer... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps | Accept (poster) | Summary: This paper mainly focuses on the problem of 3D object detection form multi-view images. It introduces MVSNet-like method for depth prediction, brings out probabilistic sampling, soft weighting and pixel-aligned Gaussian Splatting to improve the correctness, robustness of depth prediction, especially with spars... | Rebuttal 1:
Rebuttal: **[R3/Q1] Feature extractor and detection head.** Yes, they are the same with NeRF-Det. We use the ResNet50 to extract image features at multiple stages, and fuse them via a feature pyramid network as the final feature map for each image. The 3D U-Net (Line 149) outputs feature maps at three scale... | Summary: The manuscript proposes MVSDet, a multiview 3d object detection model that is evaluated on indoor scene datasets. Multiview information is lifted to 3D via an efficient per-frame depth sampling scheme. The most probable top-k depth values per pixel are used to lift 2D features into a global feature volume in a... | Rebuttal 1:
Rebuttal: **[R2/Q1] No depth offset and how to use depth offset.** The first row of **Tab. R2-YYFF/Q1** of attached PDF shows the result of removing depth offset, which is worse than our model. Please refer to **R1/Q6** on how to predict and use depth offset.
**[R2/Q2] Use GT depth as supervision.** **Tab.... | Summary: This work presents a method for multi-view 3d object detection. The method computed a MVS cost volume using a few planes, it then samples k likely depth values per pixel and builds a 3d feature volume based on the voxels close to the samples depth values weighted by their confidence. Additionally, during train... | Rebuttal 1:
Rebuttal: **[R1/Q1] How to select nearby views.** We compute the Euclidean distance between the camera location of reference view and input views to find the nearby views.
**[R1/Q2] No probabilistic sampling or soft weighting.** By comparing Row 2 and 3 of Tab 3 in our paper, we can already evaluate the ef... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their affirmation of the effectiveness of the proposed probabilistic sampling and soft weighting and the use of Gaussian Splatting for multi-view 3D object detection without using ground-truth geometry as supervision. We strongly agree with **Reviewer YYFF** that the tw... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning | Accept (poster) | Summary: This paper explores the problem of Shapley-based data selection for instruction tuning. Specifically, the proposed approach is composed of three steps–clustering the target samples, Shapley-style evaluation for each clustering, and resampling from the clusters based on the Shapley scores. The paper validates i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**W.1:**
We appreciate the reviewer's concern but note a possible misunderstanding about SHED.
SHED does consider the combinatorial effects between clusters:
- **Combinatorial Effects:** SHED calculates Shapley Value (SV) for cluster proxies, r... | Summary: The paper proposed a data refinement framework that refines datasets for fine-tuning LLMs by using the Shapley value. Based on the description, SHED is able to create smaller, high-quality datasets from large, extensive datasets without human intervention or commercial LLMs. This process involves three key com... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewer for the insightful and positive feedback. We are encouraged that the reviewer found our work meaningful, novel, and effective. For the thoughtful questions and constructive suggestions. We'd like to share our responses below.
**W.1:**
We appreciate the reviewer's ... | Summary: Tuning large language models to domain tasks is a difficult challenge requiring a dataset of high quality examples. Since noisy examples can significantly degrade performance, it is important to be able to curate these small, high-quality fine-tuning datasets. The authors propose filtering using each data poin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are encouraged by the positive comments on our well-motivated, clearly presented, and highly effective work. Below are our responses to the thoughtful questions and constructive suggestions:
**W.1:**
We sincerely thank the reviewer for highligh... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sparse Bayesian Generative Modeling for Compressive Sensing | Accept (poster) | Summary: This paper introduces a new type of sparsity inducing generative prior for the inverse problem. The authors theoretically underpin our approach by proving that its training maximizes a variational lower bound of a sparsity inducing log-evidence.
Strengths: This work can learn from a few corrupted data samples... | Rebuttal 1:
Rebuttal: We thank reviewer 34q1 for the comprehensive review. In the following, we address the reviewer’s raised weaknesses and questions.
**To Weaknesses 1:** We would very much appreciate it if the reviewer could specify which comparison methods are missing in our work. While it is difficult to track al... | Summary: The authors present an elegant new approach for dictionary based compressive sensing wherein the sparsity inducing prior is tuned from data by maximizing a lower bound on the evidence. This is a new paradigm for compressive sensing which appears to improve on reconstruction error over standard approaches and d... | Rebuttal 1:
Rebuttal: We would like to thank reviewer WoRD for the positive review and the appreciation of our work. In the following, we address the reviewer’s raised weaknesses and questions.
**To Weaknesses (bullet point 1):** We thank the reviewer for this suggestion and give it serious consideration for the final... | Summary: The paper introduces a novel training algorithm for generative models used as priors in linear inverse problems, with a specific focus on compressed sensing. The authors propose a training principle that regularizes the prior to learn a sparse representation of the signal of interest, implemented in Variationa... | Rebuttal 1:
Rebuttal: We thank reviewer y1C8 for the detailed review. In the following, we address the reviewer’s raised weaknesses and questions.
**To Summary:**
We would like to point out that we do not learn $p(\mathbf{y}|\mathbf{s})$. In fact, keeping $p(\mathbf{y}|\mathbf{s})$ fixed by a pre-known dictionary is ... | Summary: The paper proposes a set of methods to learn a generative model over the sparse representations of a signal. The dictionary basis is fixed and given, and the signal of interest is assumed to be sparse in this basis. Therefore, the generative model learns to provide such sparse representations. The method lear... | Rebuttal 1:
Rebuttal: We thank reviewer K63t for the thorough review and the appreciation of the proposed method’s ability to reconstruct via a single forward operation. In the following, we address the raised weaknesses and questions.
**To Weaknesses (first paragraph):**
We agree with the reviewer that the idea of r... | Rebuttal 1:
Rebuttal: Dear Program Chair, Senior Area Chair, Area Chair and Reviewers,
We would like to thank you for taking the time to review our paper and for the valuable feedback. For our response, please refer to our point-by-point responses to each reviewer below.
You will also find attached a PDF with additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universal In-Context Approximation By Prompting Fully Recurrent Models | Accept (poster) | Summary: In-context learning has emerged as one of the puzzling properties of language models at scale. While a lot of work has been done to understand the mechanics supporting this behavior in attention-based models, much less has been done on recurrent models. Given the renewed interest in these architectures (e.g. M... | Rebuttal 1:
Rebuttal: > The review of the different recurrent architectures is inaccurate. For example, Mamba and Hawk do not have an A matrix that is constant: it is x-dependent. On top of that, this matrix is diagonal with real values, which makes the implementation of the rotation algorithm presented in 4.2 impossib... | Summary: This paper designs a new programming language LSRL that compiles to a fully recurrent structure and shows that multiple recurrent architectures including RNNs, LSTMs, and SSMs can serve as universal in-context approximators. They also show that multiplicative gating allows more numerically stable construction.... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive opinion of LSRL and our investigation into the numerical instabilities without gating.
> In the introduction, the paper mentions that RNNs can be prompted to act as any token-to-token function over a finite token sequence. However, in the actual construction,... | Summary: The paper shows that sequence-to-sequence networks like RNNs, LSTMs, and Mamba are universal in-context approximators, which was defined as the ability to compute any function with an appropriate prompt. The authors propose a programming language called LSRL which can express any language expressible by a line... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing our work's contributions towards understanding sequence-to-sequence models.
> As such, I don't see clear weakness with the work. However, I would like the authors to be more clearer on their contributions. How is the theoretical framework diffe... | Summary: The paper explores the potential of various recurrent neural network architectures (RNNs, LSTMs, GRUs, Linear RNNs, and gated architectures) to act as universal in-context approximators, crucial for zero-shot and in-context learning without fine-tuning. It introduces the LSRL programming language to facilitate... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work to be _intriguing and innovative_ and for _adding valuable insights to the field_. We would like to address their concerns.
> The paper is very dense and hard to read and follow. The construction is rather tricky. Do you have any high-level idea in the p... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback | Accept (poster) | Summary: To learn a versatile rewards essential for the downstream policy optimization, this paper introduces a novel adaptive preference loss function inspired by distributionally robust optimization (DRO).
The proposed approach incorporates an learnable instance-specific scaling factor to accommodate varying uncertai... | Rebuttal 1:
Rebuttal: We would like to thank you for your constructive comments! In the following, your comments are first started and then followed by our point-by-point responses.
**W1: The adaptive scaling is defined on a per-instance basis, which necessitates significant compute costs and hinders real-world mini-b... | Summary: This paper studies the problem of learning from preference data and introduces a learnable scaling parameter for each preference sample. The authors propose an adaptive preference loss function that assign small scaling parameters to ambiguous preferences pairs and large scaling parameters to clear preferences... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments! We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below.
**W1: The increase in flexibility of the proposed reward function is not verified. The authors claim that one of the limitations of th... | Summary: The paper identifies a limitation in RLHF methods, noting that ranking over pairs of trajectory segments often fails to capture the varying strengths of preferences across different pairs. To address this, the paper proposes a new adaptive preference loss (Ada-DPO), underpinned by distributionally robust optim... | Rebuttal 1:
Rebuttal: We would like to thank you for appreciating the feature of the proposed method and are grateful for the constructive comments! In the following, your comments are first stated and then followed by our point-by-point responses.
**W1: It seems that only a single run was conducted for the experiment... | Summary: The paper focuses on redesigning the loss function with adaptive scaling parameters to deal with the uncertainty in the preferences and thus improving the reward modeling flexibility. In the context of both robotics and NLP, the algorithm with the new loss shows improved performance.
Strengths: 1. The propos... | Rebuttal 1:
Rebuttal: We are grateful for the valuable feedback that you have provided! Please see our response per each of your concerns below:
**W1: Weak experimental ablation. Why is the performance in summarization better in Figure 4 is not extremely clear?**
The response to weak experimental ablation is included... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the valuable feedback! Before we answer to each of the reviewers individually, we list and address common concerns below:
> **1. The experimental ablation doesn't provide concrete indications of the strength of the proposed algorithm. Only a single ru... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning | Accept (poster) | Summary: This paper presents a method to improve different tasks simultaneously by combining datasets of different fidelities and focuses, utilizing scientific laws that connect the tasks. Predicting the energy and equilibrium structure of molecules is used as an example. Two forms of consistency losses are developed b... | Rebuttal 1:
Rebuttal: Thank you for your dedicated effort in reviewing our paper! We deeply appreciate your acknowledgement of our contributions, as well as informative feedback and suggestions.
## Broader applicability
Thank you for the opportunity to elaborate on this point.
Please refer to the global rebuttal (it... | Summary: The paper proposed a scientific consistency based improvement of molecule structure and energy prediction task. Upon the overall diffusion process for structure prediction, the authors incorporated energy-guided losses, which enables direct information exchange between the two tasks. Based on the two benchmark... | Rebuttal 1:
Rebuttal: Thank you for your devoted effort in evaluating our paper! We appreciate your informative feedback and solid suggestions.
## About the title and abstract
Thank you for your feedback. Please refer to the global rebuttal (item 2).
## Utility of the proposed method
Thank you for the opportunity t... | Summary:
The authors consider the multitask learning setting for molecular structure and energy prediction where the fidelity of the labels differs between tasks [1]. The authors note that they can leverage the relationship between high fidelity labels (energy) and low fidelity labels (structure) to design loss... | Rebuttal 1:
Rebuttal: Thank you for your dedicated effort in evaluating our paper! We can feel your careful read, and are grateful for your feedback and suggestions.
## About the title
Thank you for your informative feedback. Please check item 2 of the global rebuttal.
## The minor points
1. Thank you for the profe... | Summary: To handle heterogeneity in molecular data and different computational costs, authors propose to exploit molecular tasks that have scientific laws connecting them. Their results show that the more accurate energy data can improve the accuracy of structure prediction.
Authors highlight that in contrast to conven... | Rebuttal 1:
Rebuttal: Thank you for your effort in evaluating our paper. Your informative feedback are greatly appreciated.
## Consistency Beyond Energy and Structure
Thank you for the opportunity to elaborate on this point. Please refer to the global rebuttal (item 1).
## Abundancy of involved data
In consistency ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful read, informative feedback, and sincere suggestions. We provide responses to two common questions in this global rebuttal.
1. Applicability beyond energy and structure (for Reviewers B2Rx, fhSX, p18M, 3Qs1)
Within the presented content, beyond ene... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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