title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning | Accept (poster) | Summary: This paper introduces DETAIL, a novel technique for attributing and interpreting in-context learning (ICL) demonstrations in transformer-based language models. The authors propose an adaptation of the influence function, typically used in conventional machine learning, to address the unique characteristics of ... | Rebuttal 1:
Rebuttal: # Response to the review of reviewer smfw
Dear reviewer smfw,
We would like to thank you for the review and for highlighting the novelty of our approach.
We would like to address your concern as follows.
### 1. Connection between the MNIST experiment and the LLM experiment
We wish to clarify t... | Summary: This paper proposes a new method to estimate the influence of ICL examples to a query. This influence estimation can better help ICL learning, for example, reorder ICL examples and curating ICL examples.
Strengths: - The motivation is clear, and the paper overall is well-written.
- The idea to estimate the in... | Rebuttal 1:
Rebuttal: # Response to the review of reviewer oPDt
Dear reviewer oPDt,
Thank you for the review and for acknowledging the motivation and main idea of our work.
We would like to address your concerns in the following. Kindly note that all citation numbers follow the reference list from the main paper.
##... | Summary: The paper introduces DETAIL, a novel influence-function based attribution technique to estimate the influence of each example in the demonstration sequence for the given target query for in-context learning (ICL). The authors empirically validate DETAIL’s effectiveness in stylized experiments and LLMs. Additio... | Rebuttal 1:
Rebuttal: # Response to the review of reviewer EuFx
Dear reviewer EuFx,
Thank you for your review, compliment for the innovative application of the influence function in our work, and the acknowledgment of the real-world applications of DETAIL.
We would like to address your concerns as follows. Kindly no... | Summary: The paper proposes a novel attribution method for demonstrations in ICL. The proposed method takes a perspective that the transformers learn in context by formulating an internal optimizer. The influence function is approximated as an internal kernelized ridge regression, where the representations are taken fr... | Rebuttal 1:
Rebuttal: # Response to the review of reviewer ixwr
Dear Reviewer ixwr,
Thank you for reviewing our paper and highlighting the novelty of our application of 'influence' for ICL which has the potential to inspire better instruction-tuning algorithms.
We would like to address your concerns in the following... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to provide constructive reviews and insightful suggestions. We have addressed the concerns in the respective rebuttal sections. As a general response, we would like to highlight that the main contribution of this work is to provide an interpretable at... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning | Accept (poster) | Summary: Distribution shifts in the sampling of MBRL will lead to the objective mismatch between model and policy learning.
Therefore, this paper aims to reduce the influence of the distribution shift for MBRL.
It divides the confounders into $\mu_{\pi}$ and $\mu_{c}$ and then tries to capture causal representations t... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to reviewer 8enR for their valuable feedback. The reviewer acknowledges the clarity of our problem formulation and presentation, as well as the theoretical and empirical contribution of this work. We will answer the main questions below:
**Q1. (Generality o... | Summary: The paper introduces a method to improve model-based offline reinforcement learning by addressing the objective mismatch problem using causal discovery methods. This problem stems from the fact that in this setup the learning algorithm aims to solve the following two problems at once: i) accurate learning of t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful and inspiring feedback. We are glad to know that the reviewer recognizes the novelty of our contributions, the clarity of our problem formulation, and the empirical results. We answer reviewers' questions about the causal learning process throug... | Summary: This paper aims to learn causal representations in offline RL. The authors propose a novel framework that incorporates causal graph learning through bilinear MDPs to manage mismatches in model learning and policy optimization stages. The framework models two types of confounders: ones affecting state dynamics ... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to reviewer XKke for their comprehensive and insightful feedback. We are glad to know that the reviewer recognizes the clarity of our problem formulation, the novelty and technical soundness of the proposed method, as well as the theoretical and empirical res... | Summary: This paper studies challenges in Model Based rl, namely:
1. Objective mismatch between estimating the model and policy learning for value optimization
2. Confoundedness in offline RL causing this mismatch
The paper proposes a theoretically sound algorithm and experiments extensively to support their claims.
S... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to reviewer aSxm for their insightful feedback and acknowledging the novelty of our contributions in practice and theory. We provide our response to the questions below.
**Q1. (Additional Related works): Related works about theoretical offline RL using pessi... | Rebuttal 1:
Rebuttal: ## **General Response**
We thank all the reviewers for their dedicated efforts in providing valuable feedback to our work. All of the reviewers acknowledge our clarity of problem formulation and the technical soundness in bridging bilinear MDP with causality.
In this general response, we first c... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes BECAUSE, an algorithm designed to address the objective mismatch between model and policy learning in offline model-based reinforcement learning (MBRL). The algorithm first models the spurious correlations between the current state s and the current action a, as well as between the next stat... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 8KHa for their insightful and inspiring feedback. We are glad to know that the reviewer recognizes the novelty of our contributions, the clarity of our problem formulation, the theoretical contributions, and the empirical evidence that shows the advantages compared to b... | null | null | null | null | null | null |
Resolving Discrepancies in Compute-Optimal Scaling of Language Models | Accept (spotlight) | Summary: This paper aims to resolve the discrepant conclusion about compute-optimal model size drawn from (Kaplan et al., 2020) and (Hoffmann et al., 2022), two famous scaling-law papers that have guided the exploration of early large language models. The authors concluded that the discrepancies arise from last-layer F... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review, finding that our paper presents extensive and detailed experiments and provides helpful guidance on reproducing scaling law results. We address the weaknesses and questions below - the main issue appears to be a lack of clarity regarding which scaling... | Summary: This paper studies the discrepancy between the scaling laws for Kaplan et al. and Hoffmann et al. and identifies three factors behind it: last taking last layer cost into account, warmup duration, and tuning optimizer hyperparams with model scale. They show the transition from Kaplan et al. scaling laws before... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful questions and kind words. We were glad to read that you find our paper studies the important question of scaling laws thoroughly and fills an important missing piece in the literature. Below, we address the weaknesses and questions raised in the review.
**Scal... | Summary: The paper provides an explanation for the discrepancies between the scaling laws of Kaplan et al. and Hoffman et al. The authors start by reproducing the scaling laws of Kaplan at al. They then introduce incremental changes in the methodology: accounting for the last layer computational cost, setting a more re... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and for finding our paper novel, clearly written, and our experiments well-motivated, sound, thorough, and providing valuable insights. Below we address the main concern regarding the conjecture in Hoffmann et al., as well as the two additional ques... | Summary: - Identifies and eliminates discrepancies between the scaling laws of Kaplan et al and Hoffman et al via three interventions: accounting for un-embed layer FLOPs, correcting warmup hyperparameters, and tuning optimizer hyperparameters.
- Also derives scaling laws for optimal learning rate and batch size as a f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful questions and the encouraging comments, recognizing that our work conclusively identifies and mitigates the discrepancy between the two scaling laws, and appreciating the value of findings for speeding up scaling law experiments. Below, we address the questions... | Rebuttal 1:
Rebuttal: We attach a file containing the relevant figures for the rebuttal.
Pdf: /pdf/0a110bb88f3dfe62031f3a52c909cf03a43cbe1a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers | Accept (poster) | Summary: This paper explores the theoretical foundations of in-context learning (ICL) within transformer architectures, particularly focusing on how various components contribute to ICL. The study examines a two-attention-layer transformer trained on n-gram Markov chain data, analyzing its role in ICL through gradient ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see our response below.
**Gradient flow analysis**: We focus on gradient flow to simplify the theoretical analysis, as it is a common approach for understanding the training dynamics of complex models.
We believe that the results can be extended t... | Summary: In the paper, the authors analyze a simplified two-layer transformer model trained on mixtures of Markov chains, proving that gradient flow converges to a particular configuration of copier-selector-classifier network, that implements a generalized induction head mechanism. The authors further provide numerica... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see our response below.
**First layer word embeddings**:
As stated in our general rebuttal, we study a simplified model for a better understanding of the functionality of each component in detail.
Although the first attention layer does not contai... | Summary: This research focuses on analyzing in-context learning (ICL) in transformer models specifically trained on n-gram Markov chain data. It analyzes how the components of the transformer, attention layers and feed-forward networks, contribute to processing and learning from this type of data, demonstrating their r... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see our response below.
**On $n$-gram data, simplified model & split training phases**:
We remark that the $n$-gram Markov chain data assumption is much more expressive and challenging than the bi-gram data assumption in previous works (Bietti et ... | Summary: This paper theoretically explores how simplified transformers perform in-context learning (ICL) on n-gram Markov chain data. The authors analyze a two-attention-layer transformer model with relative positional embedding (RPE), multi-head softmax attention (but second layer only scalar trainable), and an FFN wi... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see our response below.
**On the model simplification**:
- *(Residual link)*
We want to clarify that the residual link is indeed contained in our simplified model via the use of disentangled transformer. As can beseen in Eqn (2.5), the second laye... | Rebuttal 1:
Rebuttal: # General Rebuttal to all Reviewers
We thank all reviewers for their valuable feedback. Below, we summarize our contributions and address common questions.
**Summary of contributions**:
To the best of our knowledge, our work is the first to show that through gradient-based optimization, a two-lay... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline RL via Feature-Occupancy Gradient Ascent | Reject | Summary: This paper studies the offline policy optimization problem, i.e. to find a policy whose value function is close to the optimal value function using offline samples. Under the assumption of linear MDP, they proposed a gradient ascent algorithm.
The sample complexity of the algorithm only depends on the featu... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our work. We particularly
appreciate your relation of our method to the actor-critic style framework.
Please see our response to your remarks and questions below.
**Weaknesses**
**Q1.** Compared to Zanette (2021), the algorithm idea is somehow simi... | Summary: This paper propose a new algorithm for offline reinforcement learning in linear infinite-horizon discounted MDP, which achieves a strong sample complexity under the weakest data coverage assumption. Moreover, their algorithm is easy to implement and computationally efficient. Their algorithm design is based on... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our work. We appreciate your feedback on our notation and will consider your suggestion in the next draft of our paper. For now, please see our response to your questions below:
**Q1.** Is there a way to output a single policy (not a uniformly sampled... | Summary: This paper provides an approach for solving Offline RL problems in domains where the underlying MDP problem has reward and transition models that are linearly realisable under a known feature map. The paper is well presented.
Strengths: 1. Some of the tricks employed in the approach are quite interesting.
2.... | Rebuttal 1:
Rebuttal: Thank you for your critical review of our work. We understand your
concerns regarding the linear MDP assumption and significance of our
contribution. Due to space constraints, we directly respond to your individual questions in the "Weaknesses" and "Questions" sections according to the provided ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the detailed feedback on our work, particularly regarding the linear MDP assumption, possible application to the stochastic shortest path problem, and comparison with Zanette et al (2021). Please see our response to your individual comments and questions below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets | Accept (poster) | Summary: The authors introduce an active learning framework for cell sorting in two photon imaging analysis. They develop a software interface to it and conduct a large scale benchmark using multiple datasets and involving multiple domain experts. They show that their method can reduce the needed manual human input to ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our manuscript. Your feedback was extremely helpful for increasing the clarity of our manuscript, and most importantly in distinguishing ourselves from published material.
**Summary** Respectfully, we do not agree with your summary of our work in se... | Summary: This paper introduces a new semi-supervised active learning algorithm, ActSort, designed to accelerate cell sorting in large-scale calcium imaging datasets. The method leverages domain expert feature engineering and a novel active learning framework, optimizing the cell sorting process with minimal human input... | Rebuttal 1:
Rebuttal: Thank you for your detailed report and excellent summary! We truly appreciate your vote of confidence in our work and the excellent suggestions for improving our technical contributions. Thanks to you, our paper now includes several diverse, convincing control experiments. To save space, we refer ... | Summary: The paper proposes an active learning framework for improving the accuracy of cell sorting in calcium imaging datasets. The method rests upon three main components: (1) Preprocessing module which uses an already existing cell segmentation algorithm and reduces the size of the dataset using a set of engineered ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our manuscript and providing insightful feedback. We have addressed your concerns regarding where ActSort stands on within the entire calcium imaging processing pipeline and explained our fit into the NeurIPS venue in the *General Respon... | Summary: In this paper, the authors develop and open-source a software, ActSort, for cell sorting of large-scale calcium imaging datasets in neuroscience which integrates domain-expert features with an active learning framework. Alongside with the software, the authors provide a new benchmarking dataset which they use ... | Rebuttal 1:
Rebuttal: We appreciate your time and effort in reviewing our manuscript and providing invaluable feedback. You caught everything in the paper, your suggestions were extremely helpful, and your review led to new experiments that increased the readability and impact of our paper! To us, this was an extremely... | Rebuttal 1:
Rebuttal: Many thanks to all the reviewers’ comments! We appreciate your time and efforts. We have addressed all concerns either with written edits to the manuscript, by performing requested experiments (see attached PDF), and/or by citations to relevant literature. Here, we would like to address some of th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What is my quantum computer good for? Quantum capability learning with physics-aware neural networks | Accept (poster) | Summary: The paper introduces a novel quantum-physics-aware neural network (qpa-NN) architecture for quantum capability learning. The model achieves error reduction in capability prediction on both experimental and simulated data.
Strengths: 1. The qpa-NN architecture incorporates quantum physics principles, which pro... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s summary of our paper and their recognition of its strengths. Your thorough review and constructive feedback have been invaluable in refining our manuscript. Incorporating your suggestions will greatly enhance its quality and impact.
In response to your feedback, we pl... | Summary: This paper introduces a neural-network-based architecture for quantum capability learning. The idea is to utilize the architecture to predict rates of errors in quantum circuits. The authors compared their qpa-NN with previous CNN-based method and elucidated their improved performance.
Strengths: 1. qpa-NN ou... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's summary of our paper and their recognition of its strengths, especially our superior performance compared to CNNs. Your review and constructive feedback will be instrumental in improving our manuscript, significantly enhancing its clarity and impact.
In response ... | Summary: The paper presents an approach to improve the state of the art in quantum capability learning, which is the task to predict the prowess for error when running a specific quantum algorithm given a fixed quantum computer. The tackle this, the authors introduce some specializations on (graph) neural networks that... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's summary of our paper and their recognition of its strengths. We are also pleased that the reviewer finds modelling a quantum computer's performance with neural networks to be an interesting and worthwhile problem. Your detailed review and constructive feedback wi... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the referees for their time and insightful comments on our manuscript. Your thorough review and constructive feedback have been invaluable in identifying areas for improvement and clarity. We are confident that incorporating your suggestions will significantly strengthen the qua... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Convolutional Differentiable Logic Gate Networks | Accept (oral) | Summary: This paper proposes a novel computational architecture for differentiable logic gate networks (LGNs), a machine learning methodology that aims to learn networks of logic gates for fast, gate-efficient inference on logic gate-based hardware. Specifically, the authors propose extensions to a prior work on differ... | Rebuttal 1:
Rebuttal: Thank you very much for your extensive and positive feedback.
We greatly appreciate that you find our result of importance to embedded and real-time machine learning applications.
Thank you also for your praises wrt. coverage of related work, our technical soundness, our ablation studies, discussi... | Summary: In this work the authors propose a convolutional-like architecture along with two novel mechanisms oriented to differentiable logic gate neural networks, making the training and inference of such networks possible in more intense tasks in context of logic gate neural networks. More specifically, the authors au... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful and positive feedback, and for appreciating that our "paper is well organized", provides a comprehensive review on methods that target efficient inference, our in-depth discussion of the benefits of logic gate networks.
Thank you also for appreciating the techn... | Summary: The presented work is a significant extension to "Deep Differentiable Logic Gate Networks" previously presented at NeurIPS 2022 [7]. Additional contributions are the support for convolutions including logic gate trees / or-pooling and residual initializations. All additions together allow to train logic gate n... | Rebuttal 1:
Rebuttal: Thank you very much for providing such extensive and positive feedback, and for appreciating our substantial efficiency improvements, and achieving the lowest latency of all SOTA baseline results.
Due to the character limit, we keep our reponses short; please let us know if you would like us to el... | Summary: This paper introduced a convolutional logic gate network (LGN), which works effectively on high-dimensional spatial images. Inspired by LGN and convolutional neural nets (CNNs), the authors proposed (1) Logic Gate Tree as convolutional kernels (2) Logical OR as the pooling layer, and (3) residual initializatio... | Rebuttal 1:
Rebuttal: Thank you so much for your positive feedback, and for appreciating the feedforward gates during initialization, our ablation studies, our experimental performance, as well as our engineering and CUDA implementations.
We appreciate that you find our "paper is very well-written".
In the following, ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and valuable comments, which have helped us improve our paper.
We respond to each of your questions and concerns individually below.
Moreover, we would like to highlight the following additions:
* We are now providing standard deviations for our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection | Accept (poster) | Summary: To detect video forensics, the authors propose an innovative Multi-Modal Forgery Representation (MMFR) to discriminate fake videos from real ones. Besides, the authors establish a high-quality dataset including videos generated from various diffusion-based algorithems to evaluate the effectiness of the propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful and helpful feedback. We appreciate all constructive comments on the novelty, clarity, and valuable contribution of the paper. We demonstrate additional experiments and answer all weaknesses and questions mentioned in the review.
> **Q1** The usage of VQVAE
... | Summary: This work focuses on diffusion model detection, a core and popular research topic recently. It identifies limitations in previous studies that concentrate on fake face and image-level detection and explores the idea of using recent LMM to detect forgery. The proposed Multi-modal Forgery Representation (MMFR) l... | Rebuttal 1:
Rebuttal: We thank the reviewer for all insightful comments and suggestions. We appreciate the appraisal of the contribution, soundness, and clarity of the paper. We provide additional experiments and answer the weaknesses and questions in the review.
> **Q1**: Comparison with other spatiotemporal networks... | Summary: This manuscript presents a new approach to detect fake videos generated with diffusion models. This approach is based on multimodality analysis and reports promising results on a database introduced by the author(s). These results are based on both frame and video levels. This work is well written and organize... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewer for the insightful and valuable feedback. We appreciate all constructive comments on the novelty, effectiveness, and clarity of the paper. We provide additional experiments and answer the weaknesses and questions mentioned in the review as follows.... | Summary: This paper proposes a method for detecting diffusion-generated fake videos using a multi-modal approach. Key contributions include:
A Multi-Modal Forgery Representation leveraging vision and language capabilities of large multimodal models
An In-and-Across frame attention to capture spatial-temporal forgery t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable and insightful feedback. We appreciate all constructive comments on the novelty, soundness, and clarity of the paper. Here, we demonstrate additional experiments and answer the weaknesses and questions mentioned in the review.
> **Q1**: Computational analysi... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their valuable comments and suggestions. We are delighted to see (a) all reviewers give positive feedback, (b) all reviewers recognize our proposed MMFR's novelty, (c) our MMDet achieves promising and generalizable performance on diffusion forgery detection (f8wk, H... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposed a video-level detection algorithm, named Multi-Modal Detection (MM-Det) for video forensics. MM-Det consists of Multi-Modal Forgery Representation (MMFR) that discriminates fake videos from real ones, In-and-Across Frame Attention (IAFA) that balances frame-level forgery traces with informa... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewer for the insightful feedback. We appreciate the constructive positive comments on the novelty, soundness, and valuable contribution of our paper. In response, we present additional experiments and address the weaknesses and questions highlighted in the review... | null | null | null | null | null | null |
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation | Accept (poster) | Summary: This paper proposes a new method for personalized image generation, decompose the personalization process into three training stages and introducing a cross-attention map regularization term.
Strengths: The manuscript is well-written.
The authors propose to address the intrinsic issues of two classical and u... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments. We response the reviewer’s concerns as follows.
>**W1: The problems and analysis of Textual Inversion and DreamBooth introduced in the Introduction section are reiterated in Section 4.1**
Thanks for this point. The current versio... | Summary: This paper proposes a method to enhance the performance of personalizing text-to-image models by appropriately combining textual inversion approach, which learns new embeddings, and DreamBooth approach, which fine-tunes model weights. They demonstrate the effectiveness of their approach through qualitative eva... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments. We response the reviewer’s concerns as follows.
>**W1: (minor) The proposed method seems quite similar to the Magicapture [1] approach in that it separates embedding learning and weight fine-tuning and conducts regularization on a... | Summary: The author proposed a method to generate high-quality personalized images. First, a textual embedding is learned, then the cross-attention layers is finetund to refine attention map during learning the textual embedding, finally, the entire U-Net is trained to capture the subject identity.
Strengths: 1. The... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments. We response the reviewer’s concerns as follows.
>**W1: The proposed method needs a costly test-time optimization to generate personalized images. The 3 stage finetuning requires expensive computation (20 minutes in A100).**
Indee... | Summary: The submission proposes AttnDreamBooth for text-to-image personalization. It addresses the limitations of existing methods, Textual Inversion and DreamBooth, by separating the learning process into three stages: embedding alignment, attention map refinement, and subject identity capture. The method aims to imp... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments. We response the reviewer’s concerns as follows.
>**W1: My main concern is comparison with new existing work. There have been several recent works after Textual inversion and Dreambooth. Some of them like SuTI (NeurIPS 2023), Instr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive and thoughtful feedback. We are encouraged that the reviewers find our idea novel (ZpWY) and interesting (hyCc), and our method reasonable and clear (U1BU). We are pleased that they consider our results to be good (U1BU) and competitive c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise | Accept (spotlight) | Summary: The paper considers the problem of PAC learning halfspaces with margin in the presence of Massart noise. The paper provides an algorithm that well-balances sample and computational efficiency. Specifically, the dependence of the algorithm on both $\epsilon$ and $\gamma$ is near-optimal.
Strengths: 1. The stud... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reading our paper and the positive assessment. We respond to each point raised by the reviewer below.
>(Weakness 1): Results might be somewhat weaker than presented (see questions), and some phrasings in this context are too vague (for example "t... | Summary: The paper considers the problem of PAC-learning $\gamma$-margin halfspaces under $\eta$-Massart noise. The paper provides an efficient algorithm achieving error $\eta+\epsilon$ with sample complexity $\tilde{O}(1/(\epsilon^2\gamma^2))$. Since previous work provided evidence that an inverse-quadratic dependence... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reading our paper and the positive assessment. We respond to each point raised by the reviewer below.
>(Question 1): The authors claim that the computational complexity of the algorithm is linear in the samples. However, it seems to me that the com... | Summary: This paper studies the problem of PAC learning $\gamma$-margin halfspaces under Massart noise: to PAC learn any distribution $D$ such that there exists $w^*$ with the bounded norm of $1$ that has a margin of at least $\gamma$, i.e. $\mathbb{P}_{(x,y)\sim D}( |\langle w^*,x \rangle| \geq \gamma)=1$ and for ever... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper and the positive feedback. | Summary: The paper studies the problem of learning a $\\gamma$-margin halfspace with $\\eta$ Massart noise and provides the first computationally efficient algorithm having 0-1 error $<= \\eta + \\varepsilon$ with sample complexity $O(1/\\gamma^2\\varepsilon^2)$ which nearly matches the information theoretic bound of $... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the provided questions. We respond to each point raised by the reviewer below.
> (Weaknesses 1):Result is specific to a particular noise model and it is not clear if the techniques are more broadly applicable.
*Response:* We would like to poin... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, effort, and feedback. We are encouraged by the positive comments of reviewers, and that our paper was appreciated for the: (i) **significant contribution** (SyXT, JiXV, 2xNf); (ii) **technically novel** (g5LR,SyXT) and (iii) **writing quality** (g5LR, JiXV, ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper essentially resolves the *computational sample complexity* of learning $\gamma$-margin halfspaces under the Massart noise model. Here, computational sample complexity refers to the number of samples required for polynomial-time algorithms, as opposed to general statistical estimators which may be co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper and for the positive feedback. Below we provide specific responses to the points and questions raised by the reviewer.
> (Weaknesses 1): The expression "(vector) v is independent of w" (line 174, 176) is confusing. I think it's ... | null | null | null | null | null | null |
Chain-of-Thought Reasoning Without Prompting | Accept (poster) | Summary: The paper investigates the inherent capabilities of LLMs to generate CoT reasoning paths. This study introduces CoT-decoding, a method that explores alternative top-k tokens in the decoding space to uncover reasoning paths without any specialized prompting. The findings indicate that the presence of a CoT reas... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback!
> The primary contribution of the paper appears to be a method for selecting a decoding path from the top-k generated paths. While this approach is useful, it lacks novelty and significant impact.
To clarify our contribution, our first finding is that LL... | Summary: The paper explores the inherent reasoning capabilities of large language models (LLMs) without the need for explicit prompting. By altering the decoding process to consider top-k alternative tokens, the authors reveal that Chain-of-Thought (CoT) reasoning paths can emerge naturally. This approach bypasses the ... | Rebuttal 1:
Rebuttal: Thank you for the feedback!
> Can the authors provide more detailed comparisons with other state-of-the-art prompting and decoding methods? The paper could benefit from a more detailed comparative analysis with other decoding and prompting strategies to contextualize its contributions better.
- ... | Summary: The paper investigates an innovative approach to eliciting chain-of-thought (CoT) reasoning from pre-trained large language models (LLMs) without the need for explicit prompting techniques, which typically require intensive prompt engineering and can obscure the model's inherent reasoning capabilities. Instead... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review!
> The method involves generating multiple decoding paths and evaluating them to identify the most confident reasoning trajectory, which could be computationally expensive, especially when applied at scale or in real-time applications.
Thanks for the feedback.... | Summary: The paper investigates the intrinsic reasoning capabilities of LLMs without relying on prompting techniques like few-shot or zero-shot prompting. The study introduces an alternative approach by altering the decoding process, specifically by exploring the top-k alternative tokens rather than following the stand... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review!
> Does the limitation of branching only at early decoding stages restrict the flexibility and applicability of CoT-decoding? Would exploring branching at later stages in the decoding process improve overall performance?
Thanks for the question. We do have a ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active, anytime-valid risk controlling prediction sets | Accept (poster) | Summary: This paper proposes a probabilistic strategy for stream-based active learning which allows for construction of anytime valid prediction sets. The strategy stems from maximizing the variance process of an e-process, which is also used in designing the prediction sets. Relevant theoretical guarantees (validity a... | Rebuttal 1:
Rebuttal: Our responses to the highlighted weaknesses are as follows.
- **Novelty of e-processes.** Thank you for pointing out the error in the proof of Theorem 1 --- we will correct as you have described. While we agree that betting e-process was introduced in [1] and an inverse propensity weighted form w... | Summary: The authors, in their work, extend the framework of Risk Controlling Prediction Sets (RCPS) to a sequential setting where data is collected adaptively, providing anytime-valid risk guarantees. Additionally, it proposes a framework for active labeling, which allows for selective querying of true labels within a... | Rebuttal 1:
Rebuttal: Our response to each highlighted weakness and question are as follows.
- **Notational usage and clearer introduction of concepts.** We will clean up our notational usage and introduce more unfamiliar concepts more comprehensively (e.g., risk controlling prediction sets, e-processes, anytime-validi... | Summary: The setting extends the model of Bates et al.---which provide confidence-bound type guarantees on the performance of a trained ``black-box" predictor which are parametrised, and nested with respect to a monotonic parameter $\beta$---to the online setting. The goal of the original setting is to provide risk-con... | Rebuttal 1:
Rebuttal: Here are our point-by-point responses to each of the highlighted weaknesses.
1. **More examples of the method being used in practice, along with examples of choice of estimators for the labeling policy and control variate regression.** We agree that providing more examples would illustrate the ut... | null | null | Rebuttal 1:
Rebuttal: We have made point-by-point responses to each review, and posted our rebuttals to each review below. We would also like to note the following.
1. We appreciate the suggestions and concerns the reviewers have brought up about the paper. We will incorporate their suggestions for clarification and m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention | Accept (poster) | Summary: In this paper the authors introduce ProSST, a method for training protein language models on both sequence and structure. They use a geometric vector perceptron trained with a structural denoising objective to obtain a structure encoder, and then perform k-means clustering on the embeddings of local residue ne... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the novelty and contribution of our work. Your insightful comments helped us enrich the analysis a lot. With the response below, we hope to address your concerns properly.
**Weakness:**
**W1.** In fact, we do not need to train the structure encoder and the clu... | Summary: This paper focuses on the protein representation task. ProSST introduces a quantized method to combine the information from protein structure. Additionally, the authors also propose a disentangled attention mechanism on top of the quantized structures to learn the relationship between residue and structure tok... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback on our work and provide a thorough response addressing your main concerns.
**Weakness**
**W1.** We agree that our work is not the first hybrid approach to protein language models, and we have cited works such as ESM-GearNet and LM-GVP in Section ... | Summary: The paper proposes ProSST (Protein Structure-Sequence Transformer), a novel PLM incorporating sequence and structure information. ProSST can be split into two parts: a modified version of the Transformer architecture and a quantization module. The quantization module consists of a structure encoder using a Geo... | Rebuttal 1:
Rebuttal: Thank you for your meticulous feedback. Below are our responses.
**Weakness:**
**W1.** We conducted additional experiments to analyze disentangled attention.
**Experiment 1**: We replaced all structure tokens in the test set with zeros or random numbers from a uniform distribution and re-eval... | Summary: This paper presents ProSST, a new language model for protein data that captures both structural and sequential modalities of proteins. The protein's structure is tokenized using a graph-based auto-encoder architecture, where each residue's local structure is embedded into a vector in a high-dimensional embeddi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback on our work. We would like to address your questions and concerns as follows:
**Weaknesses**
**W1.** Feeding a "default/noise" structure input alongside the actual sequence was not included in the current version, but we have a solution for it, an... | Rebuttal 1:
Rebuttal: # Rebuttal
We thank all the reviewers for their detailed comments and insightful suggestions. We have incorporated additional experiments and analyses based on the recommendations.
Here, we present a concise overview of the major enhancements that have been universally implemented, focusing on:
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting | Accept (poster) | Summary: This paper studies multi-armed bandits (MAB) with heavy-tailed losses.
In the heavy-tailed bandits literature, two common assumptions are made about the losses: either a known upper bound $u$ on (1+$v$)-th raw moments (where both/either $v$ and/or $u$ are known) or truncated non-negativity (or non-positivity) ... | Rebuttal 1:
Rebuttal: We first would like to thank Reviewer q85G for carefully reading our work and providing constructive comments, which significantly help us improve our work. We are also glad to further engage in interactive discussions with the reviewer.
> Cmt 1: Why Eq. (55) holds
Re: This is indeed a mis-calcu... | Summary: This paper considers the bandit problem for heavy-tailed losses and proposes an algorithm that achieves a nearly tight high-probability regret bounds.
The proposed algorithm has a best-of-both-worlds guarantee, i.e., it achieves nearly tight bounds in both adversarial and stochastic settings.
The proposed appr... | Rebuttal 1:
Rebuttal: > Comment 1: The proposed algorithm requires prior knowledge of the parameters $u$ and $v$. This is a weakness when compared to algorithms that are adaptive to these parameters, e.g., by Huang et al.(2022) and Genalti et al. (2024).
Re: While there are $(u,v)$-adaptive algorithms proposed in [1, ... | Summary: This work studied the adversarial bandit problem with heavy-tailed distribution. It also studied the best-of-both-world setting. It relaxed the non-negative assumption and analyzed near-optimal algorithms.
===================
I would like to keep the score after reading the rebuttal.
Strengths: 1. This wo... | Rebuttal 1:
Rebuttal: > Comment 1: Is it possible to bound the expected regret of the proposed algorithm? What is the limitation?
Re: We believe that the reviewer is asking whether it is possible to bound the stronger regret $\mathbb{E}[\bar{R}_T]$ defined in line 147 in the adversarial regime. We now state the challe... | Summary: In this work, the authors consider a best-of-both-worlds multi-armed bandits problem where the losses are not bounded and instead are heavy-tailed.
To be precise, in the stochastic setting, losses are generated from fixed distributions.
In the oblivious adversarial setting, the losses are drawn from distribut... | Rebuttal 1:
Rebuttal: > Comment 1: Both the detect-switch method and FTRL with log barrier are known to be suboptimal in the standard BOBW MAB problem with bounded losses, which means that improving upon the existing results might require a completely different approach.
Re: We agree that both log-barrier (for adversa... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning | Accept (poster) | Summary: This paper shows the potential of Reinforcement Learning (RL) for designing an effective digital agent for in-the-wild control through Graphical User Interfaces (GUIs). The proposed approach relies on the advantage of the pre-trained visual language models (VLMs) while tackling real-world stochasticity by trai... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on the paper. We provide responses to the questions raised below that we will also include in the updated version of the paper. We commit to open sourcing open-source the code, environment, checkpoint, and the data. In this rebuttal period, we provide an anon... | Summary: This paper introduces a novel autonomous reinforcement learning (RL) approach, DigiRL, for training in-the-wild device control agents. DigiRL first employs offline RL to fine-tune a pre-trained vision-language model (VLM as the agent) using stale task-specific data, and then further refines the agent through o... | Rebuttal 1:
Rebuttal: Thank you for the review! At the outset, we want to clarify our scope: our goal is to show that autonomous RL can be used to build SOTA device control agents that outperform proprietary models (Gemini/GPT-4). Our methodological contribution involves identifying and designing a good RL objective to... | Summary: This paper proposes an autonomous RL approach, RL for digital agent (DigiRL), to finetune a pretrained VLM as an in-the-wild device control agent through GUI. The authors build a parallelizable Android learning environment with VLM-based evaluator to identify the key design choices for RL. The training include... | Rebuttal 1:
Rebuttal: Thank you for your positive review and feedback on the paper. To address the raised questions, we add new results to include comparisons with **CoT based planning**, and **a state-of-the-art LLM planning algorithm for device control called AppAgent [1]**. We also provide **additional results** fo... | Summary: This paper tackles AI agent training for controlling digital devices (e.g., web navigation). The proposed framework, named DigiRL, is a 3-stage training process consisting of model pre-training, offline fine-tuning (offline RL), and online fine-tuning (online RL). To achieve this goal, the authors first build ... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on our paper. To address the main concern regarding comparisons, we provide additional results and appeal to comparisons from prior work to demonstrate that DigiRL outperforms several online RL methods. These comparisons include REINFORCE, PPO, and Q-value ba... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their feedback. We are glad that the Reviewer Xhdc thinks that there is “no major weakness” in the paper and that Reviewer zDzF thinks that “the usage of RL for designing a successful agent for device control tasks is fascinating”.
At the outset, we ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Iterative Reasoning Preference Optimization | Accept (poster) | Summary: This work proposes an iterative training algorithm that enhances a model's Chain-of-Thought (COT) capabilities in reasoning tasks by combining self-improvement with preference optimization. The algorithm employs ground truth labels as supervision signals to evaluate model-generated responses, which are then in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
Weakness 1: Correct; we trained using IRPO by leveraging GSM8K/MATH/ARC training set, and we tested on the respective test sets, but not other datasets. This paper doesn’t focus on generalization to other datasets, but the reviewer raises a valid point. We... | Summary: The authors propose iterative RPO method for reasoning tasks. In particular, iteratively, the model at hand will be prompted to generate many CoT reasoning, and the ones that align with the true answers will be used as chosen, the other generated samples as rejected, for a DPO+NLL loss. The authors conduct exp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and insights.
Re: novelty.
- Our method is not self-rewarding because the reward signals come from the ground-truth labels instead of the model itself. More specifically, self-rewarding LM requires generating prompts, and using LMs to evaluate the sampled g... | Summary: This paper proposes a novel method for preference optimization for reasoning tasks. Their method involves prompting models to generate the CoT reasoning steps and answers for a set of reasoning task inputs, then labeling samples as correct or incorrect using the ground truth outputs, and training the preferenc... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and suggestions!
**Weakness 1 (longer training)**: Thank you for pointing out this issue. In each iteration, we do train longer (5000 steps) but end up selecting an earlier checkpoint – the selected checkpoints (by validation accuracy) are usually trained for 20... | Summary: The paper introduces a novel approach to improve the performance of language models on reasoning tasks through iterative preference optimization. It proposes an iterative method that generates multiple reasoning steps and final answers, constructs preference pairs based on the correctness of the answers, and t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the support!
We presented DPO results in the paper. We showed that DPO in iteration 1 does significantly worse than our RPO’s iteration 1; hence we did not try further iterations of standard DPO (aka Iterative DPO). The reviewer raises a good point that w... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments | Accept (poster) | Summary: The paper proposes a dynamic subgroup identification strategy within covariate-adjusted response-adaptive randomization (CARA) for clinical trials. This adaptive method dynamically identifies and adjusts treatment allocation to the best-performing subgroups based on ongoing trial data, and thus extend the trad... | Rebuttal 1:
Rebuttal: - Thank you for your insightful questions regarding the design objective and for kindly pointing us to the reference.
- We completely understand your concern about the practicality of identifying the best set of subgroups rather than all the benefitted ones. In many clinical settings, treating a... | Summary: This paper introduces a new dynamic treatment assignment for clinical trial to target treatment to the group most likely to benefit from it.
Strengths: The paper studies a critical problem of clinical trial design. It is clear and provides both theoretical justification and synthetic validation, demonstrating... | Rebuttal 1:
Rebuttal: Thank you for your valuable questions and suggestions!
A main limitation of our proposed approach is the assumption that outcomes are observed instantaneously following treatment. This assumption, prevalent in adaptive experiments such as Hu et al. (2015) and Zhu and Zhu (2023), simplifies the mo... | Summary: This paper studies an interesting problem in clinical trials to identify patient subgroups with the most beneficial responses to the treatment, which is essential for clinicians to create personalized treatment plans for their patients. However, most existing strategies for the design of clinical trials rely o... | Rebuttal 1:
Rebuttal: 1. Thank you for pointing us to the references. While both Guo et al. (2017) and ours are in an adaptive experiment setting, there are two major differences. (i)Their design is Bayesian, relying on prior specification, while ours is frequentist and model-free. (ii)They do not discuss theoretical p... | Summary: The paper introduces a dynamic subgroup identification strategy within the framework of covariate-adjusted response-adaptive randomization, addressing the need for more nuanced subgroup analysis in clinical trials. This strategy aims to optimize treatment allocation dynamically and identify subgroups that demo... | Rebuttal 1:
Rebuttal: - Thank you for your encouragement. Below, we provide our understanding of why adaptive designs can be preferable in identifying the best subgroups compared to other alternatives, why resampling and bootstrapping are critical in our procedure, and what technical challenges bootstrapping addresses.... | Rebuttal 1:
Rebuttal: We want to thank all of our reviewers for their very insightful suggestions and comments. We have made our best efforts to address the questions and comments raised by our reviewers.
To supplement our simulation studies and to provide additional information in response to some questions, we prov... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms | Accept (poster) | Summary: The paper is a continuation of a broad line of work exploring deterministic limits of stochastic gradient descent. In particular, the author(s) follow one specific branch of the many, which derives a solution to a coupled integro-differential equation by passing on the complex space. The novelty lies mainly in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and careful reading of our paper, including the appendix. We address below all of the reviewer’s comments. *Due to space limitations, we will include an additional "Official Comment" to answer all the reviewer's questions.*
**Practical application ... | Summary: Analyzes the behavior of AdaGrad-Norm, Line-search and Polyak step size using ODEs.
Strengths: This paper provides an interesting characterization of the behavior of several optimization methods using ODE tools, which are not as heavily used in optimization theory as they should be. This work seems technicall... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support of our paper. We answer some of the reviewer’s reservations below.
**“The methods studied are interesting to theoreticians, but they are rarely if ever used in practice, which limits the practical appeal of this work. Especially the idealized special cases... | Summary: ### Update after rebuttal
After reading the feedback carefully, I updated my score for the paper.
### Original review
In this work, the authors study SGD and its adaptive variants in the setting of noisy generalized linear models. Assuming the covariance matrix of the data is bounded by a dimension-independe... | Rebuttal 1:
Rebuttal: The reviewer's comments required more space than 6000 characters to answer. *We will include an additional "Official Comment" with the answers to the reviewer's questions.*
**Significance of results \- Main contributions**
This work introduces a framework that goes beyond traditional analysis of... | Summary: This paper studies Stochastic Gradient Descent (SGD) training with adaptive step sizes. The setting is a generalization of single-index models. There is a ground truth vector X^*, and the model must find a vector X such that a loss L(X) = E_{a,epsilon}[f(<X,a>, <X^*,a>, epsilon)] is minimized. Here epsilon is ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. We will update the paper to emphasize the difference between our analysis and that of \[14\]. We state below the main differences between this work and \[14\]:
* First, the learning rate is by itself a stochastic process. In addition, since it carries a... | Rebuttal 1:
Rebuttal: We thank the referees for their time and constructive comments that significantly helped us improve our paper. The reviewers questions were deep and so *we will add additional information as an "Official Comment."*
**Paper structural changes.**
In our next version of the paper, we will implemen... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper focuses on analytical analysis of dynamics of a class of optimization algorithms with adaptive learning rate applied to linear models with Gaussian data. The class of algorithms includes AdaGrad-Norm, RMSprop-Norm, Polyak stepsize and line search, but doesn't include, for example, Adam, classical RMS... | Rebuttal 1:
Rebuttal: We address below the questions of the reviewer.
1. **Isotropic covariance matrix $K$.** We emphasize that our results hold for *non-isotropic data covariance matrices $K$* (under mild assumption $||K||\_{op} \< C$, and the average eigenvalue of $K$ is bounded independent of $d$). We point to Rev... | null | null | null | null | null | null |
Disentangled Style Domain for Implicit $z$-Watermark Towards Copyright Protection | Accept (poster) | Summary: The current watermarks applied to AI-generated images rely on adding additional information, limiting their ability to detect unauthorized use of data. This paper introduces a new implicit watermarking scheme, which first utilizes the disentangled style domain to detect unauthorized dataset usage in text-to-im... | Rebuttal 1:
Rebuttal: Dear Reviewer NU2V, thank you very much for your careful review of our paper and thoughtful comments. We hope the following responses can alleviate your concerns.
---
**Q1: The significant problem is the writing, this paper is tough to follow and not examples.**
**R1:** Thank you for your const... | Summary: The paper introduces an implicit watermarking scheme that leverages disentangled style domains to detect unauthorized dataset usage in text-to-image models. The proposed method aims to address limitations in traditional watermarking techniques by using implicit z-watermarks for dataset copyright verification, ... | Rebuttal 1:
Rebuttal: Dear Reviewer **d18z**, thank you very much for your careful review of our paper and thoughtful comments.
**Q1: Response on "Why We Need to Protect Individual Creators' Styles and Content Copyrights in the AI Era".**
**R1:** To further alleviate your concerns, we provide more explanations.
- Fi... | Summary: This paper introduces an innovative implicit $z$-watermarking scheme using disentangled style domains to protect dataset copyrights in text-to-image models. It achieves structured delineation of copyright boundaries, self-generalization, mutual exclusivity, and effective verification for hybrid or partial infr... | Rebuttal 1:
Rebuttal: Dear Reviewer **jJoo**, thank you very much for your careful review of our paper and thoughtful comments. We hope the following responses can help clarify potential misunderstandings and alleviate your concerns.
---
**Q1:** In Table I, it is suggested to compare with some recent and SOTA waterma... | Summary: While text-to-image models excel in generating high-quality images, they also raise issues of unauthorized dataset copyright protection. This paper proposes a novel implicit watermarking scheme that detects and protects dataset copyrights by disentangling the style domain to generate watermarks. The proposed ... | Rebuttal 1:
Rebuttal: Dear Reviewer **z2Ei**, thank you very much for your careful review of our paper and your thoughtful comments. We hope that the following responses will help clarify any potential misunderstandings and alleviate your concerns.
---
**Q1:** Regarding “Moreover, we implement layer-wise guidance dro... | Rebuttal 1:
Rebuttal: ## Global Response
We would like to express our gratitude to all reviewers for their thorough reading and constructive feedback. Since all reviewers have expressed several main concerns, we will try to address these issues in the global response.
**Rethinking Personal Data Copyright Ownership in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning | Accept (poster) | Summary: This paper investigated using complex-valued diagonal RNNs for online RL to provide a small modification (RTUs), and the authors found policy performs significantly better in online RL across various partially observable prediction and control settings.
Strengths: 1. The paper is well-written.
2. Method part... | null | Summary: This paper introduces Recurrent Trace Units (RTUs), a lightweight extension to linear recurrent units (LRUs) that are more suitable for online reinforcement learning. In a range of ablations and experiments the authors show that RTU trained with Real-Time Recurrent Learning (RTRL) performs on par or outperform... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and valuable feedback. Here, we respond to the points the reviewer mentioned.
>some tasks are not very complex (Mujoco-P and Mujoco-V do not require much memory).
While Mujoco-P and Mujoco-V could be considered short-term memory tasks, Reacher POMDP and som... | Summary: This work proposes Recurrent Trace Unit (RTU) as a modified variant of the linear recurrent unit (LRU) which has gained some popularity recently as a linear complexity model.
RTU adopts cosine representations of LRU to manipulate two-coupled real-valued hidden states, and introduces non-linearity (while break... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and valuable feedback. Here, we respond to the points the reviewer mentioned.
> One major issue is the repeated claims about outperforming "Transformers" while no such experiment is provided.
We do provide the results for GPT-2 in Appendix G. Figure 21 show... | Summary: This paper introduces a novel complex-valued, diagonally connected recurrent network model based on LRUs called Recurrent Trace Units. Due to the diagonal connectivity, the model can be trained using real-time recurrent learning in linear time. The authors further propose a version of PPO that uses stale gradi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and valuable feedback. Here, we respond to the points the reviewer mentioned.
>A thorough ablation study of the proposed architecture is missing, i.e. taking discrete steps from LRUs towards RTUs and comparing all of them in one plot, including BPTT.
Thank ... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their valuable feedback on the paper. We’ve carefully considered each concern and suggestion and provided detailed responses.
While the reviewers had multiple concerns, there was no major common issue. To further address some of the reviewers' concer... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In online reinforcement learning, Recurrent Neural Networks (RNN) are still better than transformers, but there are still problems that need to be addressed. In this paper, the authors propose a modified form of the recurrence equation used for Linear Recurrent Units (LRU) – a type of RNN – called a Recurrent ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and valuable feedback. Here, we respond to the points the reviewer mentioned.
> Explaining the importance and benefits of addressing problems with online reinforcement learning in partially observable environments would strengthen the motivation.
We motiva... | null | null | null | null | null | null |
Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models | Accept (poster) | Summary: The paper proposed an unified space to enhance ZeRO-based partitioning strategies, providing a better trade-off between memory and communication. The paper also introduced a more efficient (than ring-based) collective communication method. The core motivation of this paper is to fully leverage the efficient in... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive feedback. We will explain your concerns point by point.
## Weaknesses:
**Q1: concerns about the title and claims**
**A1:** Thanks again for your insightful advice. We adjust the title to "Rethinking Memory and Communication Costs for Efficient Large Lang... | Summary: This paper introduces the Partial Redundancy Optimizer (PaRO) to improve the efficiency of training large language models (LLMs) by optimizing the trade-off between memory and communication costs. PaRO includes two main strategies: PaRO Data Parallelism (PaRO-DP), which refines model state partitioning and tra... | Rebuttal 1:
Rebuttal: Thanks for your postitive and constructive feedback. We will explain your concerns point by point.
## Weaknesses:
**Q1: Do you have much longer sequence length, for example, 4K or 8K?**
**A1:** We are currently conducting experiments with longer sequence lengths, but we have not had time to obt... | Summary: This paper recast basic known distributed training strategies (such as the ZeRo-1, 2, 3, MiCS, FSDP) in a unified framework that takes into account the trade-off between memory consumption and communication. By exhibiting natural levels of granularity for the partionning of different parts of the model and opt... | Rebuttal 1:
Rebuttal: Thanks for your positive and constructive feedback. We will explain your concerns point by point.
## Weaknesses:
**Q1: Figure.1 & 2 a bit hard to understand.**
**A1:** We have redrawn the figures, please refer to Figure 1 & 2 in the PDF.
**Q2: Experiments in Part 4.2 do not seem exhaustive.**
... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for these insightful comments and constructive advices. We offer a general response here and respond to each reviewer individually.
The proposed PaRO can be used as a standalone DP strategy or combined with other parallel strategies for n-D parallel training. PaRO-CC commu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes | Accept (poster) | Summary: Suggest a method to optimize kernel versions of Modern Hopfield Models by mapping memories onto well-separated values on a sphere in feature space. Shows that the spherical arrangement of memories is optimal for retrieval (e.g., maximize capacity) and thus can improve upon the linear kernel [Wu et al. 2024a], ... | Rebuttal 1:
Rebuttal: ### Weakness
> **W1:** the algorithm's main justification is a lower bound that is not shown to be tight, so optimizing the minimal overlap between memories is not well supported (here)....:
**Response:**
Sorry for the confusion caused. We want to emphasize that the inequality in Def. 2.7 is a ... | Summary: This work provides a theoretical analysis of the optimal capacity on both Modern Hopfield Networks (MHNs) and Kernelized Hopfield Networks (KHNs). Specifically, it seeks to answer four fundamental problems:
1. How does memory capacity affect the gap between memories?
2. Given a fixed feature dimensionality, h... | Rebuttal 1:
Rebuttal: > **W1:** There is a lack of experimentation in this work. It would be great for the work to have comparisons, similar to those detailed in Uhop.
**Response:**
Thank you for pointing that out. We recognize that many reviewers have identified the same problem of lacking experiments. **We have ad... | Summary: The paper is a theoretical analysis of the memory capacity of MHMs and KHMs. It establishes a connection between memory capacity and spherical codes. A method for approximating optimal memory capacity is introduced. Experiments are conducted to validate the theoretical findings.
Strengths: The paper is well w... | Rebuttal 1:
Rebuttal: > **W1:** The optimization procedure of U-Hop is similar U-Hop+ i.e., Gradient Descent is replaced by PGD. Due to this similarity U-Hop should have been added as baseline. Since in the standard case MHMs use learnable weight matrices (as can be seen in Eq. 10 of [Ramsauer et al., 2020] and other p... | Summary: Modern Hopfield Networks (MHNs) are limited because they require sufficient minimal separation $\Delta_{\min}$ for theoretical guarantee. Kernelized Hopfield Models (KHNs) mitigate this limitation by storing the memories in the feature space. In particular, theoretical analysis in [Wu et al., 2024a] uses a lin... | Rebuttal 1:
Rebuttal: > **W1:** Prior work on KHNs already has analysis using linear kernels. It would be helpful to showcase the improvement brought by the current algorithm to improve clarity and contextualization relative to prior work.
**Response:**
The prior work in KHN mainly explains how separation maximizat... | Rebuttal 1:
Rebuttal: ## General Response/Rebuttal Summary
Dear Reviewers,
We thank the reviewers for the insightful questions and reviews.
We have answered all the questions and addressed all the problems in detail in rebuttal and revision.
In response to the reviewers' suggestions, these revisions include additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis | Accept (poster) | Summary: This paper proposes a novel view acoustic synthesis approach based on 3D Gaussian Splatting scene representation. The framework consists of a 3D GS model, acoustics field network, and audio binauralizer. It first trains a 3D GS model to capture scene geometry. Then they use attributes from each Gaussian to ini... | Rebuttal 1:
Rebuttal: ## Weakness
**W1: Method part isn't clearly stated**
We will re-draw Figure 2 (Overview of our proposed AV-GS) to make it more clear and straightforward. And update all figures to PDF.
**W2: PCA feature of learnable acoustic points**
(The PCA plot and the correlation matrix discussed below have... | Summary: This paper proposes a new audio-visual Gaussian Splatting model for novel view acoustic synthesis. The proposed method explicitly models scene geometry and learns a point-based scene representation with an audio guidance parameter on locally initialized Gaussian points that takes the space relation from the li... | Rebuttal 1:
Rebuttal: ## Weakness
**W1: Explicit modeling of alpha**
We appreciate the reviewer's insight regarding the explicit modeling of alpha.
*"there is nothing constraining alpha to be physics-based or correlated with material parameters"*
While we acknowledge the importance of deriving alpha from material p... | Summary: This work proposes an efficient and performant method for novel view acoustic synthesis using Gaussian Splatting representations of 3D scenes. The proposed model integrates geometry and material conditioning, and offers a way to bridge the gap between visually-useful 3DGS representations and acoustically usefu... | Rebuttal 1:
Rebuttal: ## Weakness
**W1: Value of T_g**
T_g is inspired by vanilla 3D-GS paper (which empirically found a threshold of 0.0002) and consistently our threshold of 0.0004 was determined through empirical testing.
In 3D-GS optimization (trained for NVS), the point's gradient is compared with the T_g thres... | Summary: The paper proposes a 3D Gaussian splatting (3DGS) based method for improving the acoustic context modeling for novel view acoustic synthesis (NVAS). The proposed model outperforms multiple existing methods on both simulated and realworld data across different metrics. The method also trains and infers faster t... | Rebuttal 1:
Rebuttal: ## Questions (and weaknesses)
**W1, Q1: how the chosen strategy for aggregating the acoustic context in G_a affects model performance.**
L156-7: "We obtain the condition for binauralization by averaging the context across all points in G_a, post dropping points outside the vicinity of the listene... | Rebuttal 1:
Rebuttal: This attached PDF contains the PCA plot and the correlation matrix which is referred in rebuttal response for RHwj - Q2 and DaxK - W2 below.
Pdf: /pdf/36fc2f5aba8e767c6818b90eb59bbd91ed2cfeed.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity | Accept (poster) | Summary: The paper explores introspective planning to enhance robotic task execution using large language models (LLMs). The authors introduce a method for LLMs to form uncertainty-aware plans without fine-tuning while addressing hallucination and task ambiguity. Their approach integrates introspective planning with co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, acknowledging that our study includes sufficient comparison and ablation experiments. We also thank you for the comment that the write-up is easy to follow. We are looking forward to receiving additional comments and feedback from you during the dis... | Summary: This paper introduces a method that uses introspective planning to guide LLMs in forming uncertainty and ambiguity-aware task plans. The proposed method derives and quantifies the inference uncertainty of LLMs to enhance task planning and conformal prediction. Additionally, a new dataset on safe mobile manipul... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback!
> *The term "introspection" is not scientifically defined. Does introspective refer to a robotic agent, the LLM, or the proposed approach?*
We use the term “introspection” as defined in [19], referring to the human ability to assess internal va... | Summary: The paper tackles the uncertainty quantification problem for task planning with LLMs. Specifically, the paper proposes to first construct a knowledge base using LLM, that contains human-in-the-loop correction and LLM summarization / reflection. Then this knowledge base is used during inference to provide relev... | Rebuttal 1:
Rebuttal: We thank you for the feedback, acknowledging that our proposed method shows significant improvement compared to prior works in terms of addressing uncertainty alignment. We also thank you for the question and for providing an opportunity to clarify our contribution and its significance.
> *What’s... | Summary: This paper presents "introspective planning" as a method to enhance the reliability and safety of robotic task planning using large language models. The proposed method uses introspective reasoning to address uncertainty through a knowledge base consisting of sets of tasks, observations, and candidate plans, a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and thoughtful feedback!
> *Why are there significant performance gaps between the direct and conformal predictions and what is the trade-off?*
We thank the reviewer for pointing this out. We discussed the trade-off between direct and conformal prediction i... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort that all reviewers and area chairs have dedicated to providing valuable feedback and constructive advice on our manuscript. We are encouraged by the consensus among reviewers on the importance of guiding language agents to reason about their own uncertai... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new method of using LLM to do task planning. The key innovation is a retrieval-augmented generation (RAG) where the LLM retrieves few-shot introspective reasoning examples from a knowledge base that contains examples with supervised labels. The authors integrate the RAG into LLM to either... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, acknowledging that our method is sound, the writing is clear, and the proposed metrics comprehensively measure the planner's performance. We also thank you for recognizing the valuable experimental results we have presented.
> *The proposed method ... | null | null | null | null | null | null |
iVideoGPT: Interactive VideoGPTs are Scalable World Models | Accept (poster) | Summary: This work studies the setting of planning and prediction from video world models. It proposes to use a GPT-style transformer world model that incorporates action and reward information in it's context and prediction pipeline. The model is further equipped with a novel tokenization technique based on VQGAN. Fin... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 6ajb for providing a detailed review and insightful questions. We have **responded to common questions in the global response and individual questions below**.
### 1. Motivation of iVideoGPT: Best of both interactivity and scalability
In $\underline{\text{Q1 of global... | Summary: This paper introduces Interactive VideoGPT (iVideoGPT), which builds world model based on VideoGPT architecture. iVideoGPT proposes compressive tokenization, and the model is trained using millions of human or robot videos (i.e., Open X embodiment dataset). The effectiveness of iVideoGPT is demonstrated in vid... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer yh7R for providing a thorough review, valuable questions, and a positive evaluation of our paper.
### Q1: Technical novelty
Discrete tokenization and autoregressive transformer are prevelant in contemporary deep learning, due to their simplicity and generality. i... | Summary: This paper introduces a new architecture for an action-conditioned video (and reward) prediction model.
First, a VQGAN converts context frames individually into tokens.
Second, a conditional VQGAN converts future frames individually into tokens, conditioned on the context frames (using intermediate representat... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer zSnp for the thorough review and valuable questions. We also appreciate the pointed-out typos, which we will correct in a future revision.
### W1: Computational efficiency
We apologize for missing a quantitative efficiency analysis. We have reported training/inference... | Summary: The authors present a paper that attempts to utilize actionless and action conditioned trajectories to learn a large scale interactive world model. This model is subsequently adapted for robot manipulation tasks. It is evaluated on video prediction, visual planning, model based RL. The model training is tested... | Rebuttal 1:
Rebuttal: We would like to sincerely appreciate Reviewer HkK9 for the comprehensive review and insightful questions. We have **addressed common questions in the global response and indivual questions below**.
### Q1: Amount of action data needed
As described in $\underline{\text{Sec 3.2}}$, we **do not us... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive feedback. We have made every effort to address all concerns and have responded to individual reviews. We have also **answered common questions in this global response**.
Please note that **all the new figures for all responses are included i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Complexity of Teaching a Family of Linear Behavior Cloning Learners | Accept (poster) | Summary: Summary:
This paper "Optimal Teaching of Linear Behavior Cloning Learners" introduces a novel algorithm called TIE (Teach using Iterative Elimination) aimed at efficiently teaching a family of consistent linear behavior cloning (BC) learners. The algorithm focuses on constructing an optimal teaching set from ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and suggestions. Please find our replies to your individual raised concerns below.
### **1. Regarding the setting where the feature function is not well defined.**
Thank you for your feedback. We believe your comment may be referring to the ... | Summary: This paper studies optimal teaching of behavior cloning learner with a linear hypothesis class, that is, finding the minimum number of demonstrations needed to teach a target policy to the entire family of consistent linear BC learner. They first show that this problem can be transformed into a finite set-cove... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and suggestions. Please find our replies to your individual raised concerns below.
### **1. Why teaching a family instead of individual learners - any real scenario in ml?**
We appreciate your thoughtful question. In a real life scenario of teac... | Summary: The authors propose a method for determining the a minimal dataset of state-actions tuples that would allow a family of linear learning agents to learn to imitate the optimal policy of a teacher. The authors motivate their method by describing the desired set of all linear weights that lead to an optimal polic... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and their valuable feedback. Please find our responses to your individual comments below.
### **1. Clarification on notations and typos.**
- The set $2^{𝑅^𝑑}$ denotes the set of all possible subsets of $\mathbb R^d$ space. The learner... | Summary: The problem being considered is how to provide demonstrations that are useful to a family of consistent behavior cloning (BC) learners, where consistency means that the learner produces a policy consistent with the dataset. The authors study what is the smallest dataset required to teach a family of consistent... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and suggestions. Please find our replies to your individual raised concerns below.
### **1. Baselines are rather weak; provide comparison with other baselines for set cover.**
We do agree that Teach-ALL is a weak baseline. However we note that ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning | Accept (poster) | Summary: The authors define a environment-parameter sensitivity metric in terms of a Jensen gap of min-max scaled performance. The gap gives us the difference between the best average performance and the average best performance over a particular algorithms hyperparameters over a set of environments. The authors then p... | Rebuttal 1:
Rebuttal: Thank you very much for your review and comments on improving our work.
## Major comments:
1. Several performance metrics are common in reinforcement learning: best policy performance, performance during final k episodes, AUC, etc. We chose AUC because it informs us about the rate of learning. ... | Summary: The paper proposes a method to analyse how sensitive are RL methods with respect to hyperparameter tuning. The author argue that one method may perform well on average but require more HPO tuning per task which hides some computation and prevent from having comparable results. They introduce a sensitivity metr... | Rebuttal 1:
Rebuttal: First, thank you for your review and comments on improving our work. We will address your specific questions here and focus on general concerns shared between the reviews in our general rebuttal.
## Weaknesses
As requested, we have included 5 repeats of the Figure 4 plots, leaving out an enviro... | Summary: This paper proposes a new evaluation regime for reinforcement learning. As opposed to only taking into account benchmark performance (i.e., final return), as is ocmmon in previous literature, this work suggests considering an extra dimension of how sensitive algorithms are to hyperparameters in tuning based on... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your feedback.
## Weaknesses
1. We recognize that the delineation between hyperparameters and algorithm design can be fuzzy. We cite the sources used for our counts and, when possible, try to stick to hyperparameter lists identified by the original pap... | Summary: The paper introduces an empirical framework for assessing the hyperparameter sensitivity of reinforcement learning algorithms.
The framework consists of two metrics: 1. hyperparameter sensitivity, which gives a normalized difference in performance between the per-task best hyperparameters and the across-task b... | Rebuttal 1:
Rebuttal: Thank you very much for your review. Your comments are appreciated for improving this work.
## Weaknesses
Metric definition:
The effective hyperparameter dimensionality already does this by counting the number of the hyperparameters necessary to obtain a large fraction of peak performance. If ... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for your questions, comments, and suggestions on improving our work! We appreciate your feedback. We have addressed specific comments in the individual rebuttals and will provide a general response to comments shared among the reviewers here.
The main contributions of o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust | Accept (spotlight) | Summary: This paper studies edge density estimation for Erdos-Renyi random graph under node-level differential privacy. They then show that their approach can actually be used to estimating edge density for inhomogeneous graphs. In particular, this paper proposes an efficient algorithm with optimal privacy cost $O(1/(\... | Rebuttal 1:
Rebuttal: **Question 1:**
> Could you please elaborate on which part of your proof technique deviates from that in [Chen et al.]? Alternatively, are the proof techniques similar, while you are just addressing different problems in this paper?
**Response 1:**
On the one hand, the algorithm in [CDd+24] uses ... | Summary: The authors propose a robust and efficient (polynomial time) algorithm to estimate the edge density of Erdos-Renyi graph, which achieves optimal error up to logarithmic factors. The authors also design an optimal algorithm for inhomogenous graphs where edges are drawn iid from different Bern(p_i).
Strengths: ... | Rebuttal 1:
Rebuttal: **Question 1:**
> Is it possible to design algorithms when the entries of the connection probability matrix are not independent?
**Response 1:**
Yes, our algorithm guarantees can extend to some settings where the edges in the graph are not independent:
- As our algorithm is robust under node cor... | Summary: This paper gives the first polynomial time DP algorithm for estimating edge density of random graphs (Erdos-Renyi and inhomogeneous). The authors also give information-theoretic lower bounds to show that the error achieved is optimal (up to log factors). The paper utilizes the recent results of Hopkins et al w... | Rebuttal 1:
Rebuttal: **Comment 1:**
> You should define the quantity edge density formally somewhere since the whole paper hinges on the reader understanding what it is.
**Response 1:**
Thank you for pointing it out! We will add a formal definition in our proceedings version.
**Question 2:**
> Can you expand on the... | Summary: This paper presents a sum-of-squares-based polynomial-time differentially node-private algorithm for estimating the edge density of Erdos-Renyi random graphs that achieves optimal accuracy; the algorithm is simultaneously robust to corruptions. Specifically, if p is the true Erdos-Renyi parameter, then the est... | Rebuttal 1:
Rebuttal: **Question 1:**
> How does the rate achieved for inhomogeneous random graphs compare to the rate achieved by the exponential-time algorithm of [BCSZ18] for graphons?
**Response 1:**
[BCSZ18] uses the Laplace mechanism for edge density estimation of graphons.
The privacy cost of their algorithm i... | Rebuttal 1:
Rebuttal: We are very grateful to all reviewers for constructive feedback.
We will incorporate these helpful suggestions into the proceedings version of our paper.
**Question:**
What is the key conceptual contribution of our paper, compared to previous work [BCSZ18] and [SU19]?
**Answer:**
[BCSZ18], [SU19... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper concerns the graph density estimation question for random Erdos-Renyi graphs. The optimal algorithm, without additional constraints, for this problem is to output the density of edges in the graph.
The paper considers a node-private robust flavor of the question, however:
1. The output is not suppo... | Rebuttal 1:
Rebuttal: **Comment 1:**
> This is not directly a very practical problem, since Erdos-Renyi graphs do not appear frequently in natural contexts and even the generalization assumes that the selection of different edges is independent, which is not true for many models of real-world graphs.
**Response 1:**
I... | null | null | null | null | null | null |
LLM Evaluators Recognize and Favor Their Own Generations | Accept (oral) | Summary: This paper investigates whether large language models can identify their own generations in two settings: when they have to distinguish between their output and another output from another large language model or person, and when they are only given an output and must score according to a Likert-scale 1-5. The... | Rebuttal 1:
Comment: Thank you for the feedback on presentation! We will incorporate them in the revision. Here we'd like to respond to the two questions:
> why report weighted average rather than greedily selected quality score
We do so mainly due to the insensitivity of LLMs in the individual measurement setting. W... | Summary: Self-evaluation is widely adopted but can lead to self-preference in that the LLM evaluator scores its own outputs higher than others while the qualities are actually equal. This paper finds that LLMs prefer their own generation because they recognize themselves. This paper conducts experiments on two summariz... | Rebuttal 1:
Comment: Thank you for the careful review. The two experiments you suggested are in fact already in the paper (please let us know if there are better ways to highlight them). Below we provide a response which should hopefully clear up the confusion.
> Correct me if I am wrong. I think the hypothesis of thi... | Summary: The paper investigates the novel topic of self-preference and self-recognition in large language models (LLMs). The experiments are well-conceived, and the use of pairwise comparison alongside individual evaluation provides a solid framework for understanding these phenomena. Despite these strengths, the work ... | Rebuttal 1:
Comment: Thank you for the thoughtful feedback! We'd like to address the weaknesses and questions you brought up.
> Limited scope
Within the context of summarization and the constraints with compute budget, we maximized the coverage in the following aspects of the experiments, to the best of our ability:
... | Summary: The authors examine self-preference in language models through the lens of self recognition. They pose the following question: if models indeed prefer themselves, is it also because they recognize themselves? The authors explore a range of models and find correlates between self recognition and self-preference... | Rebuttal 1:
Comment: Thank you for the thoughtful feedback! We'd like to address some concerns and questions brought up in the review.
> Limitations of running the experiments on two summarization tasks
We agree that experimenting on more tasks is worthwhile and will update the paper once more experiments are complet... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Dormant Neuron Phenomenon in Multi-Agent Reinforcement Learning Value Factorization | Accept (poster) | Summary: The paper proposes ReBorn to reactivate dormant neurons in the mixing network of multiagent value-mixing algorithms. Specifically, ReBorn transfers the weights from overweight neurons to dormant neurons and this method ensures the learned action preferences are not changed after the ReBorn operation. Experimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and suggestions, which can help improve the quality of our work. We address the concerns of the reviewer as follows.
>Weakness 1: It seems that the ReBorn operation is time-consuming as it needs to compute and manipulate each neuron in the network. If ... | Summary: This paper introduces a novel approach for tackling the plasticity loss and securing sample efficiency in multi agent reinforcement learning (MARL). Different from the methods in single agent RL, this paper also shows the importance of not violating the knowledge invariant (KI) principle in MARL. With careful ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and effort in reviewing this work. Your suggestions are valuable. We have compared our method with the two approaches mentioned by the reviewer. We address the concerns of the reviewer as follows.
>The motivation behind scaling the weights in ReBorn is not cle... | Summary: The paper explores the dormant neuron phenomenon, an active research topic in RL, in the context of MARL value factorization. It describes how dormant neurons manifest mostly in the mixing network, details their effect on performance and connects them to an opposite but correlated phenomenon, overweight neuron... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer's time and effort to review this paper. We will improve the quality of the figures and add more experimental results to our work.
>The figures and figure captions are often hard to understand.
Thanks for your suggestion on Figure 2a and Figure 4. We have impr... | Summary: This work proposes a new method for resetting dormant neurons in multi-agent RL settings. It replicates previous observations on unit dormancy in deep RL in the multi-agent regime, finding that inactive neurons are correlated with reduced ability to improve performance. The proposed method differs from ReDO, w... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments, we will improve our work based on your suggestions. We address your concerns as follows.
>The knowledge invariant principle, while not entirely novel (see, e.g., the approach of Nikishin et al., which is precisely motivated by the desire to avoid changing the n... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful comments and valuable feedback. The reviewers acknowledge our work as novel (KvCg, JB6L, WpXH), important (hhg9, JB6L), significant outside the specific problem setting (hhg9), theoretical contributions (KvCg, JB6L, WpXH), good empirical result... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem | Accept (poster) | Summary: The authors consider the alignment problem between a point cloud generated from a Gaussian distribution and its noisy version under orthogonal transformation, formulated as the Procrustes-Wasserstein problem. The authors derive information-theoretic results for both high and low dimensional regime (i.e., bound... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing and all the remarks and questions that will help clarify our paper. We make sure to address the concerns raised below.
*The title seems misleading. Could the authors comment on the relation between the considered problems in line 29-39 with rand... | Summary: This paper studies the Procrustes-Wasserstein problem that aims to match two high-dimensional point clouds where one is a noisy version of the other up to an orthogonal transformation. The authors establish information-theoretic results in the high ($d \gg \log n$) and low ($d \ll \log n$) dimensional regimes.... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and questions, that will help improve the clarity of the paper. We answer the questions raised below.
*Due to technical challenges, only guarantees for one step of the proposed algorithm is analyzed.*
This is indeed true, our analysis of the Ping-pong algorit... | Summary: This submission is concerned with the problem of aligning planted graphs. To this end, both a permutation $\pi$ and an orthogonal matrix $Q$ must be estimated from observations of $X$ and $Y$. To evaluate the performance of this approach, it is proposed to measure to measure the error induced by $Q$ in terms o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough reading and reviewing, positive assessment, and very positive feedback which will help improve the quality of the paper in its second version.
We answer the questions raised in the review below.
*The analysis of the proposed ping-pong algorithm is quite limi... | Summary: This paper studies the theoretical limits of the Procruste-Wasserstein problem which consists in finding an optimal assigment of cloud of points in Euclidean space up to a global rotation. The paper focuses on the model of a cloud of point perturbed by a global rotation and some gaussian noise. The authors est... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback and the valuable comments.
We answer the question raised below.
*Why is the conical alignment loss not used in practical experiments? Is it because it gives poor practical performances?*
The conical alignment loss is useful for informational results ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Long-form factuality in large language models | Accept (poster) | Summary: This paper introduces LongFact, a dataset consisting of 2,280 questions across 38 topics, designed to assess the factuality of long-form answers generated by large language models. The authors also propose a new evaluation method called the Search-Augmented Factuality Evaluator (SAFE), which utilizes large lan... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and thoughtful reviews. Let us address your comments below.
> The layout of this paper could be improved. Many significant pieces of information have been relegated to the appendix...
Thanks for the suggestion. We agree and will move some more information on th... | Summary: In this work, the authors investigate the evaluation of LLMs’ factuality in long-form generation. Specifically, they first introduce LongFact, a multi-topic benchmark for assessing long-form factuality. In LongFact, they use GPT-4 to generate questions about specific concepts or objects from given topics. Addi... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and thoughtful reviews. Let us address your comments below.
> The overall concept is similar to previous work that decomposes long-form responses into claims, such as FactScore, which diminishes the novelty.
We agree that our evaluation method SAFE shares simil... | Summary: This paper focused on the open-domain long-form factuality problems of Large Language models. It proposes a benchmark called LongFact, which consists of more than 2k prompts across 38 domains, it also proposes an agent-based factuality detection system called SAFE with a new metric to measure the factuality: F... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and thoughtful reviews. Let us address your comments below.
> It is foreseeable that the performance of SAFE could surpass that of crowd-sourced annotators, given that crowd-sourced annotations often lack high quality. I think it would be better to compare the S... | Summary: This paper introduces a novel benchmark, LongFact, for evaluating the factual accuracy of long-form responses generated by large language models (LLMs). It proposes a method named SAFE (Search-Augmented Factuality Evaluator) to automatically assess the factuality of these responses. SAFE uses an LLM to decompo... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and thoughtful reviews. Let us address your comments below. We hope these results will help clarify our work, and we will update our manuscript to include them.
> Reliance on Google Search
Google Search indeed may have limitations as you mentioned (Line 281-290... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a 4-step pipeline for automatic evaluation of long-form answers using LLMs. Given a long-form answer, their pipeline involves (1) splitting the question into individual facts, (2) decontextualizing each fact, (3) determine each facts relevance to the original question, and (4) verifying each... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and thoughtful reviews. Let us address your comments below.
> One concern is with the "ideal number of individual facts in the answer for a given question" hyperparemeter...
We agree that the desired number of supported facts K is user-dependent, and the optima... | null | null | null | null | null | null |
Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification | Accept (poster) | Summary: This paper studies online strategic classification from a combinatorial perspective. The paper defines a new combinatorial dimension called the Strategic Littlestone dimension that jointly captures the complexity of the hypothesis class and the manipulation graph. They show that the Strategic Littlestone dimen... | Rebuttal 1:
Rebuttal: Thank you for the detailed and insightful comments.
> Deterministic learners in the agnostic setting
Our focus on deterministic algorithms is motivated by real-world applications of strategic classification where legal or regulatory requirements often demand the learner to use deterministic algo... | Summary: This paper studies an adversarial online setting where the agents can manipulate their feature vector $x_t$ to some feature vector $x'_t$ given a graph of manipulation rules $G$. The learner observes only $x'_t$ and knows in advance $G$.
The usual goal is to obtain sublinear regret, with respect to some hypot... | Rebuttal 1:
Rebuttal: Thank you for your the detailed review and valuable feedback.
> Technical contribution
We respectfully disagree that our paper lacks technical novelty because it applies ideas from standard online learning. While we build on established methodologies such as the Littlestone tree, SOA learning ru... | Summary: The authors continue the study of strategic classification in the online setting, where an agent can manipulate the instance features to potentially force a positive prediction (governed by a manipulation graph). In the *realizable* case, the authors provide a strategic variant of the Littlestone dimension and... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and the insightful comments.
> Comparison of our agnostic regret bound (Thm 4.1) to that of Cohen et al. (Prop 30), and whether there are tight lower bounds
Thank you for your question. Our agnostic algorithm that achieves the mistake bound in Thm 4.1 only req... | Summary: The paper tackles online binary classification where agents manipulate observable features for positive outcomes. It introduces the Strategic Littlestone Dimension (SLD), a new measure capturing the complexity of the hypothesis class and manipulation graph, demonstrating its role in achieving optimal mistake b... | Rebuttal 1:
Rebuttal: Thank you for your questions and comments.
> Weakness 1: deterministic learning algorithms
While we agree that many robust algorithms rely on randomization to deal with adversaries, we want to highlight that in the context of strategic classification, there are important scenarios where the lear... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper considers the problem of online binary classification when each data point can strategically manipulate its features in a discrete way which is captured by a manipulation graph.
Protocol and regret: In each round, the learner picks a deterministic classifier $h_t$, and then the data point $(x_t,y_... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback on our work. We are happy to see that you find our notion of Strategic Littlestone Dimension elegant and valuable.
> Question 1: Improved bound for the agnostic setting
This improvement results from the comparison between Theorem 4.1 in our paper and the agno... | null | null | null | null | null | null |
Differentially Private Optimization with Sparse Gradients | Accept (poster) | Summary: This paper explores differentially private optimization under the data/gradient sparsity assumption. For mean estimation with sparse data, it introduces new near-optimal bounds, improving previous results, especially in the high-dimensional setting. The corresponding lower bound is established using a novel bl... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and feedback..
In the revision, we will reorganize the content. As you suggest, we will include results for DP-SCO from Section 6 (specifically from Appendix G.2), as well as a more extensive motivation and detailed proofs in Section 5.
__Questions__
Than... | Summary: The paper studies differentially private optimization under the sparse gradient assumption. The paper first considers private and sparse mean estimation. Both lower-bounds and nearly matching upper bounds are established. The paper then use the mean estimation algorithm to construct private gradients in DP-SGD... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback.
1. Thank you for the references; we will make sure to add them to properly position our work within the field.
2. Unfortunately, it is not true that dimension-free rates are known only for unconstrained settings. In fact, that there are works providing (nea... | Summary: The paper provide algorithms for DP optimization with sparse gradient, proving both upper bounds and lower bounds, which are almost match.
Strengths: 1. The paper has a nice presentation, starting from sparse mean estimation upper bounds and lower bounds and then go into ERM with sparse gradients and deal wit... | Rebuttal 1:
Rebuttal: Thank you for your review. We will now elaborate on the comments.
Thank you for your comment on practical applications. In this regard, the idea of gradient sparsity has already impacted practical DP optimization. The most immediate references in this respect are [6: Zhang, Mironov, Hejazinia] an... | Summary: This paper addresses the problem of DP-Convex optimization and DP-SCO in scenarios where the gradient of each individual sample is $s$ sparse. The main question explored is how sparsity can help improve the known rates for DP convex optimization.
The main contributions of the paper are as follows:
DP Sparse ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable and detailed feedback.
Firstly, we apologize for the lack of clarity of the algorithmic choices and the proofs in our submission. Here is an overview of the choices behind Algorithm 2. As discussed in the paper, the near-optimal mean estimat... | Rebuttal 1:
Rebuttal: Updated table incorporating the low and high dimensional rates for DP optimization.
Pdf: /pdf/501ed5de0bfebdfc3652a49dbc69d6581b1f264a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees | Accept (poster) | Summary: The paper proposes Treeffuser, a nonparametric method for modeling the output distribution. Treeffuser learns a diffusion model using gradient-boosted trees, and uses conditional diffusion models to produce a distribution over the response variable given an input vector. Since Treeffuser user gradient-boosted ... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer knMV
We thank knMV for their review. We are encouraged that the reviewer found the paper well-written and easy to follow and that the author found our approach very flexible and valuable for tabular regression problems with UQ. Below, we provide additional results and discussi... | Summary: The authors propose a methodology for computing probabilistic predictions in regression problems using diffusion and gradient-boosted trees. In particular, their methodology can generate samples from p(y | x) from which statistics such as estimated quantiles could be recovered. They find that their method can ... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer 3Apn
We thank Reviewer 3Apn for their valuable feedback. We are pleased to hear that they appreciate our application to the newsvendor problem. Based on their comments and questions, we clarify our contributions, the applicability of our method, and its implementation below.
... | Summary: This paper proposed a method called "Treeffuser" for probabilistic prediction (density regression) of tabular data.
Strengths: The paper is well-written and clearly presented the core idea as well as the main results.
Weaknesses: There should be more related methods to compare with in the experiment in secti... | Rebuttal 1:
Rebuttal: # Rebuttal reviewer iWzb
We thank reviewer iWzb for their comments. We appreciate that the reviewer found our paper to be well-written and clearly presented. We appreciate the great questions and suggestions by the reviewer. We provide answers and discussions below.
## Weaknesses
**W1. There sh... | null | null | Rebuttal 1:
Rebuttal: # General rebuttal
We are grateful to the reviewers for their great feedback! Here, we summarize the key points from our rebuttals.
## Contributions
We clarify that our main contribution is to successfuly combine diffusion models and gradient-boosted trees (GBT), and use them to provide state-o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Instance-Specific Asymmetric Sensitivity in Differential Privacy | Accept (poster) | Summary: In this paper, authors propose a new instance specific sensitivity based method for differentially private queries. In differentially private literature, sensitivity of the query (or the function of interest) is an important quantity, which affects the amount of noise we need to add in order to guarantee DP. I... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our work and incredibly helpful feedback. In retrospect, we are disappointed with our decision to relegate much of the intuition to the appendix in deference to including more of the concrete results and will be sure to better incorporate aspects ... | Summary: This paper proposes a new instance specific asymmetric sensitivity under differential privacy by building on the well-studied inverse sensitivity mechanism which adapts to the hardness of the local data according to the inverse closeness to the underlying ground dataset. It also develops some theoretical guara... | Rebuttal 1:
Rebuttal: We know that the reviewing burden can be significant and are very sorry to have added to this burden by not presenting our results more clearly. It's clear that we attempted to fit in too many of our results in the main body and neglected better explaining the intuition of our methodology. In part... | Summary: The paper introduces a new notion of instance-dependent sensitivity, asymmetric sensitivity, to release general function queries. Compared with inverse sensitivity, it can better capture the underlying instance’s asymmetry, i.e. when changing a data point causing the function value to increase or decrease at d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our work, helpful feedback, and catching an important typo.
$\textbf{Question:}$
This is a phenomenal question and a great catch by the reviewer. To answer it really requires digging deep into the intuition upon why our method performs better w... | Summary: The paper proposes an asymmetric sensitivity mechanism for the private estimation of functions with asymmetric outputs, such as variance. This mechanism combines the inverse sensitivity mechanism with the sparse vector technique to handle asymmetric sensitivities effectively. The proposed method is efficient a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our work and their helpful feedback.
$\textbf{Weaknesses:}$
Could the reviewer please explain further why they would classify our bounds as "vacuous" in reasonable settings? We certainly agree with the reviewer that asymptotic bounds can somet... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper provides a new algorithmic framework for differentially privately computing general functions that adapts to the "local" sensitivity of the underlying dataset. It follows previous work's paradigm which is to sample outcomes with probabilities exponentially decreasing in how "far away" the current da... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our work and their helpful feedback.
$\textbf{Weaknesses:}$
We greatly appreciate the reviewer's desire for intuition and strongly agree. Unfortunately we decided to put our main intuition section in the appendix (Section C) due to space consi... | null | null | null | null | null | null |
Learnability Matters: Active Learning for Video Captioning | Accept (poster) | Summary: This work proposes an active learning algorithm via data learnability and collective outliers. The algorithm takes three complementary aspects, learnability, diversity, and uncertainty, into account. The proposed algorithm leverages off-the-shelf models (e.g., LVLM) for approximations to ground truths.
Streng... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We will provide more details on the motivation behind our design and include additional supportive observations in the final version. Please review our feedback below. We are happy to address any further questions during the discussion period.
> 1. The usage ... | Summary: The paper presents a groundbreaking exploration of collective outliers in video captioning tasks, introducing innovative active learning algorithms and an effective caption-wise learning protocol that integrates human knowledge to enhance model performance. Despite its strengths in pioneering research and soph... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments. In addition to including the cross-dataset experiments and limitations in our final version, we will double-check the text and redraw some figures to enhance clarity and aesthetics. Please see our responses to your concerns below. More figures are provided ... | Summary: This paper works on active learning for video captioning, i.e., filtering the training data for a video captioning dataset. The authors observed significant inconsistency in the captioning annotation, due to different captioning abstraction or granularity. These inconsistency makes the model training hard. The... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We are pleased to clarify our claims and will update our manuscript with additional supportive results. Below are our responses to your concerns. We are committed to addressing any further questions.
> 1. Low baseline performance, resulting in overclaims ... | null | null | Rebuttal 1:
Rebuttal: The results of our cross-dataset experiments are provided in the PDF file.
Pdf: /pdf/d9e28b23d83e1cc1c026b41628bb697be8848351.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Linear Regression using Heterogeneous Data Batches | Accept (spotlight) | Summary: This paper addresses the regression problem in scenarios where heterogeneous data is collected from multiple sources, necessitating learning tasks on small batches of samples. The approach involves dividing the data sources into k subgroups with unknown distributions. This study advances the work of Kong et al... | Rebuttal 1:
Rebuttal: We thank the reviewer for many positive comments about our paper. Below we address the remaining comments of the reviewer one by one.
1. Note that even when $k$ is arbitrarily large, the number of batches and batch sizes required in Theorem 2.1 and Corollary 2.2 are reasonable as in that case the... | Summary: The paper considers the problem of linear regression with heterogeneous data batches, where a user receives a collection of batches of data with not necessarily the same underlying signal distribution and they must attempt to infer a list of heterogeneous signals present in this data. This paper proposes a nov... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and positive comments. The reviewer’s comments are addressed herein.
1. We thank the reviewer for pointing out the typos, and will correct them along with some others we found.
2. The condition part of Theorem 2.3 can be more clearly stated as follow... | Summary: This paper explores the concurrent learning of multiple linear models from various small batches of input-output pairs, each derived from a distinct, small-sized source dataset. Although there can be a large number of sources, it is assumed that each dataset belongs to one of several fixed but unknown subgroup... | Rebuttal 1:
Rebuttal: We thank the reviewer for many positive comments. The reviewer’s main concerns involve the number of parameters and distributional assumptions, both addressed below.
While the number of parameters may seem large, each has a natural and important contribution.
Two accuracy parameters $\epsilon$ a... | Summary: The paper presents an algorithm that handles a situation of linear regression from many heterogeneous batches of data, and simultaneously learns all linear separators for which there are large enough batches of data witnessing them. The actual guarantees are more nuanced, covering the common case of a heavy-ta... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review, the appreciation of our contribution, and the two suggestions that we discuss next.
The first suggestion concerns rearranging Sections 1.1 and 1.2. Section 1.1 discusses the paper’s main results, and section 1.2 addresses the results’ improvement ove... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes an algorithm to solve linear regression problems from batched data. The regression coefficients for each batch are assumed to be heterogeneous but come from $k$ possible values. The algorithm proposes a few novel approaches to identify which medium-sized batches are close to a given batch. I... | Rebuttal 1:
Rebuttal: Thank you for the careful review and constructive feedback, and for appreciating the contribution, novelty, presentation clarity, and intuition.
Regarding your comments and questions:
Line 123: Section 1.3 summarizes the algorithm’s innovations, and we will elaborate on how they help overcome a... | null | null | null | null | null | null |
Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables | Reject | Summary: This paper investigates the problem of latent post-treatment bias in causal models where there exists some proxy variables of the latent confounder and post-treatment variables. The authors first derive a general form of latent post-treatment bias which is intractable in most situations (except in special case... | Rebuttal 1:
Rebuttal: The authors deeply appreciate your insightful comments to make our paper better. We hope that we have addressed your concerns in our responses. If you have further questions, we'd be happy to continue the discussions.
***Comment 1: Specify the post-treatment variable for the example.***
**Respon... | Summary: The authors deal with latent post-treatment bias for proxy-based methods which are employed for causal effect estimation.
They show that post-treatment variables can be latent and mixed into the observed covariates along with the latent confounders.
The authors transform the confounder-identifiability problem ... | Rebuttal 1:
Rebuttal: The authors deeply appreciate your insightful comments to make our paper better. We hope that we have addressed your concerns in our responses. If you have further questions, we'd be happy to continue the discussions.
***Comment 1: Bi-directed edges in Figure 1 are not defined properly.***
**Res... | Summary: This paper addresses the challenge of causal inference with observational data, particularly when direct measurement of confounders is infeasible. The authors propose a new method, Confounder-identifiable Variational Autoencoder (CiVAE), to mitigate post-treatment bias using observed proxies for both latent co... | Rebuttal 1:
Rebuttal: The authors deeply appreciate your insightful comments to make our paper better. We hope that we have addressed your concerns in our responses. If you have further questions, we'd be happy to continue the discussions.
***Comment 1: How CiVAE addresses interactions among latent variables and theor... | Summary: In this paper, the authors investigated the issue of latent post-treatment bias in causal inference from observational data. They showed that estimator of existing proxy-of-confounder-based methd, i.e., DEV (f(X)), is an arbitrarily biased estimator of the Average Treatment Effect (ATE), when the selected prox... | Rebuttal 1:
Rebuttal: The authors deeply appreciate your insightful comments to make our paper better. We hope that we have addressed your concerns in our responses. If you have further questions, we'd be happy to continue the discussions.
***Comment 1: How does CiVAE (with possible extension) address other types of l... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Score Distillation via Reparametrized DDIM | Accept (poster) | Summary: This paper introduces a novel algorithm called Score Distillation via Inversion (SDI) that enhances the quality of 3D shape generation. By inverting Denoising Diffusion Implicit Models (DDIM) at each step and incorporating the initial noise into the estimated score, SDI addresses the over-smoothing and detail ... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and recommendations. We provide clarifications below.
# Numerical error in choices of \kappa
Thank you for highlighting this effect in the review, we believe a more detailed explanation will indeed benefit the paper. We touch on the intuition around this question ... | Summary: The paper introduces an effective SDS modification that replaces random noise samples in the SDS objective with those obtained with DDIM inversion. The proposed technique enhances text-to-3D generation, outperforming SDS and being comparable to more sophisticated methods.
Strengths: * The paper is well organi... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive review. Below we address the points raised in the review:
# Inversion with negative CFG
Thank you for pointing this out. We agree that adding this intuition to the main paper will benefit the clarity. Below we support the intuition described in the gen... | Summary: This paper connects score distillation sampling (SDS) to a DDIM sampling process. The proposed method Score Distillation via Inversion (SDI) replaces the original random noise in SDS with DDIM inversion, and show significantly improved quality compared to SDS and other state-of-the-art prior methods.
Strength... | Rebuttal 1:
Rebuttal: We thank r6QS for the helpful review. We will address their comments in the final version and clarify some points below:
# Gray color shift
We agree that results in the main paper might seem gray or sometimes have a red/green tone. We explain this by the dark background color that is generated in ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the detailed and thoughtful feedback.
We are pleased that they appreciate the theoretical contributions and the novelty of our approach (“*well organized and provides valuable intuitions*” (4KTy), “*insights for future research in the important direction*” (r6QS)). The ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficiency of the First-Price Auction in the Autobidding World | Accept (poster) | Summary: This work studies the efficiency of first-price auctions when bidders in the market are all value maximizers or mixed value and utility maximizers. With all value maximizers, the PoA is 1/2, while with partial value maximizers, it is approximately 0.457. The paper also considers the case when the seller has ma... | Rebuttal 1:
Rebuttal: Thank you for your insightful and encouraging comments.
**PoA parametrized by number of bidders**: this is a great question, and indeed we have given some thoughts to it. In short: any nontrivial refinement (if there is one) beyond the 0.457 bound would require a more sophisticated parametrizati... | Summary: This paper studies the price of anarchy of simultaneous first-price auction, where there are $n$ bidders and $m$ auctions/items for sale. The underlying assumption of this paper is that all bidders have a **fixed** valuation. This paper considers 2 types of bidders: 1) a utility-maximizing bidder which maximiz... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments.
**Related work**: we will discuss these results but we do want to note that the third missing citation of Aggarwal et al. mentioned by the reviewer (which is one of the earliest papers on autobidding) is the first entry in our list of references.
**Submodula... | Summary: The authors study the price of anarchy of first-price auctions where the bidders can be either only autobidders (value maximizers), or a mix of autobidders and traditional bidders (utility maximizers). The setting consist of $n$ bidders and $m$ auctions. Each bidder bids a value $b_{i,j}$ for each auction $j$ ... | Rebuttal 1:
Rebuttal: Thank you for your insightful and encouraging comments.
**Paragraph on page 3**: thanks a lot for pointing this out. You are absolutely right and we will fix the paragraph.
---
Rebuttal Comment 1.1:
Comment: Thanks for the response. I'm happy to keep my score. | Summary: Autobidding is the technique of using optimization algorithms to assign ad slots to bidders while respecting their constraints (e.g., budget, ROI, ROAS, etc.). It generates about 80% of the total online ad revenue for major tech companies, and is therefore quite a significant topic to study. Within this topic,... | Rebuttal 1:
Rebuttal: Thank you for your insightful and encouraging comments.
**The Liaw et al. paper**: thank you for pointing us to the paper. This paper is indeed quite relevant and we will discuss it in our paper. In short, they consider a similar setting with autobidders (value maximizers) only, which is close ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport | Accept (spotlight) | Summary: - This paper introduces a new regularizer for learning Neural Optimal Transport. The proposed method, called Expectile-Regularized Neural Optimal Transport (ENOT), is based on the expectile regression. ENOT demonstrates competitive results on the Wasserstein-2 benchmark and Unparied Image-to-Image Translation.... | Rebuttal 1:
Rebuttal: **Q1: The ENOT and the Monge gap are similar in that they both serve as regularizers for inducing Neural Optimal Transport. Could you provide an additional quantitative comparison between ENOT and Monge gap? Currently, there are only qualitative examples in Fig 1 and 2.**
We are thankful for this... | Summary: The paper introduces ENOT (Expectile-Regularized Neural Optimal Transport), a new method for training Neural Optimal Transport (NOT) models. It improves the efficiency of NOT solvers by incorporating a novel expectile regularization on dual Kantorovich potentials. Empirically, the authors use a Wasserstein-2 b... | Rebuttal 1:
Rebuttal: We are thankful for raising important questions and for the positive feedback.
First, we would like to address the concerns about novelty:
To the best of our knowledge, ENOT is the first approach that ventures into the approximation of the c-transform by means of expectile regularization. Moreo... | Summary: Authors provided a new, theoretically justified loss in the form of expectile regularisation which stabilize the learning of Neural Optimal Transport. Importantly proposed method outperforms previous state-of-the-art approaches on the established Wasserstein-2 benchmark tasks and image-to-image by a large mar... | Rebuttal 1:
Rebuttal: **Q1: Comparison with other methods is focusing mainly on Wasserstein-2 benchmark and image-to-image translation. Comparison with some popular in OT biology problems (see 1, 2) would improve the contribution.**
We thank the reviewer for the links to these interesting articles and the application ... | Summary: This paper introduced a new framework for the training of Neural Optimal Transport, a recently emergent paradigm to enable the training of optimal transport plan in larger scale settings. The key contribution lies in the new and novel formulation of a regularized training loss that takes into account an expect... | Rebuttal 1:
Rebuttal: **Q1: Lack of references on the instability of finding optimal c-conjugate (the motivation of this work). What does it mean to make the optimization problem more "balanced" with the expectile regression regularization on (line 86)? The same applies when the authors claim expectile regression is a... | Rebuttal 1:
Rebuttal: We thank our reviewers for their constructive feedback. In the attached PDF, we provide additional experiments requested by the reviewers, showcasing the performance of ENOT. Corresponding tables are duplicated within individual responses to each reviewer.
Pdf: /pdf/9f47c11ab2783716848c618c0a05277... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work tackles the problem of estimating the dual Kantorovich OT potentials
parametrized via neural networks. They propose to approximate conjugate operator
using a well motivated loss called expectile regularization which approximates a conditional
maximum operator, well suited for estimating the c-tran... | Rebuttal 1:
Rebuttal: **Q1: The expectile loss is well suited to approximate the c-transform only if tau is large enough (close to $1$). Therefore, it would make sense to see a significant drop in performance when tau is close to $0.5$. It would be have been nice to validate this intuition in figure 4 for instance.**
... | null | null | null | null | null | null |
WATT: Weight Average Test Time Adaptation of CLIP | Accept (poster) | Summary: The authors introduce a Test-Time Adaptation technique named Weight Average Test-Time Adaptation (WATT) for Vision-Language Models (VLMs) such as CLIP. WATT is the proposed method that improves test-time adaptation by borrowing ideas from different text prompt templates to develop pseudo-labels for model updat... | Rebuttal 1:
Rebuttal: ***Q:* Limited Scope of Evaluation.**
*A:* Thank you for your valuable feedback regarding the scope of our evaluation. We appreciate the recommendation to include comparisons with more recent state-of-the-art methods in both the Experimental section and the Related Work section. Specifically, we ... | Summary: This paper proposes a test-time adaptation (TTA) method for CLIP by integrating various textual prompt templates into Weight Average (WA) methods (Ref [11], [22]). Experiments on multiple types of domain shifts show the effectiveness of the proposed method.
Strengths: - The proposed method is effective yet si... | Rebuttal 1:
Rebuttal: ***Q:* Comparison with other methods and a batch size of 1.**
*A:* Thank you for your insightful feedback. We appreciate your comments regarding the need for more comprehensive evidence to support our second contribution in Section 1. In response, we have tested the TTA methods mentioned in our p... | Summary: This paper proposes a method called Weight Average Test-Time Adaptation (WATT) to improve test-time adaptation (TTA) for the CLIP model. The core idea is to use different text templates to construct multiple text prompts and adapt the model weights using these different prompts. During the evaluation stage, th... | Rebuttal 1:
Rebuttal: ***Q:* Is it possible to combine text prompt augmentation (this method) with TTA strategies that use image augmentation to achieve better results? Some templates may be incorrect. For example, as shown in Table 2, prompt T0: "a photo of a {class k}" and prompt T2: "a bad photo of the {class k}" re... | null | null | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers' insightful and constructive comments and are pleased to note that all three reviewers voted towards acceptance. We are also encouraged by the feedback highlighting the robustness and generalizability of our method (Reviewer wUmo), its superior performance acros... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bias Detection via Signaling | Accept (poster) | Summary: The paper studies bias detection in the Bayesian persuasion model. In particular the paper wants to test for a threshold that represents the resistance of the receiver to change its mind and update their prior. The test is performed trough signalling schemes and observe the action performed by the receiver. Th... | Rebuttal 1:
Rebuttal: > The main weakness in my opinion is the definition of the baseline and its poor understanding in the larger context of PAC learning. Why the baseline is defined as such rather than considering the minimum of expected samples needed to understand if $\omega \ge \tau - \epsilon$ or $\omega \le \tau... | Summary: This paper studies the problem of determining the bias level of an agent in updating their beliefs using signaling schemes. Specifically, they detect to what degree, the agent is updating their beliefs biased towards their own prior, or 'correctly' according to the Bayesian rule. They propose a signaling schem... | Rebuttal 1:
Rebuttal: > Although the studied problem is novel, it lacks motivation and application in the real world. The authors motivate it by connecting bias to disagreement and polarization, but they don't provide any detailed discussion. Also, currently the authors assume the principal knows the agent's prior. The... | Summary: The paper studies the problem of detecting whether an agent is updating their prior beliefs given new evidence in a Bayesian way, or whether they are biased towards their own prior. The paper considers a setting where biased agents form posterior beliefs that are a convex combination of their prior and the Ba... | Rebuttal 1:
Rebuttal: > There may be an immediate future direction is like given any $\epsilon$, what is the sample complexity of narrowing down the unknown bias level $\omega$ to be in an $\epsilon$-interval? I feel $\log \frac{1}{\epsilon}$ number of rounds using binary search should suffice (though you may need to c... | Summary: The paper studies a Bayesian persuasion problem involving a biased receiver. In this model, the bias is defined by how the receiver deviates from the Bayesian posterior, which is a convex combination of the prior and the induced posterior. The authors propose algorithms to test whether this bias exceeds a fixe... | Rebuttal 1:
Rebuttal: > The sample complexity problem studied in this paper is quite different from the classical notion, where you are given two parameters, $\epsilon$ and $\delta$, and you want to output an $\epsilon$-optimal solution with probability $1 - \delta$. I would have found it much more interesting (and sta... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Dynamic Message Passing on Graphs | Accept (poster) | Summary: This work proposes a new dynamic message passing method $N^2$, which initializes a number of pseudo nodes, and apply message passing across graph nodes and pseudo nodes. The message passing scheme relies on the proximity of the node embeddings, and the node embeddings are updated through message passing. The m... | Rebuttal 1:
Rebuttal: Thank you for your kind suggestions and insightful questions about our work. We hope the following response can address your concerns. We also kindly refer you to the PDF in the global response for the new figures/tables during the rebuttal due to the limited number of characters.
**W1**: Theoret... | Summary: The authors propose a dynamic message-passing scheme where graph nodes and learnable pseudo nodes are projected into a common space. This allows non adjacent nodes to communicate immediately whilst retaining linear complexity. The model performs well on a range of benchmarks and can reduce problems with over-s... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable suggestions for our paper. Under your guidance, we have discovered new advantages of $N^2$ compared to uniform connections and rewiring. We also kindly refer you to the PDF in the global response for the new figures/tables during the rebuttal due to the limited... | Summary: This paper proposes an adaptive message passing scheme for Graph Neural Networks that is based on learnable "pseudonodes" which, to a certain extent, decouple the paths along which node features are propagated from the topology of the underlying graph. Both pseudonodes and regular nodes in the underlying graph... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestions that help us improve our paper. We respectfully refer you to the PDF in the global response for the new figures/tables during the rebuttal due to the limited character number.
**W1&Q1(a)**: Why $N^2$ mitigates over-smoothing/squashing.
**Response... | Summary: The paper considers the problem of flexible message passing with low complexity in GNNs. To tackle this concern, the paper proposes a novel dynamic message-passing mechanism for GNNs via projecting graph nodes and learnable pseudo nodes into a common space with measurable spatial relations. Based on this dynam... | Rebuttal 1:
Rebuttal: Thank you very much for your support and valuable suggestions that further broaden the potential application of our method to graph interpretations and other neural networks. We will keep studying these problems in the future.
**Q1**: Can we learn an additional pseudo node to provide global guida... | Rebuttal 1:
Rebuttal: We appreciate all the valuable suggestions and the time the reviewers take on our work. Your reviews help us a lot to improve the manuscript.
**Summary of strengths**. We sincerely appreciate that you find our method:
- novel and interesting (reviewers Qj5n, ZLZY, 4Q2o, and UBFx);
- clearly expl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization? | Accept (poster) | Summary: In this paper, the authors systematically analyzed the generalization error and optimization error of the stochastic compositional optimization problems (for black-box cases).
Strengths: 1. The paper is well-written and well-organized.
2. The paper provides the generalization analysis and optimization anal... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** What are the key challenges of extending the theoretical analysis for SCO problems from white-box cases to black-box cases? What are the technical tools employed to address these key challenges? What are the n... | Summary: This work presents the generalization upper bound for two stochastic compositional optimization methods, SCGD and SCSC, under convex and non-convex setting. For convex setting, the presented generalization bound is tighter compared to the existing work and it matches the generalization bound of SGD for optimiz... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** ...Can the authors clarify what V (bounded variance) and M (bounded function) stand for?
**A1:** Thanks for your constructive comment. As you mentioned, Lipschitz and smoothness are both assumed in our Theore... | Summary: This paper studies stability-based generalization bound for SCGD and SCSC, as well as the convergence rated of their black-box variants. The authors provide sharper generalization bounds for these algorithms, and the first convergence bounds for the black-box variants of these algorithms under PL-conditions.
... | Rebuttal 1:
Rebuttal: We are grateful to you for your valuable comments and constructive suggestions.
**Q1:** experiments might still be helpful
**A1:** Thanks for your constructive comment. We also think some experiments might still be helpful to demonstrate our more practical parameter selections than [1]. There a... | null | null | Rebuttal 1:
Rebuttal: Thanks for the comments of all reviewers. Considering the limitation of character count, we provide the table of the main differences among our main results in "global response" and upload a PDF including this table. As mentioned in the **A3** (the Rebuttal to Reviewer Xsnq), we have added the tab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling | Accept (poster) | Summary: This paper presents a new deep generative model, FlexSBDD, which advances the field of structure-based drug design (SBDD) by accounting for the flexibility of proteins when generating 3D ligand molecules. This approach addresses the shortcomings of traditional SBDD methods that assume proteins are rigid, leadi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation and valuable comments! We hope our following responses can properly address your questions.
**Comment 1**: Quality of Apobind-generated apo structures and model-generated holo structures, which is not fully discussed and evaluated
**Response 1**: Thanks... | Summary: In this research paper, the authors introduce FlexSBDD, a novel model that employs flow matching for the generation of flexible protein-based molecules. Initially, the model sample a noisy ligand based on an empirical distribution. Subsequently, it conducts flow matching, performing on both geometric character... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation and valuable comments!
**Comment 1**: As discussed in the Dynamic-Bind, many flexible aspects of protein residues involve changes in backbone atoms, such as transitions from DFG-in to DFG-out conformations. The current implementation of FlexSBDD does not... | Summary: The author identified an important missing factor in current SBDD modeling, i.e. protein structural change upon binding, and proposed an E(3)-equivariant flow matching framework named FlexSBDD that jointly models protein flexibility and molecule generation. This paper augmented apo structures for each holo pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and appreciation!
**Comment 1**: Ablation studies suggest that the biggest performance gain come from data augmentation. However, I feel that augmentation shouldn't matter that much, since the whole pipeline only implicitly utilizes the protein structur... | Summary: In this paper, the authors focus on the flexible protein setting in the structure-based drug design task. They propose a method named FlexSBDD, which is based on flow matching and utilizes E(3)-equivariant neural networks. The experiments show the advantages of the proposed FlexSBDD.
Strengths: 1. This paper ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments!
**Comment 1**: The method does not explicitly model the interaction between the ligand and the protein, especially the pocket. The authors might consider building an external interaction graph between the residues in the pocket and the atoms of the... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments and appreciation from all the reviewers!
FlexSBDD has the capability to model both the backbone and the sidechain structural changes. During rebuttal, we perform a comprehensive quantitative study on proteins with DFG-in/out confirmations to evaluate whether Fl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover | Accept (poster) | Summary: Traditional SNN methods for combinatorial optimization necessitate the use of penalty terms with slack variables to maintain feasibility constraints. The paper introduces a novel Spiking Neural Network (SNN) formulation designed to solve the Hypergraph Minimum Vertex Cover (HMVC) problem without requiring slac... | Rebuttal 1:
Rebuttal: Thanks for the feedback.
1. Despite the relatively small problem instances that can be solved by the neuromorphic hardware available (Loihi 2), we would like to point out that the selected problem instances were already sufficient to convincingly illustrate the benefit for the proposed approach. ... | Summary: This paper presents a novel approach to solving the Hypergraph Minimum Vertex Cover (HMVC) problem using spiking neural networks (SNNs). The authors introduce a slack-free formulation (SF-HMVC) that directly translates the constraints of the HMVC problem into the dynamics of SNN neurons, specifically targeting... | Rebuttal 1:
Rebuttal: Thanks for the feedback.
1. We included Algorithm 1 in the paper to make it self-contained, however, if accepted, we will move it to the appendix in the camera-ready version.
2. The motivation of the $\mathbf{W}$ matrix is to capture the connection strengths between neurons within the SF-HMVC SN... | Summary: The paper presents a novel approach to solving the Hypergraph Minimum Vertex Cover (HMVC) problem using Spiking Neural Networks (SNNs) on neuromorphic hardware, which is a significant contribution to the field of combinatorial optimization in neuromorphic computing. Here's a detailed review based on various as... | Rebuttal 1:
Rebuttal: Thanks for the feedback.
- Note that we have compared against two contemporary (non-neuromorphic) optimization techniques: integer linear programming (ILP) and quadratic unconstrained binary optimization (QUBO), both implemented using a leading optimization sofware (Gurobi) and executed on an Int... | Summary: The paper presents a method that solves a specific type of problem of combinatorial optimization (hypergraph minimum vertex cover) through spiking neural networks. The method, tested on small versions of the problem, enables a neuromorphic hardware system made by Intel to arrive at a result in cases where prev... | Rebuttal 1:
Rebuttal: Thanks for the feedback.
1. We have indicated in L79 the practical applications of HMVC in "computational biology [9], computer network security [19], resource allocation [7] and social network analysis [23]." More fundamentally, HMVC is a general problem with many related formulations (set cover... | Rebuttal 1:
Rebuttal: We thank the AC for handling our submission and the reviewers for their insightful comments.
K44F, cGCZ and 9cvo thought that the paper was technically solid. Que6 also did not report any technical flaws.
Que6's concerns were mainly on relevance and significance: note that our focus on spiking n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AHA: Human-Assisted Out-of-Distribution Generalization and Detection | Accept (poster) | Summary: The paper presents a novel approach to the problem of OOD generalization and detection. The authors introduce the AHA (Adaptive Human-Assisted OOD learning) framework, which aims to enhance both out-of-distribution (OOD) generalization and detection by strategically leveraging human-assisted labeling within a ... | Rebuttal 1:
Rebuttal: We thank you for the detailed comments and questions, which we address in detail below.
> *W1. While the paper demonstrates strong results, it is not clear how the AHA framework scales with larger and more complex datasets.*
We tested the AHA framework on the larger and more complex ImageNet ben... | Summary: This paper proposes to address both out-of-distribution detection and generalization within one joint framework under human-assistance. The proposed method first utilizes a noisy binary search algorithm to identify the most informative samples to be labeled. Then, it continues to annotate these samples with hu... | Rebuttal 1:
Rebuttal: We thank you for your thorough comments and questions, which we address in detail below.
> *W1. AHA may only work under a rather strict assumption.*
We would like to clarify that the unlabeled wild data distribution $S_{\text{wild}}$ is totally different from the test distribution. Our driving m... | Summary: This paper introduces a novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild. Extensive experiments validate the efficacy of AHA.
Strengths: 1. this paper is well wr... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and comments. We address each comment below in detail.
> *W1. There exists a strong assumption that the weighted densities of semantic and covariance ood should equalize*
Thank you for pointing out this potential misunderstanding. We would like to clarify ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution | Accept (poster) | Summary: This paper studies how to search for the best heuristics for combinatorial optimization problems (COPs) with large language models (LLM). The key idea is to use genetic programming (GP) to dynamically update the heuristics with LLM. Short-term reflection and long-term reflection are also incorporated in the GP... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in reviewing our work, and the insightful questions and suggestions you raised. We respond to your comments below.
> W1: Reliance on existing heuristic or neural solvers.
ReEvo can discover new heuristics independently but integration currently leads to... | Summary: The paper presents a LLM-enhanced evolutionary algorithm to solve diverse combinatorial optimization problems. The method is evaludated on various COPs and heuristics, however, the advantage of proposed method compared to recent ML4CO methods that does not use LLM requires more specification.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in reviewing our work, as well as the insightful comments and questions you raised.
> W1: Details of reflections.
We will revise the writing for better clarity according to your suggestions. An example of both short- and long-term reflection is illustrat... | Summary: This article proposes a large language model (LLM) assisted evolutionary computation (EC)-based method, to solve combinatorial optimization problems. It incorporates a reflection mechanism to enhance performance in black-box settings.
Strengths: 1. The method shows an impressive performance on several CO prob... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in reviewing our work, as well as the insightful comments and questions you raised.
> W1: The reflection technique has been well-developed and widely used in prompt engineering and code generation.
Thank you for raising this point. Our work introduces a ... | Summary: The paper's contributions consist of multiple sections, which could be orthogonal:
1. The paper proposes a new ReEvo algorithm within the class of string-mutation evolutionary search methods. The core idea is to add a "Reflection LLM" which observes patterns over the history of trials, and proposes a new gener... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in reviewing our work, the insightful questions and suggestions you raised, and your recognition of our contributions. We respond to your comments below.
> W1: The paper's evolutionary focus may be limiting its appeal and perceived novelty to a broader au... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection | Accept (oral) | Summary: This paper introduces a novel end-to-end algorithm named E2E-MFD for multimodal image fusion and object detection. Unlike existing joint learning methods, its key innovation lies in the synchronous joint optimization approach, simplifying the fusion detection process into a single training step and enhancing e... | Rebuttal 1:
Rebuttal: # Reviewer cEzg
Thank you for your feedback.
**A1**: Regarding the typo in Equation (7) and the inconsistency with $\mathcal{L}_{\text {SSIM }}$ on line 179, we apologize for the oversight. We have revised line 179 to ensure consistency with the description in Equation (7).
**A2**: The dashed ... | Summary: This paper focuses on the task of multimodal image fusion detection, combining texture details and target semantic information. An end-to-end multimodal fusion detection algorithm named E2E-MFD is proposed, which employs synchronous joint optimization, differing from existing independent or cascaded joint meth... | Rebuttal 1:
Rebuttal: # Reviewer 6qWk
Thank you for your constructive insights.
**A1**: We acknowledge your point regarding comparing YOLOv5s with the latest version of YOLO. We chose YOLOv5s as it represents a well-established baseline in the field, and our focus was on evaluating the results of the detection accura... | Summary: This paper proposes a joint learning diagram for multimodal fusion and object detection with task alignment module.
The suggested network achieves SOTA performance with affordable computational cost.
Strengths: This paper presents a novel approach to learning image fusion and objection detection in a synchro... | Rebuttal 1:
Rebuttal: # Reviewer G1Ro
Thank you for your feedback.
**A1**: Reviewer 6qWk and reviewer cEzg both affirmed the novelty of our paper. As reviewer cEzg commented, "The idea of simultaneously learning image fusion (MF) and object detection (OD) tasks to mutually benefit each other is intriguing and reasona... | Summary: This paper proposed an end-to-end algorithm for multimodal fusion detection, experiments on fusion and detection tasks showed the better performance than some methods.
Strengths: This paper proposed an end-to-end algorithm with one-stage training process，for multimodal fusion detection, experiments on fusion ... | Rebuttal 1:
Rebuttal: # Reviewer bix9
Thanks for your comments.
**A1:** This is a common default setting in the field such as "CVPR23 MetaFusion". V (brightness) channel can effectively measure the algorithm’s ability to handle low-light environments.
**A2:** We have made revision in Tab. 2. It's common to observe fl... | Rebuttal 1:
Rebuttal: # Global Author Rebuttal
We thank the reviewers for their comments. We are encouraged that the reviewers appreciate the **sound technology** (6qWk, cEzg), **well-organized writing** (bix9, 6qWk, cEzg), **certain influence** (bix9, 6qWk, cEzg), **clear motivation** (bix9, 6qWk, cEzg), and **excelle... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation | Reject | Summary: This paper proposes a methodology called Rockafellian Relaxation (RR) to mitigate the impact of labeling errors in neural network training. The method is architecture-independent and integrates concepts from adversarial training to address dataset imperfections robustly. Through theoretical justifications and ... | Rebuttal 1:
Rebuttal: ## **Question**
Scalability: How does the RRM scale in terms of computational cost and effectiveness with larger and more complex datasets?\\
## **Rebuttal**
Each iteration of RRM, as outlined in Algorithm 1 on page 5, is comprised of two tasks: (1) a gradient step and (2) a poly-size (in training... | Summary: This work proposes a loss reweighting scheme to train models in the presence of label errors. When training an NN with empirical risk minimization in this setting, one would want to assign a weight of zero to all datapoints that are mislabeled and a weight of one to all datapoints that are correctly labeled. T... | Rebuttal 1:
Rebuttal: ## **Question**
Could you expand on what you mean by RRM producing sparse weight vectors? Does assigning zero weight to data points with high losses result in sparsity in the parameter space?
## **Response**
In equation (3), the expressions $(\frac{1}{N} + u_i)$ are to be understood as the weight... | Summary: The paper presents Rockafellian Relaxation (RR), a new method to address labeling errors in machine learning datasets. RR is a loss reweighting technique that enhances neural network robustness against labeling errors and adversarial attacks, working across various data domains and model architectures. The key... | Rebuttal 1:
Rebuttal: # Question
Could the authors provide insights into the computational complexity of the RR algorithm...?
# Response
Each iteration of RRM, as outlined in Algorithm 1 on page 5, is comprised of two tasks: (1) a gradient step and (2) a poly-size (in training data size) linear program that constitutes... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and input! We have provided a separate rebuttal to each of you, and hope that we have addressed all questions/concerns. Looking forward to engaging with you in the discussion period to come. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PMechRP: Interpretable Deep Learning for Polar Reaction Prediction | Reject | Summary: This paper attempts to address the problem of low interpretability of reaction prediction methods by proposing modeling step-wise polar reactions. To model such mechanisms it uses an existing dataset PMechDB. The authors propose an approach to model such reaction by first selecting the right atoms to react fro... | Rebuttal 1:
Rebuttal: Reviewer Comment:
The paper has substantial clarity problems:
Table captions are insufficiently informative, requires going deeper into the text to understand what results are actually presented (e.g. 'Table 3: Top-N Accuracy of Trained Models')
Figures 5 and 6 are formatted inconsistently with th... | Summary: Current reaction prediction models lack interpretability for chemical reaction prediction. This paper evaluates the various machine learning models on the PMechDB dataset which contains polar elementary steps. Besides, this paper proposes a new system: PMechRP, which achieves the highest top-5 accuracy.
Stren... | Rebuttal 1:
Rebuttal: Reviewer Comment:
This paper seems like a technique report. The main conference track is not suitable for this paper. I think the dataset & benchmark track is more suitable. Writing is poor.
Response:
We address a very important problem in Chemistry, the prediction of polar reactions. Polar react... | Summary: Previous reaction prediction models formulate the forward chemical reactions in an end-to-end manner, which only considers the input state and output state while ignoring intermediate states describing the electron redistribution changes. This work tries different models on a new benchmark dataset PMechDB. Exp... | Rebuttal 1:
Rebuttal: Reviewer Comment:
The technical contribution of this work is very limited. This reviewer does not see enough improvements from the algorithm side. Also, it seems the dataset is not proposed by this work. The contribution of this work is overall limited.
Response:
For novel ML architectures, we pr... | Summary: the paper describes a new approach to predict polar reaction mechanisms, which is the most important class of chemical reaction mechanisms. this can be quite useful for chemical reaction prediction.
this reviewers rating is based on the current presentation of the manuscript, if the authors are willing to en... | Rebuttal 1:
Rebuttal: Reviewer Comment: Model and data processing descriptions are quite short and should be expanded, and presented coherently in one location in the manuscript. From the description in the manuscript I would likely not be able to re-implement the method
Response: The transformer models are publicly a... | Rebuttal 1:
Rebuttal: We have carefully considered all comments given by the reviewers, and wish to thank them for helping us enhance the quality of the manuscript. In response to the reviewers’ feedback, we have prepared an additional pdf with figures to address specific comments.
Pdf: /pdf/4cdcae9663eb69eb63e843d1608... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing | Accept (poster) | Summary: The paper introduces COrrespondence-guided Video Editing (COVE), a method to improve video editing with pretrained text-to-image (T2I) diffusion models. It addresses the challenge of maintaining temporal consistency by using diffusion feature correspondence. COVE identifies and samples highly corresponding tok... | Rebuttal 1:
Rebuttal: Dear Reviewer nziD,
Thank you for your time and thoughtful feedback on COVE. We are pleased that you recognize the effectiveness of the sliding window strategy. We provide our feedback as follows.
# Experimental performance
> Experimental performance improvement seems limited. Most of the editing... | Summary: In this paper, the authors tackle the problem of video editing using Text-to-Image diffusion models. To achieve this, the authors make use of strong diffusion model’s feature correspondence abilities. The authors propose a sliding-window based strategy to track features of source video based on correspondences... | Rebuttal 1:
Rebuttal: Dear reviewer Ht4d,
Thanks for your comprehensive review and insightful comments on our paper. We appreciate that you recognize the advantages of training-free and impressive results of our method. The response to your concerns is shown below.
# Temporal layers and 3D Unet
> The paper clearly st... | Summary: This paper focused on improving the temporal consistency of video editing. They propose to leverage the inherent diffusion feature correspondence with a sliding-window based strategy. With this design, the tokens in noisy latents can be sampled based on the “one-to-many” correspondence. The experiments demonst... | Rebuttal 1:
Rebuttal: Dear Reviewer LETZ,
Thank you for your comprehensive and detailed review of our paper and the recognition of our work's clarity and effectiveness. We provide our feedback as follows.
# Accuracy of correspondences
> The proposed method highly relies on the correspondences of diffusion features $\c... | Summary: The paper proposes using the correspondence features already exist in diffusion models to find matched tokens between different frames in a video for consistent video editing. The motivation is that video editing models using optical flow to find matched features can exhibit one-to-many matching issue which li... | Rebuttal 1:
Rebuttal: Dear Reviewer sLJK,
Thanks for your time and thoughtful review. We appreciate your recognition of the satisfying experimental results and clear writing of the paper. We provide our feedback as follows.
# Optical flow model for one-to-many correspondences
> Optical flow is $\cdots$ by an optical f... | Rebuttal 1:
Rebuttal: # Overall reply for all reviewers
We thank all reviewers for their constructive comments. We have responded to each of the concerns below. In the following response, Figure 1-12 and Table 1-4 are in our main paper, ***Figure 13-16 are in the uploaded PDF of the rebuttal, and Table 5-10 are in our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising | Accept (poster) | Summary: This paper introduces AsyncDiff, an acceleration framework for diffusion models that transforms the traditional sequential denoising process into an asynchronous process. The key insight is that hidden state features in consecutive sampling steps exhibit high similarity. Therefore, feeding the output of the pr... | Rebuttal 1:
Rebuttal: ## **Q1: Can the similarity of the hidden states be quantitatively measured? For instance, does a low MSE between hidden states indicate that the two states are “similar”?**
Thanks for the valuable comment. We provide quantitative analysis and visualization of the hidden state similarities in **Fi... | Summary: This paper proposes AsyncDiff, a plug-and-play acceleration scheme that enables model parallelism across multiple devices. The core method involves dividing the diffusion model into multiple components and executing the inference in parallel. This is facilitated by the high similarity between hidden states in ... | Rebuttal 1:
Rebuttal: ## **Q1: Some concepts, such as the “dependency chain” in ABS, are not well explained. It would be beneficial if the author could provide a minimal explanation for these concepts**
Thank you for the valuable feedback. This is indeed a question that requires further explanation. In the diffusion pr... | Summary: This work introduces an acceleration method for diffusion models by distributing the model blocks across multiple GPUs and running different blocks asynchronously. The core motivation of this paper lies in the observation that the hidden states of a block exhibit high similarity across consecutive diffusion st... | Rebuttal 1:
Rebuttal: ## **Q1: Is the communication cost huge compared to the inference cost? Will there be issues if this is extended to 8 or 16 GPUs? Is this method still competitive when the number of devices exceeds 8, where cross-node communication becomes substantial?**
Thanks for the valuable comment, this is in... | Summary: This paper proposes AsyncDiff to enable diffusion model parallelism across multiple devices and achieve a very impressive speedup with negligible degradation. Specifically, the denoising model is split into several components, each assigned to a different device. The conventional sequential denoising process i... | Rebuttal 1:
Rebuttal: ## **Q1: Whether AsyncDiff can be combined with other diffusion samplers such as DPM-Solver**
Thanks for the valuable feedback. AsyncDiff is a universal method that can be used with various samplers, including the DPM-Solver. Table 1 below provides the quantitative evaluation of AsyncDiff on the S... | Rebuttal 1:
Rebuttal: Dear Chairs,
We sincerely appreciate the time and effort you have spent evaluating our submission, and we look forward to the discussion stage. We will include the review stage results in the appendix of our next version.
Pdf: /pdf/e42b2193df26cdfbec8899c3a3ff728ae36e1146.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AP-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models | Accept (poster) | Summary: This paper proposes a new task called MGAPO, aimed at addressing the generalization problem of existing APO methods on unseen text-to-image generation models. To achieve this, the authors present a two-stage method called AP-Adapter. In the first stage, keyword prompts are generated through a large language mo... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We will address the concerns below.
Q1. The authors did not conduct relevant ablation experiments on CLIP.
A1. The computation of domain prototypes indeed relies on the image encoder. Moreover, domain prototypes are concatenated with text representation... | Summary: The paper proposes model-generalized automatic prompt optimization (MGAPO), an automatic prompt optimization (APO) method which trains on a set of known models to enable generalization to unseen models during testing. MGAPO presents significant challenges, a perspective missing in previous methods. MGAPO inclu... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We will address the concerns below.
Q1. The quality of writing can be improved.
A1. Thank you for your suggestions. We will optimize the writing in the methodology section to make it more concise and easy to understand.
Q2. What’s the difference betwee... | Summary: 1. The authors propose model-generalized automatic prompt optimization (MGAPO), which targets the effectivenss of automatic prompts on unseen models.
2. The authors propose AP-Adapter which include in-context learning based prompt rewriting and prototype-based prompt adaptation.
3. The authors build a multi-... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We will address the concerns below.
Q1-1. What’s the the main reasons for this substantial gap between automatic and manual prompts?
A1-1. The effectiveness of manual prompts lies in their iterative nature, allowing humans to refine prompts based on the... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Recurrent neural networks: vanishing and exploding gradients are not the end of the story | Accept (poster) | Summary: This paper studies the vanishing and exploding gradient phenomenon in RNNs. In particular, it answer the question: "is solving those issues really enough to ensure well-behaved loss landscapes?" This paper reveals the importance of the element-wise recurrence design pattern combined with careful parametrizatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback. We address their concerns below.
**Weaknesses**
1. **Wide-sense stationarity.** A discussion of this assumption was indeed missing. We address it in the global response. In short, this assumption is standard in the analysis of linear time filter... | Summary: In this work, the authors present an analysis of Recurrent Neural Networks (RNNs), focusing on problems which impede optimization as the length of input sequences and model memory increase. The authors first focus on the well-studied problem of vanishing/exploding gradients, arguing that this problem by itself... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback on our paper and address their concerns below.
1. **Typos**. We thank the reviewer for their detailed report of our typos. It will help us make the manuscript as clear as possible and without typos. Even though the system does not allow for the uploa... | Summary: The paper explores challenges RNNs face in learning long-term dependencies. While generally attributed to the exploding and vanishing gradients problem (EVGP), the authors reveal that as a network's memory increases, parameter changes cause an explosion of the second moment of hidden states and their gradients... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s very positive feedback and their valuable input regarding these additional references and important details. We address their questions below.
> in l. 180 you assume $\gamma$ is independent of $\lambda$ yet in the Diff. eq. in l. 182 you introduce the dependency again... | Summary: The paper discusses "the curse of memory" which is the hypersensitivity of hidden states to parameters as the memory increases. This can lead to optimization issues, even if the exploding/vanishing gradient issue is addressed. The authors discuss solutions to this problem: complex diagonalization, normalizatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and address their concerns below.
**On the contributions of the paper**
> What are the main contributions of the paper?
We provide a detailed list of our contributions in the global answer.
> If the solution to the curse of memory is not the contribution ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their high-quality feedback and their positive comments about our paper. We answer the main points below and respond to each reviewer more specifically in corresponding threads.
**Contributions-limitations.** Many reviewers highlighted that the contributions a... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper provides a theoretical and practical analysis on the difficulties of training recurrent neural networks. In particular, given the current rise of interest in leveraging recurrent mechanisms for long sequence processing -- due to novel architectural components and solutions (deep state-space models, l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging review. We answer their questions below:
1. **SSM terminology.** We agree with the reviewer that the SSM term has been overloaded. This is the reason why we try to use the RNN terminology as much as possible. That said, we appreciate the suggestion of t... | null | null | null | null | null | null |
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain | Accept (poster) | Summary: The paper presents a novel framework for the long-standing monocular depth estimation. The task is first formulated as a progressive regression in the discrete cosine domain. The authors propose two modules: the PPH module progressively estimates higher-frequency coefficients based on previous predictions, and... | Rebuttal 1:
Rebuttal: 1. **Clarification about Contribution:** Thank you for your comment. We believe there may be a misunderstanding regarding our contributions for the following reasons:
1. As stated in lines 53-54, our main contribution is the first to formulate monocular depth estimation as a progressive regress... | Summary: The paper introduces a frequency domain-based method for monocular depth estimation. The proposed method begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. The prop... | Rebuttal 1:
Rebuttal: 1. **Discussion on TOFDC Result:** Thanks for your valuable suggestion. The TOFDC dataset is challenging due to its small number of training images with limited diversity. NewCRFs, PixelFormer, and IEBins independently predict pixel-wise depth without modeling the correlations among them. We belie... | Summary: This paper introduces DCDepth, a framework designed to tackle the long-standing monocular depth estimation task. In general, the proposed methods are quite interesting, the motivations for this work are clear, the experiments conducted are comprehensive, and the paper is well-structured.
Strengths: This paper... | Rebuttal 1:
Rebuttal: 1. **Table Result Presentation:** Thank you for your valuable feedback! Based on your suggestions, we plan to update our tables in the following two aspects:
1. Updating the accuracy metrics in Tables 1 and 2 to percentages and multiplying the error metrics in Table 1 by 100, keeping two decim... | Summary: The author has proposed the DC depth, which aims to predict a depth map from a monocular image.
The authors introduce a novel technique that implements depth estimation of the frequency coefficients from the discrete cosine domain and enables modeling the local depth correlations.
The author conducted experime... | Rebuttal 1:
Rebuttal: 1. **Clarification of Experimental Comparison:** Thank you for your comment. We would like to clarify the following points:
1. Our research goal is different from that of Depth Anything and Metric3D. We focus on the novel network and algorithm design for monocular depth estimation task, while ... | Rebuttal 1:
Rebuttal: Dear Reviewers and Chairs,
Thank you for your constructive feedback and efforts.
We have individually responded to each reviewer’s comments. In the submitted PDF, we have visualized the prediction evolution of our method in both the spatial and frequency domains.
If you have any questions, plea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning symmetries via weight-sharing with doubly stochastic tensors | Accept (poster) | Summary: In contrast to many works that impose strict architectural constraints to parameterize neural networks that are exactly equivariant to a known group, this work considers learning approximate equivariances to unknown groups. This is done using soft weight sharing schemes, where doubly stochastic tensors are lea... | Rebuttal 1:
Rebuttal: **Response:**
Dear reviewer, we thank you for your time and valuable comments, which we address in the following.
- On the use of element-wise activations: Regular group convolutions offer the benefit of having scalar feature field co-domains, permitting the use of any point-wise activation fun... | Summary: Overview:
This paper claims to build upon a long line prior work on symmetry detection and learning equivariant kernels. [18,19,20,22, 25-31. Eespecially reference [30] which is an identical weight and parameter sharing scheme that learns and discovers equivariances solely by row-stochastic entries. This pa... | Rebuttal 1:
Rebuttal: Dear reviewer, we thank you for your time and comments, which we address in the following.
Regarding the motivation for double stochasticity and its comparison to prior works, we point out that there are key benefits about employing double stochasticity over single stochasticity:
- Theoretical m... | Summary: The paper proposes a symmetry discovery through learning parameter-sharing in weight matrices. The parameterization relies on relaxing underlying permutation matrices by transforming them as doubly stochastic matrices. In combination with additional regularization, the parameterization can be used to successfu... | Rebuttal 1:
Rebuttal: Dear reviewer, we thank you for your time and valuable comments, which we address in the following.
Regarding employed regularizers, we acknowledge that the motivation could have been more explicitly mentioned, and we provide some added details:
- Entropy Regularizer: The primary motivation for ... | Summary: This paper introduces a parameterisation that contains the ability to represent weight tying corresponding to arbitrary (?) group equivariances. In practice it can represent interpolations of weight tying, but it is argued that this is a feature (not a bug!) since strict equivariance is often too strong a cons... | Rebuttal 1:
Rebuttal: **Response:**
Dear reviewer, we thank you for your constructive feedback and engaged questions, which we address in the following.
- We thank the reviewer for pointing out the connection of our proposed method to low-rank approaches. It is possible to make our doubly stochastic matrices converge ... | Rebuttal 1:
Rebuttal: # General response
We are grateful for the time, effort and invaluable feedback provided by the reviewers. We next address general points raised jointly amongst reviewers, and proceed to respond to specific comments in the individual author rebuttals below.
Firstly, we are glad that you **found ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boundary Matters: A Bi-Level Active Finetuning Method | Accept (poster) | Summary: In this paper, the authors aim to address the problem of active fine-tuning learning. The difference with active learning is that active fine-tuning models are pre-trained rather than trained from scratch. Therefore, the characteristics of the pre-trained model are important for this problem. While the uncerta... | Rebuttal 1:
Rebuttal: >***Q1: Fine-grained classification?***
**R1:** Following your suggestion, we utilized the CUB-200-2011 dataset, which includes $200$ bird species with a total of $11,788$ images. According to the default configuration of the dataset, $5,994$ samples are used for training, with the remainder used... | Summary: The paper proposes an active fine-tuning method that considers both diversity and uncertainty. This method selects uncertain samples through an unsupervised denoising approach and boundary score evaluation. The efficiency and effectiveness of this method, which involves selecting central and boundary samples, ... | Rebuttal 1:
Rebuttal: >***Q1: Figure 2 should be modified.***
**R1:** Thank you for your suggestion. In the revised version, we will enhance the figure to improve its detail and clarity.
>***Q2: Is there consistency between the decision boundaries of the K pseudo-classes and the decision boundaries of the true task c... | Summary: In this paper, the authors introduce a novel Bi-Level Active Finetuning Framework (BiLAF) designed to address the limitations of existing active learning methods in the context of the pretraining-finetuning paradigm. The framework aims to optimize sample selection for finetuning models within a limited annotat... | Rebuttal 1:
Rebuttal: >***Q1: Threshold: Whether BiLAF requires a certain threshold of fine-tuning data to be effective?.***
**R1:** This is an excellent question. The performance gap with just 0.5% of CIFAR-10 data indeed triggers considerations about scale. In traditional active learning, the applicability of differ... | Summary: This paper proposes a Bi-Level Active Finetuning Framework (BiLAF) for optimizing sample selection in the pretraining-finetuning paradigm. BiLAF combines global diversity and local decision uncertainty through two stages: core sample selection and boundary sample selection. Without requiring labels, the method... | Rebuttal 1:
Rebuttal: ***Important Notes***. Reviewer’s recognition of "numerous hyperparameters can cause instability in different scenarios" in Limitations is not justified. Except *Core Number*, we **consistently employs the same hyperparameters across all six different tasks**, such as *nearest neighbors number*, *... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their time and insightful feedback. Overall, we are heartened by their recognition of our paper's clear writing and structure (V2Vc, 2Ep4, 8trC, kN8s), insightful balance of diversity and uncertainty (2Ep4, kN8s, vV4Z), technical novelty and effectiveness (2Ep... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces BiLAF, a novel approach for selecting boundary examples alongside core samples to enhance the fine-tuning of pre-trained models for downstream tasks. Specifically, the boundary selection strategy leverages the distinction between intra- and inter-class distances within the pre-trained fea... | Rebuttal 1:
Rebuttal: >***Q1: Concern about pre-trained features. Would there be a consistent improvement if we first apply unsupervised pre-training on the unlabeled fine-tuning set?***
**R1:**
1. Our main experiment utilized a checkpoint pre-trained on ImageNet using DINO. We conducted studies both **on consistent ... | null | null | null | null | null | null |
TableRAG: Million-Token Table Understanding with Language Models | Accept (poster) | Summary: This paper primarily concentrates on the scalability challenges associated with encoding entire tables as input for LLM reasoning. It introduces a retrieval-augmented generation (RAG) framework, named TableRAG, which utilizes query expansion along with schema and cell retrieval to identify essential informatio... | Rebuttal 1:
Rebuttal: ## Generalizability
We appreciate the reviewer's question regarding the generalizability of our schema-cell retrieval method. It's important to note that our work focuses on general TableQA, which often involves complex understanding and reasoning beyond simple table manipulation, as shown in the... | Summary: The paper introduces TableRAG, a framework that enhances LM-based table understanding by incorporating query expansion and retrieval mechanisms. TableRAG aims to improve the performance of large-scale table understanding tasks by efficiently encoding data and utilizing precise retrieval techniques. The framewo... | Rebuttal 1:
Rebuttal: ## Contribution
> The contribution of the paper is relatively small. The author mainly builds upon the original Row Column Retrieval and further reduces the retrieval cost by Scheme Cell Retrieval. The original work has included column selection, but this paper only further reduces the returned ro... | Summary: This paper introduces a novel approach to retrieving cell values and database schema within the table question answering domain. The method operates as follows:
1) It expands the initial question by identifying potential queries for column retrieval or cell value retrieval.
2) For each query generated in the f... | Rebuttal 1:
Rebuttal: ## Baselines
> Papers like CodeS [1] have proposed using BM25 retrieval to find cell values, which would serve as an excellent baseline for comparison. Additionally, schema linking—the process of identifying the correct rows and columns—is well-explored. Approaches proposed in studies like TaBERT ... | Summary: The paper introduces TableRAG, a novel framework that improves LM-based table understanding by incorporating advanced query expansion and retrieval mechanisms. TableRAG addresses the critical scalability challenges associated with large-scale tables by efficiently encoding data and employing precise retrieval ... | Rebuttal 1:
Rebuttal: ## Novelty
>Many papers about RAG reveals that the accuracy of the evidence is crucially important for the performance, and the idea of this paper is to is another practice in the TableQA domain.
Thank you for highlighting the importance of evidence accuracy in RAG systems. While this principle i... | Rebuttal 1:
Rebuttal: ## General Response
We thank the reviewers for their constructive feedback. They found our solution addresses the critical scalability challenge with superior performance in comprehensive evaluations. The work contributes to a better understanding of LLM capabilities in large-scale table analysis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Equivariant Neural Diffusion for Molecule Generation | Accept (poster) | Summary: This paper presents Equivariant Neural Diffusion (END), a novel diffusion model for 3D molecule generation. The major novelty of END over previous molecule diffusion models lies in adopting a learnable forward process based on neural flow diffusion models. Experiments show that END can achieve good performance... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
We address the reviewer’s concerns/questions here below:
**Limited technical novelty**
As mentioned in our general rebuttal, END is indeed a combination of existing ideas. We however want to emphasize that (1) our work is the first to se... | Summary: Paper presents, END, a diffusion models for 3D molecule generation that
- is equivariant to euclidean transformations and
- includes a learnable forward process.
Specifically, the forward process in the presented models, is defined as a learnable transformation, dependent on both time and data such ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We are happy that our work was positively received by the reviewer.
We address the reviewer’s concerns/questions here below:
**Ablation**
The key component in our method is the learnable forward process. Hence, the logical ablation is wh... | Summary: The Equivariant Neural Diffusion (END) model is a novel approach for molecule generation in 3D that maintains equivariance to Euclidean transformations. Unlike traditional diffusion models that use a pre-specified forward process, END introduces a learnable forward process, parameterized through a time- and da... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We are happy that our work was positively received by the reviewer.
We address the reviewer’s concerns/questions here below.
**Performance Consistency**
Regarding validity, we first note that (1) cheminformatics software implicitly adds... | Summary: This paper proposes an extension of diffusion models dubbed Equivariant Neural Diffusion, which leverages a learnable forward diffusion process to enhance flexibility. The entire framework has been constructed such that physical symmetry, i.e., equivariance/invariance, of the density is preserved. Experiments... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We address the reviewer’s concerns/questions here below:
**Limited technical novelty**
As mentioned in our general rebuttal, END is indeed a combination of existing ideas. We however want to emphasize that (1) our work is the first to seek... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback.
We are pleased that the reviewers [**hMME,5dRq,K86T**] found the presentation of END clear, its construction original **[K86T]**, that it is a potentially significant contribution to the field **[K86T],** and that it demonstrates experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Asynchronous Multi-Agent Reinforcement Learning with General Function Approximation | Reject | Summary: In this paper, the authors study multi-agent reinforcement learning where agents cooperate through asynchronous communications with a central server to learn a shared environment. They consider the following two settings: multi-agent contextual bandits with general function approximation, and multi-agent RL wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the numerous feedback and suggestions to improve our paper. We have corrected the relevant typos and small mistakes, and address the reviewer’s major concerns below:
---
**Q1.** It seems that part of the techniques is from previous results, such as the bonus function or... | Summary: The authors propose two algorithms for asynchronous communication in multi-agent reinforcement learning with generalized value function approximation: Asynchronous-NLin-UCB for context bandit scenarios and Asynchronous-NLSVI-UCB for episodic MDP scenarios. These algorithms achieve near-optimal regret with low ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and thoughtful questions. We have fixed all typos mentioned in the review, and address the reviewer’s notable concerns as follows:
---
**Q1.** Does the proposed scenario adapt the Centralized Training Decentralized Execution (CTDE) framework in MARL... | Summary: This paper studies the asynchronous multi-agent bandit and RL problem with general function approximation (measured by Elude dimension). The main contribution is to establish $\tilde{O}(\sqrt{\text{dim} T})$ regret bound with $\tilde{O}(M^2 \text{dim})$ communication complexity.
Strengths: This paper is well ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the affirmative review, and address their concerns in the following:
---
**Q1.** The dependency of regret upper bound on $H$ is non-optimal. Also, what is the current best lower bound for the communication cost to reach an $T$ regret bound?
**A1.** Regarding the opti... | Summary: This paper studied the distributed federated contextual bandit and federated reinforcement learning (FRL) in the presence of a trusted server. In both problems, nonlinearity and asynchronous communications are explored. Similar algorithms for contextual bandit and FRL that encourage exploration via bonus funct... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our submission in great detail and pointing out errors and shortcomings. We have fixed all the mentioned typos, and we address other notable concerns in detail below:
---
**Q1.** What are the definitions of $\tilde {\beta}_1$ in Theorem 4.3 and $\tilde {\beta}_2... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses | Accept (poster) | Summary: This paper proposes an efficient transformer-based local feature matching approach named ETO. ETO consists of three steps: hypothesis estimation, segmentation and refinement. The first and second steps can obtain coarse matching results whose resolution is similar to LoFTR’s coarse results. The third step can ... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.
1.1 What is the computation manner of the Transformer before the hypothesis estimation step? In line 237, the authors state that "then we perform transformer five times at M_1". Does the transformer contain some cross-attention processes? Or does i... | Summary: The authors propose a local feature matching method that leverages homography to accelerate the transformer-based feature matching pipeline. Additionally, they employ unidirectional cross-attention in the refinement stage to further reduce computational overhead. Experimental results demonstrate the efficiency... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.
1. Though the time usage is decrease, the accuracy is also decrease from Tab.1&2&3.
Yes, you are right. But we claim that the huge improvement in runtime is valuable in realtime applications such as robotics or SLAM.
2. The paper lacks significan... | Summary: This paper proposes a novel framework for efficient transformer-based local feature matching. Transformer-based local feature matching usually contains two stages: a coarse matching stage which applies self-attention and cross-attention on coarse-level features (usually H/8 x W/8) to obtain coarse matches, and... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.
1. The symbols used in the method part are too complicated, making it hard to read.
Thank you for pointing this out and I'm so sorry for it.
2. The symbol $\mathscr{H}$ used in figure 4 is undefined in the text.
Thanks for pointing this out, th... | Summary: This paper presents a local feature matching method based on the multiple homography hypotheses. The paper explicitly introduces the homography hypotheses for coarse matching, combined with homography segmentation, cross-attention, and sub-pixel refinement to obtain fine matching results. The introduction of t... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.
1. The pre-compiled transformer model may have some impact on inference speed, can additional tests be conducted on the efficiency of a normal transformer module?
Without the pre-compiled transformer model, the runtime of ETO will be delayed from ... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.We will correct the problems mentioned by the reviewers.
Here we add some comparative experiments on some recent methods, compared with these methods, ETO still has a high advantage in efficiency. We then provide a graphical representation of how o... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper propose an efficient framework to reduce the computational load of transformer-based matching approaches. It is a coarse-to-fine two-stage solution. During the coarse matching phase, multiple homography hypotheses are estimated to approximate continuous matches. Each hypothesis encompasses few feat... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable comments.
1. Some descriptions in Sec 3.4 need further clarification.
Yes. Due to space limitations, we have included a more intuitive description and illustrations of this step in Sec.2 in the supplementary materials. Specifically, since the corresponding ... | null | null | null | null | null | null |
Higher-Order Causal Message Passing for Experimentation with Complex Interference | Accept (poster) | Summary: The paper considers the problem of total treatment effect estimation under interference, when the interference structure is unknown. It proposes an estimation approach based on fitting a data-driven model for low-dimensional dynamics of the mean and variance of observed experimental outcomes over time, motiva... | Rebuttal 1:
Comment: We thank the reviewer for carefully reading our paper and providing insightful comments. Below we provide responses to the weaknesses and questions you raised.
Response to Weaknesses:
Thanks for helping us clarify the scope of our algorithm. In the introduction, we highlighted our algorithm can ... | Summary: This paper discusses the causal effect estimation under spatiotemporal interference characterized by a dynamic system. The authors are devoted to improving the causal message-passing framework by adding interaction terms of inputs in the summary function $f_\theta$ (a linear regressor). Synthetic and semi-synt... | Rebuttal 1:
Comment: Thank you for reading our work. As outlined in our global response, this research extends the estimation method of [SB23] by adopting a more data-driven approach. We will carefully revise the manuscript to ensure that the unique contributions of our work are clearly discussed and distinguished.
Re... | Summary: This paper studies experimental interference, in which the treatment assignments of one unit affects the outcome of another. The majority of work in this area assumes interference acts through a network, and requires knowledge of that network to reduce bias in the resulting estimator. This paper takes a genera... | Rebuttal 1:
Comment: Thank you for your thoughtful comments. Indeed, the reviewer is right, and as explained in our global response, this work builds on the foundation laid by [SB23] by extending their estimation method. We would also like to clarify that there was no intention to mirror the writing of [SB23]. However,... | Summary: The authors motivate a regression framework using Causal Message Passing to estimate global average treatment effects under some unknown forms of interference. The simplest way to think about their proposed algorithm is that CMP is used to motivate particular sufficient statistics for the interference dynamics... | Rebuttal 1:
Comment: 1. We thank Reviewer p2A8 for helping us make this point more clear. The framework of HO-CMP enables us to learn an arbitrary functional form of \nu_{t+1}(w). In HO-CMP-i, we specifically model this as a linear function of \bar{w}_{t+1}. It is important to note that \pi varies during the experiment... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space | Accept (poster) | Summary: This paper first introduce some common phenomena for TD3 and RAD agents, and then introduce a novel deep reinforcement learning algorithm which perform a novel temporal SVD along the policy learning path called Policy Path Trimming and Boosting (PPTB). This algorithm offers us an angle to view how policy evolv... | Rebuttal 1:
Rebuttal: > Q1: The learning curves of TD3 and TD3-PPTB
We provide the missing learning curves in **Figure 9** (for RAD and RAD-PPTB) and **Figure 10** (for TD3 and TD3-PPTB) of the one-page pdf uploaded.
We hypothesize that the **higher convergence performance** in Ant and Hopper can be explained by **PP... | Summary: This paper investigates the evolving path of policy network parameters in deep reinforcement learning (DRL). The author conducts experiments on multiple tasks in MuJoCo using TD3 and on multiple tasks in DMC using RAD. The findings reveal significant discrepancies in the amount of change among policy parameter... | Rebuttal 1:
Rebuttal: > Q1: Why TD3 and RAD are selected?
The motivation for this work starts from the investigation of the dynamics of policy network parameters. Thus, we choose TD3 and RAD for the following reasons:
- They are popular deep AC methods (note RAD is SAC-based), where an explicit policy network is train... | Summary: Off-policy actor-critic Deep RL has unstable and seemingly oscillatory learning dynamics, which are poorly understood. This paper looks closely at the trajectories taken by policy networks during training. An SVD analysis, performed over sequences of policy parameter snapshots, reveals near-monotonic parameter... | Rebuttal 1:
Title: Missing Rebuttal
Comment: > Q1: The additional investigation for Behavior Cloning policy
We appreciate the reviewer for pointing out this insightful and inspiring point.
We provide additional results in Figure 11 of the one-page pdf to present additional investigation for **Behavior Cloning** in D4... | Summary: This paper investigates the evolution of parameters over time during policy optimization with TD3 and RAD. By analysing the SVD of a matrix containing parameters over time, the authors find that there are a few directions in which the parameters move consistently and many with more oscillations. Using this ins... | Rebuttal 1:
Rebuttal: > Q1: the evaluation of the algorithms. For example, reporting the max return in the evaluation of algorithm or using the standard deviation across runs, whereas the standard error or boostrapped confidence intervals would be more appropriate
Technically, we are not reporting the “max return” in ... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers’ careful review and valuable comments. Please refer to the individual rebuttals for our responses.
In the one-page pdf uploaded, we provide additional results:
- **[(Suggested by Reviewer zpVk) Additional empirical investigation on Behavior Cloning in D4RL ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors examine the trajectories of policy learning in continuous control reinforcement learning tasks.
They begin by measuring how directly parameters go to their destination and observe large detours and differing update behaviour for different layers. They then examine the singular value decomposition... | Rebuttal 1:
Rebuttal: > Q1: “Computing SVD is both time intensive and requires storing a wide range of previous parameters. It therefore requires a lot more compute, which is why such methods are typically not used”
We apologize for causing the reviewers to misunderstand the memory consumption and computational overhe... | Summary: The authors study how parameters evolve in Deep RL. They perform SVD on updates and find that parameters advance along a small number of directions. They then propose a method to trim the policy learning path by focusing the updates on these major directions. They show that their methods improve performance in... | Rebuttal 1:
Rebuttal: > Q1: “Do you think these results apply beyond just RL? I see no reason why this phenomenon is RL-specific. Doesn't the fact that these methods use momentum (e.g. Adam) make this phenomenon obviously true? Does this phenomenon persist when using plain SGD? What about when you observe the gradient ... | null | null | null | null |
Revisiting Differentially Private ReLU Regression | Accept (poster) | Summary: This paper studies the problem of learning a planted (student-teacher) ReLU regression model, privately. The paper proposes two algorithms (DP-GLMtron and DP-TAGLMtron) to do this. For both algorithms, privacy utility tradeoffs are computed. The results do not explicitely contain the ambient dimension $d$, whi... | Rebuttal 1:
Rebuttal: We thank the Reviewer M8TJ for the thorough review and the insightful comments.
**Response to the Weakness 1 and Question 1**
We thank reviewer M8TJ for the constructive feedback and we will include these comments in the updated version, thanks! We acknowledge that our main improvement is highli... | Summary: The paper provides an algorithm for differentially private RELU regression. They claim that their results outperform DPSGD. Additionally, for the case of a small privacy budget, they provide a tree aggregation protocol that balances privacy and utility. Finally, extensive experimental results are provided to s... | Rebuttal 1:
Rebuttal: We thank the Reviewer JyY5 for the valuable review as well as the positive feedback!
**Response to the Weakness 1**
Thank you for your suggestion. We agree that adding a literature survey on privacy-utility tradeoffs would be beneficial. We will include more related work in the revised paper and... | Summary: This paper revisits the problem of differentially private (DP) ReLU regression in overparameterized regimes. The authors propose two novel algorithms, DP-GLMtron and DP-TAGLMtron, which outperform conventional methods like DPSGD. The paper provides theoretical analysis of these algorithms, including privacy gu... | Rebuttal 1:
Rebuttal: We thank Reviewer 7Kd7 for the careful and detailed review as well as the constructive feedback.
**Response to the Weakness 1**
**We kindly disagree with the reviewer's opinion.** It is important to note that our work is theoretical, and our assumptions are standard in theoretical studies of ReL... | null | null | Rebuttal 1:
Rebuttal: To all reviewers:
We would like to thank all the reviewers for their great efforts and insightful comments! Based on their suggestions, we address some common concerns and discuss them in the revised paper.
**1. Motivation for Proposing the DP-GLMtron Algorithm and Focus Away from DP-SGD:**
Our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering | Accept (poster) | Summary: This paper proposes a test-time approach that adaptively modifies the attention map during inference to enhance the consistency and plausibility of novel view synthesis. Experimental results on GSO demonstrate improved performance with the proposed method, and the authors conducted ablation studies to assess t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for insightful feedback and comments.
**1. Setting the Hyperparameters R and alpha.**
All hyperparameters were tuned based on a small random set of objects. Due to limited computational resources, the tuning process was not exhaustive. We found that the method's ... | Summary: This paper proposes a novel approach to generate realistic images from arbitrary views based on a single source image. The authors introduce a test-time method that enhances view synthesis by manipulating attention maps during the denoising process of diffusion models. This process improves geometric consisten... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for insightful feedback and comments.
**1. Additional Qualitative Results.**
We kindly refer the reviewer to General Comment #1 and the supplementary material, where we have included further qualitative results.
**2. Applicability of Zero-to-Hero in Multiview Dif... | Summary: This paper proposes attention map filtering to enhance the novel view synthesis performance of Zero-1-to-3. The method is composed of several changes to the original sampling method, but the main contribution is an aggregation of attention map strategy by resampling the same denoising step multiple times. The ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for insightful feedback and comments.
**1. Effect of AMF on Generation Diversity.**
We agree that excessive use of Attention Filtering (R>>1) may reduce generation diversity, as analyzed in the appendix. We will incorporate these insights into the limitations sect... | Summary: This paper experimentally analyzes which parts are important and responsible for generation artifacts.
To solve it, this paper propose an attention map filtering process.
This process share the similar idea with SGD, which reduces the error of the generation process by repeated sampling.
This paper also propos... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and comments.
**1. Inference cost analysis**
Our proposed modules add a computational overhead to the base model. In the paper, we addressed this by counting the overall number of function evaluations (NFE) and keeping it on par with ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers and ACs for their efforts in reviewing our work.
We are encouraged by the recognition of our work's innovative perspective of attention map filtering as analogous to optimization process (hzMx, 3GQx), applicability and effectiveness (UpfM, qQr5). We are pleas... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SA3DIP: Segment Any 3D Instance with Potential 3D Priors | Accept (spotlight) | Summary: The paper "SA3DIP: Segment Any 3D Instance with Potential 3D Priors" presents a novel method for 3D instance segmentation by incorporating geometric and textural priors to generate 3D primitives. It addresses the limitations of existing methods that rely heavily on 2D foundation models, leading to under-segmen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and detailed feedback.
**W: Innovation of the proposed approach**\
In Fig. 1 of our paper, we demonstrate the existing problem of previous methods, which use under-segmented 3D primitives and inherit the over-segmented 2D masks to the final 3D instance s... | Summary: The paper proposes a pipeline to perform open-vocabulary 3D instance segmentation of scenes, incorporating geometric and RGB information. The method is based on constructing a super-points graph, which is then refined by SAM and a 3D detector (V-DETR). Also, the paper provides an enhanced version of ScanNetV2,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and detailed feedback.
**W1: Novelty of the proposed approach**\
It is true that other methods, including ours, rely on SAM, and **this heavy reliance of 2D segmentation results is exactly what we were trying to avoid by introducing the 3D prior.** In Fi... | Summary: This study introduces SA3DIP, a novel 3D instance segmentation model based on SAM. SA3DIP leverages texture priors from point cloud color channels to generate complementary primitives and incorporates 3D spatial priors when merging 2D masks by integrating a 3D detector. These enhancements enable SA3DIP to gene... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and detailed feedback.
**W1/Q: RGB values as texture prior are not robust enough / Motivations for using color values / More experiments**\
In the first place, our motivation is that we found **distinct instances with similar normal often exhibit differe... | Summary: The paper introduces SA3DIP, a novel method for 3D instance segmentation that leverages both geometric and textural priors to enhance the accuracy of segmentation tasks. The goal is to improve open-world 3D instance segmentation by addressing the limitations of current methods, which often result in under-segm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and detailed feedback.
**W1: Ablation studies on each feature individually**\
We have conducted ablation studies on each feature we intend to exploit (e.g., geometry, texture and 3D space prior). The results are provided in the following tables. APs wit... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback, we appreciate their detailed suggestions. We reply to each reviewer’s questions and concerns in the individual responses, and we have added tables and figures in the attached rebuttal PDF, which we reference and explain in the responses.
We ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning | Accept (poster) | Summary: This paper discusses the safe dataset mismatch (SDM) problem, highlighting how low-reward or unsafe samples in datasets can harm offline safe RL. Conditional distribution shaping (OASIS) is proposed to mitigate this problem by generating high-reward and safe samples via diffusion models and promoting general o... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for recognizing the novelty, comprehensive experiment validation, and theoretical analysis contribution of our work. We provide our response to the comments below:
> W2: Assumptions 1 and 2 seem kind of idealized to directly bound the distribution and policy discr... | Summary: Offline Safe Reinforcement Learning (RL) is used to learn policies satisfying cost constraints from a given dataset. This proves to be a challenge when the dataset is biased in a certain way. This paper introduces a method to use the offline training dataset to capture the environment using a diffusion model t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the insightful suggestions and the praise of our theoretical results and experimental performance. We provide our response to the reviewer's comments and questions as follows:
> W1: Data generation and training can be a slow process.
We agree that generating new da... | Summary: This paper proposes OASIS, which uses a conditional diffusion model to reshape the dataset distribution and achieve effective offline safe RL learning. Theoretical analysis gives the error upper bound of distribution reshaping and constraint violation upper bound. A large number of experiments show that the pr... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for the valuable feedback. We are glad to know that the reviewer recognizes the clear logic, sufficient theoritical analysis, and experiments proving the effectiveness. We provide our response to the questions and concerns below.
> W1: Some b... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you all for your careful review and valuable feedback. In addition to addressing each reviewer’s comments, we would like to highlight the new examples and experiments during the rebuttal phase. The figures we refer to can be found in the attached PDF file.
1. **Additiona... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning | Accept (poster) | Summary: The paper studies the scalable oversight problem in the goal-conditioned hierarchical reinforcement learning setup. The starting point of the paper is that, if the time horizon $H$ is large, there is only a limited amount of feedback that can be given (authors use an example of essay or code writing, which req... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review, reviewer z273! These are thoughtful questions and we address the main concerns below due to the space limit. We will be sure to clarify the notation (e.g. $\Pi^h$) as you suggest.
```
…The task studied is then to find a high-level policy and a set of ... | Summary: Provides proofs of the regret bounds for cardinal and ordinal feedback using the sub-MDP framework of goal-conditioned HRL. Sub-MDPs are defined by a starting state, fixed horizon, high-level action and subspace. The high-level policy selects transitions between sub-MDPs. This work first proposes an upper conf... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review, reviewer poPw! These are great questions, which have definitely helped to improve our paper and its presentation.
```
Algorithm 1 is somewhat difficult to follow. In particular, while the components make sense, the core insight of the bounds on the lo... | Summary: This paper analyzes scalable oversight in the context of goal-conditioned hierarchical reinforcement learning. Specifically, it theoretically shows that it is possible to efficiently use hierarchical structure to learn from bounded human feedback.
Strengths: * The problem is significant and of high importance... | Rebuttal 1:
Rebuttal: Thank you for your review, reviewer Hzkp! It has definitely helped to improve our paper and its presentation.
```
Paper had quite a few typo’s. I would encourage the authors to use an automated tool to check for spelling / grammar mistakes.
```
Thank you for your careful reading! We will be sure... | Summary: In this paper, the authors study how to scale human feedback, in the context of goal-conditioned hierarchical reinforcement learning. For this work, the authors assume that humans can only provide feedback for outputs with length below a certain threshold. Thus, it is necessary to scale the little feedback pro... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review, reviewer 2zMJ! It has definitely helped to improve our paper and its presentation.
```
The results are based on a very strong assumption that there is a well-defined goal function available (that is, a goal function that only proposes feasible goals).... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Unraveling and Improving Generalization in World Models | Reject | Summary: This paper investigates the generalization capabilities of world models in RL, particularly with respect to latent representation errors, which arise when observations are encoded into a low-dimensional latent space. The authors provide a bound on latent representation error when using CNN encoder-decoder arch... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful feedback on our work! We respond to your questions as follow:
### **A1. On the analysis of compounding model error**
Thank you for highlighting this important aspect. We agree that compounding model error is critical in world model analysis. During both train... | Summary: The paper studies the generalization capability of world models via a stochastic differential equation formulation. They try to understand latent representation errors on generalization, with both zero-drift representation errors and non-zero-drift representation errors. They found that zero drift latent repre... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful comment!
- _“The unseen images are produced via global/partial Gaussian noises and rotation, which seems more on the robustness side rather than the generalization of unseen images;”_
Following your valuable feedback, we added a new set of experiments invol... | Summary: This paper explores the generalization capability of world models in reinforcement learning. In particular, they investigate the latent representation error in world models. They show that zero-drift representation error is inherently a regularizer for the learned model functions. On the other hand, they show ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and suggestions! Based on your valuable feedback, we have improved the empirical analysis of our proposed regularization schemes with some new experiments and visualizations.
### **A1. Additional Experiments on Benchmark Environment**
Thanks for the construc... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely thank your comments and constructive suggestions. Below, we address the reviewer’s concerns point by point. We also attach a one-page PDF for visualizations and graphs.
Pdf: /pdf/41ad33ecd4cbef933c30e3d67a10108fa3094862.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearly Optimal Approximation of Matrix Functions by the Lanczos Method | Accept (spotlight) | Summary: The authors investigate the convergence of the Lanczos method for computing $f(A)v$ for a matrix $A$ and a vector $v$. They show a near instance optimality bound that involves the product of the condition numbers of matrices related to $A$ and empirically investigate the bound.
Strengths: The authors investig... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We will respond to each of the questions in turn:
> “Can you generalize your results to other types of Krylov subspace methods such as rational Krylov methods?”
It would be interesting to see whether bounds of the flavor we describe can be extended to rati... | Summary: A matrix function f(A) for a real, symmetric matrix A and a univariate function f is given as \sum f(\lambda_i) u_i u_i^\top, where \lambda_i are the eigenvalues of A and u_i the corresponding eigenvectors. In practice, one is often interested in matrix-vector products f(A)b for some vector b. In the paper, th... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback! We will address the two questions in turn:
The first asks for clarification about Line 145. The projection of a vector $x$ onto a linear subspace $S$ is defined as $\underset{y \in S}{\operatorname{argmin}} \||y - x\||_2$. The $A$-norm projection is defined ... | Summary: The submission discusses the optimality (in terms of approximation error) of approximation $(A, b) \rightarrow f(A)b$ with Lanczos' algorithm.
The main result (Theorem 1) states that the Lanczos iteration is "near instance optimal". Near-instance-optimality relates to the optimal reconstruction of $r(A)b$ for ... | Rebuttal 1:
Rebuttal: Thank you for the thorough and thoughtful comments. This review raises several valuable points:
On the issues of exact vs. floating point arithmetic, please see the global response.
Regarding the leading constant in Theorem 1, it is true that our result is weaker if the condition number of each ... | Summary: The authors study computation of matvecs of a matrix function via the Lanczos method, an in particular try to answer the question of why the basic Lanczos method is competitive or even superior to sophisticated implementations targeting specific matrix functions. They provide theoretical bounds on the error of... | Rebuttal 1:
Rebuttal: Thank you for the thorough review! The point about floating point arithmetic is well-taken; see the global response for more. Thank you as well for the grammar corrections. These have all been corrected in our revision. | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments. We were pleased with the quality and number of reviews we received, and have taken the feedback into account in order to improve the paper. In our global response we address some common points raised by the reviewers.
Several of the reviewers... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The goal of this paper is to better understand the performance of using Lanczos for matrix-function times vector computations. Using Lanczos for this task is the de-facto standard, since it works very well (best vs competitors) in practice. Previous theory is very weak: there is a big gap between the bounds it... | Rebuttal 1:
Rebuttal: Thank you for the thorough and thoughtful comments! This review raises many valuable points:
Regarding the difference between exact arithmetic and finite precision, please see the global response.
The review asks if it is realistic to build a rational approximation with poles outside the interva... | null | null | null | null | null | null |
Efficient Multi-task Reinforcement Learning with Cross-Task Policy Guidance | Accept (poster) | Summary: The paper presents Cross-Task Policy Guidance (CTPG), a novel framework designed to improve multi-task reinforcement learning (MTRL) by leveraging cross-task policy similarities. CTPG trains a guide policy for each task to select the most suitable behavior policy from a pool of all tasks' control policies. Thi... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Scalability to complex environments.
We have tested CTPG on MetaWorld and HalfCheetah, the commonly used and authoritative benchmarks in the MTRL community, c... | Summary: This paper tackles the problem of multi-task reinforcement learning. Previous works have primarily focused on MTRL through specialized network structures or methods to resolve conflicting gradients. This paper considers the problem from an orthogonal perspective, by actively sharing control policies for each t... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts.
> Floating pieces
1. Hindsight off-policy correction: On the one hand, it is deeply integral to our CTPG framework, instead of just a floating piece. It addresses the unavoidable non-stationarity issue in off-p... | Summary: This paper addresses the problem of multi-task reinforcement learning (MTRL). To this end, the paper proposes a method that selectively shares behaviors from the policies learning to solve other tasks. The experiments conducted in a locomotion domain (multi-task Half-Cheetah) and a robot arm manipulation domai... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Sample efficiency comparison.
Please refer to **Figure 10 (Appendix D.1)**, which presents the full training curves for our main experiment (Table 1). It show... | Summary: This paper proposes a method called CTPG to enable policies trained on different tasks to learn from each others' generated trajectories. CTPG operates by learning a guideline policy that determines which control policy in a given set should best generate trajectories to enable agents to learn a particular tas... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback and acknowledgment of our efforts. Following are our responses to all your concerns.
> Additional comparison with a simple transfer learning strategy where experience is only transferred from the policy that has the best performance.
[1] focus o... | Rebuttal 1:
Rebuttal: To AC and all the reviewers:
We would like to express our sincere gratitude to AC and all the reviewers for their great efforts in evaluating our paper. Your valuable insights and suggestions are greatly appreciated. We have carefully addressed all the questions and concerns raised in the reviews... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conformal Classification with Equalized Coverage for Adaptively Selected Groups | Accept (poster) | Summary: This paper presents a conformal inference method to assess uncertainty in classification by generating prediction sets with valid coverage based on adaptively chosen features. Falling between marginal and strictly conditional coverage, the features in the proposed method are adaptively selected to address pote... | Rebuttal 1:
Rebuttal: Thank you for your careful review and insightful feedback.
## Seeking Different Fairness Criteria
The potential for our method to be adapted based on different fairness criteria, going beyond our notion of selectively equalized coverage, is indeed both intriguing and promising. Although explorin... | Summary: The paper introduces a conformal inference method to assess uncertainty in classification by generating prediction sets with valid coverage, conditional on adaptively chosen features. These features are selected to address model limitations or biases, balancing efficiency and fairness by ensuring equalized cov... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, which allows us to clarify key points and address potential misunderstandings. We believe these clarifications will enhance the paper's accessibility.
## Clarification on Method Aims
While other reviewers have praised the paper for its clarity, we appreciate t... | Summary: The paper presents a novel conformal inference method aimed at generating prediction sets with valid coverage, conditional on adaptively chosen features. This method is intended to address the dual concerns of efficiency and algorithmic fairness by ensuring equalized coverage for the most sensitive groups, thu... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper and providing constructive feedback. Please find our responses below.
## Computational Complexity Analysis
As (perhaps too) briefly mentioned in Section 1.4, Appendix A6 provides a detailed computational complexity analysis for implement... | Summary: The paper focuses on the problem of conformal inference with equalized coverage introduced in [1]. The authors propose a new method, Adaptively Fair Conformal Prediction (AFCP) that (i) adaptively selects a sensitive attribute corresponding to the group most negatively affected by algorithmic bias as evaluated... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We have addressed your questions and concerns point-by-point below.
## Additional experiments
Might you have missed some experiments described in the Appendix and mentioned in Section 3.4? We will highlight these experiments more clea... | Rebuttal 1:
Rebuttal: We are grateful to the four referees for their detailed reviews and constructive suggestions. We have addressed their questions and concerns point-by-point below. In addition to providing several clarifications, which can be easily reflected in the camera-ready manuscript to enhance its accessibil... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup | Accept (poster) | Summary: This paper proposes the D-Miso pipeline, which includes multi-Gaussian components and two deformation networks to model global transformation and local deformation. A good training strategy is introduced to fit the proposed methods. The rendering quality looks good, and the editing results seem better than tho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the remarks and excellent comments and appreciate the time taken to review our manuscript. We have responded to these remarks below.
***W1 Multi-Gaussian representations are similar to SC-GS's control points ...***
Our solution differs in architecture and training strat... | Summary: This paper is developed based on SC-GS, which enhanced 3DGS with Deformed Control Points to model low-rank dynamics and modify dynamic objects over time. SC-GS necessitates selecting elements that need to be kept fixed and centroids that should be adjusted throughout editing, and it poses additional difficulti... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the feedback and for pointing out improvements to our paper. We respond to these concerns in the points below.
***W1: Lack of training time comparisons.***
Here, we showcase a time comparison between SC-GS and D-Miso models, with the former operating noticeably faster ... | Summary: This paper proposes a novel framework for modeling and editing dynamic scenes. The authors used a two-pass Multi-Gaussian approach to represent the entire scene. First, they obtained relatively stable Core Gaussians through initialization, and then used the Core Gaussians to drive Sub-Gaussians to fit the enti... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the vote of confidence in our manuscript and for the in-depth feedback and suggestions, which we are happy to incorporate and feel will improve the manuscript. We respond to all the questions and concerns raised in the answers below.
***W1: I think the following papers s... | Summary: This paper introduces a Dynamic Gaussian Splatting representation that allows for easier object shape editing at test time. This is achieved through the use of Dynamic Multi-Gaussian Soup (D-MiSo), a mesh-inspired multi-gaussian system. Specifically, a GS is divided into two components: Core-Gaussian, which mo... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive remarks regarding our manuscript, to which we responded next to the posted questions below. We will revise the manuscript in accordance with the raised concerns.
***W1 “I find the paper's writing style difficult to follow … I would appreciate a revision... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their excellent comments and constructive remarks regarding our paper, as well as for their positive feedback. We are also thankful for noticing the main contribution of our model, which is “enabling the editing of more extreme large motions” that “matches the rendering ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficiently Parameterized Neural Metriplectic Systems | Reject | Summary: This paper proposes a new parameterization for neural metriplectic systems which explicitly incorporates structural information about the degeneracy conditions $\{ S, \cdot \} = 0, [E,\cdot ] = 0$ into the model. The model requires $\sim O(n^2)$ learnable parameters for a problem with $n$ state variables inste... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Below you will find responses to your comments and questions. Please let us know if we can do anything further to aid you in your final decision.
**Weaknesses**
*While the authors improve the scaling from cubic to quadratic in the number of state variables... | Summary: This paper proposes a parameter efficient parameterization for neural networks simulating metripletic systems, which are differential equations that have both an energy dissipative piece and an energy conservative piece. The method works by learning several of the required quantities (L and M, which trade off ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad to hear that you found our paper well-written and could use it to study the current literature in this interesting area of ML.
Below you will find responses to your comments and questions. Please let us know if we can do anything further to aid ... | Summary: This work presents a method for learning metriplectic systems from data. Metriplectic systems are a model which conserve energy and produce entropy, two desirable features. Their method, termed “neural metriplectic systems” (NMS), is based on a more efficient system parametrization. The authors also prove univ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad to hear that you found our paper well-written and reflective of a valuable contribution.
Below you will find responses to your comments/questions, including your main concern that the work may not be well-suited for a machine learning audience. ... | null | null | Rebuttal 1:
Rebuttal: Thanks to the reviewers for their helpful feedback and interest in our article. We are pleased to hear that all reviewers consider it a valuable contribution to the field of structure-preserving machine learning.
In response to reviewer dk6W's comment, we are attaching a set of figures for the d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training | Accept (poster) | Summary: The manuscript introduces a slicing privacy mechanism for training generative models without noisy gradients. This mechanism injects noise into random low-dimensional projections of private data, providing strong differential privacy guarantees. The study introduces the smoothed-sliced
$f$-divergence and a ke... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and for appreciating the merits of the work! If there are any further questions that might lead to improving your score please let us know. | Summary: The paper proposes to add noise to randomly projected private data along with optimizing a newly proposed metric smoothed-sliced f-divergence to train generative models. Such paradigm can circumvent adding noise to gradient and enable more architecture choices. Experiment show the proposed method perform compe... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful reading of our paper and thoughtful comments!
---
**Q1. Weak baselines**
A1. We have added experimental comparisons to the state-of-the-art MERF method in the attached pdf. In Figure 1 of the attached PDF, we have considered private image generative modeling ... | Summary: This paper proposes a DP generative modeling technique via f-divergence and random projection. Specifically, both real data and synthetic data are randomly projected into a lower-dimensional space, where the noise is added to the aggregation of the projected data such that the effect of individual data point i... | Rebuttal 1:
Rebuttal: PART 1
We thank the reviewer for the thoughtful comments and for appreciating the novelty of the work!
---
**Q1. Comparison with MMD-based methods.**
A1. Indeed, this is a great point! As mentioned in the general response, we have added new experiments to address this.
In Figure 1 of the attac... | null | null | Rebuttal 1:
Rebuttal: General response:
We thank the reviewers for their thoughtful and helpful comments. Due to the suggestions of several reviewers, in the attached pdf we have included results for two new experiments showing the advantage of our methods over the state-of-the-art MMD-based MERF method. We will add b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection | Accept (poster) | Summary: This paper focuses on out of distribution detection. The detection is based on the reconstruction error. The authors use a diffusion model as their generative model and focus on the feature space instead of the original images. The authors test their proposed methods on several different datasets.
Strengths: ... | Rebuttal 1:
Rebuttal: 1. Novelty of this paper
Thanks for the valuable comments. I believe the reviewer may misunderstand our contributions on OOD detection by stating that “It looks like it simply replaces a generative model with diffusion model”. Our main novelty lies in the following three aspects:
Firstly, we a... | Summary: The authors introduce a method for unsupervised out-of-distribution (OoD) detection for image classification tasks. Their approach is based on the semantic reconstruction of latent features in multiple layers of an image encoder using a diffusion model and does not require any OoD data for training. Unlike exi... | Rebuttal 1:
Rebuttal: Q1. Comparison against other methods using the multi-scale feature encodings as the input.
A: We have made comparison of our method against AE and VAE using the multi-layer feature encodings as inputs.
For AE (AutoEncoder), we use the LFDN network without the timestep embedding, i.e., a 16-... | Summary: The paper addresses the problem identification of out of distribution models in an unsupervised manner. The idea is that OOD samples have the largest reconstruction error. The method is evaluated on a plethora of setups including SVHN [Netzer et al., 2011], LSUN(and variants) iNaturalist, Textures, Places365.
... | Rebuttal 1:
Rebuttal: Q1.at the core, the idea is that reconstruction error points to OOD. This is new, but is in a broader family (including AE, contrastive leraning), thus some limited.
A: Thanks for the insightful comment. We agree that the core idea of using reconstruction error to indicate OOD is indeed related ... | Summary: This paper proposes a diffusion-based layer-wise semantic reconstruction strategy for unsupervised Out of Domain (OOD) detection. Specifically, they leverage the intrinsic data reconstruction ability of the diffusion model to differentiate between the In-distribution (ID) and OOD samples in the feature space. ... | Rebuttal 1:
Rebuttal: Q1. Writeup revision.
A: Thank you for your valuable feedback. We appreciate your observation regarding the repetition of the contributions and the main idea of the paper. We will address these issues to ensure clarity and conciseness.
Q2. Clarification of OOD detection step.
A: During i... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback, with three reviewers (yKR8, ch15 and aPqn) supporting our work. We are encouraged that reviews think our paper:
- **A novel and interesting unsupervised OOD detection scheme.** (by Reviewer yKR8, ch15, aPqn)
- **State-of-the-art performances on b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Decouple the Lights for 3D Face Texture Modeling | Accept (poster) | Summary: The paper presents a new face texture modeling framework by learning to decouple environmental illumination into multiple light conditions with neural representations. The proposed method can reconstruct a cleaner texture compared to previous work that use a single environment map. The proposed method is neat ... | Rebuttal 1:
Rebuttal: **Q1: Line 119 says the n lighting coefficients are initialized differently. Can you provide some insights behind these design choices? Is the method sensitive to the initialization of lighting?**
**A1:** Sure. The lighting coefficients are just simply initialized as a uniform distribution betwee... | Summary: This paper proposes a method to recover face albedo by disentangling the input image into an albedo and shading maps (called "light conditions"). The shading maps are generated by a network which are combined with the albedo texture (generated from AlbedoMM) that is rendered under a set of $n$ lighting conditi... | Rebuttal 1:
Rebuttal: **Q1: The authors have not compared against optimizing in haar-wavelets[1] which are designed specifically to model sharp illumination dependent effects.**
[1] Triple Product Wavelet Integrals for All-Frequency Relighting
**A1:** Thank you for your suggestion. Please note that our contribution l... | Summary: This work tackles the problem of external shadows in single image face reconstruction. Specifically, when the input image contains foreign shadows, this often affects the quality of the estimated facial texture, as the external shadows often become baked into the texture or leave behind undesirable artifacts i... | Rebuttal 1:
Rebuttal: **Q1: Quantitative evaluations for ablations.**
**A1:** Thank you for your suggestion. Here, we provide the quantitative ablation study of our proposed components below, following the same setting as Sec. 4.4 of the main paper. GP, LP, and HP denote our proposed constraints $L_{GP}$, $L_{LP}$, an... | Summary: The paper presents a method for reconstructing 3D face textures from monocular images in the presence of occlusions, both self-occlusions and occlusions by other scene elements such as hats. The paper identifies a key limitation in existing works -- they all get impacted by the lighting changes introduced by a... | Rebuttal 1:
Rebuttal: **Q1: Lack of discussion and comparisons with 3D face reconstruction works dealing with de-occlusion, such as [1, 2].**
[1] Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation (CVPR'23)
[2] Occlusion-aware 3D Morphable Models and an Illumination Prior for Face I... | Rebuttal 1:
Rebuttal: We thank all reviewers for your valuable comments. We are inspired that all reviewers recognize the sufficient motivation and good performance of our method. We apologize for any confusion caused by missing comparisons, citations, unclear definitions, and other issues. In this rebuttal, we make re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm | Accept (poster) | Summary: The paper explores RL in Infinite-horizon Ergodic Average-reward constrained
MDPs under general policy parametrization. In this setting, the authors propose
a primal-dual based policy gradient algorithm that simultaneously optimizes the
policy parameter and a constraint violation penalty term $\lambda$. The... | Rebuttal 1:
Rebuttal: Response to **Question 1**: Note that a constrained MDP can be considered as an unconstrained MDP where the (equivalent) reward function is $r+\lambda c$ and the optimization is performed over all policies $\pi\in \Pi$ and $\lambda\geq 0$. In other words, the original constrained optimization is e... | Summary: This paper tackles the infinite horizon average reward Constrained MDP setting and proposes an analysis of regret and constraint violation under a general policy parametrization. They devise a primal-dual policy gradient algorithm achieving global optimality while ensuring sublinear bounds on the regret and co... | Rebuttal 1:
Rebuttal: Response to **Weakness 1**: Thank you for suggesting the paper by Patel et. al. where the algorithm works without the knowledge of $t_{\mathrm{mix}}$. We would like to point out that such a feat is achieved by introducing an additional assumption and weakening the convergence result. In particula... | Summary: This paper studies learning in constrained MDPs with the long-run average-reward objective.
It gives the first sample complexity, regret bound and constraint violation bound in this setting with general parameterization, whereas all prior work is restricted to either tabular or linear parameterizations.
Stre... | Rebuttal 1:
Rebuttal: Response to **Weakness 1**: Unfortunately, the proposed algorithm may not be extended to the weakly communicating (WC) setting. In general, it is difficult to propose a model-free algorithm with provable guarantees for Constrained MDPs (CMDPs) without considering the ergodic model. WC CMDPs impose... | Summary: This paper propose a primal dual policy gradient method for solving average reward constrained MDPs. The primal problem is minimizing the usual RL objective with a averaged reward, plus a penalty induced constraint violation term. The dual problem is to find appropriate Lagrangian multiplier that balances the ... | Rebuttal 1:
Rebuttal: Response to **Weakness 1**: We would like to clarify that we do not assume access to an accurate knowledge of the optimal policy since that would contradict our learning objective. We merely assume that the policy belongs to a class where each member is indexed by a parameter $\theta$. The goal is... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a primal-dual policy gradient algorithm for infinite horizon average-reward Constrained Markov Decision Processes with general policy parametrization. It uniquely addresses minimizing regret and managing constraints simultaneously, providing the first analysis showing sub-linear bounds of O(... | Rebuttal 1:
Rebuttal: Response to **Weakness 1**: Note that the framework of UCRL that works for Weakly Communicating (WC) MDPs is a model-based method that cannot be extended to large state space. Designing model-free algorithms, especially for Constrained MDPs (CMDPs), is an extremely difficult problem. This is evide... | null | null | null | null | null | null |
Multi-Agent Coordination via Multi-Level Communication | Accept (poster) | Summary: This paper proposed a multi-level sequential communication framework that has two communication phases. It also proved that the policies learned in this way are guaranteed to improve and converge. Instead of the observations, this paper focus on make agents communicate about the action selections. Each agent i... | Rebuttal 1:
Rebuttal: To begin with, we thank the reviewer for the carefully reviewing and insightful advice.
>If the authors formulate this problem as a multi-agent sequential decision-making problem, the Markovian property required for formulating the problem as an MDP is no longer satisfied and there is a need to p... | Summary: This paper introduces SeqComm, a novel multi-level communication scheme for multi-agent coordination in reinforcement learning. The key contributions are:
1. A new approach treating agents asynchronously with two communication phases: negotiation and launching.
2. Theoretical analysis proving monotonic improv... | Rebuttal 1:
Rebuttal: To begin with, we thank the reviewer for the carefully reviewing and insightful advice.
>Computational complexity.
The computational complexity of SeqComm is mainly related to communication overhead.
For full communication, SeqComm needs more rounds, but it only transmits observation informat... | Summary: This paper introduces SeqComm, a novel multi-level communication scheme for multi-agent reinforcement learning. SeqComm enables agents to coordinate asynchronously, with upper-level agents making decisions before lower-level ones. The approach involves two communication phases: negotiation and launching. In th... | Rebuttal 1:
Rebuttal: >The assumptions regarding local observations and communications might not be realistic for all applications.
Thank you for pointing out the limitations. We agree that the settings cannot be applied to all the applications. However, our setting is more realistic compared to other full communicati... | Summary: This paper introduces SeqComm, a novel multi-agent reinforcement learning (MARL) method that addresses coordination issues through sequential decision-making and multi-level communication. The main contributions include:
1. A new asynchronous perspective on MARL, allowing agents to make decisions sequentially... | Rebuttal 1:
Rebuttal: To begin with, we thank the reviewer for the carefully reviewing and insightful advice.
>Could you elaborate on your choice of SMACv2 as the primary benchmark for evaluating SeqComm?
In cooperative MARL, SMAC is the most popular testbed for the centralized training and decentralized execution p... | Rebuttal 1:
Rebuttal: First of all, we are very grateful to the reviewers for their thorough review of our paper. We highly appreciate your valuable comments. We will emphasize the key points mentioned during the rebuttal period in the revised version.
Additionally, we have provided some extra experiments: Figure 1 i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fearless Stochasticity in Expectation Propagation | Accept (spotlight) | Summary: The authors introduce two methods for Expectation Propagation [EP] (which itself can be understood as a method for approximate Bayesian inference) that are robust to the stochastic noise introduced by using approximate expectations in the inner loop of EP. To do so the authors re-interpret the moment-matching ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to read our paper and for providing thoughtful feedback. We agree with the reviewer that the exposition can be dense at times; as the reviewer correctly mentioned, this was to some extent unavoidable due to the nature of the ideas being discu... | Summary: This work addresses the sensitivity of expectation propagation (EP) to the randomness of Monte Carlo (MC) estimates involved in its update steps. It tackles this issue by recasting the moment matching step in EP as natural gradient descent (NGD) in the mean space of an exponential family distribution. The auth... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to read our paper and for providing thoughtful feedback. With regards to the novelty of our NGD interpretation, we agree that the link between NGD and exponential family moment matching is known, and our intention is not to claim otherwise; w... | Summary: This paper considers new inference algorithms for EP. By framing the moment-matching equations of EP as a natural gradient update of a variational objective, they propose two new algorithms that are better suited for reducing/removing the bias introduced when sampling is required.
Strengths: I like the attemp... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to read our paper and for providing thoughtful feedback. The reviewer’s concerns relate to the paper presentation. We will take specific steps to address these, detailed below. In light of these changes, would the reviewer be willing to recon... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for taking the time to review our paper. All reviewers offered valuable feedback, which we have taken on board. Two reviewers highlighted weaknesses related to the paper presentation; we have taken specific steps to address the points raised, which are d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention | Accept (spotlight) | Summary: This paper proposed a novel attention method for graphs, focusing on different resolutions of nodes. Instead of attention calculation on the coarsened graph, it views the clustered nodes as sets, and calculates the attention between cluster and node level. Furthermore, it leverages the kernel method for more e... | Rebuttal 1:
Rebuttal: Thank you for the valuable questions. We provide the following detailed responses to your major concerns.
> **Q1. The only novelty is the dual granularity/resolution/hierarchy attention. The kernelization and the multi-kernel are already investigated.**
We acknowledge the reviewer's comment rega... | Summary: This paper considers graph transformers. In previous methods that consider cluster info, node clusters are pooled, which may lose node level information. This paper proposes node-to-cluster attention, where the nodes in the clusters are not compressed, and each cluster can interact with every node in other clu... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's comments and suggestions. Below, we provide detailed responses to address the main concerns.
> **Q1. Even though it may be unavoidable, the amount of notations make reading the paper somewhat difficult.**
We fully understand the reviewer's concern about the num... | Summary: The paper introduces the Node-to-Cluster Attention (N2C-Attn) mechanism, which captures both node and cluster-level information using multiple kernels. N2C-Attn can be implemented in the form of linear-time complexity by a cluster-wise message-passing framework. Based on N2C-Attn, the authors propose a Cluster... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comments and suggestions. Here are our detailed responses to your main concerns.
> **Q1. The paper emphasizes "without resorting to the graph coarsening pipeline," suggesting that the proposed method eliminates graph coarsening in their model pipeline. However, it act... | Summary: The paper proposes an attention-based methodology for supervised graph classification and regression. It adopts a pipeline similar to GraphViT, involving graph partition, cluster-wise representation learning, and aggregation. However, the core mechanism for learning cluster-wise representations is novel. Speci... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback. We provide details to clarify the reviewer's major concerns.
> **Q1. The comparison with graph pooling methods misses MVPooL [1], a recent baseline that has achieved higher accuracy on these benchmarks.**
We appreciate the reminder from the review... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the reviewers for their insightful feedback.
We are delighted to see the comments regarding the paper's **presentation**: "The paper is well written and illustrated" (Reviewer 2taa), its **motivation**: "The paper presents a clear and well-supported motivation ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Text-to-Video Generative Model with MLLMs Feedback | Accept (poster) | Summary: Recent text-to-video models like Sora show potential but suffer from poor video quality and misalignment with text prompts due to variable dataset quality. This study addresses these issues by leveraging Reinforcement Learning from Human Feedback (RLHF) to align outputs with human preferences. Due to the high ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your feedback point by point below.
---
>**Q1**: Although existing research has demonstrated that GPT-4 can be used for data annotation, it is essential to perform a sample check of the annotated data to assess its quality.
**A1**: Thank you for... | Summary: Recent advancements in text-to-video generative models, such as Sora, have shown impressive capabilities, generating significant interest for their potential applications. However, these models often rely on extensive datasets of variable quality, resulting in generated videos that may lack aesthetic appeal an... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your feedback point by point below.
---
>**Q1**: **Representativeness of Generated Video Quality**: Over 98% of the videos in VIDEOPREFER are generated using open-source models (as indicated in Table 5). This high percentage raises a crucial questi... | Summary: The paper presents a dataset and a learned reward model to better align the generated outputs of text-to-video models with human preferences. Specifically, the authors make use of GPT-4V to provide preference scores for a large dataset of videos by learning from human feedback on a smaller set of videos. The a... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Due to space constraints, we address your questions in the **Rebuttal** below as well as at the top of this page in the **Author Rebuttal**:
---
>**Q1**: While GPT-4V has the best agreement with human feedback, it is still not very high. Did the authors explore any pr... | Summary: This paper introduces a new dataset called VideoPrefer, a collection of (simulated) human preferences on videos conditioned on certain language prompts. VideoPrefer utilizes GPT-4v as an automatic reward assessor, which contains videos from both machine-generated and real-world curated videos. The work then ut... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Due to space constraints, we address your questions in the **Rebuttal** below as well as at the top of this page in the **Author Rebuttal**:
---
> **Q1**: Solely performing statistical analysis on the VideoPrefer dataset is still required. It remains questionable how ... | Rebuttal 1:
Rebuttal: ## Supplementary rebuttal for Reviewer nZ7n
---
> **Q6**: What or who is deciding the “win” in Figure 2? The GPT-4v assessor?
**A6**: The "win" in Figure 2 is determined by scores from six human experts. The sample with the highest average expert score is considered the ground truth. We apologi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LaSe-E2V: Towards Language-guided Semantic-aware Event-to-Video Reconstruction | Accept (poster) | Summary: This paper proposes a language-guided event-based video reconstruction method LaSe-E2V, which introduces the popular language model into event-based imaging tasks. The LaSe-E2V is generally based on a diffusion model. In order to further improve the method, this paper proposes a series of designs, such as even... | Rebuttal 1:
Rebuttal: Thank the reviewer for the constructive comments and valuable concerns.
> **Can the model handle the situation when the description of the scene is unknown?**
>
- Yes, our model can handle such scenarios. If the text description is unavailable, our framework defaults to a conventional event-to-v... | Summary: The paper explores the use of Denoising Diffusion Models (DDM) for event-based video reconstruction task. The most important contribution, in my opinion, is the improvement in video quality. The researchers adapted an existing model (ModelScope Text-to-Video) for the event-based video reconstruction task. The... | Rebuttal 1:
Rebuttal: Thank the reviewer for appreciating our work with valuable suggestions. We address the comments below.
> **About the "hallucination" issue of diffusion-based models.**
>
- Yes, this is indeed a problem for the diffusion models. To address this, we have proposed novel techniques (i.e., ESA module... | Summary: This paper addresses the issue of artifacts and regional blur in existing event-to-video (E2V) reconstruction algorithms by leveraging the rich semantic information in language to enhance the semantic consistency of reconstructed videos. The authors propose a language-guided E2V generation model that employs e... | Rebuttal 1:
Rebuttal: Thank the reviewer for the valuable suggestions. We address the questions below.
> **Detailed comparison of inference speed and model size.**
>
- Please refer to the **Global R*esponse to All Reviewers***. As shown in Tab. A-1, our method requires considerable inference time due to multiple deno... | Summary: This paper uses abundant semantic information and raw event information to guide the reconstruction of RGB images from event images based on U-Net. Furthermore, this paper introduces event-aware mask loss to ensure temporal coherence and a noise initialization strategy to enhance spatial consistency. Experimen... | Rebuttal 1:
Rebuttal: Thank the reviewer for valuable comments and suggestions. We address the concerns below:
> **About ablation experiments for ESA on raw events and text.**
>
- The reviewer might misunderstand the ESA module. Indeed, the ESA and the text are **separate** parts in our framework. As outlined in Sec.... | Rebuttal 1:
Rebuttal: ### **Global Response to All Reviewers**
We sincerely thank the reviewers for their constructive feedback. We are pleased that the reviewers found our **method to be novel and effective**, the **performance to be strong and with high-quality**, which is believed to **inspire future work** in the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links | Accept (poster) | Summary: Traditional DML methods are limited to (1) data heterogeneity and (2) time-varying communication links. In this work, they present a non-linear class aggregation framework HyperPrism that leverages Kolmogorov Means to conduct distributed mirror descent with the averaging occuring within the mirror descent dual... | Rebuttal 1:
Rebuttal: R3-1: The difference between Hypernetwork and MLP.
Response: You make an excellent point about HN and MLP. The HN itself is basically a simple MLP. However, in terms of parameter selection, there are fundamental differences between the two. First, the MLP learns a direct mapping from the determi... | Summary: This paper addresses challenges in distributed machine learning (DML) caused by non-IID data and
unstable communication links. The proposed HyperPrism framework uses mirror descent and adaptive
mapping to project models into a mirror space for better aggregation and gradient steps.
It employs adaptive weigh... | Rebuttal 1:
Rebuttal: R2-1: Some sections are densely packed with technical details, which might be challenging for readers.
Response: To the best of our knowledge, HyperPrism is the first non-linear aggregation DML framework that combines mirror gradient descent and hypernetwork techniques. Therefore, a comprehensive... | Summary: This paper presents HyperPrism, a novel framework for decentralized machine learning (DML) that aims to address the challenges of non-IID data and time-varying communication links. The authors propose a non-linear aggregation method based on Kolmogorov Means and adaptive mapping functions, which they argue imp... | Rebuttal 1:
Rebuttal: R1-1: Experiments are limited to MNIST and CIFAR-10 and the non-IID setting includes only two extreme cases.
Response: The MNIST and CIFAR-10 are the most commonly used datasets in the DML field, and recent works such as [R1] have also chosen these datasets for verification. Moreover, due to spac... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their careful reviews and constructive suggestions. Our responses to the main issues are below： | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Low Degree Hardness for Broadcasting on Trees | Accept (poster) | Summary: The authors study hardness of broadcasting on trees for low degree polynomials. The main result shows that log(N) degree polynomials of the leaf values have vanishing correlation with the root, resolving the main open question of Kohler and Mossel (NeurIPS 2022).
The tree broadcasting problem consists of a $d... | null | Summary: The paper shows that for a Markov process in a tree, propagating a starting state at the root to the leaves it is "hard" to infer the starting state from the leaf states.
Concretely they show that for any function $f$ of the leaf states with bounded Efron-Stein degree the variance of $f$ conditioned on the ro... | null | Summary: This paper obtained low degree hardness results for broadcasting on trees (BOT).
The BOT problem is as follows.
Consider a rooted tree and an associated Markov process defined by a transition matrix $M$.
The process is initialized by a distribution on the root.
Then for each vertex with state $s$, its chil... | Rebuttal 1:
Comment: We sincerely appreciate the reviewer to carefully go over the manuscript. The feedback is valuable and we will address the issues raised by the reviewer in the revised version of the paper. Below we will address the some major issues raised by the reviewer:
1. Low-degree polynomial
Thanks for ... | Summary: This submission considers the broadcasting on trees problem, where given a rooted tree, information is propagated from the root to the leaves using a Markov process. The algorithmic task is to infer the information at the root given only the information at the leaves. Previous works had identified that the KS-... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Topological obstruction to the training of shallow ReLU neural networks | Accept (poster) | Summary: This works studies how topological properties affect loss landscape of neural networks, showing that the loss can be divided into disconnected regions where reaching one region from the other using GF is impossible. The theory is shown to have consequence for understanding the training of simple networks
Stre... | Rebuttal 1:
Rebuttal: Dear Colleague, thank you very much for your useful and insightful review.
___
**Weaknesses.**
**W1.**
- **(a)** Thanks for this question, an answer to which we believe can improve the quality of our paper. There are several differences, namely: in [35], the input-output layers are restricted to... | Summary: This paper considers the landscape topology of one hidden layer networks with non-negative homogeneous activations. The main result of this work is that in some cases, the loss landscape may consist of disconnected components that cannot be traversed by gradient flow dynamics. This leads to an obstruction, in ... | Rebuttal 1:
Rebuttal: Dear Colleague, thank you very much for your useful and insightful review.
___
**Questions.**
**Q1,Q2.**
Thank you for the corrections.
**Q3.**
That is correct. Particular activations or datasets may induce other symmetries but our results are independent of their choice.
**Q4.**
Thank you ... | Summary: In this paper, authors analyze the performance of gradient-descent optimization over a two-layer neural network. Authors reveal the presence of obstructions in the loss landscape and explore their topology. Finally, they identify the cause of those optimization obstructions in the so-called `pathological’ neur... | Rebuttal 1:
Rebuttal: Dear Colleague, thank you very much for your useful and insightful review.
___
**Weaknesses.**
**W1.**
As we mention in lines 245 and 246, one can easily control the initialization to have $c_k\geq 0$ for every hidden neuron by employing, for example, Proposition 4. Once that holds we have tha... | Summary: This paper studies the topological obstruction in the loss landscape of two-layer ReLU neural networks under gradient flow. Using conserved quantities in gradient flow, the authors define invariant sets, to which gradient flow trajectories are constrained. They then show that when the input or output of the ne... | Rebuttal 1:
Rebuttal: Dear Colleague, thank you very much for your useful and insightful review.
___
**Weaknesses.**
**W1.**
It is true that our results are limited to one-layer neural networks but we believe that, even if they cannot be generalized to the multi-layer case, they could be interesting when studying arc... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and thorough assessments of our work.
Prompted by some of the reviewers' questions, we performed three additional experiments to clarify some of the points raised.
### **1. Probability of obstruction**
Let us consider the following question: *what is t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Summary of the paper:
* The paper undertakes a theoretical 'reachability' analysis for neural
networks trained with gradient flow, identifying 'obstructions' separating
certain regions of parameter space from each other.
* The setting is one-hidden-layer networks with homogeneous activation
functions, i... | Rebuttal 1:
Rebuttal: Dear Colleague, thank you very much for your useful and insightful review.
---
**Weaknesses**
**W1.**
At the moment it is hard to foresee if and how this can be relevant to deep learning practice as the results need to be extended to more general settings.
Provided the same holds also for mult... | null | null | null | null | null | null |
SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers | Accept (poster) | Summary: This paper presents a novel neural network architecture (SEA) for solving partial differential equations (PDEs) in physical problems. This architecture effectively utilizes information exchange between multiple fields and the conservation quantities of physical systems to correct model predictions, achieving s... | Rebuttal 1:
Rebuttal: ***
**W1. Further exploration of the model's generalization ability is needed, such as evaluating model errors under more complex boundary conditions. Additionally, presenting the training and inference costs of the model would provide a better assessment of its potential for industrial applicatio... | Summary: The paper introduces the State-Exchange Attention (SEA) module, a new cross-attention module that enables information exchange between state variables in physics-domain transformer modules. The authors evaluate the performance of this module in both single-phase and multi-phase 2D fluid settings.
Strengths: 1... | Rebuttal 1:
Rebuttal: ***
**W1. Scalability Concerns: The current architecture is relatively small, using a transformer with only 1 layer and 8 attention heads. I am curious how the model will scale at larger architecture sizes. Moreover, as the authors themselves note, there are also concerns about how the model will ... | Summary: The authors present a novel and interesting approach to physics-domain transformer models that exchanges information between state variables and demonstrates strong performance improvements relative to SOTA models on hard fluid dynamics problems. The paper is well written and the results compelling.
Strengths... | Rebuttal 1:
Rebuttal: ***
**W1 and W2: Perhaps I missed this somewhere, but will code be made available with this paper? It seems too much to require readers to re-implement this architecture to use it.**
The code is prepared and will be released with the paper. We will also add more information in the implementation ... | Summary: The study proposes an approach for autoregressive spatiotemporal estimation of a dynamical system state, e.g. solution of time-dependent PDE. In particular, a Vision Transformer (VIT) model is adapted to solving PDEs where each state variable is tokenized similarly to tokenizing an image and a novel State-Exch... | Rebuttal 1:
Rebuttal: ***
**W1. It is claimed that the rollout errors are effectively controlled by the SEA model. There's very little explanation/derivations of the source of these errors. It would be helpful to define these errors rigorously and also if possible to show (graphically or rigorously) how SEA model allow... | Rebuttal 1:
Rebuttal: ***
We would like to appreciate all referees for spending time giving great feedback, and instructions on how to improve the paper.
Here we would like to address two main points that were questioned by all the referees, first the limitation of the work and then the efficiency of the model.
***
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals | Reject | Summary: The paper highlights how temporal autocorrelations in EEG data can lead to misleadingly high decoding accuracy in brain-computer interface (BCI) tasks. Using a novel approach with a "watermelon EEG dataset," the authors demonstrate that many reported high performances may exploit these autocorrelations rather ... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions.
We are pleased that you mentioned
"The findings have significant implications for BCI research,
highlighting a critical issue that could affect the validity of many existing studies",
as this was the initial objective of ou... | Summary: The paper investigates the potential overestimation of decoding accuracy in brain-computer interface (BCI) tasks that utilize EEG signals. The authors address concerns that high reported decoding accuracies may be attributed to the inherent temporal autocorrelation present in EEG signals rather than the actual... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions.
We are pleased that you also mentioned "emphasizing the need for careful experimental design to ensure the robustness and reliability of BCI systems",
which is exactly what we intended to achieve with this work.
**W1: This ... | Summary: The authors have correctly identified a significant issue of numerous hyperbolic or irreproducible results in EEG decoding or classification tasks. However, their evaluation approach of recording signals from electrodes placed on a watermelon needs correction. The authors are advised to consult the definition ... | Rebuttal 1:
Rebuttal: Thanks for your comments.
It is a pity that presentation of the key points and the specific contribution of this work was not clear enough for you,
but we hope our responses to your comments and questions can make it clearer.
**S1: Many reputable researchers defend their approaches with leave-on... | Summary: Authors hypothesise that the high temporal correlation of EEG data contributes to the high BCI decoding accuracies reported in some prior BCI studies. Specifically, the highly questionable data partitioning practice of splitting continuous EEG data with the same label (or subject) across train/test sets. They ... | Rebuttal 1:
Rebuttal: # Reviewer1 7nx7
We sincerely appreciate you for the thorough review and constructive comments.
We would like to express our gratitude to you for the high praise on the originality, quality, clarity, and significance of our work.
**W1: Adding the result of other BCI datasets and actual datase... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for their valuable suggestions and for recognizing our intention to reveal the fatal drawback of using DNN on general BCI decoding tasks.
Although three reviewers rated the contribution of this work as "good",
it is a pity that the other one concluded it in an inappro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving the Learning Capability of Small-size Image Restoration Network by Deep Fourier Shifting | Accept (poster) | Summary: The authors introduce a theoretically sound Fourier shifting operator designed to enhance the learning capability of small-size image restoration models. This work represents the first comprehensive attempt to model the shift mechanism within the Fourier domain. The proposed operator is versatile and can be se... | Rebuttal 1:
Rebuttal: **1,generalization.**
Fourier shifting exhibits enhanced generalization due to its operation in the frequency domain, which captures global image information and maintains feature integrity while minimizing domain-specific artifacts. This approach is particularly advantageous for managing low-lig... | Summary: This paper addresses challenges in current image restoration methods, which are often too computationally demanding for edge devices. It proposes Deep Fourier Shifting, a novel approach inspired by spatial-shift operators adapted for low-level image tasks. By leveraging the Fourier domain and ensuring informat... | Rebuttal 1:
Rebuttal: **1, types.**
We appreciate the suggestion and will expand our experiments to include various types of images with different textures, structures, and noise characteristics. This will provide a more comprehensive assessment of how Deep Fourier Shifting performs under a wider array of conditions, ... | Summary: The authors in this paper aim at exploring more into image restoration task via the concept of spatial shift operation that facilitates efficient spatial communication and has achieved significant advancements in several high-level vision tasks. As per their observation, since the image restoration is more sp... | Rebuttal 1:
Rebuttal: **1, Fig.6.**
Thank you for your feedback. Firstly, the Fourier transform is an efficient tool that amplifies image degradations in the Fourier domain, and our learnable parameters act as filters to eliminate these artifacts. Secondly, the artifacts arise from two aspects: (1) features are extrac... | Summary: This paper explores the use of a spatial-shift operator for image restoration tasks, addressing the issue of information loss identified through experimental analysis. The authors propose a shift operator in the Fourier domain, leveraging the periodicity and cycling properties of the Fourier transform to devel... | Rebuttal 1:
Rebuttal: **1, Complexity.**
Thank you for your feedback. In Table 5, we compared the performance of our proposed Fourier shifting operator against the spatial shift operator and the baseline 3x3 convolution. Replacing convolutions with Fourier shifting not only reduced the number of parameters by half but... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can Transformers Smell Like Humans? | Accept (spotlight) | Summary: This paper mainly focus on the question "Can transformers smell like humans". By using a chemical structure transformer called MoLFormer to encode the odorant molecules, this paper proposes that the MoLFormer pre-trained representation can classify the odors of variety of molecules without careful fine-tuning.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and for the time spent reviewing our paper We now address the questions (Q), weaknesses (W), and limitations (L) raised by the reviewer:
* **(W2, Q2, L1) Fine-tuning results**: We thank the reviewer for the suggestion. We present the results of fine-tuning ... | Summary: This paper took a transformer model that was pre-trained on general chemical structures and tested whether the resulting model representations aligned with human olfactory perception. Specifically, the authors used a transformer for chemical structures called MoL-Former. MoL-Former was trained via masked token... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and for the time spent reviewing our paper. We now address the questions (Q) and weaknesses (W) raised by the reviewer:
* **(W1.1) Dimensionality Reduction**: In this work, we use PCA to reduce the dimensionality of the representations for MoLFormer and Open-... | Summary: This paper investigates the ability of MOLformer, a model trained on SMILES strings in a BERT-like fashion, to predict the human assessment of odors. The assessments were based on natural language descriptors of odors by human experts, ratings on different NL descriptors by naive subjects, and finally, on simi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and for the time spent reviewing our paper. We address the questions (Q), weaknesses (W), and comments (C) raised by the reviewer as follows:
* **(W1) Description of GS-LF dataset**: GS-LF dataset is a multi-label binary dataset, where each data point (the ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for the constructive and interesting questions and also for the positive and encouraging feedback. We did the following extra experiments, analysis, and visualizations to answer the questions raised by the reviewers.
1. We visualized representational similarity matrices... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning | Accept (poster) | Summary: This paper identifies an overlooked problem in offline model-based reinforcement learning (RL).
Offline model-based RL approaches commonly perform online RL inside a learned dynamics model and
due to model errors, they generally use truncated $k$ step rollouts, instead of full trajectories (episodes).
In this ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their obvious extreme care in providing us with feedback. We are encouraged by the reviewer’s comments that the paper “should be accepted,” "will be an important contribution to the community,” and are grateful for their detailed comments which will help us ... | Summary: The paper presents a novel analysis of the challenges faced by offline model-based reinforcement learning (RL) when the dynamics model becomes increasingly accurate. The central thesis is that existing methods fail under true, error-free dynamics due to a previously unaddressed issue termed the "edge-of-reach"... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their careful feedback on our submission.
We are grateful that they appreciated our “thorough investigation, theoretical analysis, and empirical evidence to support [our] claims” and “robust performance across benchmarks.”
We found their questions particu... | Summary: This paper identifies and investigates a previously neglected issue in offline model-based RL called the "edge-of-reach problem", which is due to the truncated rollouts used in model-based RL to mitigate the compounding errors of the dynamics model. The authors proposed Reach-Aware Value Learning (RAVL) to add... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission.
We are encouraged that the reviewer found the problem to be “interesting,” and the experiments “comprehensive, well-designed”, and are grateful they appreciated that the work is “the first to formally investigate the problem”. We h... | Summary: The paper investigates a phenomena in model-based offline RL that most existing algorithms fail when they are given the perfect model of the environment. Through multiple experiments, the paper argues the failure is due to the wrong estimated values at the states in the end of model rollouts. These states are ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable feedback on our submission.
We are particularly encouraged by their appreciation of the care we took with the empirical investigations, in particular with the comments that we provide “a very convincing set of evidences for the paper's claim,... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers. It is clear they have each taken time and care, and their feedback has greatly helped us improve and refine our work.
We are gratified by the reviewers' overall positive assessment of our work, including comments that the paper “should be accepted,” "will be ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decoding-Time Language Model Alignment with Multiple Objectives | Accept (poster) | Summary: The paper introduces a decoding-time alignment method called Multi-Objective Decoding (MOD) for aligning language models (LMs) with multiple objectives. MOD combines a set of models aligned for individual rewards and allows any weightings (even not-all-positive) for rewards. MOD leverages a common form among f... | Rebuttal 1:
Rebuttal: **Q1**
*Q: Is the conditions of Eq. 6 correctly?*
A: We omit some details in the main content. The formal conditions of Eq. 6 are provided in **Appendix D.3 Theorem 5**.
Briefly speaking, we require the $f$ to be strong-barrier function and the set of $\{\pi_{i=1,..,M}\}$ to be optimial for each... | Summary: The authors propose a decoding method that aims to combine the predictions of diverse models that are aligned with different objectives. In their multi-objective setting, the goal is to find an optimal policy that maximize a weighted, multi-objective reward, given the policy aligned to each of the individual r... | Rebuttal 1:
Rebuttal: **W1**
*Q: The baselines appears weak. For example, in Appendix F, the main comparison is against RS. However, RS cannot even outperform the best individual model in all experiments (Tables 7,8,9,10).*
A: We have pointed out the sub-optimality of rewarded soup in **Section 6.1**. Although it is ... | Summary: In many practical uses of RLHF the reward function is the convex combination of several rewards. Instead of training a single policy attempting to maximize the expected aggregate reward (subject to the usual regularization keeping it close to an anchor policy), the authors show that one can train separate poli... | Rebuttal 1:
Rebuttal: **W1**
*Q: The presentation could be much simpler, starting from the ubiquitous case of using KL divergence for regularization, which also leads to the elegant log-linear combination in Eq. (7). The general case for f-divergence could be mentioned, but relegated to the already prodigious appendix... | Summary: This paper presents Multi-Objective Decoding (MOD), a novel algorithm designed to align language models (LMs) with multiple human preferences simultaneously during decoding. MOD addresses the limitations of existing methods that optimize LMs for a single reward function, thereby providing flexibility and effic... | Rebuttal 1:
Rebuttal: **W1**
*Q: Although MOD circumvents the need for retraining, it requires loading multiple models concurrently, which can be computationally intensive and may not scale efficiently for a larger number of objectives or bigger model sizes.*
A: Please see **global response**.
**W2**
*Q: The paper ... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for their timely and positive feedbacks. Reviewer dVwo describes our approach, MOD, as “novel, effective and practical”, and notes that “the theoretical framework is robust”; Reviewer pv2M thinks MOD is “novel, simple, practical, and mathematically sound”; Reviewer ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning-Augmented Algorithms for the Bahncard Problem | Accept (poster) | Summary: This work studies the Bahncard Problem and proposes a new learning-augmented algorithm, PFSUM. The writing of the paper is clear and easy to follow. The work also provides detailed mathematical proofs for six patterns followed by the experiments validate the theoretical findings.
Strengths: This paper present... | Rebuttal 1:
Rebuttal: ### **Weakness 1 & Question 1**
Our specific contributions are to (1) develop an effective learning-augmented algorithm PFSUM for the Bahncard problem **using predictions on an immediate future only, which is more practical since the predictor is much easier to train**; (2) analyze the competitive... | Summary: In this paper, the authors investigate the Bahncard problem in the learning-augmented context. The Bahncard problem is a generalization of ski rental, originating from the railway pass of the German railway company of the same name, where an algorithm must choose between a cheap short-term solution (purchaing ... | Rebuttal 1:
Rebuttal: Dear Reviewer AGsA, we deeply appreciate your acknowledgment and support of our work! Below, we respond to the weakness and questions 1-5, one by one.
### **Weakness**
We greatly appreciate your constructive comments. In particular, we greatly appreciate that you understood our paper in depth. We... | Summary: In this paper, the authors provide a learning-augmented approach for solving the Bahncard problem, which is a generalization of the ski-rental problem. The authors provide theoretical guarantees for the consistency and robustness of the proposed algorithm PFSUM, which measures the performance of the proposed m... | Rebuttal 1:
Rebuttal: Dear Reviewer m14s, we are immensely grateful for your positive comments! Below is our response to the weakness.
### **Weakness**
We greatly appreciate your keen observation between the experimental results and the theoretical robustness bound $1 / \beta$. Allow us to start by revisiting the foll... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EGonc : Energy-based Open-Set Node Classification with substitute Unknowns | Accept (poster) | Summary: This paper introduces a new method EGonc for open-set node classification in graphs. EGonc performs open-set classification by incorporating energy-based models into the graph learning diagram. The training of the model only requires in-distribution data, while out-of-distribution training data are generated s... | Rebuttal 1:
Rebuttal: **W1:** An important claim made …
**A-W1:** We support the claim through three propositions. The defined propositions can be found in Section 3, and the proofs can be found in Appendix C. In proposition 1, we proved loss $l_1$ and $l_2$ would result in lower energy scores for the model's outputs ... | Summary: This paper proposed a new energy-based generative open-set node classification method to achieve open-set graph learning. It uses energy-based models to estimate the underlying density of the seen classes and to evaluate the probability of a given input belonging to the IND classes or OOD classes. Besides, it ... | Rebuttal 1:
Rebuttal: **Review 3:**
**Weakness 1:** What is difference between open-set node classification and out-of-distribution detection problem? The authors should illustrate the differences and whether the proposed method can solve these two problems simultaneously.
**Answer W1:** Thanks for your suggestion. W... | Summary: This paper focuses on energy-based open-set node classification, and adopted a generative method to obtain the explicit specific score of a node belonging to the ‘unknown‘ class. To generate good substitute unknowns, it adopted energy-based models to estimate the density of classes and guarantee the nice theor... | Rebuttal 1:
Rebuttal: **Review 2:**
**Weakness 1:** In the main part of the paper, some important experiment settings are missing, which makes the readers feel confused about the motivations of the experiments.
**Answer W1:** Thanks for your comment. A more detailed experimental setting can be found in **subsection ... | Summary: In this paper, the authors proposes a new generative open set node classification method (EGonc) to address the challenge of Open Set Classification (OSC) for safely deploying machine learning models in an open world. Traditional softmax-based methods are overly confident on unseen data, making them vulnerable... | Rebuttal 1:
Rebuttal: **Review 1:**
**Weakness 1:** The motivation primarily focuses on the limitations of softmax-based neural networks without delving deeply into the broader implications and potential impact of improved open set classification across various domains and real-world application.
**Answer W1:** Thank... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Global Rewards in Restless Multi-Armed Bandits | Accept (poster) | Summary: The authors study restless multi-arm bandits where we do not observe separate reward for individual arms and instead observe a global reward which is the sum of reward across arms. They propose Linear and Shapley-Whittle indices, which extend classical Whittle indices (designed for settings where separate rew... | Rebuttal 1:
Rebuttal: Dear Reviewer a47J,
We thank you for your comments and suggestions. These comments will greatly help improve our final manuscript. We appreciate that you find our empirical evaluation comprehensive, and find that our empirical results strengthen the validity of our policies. We are additionally g... | Summary: This paper studies RMAB (restless multi-armed bandits) with global
rewards. Standard RMAB assumes that the total reward is a sum of the
rewards across arms. Even though this type of RMAB incurs
curse-of-dimensionality, the linear reward structure allows Whittle
index to be defined. In contrast, in this paper t... | Rebuttal 1:
Rebuttal: Dear Reviewer t2ns,
We thank you for your suggestions and comments on our paper, and these will greatly help improve our final manuscript. We are glad that you appreciate how RMAB-G has not been previously studied in the literature. Additionally, we are happy that you appreciate the approximation... | Summary: Conventional RMAB problems model the rewards with respect to individual arms. Authors claim that for some instances (such as food rescue), a global non-separable reward function exists, because of which solutions to RMAB cannot be applied to such problem.
To address this NP-hard problem, authors modify Whittl... | Rebuttal 1:
Rebuttal: Dear Reviewer oZCz,
We thank you for your suggestions and these comments will help improve our final manuscript. We appreciate that you view our work as “opening a new topic” and are glad that you recognize our theoretical bounds. Additionally, we are happy that you appreciated our empirical resu... | Summary: This paper studies the popular Restless Multi-Armed Bandit (RMAB) problem and aims to tackle a key limitation – which is that, in RMABs, the rewards are assumed to be separable across arms. This is a limitation because in many scenarios, the overall reward may not simply be a sum over individual rewards of arm... | Rebuttal 1:
Rebuttal: Dear Reviewer TFrk,
We thank you for your suggestions and insights, and your comments will help improve our final manuscript. We are happy that you find our study novel, and find the RMAB-G model to be a key strength of our paper. Additionally, we are glad that you find our policies intuitive and... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and for taking the time to carefully read through our work. We are pleased that reviewers find our problem formulation novel (reviewers TFrk18, oZCz07, and t2ns05) and motivated by real-world applications (reviewer a47J02). We are happy to see t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Inductive Bias of Stacking Towards Improving Reasoning | Accept (poster) | Summary: This paper examines the inductive bias of gradually stacking layers to increase the depth of a smaller model. The proposed stacking variant, MIDAS, enhances training efficiency and discovers a compelling inductive bias that boosts downstream performance, particularly in reasoning tasks.
Strengths: 1) This wor... | Rebuttal 1:
Rebuttal: Thank you for the feedback and many suggestions. We tried to address them below by running new experiments.
**Q1**: *The benchmarked models of size 1B and 2B are small compared to commonly used models like 7B, 13B etc. It’s hard to conclude yet whether this strategy will scale well.*
A: Based on... | Summary: The authors propose MIDAS, an efficient and effective framework for gradually increasing model depth. Their method achieves better performance on some reasoning primitive problems. Also, the authors further provides empirical analysis to support their findings.
Strengths: 1. The authors propose MIDAS, a novel... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions. We tried to address them below by running new experiments.
**Q1**: *Can the authors evaluate on more reasoning and common-sense questions for better comparison, such as tasks tested in the Llama paper?*
A: Based on the reviewer’s suggestion, we run eva... | Summary: Gradual stacking involves incrementally growing a model by stacking its last few layers to initialize the next stage. A new variant called MIDAS (MIDdle grAdual Stacking) is proposed, which stacks the middle block of layers instead of the last block. This method is found to be more efficient and shows a bias t... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and suggestions.
**Q1**: *I think at least one more reasoning benchmark is needed in each category in order to justify that the improvement difference is indeed due to memorization instead of some dataset artifacts.*
A: Based on the reviewer’s suggestion, we e... | Summary: The paper proposes an improvement of the gradual stacking method proposed in Reddi et al. 2023 for efficient training. The improved method relies on an observation that stacking the layers at the end exhibits the similarity between layers at the end and this might be a suboptimal choice but stacking the layers... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and insightful questions.
**Q1**: *the improvement of MIDAS on downstream tasks (open book QA and math word problems) is marginal compared to the improvements on these tasks [reasoning primitives].*
A: While the improvement on these benchmarks is lower compare... | Rebuttal 1:
Rebuttal: We thank the reviewers for constructive feedback and their appreciation for the results in the paper. We have responded to reviewer questions independently. Following reviewer suggestions, we ran and reported the following new experiments:
- Run GradStack for the 2B UL2 model that was missing in ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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