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Zero-Inflated Bandits | Accept (poster) | Summary: This paper considers stochastic multi-armed and contextual bandits where the reward distributions are zero-inflated distributions. Formally, each reward observation is a draw of a product random variable $R_t = X_tY_t$ where $X_t$ is a distribution with mean $\mu$ and $Y_t$ is a Bernoulli distribution with par... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and encouraging review. Below, we provide point-by-point responses to your comments. We look forward to refining our manuscript to address these valuable suggestions.
*Theoretical Claims:*
We sincerely thank you for your thoughtful and encouraging comment.... | Summary: This paper “Zero-Inflated Bandits” focuses on the issue of sparse rewards in multi-armed bandit (MAB) and contextual bandit applications. The authors propose a zero-inflated bandit (ZIB) algorithm framework to enhance learning efficiency by leveraging the zero-inflated distribution structure.For zero-inflated ... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. Below, we respond to each of your points. We hope this addresses your concerns.
*Claims & Evidence:*
Our main independence assumption is that $X$ (nonzero rewards) and $Y$ (the indicator of a nonzero outcome) are independent in the decompos... | Summary: The submission studies multi-armed bandits whose reward function follows the zero-inflated (ZI) distribution. The motivation is to investigate the advantages of distribution modeling and exploiting the problem specific structure. UCB and TS are modified to solve the MAB and the contextual bandit problems under... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We greatly value your time and suggestions, and we hope the following clarifications and enhancements address your concerns. We respectfully ask you to consider revising your evaluation score if our replies resolve your reservations.
*Theoretical Claims:*
Our ... | Summary: This paper considers a multi-armed bandit setting, where reward distributions are contaminated with a zero point mass with weight $p$. To accommodate this special reward structure, which returns rewards of 0 with probability $1-p$, and rewards distributed according to an arm-specific sub-Weibull distribution o... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful and encouraging feedback. Below, we respond to your comments point by point, and we hope our replies provide clear and satisfactory answers to your questions.
*Claims & Evidence:*
While sub-Gaussian or sub-Weibull assumptions allow existing algorithms t... | null | null | null | null | null | null |
Measuring Diversity in Synthetic Datasets | Accept (poster) | Summary: The paper introduces DCScore, a novel method for measuring diversity in synthetic datasets from a classification perspective. From my analysis, the key innovation is reformulating diversity evaluation as a sample classification task, where each sample should be distinguishable enough to form its own class. The... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our work. We respond to the reviewer’s question as follows. **Limited by the space, we present our additional experiments in an anonymous URL** (https://anonymous.4open.science/r/ICMLRebuttal_DCScore).
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>Q1: It would be better to add more investigati... | Summary: - The paper introduces DCScore, a novel method for measuring diversity in synthetic datasets generated by large language models (LLMs).
- Key innovation: DCScore formulates diversity evaluation as a sample classification task, leveraging mutual relationships among samples, rather than using traditional n-gram... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper. We respond to the reviewer’s question as follows. **Limited by the space, we present our additional experiments in an anonymous URL** (https://anonymous.4open.science/r/ICMLRebuttal_DCScore).
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>Q1: The claim about DCScore's correlation with human ju... | Summary: The paper introduces DCScore, a novel metric for measuring diversity in synthetic datasets. Unlike traditional methods (e.g., Distinct-n, VendiScore), DCScore models diversity as a classification task and uses semantic embeddings to compute pairwise similarity among samples. It leverages a softmax-based classi... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our paper. We respond to the reviewer’s question as follows. **Limited by the space, we present our additional experiments in an anonymous URL** (https://anonymous.4open.science/r/ICMLRebuttal_DCScore).
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>Q1: it does not sufficiently justify why treating each ... | Summary: The paper introduces a classification-based evaluation metric, DCScore, for assessing the diversity of synthetic datasets. The authors address computational challenges while satisfying axiomatic requirements and providing a holistic analysis. Additionally, they evaluate the diversity of generated datasets acro... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing detailed review on our submission. We respond to the reviewers’ concerns and questions one by one. **Limited by the space, we present our additional experiments in an anonymous URL** (https://anonymous.4open.science/r/ICMLRebuttal_DCScore).
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>Q1: Relying so... | null | null | null | null | null | null |
Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks | Reject | Summary: This paper considers the convergences of a certain class of PINNs with 2 layers in the overparametrization regime (NTK) and makes two contributions: 1) improves the convergence of GD (conditions for LR) and 2) shows quadratic convergence of natural gradient descent. Specifically for 1) the LR dependency on the... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our article and for your insightful comments. We apologize for the oversight in notations. We agree that adding some context before diving into derivations would improve readability, especially for a broader audience. In the revised version, we will make sur... | Summary: The manuscript concerns convergence results for PINNs for shallow neural networks with $\operatorname{ReLU}^3$ (or certain smooth) activation functions in the overparametrized setting. Both gradient descent and natural gradient descent are considered. The considered model PDE is a heat equation.
## update aft... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and valuable feedback on our manuscript. Let us address your questions point by point.
**Q1: Relations to other methods.**
**A1**: We apologize for the insufficient background discussion of Natural Gradient Descent (NGD) in our work. Although NGD sha... | Summary: The paper investigates the theoretical convergence of natural gradient descent for overparameterized physics-informed neural networks under NTK regime. The paper extends the previous works on gradient descent to natural gradient descent, with improved bounds. The improvement is based on some inequality techniq... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and valuable feedback on our manuscript. Let us address your questions point by point.
**Q1: Experimental investigations on NGD for PINNs.**
**A1**: Thanks for the suggestion. We provide some experiments to validate our theoretical results. In the rev... | Summary: The paper investigates the convergence properties of gradient descent (GD) and natural gradient descent (NGD) for training two-layer Physics-Informed Neural Networks (PINNs). The authors improve the learning rate of GD from $\mathcal{O}(\lambda_0)$ to $\mathcal{O}(1/\|H^{\infty}\|_2)$, where $\lambda_0$ is the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable time and constructive suggestions on our work.
**Q1: Methods to help the PINNs' design.**
**A1:** Thanks for the kind suggestion. The results of the paper motivate us to establish "good" Gram matrix $H^{\infty}$ to reduce the strictly learning ... | null | null | null | null | null | null |
How Far Is Video Generation from World Model: A Physical Law Perspective | Accept (poster) | Summary: This paper investigates whether state-of-the-art video generative models can learn fundamental physical laws from purely visual data. Inspired by the vision of video models as “world simulators” (e.g. OpenAI’s Sora), the authors conduct a systematic study using a controlled 2D physics environment. They constr... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for appreciating our method design. Please find our responses to your questions below.
`1. More qualitative examples of failure cases in OOD or combinatorial scenarios`
Thank you for the helpful suggestion. In Appendix A.8, we have included failure cases wi... | Summary: This paper explores whether scaling video generation models enables them to learn physical laws. It first provides a thorough problem definition and then evaluates video generation models under three scenarios: in-distribution, out-of-distribution, and combinatorial generalization. The authors develop a 2D sim... | Rebuttal 1:
Rebuttal: `1. The prioritization conclusion is drawn from single uniform linear motion, rasing concerns about its generalizability to other physical contexts.`
Thank you for your thoughtful comment. We selected the uniform linear motion scenario as it provides **a highly representative and clean setting**—... | Summary: The authors create a benchmark to evaluate the physical understanding of large video models at scale. Specifically, they measure the generalization performance of the model under variations of physically meaningful quantities such as color, shape, size and velocity.
Claims And Evidence: They claim that the mo... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for supporting the acceptance of our work.
We appreciate you pointing out these relevant works on evaluating the physical understanding of large video models. We agree that incorporating them will help position our contributions within the broader landscape... | Summary: This paper evaluates if scaling video generation models enables learning physical laws. Using a 2D simulator for object motion governed by classical mechanics, experiments reveal: (1) near-perfect in-distribution (ID) generalization with scaling, (2) failure in out-of-distribution (OOD) scenarios despite scali... | Rebuttal 1:
Rebuttal: Thanks for supporting the acceptance of our work. Please find our responses to your questions below.
`1. pixel-level metrics (SSIM/PSNR) may not fully capture physical plausibility; over-reliance on human evaluation for combinatorial cases`
Thank you for your comment. We agree that pixel-level m... | null | null | null | null | null | null |
Efficient Robotic Policy Learning via Latent Space Backward Planning | Accept (poster) | Summary: The authors introduce LBP (Latent space Backward Planning), a novel approach for robotic planning. LBP works by grounding tasks into final latent goals and recursively predicting intermediate subgoals backward toward the current state. The authors evaluate LBP on simulation benchmarks and real-robot environmen... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and recognition of our work! If you have any further concerns or questions related to LBP, we would be happy to discuss them. | Summary: In this work, the author proposed a robotic manipulation method called LBP. This method first grounds the task into final latent goals and then recursively predicts the intermediate subgoals closer to the current state. Compared to previous fine-grained approaches, LBP is more lightweight and less prone to acc... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's positive feedback and recognition of our work! Below are our responses to the concerns raised.
## Experimental Designs Or Analyses
>The presentation of real-world results is great...It could be better to involve more contact-rich tasks.
Thanks for your suggestion! We ar... | Summary: This paper focuses on latent space planning to accomplish robotic tasks. It breaks down a long horizon language conditioned manipulation task into predicting the final goal. Then using the final-goal to predict sub-goals moving from the goal state to the initial state. Once these have been learned a sub-goal/f... | Rebuttal 1:
Rebuttal: ## **Experimental Designs Or Analyses**
>Other long horizon tasks for comparison (e.g. AntMaze etc).
Thanks for the positive comments to our experimental designs. While other long-horizon tasks like variations of AntMaze exist, we excluded them from our primary benchmark suite for specific reason... | Summary: To enable real-time planning for long-horizon and multi-stage tasks, the paper proposes LBP, a backward planning scheme in the latent space. By eliminating the need for pixel-level generation, the proposed scheme significantly improves inference speed while alleviating compound errors. Additionally, it enhance... | Rebuttal 1:
Rebuttal: Thank you for your efforts and valuable feedback!
# Claims and Evidence
> **Missing ablations to forward planning**
We add an ablation study comparing LBP to latent forward planning. The results demonstrate that LBP significantly outperforms the forward planning paradigm in both **subgoal predict... | null | null | null | null | null | null |
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger | Accept (spotlight poster) | Summary: This paper introduces a novel framework to enhance Large Vision Language models (LVLMs) capability for visual question answering. Two major contributions are demonstrated in this paper. First is about creating a comprehensive knowledge base enriched with automatically generated reasoning contexts. Second is a... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews and address your concerns as follows.
**Q1**: From the Table 2, it is not clear why for InternVL-2, the Vanilla-Rag performs worse than the Zero-shot. Is that because of the quantization method for models over 7B?
**A1**: Thank you for pointing out this issu... | Summary: This paper focuses on RAG for VQA tasks. The authors propose constructing a reasoning-based KB with examples of successful reasoning and then introduce an MCTS-based method for finding the best set of ICL examples, motivated by the fact that existing models can only take a fairly small number of ICL examples (... | Rebuttal 1:
Rebuttal: We thank you for your insightful and valuable reviews and address your concerns as follows.
**Q1**: What dataset is Vanilla RAG retrieving from? Is the info coming from the same KB but without MCTS reranking? And I think the setup for the Vanilla-RAG is not clear. It's later improved by Fig. 6 bu... | Summary: This paper presents a method to refine the selection of retrieved examples for multimodal language model in-context learning. It has two key components. The first component is to ask LLM to generate a set of rationale/reasoning contexts given a QA pair and select the context that has the highest probability of... | Rebuttal 1:
Rebuttal: We thank you for your constructive reviews and address your concerns as follows.
**Q1**: One major concern is the assumption of this paper. Unlike other KB-VQA that mostly focuses on an external knowledge base, the knowledge base in this paper is a large number of QA pairs for each question.
**... | Summary: This work proposed a multi-modal RAG method to retrieve reasoning context examples from the knowledge base. The main components of this work are (1) a CoT knowledge base, (2) knowledge retrieval metrics with hybrid vision-language embeddings, and (3) Monte Carlo Tree Search (MCTS) to retrieve the most related ... | Rebuttal 1:
Rebuttal: We thank you for your constructive reviews and address your concerns as follows.
**Q1**: Why in-context example retrieval needs such a complex tree-based search strategy? Does the order of retrieved examples matter?
**A1**: As demonstrated by Tan et al.[1] and Liu et al.[2], the order of retrie... | null | null | null | null | null | null |
PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation | Accept (poster) | Summary: The paper proposes PhantomWiki, a benchmark that dynamically generates a fictional universe for evaluating retrieval and multi-hop reasoning. The proposed benchmark can be generated on-demand and is free of data leakage because the fictional events are independent with the real-world.
The PhantomWiki data gen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on the design of PhantomWiki and for acknowledging its novelty in providing a scalable and data leakage-resistant evaluation framework. We address your concerns in turn.
**1. Using templated articles**
> Article content and questions lack realism... | Summary: This paper presents a solution for creating a high-quality benchmark to evaluate the RAG and reasoning abilities of LLMs. Specifically, the proposed method, PhantomWiki, introduces a novel pipeline for generating unique, factually consistent document corpora with diverse question-answer pairs for evaluation. P... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and encouraging assessment of PhantomWiki. We are pleased that you find PhantomWiki a high-quality benchmark for fine-grained evaluation of reasoning and retrieval. We are also delighted to hear that our work is **well-documented**, and presents **solid exp... | Summary: The manuscript introduces PhantomWiki, a generator for fictional universe of characters in the form of a fandom wiki. The knowledge graph for characters, their relations, and facts about them, are generated by sampling simple distributions. Articles are generated from these facts using templates. Questions are... | Rebuttal 1:
Rebuttal: **1. Question 1 / Evaluating language models fine-tuned on PhantomWiki data**
> Another avenue is to try to actually fine-tune models on multiple instances of PhantomWiki, and assess how this affects performances on fresh instances.
We add a **new experiment** and find that **fine-tuning on Phan... | Summary: The paper presents PhantomWiki, a pipeline that generates synthetic large-scale document corpora and question-answer pairs. PhantomWiki works by generating articles containing facts about characters (e.g., “The job of x is y.”), and generating question-answer pairs from templates. PhantomWiki allows generating... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions, which we believe improve our manuscript significantly. We are pleased that you find PhantomWiki a **promising alternative to the popular “needle-in-a-haystack” test**. We address your comments below and will add results for suggested experime... | null | null | null | null | null | null |
Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop | Reject | Summary: This paper proposed a multi-modal prediction framework that integrates a prototype-based time series encoder with three collaborating LLMs to deliver more accurate predictions and interpretable explanations. The closed-loop workflow – prediction, critique, and refinement – continuously boosts the framework’s p... | Rebuttal 1:
Rebuttal: **Question 1 and Weakness 1,2: Innovation and scope:**
We appreciate the reviewer’s comments and the opportunity to clarify our contributions. Our work is motivated by the need for effective explanation, multi-modal time series understanding, and contextual reasoning—areas that are underexplored ... | Summary: The paper introduces TimeXL, a multi-modal prediction framework designed to integrate both time series data and textual information, addressing a common limitation in existing time series models that often neglect auxiliary textual data available in real-world scenarios. A key contribution of the paper is a ne... | Rebuttal 1:
Rebuttal: **The regression-based setting:**
We sincerely appreciate the reviewer for the comments. In Appendix F (Pages 23-25), we implemented a regression-based variant and provided a demonstration on the same finance dataset with numerical ground truths to show its capability for numerical value forecast... | Summary: In this paper, the authors proposed a new Multi-model time series prediction model that uses prototype-based encoder with 3 LLMs to predict, reflect and refine the semantic information. Experiments on real-world datasets show the effectiveness of the proposed method.
Claims And Evidence: Experiments are not e... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful feedback and provide our response below.
**Q1 and Weakness 2: Regression tasks**
We appreciate the reviewer’s interest in understanding how our approach performs on regression tasks. Below are the results of our method and state-of-the-art base... | null | null | null | null | null | null | null | null |
Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification | Accept (poster) | Summary: This paper is related to the best arm identification (BAI) problem in multi-armed bandits, where the objective is to recommend the (unique) arm with highest expected reward at a fixed error rate $\delta$ ($\delta$-correctness property) after collecting as few observations as possible. The stopping time of a r... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the significance of our problem, its novelty in bandit literature, and the strength of an exponentially decaying stopping time over high-probability bounds. The detailed description and anonymous link of the additional experiments we did are in **Additional em... | Summary: This paper studies the fixed-confidence best-arm identification problem for $1$-sub-Gaussian distributions. The authors remark that asymptotic guarantees on the expected sample complexity doesn’t prevent a large tail of the empirical stopping time. Worse, high probability guarantees of the sample complexity do... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the value of our proposed exponential stopping tail property and finding it helpful. Please see empirical results in rebuttal to 94Yb. The maximum characters limit our rebuttal content, will add more in discussion.
**On Theorems 2.4 / 2.5**: We agree that the... | Summary: This paper considers the distribution of the stopping time in the fixed confidence BAI problem. It discovers that while most existing algorithms only have stopping time bounds in expectation or in high probability, which fail to achieve exponential decreasing rate (in time step $T$) for the misidentification p... | Rebuttal 1:
Rebuttal: We thank the reviewer for understanding our theoretical contribution and your interest in additional empirical studies. We address your comments below.
Regarding the reviewer's comment that "FC-DSH is a lot like the algorithm in Zhao et al. (2023), with just a change to the stopping rule," we wou... | Summary: This paper address the best arm identification problem under fixed confidence, emphasizing the importance of exponential-tailed stopping time guarantees. Unlike existing algorithms prone to heavy tails or indefinite stopping, the authors propose two novel theoretical results: (1) an algorithm with exponential-... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the significance and broader impact of our work in identifying an intriguing problem. We address your comments below. Please see empirical results in rebuttal to 94Yb.
1. Our goal was to highlight a surprisingly overlooked issue in the bandit literature and p... | null | null | null | null | null | null |
Scaling Laws for Differentially Private Language Models | Accept (poster) | Summary: This paper formulates scaling laws that accurately reflect the complexities of training Differentially Private (DP) Large Language Models (LLMs).
Claims And Evidence: Yes, the claims made in the submission are supported by clear and convincing evidence.
Methods And Evaluation Criteria: It is not clear whethe... | Rebuttal 1:
Rebuttal: We appreciate the feedback, and thank you for sharing ideas for specific ways to improve the paper. We are glad that you found the paper well-written and thorough, but we respectfully disagree with some of your comments, and respond to the main critiques below.
**[Motivation]** There are several ... | Summary: The paper formulates the problem of identifying scaling laws for differentially private training of Bert models, as identifying the optimal training configurations (model size, batch size, noise-batch ratio, and iterations) given fixed data, compute, and privacy budget. Here clipping thresholds and stepsize ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and feedback, and we are glad you liked the paper. Below we respond to the main questions / critiques:
**[Missing details]** We will be sure to update our discussion around Figure 1 and Section 4.5 to clarify what is shown.
* Regarding Figure 1, we ... | Summary: This paper explores the scaling laws applicable to the training of masked language models under differential privacy (DP) constraints. The authors establish that traditional scaling laws, which do not account for privacy considerations, are suboptimal when applied in DP settings. Key findings: optimal model si... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and feedback, and for recognizing the various strengths of this work. Below we respond some of the questions / criticisms:
**[Masked Language Modeling vs. Other Tasks]** The reviewer is correct to point out that our focus on BERT models raises questio... | null | null | null | null | null | null | null | null |
Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval | Accept (poster) | Summary: The paper introduces a test‐time, plug‐and‐play approach VISA for text-to-visual retrieval. VISA converts visual content into dense, natural language descriptions using off‐the-shelf large multimodal models (LMMs). It then refines these descriptions via a question-answering module that leverages chain-of-thoug... | Rebuttal 1:
Rebuttal: > **Q1:Concern of computational latency**
We would like to clarify that our method is **compatible with smaller, more efficient retrievers**, and we have observed that models with significantly fewer parameters (e.g., 400M or 1.5B) still achieve **comparable performance** (see our response to Rev... | Summary: This paper introduces Visual Abstraction (VISA), a novel test-time approach for enhancing text-to-visual retrieval by converting visual content into textual descriptions using large pre-trained models. VISA utilizes a question-answering mechanism to refine these descriptions to match the granularity of user q... | Rebuttal 1:
Rebuttal: > **Q1:Integrating VLMs**
Thank you for this insightful suggestion. To explore this idea, we integrate SigLIP (as the base model) with EVA-CLIP (18B parameters), which has a significantly higher parameter count compared to the text retrieval model gemma-9B. This hybrid setup yields notable perfor... | Summary: This paper proposes Visual Abstraction (VISA), a plug-and-play approach designed to enhance text-to-visual retrieval. Unlike traditional retrieval methods that operate in a cross-modal embedding space, VISA transforms visual content into textual descriptions using off-the-shelf large models. This transformatio... | Rebuttal 1:
Rebuttal: > **Q1:Efficiency Concerns**
Thanks for the suggestion regarding the efficiency of using large text retrievers. Importantly, VISA does not require LLMs with over 7B parameters to work effectively. To demonstrate this, we conducted experiments using smaller text retrievers with **400M and 1.5B par... | Summary: The paper studies the problem of text-to-visual retrieval, which involves both text-to-image retrieval and text-to-video retrieval. The authors propose a framework to enhance the retrieval via converting the visual content to the text domain, and then do the retrieval. Experiments show improvement of the propo... | Rebuttal 1:
Rebuttal: > **Q1: Efficiency Concerns (FLOPs and latency)**
Thank you for the valuable suggestions. We include the FLOPs and latency comparisions on the Flickr dataset below. For clarity, we divide the retrieval process into two stages:
- Offline stage precomputes visual features (via VLM) and generate... | null | null | null | null | null | null |
Robust Automatic Modulation Classification with Fuzzy Regularization | Accept (spotlight poster) | Summary: The paper introduces Fuzzy Regularization (FR) as a novel solution to mitigate prediction ambiguity in Automatic Modulation Classification (AMC). This ambiguity is caused by similar characteristics between modulation schemes, especially under noisy conditions. The FR approach is characterized by three key feat... | Rebuttal 1:
Rebuttal: Thank you for professional comments. We have tried our best to address your questions and revised our paper by following suggestions from all reviewers.
**Q1: Can you provide a more formal and detailed explanation of the adaptive gradient mechanism used in Fuzzy Regularization? This would help ... | Summary: This paper proposes a regularization method aimed at enhancing classification performance in signal classification tasks, particularly for those with low signal-to-noise ratios. It achieves this by constraining the model's predictive ambiguity for samples during the task, thereby increasing the inter-class dis... | Rebuttal 1:
Rebuttal: Thank you for professional comments. We have tried our best to address your questions and revised our paper by following suggestions from all reviewers.
**W1: The work appears to bear some resemblance to curriculum learning. Could the authors provide a detailed explanation of the similarities ... | Summary: This paper proposes a method to improve the reliability of signal classification models by means of fuzzy regularization. Starting from the prediction fuzzy phenomenon, the authors first experimentally prove that the prediction fuzzy phenomenon is a common phenomenon in automatic modulation recognition, and th... | Rebuttal 1:
Rebuttal: Thank you for professional comments. We have tried our best to address your questions and revised our paper by following suggestions from all reviewers.
**W1: Does FR sharpen misclassified samples?**
RE: Thanks for your valuable comment. We can suppress this phenomenon by adjusting the hyper... | Summary: This article primarily introduces a fuzzy regularization method applicable to the field of automatic modulation. The authors first measure the prediction ambiguity by an entropy function or a regular function, and then gradually introduced adaptive gradient and exponential normal distribution to further optimi... | Rebuttal 1:
Rebuttal: Thank you for professional comments. We have tried our best to address your questions and revised our paper by following suggestions from all reviewers.
**W1: Section 4.5 lacks experimental validation regarding the time taken for a single training round. Fast convergence in terms of training ep... | null | null | null | null | null | null |
Distilling the Knowledge in Data Pruning | Accept (poster) | Summary: Dataset pruning is the task of reducing the number of samples within a dataset without impairing accuracy. This article combines dataset pruning with methods inspired by the knowledge distillation (KD) literature by augmenting the training process with soft labels from a teacher network that was trained on the... | Rebuttal 1:
Rebuttal: We sincerely thank **Reviewer xePE** for the thoughtful reading, positive feedback and appreciation for the novelty and simplicity of our proposed approach. Below, we kindly address each of the reviewer's questions and concerns:
**1. Comparison with a Stronger Baseline (100% Data, KD)**
We thank... | Summary: The key contribution of this paper is to show that using soft predictions from teachers trained on complete data combined with prune data improves the accuracy of students trained on pruned data. The authors conduct a series of experiments with ResNet variations on small datasets, including CIFAR-10, CIFAR-100... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable feedback and for highlighting the strengths of our work. We appreciate the constructive suggestions and are committed to addressing the concerns raised.
**Experimental Results with Disjoint Datasets**
Following the reviewer's suggestio... | Summary: The paper explores the use of knowledge distillation (KD) for enhancing training on pruned datasets, demonstrating that simple random pruning with KD can achieve superior accuracy compared to recent data pruning methods. The work also reveals that, when using teachers with smaller capacities, the student can b... | Rebuttal 1:
Rebuttal: We sincerely thank **Reviewer RfVw** for their careful reading, thoughtful remarks and the positive feedback. In addition, we appreciate their acknowledgement of the novelty, simplicity and effectiveness of our proposed method.
Below, we kindly address each of the reviewer's questions and concer... | Summary: This article explores the use of knowledge distillation (KD) to improve model performance when training on pruned datasets. The authors investigate how transferring soft predictions from a teacher model can eliminate the accuracy loss caused by aggressive data pruning.
Claims And Evidence: Yes, the article's ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comprehensive and constructive feedback and for highlighting the strengths of our paper. We appreciate the insightful questions and will make efforts to address the reviewer’s concerns.
**Incorporating Recent Pruning Methods**
Following the reviewer’s sugge... | null | null | null | null | null | null |
Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging | Accept (poster) | Summary: The authors use multi-objective optimization to create a framework for users to give preferences to which tasks are important to them when merging models. Given a merged model, they build another PEFT architecture that takes preference weights as input This new PEFT is trained either data-free or using unlabel... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. Below, we provide a detailed response to each point.
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**R1: Direct incorporation of preference to Task Arithmetic and AdaMerging**
In Section 3, we frame model merging as a MOO problem and propose both straightforward methods (Section 3.1) and our Paret... | Summary: This paper proposes a novel preference-aware multi-objective model merging method called Pareto Merging (PM) to generate a Pareto set of merged models (the number of models might be infinite) by a single merging process. The main contributions include 1) the preference-aware multi-objective model merging formu... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. Below, we provide a detailed response to each point.
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**Response to Claims And Evidence**
**R1: Comparison with [1]** Note that [1] is indeed a concurrent work of ours but with different goal. [1] aims to improve the efficiency of general Pareto set l... | Summary: This paper introduces a new method for merging models with trade-offs by finding the Pareto front. They included both data-free version and using unlabeled data version of the method.
Claims And Evidence: Most of the claims are clear. I've detailed unclear points in the following comments.
Methods And Evalua... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. Below, we provide a detailed response to each point.
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**Response to Questions For Authors**
**R1: Parameter count in Table 1**
The "parameter count" refers to the number of parameters that need to be stored after merging.
Note that for each merging m... | Summary: The paper introduces a novel method named "Pareto Merging," which is designed for the efficient merging of multiple pre-trained machine learning models into a single model, taking into account the preferences of different users. The approach learns a set of models, each optimized for different trade-offs or pr... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. Below, we provide a detailed response to each point.
---
**Response to Essential References Not Discussed**
**R1: MGDA Reference** Thanks. We'll include it in our final version.
**R2: Relation with preference-based MOO works [1,2,3]** They differ fundame... | null | null | null | null | null | null |
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing | Accept (poster) | Summary: This paper introduces an adversarial training algorithm aimed at enhancing cost-sensitive robustness. The writing is clear, and the methodology appears sound within the context of the paper. However, I have concerns regarding the motivation, and the evaluation lacks several critical experiments.
## Update aft... | Rebuttal 1:
Rebuttal: **1. It is essential to report the robust accuracy of both cost-sensitive and non-sensitive adversarial examples separately.**
|Method|$Rob_{cs}$|$Rob_{normal}$|
| -------- | -------- | --------
|Gaussian |22.9|49.8|
|SmoothAdv|26.3 |52.5|
|SmoothMix |16.8|52.7|
|MACER|27.4|54.3|
|Gaussian... | Summary: This paper considers certified robustness when the cost between the correct label and the incorrect one is non-uniform. Specificially, author proposed a certification method via randomized smoothing and the corresponding provable training algoriothm in this context.
Claims And Evidence: The claims in this pap... | Rebuttal 1:
Rebuttal: **1. The authors should report the performance variance**
We conduct three independent runs of the Imagenette S-Seed setting and report the corresponding standard deviations. The experimental procedure comprises two distinct stages: the training phase and the certification (evaluation) phase. The... | Summary: The paper introduces a novel framework for adversarial robustness that incorporates cost-sensitive learning using randomized smoothing. Unlike existing defenses that assume uniform misclassification costs, this method optimizes robustness with a cost matrix that accounts for real-world risk variations (e.g., m... | Rebuttal 1:
Rebuttal: **1. Theorem 4.4 (Certified robustness estimate is statistically valid). The logic appears correct, but empirical verification (e.g., checking Monte Carlo estimates converge) would strengthen confidence.**
We conduct the certification process using varying numbers of Monte Carlo samples, where ... | null | null | null | null | null | null | null | null |
DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning | Accept (poster) | Summary: The paper introduces DINO‐WM, building task‐agnostic world models for control and planning. Instead of operating directly in pixel space, the method leverages pre‐trained patch features from DINOv2 to encode observations into a rich, spatially-aware latent representation. The world model is trained offline usi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review, especially in highlighting that DINO-WM demonstrates “robust generalization” and “is a significant step toward more general-purpose models.” We now address the issues raised by the reviewer.
>**“I am a little worried about the real-world planning... | Summary: This work aims to train a world model using large vision pre-trained features on offline trajectories in a task-agnostic fashion. They use the resulting trained world model to plan out Push-T, Maze, Reach, Rope, and Granalur control tasks in a zero-shot manner. Specifically, the authors train a world model usi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and constructive feedback, and for acknowledging that DINO-WM “expands the current MBRL setting by incorporating the learned visual features from large vision models”, and the idea of using pretrained features to be “neat and simple”. We now address the q... | Summary: The paper introduces DINO-WM, a world model that operates within the DINOv2 representation space without the need for reconstruction. The model is trained using teacher forcing with a frame-level causal mask. Due to its task-agnostic nature, DINO-WM can be used for zero-shot model predictive control without de... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, and for acknowledging that DINO-WM “demonstrates superior performance and could serve as a solid baseline for future research” and “could inspire future research a lot.” We address the issues raised in the review below.
>**“why DINOv2 perfo... | Summary: This paper proposes DINO-WM, a task-agnostic world model that predicts future visual features using DINOv2 embeddings instead of reconstructing raw observations. Trained on offline trajectories with a Vision Transformer, it enables zero-shot test-time optimization via model predictive control. DINO-WM outperfo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, for identifying that DINO-WM “significantly improves world modeling quality” and shows “a good embedding space makes world modeling + MPC a winning recipe”. We address the issues raised in the review below.
>**“it is uncertain whether frozen... | null | null | null | null | null | null |
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation | Accept (poster) | Summary: The paper introduces CHATS, a text-to-image generation framework that integrates human preference alignment with test-time sampling. It employs two distinct models to capture preferred and dispreferred distributions, trained with a new objective based on Direct Preference Optimization (DPO), and uses a proxy-p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable and insightful feedback！
### **1. Clarification on approximating an intractable expectation using Jensen’s inequality**
We acknowledge the reviewer's concern regarding the use of Jensen’s inequality in our derivation of Eq. 12. Specifically, we approximate t... | Summary: This paper presents CHATS, a framework for text-to-image generation (T2I) that enhances both text-image alignment and generation quality. Unlike traditional approaches that separately apply human preference alignment and classifier-free guidance, CHATS integrates both components to optimize text-to-image diffu... | Rebuttal 1:
Rebuttal: Thank you for your comments.
### **1. Novelty**
We respectfully disagree with the assertion that CHATS merely *combines existing technologies*. In our work, we explicitly differentiate our approach from prior DPO methods for diffusion models in related work [1–3] in Line 135-146 (left column).... | Summary: This paper aims to improve the performance of text-to-image diffusion models by using a human preference dataset. To make better use of DPO and CFG, they propose a training objective that trains a preferred model and a dispreferred model. During the sampling step, they introduce a new guidance method that inco... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback and questions! Due to space limitations, we provide responses to your main comments. Further questions can be discussed in subsequent responses.
### **1. Theoretical foundations & convergence properties of CHATS**
Given that DPO is invariant to affine transform... | null | null | null | null | null | null | null | null |
Adjustment for Confounding using Pre-Trained Representations | Accept (poster) | Summary: This paper explores how non-tabular data, such as images and text, can be incorporated into average treatment effect (ATE) estimation to account for confounding factors. The authors propose using latent features from pre-trained neural networks for adjustment. They formalize conditions under which these featur... | Rebuttal 1:
Rebuttal: **Additional experiments** can be found at https://anonymous.4open.science/r/icml2025-6599/add_exp_r3.pdf
- - - -
### **Theory & Practical Relevance**
> I found this paper to be a primarily theoretical paper with minimal experimental support.
This is correct. Our paper is a theoretical contrib... | Summary: The paper revisits the problem of estimating Average Treatment Effects (ATE) in observational studies under an assumption of ignorability where confounding factors are available as images or text (non-tabular data). The authors develop a theoretical argument that describes under what conditions the use of pre-... | Rebuttal 1:
Rebuttal: ### **Theoretical Results**
> Why frame the results so closely to ATE estimation? Is there not an opportunity here to provide guarantees for any functional of pre-trained representations?
Indeed, the derived convergence rates could be used for convergence guarantees of any functional of the pre-... | Summary: This paper investigates the application of Double Machine Learning (DML) for estimating the Average Treatment Effect (ATE) in non-tabular data contexts, such as text and images. The authors highlight the limitations of traditional causal inference methods in handling non-tabular data and propose leveraging pre... | Rebuttal 1:
Rebuttal: **Additional experiments** can be found at https://anonymous.4open.science/r/icml2025-6599/add_exp_r1.pdf
- - - -
### **Impact of ILTs on ATE**
> How does ILT invariance specifically impact DML estimation?
The ILT invariance of pre-trained representation does not only have theoretical but also... | null | null | null | null | null | null | null | null |
MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation | Accept (poster) | Summary: The paper employs a multi-time stepping procedure in conjunction with a numerical integrator, physical operators, and neural networks to process downsampled data in both space and time. The proposed method applies time stepping on a finer temporal grid while utilizing the RK4 scheme for PDE integration. Superv... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments. To enhance clarity, we have thoroughly proofread the manuscript and corrected all identified typographical errors. We believe these revisions will significantly improve the presentation. The updated version will be uploaded as soon as file submissions are ... | Summary: The paper introduces MultiPDENet, a PDE-embedded neural network with multiscale time stepping to accelerate flow simulations by integrating numerical methods with machine learning. It employs finite difference-based convolutional filters to approximate spatial derivatives on coarse grids, while a Physics Block... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments and suggestions! We have carefully addressed them, and the following responses have been incorporated into the revised paper.
### **Questions**
>**Q1. Essential References Not Discussed.**
**Re:** We appreciate your comment. We have noted that our initial ... | Summary: The authors proposed a framework for data-driven fluid flow simulation on uniform grid. Trying to soft-embed partial differential equations in data-driven flow simulations, the authors design a convolutional filter based on the constraints of the central difference discretization of the first and second order ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments! We have addressed them thoroughly and added new figures/tables (see the **rebuttal.pdf** via https://anonymous.4open.science/r/Rebuttal-5D8B/rebuttal.pdf). These results will be added to the revised paper.
### **Weaknesses**
>**W1. Uniform grids.**
**Re:*... | null | null | null | null | null | null | null | null |
Deep Sturm–Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions | Accept (poster) | Summary: This paper proposes a novel method (Deep Sturm-Liouville, DSL) that combines the Sturm-Liouville theorem with neural networks. By solving one-dimensional Sturm-Liouville problems, DSL generates orthogonal basis functions from the resulting eigenfunctions to approximate target functions and employs implicit gra... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for carefully reading our paper, providing thoughtful feedback and recognizing the novelty of our approach.
I — In response to the reviewer's main concerns:
1 — 1D Regularization vs. 0D Regularization: By 0D regularization, we refer to regularization applied a... | Summary: In the present contribution authors describe a novel approximation scheme suitable for general mappings $\mathbb{R}^{m}\rightarrow \mathbb{R}^{n}$ where both $m$ and $n$ may be large. The scheme is suggested to be used as an alternative to neural networks.
A simplified description of the proposed mapping $y =... | Rebuttal 1:
Rebuttal: The authors would like to thank you for your detailed review, for carefully examining the demonstrations and for recognizing the novelty of our approach.
I — Demonstration 2
First, we would like to address the mistake you identified in Demonstration 2, specifically in lines 728–732. The reviewer... | Summary: This paper introduces Deep Sturm-Liouville (DSL), a novel function approximator that integrates the Sturm-Liouville Theorem (SLT) into deep learning to achieve continuous 1D regularization along field lines in the input space. Demonstrates competitive performance and improved sample efficiency on diverse datas... | Rebuttal 1:
Rebuttal: We thank the reviewer upBa for taking the time to review our paper in detail and for recognizing the novelty of our approach. The novelty of our approach was highlighted also by reviewer 2moq, who described our article as "highly original with no directly related techniques in a published literatu... | null | null | null | null | null | null | null | null |
SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation | Accept (poster) | Summary: The paper proposes a diffusion based CAD sketch generation framework using a mixture of continuous and discrete diffusion. Technically, it introduces Gaussian-softmax diffusion, which is able to model categorical distributions with diffusion models. The paper provides a detailed theoretical derivation for thei... | Rebuttal 1:
Rebuttal: 1. While this claim is supported by the paper's superior CAD sketch generation results compared to baselines quantitatively, it would be great to also see qualitative examples compared with the baselines.
1. We have a qualitative analysis written and ready for the final paper. However, we onl... | Summary: Authors proposed a diffusion-based generative model for 2D CAD engineering drawings of parametric primitives. Main contributions include the use of Gaussian-softmax to unify discrete and continuous parameters, which works well for a modified diffusion model without positional encoding. And also a superposition... | Rebuttal 1:
Rebuttal: 1. Line 108: “we propose the first diffusion-based generative model for parametric CAD sketches”.
1. Sorry, you are right, what we meant to write was “the first ‘sketch space’/’data space’ diffusion-based generative model for parametric CAD sketches.
2. No closest-retrieval results to demonst... | Summary: This paper introduces a diffusion-based generative model for CAD sketch primitive generation. The technical innovation is a Gaussian-Softmax based diffusion paradigm. The method addresses key challenges in the heterogeneity of primitives (each primitive type is defined by its distinct parameterization) and per... | Rebuttal 1:
Rebuttal: 1. No Qualitative Analysis
1. We have now written a qualitative analysis ready for the final paper. However, we only compare ours against Vitruvion, as other prior arts like SketchGen don’t have published code. We chose not to do a qualitative analysis against Sketchgraphs since Vitruvion is ... | null | null | null | null | null | null | null | null |
EditLord: Learning Code Transformation Rules for Code Editing | Accept (poster) | Summary: This paper seeks to decompose the traditional end-to-end LLM-assisted code editing task into discrete and step-wise processes. To this purpose, this paper adopted LLM to summarize meta editing rules from 3 editing tasks: optimization, decompilation and security hardening, and augmented LLM performance in a ret... | Rebuttal 1:
Rebuttal: We really appreciate your time and effort in leaving constructive comments.
**Q1: The three tasks it focuses on may limit real-world applicability because they are not highly frequent in real-world editing scenarios.**
The three editing tasks we considered are extensively studied in the literat... | Summary: The paper introduces a method for editing code in a decompositional way, where it extracts editing steps, obtains functional specifications, and performs rule-based code editing by prompting LMs.
Claims And Evidence: The paper claims that their method improves code efficiency by leveraging the decompositional... | Rebuttal 1:
Rebuttal: Thank you so much for your taking the time and effort to read our paper and leave constructive comments.
**Q1. Why extracted editing rules are effective in improving code efficiency or what the rules actually look like.**
These extracted editing rules serve as editing guidelines that help the m... | Summary: EditLord is a system designed to improve performance on code editing tasks (e.g. performance/readability/security). The proposed pipeline involves a few steps, starting from a training dataset (editing task-specific) with pre/post edit programs. In the first step, a LLM is used to produce a set of editing ‘met... | Rebuttal 1:
Rebuttal: We are grateful for your effort in leaving us such encouraging comments.
**Q1. It’s unclear how robust the meta rule creation process is. And there aren’t really any guarantees on the quality without human interaction.**
Great point. We observed that if we simply bootstrap per-sample rules, the... | Summary: Traditionally, for code editing, language models (LMs) are often used to directly generate the output code (or diff) given the input code in a single turn. There have also been approaches that prompt LMs in a CoT-style manner to generate some reasoning before outputting the edited code. Similarly, existing app... | Rebuttal 1:
Rebuttal: We really appreciate your time and effort in reviewing our paper and giving us constructive comments!
**Q1: More well-known benchmarks should be included.**
Thanks for pointing this out. We originally focused on individual tasks where the corresponding papers also proposed tailored solutions (e... | null | null | null | null | null | null |
Learning Input Encodings for Kernel-Optimal Implicit Neural Representations | Accept (poster) | Summary: This paper first established a theoretical insight that the neural tangent kernel of implicit neural representation can approximate any positive semidefinite dot-product kernels. Developing on this insight, the paper propose a kernel alignment regularizer to improve the INR system. Experiments show the propose... | Rebuttal 1:
Rebuttal: **All the Table/Figure Rx can be found in https://anonymous.4open.science/r/ICML-Re-3333**
**Q1:** The proposed method should be applied to other more challenging real-world problems.
**A1:** Thank you for your valuable suggestion. We have conducted additional experiments on more complex prob... | Summary: The paper proposes two theoretically-motivated changes to implicit neural representation architecture and training, based on comparisons to the infinite width neural tangent kernel. The first change is a regularization strategy to encourage alignment with the optimal NTK, and the second is a trainable encoding... | Rebuttal 1:
Rebuttal: **All the Table/Figure Rx can be found in https://anonymous.4open.science/r/ICML-Re-3333**
**Q1:** A comparison of model size and training/inference time, with a fixed number of trainable parameters, is necessary for a fair evaluation.
**A1:** We have conducted a comparison regarding parameter ... | Summary: The paper summarises NTK-theory related contributions on INRs, and derives the optimal kernel for INRs under certain conditions. It then proposes an algorithm, named PEAK, to approximate a "Kernel Alignment Regularizer" and apply it to an INR, so that its kernel is encouraged to move towards the optimal one, i... | Rebuttal 1:
Rebuttal: **All the Table/Figure Rx can be found in https://anonymous.4open.science/r/ICML-Re-3333**
**Q1:** The paper should compare PEAK with SIREN, MFN, BACON, Gauss, WIRE, FINER and SAPE, and explore whether the framework can be applied to and improve these methods.
**A1:** Thank you for the concern ... | Summary: The paper studies Implicit Neural Representation (INRs) from a Neural Tangent Kernel (NTK) perspective. It introduces the *Kernel Alignment Regularizer* (KAR), which encourages alignment between the INR’s NTK and an optimal kernel and *Plug-in Encoding for Aligned Kernels* (PEAK). PEAK is a method to integrat... | Rebuttal 1:
Rebuttal: **Q1:** The choice of the current baselines needs clarification.
**A1:** We appreciate your inquiry regarding the choice of baselines. Our PEAK algorithm is designed to find an encoder $\gamma(\mathbf{x})$ that enhances the generalization ability of the composed function $f_{\boldsymbol{\theta}}... | null | null | null | null | null | null |
Learning Utilities from Demonstrations in Markov Decision Processes | Accept (poster) | Summary: This paper considers learning a utility function from demonstration using inverse reinforcement learning and risk sensitive RL. The reward function is assumed to be known. The utility function mapping cumulative rewards to a scalar value is to be inferred. The authors proof the partial identifiability of utili... | Rebuttal 1:
Rebuttal: We thank the Reviewer for recognizing the validity of our proposal, specifically of learning a utility with known reward and to use demonstrations from multiple environments to reduce identifiability issues. Below, we answer to the Reviewer's comments and questions.
> Is the purpose of including ... | Summary: This paper introduces a framework for learning utility functions from expert demonstrations inMDPs, where the utility function captures the agent’s risk sensitivity. As the utility may not be identifiable in a single environment, the authors consider learning from demonstrations in multiple environments. The p... | Rebuttal 1:
Rebuttal: We are glad that the Reviewer appreciated the significance of the problem setting considered and the analysis conducted. Below, we report answers to the Reviewer's comments.
> On the experiments conducted.
The ultimate goal of the paper is to introduce a new problem setting, and to characterize ... | Summary: The paper introduces a new risk-sensitive model for inverse reinforcement learning in MDPs, explicitly accounting for the non-Markovian policies induced by risk-sensitive utility functions. The main contributions include formulating the Utility Learning problem to learn an agent's risk attitude, characterizin... | Rebuttal 1:
Rebuttal: We thank the Reviewer for recognizing the novelty of the proposed problem formulation, and the strength of our theoretical contributions on the identifiability problem of utilities. Below, we answer to the Reviewer's comments and questions.
> On the comparison with literature.
We thank the Revie... | Summary: The paper considers a specific type of risk-sensitive MDPs where the risk sensitivity is captured via a continuous, strictly increasing utility function. Given a set of optimal expert demonstrations and the expert reward, the aim is to recover the expert's utility function. The authors first provide a few impo... | Rebuttal 1:
Rebuttal: We are glad that the Reviewer appreciated the novelty and the significance of the problem setting introduced, and that the Reviewer recognized the solidity of the theoretical results presented. Below, we report answers to the Reviewer's comments.
> The authors stress the ability of their approach... | null | null | null | null | null | null |
Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning | Accept (poster) | Summary: This paper investigates few-shot class incremental learning for 3D object classification using foundation models. Building on the work of FoundationModel (Ahmadi et al.), the authors employ a frozen, pre-trained large-scale 3D encoder (Uni3D) to extract generalizable features for each point. They then construc... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback, which has guided us in refining the manuscript and addressing key concerns. Below, we provide detailed responses to each of your questions, supported by additional analyses and clarifications.
**Q1: Clarification of Ablation Studies in Table 3** ... | Summary: The paper proposes 3D-GLEG, a method to improve 3D few-shot class incremental learning by incorporating geometric features into the learning process. The authors propose two modules: a geometric feature extraction module and a geometric feature embedding module. By leveraging geometric information, 3D-FLEG ach... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback, which has helped us significantly improve the clarity and rigor of our manuscript. Below are our detailed responses:
**Q1: Claims (Laplacian Eigenmaps, AdaptiveAvgPool1d, geometric feature) are not well supported**
**A1: (1) Laplacian Eigenmaps for Geometr... | Summary: The paper proposes a model called 3D-FLEG for the 3D few-shot class incremental learning task. The model has a geometric feature extraction module that obtains geometric features through clustering and Laplacian eigenmaps, and it includes a geometric feature embedding module to fuse these geometric features wi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We deeply appreciate your insights, which have guided us in refining our manuscript. Below, we outline the specific revisions made in response to your concerns:
**Q1: Ablation Studies on the Number of Clusters**
**A1**: We deeply apprecia... | null | null | null | null | null | null | null | null |
Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers | Accept (poster) | Summary: This paper proposes Dynamic DETR to reduce token redundancy within the encoder for improving the efficiency of DETR-like object detectors. This problem has also been studied by previous work such as Sparse DETR and Focus-DETR. Compared to these existing efforts, this work proposes a finer-grained sparsificatio... | Rebuttal 1:
Rebuttal: We appreciate your valuable suggestions, and next we respond to each comment as follows.
## W1: There is some lack of clarity in the experimental setup.
- *The paper does not indicate what device the FPS in Table 1 was measured on.*
- *For which backbone is Figure 1(a) obtained?*
**Response**:... | Summary: This paper introduces Dynamic DETR designed to enhance the computational efficiency of DETR-based methods. The study identifies the encoder as the primary computational bottleneck and proposes a dynamic token sparsification strategy to reduce redundant tokens, effectively lowering computational complexity whil... | Rebuttal 1:
Rebuttal: We really appreciate your constructive comments. We respond to each comment as follows.
## W1: A breakdown of token importance computation and matching strategy in terms of FLOPs or latency would provide better clarity
**Response**: To quantify this overhead, we provide a detailed analysis of th... | Summary: It is known in the object detection literature that the detection transformers are notorious for their long and computationally demanding training requirements. To partially address this issue, this work proposes a novel token aggregation strategy for detection transformers based on the recent token merging st... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. Below we response to these concerns.
## C1: Comparable performance with Lite DETR
**Response**:
First of all, we are sorry for the incorrect parameter descrition about DINO and Dynamic DINO in the initial submission, while the corrected verison is as follows... | Summary: The paper **"Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers"** proposes a novel framework called **Dynamic DETR**, aiming to address the computational efficiency bottleneck in **Detection Transformers (DETRs)**. DETRs require high computational resources,... | Rebuttal 1:
Rebuttal: We sincerely appreciate your selfless dedication and thoughtful comments. Below we response to these concerns.
## W1&Q1: The paper does not discuss the real-world deployment performance of Dynamic DETR, such as its effectiveness in real-time detection tasks
**Response**:
To verify the potential... | null | null | null | null | null | null |
Conformal Anomaly Detection in Event Sequences | Accept (poster) | Summary: The paper introduces CADES, a novel anomaly detection method for continuous-time event sequences under the conformal inference framework. The authors propose two new non-conformity scores tailored to event sequences based on the time-rescaling theorem, which address the non-identifiability issues of existing t... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work and suggestions. We provide answers to your concerns as follows:
**Q1**: While the authors provide theoretical guarantees on FPR control, the practical implication and significance of these guarantees in real-world scenarios could be explained in more d... | Summary: The paper is the first to extend conformal inference to anomaly detection in event sequences and proposes a novel method called CADES, which provides statistical guarantees of validity. Notably, CADES addresses the severe non-identifiability issues found in previous methods by developing two new powerful non-c... | Rebuttal 1:
Rebuttal: Thank you for your recognition and feedback. We provide answers to your concerns as follows:
**Q1**: It would be more comprehensive if the literatures on applying conformal inference to time series were discussed.
**R1**: Thank you for your valuable suggestion and the references you provided. We... | Summary: This paper proposes a novel test procedure based on conformal inference for detecting anomalous event sequences, with rigorous control over the false positive rate (FPR), a crucial factor for deploying anomaly detection methods in safety-critical applications. Specifically, it designs two new non-conformity sc... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions. We provide answers to your concerns as follows:
**Q1**: It is suggested to explain the reason behind the poor performance of the 3S statistic under the Uniform scenario in Section 4.1.
**R1**: This is because the 3S statistic is not sensitive... | null | null | null | null | null | null | null | null |
Training Software Engineering Agents and Verifiers with SWE-Gym | Accept (poster) | Summary: The paper introduces SWE-Gym, a novel training environment specifically designed for developing software engineering agents. The environment comprises 2,438 real-world Python task instances extracted from GitHub issues; each instance includes a codebase, an executable runtime environment with pre-installed dep... | Rebuttal 1:
Rebuttal: > Could you clarify why a larger dataset (SWE-Gym) with executable environments is necessary, given that SWE-Bench already offers multiple, human-verified versions?
We’d like to clarify that SWE-Bench doesn’t include executable environments or unit tests for its training split. This makes it impo... | Summary: This paper introduces SWE-Gym, the environment for training software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python tasks from 11 popular GitHub repositories, each equipped with pre-installed dependencies, executable runtime environments, unit tests, and natural language task descriptions. ... | Rebuttal 1:
Rebuttal: > computational budget constraints limited the number of training trajectories to 491, which may affect the generalizability of some findings.
We would like to emphasize that our results represent the state-of-the-art open-model results at the time of submission, and used a substantial compute b... | Summary: The paper proposes SWE-Gym, which is a training environment for coding agents tasked to resolve GitHub issues.
They provide a collection of 2438 python-based SWE tasks.
They used filtered fine-tuning and showed improvement by fine-tuning LLMs in their training environment.
Finally, to show effectiveness author... | Rebuttal 1:
Rebuttal: We thank Reviewer aRo7 for their insightful feedback. Below, we address the primary concerns and suggestions provided.
> I think Novelty is limited given that swe-bench training data already provide training data
To clarify, SWE-Bench doesn’t include unit tests or executable environments for the... | null | null | null | null | null | null | null | null |
Maximum Entropy Reinforcement Learning with Diffusion Policy | Accept (poster) | Summary: This paper focuses on adapting diffusion-based policies to maximum entropy reinforcement learning (MaxEnt RL) for better exploration. The primary obstacles are: 1) policy evaluation involves computing the log-probability over the clean actions, which for diffusion policies is non-trivial; and 2) policy improve... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. Here, we aim to address the questions raised in the review.
>**Q1: Include results on tasks like MetaWorld or DMControl.**
We compare MaxEntDP with SAC on DMControl and MyoSuite benchmarks. The results are shown in https://anonymous.4open.sci... | Summary: This paper introduces Maximum entropy Reinforcement Learning with Diffusion Policy (MaxEntDP). More specifically, this method proposes solutions to the well-known problems on how to approximate the target distribution composed of the exponential of the Q-function and how to calculate the log likelihoods of the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback. Below, we will address your comments and hope that this clarifies the context of our work.
>**Q1: Consider more sophisticated RL tasks.**
We compare MaxEntDP with SAC on DMContorl and MyoSuite benchmarks. The results are shown in ht... | Summary: This paper introduces MaxEntDP, a new online diffusion-based RL algorithm that integrates diffusion models into the maximum entropy framework. The method proposes a Q-weighted noise estimation for policy improvement and use numerical integration to estimate action probability for policy evaluation. Experiments... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. Below we will address each concern raised in the review.
>**Q1: Can MaxEntDP learn different solutions in high-dimensional multi-goal tasks from DDiffPG? And provide evidence for improved exploration.**
We tested MaxEntDP and SAC on four v... | null | null | null | null | null | null | null | null |
Gaussian Mixture Flow Matching Models | Accept (poster) | Summary: This paper introduces GM-Flow, a novel variant of flow matching that explicitly parameterizes the entire velocity distribution using a mixture of Gaussians, rather than learning only the mean velocity as in conventional approaches. Unlike CFG, which extrapolates class-conditional and unconditional velocities, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have uploaded a **revised manuscript** and essential **code** in this anonymous link (full code will be released upon publication):
https://anonymous.4open.science/r/anonymous_gmflow-63FE
backup: https://limewire.com/d/CgAn9#jkBxDmC3qh
### **Weaknesses ... | Summary: This paper proposes Gaussian mixture flow matching (GMFlow) model, which captures the flow
velocity distribution rather than only predicting its mean based on single-Gaussian assumption. In
addition, the paper utilizes the Gaussian mixture sampling framework to provide probabilistic
guidance via Gaussian mixtu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have uploaded a **revised manuscript** and essential **code** in this anonymous link (full code will be released upon publication):
https://anonymous.4open.science/r/anonymous_gmflow-63FE
backup: https://limewire.com/d/CgAn9#jkBxDmC3qh
> The GM idea is ... | Summary: The authors present a new formulation of diffusion models, termed as Gaussian mixture flow matching (GMFlow). Unlike existing diffusion models, GMFlow models the PDF of velocity by predicting the parameters of a Gaussian mixture (GM) distribution. Based on this formulation, GMFlow can generate high-quality ima... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have uploaded a **revised manuscript** and essential **code** in this anonymous link (full code will be released upon publication):
https://anonymous.4open.science/r/anonymous_gmflow-63FE
backup: https://limewire.com/d/CgAn9#jkBxDmC3qh
> Pre-defined num... | Summary: This paper proposes a Gaussian mixture (GM) flow matching (FM) model. The traditional FM model uses a Gaussian modeling velocity field, while the proposed GMFlow method in this paper uses a Gaussian mixture modeling velocity field. The author shows that GMFLow can produce better results with fewer steps. The a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have uploaded a **revised manuscript** and essential **code** in this anonymous link (full code will be released upon publication):
https://anonymous.4open.science/r/anonymous_gmflow-63FE
backup: https://limewire.com/d/CgAn9#jkBxDmC3qh
> Boundary of exp... | null | null | null | null | null | null |
EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph | Accept (poster) | Summary: This paper introduces a new epidemic forecasting framework, EARTH, which combines neural ODEs with traditional compartmental models. The core idea behind EARTH is to address common forecasting challenges such as irregular data sampling and missing values by integrating two main modules: a local transmission mo... | Rebuttal 1:
Rebuttal: # Response to Reviewer XKTr
We sincerely thank you for your thorough review and positive assessment of our work. We are grateful for your recognition of EARTH's
novelty and contributions to epidemic forecasting. We address your questions below:
> `Weakness 1`: No detailed analysis of the comput... | Summary: The authors tackle the problem of effectively forecasting epidemics and propose the so-called EARTH method, which combines an epidemiology-aware neural ODE with a continuous disease transmission graph. More specifically, they leverage a **neural ODE-based component** (EANO) based on the **common epidemic SIR m... | Rebuttal 1:
Rebuttal: # Response to Reviewer rfdg
We sincerely thank you for your thorough review and hope our responses below will help improve your assessment of our work:
> `Weakness S1 (Theoretical Claims & Claims And Evidence)`: Presentation and Design choices of the epidemic compartment
Our approach addresses ... | Summary: The paper proposes EARTH, an Epidemiology-Aware Neural ODE with a Continuous Disease Transmission Graph, as a novel framework for epidemic forecasting. The authors integrate neural ODEs with epidemiological mechanisms, capturing both continuous-time disease transmission and global infection trends. The Global-... | Rebuttal 1:
Rebuttal: # Response to Reviewer uXQM
We sincerely thank you for your positive assessment of our work and for recognizing the innovation in integrating neural ODEs with epidemiological models. We appreciate your thorough evaluation and answer your questions below:
> `Cons`: On applying neural ODEs to epid... | Summary: The paper presents EARTH (Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph), a novel framework for epidemic forecasting that integrates neural ordinary differential equations with epidemiological mechanisms. The authors address challenges in current approaches by modeling the continuous... | Rebuttal 1:
Rebuttal: # Response to Reviewer Q6Uq
We thank you for your positive assessment and for recognizing the importance of our work in epidemic modeling. We address your concerns below:
> `Question & Weakness`: Only evaluated for COVID-19, with potential limitations for heterogeneous datasets and different dis... | null | null | null | null | null | null |
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization | Accept (poster) | Summary: This submission investigates the extent to which spurious correlations are used for learning in two models, linear ridge regression and random feature models, under the setting that the input dimension grows proportionally with the sample size. The key definition is $\mathcal{C}(\hat{\theta})$ in (3), and for ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the remarkable care in reviewing our work and for the positive evaluation. We address concerns below.
---
**Lack of clarity when citing (Han&Xu2023)**:
To address this and ease the comparison of our claims with the results in Han&Xu2023, we will add to the appendix a d... | Summary: This paper quantifies the notion of spurious correlations -- where the feature of an image which determines it's classification correlates with another feature which does not determine the label -- and sudies the effect of spurious correlations for linear ridge regression and random-feature ridge regression. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the rigor, clarity and importance of our results. We address concerns below.
---
**Joint requirements $p = \omega(n)$ and $\log p = \Theta(\log n)$**:
We note that the regime $p = \omega(n)$ and $\log p = \Theta(\log n)$ formally includes all scalings where... | Summary: This paper investigates spurious correlations in high-dimensional regression, focusing on the effects of regularization, simplicity bias, and over-parameterization. Using linear regression, the study quantifies how regularization influences the reliance on spurious correlations, revealing a trade-off where inc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and the several interesting suggestions for extensions. We answer questions and address concerns below. We will incorporate the discussions in the revision.
---
**Comparison with (Bombari et al., 2024):**
Our work concerns the problem of spurious ... | Summary: The paper characterizes the learning of spurious features in linear regression as function of $\ell_2$ regularization strength and spurious feature simplicity. They also show that under overparametrization incurred by random features the effect of regularization is modified in a way that explains empirical res... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and helpful comments. We address concerns below.
---
**Improving experimentation**:
Following the reviewer’s suggestion, we will add the following experiments to the revision.
In https://ibb.co/21W8qbLJ, we consider Color-MNIST including all di... | null | null | null | null | null | null |
Graph Transformers Get the GIST: Graph Invariant Structural Trait for Refined Graph Encoding | Reject | Summary: This paper proposes a graph structural encoding method named Graph Invariant Structural Trait (GIST), aiming to improve Graph Transformers' ability to encode structural information. GIST captures structural features based on the intersection cardinality of pairwise nodes' k-hop neighborhoods. Empirical evaluat... | Rebuttal 1:
Rebuttal: ### **[W1 - The theoretical analysis lacks clarity in terms of its practical implications for model improvements]**
In Section 4, we present a theoretical analysis showing that GIST is invariant under graph isomorphism. This means GIST consistently captures the true structure of a graph, regardle... | Summary: In this paper, authors propose a new Graph Invariant Structural Trait (GIST) for higher-order structural relationship modeling within graphs and utilize randomized hashing to accelerate the corresponding calculation. The usage of GIST in the graph transformer has proven to be effective through experiments on s... | Rebuttal 1:
Rebuttal: ### **[W1 - Exra overhead cost of GIST computation]: Sure. Here are an analysis of GIST computation overhead cost and training efficiency of our proposed method.**
We kindly direct them to our new analysis on the one-time pre-computation overhead of GIST features and the overall training efficien... | Summary: Graph classification, a fundamental machine learning task with broad scientific applications, has been advanced by Transformers, which address oversmoothing/oversquashing limitations of traditional GNNs using attention mechanisms. However, effectively encoding graph structural information within Transformers' ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. Given the 5k char limitation and 8 distinct questions raised by the reviewer, unfortunately many of our responses will be condensed and citing other replies. Should the reviewer be interested in an elaboration in any particular response, please let ... | Summary: This paper is aimed to effectively encode graph structure into formation within the attention mechanism. Authors propose a new structural encoding based on Graph Invariant Structural Trait (GIST) to capture substructures within a graph by estimating pairwise node intersections. Both theoretical analysis and em... | Rebuttal 1:
Rebuttal: ### **[W1 - The novelty of this paper is somewhat limited, since the general idea to add structural bias to the attention mechanism is not very novel.]: Our novelty lies in the GIST features, which offer a more effective way to encode structural information.**
We agree with the reviewer that addin... | null | null | null | null | null | null |
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD | Accept (poster) | Summary: The authors propose a new theoretical framework with fairly weak assumptions, within which they are able to establish convergence rates for Adam.
Claims And Evidence: N/A
Methods And Evaluation Criteria: No simulation sutdy nor application on real datasets.
Theoretical Claims: Given the short time available... | Rebuttal 1:
Rebuttal: Dear Reviewer LTUb,
We sincerely appreciate your thorough evaluation of our manuscript and your positive feedback. Your recognition of our theoretical framework and the establishment of convergence rates for Adam under weak assumptions is highly encouraging.
We acknowledge your suggestion to inc... | Summary: The paper studies the convergence properties of Adam under smooth nonconvex settings. The paper presents convergence results in the sense of almost sure, $L_1$ and non-asymptotic, under relaxed noise assumption, i.e. the ABC inequality. The non-asymptotic convergence result is in the order of $O(1/\sqrt{T})$, ... | Rebuttal 1:
Rebuttal: **Rebuttal to Reviewer nypN**
Dear Reviewer nypN,
Thank you very much for your thoughtful feedback and constructive comments on our manuscript. We sincerely appreciate the time and effort you have put into reviewing our work. We are grateful for your insights, and we have carefully addressed eac... | Summary: This paper presents a unified analytical framework for understanding Adam’s convergence under weaker assumptions than those typically used. Specifically, the authors rely on standard L-smoothness and ABC inequality for stochastic gradients to show that Adam achieves non-asymptotic and asymptotic convergence.
... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough evaluation of our manuscript and your insightful feedback. Your recognition of our theoretical framework is highly encouraging.
**Incorporation of Assumption Comparisons into the Main Text:**
We acknowledge your suggestion to move the discuss... | Summary: In the past several years, many efforts have been made to understand the convergence of Adam-like algorithms under different noise assumptions. This paper is a novel paper among these works and is based on an even weaker version of the noise condition called the ABC condition. Under the ABC assumption, the aut... | Rebuttal 1:
Rebuttal: Dear Reviewer JhWZ,
Thank you for your thoughtful and constructive feedback on our paper. We greatly appreciate the time and effort you’ve put into reviewing our work. We carefully considered your comments, and we would like to address the main concern regarding the differences between our approa... | null | null | null | null | null | null |
Tensor Product Neural Networks for Functional ANOVA Model | Accept (poster) | Summary: The authors propose an approach for learning functional ANOVA decompositions form data. The neural network-based architecture they propose is designed such that it admits a unique functional ANOVA decomposition. The authors prove their architecture is a universal approximator for smooth functions which satisfy... | Rebuttal 1:
Rebuttal: Thank you for your valuable and insightful feedback.
We have made every effort to address your comments.
Due to character limits, "Comment" is abbreviated as "C".
>**C1 in Claims and Evidence** : One of the claims which...
>**C2 in Supplementary Material :** I struggled to understand...
**Respo... | Summary: The paper proposes an approach for constructing interpretable machine learning models based on the functional ANOVA decomposition. The authors consider a decomposition of small order (1-2), and the decomposition terms are constructed with the basis functions represented by neural networks. To satisfy the condi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions.
We have made every effort to address your insightful questions.
> **Weakness 1 in Claims And Evidence.** In the introduction to your paper, you explicitly formulate the problem of interpretability of AI models. In this context, the task seems to... | Summary: This paper introduces ANOVA Tensor Product Neural Network (ANOVA-TPNN), a novel neural network framework designed to estimate the functional ANOVA model with greater stability and accuracy. Theoretical analysis confirms that ANOVA-TPNN has universal approximation capabilities for smooth functions. Empirical st... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions.
We have made every effort to address your insightful questions.
> **W1.** While ANOVA-TPNN improves efficiency ...
**Response to W1.**
We have conducted runtime experiments for the functional ANOVA model only with the main effects in Appendix K... | null | null | null | null | null | null | null | null |
On the Similarities of Embeddings in Contrastive Learning | Accept (poster) | Summary: This paper investigates the geometry of embeddings learned by contrastive learning. This paper first extends the geometry of optimal embeddings (perfectly aligned positives and negatives with cosine similarity $-1/(n-1)$) to an inclusive form of contrastive loss. Then it proves that over-separated negatives wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive review, especially the effort to understand and verify our theoretical results. We are glad the reviewer saw our analysis as a meaningful **extension of existing CL theory** and appreciated its **new perspective** on the role of negative pa... | Summary: This paper mathematically analyzes the geometric properties of the positive pairs’ embeddings as well as negative pairs’ embeddings in different contrastive learning objectives. The authors mathematically find the optimal threshold for the expected negative pair similarities that results in preventing a misali... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our mathematical analysis **provides important insights for contrastive learning in both unimodal and multimodal settings**. We are also encouraged by the positive evaluation of our experimental claims and the effectiveness of our proposed VRN approach ... | Summary: The paper analyzes the distribution of positive and negative pairs in contrastive learning and shows that perfect alignment becomes impossible when expecting negative pairs to fall below the optimal threshold. The paper also proposes variance reduction for negative-pair similarity loss to reduce the variance o... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback, including that our paper is **well-written**, provides **a clear explanation of results**, and may be **useful for future CL research**. Below, we address each of the reviewer’s comments in detail.
---
### W1. When VRN loss term downgrades the perfo... | null | null | null | null | null | null | null | null |
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators | Accept (poster) | Summary: This paper studies LLM judges as evaluators for test-time scaling and introduces a new benchmark JETTS (Judge Evaluation for Test-Time Scaling). The benchmark assesses different models across three tasks: 1) Response reranking: ) Response reranking, where judges select the best from multiple candidate response... | Rebuttal 1:
Rebuttal: We thank reviewer Mn8F for the constructive review and are delighted that they found our evaluation criteria comprehensive. We respond point by point below.
> The random tie-breaking method used for single-rating protocols introduces unquantified variability that affects result reliability;
We ... | Summary: This paper introduces a benchmark designed to assess the feasibility of using large language model (LLM) judges as evaluators in test-time scaling scenarios. The study compares LLM-judges to traditional reward models (RMs) and process reward models (PRMs) in three key tasks: Response Reranking, Step-Level Beam... | Rebuttal 1:
Rebuttal: We thank the reviewer for their considerate review, and are happy that you found our paper well designed and interesting.
> What are the possible reasons for the Critique-Based Refinement Task being largely ineffective, despite the success of self-reflection and similar methods…? …what potential... | Summary: The authors propose the JETTS Benchmark for evaluating LLM-as-Judge evaluators for test-time scaling where the judges are used to improve the final output from the generator. The benchmark covers Best-of-N reranking, (2) step-level beam search, and (3) critique-based refinement across the math reasoning, code... | Rebuttal 1:
Rebuttal: We thank Reviewer Bcf6 for their thoughtful review and are grateful that you found our work well-motivated.
> Why were the 3 domains selected…
This is an excellent question. We will revise our paper to motivate our choice of domains more concretely:
*Instruction following (IF):* Much recent w... | Summary: This paper proposes a benchmark called JETTS (Judge Evaluation for Test-Time Scaling) to evaluate the performance of LLM-as-judges in test-time scaling scenarios. The benchmark consists of three tasks: response reranking, step-level beam search, and critique-based refinement.
The main findings of the paper are... | Rebuttal 1:
Rebuttal: We thank Reviewer gykS for their thoughtful review. In particular, we are happy that you found our metrics intuitive and our experimental setup sound. We respond point-by-point to questions and comments below.
> Specifically, on Line 218, most judges are fine-tuned using a fixed prompt template, ... | null | null | null | null | null | null |
Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries | Accept (poster) | Summary: This paper proposes a new type of query in rlhf, the distinguishability query (DQ). Rather than directly comparing two sets of trajectories, the authors compare two sets of trajectories and selects the one that is easier to give feedback on. They then provide feedback on the easier pair, and can learn from tha... | Rebuttal 1:
Rebuttal: 1. Claim about performance of DQ (half):
(1) For query budget and fairness comparison discussion, please refer to **point 1-(1)&(2) in our response to reviewer ufMA**.
(2) For effectiveness, please refer to **point 1-(3) in our response to reviewer ufMA**. Besides, we show in Fig 2 in https://... | Summary: This paper proposes a three-stage approach to optimizing the PbRL pipeline:
1. **Selecting the top N informative (based on the variance of reward ensembles) Pairwise Comparison Queries (PCQs)** for comparison. This step explicitly enables the reward model to distinguish high-uncertainty pairs better, accel... | Rebuttal 1:
Rebuttal: 1. About experiments on more challenging tasks:
**Please refer to point 1 in our response to reviewer pH8H**. Besides, it is worth mentioning that the suggested harder tasks are not evaluated in all our baselines either. Therefore, we need to determine workable hyper-parameters for all the metho... | Summary: This paper proposes Distinguishability Queries (DistQ), a new method for improving RLHF. DistQ reduces cognitive load by letting humans first choose which of two trajectory comparisons is easier to evaluate and then provide feedback on the easier pair. This approach captures both preference strength and ordina... | Rebuttal 1:
Rebuttal: 1. About more experimental environments:
For the current version of our paper, we selected representative control tasks of different difficulty levels (in terms of query budget needed to accomplish the task) and of different types (locomotion and robotic manipulation) to demonstrate the performa... | Summary: * This paper proposes a novel human feedback type for RLHF and an algorithm allowing robots to learn reward functions from such human feedback.
* The novel feedback is that the robot first gives a human 2 pairs of trajectories, has the human choose the pair that is easier to choose, and then has the human ch... | Rebuttal 1:
Rebuttal: 1. About experimental settings:
(1) To address potential misunderstanding in the review, we first clarify some terminologies we used in our paper. One distinguishability query (DQ) consists of 2 pairwise comparison queries (PCQs). The human first chooses the more distinguishable PCQ and then cho... | null | null | null | null | null | null |
Theoretical Limitations of Ensembles in the Age of Overparameterization | Accept (oral) | Summary: This paper studies ensembles of M random feature networks when the number of features D is greater than the number of data points N (overparameterized regime). Large ensembles are found to be asymptotically equivalent to a single large network and convergence bounds are given for finite M. There are numerical ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review, as well as their suggestions for improving our paper. Below, we address the concerns and questions raised, and outline the changes we will make in response.
**Claims And Evidence:**
- **Clarification of "hockey stick" pattern in Fig. 2:** We agree... | Summary: This paper presents a theoretical analysis of ensembles of overparametrized models (more parameters than training data) in which authors claim that an Infinite Ensemble is equivalent to an Infinite-Width Single Model. This analysis is done by using the equivalence between random feature regressors (RF) and neu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and feedback on our paper. Below, we address the concerns and questions raised and outline the changes we plan to make to the manuscript based on your suggestions.
**Regarding the Experiments:**
We agree that explicitly including the experimental... | Summary: This paper proves that in the random feature (RF) regression, the ensemble estimator is approximately equivalent with the simple regressor, as long as the model size is sufficiently great. This result can be applied not only to ridgeless models, but also to models with small ridge parameters.
Besides, the pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback on our paper. Below, we address the concerns and questions raised.
**1\. Concerns about Theorem 3.3:**
> "Firstly, there is some problem with Theorem 3.3. The bound term on the right-hand side does not converge to zero as the number of featur... | null | null | null | null | null | null | null | null |
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning | Accept (poster) | Summary: This work seeks to further advance Parameter-Efficient Fine-Tuning (PEFT) techniques, which reduce memory usage and computational cost compared to full fine-tuning. It draws insights from the Neural Engram (NE) phenomenon, where the brain processes new knowledge by strengthening or weakening existing connectio... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 1yzx
Thank you for your thorough review and feedback. Your positive comments (such as acknowledging our clearly explained SAN and its connection to LTP/LTD) and suggestions have greatly helped us improve our paper. To address your concerns, we have carefully read your feedb... | Summary: This paper proposes a fine tuning method based on ideas from the Neural Engram and LTPD literature which gives an alternative method to the popular LORA and DORA updates that have recently been employed for large models. The update consists computing scale vectors $\gamma_\ell$ at each hidden layer $\ell$ of t... | Rebuttal 1:
Rebuttal: ## Response to Reviewer bAk4
Thank you for your thorough review and feedback. Your positive comments (such as acknowledging our solid experiment and idea) and suggestions have greatly helped us improve our paper. To address your concerns, we have carefully read your feedback and provided point-by-... | Summary: The authors of this paper introduce a method called Synapse and Neuron (SAN), which decomposes and propagates scaling components from anterior feature adjustment vectors to posterior weight matrices. Extensive experimentation is performed by combining SAN with multiple PEFT strategies demonstrating the perform... | Rebuttal 1:
Rebuttal: ## Response to Reviewer WoLR
Thank you for your careful review and feedback. Your positive comments (such as praising our "sound evaluation criteria" and "easy to follow writing") have greatly support our paper. We have thoroughly reviewed your comments and address your key questions below:
1. Fo... | null | null | null | null | null | null | null | null |
Emergent Response Planning in LLMs | Accept (poster) | Summary: In this paper, the authors aim to explore whether LLMs plan before token generation. Specifically, they examine three types of attributes:
- Structural attributes refer to whether LLMs plan the response length and reasoning steps.
- Content attributes refer to whether LLMs plan character choices in story wri... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and constructive feedback. In response to your inquiries:
---
> Q1. Detailed illustration of probing strategies.
Thank you for the helpful suggestion. To improve clarity, we will add this explanation to the final version: Probing trains auxiliary models (e.... | Summary: The paper provides a simple definition of response planning, and then shows that according to this definition, multiple LLMs do in fact plan responses on various dimensions.
## update after rebuttal: As described in my comment later in the thread, I am keeping my initial rating, which was already high.
Claim... | Rebuttal 1:
Rebuttal: We are grateful for your comprehensive remarks and thoughtful advice, and we are really glad that you find our paper interesting.
---
> Q1. Defining "planning" and designing experiments to prevent spurious correlations.
We really appreciate your thoughtful insights. Indeed, results may be biase... | Summary: The paper presents evidence of emergent planning behavior in LLMs by analyzing patterns in global attributes – structural, content, and behavioral – across different models and sizes. The authors identify four key insights that showcase how this planning behavior emerges, analyzing how each attribute is proces... | Rebuttal 1:
Rebuttal: Thank you for the time, thorough comments, and nice suggestions. We answer the comments/questions point-by-point:
---
> Q1. Adding annotations on model orders of Figure 4.
Thank you for your constructive feedback. We agree that explicitly annotating model orders will improve clarity, and we gre... | null | null | null | null | null | null | null | null |
Leveraging Predictive Equivalence in Decision Trees | Accept (poster) | Summary: The paper presents an intuitive boolean logical representation of decision trees. This representation removes predictive equivalence. They then show in 3 settings (feature importance, missing data, and improving cost efficiency) that this representation can yield improvements over standard tree representations... | Rebuttal 1:
Rebuttal: Thank you for your review! We are glad that you appreciated the novelty of our method through our experimental results.
To answer your question, we are unaware of any substantially simpler procedure to improve cost efficiency. Naïvely, we could attempt to perfectly fit an optimal, cost sensitive... | Summary: This paper addresses the issue of "predictive equivalence" in decision trees, where different tree structures can represent the same decision boundary but imply different evaluation procedures. The authors propose a boolean logical representation using Disjunctive Normal Form (DNF) to abstract away the evalua... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and thorough review! We hope that the comments below address your concerns.
**Complexity of Algorithm 1 and Quine-McCluskey**
The DNF simplification problem solved by the Quine-McCluskey algorithm is NP-Complete in the number of variables used by the tree. Since tre... | Summary: The authors propose the use of a minimal Boolean formula as a DNF in order to represent a decision tree that has been learnt. This representation is useful for the authors, as one no longer requires the evaluation of the learnt function to happen in a top-down manner (i.e., start from the root node of the tre... | Rebuttal 1:
Rebuttal: Thank you for the thorough and thoughtful review and for the suggested additions in describing the
Rashomon set and TreeFARMS. If accepted, we will use our extra page to incorporate this discussion into
the camera-ready version. Thank you also for flagging the off-by-one typo in lines 258-259, and... | Summary: The paper addresses the challenge of predictive equivalence in decision trees, where multiple trees with identical decision boundaries but different evaluation processes complicate model selection. To resolve this, the authors propose a Boolean logical representation of decision trees that eliminates predictiv... | Rebuttal 1:
Rebuttal: Thank you for the review! We are glad that you appreciated our theory, experiments, and motivation. We would be happy to engage in further discussion on the modernity of our work. We interpreted Weakness 1 as saying that research on decision trees is outdated -- if we misunderstood the weakness, p... | null | null | null | null | null | null |
OR-Bench: An Over-Refusal Benchmark for Large Language Models | Accept (poster) | Summary: The paper introduces OR-Bench - a large-scale benchmark for evaluating over-refusal in LLMs. The authors propose an automated pipeline to generate prompts that might trigger over-refusal, but are deemed safe by an ensemble of LLM judges. The authors evaluate 32 LLMs across 8 model families on OR-Bench, measuri... | Rebuttal 1:
Rebuttal: Thank the reviewer for your great feedback and suggestions. We really appreciate it. Please see our response below.
**Q1:However, I think important details are missing here. Specifically, it is not clear how the ensemble LLMs were prompted, nor is it clear how the human annotators were prompted.... | Summary: The paper introduces OR-Bench, a large-scale dataset for measuring over-refusal in LLMs.
Claims And Evidence: One of the main issues with this paper is that it focuses on over-refusal, yet fails to appropriately define it. The authors, by attempting to extrapolate from previous definitions, define over-refusa... | Rebuttal 1:
Rebuttal: Thank the reviewer for the great feedback and suggestion. Please see our response below.
**Q1: Question about the definition**
We are sorry for the confusion, due to limited space, please see our response to reviewer fdkf's Q2 above.
**Q2: Incorrect claim about existing datasets**
**A2**: Than... | Summary: The paper introduces OR-Bench, a large-scale benchmark designed to assess over-refusal in Large Language Models (LLMs), where models unnecessarily reject safe prompts. It employs an automated pipeline to generate 80,000 prompts across 10 categories, including a harder subset of 1,000 prompts and an additional ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our motivation and providing insightful feedback. We really appreciate it. Please see our detailed responses below.
**Q1: I notice that system prompts are not used during evaluation. This is very strange and not reflecting the practical scenario. I recommend... | Summary: This paper introduces OR-bench, a novel large-scale benchmark for quantifying over-refusals in LLMs. Authors leverage an adversarial synthetic data generation pipeline with filtering to create 80k examples of seemingly toxic but benign input prompts, with a 1k-sized hard subset that fools even the most capable... | Rebuttal 1:
Rebuttal: Thank you so much for recognizing the contribution of our work and all the great feedback. We will make sure to address them, please see our detailed responses below.
**Q1: In the related work section, I would suggest adding a section on model refusals, which have a history before LLM safeguardin... | null | null | null | null | null | null |
Sample Complexity of Branch-length Estimation by Maximum Likelihood | Accept (poster) | Summary: This paper focuses on the branch lengths maximum likelihood estimation problem. Arises in phylogenetic inference, this problem aims at estimating the transition probability over each edge of a bifurcating tree give repeated and independent observation of leaf node states.
The authors prove that, with the assum... | Rebuttal 1:
Rebuttal: Thank you for the review and thoughtful comments.
* The equation (2) does not include a tree shape component, which may also affect the estimation. Have the author considered how to perform MLE with additional freedom on tree shapes?
**Response**
>In general, optimizing over both the edge length... | Summary: This work concerns the maximum-likelihood estimation in a particular model for branch-length estimation relevant in phylogenetics. This work seems to provide theoretical support for the finding that a rather naive coordinate ascent algorithm works well for this problem even though the likelihood is known to be... | Rebuttal 1:
Rebuttal: Thank you for your comments and for pointing out the typos. We will incorporate them in the revision.
1. Given the submission to a machine-learning conference, how does this work relate to machine learning?
**Response**
> We appreciate the reviewer’s question. Maximum Likelihood Estimation (MLE)... | Summary: The paper provides analysis of optimization landscape of the MLE problem in phylogenetics under the Kesten-Stigum (KS) regime. As a corollary, they obtain quantitative results for consistency of the MLE and convergence rate for coordinate descents, which are often used in practice.
Claims And Evidence: The pa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and remarks.
* My biggest concern is that the related work session is not well down, with very few direct comparisons to relevant literature. Indeed I think the paper has substantial overlaps with Clancy, Ryu and Roch's preprint on "Likelihood landscape of b... | null | null | null | null | null | null | null | null |
Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme | Accept (poster) | Summary: The paper analyzed the consistency model of VE process and decay step size, and proved the discretization complexity of the consistency model.
Claims And Evidence: The paper bridges the gap between theory and application of consistency models by analyzing the discretization complexity through mathematical der... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**Weakness & Suggestion: The analysis for consistency training and continuous time consistency models.**
As shown by the professional reviewer, the consistency training and continuous-time co... | Summary: The paper proposes a novel discretization complexity analysis of Consistency Models, by incorporating the variance exploding kernel and the non-uniform step size. The results are closer to diffusion models than previous methods, providing a better analysis of conistency models.
## Update after rebuttal
The se... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**Weakness 1: The guidance on the design of better consistency models.**
This paper makes the first step to elucidate the design space of consistency models under the different diffusion proce... | Summary: This paper examines the convergence of the consistency model under the VE process with a decaying step size. It focuses on consistency distillation and establishes convergence results based on the Wasserstein distance between the generated and target distributions. Additionally, it demonstrates that 2-step sam... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**Q1: Theoretical Claims: The discussion on $L_{f,0}$ assumption and remove it.**
Following the suggestion of the professional reviewer, we consider the $L_{f,0}$ in 2-mode GMM in the followin... | Summary: This paper aims to provide a theoretical explanation for the strong empirical performance of consistency models — specifically focusing on how many discretization steps $K$ are needed during training to guarantee high-quality one-step sampling at test time. Prior theoretical analyses of consistency models typi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**Weakness 1: The approximated score and consistency function error (end-to-end analysis).**
In this work, we assume the pretrained score and consistency function are accurate enough to achiev... | null | null | null | null | null | null |
ResearchTown: Simulator of Human Research Community | Accept (poster) | Summary: The paper proposes ResearchTown, a multi-agent simulation framework for research community simulation. The research community is simplified as an agent-data graph, where researchers are modeled as agent nodes and research outputs (such as papers and reviews) as data nodes. The interactions, including paper rea... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and constructive comments. We ddress each of your comments in detail.
**[Fine-grained evaluation with LLM and human]** Please check the same tag under **Reviewer YZ7h** for more human and LLM eval.
**[Novelty+feasibility evaluation with LLM and human]** Pl... | Summary: The paper introduces RESEARCHTOWN, a multi-agent framework for simulating human research communities using Large Language Models (LLMs). The key idea is to model the research community as an agent-data graph, where researchers (agent nodes) and papers (data nodes) interact through edges representing authoring,... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and constructive comments. We address each of your comments in detail.
**[Novelty+feasibility evaluation with LLM and human]** Please check the same tag under **Reviewer YZ7h** fo novelty and feasibility evaluation.
**[Fine-grained evaluation with LLM and ... | Summary: This paper aims to simulate the human research community (called ResearchTown), which is modeled as a graph structure, where researchers and papers are represented as nodes and they are connected based on their relationships. Also, each researcher over the graph structure is powered by Large Language Models (L... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and constructive comments. We address each of your comments in detail.
**[Novelty+feasibility evaluation with LLM and human]** Please check the same tag under **Reviewer YZ7h** for novelty and feasibility evaluation conducted by both LLMs and humans.
**[Fi... | Summary: This research work starts from the idea that we can leverage LLMs to simulate human research communities and proposes ResearchTown, a multi-agent framework designed to model human research societies and behaviors. This work also introduces TextGNN to model various research activities, including paper reading, ... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and constructive comments. We address each of your comments in detail.
**[Input and output of ResearchTown]**
Please check the same tag under **Reviewer Cny3** for a detailed explanation.
**[Creativity of ResearchTown's output]**
LLMs have been shown capab... | null | null | null | null | null | null |
Curse of High Dimensionality Issue in Transformer for Long Context Modeling | Accept (poster) | Summary: This paper explores the challenge of the curse of dimensionality in Transformer architectures for long-context modeling, with a particular focus on redundant attention computations. To address this issue, the authors introduce a novel approach called Dynamic Group Attention (DGA), which minimizes redundant com... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer's kind words and detailed suggestions. Here are our responses:
>Q1. "...**well-structured and insightful theoretical analyses**... **strong foundation**...", "The experimental designs...are **well-structured and robust**...", "...theoretical exploration offers a ... | Summary: In this paper, the authors propose a novel approach called Dynamic Group Attention (DGA) to address the computational inefficiencies in long-context modeling for transformer-based large language models. DGA leverages a group coding strategy to dynamically aggregate less important tokens while preserving critic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments and detailed suggestions. Responses are below:
>Q1. "...**solid evidence** from **theoretical analysis** and **extensive experiments**", "...**theoretical analysis clearly demonstrates** how DGA reduces computational redundancy...**well-justified... | Summary: This paper addresses the computational inefficiency in Transformer-based models for long-context modeling caused by redundant attention computations. The authors reformulate probabilistic sequence modeling as a supervised learning task, providing a theoretical foundation for analyzing redundancy. Building on t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments and suggestions. Responses are below:
>Q1. "...**clear theoretical insights**...**deepen the understanding** of the redundancy in the attention, providing a **foundation** for the proposed method...", "...**comprehensive empirical results**...The... | Summary: In this paper, the author proposes a new method for long context LLMs. It uses dynamic grouping to divide tokens into several groups, the attention over coarse granularity of token groups achieves faster inference.
Claims And Evidence: 1. The LLM sparsity discovered in Sec 4 has actually already been reveale... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. Responses are below:
>Q1. The LLM sparsity discovered in Sec 4 has already been revealed in several previous works (e.g., StreamingLLM).
**A1.** We thank the reviewer for noting prior sparsity on self-attention weight like in StreamingLLM [r1]. U... | null | null | null | null | null | null |
Rethinking Aleatoric and Epistemic Uncertainty | Accept (poster) | Summary: This paper revisits the concepts of aleatoric and epistemic uncertainty in machine learning. It identifies inconsistencies in how these uncertainties are commonly discussed. The authors argue that traditional definitions are overly simplistic, causing confusion. They propose a decision-theoretic approach to cl... | Rebuttal 1:
Rebuttal: Thank you for your review.
We are pleased to see positive feedback on multiple aspects of the paper:
1. Strong motivation
2. Interesting use of decision theory
3. Comprehensive view on reasoning and learning
4. Clear writing, proofs and overall presentation
We also appreciate your thoughtful, i... | Summary: This paper argues that the current view on the decomposition of uncertainty into (reducible) epistemic and (non-reducible) aleatoric uncertainty is not only insufficient but also inappropriate from the theoretical viewpoint. They argue that the whole notion of predictive uncertainty should be grounded in a los... | Rebuttal 1:
Rebuttal: Thank you for your review.
We are happy to see you highlight a number of positive aspects of the paper:
1. Clear writing
2. Reasonable theory
3. Useful experimental results
4. Clear contextualisation within the literature
5. Refreshing style of contribution
We also recognise there are some thin... | Summary: The paper examines the concepts of aleatoric and epistemic uncertainty. It highlights inconsistencies in existing discussions of these concepts, attributing them to the limited expressiveness of the aleatoric-epistemic framework in capturing the diverse uncertainty quantities. To address this, the authors prop... | Rebuttal 1:
Rebuttal: Thank you for your review.
Your critical feedback is much appreciated. We hope our responses and new demonstrative plots help alleviate your concerns.
### **Loss functions and uncertainty**
> "Given this, we argue that a principled notion of predictive uncertainty cannot be detached from this l... | Summary: This paper critiques the concepts of aleatoric and epistemic uncertainty in machine learning predictions, identifying inconsistencies and limitations in existing discussions. The authors argue that the traditional aleatoric-epistemic framework is insufficient to capture all relevant aspects of uncertainty in p... | Rebuttal 1:
Rebuttal: Thank you for your review.
We appreciate your positive feedback on a number of points:
1. Careful writing
2. Theoretical rigour
3. Convincing empirical evidence
4. Clear coverage of prior work
5. Potential benefit to the community
We are also grateful that you highlighted some ways to improve t... | null | null | null | null | null | null |
LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data | Accept (poster) | Summary: The paper introduces LOB-Bench, a benchmark designed to evaluate the quality of generative models for limit order book (LOB) data. The authors propose a quantitative evaluation framework that measures distributional differences between generated and real LOB data. LOB-Bench assesses key LOB metrics such as spr... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer Nn9N for their detailed remarks and for recognizing the potential of this work in establishing a foundation for developing more robust and interpretable generative models in finance. We address the queries and concerns below, and would welcome any follow-up questions or... | Summary: This paper introduces LOB-Bench, a novel benchmark implemented in Python for evaluating the quality and realism of generative AI models applied to Limit Order Book (LOB) data in the LOBSTER format. The benchmark addresses the lack of quantitative evaluation paradigms in financial sequence modeling by providing... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer KDf5 for their detailed review and thoughtful comments, which not only provide valuable feedback but also highlight key strengths of our paper. In particular, we appreciate the recognition of the important gap our work addresses by introducing a well-founded, fully dist... | Summary: This is a great study with multiple important contributions:
1. The paper introduces a new benchmark for evaluating limit order book (LOB) generated data, applying aggregator functions to extract LOB-specific statistics and measuring the distribution distance between real and model generated data in both unco... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer **EUwk** for their thorough review and detailed feedback. We appreciate their recognition of the key strengths of our paper and framework, including the robust justification for our methods and scoring functions, the generality of the methodology, market impact evaluati... | null | null | null | null | null | null | null | null |
Near-Optimal Sample Complexity for MDPs via Anchoring | Accept (poster) | Summary: The authors propose a new model-free algorithm for solving average-reward weakly communicating MDPs with a generative model. The authors achieved the sample complexity of order $\widetilde{O}(SAH^2 / \varepsilon^2)$, where $S$ is a number of states, $A$ is a number of actions, $H$ is a span of an optimal bias ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. Both of the Reviewer's questions are central to our motivation for writing this work. For the list of references please refer to the reply to Reviewer BWFD.
1. Use of a related variance reduction technique
The paper [16] is related to ours in the sense that... | Summary: This paper introduces a novel value iteration algorithm that achieves near-optimal sample complexity in the setting of *weakly communicating* MDP. A weakly communicating MDP is an MDP whose state space is comprised by a set of states that are accessible from one another and an additional set of transient state... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments.
1. What stands in the way of achieving linear dependence on $\\|h^*\\|_{sp}$?
This is a very interesting and relevant question which we tried to address without success so far. From a technical viewpoint, the quadratic dependence of our sample co... | Summary: This paper studies the sample complexity of weakly communicating average-reward MDPs assuming access to a generative model. The authors focus on developing model-free algorithms by using a stochastic version of Halpern iteration. They show that this approach achieves a sample complexity bound in terms of the s... | Rebuttal 1:
Rebuttal: We thank the reviewer's constructive feedback. For the list of references please refer to the reply to review BWFD.
1. Advantage of SAVIA+ compared to [8] and [20]
The papers [8] and [20] propose model-free and model-based methods under the mixing time and weakly communicating assumptions, respe... | Summary: The established sample complexity of $O(\frac{1}{\epsilon^2})$ for average reward MDPs using a interesting approach of Halpern's iteration. The result is independent of mixing time which is very important.
Claims And Evidence: Looks good.
Methods And Evaluation Criteria: Seems so.
Theoretical Claims: I didn... | Rebuttal 1:
Rebuttal: We thank the reviewer for the opportunity to clarify some points that were not transparent
in our manuscript, and to better put in perspective our contribution with respect to
previous work. For the list of references please refer to the reply to Reviewer BWFD.
1. Comparison with [19]
There a... | null | null | null | null | null | null |
LapSum - One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection | Accept (poster) | Summary: LapSum introduces a unified method for creating differentiable versions of ordering operations—such as ranking, sorting, and top‑k selection—by leveraging a closed-form inversion of the Lap-Sum function (the sum of Laplace CDFs). This approach allows efficient gradient computation in $O(n \log n)$ time while u... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive suggestions for improving our paper.
1. Applications of our method
Our paper introduces several applications including top-k learning, soft ranking, sorting, and permutation learning. To address your request for concrete examples:
... | Summary: Authors propose “LapSum” - that yields differentiable versions of ranking, sorting, top-k selection, and permutations, all in closed form, with low time complexity: O(nlogn) (same as any sorting algorithm), and a linear memory.
Authors define this F-sum function and then define the ranking task in terms of F-... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper.
1. Related references
We appreciate your suggestions regarding additional references. Our literature review focused primarily on differentiable soft ordering, ranking, sorting, and top-k methods as addressed in works by Cuturi, Lapin, Berr... | Summary: This paper proposes a new method for computing differentiable approximations of ranking, sorting and top-k operators. This method is based on considering sums of the CDF of the Laplace distribution, which defines the approximations for well chosen arguments, with a regularization term $\alpha$. The choice of t... | Rebuttal 1:
Rebuttal: Thank you for your very careful reading and insightful review of our paper. We address your questions as follows:
1. Why haven't we considered sorting and ranking applications?
Our research focused on designing an efficient model with a closed form for soft ordering tasks, and we defined m... | null | null | null | null | null | null | null | null |
Generalization Performance of Ensemble Clustering: From Theory to Algorithm | Accept (poster) | Summary: This paper explores the theoretical foundations of ensemble clustering, focusing on its generalization performance, including generalization error, excess risk, and consistency. The authors derive convergence rates for both generalization error and excess risk, which are bounded by $\mathcal{O}(\sqrt{(\log n /... | Rebuttal 1:
Rebuttal: **We sincerely thanks for all your constructive comments!**
>**Weakness 1. Statistical significance tests**
We conducted paired t-tests on our method using NMI, ARI, and Purity metrics, and the results show that our method significantly **outperforms the sota methods** compared across almost all... | Summary: This paper investigates the theoretical foundations of ensemble clustering, focusing on its generalization performance, including generalization error, excess risk, and consistency. The authors derive theoretical bounds for these indicators and propose a new ensemble clustering algorithm based on their finding... | Rebuttal 1:
Rebuttal: **We sincerely thanks for all your constructive comments!**
We denote "Experimental Designs Or Analyses" as E, "Questions For Authors" as Q, and "Weakness" as W to save space.
>**E2 & W7: Global optimal solution**
Our model is a **convex optimization problem of w** (optimization function is conv... | Summary: The ensemble clustering is the problem of combining multiple base clusterings into a more accurate final clustering result. Prior research shows advances of ensemble clustering in practice while the theoretical analysis has fallen behind.
This paper provides the first generalization bound of emsemble cluster... | Rebuttal 1:
Rebuttal: **We sincerely thanks for all your constructive comments!**
>**Weakness 1: Improve writing quality and clarify some concepts**
Thanks for your suggestions! We will make the following changes in the final version of the paper:
- Provide a more detailed explanation of ensemble clustering and motiva... | null | null | null | null | null | null | null | null |
More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions | Accept (poster) | Summary: This paper presents MOTE , a novel multi-object tracking algorithm designed to tackle the persistent challenge of tracking occluded objects. MOTE introduces a unique approach by integrating deformable detection transformers with a custom disocclusion matrix, which significantly improves the ability to track o... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work and insightful comments.
Computational complexity: MOTE's end-to-end processing time is $\sim$45ms per frame on an A100 GPU, compared to $\sim$20ms for MOTR and $\sim$15ms for ByteTrack. The additional overhead comes primarily from optical flow e... | Summary: This paper presents MOTE, a novel multi - object tracking (MOT) algorithm aiming to solve the problem of tracking occluded objects. It combines deformable detection transformers, optical flow estimation, and softmax splatting. By leveraging optical flow to generate features and using a softmax splatting layer ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback on our MOTE framework.
ID switches: You raise an important point. The videos show some ID switches in extremely challenging scenarios with prolonged, complete occlusions. These represent edge cases where even our approach struggles. Our primary focus was o... | Summary: The paper introduces MOTE, an end-to-end multi-object tracking framework that integrates optical flow estimation and softmax splatting to robustly handle prolonged occlusions.
## update after rebuttal
The authors did not provide a detailed FLOPS analysis, leaving key computational efficiency aspects unaddress... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work and thoughtful questions.
Computational costs: Our MOTE framework adds only 25ms of additional processing time per frame compared to the baseline MOTR method's 133ms inference time (7.5 FPS) on high-resolution (1536x800) inputs, representing just... | Summary: This paper propose leveraging optical flow with soffmax splitting to estimate the motion of occluded objects. Together with the proposed enhanced track embeddgins module (ETEM), the model, i.e. MOTE, achieves state-of-the-art (SOTA) performance on various multiple object tracking (MOT) benchmarks.
Claims And ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our MOTE framework. We appreciate your recognition of our method's performance improvements and effective use of optical flow and memory mechanisms.
Qualitative analysis: We understand your concern about limited qualitative results. While Fig. 5 demonstra... | null | null | null | null | null | null |
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration | Reject | Summary: The paper proposed Adaptive Normalization-free Feature Recalibration (ANFR) to address data heterogeneity in federated learning. Instead of using normalization layers as in common neural networks, ANFR normalizes convolutional layer weights with a learnable scaling factor. This approach alleviates data heterog... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thoughtful engagement. Their concerns highlight important aspects that warrant clarification, as addressed comprehensively below:
---
# W1: Relation to previous work
We thank the Reviewer for suggesting additional related work. To clarify our contribution's positio... | Summary: This paper proposes a novel architecture-level approach to address statistical heterogeneity in Federated Learning(FL). While most previous works have focused on aggregation strategies, this paper directly modifies model architecture to enhance the generalization performance over heterogeneous clients. The pr... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their thorough and thoughtful review and positive assessment of our paper. Their recognition of ANFR as a "pioneering contribution to FL research" is deeply appreciated! Below we touch upon the suggestions the Reviewer made:
---
The suggestion to explore means ... | Summary: This paper introduces Adaptive Normalization-Free Feature Recalibration (ANFR), an architecture-level approach designed to combat heterogeneity in Federated Learning (FL). The authors explore how architectural components, more specifically weight standardization and channel attention can be used to enhance r... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their thorough and encouraging review and their insightful feedback. Their appreciation of our work's contributions and experimental rigor is very welcome. We are particularly pleased that they recognized the novelty of our architectural approach to federated le... | Summary: This paper focus another aspects to design a new architecture to address the heterogeneous data in FL. This architecture uses the weight standardization. Channel attention gets learnable scaling factors for feature maps for consistent features. This strategy improves the class selectivity and channel attentio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments, which have helped us identify areas for improved clarity and comprehensiveness. Below we address each point in detail:
---
# $C_R$, $C_{NR}$ and their computation
$C_R$ and $C_{NR}$ are conceptual tools used to explain the underlying mechanisms... | null | null | null | null | null | null |
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention | Accept (poster) | Summary: This paper targets cross-problem learning for vehicle routing problems. It proposes Constraint-Aware Dual-Attention (CaDA), which introduces a constraint prompt to enhance constraint awareness and employs a dual-attention mechanism consisting of a global branch and a sparse branch. The sparse branch utilizes a... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are very glad to know you find the paper is well-structured and easy to follow. We address your concerns as follows.
> **E1. Prompt Ablation Study**
To address your concern, we conduct the ablation study on adding prompt to b... | Summary: The paper proposes a novel architecture to tackle various variants of vehicle routing problems (VRP). The main idea of this architecture is to encode the different possible constraints in a so-called "constraint prompt" and use in conjunction two attention-based encoders of a VRP instance, one global and one s... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our manuscript. We are pleased to hear that you found the paper to be well-written and clear. Below, we address your concerns and questions point by point.
> **E1, Q2. Generalization Capability**
Thank you for raising this valuable point. We conduct experi... | Summary: This paper presents Constraint-Aware Dual-Attention (CaDA), a new neural architecture for solving multi-task vehicle routing problems (VRPs). CaDA integrates a constraint prompt to help the model recognize the specific constraints of the current task, along with a dual-attention architecture that combines a st... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are very glad to know that you find our proposed method efficient and interesting. We address your concerns as follows.
> **W1&Q1. Results on Other Distribution**
Thank you for raising this point. To evaluate cross-distribut... | Summary: The paper "CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention" presents a novel cross-problem learning method for Vehicle Routing Problems (VRPs) that enhances constraint awareness and representation learning through a Constraint-Aware Dual-Attention Model (CaDA).
1.Main Contributions
(... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our manuscript. We are glad to know that you find our method innovative and it addresses potential weaknesses in existing methods, and the paper well written and well structured. Point-to-point responses to your concerns and questions are presented b... | null | null | null | null | null | null |
Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs | Accept (poster) | Summary: This paper presents eva, a new minimax algorithm for RLHF which pushes beyond the static prompt set used by the majority of RLHF algorithms. In eva, the creator is trained to generate prompts that are solvable by the the solver. The largely empirical work focuses on evaluating a large number of design choices ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed review and insightful questions. Below we provide a high-level summary with detailed rebuttal, and will add relevant discussions in the reivisions.
---
> **Q2 & Q3**: *Could the authors provide more details on how they evaluate `eva`? What subse... | Summary: This paper studies a new paradigm for post-training where prompts are sampled adaptively. In particular, this paper proposes eva, in which a creator is addtionally introduced to select prompts for the solver to optimize. It provides extensive empirical results to show the advantage of eva.
## update after reb... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed and thoughtful review, which helps us a lot in shaping a better submission.
---
**Overview:** We would like to clarify several potential misunderstandings in the review:
1. **Problem 1 and Minimax Game:**
- (**i**) "*Problem 1 is a max-max col... | Summary: The paper "Evolving Alignment via Asymmetric Self-Play" presents EVA, an innovative framework that addresses a critical limitation in current RLHF methods by replacing static prompt distributions with an adaptive, generative approach. By framing alignment as a game between a prompt creator and response solver,... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer's in-depth evaluation and new insights on the game. Below we provide a high-level summary with detailed rebuttal, and will add relevant discussions in the reivisions.
---
**TL;DR**: Under reasonable assumptions, we can evolve to a *local* minimax optimum. (However... | Summary: The paper proposes Evolving Alignment via Asymmetric Self-Play (Eva), which treats post-training as an infinite game involving two roles: the creator, responsible for generating new prompts, and the solver, which optimizes responses. Eva implements prompt evolution via a regret-based reward objective combined ... | null | null | null | null | null | null | |
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training | Accept (poster) | Summary: This paper combines two classic technique normalization and whitening to improve SGD and achieves better performance than Adam and other optimizers while saving memory cost by not saving optimizer state. Theoretical insights are also provided to explain the effect of each technique.
Claims And Evidence: The s... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their thorough and constructive feedback. We are grateful that the reviewer acknowledged the novelty, writing, empirical performance, and the overall significance of our work. Below, we address each point raised:**
---
**1. On Combining GradNorm and GradWhitening:** ... | Summary: This paper introduces a new stateless optimizer SWAN (SGD with Whitening And Normalization) with the same performance as the Adam optimizer for LLM training. The author analyses that SGD with GradNorm and GradWhitening applied in tandem can minimize the condition number, stabilize gradient distributions across... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their insightful feedback and for acknowledging many of the strengths of our work. We address the specific points raised below:**
**1, Iteration Count and Motivation for Whitening:**
Regarding the number of iterations in the Newton–Schulz procedure, our ablation expe... | Summary: The paper proposes SWAN, an optimizer which is completely stateless. They claim that SWAN outperforms existing optimizers while also using lesser memory (since it is stateless). They support these claim by doing LLM pretraining experiments.
SWAN is similar to another previously proposed optimizer Muon the fo... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their detailed and constructive feedback. We address the main points raised below:**
1. **"The claim that SWAN outperforms Muon is problematic"**
- Our core contribution is to push the boundaries and demonstrate that: **it is possible to train LLMs matching the pe... | Summary: The paper proposed a "stateless" optimizer using gradient-normalization and gradient-whitening. The proposed method saves half memory over Adam and reaches 2x speedup. The idea is interesting and the writing is clear.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: yes
Exp... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their careful reading and for the positive comments regarding the clarity of our writing and the significance of our contribution. We address each question below:**
**Q1:**
Regarding the sufficiency of 12B tokens for 1B models, we have conducted additional experiment... | null | null | null | null | null | null |
Tracking Most Significant Shifts in Infinite-Armed Bandits | Accept (poster) | Summary: The paper studies the non-stationary infinite-armed bandit problem where arms' mean rewards are initially sampled from a $\beta$-regular reservoir distribution and evolve under adversarial/non-stationary dynamics. Prior works focused on stationary rewards or specific non-stationary cases requiring prior knowle... | Rebuttal 1:
Rebuttal: Thank you for the insightful review, writing suggestions, and careful discussion about the similarities with the blackbox MASTER algorithm of Wei \& Luo, 2021.
> In my opinion, the methods of this work primarily extends the black-box technique from [Wei and Luo, 2021] to the infinite many-armed... | Summary: This paper considers an infinite-armed bandits problem in a non-stationary setup. The means of the arms are drawn from a reservoir distribution and are chosen by an adaptive adversary in later rounds. Two algorithms are proposed with theoretical guarantees and experiments are conducted to illustrate the empiri... | Rebuttal 1:
Rebuttal: Thank you for the detailed review, and astute questions.
**On Comparison of Regret Bounds and $V, V_R, S_T$ Depending on Agent**: You're correct that, in general, a direct comparison of bounds between algorithms is tricky since the adaptive adversary may vary its behavior depending on the algori... | Summary: This manuscript deals with the problem of regret minimization in an infinite-arm bandit model with a reservoir distribution where shifts can occur. As such, this work is at the crossroad of infinite bandit and non-stationary bandits. Recently, Kim et al. (2024) have characterized the minimax cumulative regret ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and positive comments.
> the authors should emphasize that the "subsampling idea" for infinite-arms bandit is much older than Bayati et al[2020], see the earlier work of Berry. It is in fact standard in the infinite arm field.
This is correct - subsampling was i... | null | null | null | null | null | null | null | null |
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models | Accept (poster) | Summary: The paper proposes AQUA-KV, a KV cache compression method for autoregressive LLMs that explots inter and intra layer dependencies for improving cache quantization accuracy. It can be combined with additional compression techniques such as pruning. To this effect, they train predictors for predicting the value ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions. We appreciate that you highlight the modularity of AQUA-KV and address your concerns below.
> My main concern is that in Table 2, where the authors compare their method to other quantization approaches across various LLMs, their method sho... | Summary: This work proposes AQUA-KV, a method of using inter-layer and intra-layer information to reduce the size of the KV cache with minimum overhead via a supplementary probe. AQUA-KV supplements a “backbone” quantization algorithm, where it functions to improve accuracy by using the information available from the p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Overall, the review appreciates the efficacy and simplicity of AQUA-KV, but suggests additional evaluations on extra benchmarks and asks follow-up questions about implementation details. We do our best to address these below.
# Additional evaluat... | Summary: The paper presents AQUA-KV, an approach that leverages dependencies between keys and values across adjacent attention blocks. The method employs linear predictors trained to estimate KV caches for a given block based on previously generated keys and values. Subsequently, the residuals are quantized to low bit-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and valuable suggestions. We agree that improving clarity and expanding evaluations would strengthen the paper, and we address these points below:
> What does "high-compression mechanisms for internal network states" mean in the abstract? Is it an... | Summary: The paper proposes a learned predictor based adaptive quantization for KV Cache compression. The idea is this -- transformer models are residual in nature, i.e. each subsequent layers add smaller and smaller deltas to the outputs -- this means that the intermediate representations are highly dependent. This tu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and are glad that they appreciate our method's design and empirical results.
Below, we provide detailed answers to the questions posed in the review:
> If you train your predictors post-ROPE for 8192 sequences, do you see any deterioration in in... | null | null | null | null | null | null |
VCT: Training Consistency Models with Variational Noise Coupling | Accept (poster) | Summary: The authors propose an improved consistency training (CT) method by introducing a variational noise coupling scheme. The core idea involves training a data-dependent noise emission model using an encoder architecture inspired by Variational Autoencoders (VAEs). The method is theoretically linked to the VAE fra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments.
### **W1. On Eq. (8), Typos, and Its Tightness**
**R1.** The correct form of Eq. (8) should be:
$$||x_0 - f_\\theta(x_1,1)||^2 \leq
N\\sum_{i=0}^N||f_\\theta(\\psi_{t_{i+1}}(x_0; x_1),t_{i+1})- f_{\\theta^-}(\\psi_{t_i}(x_0; x_1),t_{i})||^2.$$
... | Summary: The paper aims to improve the training dynamics of Consistency Training (CT) by replacing the independent joint distribution between the source (data) and target (Gaussian noise) with a learned coupling. This coupling is parameterized as an encoder that maps each data point to a conditional noise distribution.... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the careful and thoughtful review. Below we address some of the points raised by the reviewer, especially the correctness of Eq. (8), how our results compare with the baselines, and the novelty of the method.
### **W1. About Eq. (8).**
**R1.** We thank the revi... | Summary: This paper proposes a method that combines VAE and Consistency Model, specifically using the encoder to predict the noise corresponding to the data. The resulting data-noise coupling is used to train the consistency model. The authors claim that this approach can reduce the variance in consistency model traini... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and feedback. We address here some of the points and concerns raised.
### **W1. Figure 3 Does Not Have Enough Support.**
**R1.** We believe that the initial disadvantage of our method compared to the baseline is due to the fact that the encoder is still in early... | null | null | null | null | null | null | null | null |
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs – No Silver Bullet for LC or RAG Routing | Accept (poster) | Summary: The paper investigates whether Retrieval Augmented Generation (RAG) or Long Context (LC) generation is superior to answer questions with LLMs. For this, it first identifies shortcomings of evaluations in current studies and then introduces its own dataset (LaRA) that aims to mitigate these shortcomings. In an ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback, which helps enhance the clarity of our paper! Let us answer your question below.
>**More details about the QA generation process and answer to question “Could it be that the generation produces wrong answers, and the final LC or RAG answers are ... | Summary: The paper proposes LaRA, a benchmark that attempts to answer if RAG is still necessary compared with long-context LLMs.
The LaRA dataset is constructed from novels, academic papers, and financial statements with four tasks: locating specific information, comparing different parts of the text, reasoning about ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback, and we are very happy that the reviewer liked how our dataset is structured! Let us answer your question below.
>**RAG retrieval setup: the potential unfairness in the RAG setup, as it relies on a limited number of chunks without further experi... | Summary: This paper introduces a new benchmark called LaRA, which is designed to systematically compare Retrieval-Augmented Generation (RAG) and long-context (LC) large language models (LLMs). It evaluates 11 models on 2,326 test cases across four key tasks (information retrieval, reasoning, comparison, and hallucinati... | Rebuttal 1:
Rebuttal: We would like to Thank you for your constructive review, as well as your positive feedback! Let us answer your question below.
>**Some implementation details are missing from the main paper---How many chunks were used in the setting of RAG experiments?**
In Section 4, paragraph “Implementation o... | Summary: This paper studies the problem of benchmarking RAG and long-context LLMs. The authors first revisit the existing benchmarks to compare RAG and long-context LLMs. They further construct a dataset called LaRA, which contains location-related question, reasoning-related question, comparison-related questions and ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! Please see our responses below.
>**Discuss with [1]**
Thanks for pointing out this important related work. [1] mainly investigated the phenomenon that increasing the number of retrieved passages does not consistently improve the performance of LLMs. ... | null | null | null | null | null | null |
Learning from True-False Labels via Multi-modal Prompt Retrieving | Accept (poster) | Summary: This paper propose a novel weakly supervised labeling setting, namely True-False Labels (TFLs) which can achieve high accuracy when generated by pre-trained Vision-Language Models (VLM). Moreover, the paper derived a risk-consistent loss for this setting and propose a convolutional-based Multi-modal Prompt Ret... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive comments. We will address each concern point by point:
**Q1&Method (Novelty & Comparison with Self-Training)**
R1: Our TFL framework introduces three key innovations that fundamentally address limitations in standard self-training and prior work lik... | Summary: This paper proposes a novel weakly supervised setting called True-False Labels (TFLs), leveraging VLM to reduce the difficulty of manual annotation. TFLs indicates whether a sample belongs to a label randomly and uniformly sampled from a candidate label set. In addition, this paper derives a risk-consistent es... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive comments. We will address each concern point by point:
**Q1: Assumption 1 appears problematic**
R1: In fact, Assumption 1 emphasizes that consistent classifiers can learn from pre-existing VLM-generated or human-annotated TFLs data, i.e., TFLs consi... | Summary: The paper proposes TFLs, a weakly supervised framework leveraging VLMs to generate high-accuracy labels efficiently. A risk-consistent estimator exploits TFLs’ conditional probabilities, and MPR aligns VLMs with target tasks. Experiments show significant gains over baselines.
Claims And Evidence: The claims a... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive comments. We will address each concern point by point:
**Q1: Class Imbalance in TFLs**
R1: We appreciate the reviewer's insightful observation regarding class imbalance. The imbalance between True and False labels is an inherent characteristic of th... | Summary: This paper introduces a weakly supervised learning framework that leverages True-False Labels (TFLs) to enhance annotation quality and efficiency. In this setting, each instance receives a binary label indicating whether it belongs to a randomly sampled candidate class, thereby mitigating errors common in conv... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive comments. We will address each concern point by point:
**Experimental Designs (Ablation study on the hyperparameters $K_T$ and $K_I$)**
R1: We conducted comprehensive ablation studies on the hyperparameters $K_T$ and $K_I$. Experimental results demo... | null | null | null | null | null | null |
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks | Accept (poster) | Summary: In this paper, the authors propose a method that leverages gradient guidance in the context of structure-based drug design. In particular, they augment MolCraft (that uses Bayesian Flow Networks as generative model for structure-conditioned ligand design) to be compatible with gradient guidance according to so... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough reading and insightful feedback, which have helped us identify areas for improved clarity and presentation. We shall address each point in our responses below, and we welcome further questions.
**Q1: Explanation of BFN Fundamentals**
We thank the r... | Summary: This paper propose a gradient-based molecule optimization framework for the SBDD task, which in experiment achieves state-of-the-art performance on CrossDocked2020 benchmark. Besides, it extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough reading and insightful feedback, which have helped us improve clarity and presentation. We shall address each point in our responses below, and we welcome further questions.
## Questions
**Q1: Similarities and Differences between Gaussian BFN and Gu... | Summary: This paper proposes MolJO, a framework that jointly guides continuous 3d coordinates and discrete atom types of 3d molecules based on the geometry of the target protein pocket and one or more molecular property classifiers. The paper also proposes a backward correction strategy that corrects parameters of Baye... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the careful reading and insightful feedback that helps improve the clarity and completeness of our work.
## Questions
**Q1: Table Value Inconsistencies**
We apologize for the confusion. The values in Table 1 and 3 were obtained from different runs. For Table 3... | Summary: The paper proposes MoIJO, a gradient-guided framework for SBMO. The key contributions are:
Joint gradient guidance over both continuous (coordinates) and discrete (atom types) modalities via Bayesian Flow Networks, avoiding modality inconsistency.
Backward correction strategy with a sliding window to balance... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough reading and insightful questions, which have helped us identify areas for improved clarity and presentation. We shall address each point in our responses below as well as our revised manuscript, and we welcome further questions.
**Q1: Smoothness of ... | null | null | null | null | null | null |
Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion | Accept (poster) | Summary: This paper proposed a novel audio-driven DiT-based portrait animation pipeline with customized emotion control and driving video control. The major contributions are 1) a motion-decoupled module with perceptual loss and adaptive normalization, 2) an emotion-control module with DiT blocks, and 3) an implicit 3D... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your recognition of our method's innovation and applicability. Here are our responses to your comments.
**Q1:However, it's unclear how the head pose and facial dynamics transfer and the perceptual loss improve the disentanglement, is there any detailed e... | Summary: The work introduces Playmat, a diffusion transformer based talking face generation model. Playmate is able to generate talking heads (portrait animation) given reference image and audio signal, as well as an emotion signal. It splits training into two stages, first training the talking face generation model (d... | Rebuttal 1:
Rebuttal: We are grateful for your review and valuable comments, and we hope our response fully resolves your concerns.
**Q1:precise motion decoupling... (a) confusing and (b) lacking evidence...**
About the motion decoupling. We emphasize motion decoupling because it is the foundation for Playmate to ach... | Summary: This work targets to generate lifelike talking videos for arbitrary identity, guided by a speech clip. Emotional and pose conditions are carefully devised to control the talking status. Specifically, a motion-decoupled module and emotion-control module are designed to enhance the performance.
Claims And Evide... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for your careful reading and providing numerous constructive comments! Below we address the concerns mentioned in the review.
**W1:The attached website does not include head pose control videos.**
Thank you for pointing this out. We have uploaded multip... | null | null | null | null | null | null | null | null |
From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models | Accept (poster) | Summary: This paper studies MLLMs’ ability on theory of mind. The authors first construct a benchmark testing MLLMs’ first-order and second-order theory of mind based on grid world setting, and then probe MLLMs’ understanding of beliefs with linear probing. Experiments show that some attention heads show distinguishmen... | Rebuttal 1:
Rebuttal: Thank you for your helpful and constructive reviews. We respond to your concerns and questions below.
# Claims And Evidence
The term "robustness" refers to the internal consistency of our task design. The GridToM dataset is generated via a fully automated pipeline with systematically controlled ... | Summary: This paper develops a new approach to evaluate Theory of Mind (ToM) abilities of Large Language Models. Taking as a starting point the potential limitations of previous ToM experiments (difficulties in capturing an agent’s perception, ToM tasks not addressing internal representations), it designs a specific te... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work and thank you for the thoughtful suggestions. We respond to your concerns and questions below.
# Methods And Evaluation Criteria
Our benchmark is designed as a foundational framework, using binary labels to establish clear distinctions between... | Summary: This paper aims to explore the Theory of Mind (ToM) capabilities for multimodal large language models (MLLMs).
To this end, it proposes a new dataset, GridToM, which is designed to evaluate MLLM Tom reasoning from multiple perspectives.
Based on GridToM, they then conduct experiments using different tech to d... | Rebuttal 1:
Rebuttal: Thank you for your helpful and constructive reviews. We respond to your concerns and questions below.
# Methods And Evaluation Criteria
We agree that the construction of the GridToM dataset should be described in greater detail. We provide further clarification here and will include the full pip... | null | null | null | null | null | null | null | null |
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation | Accept (poster) | Summary: This paper studies distributional RL, in particular the statistical functional formulation of it. They begin by introducing the concept of Bellman-unbiasedness, obtain results on which equivalent conditions lead to this, and exactly characterize which sets of statistics are both Bellman-unbiased and Bellman-cl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. **Due to character limits, we have focused on addressing what we considered to be the most important comments. We ask for your understanding that we could ... | Summary: This paper considers learnability and provable efficiency of distributional RL (distRL). The proposed notion of *Bellman unbiasedness* extends *Bellman closedness* in the literature to address the estimation errors stemming from finite samples. They show that moment functionals are the only finite statistical ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. If any of our responses fail to address the intent of your questions or if you have remaining concerns, please let us know.
---
### 1. The advantage of SF-... | Summary: The paper aims to design provably efficient and exactly learnable distributional reinforcement learning algorithm in an online setting, especially under general value function approximation.
For the main findings, they introduce two key properties for statistical functionals:
(1). Bellman Closedness: The sket... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. If any of our responses fail to address the intent of your questions or if you have remaining concerns, please let us know.
----
### 1. Lack of empirical v... | Summary: The paper proposes a distributional RL algorithm in the finite horizon episodic MDP setting. They propose bellman unbiasedness, a notion complementary to bellman closeness in prior work. They analyze the regret bound of the algorithm and compare against prior work in the space, showcasing theoretical improveme... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We have organized our responses to your comments below. If any of our reconstructed responses miss the intent of your questions or if there are remaining concerns, please let us know so we can address them.
-------
### 1. Con... | null | null | null | null | null | null |
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them) | Accept (poster) | Summary: The paper studies issues in LM role-learning for security purposes (e.g., following instructions in system prompts over user instructions), and identifies two key issues: task type exploitation (the model following user tasks that are similar to the system prompts) and proximity to beginning of text (the model... | Rebuttal 1:
Rebuttal: We are glad you like this work!
**Open-domain performance** Thank you for your positive feedback! We choose a close-domain setting as it is easy to validate and evaluate the model role-separation capability. This is also a common choice in many security-related works, such as [StruQ](https://arxi... | Summary: The paper studies how well LLMs are able to distinguish between different input roles like system, user etc. The authors motivate their work by claiming that existing fine tuning approaches do not teach the LLM genuine role differentiation but rely on spurious patterns picked up by the model during training. T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions! We are glad that you find the paper well written, and the empirical evidence strong.
Many of your questions about theoretical results and other embedding-based methods are great suggestions! We will study them in the future projects. More spe... | Summary: The paper introduces the concept of role-separation learning, which reflects the LLM capability to distinguish the system instructions and user queries. The authors evaluate the role-separation capability of LLMs through a controlled experimental framework and conclude current fine-tuned models use task type e... | Rebuttal 1:
Rebuttal: Thank you for your review! We are glad you find the problem of role-separation important, and the experiment designs make sense.
For your questions about whether other factors (such as model capability or evaluation metrics) confound the conclusions, we discussed in the paper that controlled exp... | null | null | null | null | null | null | null | null |
Sable: a Performant, Efficient and Scalable Sequence Model for MARL | Accept (poster) | Summary: This paper proposes to use retentive networks to process multiple agents' observations and actions in MARL. The proposed framework can scale to a large number of agents. Extensive experiments and analysis are conducted. As a result, the proposed framework shows performance improvements in 34/45 of tasks and al... | Rebuttal 1:
Rebuttal: Thank you for the feedback. Your comments on the retention equations and other aspects of our work have helped us identify areas for improvement. We address your questions and comments below.
>wondering if agents share parameteres, espicially on Q, V, K matrices
Sable uses a single network for a... | Summary: The paper proposes to replace the attention mechanism in Multi-Agent Transformers with Retentive Networks and shows that such tweak (called Sable method by the paper) leads to improvement in the following three dimensions: strong performance, memory efficiency and scalability. The paper evaluates the Sable met... | Rebuttal 1:
Rebuttal: Thank you for your clarifying questions and feedback. We provide detailed responses below.
> The claim on the scalability may be a bit untenable
RetNets' chunkwise formulation allows us to process long sequences in small chunks, scaling to arbitrarily long sequences regardless of whether they co... | Summary: This paper presents a novel sequence modeling approach for MARL. It adopts the retention mechanism instead of the attention mechanism in MAT to achieve computational efficiency, memory efficiency, and scalability.
## update after rebuttal
During the rebuttal, the authors adequately addressed most of my concer... | Rebuttal 1:
Rebuttal: Thank you for your feedback, especially the close attention paid to our equations/notation. We provide detailed responses below.
>(Q1) Do all baseline methods are trained in via centralized training?
In addition to answering the above question, we also wish to clarify a misunderstanding evidence... | Summary: The work proposes a novel sequence model architecture for multi-agent reinforcement learning (MARL) and conducts a large empirical evaluation to validate the efficacy of the new approach. The architecture is based on retention networks and optimises the sequence model architecture similar to the prior multi-ag... | Rebuttal 1:
Rebuttal: Thank you for your feedback, especially your comments on our positioning within the context of different MARL algorithms, and questions on implementation and experimental details, which helped us improve the paper. We provide detailed responses below.
> Confusion from the introduction's contrast ... | null | null | null | null | null | null |
Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics | Accept (poster) | Summary: The paper addresses the problem of optimal sensor scheduling and selection in Continuous-Discrete Kalman Filtering (CD-KF) for Bayesian State-Space Models (SSMs), where continuous-time processes are observed through multiple sensors with discrete, irregularly timed measurements. The novelty of the work lies in... | Rebuttal 1:
Rebuttal: Q1: Besides the auxiliary dynamics, our method considers the continuous-discrete setup and the flexibility of not requiring an upper bound on the number of measurements. The method by [Le Ny et al., 2009] is for a continuous-continuous setup, while the method in [Marelli et al., 2019] is for a dis... | Summary: In this paper, the authors are concerned with optimizing temporal event sequences of measurements for minimizing the uncertainty of continuous-discrete Kalman filter (CD-KF). In particular, they consider a general case where the measurements may affect the underlying states of sensors themselves as well as the... | Rebuttal 1:
Rebuttal: Unknown dynamics:
Our formulation as an Optimal Control Problem (OCP) with a differentiable cost function and constraints opens avenues for extension to uncertain dynamics using robust/stochastic OCP methods [2,3]. For completely unknown dynamics, planning is challenging since we must schedule m... | Summary: This work considers continuous-time state-space models in which observations are taken at discrete and potentially irregular time intervals from a finite collection of different kinds of sensors, each with a potentially different accuracy and potentially different cost incurred per measurement.
In this conte... | Rebuttal 1:
Rebuttal: Q1:
In the last paragraph of Section 8.1, we leverage the fact that Gaussian process (GP) regression (with many common stationary covariance kernels) is equivalent to Kalman smoothing of a specific linear state-space model (see ref. Sarkka \& Hartikainen, 2012 in the paper). This equivalence allo... | null | null | null | null | null | null | null | null |
Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination | Accept (poster) | Summary: This paper introduces optimal pursuit strategies for feature selection and elimination in best subset selection problems. It challenges classical feature selection methods by offering new selection and elimination criteria, which focus on feature interactions as opposed to individual significance alone. The au... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback and constructive suggestions on our paper, which will help enrich the original content. In this rebuttal, we address the concerns raised in the reviews. For references [1-8] in the rebuttal, please refer to Reviewer uuYf.
**Q1: (Complexity)** Thank you for y... | Summary: This paper proposes two criteria for feature selection and feature elimination in the context of solving the best subset selection problem. The authors approach these criteria from an optimization perspective. These criteria can be incorporated into various heuristic subset selection algorithms. Additionally, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful question, which has driven us to further theoretical advancements. In this rebuttal, we address concerns raised in the reviews. For references [1-8], please refer to Reviewer uuYf.
**Q1 (Theoretical Assumptions):**
Thank you for your question. The theoreti... | Summary: The paper proposes a new criterion for selecting and rejecting features in the context of the best subset selection problem. While previous methods primarily focused on the significance of individual features, the proposed approach offers the flexibility to capture interactions between features.
## update aft... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback on our theoretical foundation and flexibility. We have incorporated further explanations and experiments accordingly, and this rebuttal will be integrated into revised paper. For the references [1-8] in the rebuttal, please refer to **Reviewer uuYf**.
---
**... | Summary: The paper presents two novel criteria for feature selection, which are refinements on well studied approaches for identifying features which maximally improve (or reduce) prediction accuracy. By more rigorously considering the impact of features selected as a subset, rather than just individually, similar effi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your deep understanding and kind words on our work. We have carefully addressed your comments below and will incorporate this rebuttal into the revised version of the paper.
---
**Q1 (Why CoSaMP Fails):**
Thank you for your question. We implemented Algorithm 1 of CoSaMP [... | null | null | null | null | null | null |
Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models | Accept (poster) | Summary: This paper introduces a collection of methods for controlling likelihood of samples generated by a flow/diffusion model. Authors provide a comprehensive review of prior work on density control, in particular providing a more formal analysis of latent scaling [Song 2021]. They further introduce density guidance... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their time and efforts to scrutinize our submission. We address the raised concern below.
**File with new figures:** https://anonymous.4open.science/r/DensityGuidance-20E6/Density_guided_sampling___Rebuttal.pdf
>The practical utility of the proposed method... | Summary: This paper studies the control of the amount of details in samples from diffusion models. The authors first established a theoretical framework (Score alignment) to explain a trick to increase sample details in prior literature. Then, the authors explore a suite of methods that can be used to control the exact... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thorough evaluation of our work and very insightful questions! We address the raised points below.
**File with new figures:** https://anonymous.4open.science/r/DensityGuidance-20E6/Density_guided_sampling___Rebuttal.pdf
Glossary:
* Density Guidance - DG
* Prior Gu... | Summary: This work introduce a method to control the sampling density in diffusion models. The main contribution is using score alignment to scale and control the sampling guidance, which works for both deterministic and stochastic sampling.
The experiments demonstrate that density guidance and its stochastic extensi... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their support of our work. Below we address the raised concern.
**File with new figures:** https://anonymous.4open.science/r/DensityGuidance-20E6/Density_guided_sampling___Rebuttal.pdf
> I would appreciate it if the authors could provide practical advice o... | Summary: The paper proposes a novel method, Density Guidance, to control the level of detail in generated images of flow models. It addresses the observsed mismatch between image likelihood and perceptual quality. The samples of high-likelihood are usually overly smooth, while the low-likelihood ones are more detailed.... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their positive comments and constructive feedback. We address the raised points below.
**File with new figures:** https://anonymous.4open.science/r/DensityGuidance-20E6/Density_guided_sampling___Rebuttal.pdf
> Discuss some papers about perceptual quality metrics and det... | null | null | null | null | null | null |
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off | Accept (poster) | Summary: The paper proposes a novel federated learning framework, FedCEO, aimed at balancing model utility and user privacy through collaboration among clients. The authors introduce a compelling case study to illustrate the potential of semantic collaboration among clients in enhancing the utility of the global model,... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback on our theoretical and empirical contributions. Below are our point-to-point responses to the raised concerns and suggestions.
> **W1**: Lack of Depth in Technical Details: The detailed discussions on parameter selection are somewhat brief ... | Summary: The authors propose a novel federated learning framework with the differential privacy mechanism, focusing on improving the trade-off between model utility and user privacy. By leveraging tensor decomposition techniques, the proposed method can model the dynamic semantic relationships among different clients, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback and recognition of our work. Below are our point-by-point responses:
> **W1 & Comments1, 2**
Condensing Background Knowledge: We agree with the suggestion. In the revised manuscript, we will streamline Section 2 (Preliminaries) by movi... | Summary: The paper introduces FedCEO, a federated learning framework that aims to balance model utility and differential privacy by applying tensor low-rank proximal optimization (via T-tSVD) on noisy client parameters. The idea of smoothing the global semantic space is interesting, though its novelty is not entirely c... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and the opportunity to improve our paper. Below are our point-by-point responses to your comments:
> **W1**
**Figure 4 in the paper** visualizes the robustness of our method to different noise levels on CIFAR-10. Compared to other methods, our F... | Summary: The paper introduces a new method for FL training with DP guarantees. The authors argue that the method improves on existing work in terms of the utility-privacy trade-off, supporting their algorithm with a theoretical analysis and experiments. The method is based on a tensor low-rank proximal optimization of ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for taking the time to provide thorough reviews! Below are our point-by-point responses to your comments:
> **Claims And Evidence (Concern 1)**
We appreciate the reviewer's suggestion.
- The product form was chosen because it directly reflects the **j... | null | null | null | null | null | null |
Which Attention Heads Matter for In-Context Learning? | Accept (poster) | Summary: The authors investigate the nechanisms behind in-context learning (ICL). Specifically, they study about two special types of attention heads called "induction heads" and "function vector heads" (FV heads). By detecting these two heads, they find that: 1) The induction heads and FV heads are distinct; 2) FV hea... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback, and we especially appreciate the clear questions to resolve reviewer concerns!
We first address reviewer concerns on the claims:
1. We used Pythia models mostly to facilitate the analysis over training dynamics, which other open-source models do not enable sin... | Summary: This paper studies the functionality of attention heads in in-context learning. Specifically, two functionalities are investigated, namely induction heads and function vector heads. By using the established metrics to locate induction heads and function vector heads across layers, several observations have bee... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We will first address the concerns around claims and evidence:
* On the layer distribution, we will clarify that the claim on the layer depth difference is more of a speculative observation than a claim sufficiently backed up statistically.
* On the selection of 2%: t... | Summary: This paper presents a comprehensive study on attention heads and their impact on in-context learning. Specifically, it examines two types of attention heads: induction heads and function vector heads. Through ablation studies, the authors found that function vector heads play a more significant role in in-cont... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
We will first respond to the questions about claims and evidence:
* Choice of percentile: In many cases, we had to decide on a threshold to differentiate meaningful FV/induction heads from the long tail of other heads. The 2% threshold was carefully chosen following p... | Summary: This paper investigates the mechanisms behind ICL. Specifically, it focuses on analyzing two popular explanations for the mechanism of ICL: induction heads (token-copying) and function vector (FV) heads (task-encoding). Through extensive experiments on 12 LLMs and 45 tasks, multiple interesting findings are pr... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment! We also address minor concerns:
* Larger models: we will consider experiments on models in the 13B range for our camera-ready. However, computational constraints on our end prevent testing on larger models due to memory limits, since the FV score computation... | Summary: The authors study Induction Heads and Function Vector Heads in relation to In-Context Learning in a variety of small and medium-sized models.
Their experiments suggest that induction heads and FV heads are distinct, though there is some overlap in their behaviours.
They also show that removing FV heads greatly... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback! We address concerns below:
1. Our task suite does include dictionary-style tasks as you mentioned, and we plotted the task-specific ablations in Figure 18 of the appendix - for the dictionary tasks (e.g. national_parks, park-country, person-occu... | Summary: This paper explores the mechanisms behind in-context learning (ICL) in large language models (LLMs), specifically examining two types of attention heads: induction heads and function vector (FV) heads. The authors conduct experiments to determine which of these mechanisms is primarily responsible for ICL. Thro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback!
To address your weaknesses:
* On the shift from induction to FV: we will elaborate on why this shift might be necessary for larger models. We hypothesize that induction is a more lightweight mechanism (based on just attending to the subsequent token of a... | null | null |
Detecting Strategic Deception with Linear Probes | Accept (poster) | Summary: The paper evaluates whether deceptive behaviour in LMs can be detected by linear probes on the activations. The paper includes extensive experiments and ablations with strong positive results.
Claims And Evidence: The paper is clear about its limitations and scope, e.g.,
>Thus, our experiments do not attemp... | Summary: The paper explores the use of logistic regression probes, trained on the residual stream activations of an LLM, to detect whether the LLM is executing deceptive behavior.
Their results demonstrate that such white-box deception probes can be trained using teacher forced (off-policy) data, and transfer to detec... | Summary: The paper studies the problem of strategic deception in AI models by training linear probes on datasets that elicit dishonesty in certain ways and check whether the probes generalize to more realistic examples of deception. The paper provides experimental validation that the probes can distinguish between hone... | Summary: The paper evaluates whether linear probes can effectively detect strategic deception in language models by monitoring their activations. The authors train probes on simple datasets (instruction pairs and roleplaying scenarios) and test if they generalize to realistic deceptive behaviors like concealing insider... | Summary: The authors address the question of whether linear probes trained on simple deception datasets can generalise to more complex examples of deception. They train linear probes for Llama-3.3-70b-Instruct using a simple dataset of contrastive pairs. They then test generalization to four out-of-distribution decepti... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
We agree that it would be useful to compare results on other models. During writing we ran experiments on gemma-2-27b and llama-3.1-70b and saw broadly similar results, but these results are not included in the main paper (and were not run with the latest datas... | null | null | null | null | ||||
Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision | Accept (poster) | Summary: The authors address the vulnerability of LVLMs to adversarially perturbed or maliciously injected input images. Their motivation is to solve the issues of response quality degradion that comes with the existing defense methods which rely on image modifications that could possibly cause semantic distortions. Th... | Rebuttal 1:
Rebuttal: We greatly appreciate your time and effort in reviewing our work and would like to address each of your concerns.
### 1. The incremental improvement
We appreciate the reviewer’s recognition of our idea novelty. In addition to the novel idea, we respectfully argue that the reviewer should reconsid... | Summary: This paper proposes a black-box, training-free defense method called DPS (Defense via Partial Perception Supervision) to protect large vision-language models (LVLMs) from visual adversarial attacks. Unlike existing defenses (e.g., majority voting), which suffer from semantic distortion due to image cropping an... | Rebuttal 1:
Rebuttal: We greatly appreciate your time and effort in reviewing our work, and would like to address each of your concerns.
### 1. The definition of confidence
> However, I am skeptical about this confidence variation because the definition of confidence in this context is unclear. Specifically, I quest... | Summary: The paper proposes a improved ensemble based defense of VLMs. The idea improves upon SmoothVLM which leveraged partial crops or random perturbations of the input image and ensembled the responses, by adding a secondary step of using the VLM responses on the partial crops as supervisory inputs to the VLM. The a... | Rebuttal 1:
Rebuttal: We greatly appreciate your time and effort in reviewing our work, and would like to address each of your concerns.
### 1. Error Bar
> I would suggest adding error bars to the results where ever possible to account for the random nature of the evaluations.
Thanks for your suggestion! We conducte... | Summary: This paper proposes a novel method, named Defense through Partial Perception Supervision (DPS), which focuses on evaluating and improving the robustness of Large Vision-Language Models (LVLMs) against vision attacks. Specifically, DPS leverage the outputs from cropped image processing to supervise the outputs ... | Rebuttal 1:
Rebuttal: We greatly appreciate your effort and address each of your concerns.
### 1. Computational cost
We report the computational costs of baselines and our method, and would like to highlight that the computational overhead of our DPS delivers proportionally higher defense effectiveness. Specifically... | null | null | null | null | null | null |
Arrow: Accelerator for Time Series Causal Discovery with Time Weaving | Accept (poster) | Summary: The authors proposed an accelerator framework named ARROW to address the efficiency bottleneck in multivariate time series causal discovery. By introducing time weaving encoding (capturing contextual trends between time points), an optimal time lag determination theorem based on XOR operations, and an intellig... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and our responses are detailed below.
**Response to W1**: Our method is better suited for monotonic causal relationships and less applicable to purely nonlinear ones. Future work should explore trend patterns in nonlinear relationships, such as periodicity. I... | Summary: This paper presents ARROW, an accelerator for time series causal discovery that overcomes the efficiency bottleneck of existing causal discovery methods. The concept of time weaving is introduced, along with an XOR-based time lag discovery strategy, which leverages theoretical derivations to rapidly determine ... | Rebuttal 1:
Rebuttal: We appreciate the positive comments and our responses are detailed below.
**Response to W1**: Calculating time lag is the same for both constant and multiple lags, with no additional complexity for multiple lags. Our pruning strategy reduces the time dimension and variable count, while binary com... | Summary: This paper presents ARROW, an acceleration framework for causal discovery in time series data. ARROW aims to improve the efficiency of causal discovery algorithms by reducing computational complexity through three sequential steps: Time Encoding with Time Weaving transforms time series into binary tuple repres... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions.
**Response to Q1 and W1**:
1. **Response to the assumption of Theorem 4.2:** Our method is well-suited for scenarios with monotonic causal relationships, as assumed in Theorem 4.2, where trend changes show consistent positive or negative correlations in mos... | Summary: The paper investigates the computational efficiency of causal discovery in multivariate time series. Existing methods face high computational costs when applied to large-scale data, primarily due to issues such as data binning, time lag selection, and candidate set explosion. To address these challenges, the a... | Rebuttal 1:
Rebuttal: We appreciate the positive comments and our responses are detailed below.
**Response to W1**: Causal relationships rely more on trend synchronization than numerical transmission. Performance improvements come from three aspects:
* Noise Robustness – Binary encoding filters noise and highlights st... | null | null | null | null | null | null |
Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis | Accept (poster) | Summary: This paper proposes a noise-robust method for surface electromyography (sEMG)-based gesture recognition. The authors emphasize the importance of short-term signal learning in mitigating the interference of local noise, which could otherwise degrade the modeling of long-term signals. Specifically, the paper int... | Rebuttal 1:
Rebuttal: # Response to Reviewer QgDP
Thank you for your professional review and valuable time. Your positive assessment is incredibly encouraging to our research team. We sincerely appreciate your thoughtful comments and would like to address each of your concerns as follows:
## Broader Scientific Litera... | Summary: This paper specially captures the short-term temporal dependencies in sEMG-based gesture recognition. By designed a self-supervised pretrained method and two short/long-term heads, the proposed method achieve SOTA performance.
## update after rebuttal
The authors have resolved most of my concerns.
Claims And... | Rebuttal 1:
Rebuttal: # Response to Reviewer ybGK
We sincerely appreciate your thorough review and valuable feedback on our manuscript. We are grateful for your recognition of our paper's strengths, including the **clear writing style**, **effective visualizations**, **strong performance** , **comprehensive ablation ... | Summary: The paper addresses the problem of noise resilience in surface electromyography (sEMG)-based gesture recognition. The authors propose a novel Short-Term Enhancement Module (STEM), which focuses on capturing short-term dependencies in sEMG signals to enhance noise resistance. Further, results on GRABMyo and Nin... | Rebuttal 1:
Rebuttal: # Response to Reviewer AUAg
We sincerely thank you for your thorough review and **insightful feedback** on our paper. We particularly appreciate your recognition of our **well-motivated problem statement** and the **novel approach of using short-term feature extraction**. Your professional sugges... | null | null | null | null | null | null | null | null |
On the Robustness of Transformers against Context Hijacking for Linear Classification | Reject | Summary: This paper studies the robustness of Transformers against context hijacking in a linear classification setting.
Empirically, the paper observes deeper transformer can achieve higher robustness. Theoretically, the paper explains this phenomenon as deeper model corresponding to more fine-grained optimization st... | Rebuttal 1:
Rebuttal: We appreciate your constructive questions and suggestions! We address them as follows:
>**Q1:** More clarity on the design of hijacked context data in the linear classification setting, and its connection to context hijacking in practice.
**A1:**:
We carefully design the context hijacking sample... | Summary: This paper investigates the context hijacking phenomenon of transformer models, where incorporating multiple hijacking context samples can successfully flip the original model prediction. The paper conducts theoretical analysis on the linear transformer case for in-context learning, and verifies it on linear t... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and your constructive questions!
>**Q1:**
Are the distributions $\mathcal{D}\_{\mathrm{te}}$ and $\mathcal{D}\_{\mathrm{tr}}$ the same? How does the difference between the two distributions affect the theoretical analysis?
**A1:**
The distributions $\ma... | Summary: The authors here have studied how the concept of context hijacking affects the transformer models. The context hijacking problem deals with the problem where giving some other informations to the model might affect it's output even if the informations are factually correct. The authors here tried to study this... | Rebuttal 1:
Rebuttal: Thanks for your informative feedback! We address your comments as follows:
>**Q1:** More clarity on the data model.
**A1:**
We model the context hijacking problem as a binary linear classification task, following general theoretical studies on transformer in-context learning [1, 2, 4, 5, 7]. Our... | null | null | null | null | null | null | null | null |
Strategic A/B testing via Maximum Probability-driven Two-armed Bandit | Accept (poster) | Summary: This paper builds on Strategic Two-Sample Test via the Two-Armed Bandit Process to enhance the detection of small average treatment effects. It proposes a more powerful one-sided two-sample test by adjusting the balance between the mean and volatility terms, yielding a statistic that is more concentrated under... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback and valuable suggestions. We also sincerely apologize for any difficulties or confusion arising from insufficient clarity in the presentation of some fundamental equations and references in the paper. Below, we will discuss the issues you ... | Summary: **Problem:**
This work aims to address the limitations of traditional A/B testing in detecting minor treatment effects.
The key challenges are: (i) data distributions between the treatment and control groups may differ due to confounding effects, (ii) even when distributions are balanced, measured outcomes c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comprehensive and insightful feedback. Below, we provide a point-by-point response to the issues raised:
**1. The Use of “Exchangeability”**
We appreciate the reviewer’s observation regarding the term “exchangeability” and fully agree that a clear definit... | Summary: This paper introduces a novel approach to A/B testing focused on detecting minor average treatment effects (ATEs) in large-scale applications. The authors propose a maximum probability-driven two-armed bandit process with a weighted mean volatility statistic and incorporation of permutation methods. The key th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s careful evaluation of our work and the constructive feedback provided. Below, we address the key weaknesses and questions raised:
**1. Adding a More Intuitive Explanation of Mathematical Densities**
We thank the reviewer for this valuable suggestion. We ac... | null | null | null | null | null | null | null | null |
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning | Accept (poster) | Summary: This paper introduces DEMO3 (Demonstration-Augmented Reward, Policy, and World Model Learning), a novel framework for solving long-horizon, multi-stage manipulation tasks with sparse rewards. The authors address the challenge of designing dense reward functions and effectively exploring large state-action spac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We address your comments in the following.
---
**Q: Discussion on limitations and future work**
**A:** Thank you for pointing this out. In response, we have prepared an expanded discussion on limitations and future work. We will incorporate th... | Summary: This paper introduces a demonstration-augmented reinforcement learning method to solve data-efficient manipulation tasks with sparse rewards. By utilizing limited demonstrations, the policy, world model, and dense reward are effectively modeled, thus the long-horizon tasks can be solved in a multi-stage manner... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We address your comments below.
---
**Q: Benchmarks/tasks have a limited number of stages**
**A:** We appreciate the reviewer’s concern. While many tasks in our suite contain 2–3 defined stages, we argue that this already reflects significant ... | Summary: This paper proposes DEMO, a framework that learns dense rewards from demonstrations and interactions with the environment to aid model-based RL learning. DEMO uses a multi-stage paradigm where it learns dense rewards from sparse reward signals (from stage indicators) to indicate "progress" and uses the learned... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We address your comments in the following.
---
**Q: Suggestion about live plots with policy rollout (1 to 1 video with reward evolution and policy rollout)**
**A:** We fully agree with the reviewer that it is important to verify whether the le... | null | null | null | null | null | null | null | null |
MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product | Accept (poster) | Summary: This paper considers the problem of metric nearness and proposes a new method for solving the problem. In particular, they define a tropical neural network that uses tropical operations instead of regular operations, a new loss function to be used to train the model, and then experimental results validating th... | Rebuttal 1:
Rebuttal: **Q1:Undefined error rate in Sec 4.3**
A1:Thank you for pointing this out. We agree that the evaluation metric in Section 4.3 should be clarified.
All experiments in Section 4.3 use **Normalized Mean Squared Error (NMSE)**, as defined in Section 4.2, to measure reconstruction quality. Specifical... | Summary: The paper introduces MetricEmbedding, a novel approach using the ropical inner product (max-plus operation) to efficiently solve the Metric Nearness Problem (MNP) while ensuring metric properties like the triangle inequality. The authors showed the equivalence (up to diagonal elements) between the class of non... | Rebuttal 1:
Rebuttal: **Q1：Please clarify whether the proposed training procedure for MetricEmbedding is guaranteed to achieve a plausible minimum and under which conditions.**
A1：The short answer is **no**, because it is a **non-convex** optimization problem. Nevertheless, our algorithm is guaranteed to converge to a... | Summary: The authors propose the use of tropical algebra to frame the metric nearest problem within a continuous optimization framework. They first demonstrate that the set of non-negative matrices satisfying the triangle inequality can be fully represented using a combination of tropical algebraic representations. The... | Rebuttal 1:
Rebuttal: **Q1:but one can also easily optimize over multiple starting matrices A in parallel as another means of avoiding local minima.**
A1:Thanks for your valuable suggestion. We conducted experiments using the proposed strategy with a single-layer model, and indeed observed further performance improvem... | null | null | null | null | null | null | null | null |
A General Representation-Based Approach to Multi-Source Domain Adaptation | Accept (poster) | Summary: This paper addresses the issue of multi-source domain adaptation with a focus on identifiability. It introduces a causal framework that avoids restrictive assumptions such as independent latent variables or invariant label distributions. The authors theoretically establish identifiability and validate the effe... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's constructive comments, helpful feedback, and time devoted. Please see below for our response.
**Q1:** "the experiments are limited to datasets with covariate shifts" and "experiments involving other types of distribution shifts"
**A1:** Thanks for your comm... | Summary: The manuscript presents a general representation-based approach for multi-source domain adaptation (GAMA). It aims to improve knowledge transfer across domains by leveraging theoretical identifiability results for latent variables and adapting a variational autoencoder (VAE)-based framework. The authors propos... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's time and valuable comments, many of which will help improve the clarity of our paper. Our responses to these comments are given below.
**Q1:** "some assumptions, such as linear independence of certain distributions, may be restrictive and require further discu... | Summary: The paper proposes a multi-source domain adaptaion approach. It is a generative based approach which projects feature representation into a latent space using VAE. The latent space (Markov blanket) is then partitioned into the subspace of label’s parents, children and spouses. Next, two VAE are used to Z_pa an... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time dedicated to reviewing our paper and the valuable comments. We have tried to address all the concerns in the following.
**Q1:** "More experiments would be better" and "Domainnet is a popular dataset for multi-source domain adaptation"
**A1:** Thank yo... | null | null | null | null | null | null | null | null |
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images | Accept (poster) | Summary: The paper introduces Querent, a query-aware long contextual modeling framework for whole slide image (WSI) analysis, addressing the challenge of computational efficiency in gigapixel images. Unlike standard transformer architectures with quadratic complexity, Querent dynamically selects relevant regions for ea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions. We answer the reviewer's questions in the following one by one:
`Weakness`
> The assumptions in the theoretical proofs may be challenging to satisfy ...
We appreciate the reviewer's concerns. Regarding the Lipschitz continuity of $f_{min}$ and ... | Summary: This paper introduces Querent, a framework for dynamic long-range contextual modeling of gigapixel WSIs through the adaptive determination of patch relationships. The key idea is to maintain the modeling power of full self-attention while achieving computational efficiency through dynamic sparsification. The m... | Rebuttal 1:
Rebuttal: We thank the reviewer for these constructive comments. We answer the reviewer's questions in the following one by one:
`Experimental Designs or Analyses`
> However, the comparative methods delineated in the paper appear to diverge from the tasks ...
We appreciate the reviewer's concern regarding... | Summary: This paper introduces Querent, a query-aware dynamic modeling framework for analyzing whole-slide images in computational pathology. To address the computational inefficiency of standard transformer architectures, which struggle with the quadratic complexity of self-attention in large-scale WSI analysis, the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We respond to the comments one by one as follows:
`Weakness`
> The performance of the model depends on the quality of the region-level metadata ...
We acknowledge that any approach to summarizing region-level metadata, whether it be min/max, m... | Summary: To alleviate the self-attention o(n^2) complexity when modeling WSI, this paper introduces query-based lager-region pruning method to replace linear-attention and local-global attention mechanisms. By ignoring the irrelevant regions to current patches, all the computational cost between current patch and all p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. We respond with detailed interpretations as follows:
`Claims and Evidences`
> The claim in line 023-025 (abstract) 'the query-aware ...
We should clarify that Querent provides a theoretically bounded approximation of full self-attention rather tha... | null | null | null | null | null | null |
PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models | Accept (poster) | Summary: In this work, we proposed PolyConf, a pioneering tailored polymer conformation generation method that leverages hierarchical generative models to unlock new possibilities for this task. The authors decompose the polymer conformation into a series of local conformations generating these local conformations thro... | Rebuttal 1:
Rebuttal: Thanks for your comments. Below, we try to resolve your concerns one by one.
**W1: Clarification of model construction and contribution**
As shown in Figure 2, we design PolyConf as a hierarchical generative framework with a two-phase generating process.
As described in Section 3.2, in the fir... | Summary: Deep learning for polymer design is a severely underexplored area, and this work attempts to address two major challenges: the lack of methodology and the scarcity of high-quality data. In PolyConf, the authors generate polymers autoregressively, generating conformers for individual building blocks and linking... | Rebuttal 1:
Rebuttal: Thanks for your comments and suggestions. Below are our responses to your questions.
**Q1&Q5: Code availability and Validation of MD**
The code, data, and various scripts for our PolyConf and MD simulations are available in this anonymous link (https://anonymous.4open.science/r/PolyConf). It has... | Summary: This paper introduces PolyConf, a novel hierarchical generative framework for polymer conformation generation. Addressing the unique challenges of polymers—such as high flexibility, large chemical space, and lack of prior datasets—PolyConf decomposes the task into two phases: Repeating Unit Conformation Gener... | Rebuttal 1:
Rebuttal: Thanks for your comments. Below, we categorize and resolve your concerns.
**W1&Q1: Why not test on proteins?**
Our work focuses on polymers, aiming to explore polymer conformation generation (all-atom conformation).
Polymers present unique challenges compared to proteins, such as greater struc... | null | null | null | null | null | null | null | null |
Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation | Accept (poster) | Summary: The paper provides a theoretical analysis of why estimating a history-dependent behavior policy in off-policy evaluation (OPE) can reduce mean squared error (MSE). The authors derive a bias-variance decomposition for OPE estimators and show that history-dependent behavior policy estimation reduces variance at ... | Rebuttal 1:
Rebuttal: **Choice of History Length** Excellent comment. We fully agree that optimal selection of history length is crucial for applying our theory to practice. In response, we have **developed a method during the rebuttal, supported by promising simulation results**. Our approach is motivated by the bias... | Summary: The paper discusses a paradox in offline policy evaluation through importance sampling, where the performance of the target policy is estimated from a weighted average of the reward value by the ratio of the target policy and the behavioral policy. The paper suggests that the mean-squared error of the said est... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback regarding the proof of Lemma 1. We acknowledge that its reliance on Neyman orthogonality may not be immediately familiar to general audience. Apart from this concept, the proof employs standard techniques, including basic calculus such as Taylor expansions.... | Summary: The paper investigates the bias-variance trade-off and the MSE in IS-based off-policy evaluation. A first part of the paper investigates bandits and shows that context-conditioning and visitation-count based approximation of the behavior policy can reduce the MSE (even though the policy is context-independent)... | Rebuttal 1:
Rebuttal: Many thanks for your excellent comments and positive assessment of our paper. We will include these references, use the extra page to expand the related work and address all minor comments. In the following, we focus on clarifying your questions.
**Neyman Orthogonality**. This property enables u... | null | null | null | null | null | null | null | null |
ADDQ: Adaptive distributional double Q-learning | Accept (poster) | Summary: Based on double Q-learning, this paper proposes a set of theoretical and practical solutions to reduce the bias of Q value estimation. The paper conducts some experiments in Atari, mujoco and table environments. Some results have demonstrate their effectiveness.
## Update after rebuttal
During the rebuttal, t... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your careful reading and thougths on our article!
**Please check the anonymous repository https://anonymous.4open.science/r/ADDQ-B776 for figures addressing some of your thoughts.**
**Methods and evaluation criteria:**
* The metrics that you claimed to be ... | Summary: The authors propose ADDQ, a distributional reinforcement learning (DRL) algorithm which adaptively combines two RL algorithms to combat overestimation. Specifically, sample variances from DRL are used to adaptively balance the updating scheme of an algorithm with a tendency to overestimation (e.g. Q-Learning) ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your careful reading and thougths on our article!
**Please check the anonymous repository https://anonymous.4open.science/r/ADDQ-B776 for figures addressing some of your thoughts.**
**Bandit example:**
* We agree with your summary about the main contributio... | Summary: This paper proposes ADDQ, an adaptive distributional double Q-learning method that mitigates Q-value overestimation bias by locally adjusting update weights based on distributional uncertainty estimates. Built upon distributional RL frameworks (e.g., C51, QRDQN), ADDQ dynamically combines Q-learning and double... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your careful reading and thoughts on our article!
**Please check the anonymous repository https://anonymous.4open.science/r/ADDQ-B776 for figures addressing some of your thoughts.**
**Weaknesses:**
* **Ablation study**: We included a more comprehensive ab... | Summary: The paper introduces ADDQ (Adaptive Distributional Double Q-learning), a novel reinforcement learning (RL) algorithm that addresses the overestimation bias in Q-learning by leveraging distributional reinforcement learning (DRL). The key claim is that ADDQ provides a flexible and computationally efficient way t... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your careful reading and thougths on our article!
**Please check the anonymous repository https://anonymous.4open.science/r/ADDQ-B776 for figures addressing some of your thoughts.**
* You are totally right that the suggested **choice of $\beta$** is somewh... | null | null | null | null | null | null |
Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry | Accept (poster) | Summary: The authors investigate the problem of LTH masks not being compatible with random initializations. They find that by aligning the loss basins via a matching permutation, an LTH mask can be used with a random initialization, not associated with the mask.
This work shows that LTH masks can be reused with random ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the detailed feedback provided; we provide more details below:
1.
In our work, we used 2 trained dense models to find permutation (perm) mapping, as primary aim of the paper was to understand why winning tickets don't work with different random init.
One can also find ... | Summary: This paper hypothesizes that Iterative Magnitude Pruning (IMP) fails to generalize its sparse mask to other random initialization because the basin in which other random initialization resides does not match the basin constructed by the IMP sparse mask.
To address this, the authors propose to permute the IMP m... | Rebuttal 1:
Rebuttal: >The authors claim that the IMP sparse mask may not match the basin in which other random init resides. evidence for this claim is just analysis in Figure 1 with assumption of a single layer with two parameters case.
We validate our hypothesis through comprehensive experiments conducted across mu... | Summary: The paper studies the property of winning tickets, where the combination of the sparse mask and initial weight values determines eventual generalization performance. When these networks are trained using the same sparse mask but different weight initializations, their performance deteriorates. This is a well k... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback and helpful insights. We have added new experiments based on your insights/questions and added our response below:
> a natural question that arises is whether the permutation function can be obtained early in training eliminating the requirement for fully trai... | null | null | null | null | null | null | null | null |
A Online Statistical Framework for Out-of-Distribution Detection | Accept (poster) | Summary: The paper focuses on the out-of-distribution (OOD) detection task. Unlike previous research that primarily focuses on designing powerful score functions, this paper introduces a novel perspective by framing OOD detection as a online multiple hypothesis testing problem. The authors propose a Generalized LOND (g... | Rebuttal 1:
Rebuttal: __W1__. In Algorithm 1, the proposed method utilizes a calibrated set for hypothesis testing. I think a detailed explanation for it would be helpful.
__Ans-W1__. In practice, we just need to randomly sample a small number of examples from the ID training set as the calibrated set, without any spe... | Summary: this paper thinks the OOD detection task from an perspective of online multiple hypothesis testing.
the g-LOND algorithm controls false discovery rate (FDR) at pre-specified level without the consideration for the dependence between the p-values.
Along with thorecticla analysis, the empirical effectiveness o... | Rebuttal 1:
Rebuttal: __Q1__: about the reference.
__Ans-Q1__: Thank you for providing these meaningful references [a]-[i]. We will discuss these papers in related work section.
__W1,2__. the evaluiation on CIFAR-10 and ImageNet-1k lacks. the comparision with the mostly recent baselines [a,b,c,d,e,f,g,h,i] lacks.
_... | Summary: This paper studies the OOD detection task as an online multiple hypothesis testing problem. It presents a new algorithm, called the generalized LOND algorithm (g-LOND), built upon the well-known LOND algorithm. They provide theoretical results about the false discovery rate (FDR) and false positive rate (FPR) ... | Rebuttal 1:
Rebuttal: __Q1__. While the paper rigorously develops its theoretical framework, it would be beneficial to outline any underlying assumptions made in this paper.
__Ans-Q1__. In Theorem 4.5 and 4.6, we have no underlying assumptions. In Theorem 5.3, we just assume that the testing statistic follows generali... | Summary: This work investigates out-of-distribution (OOD) detection from the perspective of online multiple hypothesis testing. This paper proposes a generalized LOND algorithm that controls the false discovery rate even under dependent p-values. This work also derives the asymptotic false positive rate of the g-LOND a... | Rebuttal 1:
Rebuttal: __Q1__: about the reference.
__Ans-Q1__: Thank you for providing these meaningful references [1]-[3]. We will discuss these papers in related work section.
__Weakness 1__. The proposed method may be sensitive to both the size and quality of the calibration set.
__Ans-w1__. In practice, we jus... | null | null | null | null | null | null |
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain | Accept (spotlight poster) | Summary: This paper explores a novel method for adversarial purification by analyzing the impact of adversarial perturbations on images in the frequency domain. The authors propose a method that selectively preserves low-frequency components of images during the purification process to minimize damage to semantic info... | Rebuttal 1:
Rebuttal: Thank you for recognizing our paper. To the best of our knowledge, our paper is the first to improve the purification effect of diffusion models from the perspective of the frequency domain. Compared to pixel space, the frequency domain makes it easier to decouple the perturbed components from the... | Summary: The paper proposes FreqPure, a frequency-aware adversarial purification method that addresses the limitations of existing diffusion-based approaches by preserving critical semantic information during purification. Through frequency domain analysis, the authors demonstrate that adversarial perturbations disprop... | Rebuttal 1:
Rebuttal: We greatly appreciate your responsible and meticulous review. Your valuable feedback will improve our work greatly.
## Q1.1: Assumption of Theorem 3.2
This assumption is indeed somewhat strong, especially for the amplitude spectrum of low frequencies. Therefore, to eliminate this assumption, we re... | Summary: This study discovered that the damage caused by adversarial perturbations tends to increase monotonically with the rise in frequency. Nevertheless, existing purification efforts impact both low-frequency and high-frequency components. Based on this finding, this study retains the low-frequency information of t... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, which will enhance the completeness and persuasiveness of our article.
## Claims And Evidence
Figure 1 shows the extent of damage caused by adversarial perturbations to the phase spectrum and amplitude spectrum of images at different frequency components. ... | Summary: The paper focuses on adversarial defense methods, particularly addressing challenges in accurately and quickly calculating gradients, which is crucial for evaluating the effectiveness of defense mechanisms. The authors propose a method that significantly outperforms other approaches in terms of both standard a... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, which will enhance the integrity of our paper. To address your concerns, we have provided additional theoretical proofs and experiments. We sincerely hope our response resolves the concerns raised, and we would greatly appreciate reconsideration of the score.
... | Summary: The paper proposes a novel adversarial purification method called FreqPure through frequency domain analysis and theoretical proof. The core idea of the method is to provide effective prior guidance for image purification by selectively retaining low-frequency spectral information. Experimental results demonst... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable feedback. We have made every effort to address your concerns. We believe that investigating diffusion model-based adversarial purification from a frequency-domain perspective enables further research.
## Q1 Results for ASE+PSE without PSP
Thank you fo... | null | null | null | null |
Not All Wrong is Bad: Using Adversarial Examples for Unlearning | Accept (spotlight poster) | Summary: This paper proposes an algorithm for machine unlearning with an interesting finding.
The authors observe that fine-tuning models on adversarial examples closest to the corresponding forget samples can
avoid drastic changes to the global behavior of the model.
Experimental results on CIFAR-10 show promising ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We are excited about the reviewer’s acknowledgment of our interesting approach toward unlearning. Below are our responses to their questions and concerns:
**Theoretical guarantees:** Although most prior SOTA methods in approximate unlearning ar... | Summary: This paper proposes the Adversarial Machine UNlearning (AMUN) method, which reduces the prediction confidence of the model for the forget samples by fine-tuning the model on adversarial examples, while maintaining the accuracy of the model on test samples. Experimental results demonstrate that AMUN outperforms... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We are excited about the reviewer’s acknowledgment of our interesting approach toward unlearning. Below are our responses to their questions and concerns:
**W1. Additional experiments:** We have performed new experiments on VGG19 models (12 tim... | Summary: The paper proposes Adversarial Machine UNlearning (AMUN), a novel method for efficient machine unlearning in classification models. The core idea is to leverage adversarial examples corresponding to the forget set to fine-tune the model, thereby reducing its confidence on $D_F$ while preserving test accuracy. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We are excited about the reviewer’s acknowledgment of the novel connections our work makes with adversarial training and Lipschitz-constrained models, in addition to a more rigorous analysis by leveraging RMIA rather than MIA. Below are our resp... | Summary: This article introduces AMUN, an unlearning method that uses adversarial examples to remove the influence of specific training samples from a trained model while preserving overall model accuracy. The key insight behind AMUN is that fine-tuning a model on adversarially modified versions of the forget set (DF) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and insightful comments. We are excited about the reviewer’s acknowledgment of the uniqueness of our approach for unlearning. Below are our responses to their questions:
**Q1+S1+W1. Theoretical guarantees:** Although most prior SOTA methods in approximate ... | null | null | null | null | null | null |
TLLC: Transfer Learning-based Label Completion for Crowdsourcing | Accept (spotlight poster) | Summary: To complete the missing labels, this paper proposes a novel label completion method for crowdsourcing by utilizing transfer learning. All high-confidence instances from the original data are selected as the source domain, and a Siamese network is pretrained based on the instances coming from the source domain.... | Rebuttal 1:
Rebuttal: **Q1:** There appears to be an error in Equation (11). In the MSE loss, there should be a minus sign ‘-’ instead of a comma ‘,’. Additionally, the notation for $x_{i1}$ and $x_{i2}$ is somewhat confusing to me, as it could easily be interpreted as the values of two attributes for the same instance... | Summary: Existing worker modeling-based label completion methods have successfully improved the performance of label completion, but they remain constrained by the insufficient annotated instances per worker. To address this issue, this paper proposes a transfer learning-based label completion (TLLC) method. TLLC begin... | Rebuttal 1:
Rebuttal: **Q1:** This paper uses three algorithms to describe the construction of source and target domains, worker modeling, and label completion, respectively. However, how these three algorithms are combined to form the complete TLLC remains unclear. A framework diagram is needed to provide a complete i... | Summary: This paper at first reveals the limitations of existing methods that leverage worker modeling to improve label completion for Crowdsourcing and then proposes a novel transfer learning-based label completion (TLLC) method, which introduces transfer learning to avoid insufficient worker modeling and leverages th... | Rebuttal 1:
Rebuttal: **Q1:** The proposed TLLC transfers the pretrained Siamese network to the target domain. In the paper, the authors just said: “Specifically, we set up both and as Siamese networks with the same structure (Li et al., 2022).” What are the detailed network structure and parameter settings?
**Author ... | Summary: The paper proposes a Transfer Learning-based Label Completion (TLLC) method for crowdsourcing scenarios. The authors address the issue of sparse label matrices, where individual workers annotate only a few instances, leading to insufficient worker modeling and poor label completion. The key idea of TLLC is to ... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments. Please find our detailed responses to your concerns as follows.
**Author Response to Contributions:** This paper is the first to identify and address the limitation from insufficient worker modeling. Moreover, based on our review of related work, this paper is also... | null | null | null | null | null | null |
A standard transformer and attention with linear biases for molecular conformer generation | Reject | Summary: In this work, the authors introduce a transformer architecture with a new positional encoding scheme and training method for molecular conformer generation (MCG), achieving comparable performance as MCF, a prevailing non-equivariant MCG architecture, using a fraction of MCF's number of parameters. The proposed... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for the insightful comments. We have performed additional analyses, which we hope will address the reviewer's concerns.
> *"...no analysis of the training efficiency of the proposed method versus baselines"*
Training time should be roughly proportional to single step in... | Summary: This paper presents "S23D", a molecular conformer generation approach based on a standard transformer augmented with linear attention biases (similar to ALiBi). Unlike specialized equivariant models, S23D relies on graph-relative positional encodings and achieves competitive results at smaller parameter counts... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive critique. We made additional analyses including ablation experiments that will be added to the manuscript to address the comments.
We made 3 runs with ablation on PE for the S23D-S-1/9 (S) model using 1-stage training (with hydrogen) for 100 ... | Summary: The paper introduces a novel relative positional encoding technique similar to the ALiBi technique found in NLP for non-equivariant Transformer diffusion models for generating molecular conformations. The proposed approach allows scaling down non-equivariant Transformer, which typically requires a large model ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing the manuscript and providing valuable comments. We have performed additional analyses, described below, in light of these comments, and hope that they alleviate the reviewer's concerns.
> *"I would be convinced by a comparison of the performance of two identi... | Summary: In this paper the authors propose a new method for sampled-based molecular conformer generation that is built on top of a non-equivariant model. They show that unlike prior methods, with the right modifications to the architecture and the right training procedures, one can train non-equivariant architectures t... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments. To address the comments, we made additional analyses and hope our response will alleviate the reviewer’s concerns.
> *"I'd argue that the evidence here is quite limited, and is in fact, limited to 2 data points (8.6M and 25M parameters). While this is... | null | null | null | null | null | null |
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning | Accept (poster) | Summary: The paper introduces OpenworldAUC, a unified evaluation metric for open-world prompt
tuning (OPT) that jointly assesses base-to-new detection (P1), domain-specific classification
(P2), and insensitivity to domain distribution (P3). To optimize OpenworldAUC, the authors
propose Gated Mixture-of-Prompts (GMoP), ... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in providing us with such constructive comments. We would like to respond to them as follows:
> Q1: Generalization to truly unseen domains, e.g., cross-dataset evaluation.
Following your insightful suggestion, we extend our evaluation to investigate the ... | Summary: This paper explore a new evaluation metric openworldauc for the practical open-world prompt tuning (OPT) task, which jointly measure the inter-domain detection and intra-domain classification performance and remains insensitive towards the varying data distributions. Further, the mixture-of-prompt learning fra... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in providing us with such constructive comments. We would like to respond to them as follows:
> **Q1:** More detailed explanation of the challenges associated with generalization analysis.
- Our main theoretical findings focus on how well our optimizatio... | Summary: This paper addresses the open-world prompt tuning problem and uncovers the fundamental limitations of current evaluation metrics in this field. To tackle this challenge, the authors propose a novel, unified metric, OpenWorldAUC, which jointly evaluates the model’s detection and classification performance witho... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Q1:** The necessity of introducing the Gated Mixture-of-Prompts remains unclear. A more detailed explanation is needed to justify this design choice.
The three components $g$, $h$, and $r$ have **conflic... | Summary: Since existing evaluation metrics cannot comprehensively assess performance in open-world prompt tuning, this paper proposes a unified evaluation metric called OpenworldAUC. This metric not only measures the detection capability of base/new samples (P1) and classification accuracy (P2), but also ensures robust... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! Due to space constraints, we include full tables and figures https://anonymous.4open.science/r/R-4D06. References to Tab.X, Fig.X correspond to those provided in this link.
> Q1: OpenworldAUC for fine-grained model capability analysis
The OpenworldAUC comprehe... | null | null | null | null | null | null |
Representative Language Generation | Accept (poster) | Summary: This paper introduces a theoretical framework to characterize generative models’ capacities/abilities to produce samples that reflect the diversity seen in the data whose distribution the model is trying to approximate. Such characterizations are of interest to the machine learning community as they let us qua... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We address the reviewer's concerns below.
> There are several works on generative model evaluation that use notions of precision and recall (e.g, Sajjadi et. al. 2018, Kynkäänniemi et. al. 2018) in the attempt to assess whether models capture the... | Summary: The paper defines a new property of generators called “representative generatability” to provide a theoretical framework for comparing a generator's representation (occurrence) of distinct groups present in the training distribution with the representation in the distribution of output sequences. The contribut... | Rebuttal 1:
Rebuttal: > Re: Practical applicability of results
Thank you for your question. While we maintain that this is primarily a theoretical work, we've addressed how our research may or may not relate to practical applications in our global comment to all reviewers above. We will plan to incorporate more of thi... | Summary: This work introduces the concept of "Representative Generation" and its variants, aiming to provide a theoretical framework that characterizes generators (i.e. generative models) such that, when the training data distribution consists of multiple groups of interest, the generator outputs closely approximate th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> Could you use this example to help clarify, for a non-expert like me, how is "representative generation" different from "generation in the limit" and how to interpret the negative result (Lemma 4.9) proved in this work?
In the original notion of "gene... | Summary: This paper extends the study of the recent model of language generation introduced by Kleinberg and Mullainathan in a 2024 paper. In this model, this paper defines a notion of representative generation where the generator is roughly speaking required to assign similar probability masses to each group A (from a... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding that our paper studies an interesting and fundamental learning theoretic model for generation. We address the reviewer's comments/questions below.
> Finite Support Size Assumption
We can certainly add some examples to clarify the definition. We'll highlight a f... | null | null | null | null | null | null |
ParallelComp: Parallel Long-Context Compressor for Length Extrapolation | Accept (poster) | Summary: ParallelComp processes the input sequence chunk by chunk independently. Therefore, they can parallel process the attention matrix, except for the last chunk of attention.
Claims And Evidence: No, the supporting evidence is not clear and not well connected to their claims.
Methods And Evaluation Criteria: No,... | Rebuttal 1:
Rebuttal: Thank for your suggestions!
Q1：**Key differences**
|Method|NARR|QAS|MUlT|HOPT|2WKI|MUS|GOV|QMS|NEWS|TREC|TRIV|SSM|PCNNT|PREN|LCC|REP|AVG|
|------|----|---|----|----|----|---|---|---|----|----|----|---|-----|----|---|---|----|
|APE|23.63|39.11|50.06|49.47|43.70|25.99|27.78|22.79|11.22|43.50|90.... | Summary: This paper introduces ParallelComp, which the authors claim to be able to extend the context window of off-the-shelf LLMs from 4K to 128K tokens while maintaining computational efficiency. The authors address the critical challenge of attention sink phenomena in parallel attention mechanisms, where models disp... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your fruitful suggestions, and for all your questions below!
Q1: **The authors conduct a thorough comparison against existing length extrapolation methods for tasks such as single/multi-document QA. However, these tasks could potentially benefi... | Summary: This paper proposes a method for training-free length extrapolation of LLM i.e. extending an LLM to process sequence longer than the sequence length it is pretrained on. The key idea is to split the input into chunks that fit in the LLM's context window and perform global attention over the chunks. While previ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your fruitful suggestions, and for all your questions below!
Q1: **Minimal Performance Difference Between "Ours" and Other Methods in Token Eviction Settings.**
- "Ours" implements the eviction scheme for the entire chunk's KV cache.
- "Ours-co... | Summary: Motivation is also for better generalization on long seqs. And to use existing LLMs and extend their attention context size. And for more efficient attention on long seqs.
The proposed method does not need any finetuning. Any existing LLM can be used, with the attention mechanism and caching adapted.
The pro... | Rebuttal 1:
Rebuttal: Thank for your suggestions!
Q1:**For general inquiries such as typos and others.**
- We promise to release our github and will correct typo error in future paper version.
- Regarding citation issues, the NTK-by-parts position interpolation method was not proposed in a formal paper. It was also... | null | null | null | null | null | null |
RBench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation | Accept (poster) | Summary: This paper proposes a new benchmark called R-Bench, with features of Comprehensiveness, Difficulty, Multimodality and Multilingualism.
The paper also conducted various experiments on current mainstream LLMs and MLLMs using R-Bench.
Claims And Evidence: Yes. All claims are supported by clear and convincing evi... | Rebuttal 1:
Rebuttal: Thank you for the selfless efforts and constructive comments for improving the quality of this work. The followings are detailed response to your concerns.
### Q1:
The paper doesn't fully explore whether R-Bench needs more reasoning abilities than current benchmark.
### A1:
Evaluating the reas... | Summary: This paper proposes R‑Bench, a benchmark designed to evaluate complex reasoning in language and multimodal models. The dataset spans a wide range of subjects and includes more than 1,000 text-based and 665 multimodal questions. The questions are carefully selected and filtered to ensure that they require deep ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which are crucial to improving the quality of this work. The followings are detailed response to your concerns.
### Q1:
In the conclusion, authors claim R-Bench achieves competition-level difficulty, but this is not fully supported by the evidence provided... | Summary: This work proposes R-Bench, a graduate-level multidisciplinary, multilingual benchmark for both LLM and MLLM reasoning evaluation, which has coverage similar to MMLU and MMMU while reaching the difficulty of mathematical competitions such as AIME@2024. The authors evaluate multiple closed-source and open-sourc... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which are crucial to improving the quality of this work. In addition, thank you for your positive evaluation of our work, which has been a great source of encouragement for us. The followings are detailed response to your concerns.
### Q1:
The related work... | Summary: This paper introduces R-Bench, a new benchmark designed to evaluate complex reasoning capabilities in both LLMs and MLLMs. The benchmark contains questions in two languages: English and Chinese. There are 1,094 questions in 108 subjects
for textual evaluation and 665 questions in 83 subjects for multimodal eva... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which are crucial to improving the quality of this work. The followings are detailed response to your concerns.
### Q1:
Despite the broad subject coverage, the total number of collected questions, 1,094 for textual and 665 for multimodal, is relatively lim... | null | null | null | null | null | null |
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization | Accept (poster) | Summary: This work studies last-iterate guarantees under different shuffling models. In the RR model, the authors establish last-iterate (and suffix-averaging) guarantees, similar to the iterate-averaging results of (Koren et al., 2022), with slightly improved rates in the SS model. The obtained guarantees generally ma... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback. We will answer the reviewer's question below.
**Question.** Thanks for the deep question. We will discuss it from the following two perspectives.
- As mentioned in our Subsection 1.2 (see Lines 122-142, right column), the lower bound in [1] can... | Summary: This paper studies the Last iterate convergence of proximal gradient methods for non-smooth (strongly) convex optimisation problem, with Random Reshuffle (RR) and Single Shuffle (SS) strategies. The paper considers the General Proximal Gradient Method, where proximal step is implemented at every step which is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments. We will answer the reviewer's questions below.
**Q1.** Our analysis is still inspired by and related to [1] but with some necessary changes to fit the shuffling method. However, whether the framework of [2] can be used to simplify the proof is uncl... | Summary: - The paper investigates the convergence rates of shuffling SGD for nonsmooth (strongly) convex function. While the convergence behavior of shuffling SGD under smoothness assumption has been widely studied in recent literature, its investigation under Lipschitz continuity remains relatively less explored. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We would like to answer the reviewer's questions below.
**Typos&Suggestions.** Thanks for carefully reading and the helpful comments. We have corrected/modified them accordingly.
**Q1.** Thanks for the interesting question. Honestly, we do not hav... | Summary: This paper studies shuffling-based variants of proximal SGD on nonsmooth convex optimization, where proximal SGD steps are taken using indices following randomly sampled or arbitrary permutations. Prior works on shuffling-based SGD mostly focus on smooth cases, and this paper tackles the more difficult nonsmoo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the endorsement of our work. We would like to address the reviewer's concerns below.
**W1&Q2.** To make the bounds for SS decrease to $0$, it indeed needs some other technique in addition to the analysis sketched in Section 5 (as mentioned at the end of the same section)... | null | null | null | null | null | null |
DCBM: Data-Efficient Visual Concept Bottleneck Models | Accept (poster) | Summary: This paper proposed a data-efficient Concept Bottleneck Model (DCBM) that enables concept generation while maintaining interpretability with minimal training samples. DCBM defines concepts as image regions using segmentation and object detection models, eliminating the reliance on textual descriptions or large... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for recognizing the flexible and interpretable design of DCBM. We highly appreciate your helpful comments and hope to provide the missing details in our answers below.
## Efficiency
We derive concept proposals based on 50 samples per class and train the CB... | Summary: The paper proposes a Data-efficient CBM (DCBM) that enhances interpretability while reducing the reliance on large datasets. Specifically, DCBM defines concepts as image regions detected through segmentation and object detection foundation models, rather than relying on textual descriptions. This allows DCBM t... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work. We appreciate your positive feedback on our concept extraction pipeline that leverages foundation models, and we’re pleased that you recognize the interpretability and localization capabilities of our DCBM.
## Data efficiency in real-world setting
DCBM is designe... | Summary: The paper proposes a novel framework to enhance the practicality of concept bottleneck models (CBMs) by reducing their reliance on extensive labeled concept data. DCBM decouples concept learning from task adaptation through self-supervised pretraining (e.g., using vision-language models like CLIP) to autonomou... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We appreciate your recognition of our framework’s originality and data efficiency—requiring 10x fewer concept labels than comparable CBM approaches while achieving similar performance. Below, we provide detailed responses organized by the ke... | Summary: The paper introduces Data-Efficient Visual Concept Bottleneck Models (DCBMs), which generate interpretable visual concepts using segmentation and detection foundation models, enabling Concept Bottleneck Models (CBMs) to work effectively with limited data. By clustering image regions into concepts without relyi... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive feedback. We appreciate your acknowledgment of DCBM's strengths being simple, adaptable, and avoiding large-scale pre-training. Below, we outline our responses structured by the key points you raised:
---
## Performance on Large-Scale Datasets
In our... | null | null | null | null | null | null |
EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification | Accept (poster) | Summary: This paper introduces a unified framework that jointly considers graph-level and parameter-level sparsification. It incorporates dual experts and consensus-based sparsification to ensure a stable sparsification process. Extensive experiments demonstrate that the proposed method is effective, efficient, and eco... | Rebuttal 1:
Rebuttal: # Dear Reviewer h7Za
We sincerely thank you for your insightful feedback and have provided detailed responses to your questions.
> ` W1: Without quantitative analysis of heterogeneity reduction.`
We provide a quantitative analysis of heterogeneity reduction at [this link](https://anonymous.4ope... | Summary: The paper introduces EAGLES, a unified sparsification framework designed to enhance FGL by addressing computational and communication challenges. EAGLES optimizes both graph structures and model parameters through client-consensus parameter sparsification, which generates multiple unbiased subnetworks at vario... | Rebuttal 1:
Rebuttal: # Dear Reviewer Fguv
We sincerely thank you for taking the time to evaluate our work and have adressed your concerns as follows:
> ` W1: Additional empirical studies addressing the significant computational challenges faced by FGL will further strengthen this analysis.`
In the theoretical model... | Summary: EAGLES introduces a framework designed to reduce computational and communication costs in federated graph learning by jointly sparsifying both model parameters and graph structures. It employs client-consensus pruning to generate subnetworks at different sparsity levels and utilizes a mixture of experts for gr... | Rebuttal 1:
Rebuttal: # Dear Reviewer jagF
We sincerely appreciate your detailed review and invaluable feedback. In the response below, we provide a thorough reply to address your concerns and offer a clearer explanation of our method.
> ` W1: what $W_{gate}$ refers to in eq (12) lacks the necessary explanation in th... | Summary: This work introduces EAGLES, a framework for Federated Graph Learning (FGL) that reduces computational demands while maintaining high performance. By unifying graph and model sparsification, it simplifies graph structures and prunes model parameters efficiently. EAGLES uses multi-criteria experts to sparsify g... | Rebuttal 1:
Rebuttal: We sincerely appreciate for taking the time to review our manuscript and hope our response will address your concerns and contribute to an improved score.
> ` W1: How the GSyE processes and combines the outputs of various sparsification experts.`
In our method, GSyE (Graph Synergy Expert) is use... | null | null | null | null | null | null |
X-Hacking: The Threat of Misguided AutoML | Accept (poster) | Summary: The paper introduces the concept of X-Hacking, which refers to the practice of deliberately exploiting model multiplicity, where different ML models can have comparable performance, to select a model that has certain desirable explainability characteristics (like SHAP). The paper demonstrates the ability to se... | Rebuttal 1:
Rebuttal: We thank the reviewer for their strong approval of the paper and the valuable feedback. Below we address the questions raised by the reviewer.
“**Selecting a model because it uses some characteristics rather than others (as accurately represented by SHAP) is not inherently problematic...**”:
C... | Summary: In this work the authors propose the concept of “X-hacking,” where scientists or ML service providers leverage the multiplicity of ML systems to provide misleading explanations despite maintaining performance.
Claims And Evidence: The authors claim the potential for X-hacking in ML systems, especially AutoML ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and supportive comments. Furthermore, we will incorporate your suggestions to further strengthen the exposition and clarify details around our adversarial concepts, notation, and experimental setups. Specifically:
We agree that our discussions of adversa... | Summary: The paper notes that AutoML pipelines allow training of multiple ML models, including sets of models with 'defensible' performance.
Thus, AutoML tools make it easier for authors to cherry-pick models to fit preconceived notions, as embodied by explainability metrics. The paper uses the Shapley value as its r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. The following addresses the reviewer’s questions and concerns.
**p-hacking and X-hacking**: Both p-hacking and X-hacking can arise from scale, chance and collinearity, which are interrelated rather than distinct. For instance, collinearity can c... | Summary: The paper introduces X-hacking that manipulates XAI metrics by exploiting model multiplicity to find explanations supporting desired conclusions. Bayesian optimization helps to find models that support a desired explanation while maintaining acceptable accuracy, allowing for manipulation of SHAP values. Datas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and valuable references.
We agree that the phenomenon of having many equally accurate, yet interpretively distinct, models is well established, and we cite several related papers (e.g., Fisher et al., 2019; Brunet et al., 2022) to acknowledge ... | null | null | null | null | null | null |
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