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Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition | Accept (poster) | Summary: This paper studies a principal-agent problem, where the principal aims at learning the optimal scoring rule for that problem. Notably, it proposes pure exploration algorithms in both settings of fixed budget and fixed confidence
Claims And Evidence: See below
Methods And Evaluation Criteria: See below
Theor... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, which has greatly contributed to improving our work.
**Q1:** My main concern about this work is about the (over)complexity of the setting ... help in that direction.
**A1:** Thank you for your insightful comment. We understand the concern about complexity, a... | Summary: The paper studies how to learn scoring rules within the principal-agent problem for information acquisition. In particular, the authors study how to identify approximately optimal scoring rules with high probability through interaction with the environment. They design two algorithms depending on whether the s... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments and questions, which have helped us improve the quality of our work.
**Q1:** Why are the algorithms of Chen et al. and Cacciamani et al. suboptimal for sample complexity problems?
**A1:** Thank you for the question.
The primary reason for the... | Summary: The paper addresses the Best Scoring Rule Identification problem, proposing two algorithms: OIAFC (Online Information Acquisition Fixed Confidence) and OIAFB (Online Information Acquisition Fixed Budget). These algorithms aim to identify the optimal scoring rule through online learning. The key contributions a... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments and questions.
**About the references:**
Thank you for pointing out these relevant references. We will include the three suggested papers in our revised manuscript. We greatly appreciate your helpful suggestion.
**About the Experiments:**
Thank you for ... | Summary: This work considers optimal scoring rules for principal/agent problems in online settings, in two variants: with a fixed budget, or fixed confidence, where the principal is trying to make investment decisions based on the knowledge/actions of the agent. The utility of the principal is driven by the quality of ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and importance of our work. We truly appreciate your encouraging feedback. | null | null | null | null | null | null |
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization | Accept (poster) | Summary: The paper considers the problem of constructing efficient denoisers that map noisy samples from a manifold to the manifold. An online learning approach is used to construct a graph for optimization, and a mixed-order method is used to aid optimization in order to achieve good performances. Theoretical analyses... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s positive feedback and insightful suggestions! We are delighted that the reviewer found our proposed method satisfactory and valued both the supporting experimental results (particularly the strong performance compared to the natural baseline of nearest neighbor ... | Summary: This paper addresses the problem of efficiently denoising new noisy data sampled from an unknown manifold M, relying only on noisy samples. To this end, a framework for test-time efficient manifold denoising was proposed. In the theoretical analyses, the optimality of the proposed methods was elucidated. In th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our writing and theoretical analysis. At the core of our work is a novel, efficient and accurate algorithm for manifold denoising, which reframes learning-to-denoise as learning-to-optimize. Our key technical innovations includes an accurate and effi... | Summary: This paper proposes a new framework for efficient denoising of noisy data sampled from an unknown manifold, which treats "learning to denoise" as "learning to optimize." The key innovations include: 1) online learning that learns to optimize clean signals using only noisy data and improve the optimizer on the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and thoughtful questions! We're glad the core idea of learning-to-optimize for denoising, along with our mixed-order method and scalable online learning approach, was well received. We also appreciate the recognition of our experimental and theoretic... | Summary: This paper studies the interesting manifold denoising problem (ref. 1) with the focus “learning-to-optimize” and proposes test-time efficient denoising algorithm. Also mixed-order optimization is proposed to help achieve near optimal results, given first-order gradient only optimization is more efficient.
St... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable feedback to improve our work. We are pleased that the reviewer finds the manifold denoising problem interesting, as well as our idea of rethinking learning-to-denoise as learning-to-optimize, and our accurate and efficient mixed-order method. We are ... | null | null | null | null | null | null |
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations | Accept (poster) | Summary: This paper addresses the challenge of checking equivalence among modeling formulations. The authors introduce quasi-Karp as an equivalence criterion for determining model equivalence. The primary concept of this criterion involves verifying the existence of "equivalence mapping" (quasi-Karp) between models. Th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed feedback and for acknowledging the importance of our work:
> "This work tries to address an important challenge in modeling equivalence checking, where previous works have not provided sufficiently satisfying solutions."
and for recognizing the s... | Summary: The submission proposed a new method for determining whether two optimization problem formulations are equivalent. The authors introduce Quasi-Karp Equivalence, based on Karp reductions, and propose EquivaMap, a framework that utilizes LLMs to identify mappings between decision variables of different formulati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed feedback and for acknowledging the contribution of our work:
> "The introduction of Quasi-Karp Equivalence is an important conceptual contribution."
>” The EquivaFormulation dataset is a valuable resource for future research.”
>” The work is tim... | Summary: This paper proposes a new method to assess whether two mathematical MILP formulations of a combinatorial optimization problem are equivalent. To this end, it proposes a formal notion of equivalence inspired by standard Karp reductions, creates a new dataset called EquivaFormulation for assessment, and compares... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's recognition of both the conceptual novelty of quasi-Karp equivalence and the empirical strength of EquivaMap on the benchmarked transformations. Below, we address the reviewer’s questions individually.
---
>**C1**: “It's not clear whether EquivaMap will also wo... | Summary: The paper introduces EquivaMap, an LLM-based framework for automatically verifying equivalence between combinatorial optimization formulations (MILP). It defines a new theoretical criterion called quasi-Karp equivalence, enabling robust equivalence checks even under transformations like variable scaling or aux... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive and thoughtful review, and for appreciating the design of our experiments. We also appreciate your suggestion to include a runtime discussion of the EquivaMap algorithm.
Compared to execution accuracy, EquivaMap does not require additional MILP so... | Summary: This paper discusses how LLMs (+ NLP) can help with automatic checking of equivalences in the context of combinatorial optimization reductions.
Claims And Evidence: Yes, the experiments are convincing.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: N/A.
Experimental Designs Or Analyses: Yes, the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive and thoughtful comments, and for recognizing the novelty of our work:
> “The paper gives a novel connection between computational complexity and LLMs.”
Below, we address the specific questions and suggestions:
---
> **Q1**: "Since you are me... | null | null | null | null |
Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders | Accept (poster) | Summary: The authors propose to challenge the assumption of independence between unimodal experts in computing the joint posterior in multimodal VAEs. Therefore they propose the CoDE-VAE that uses a Bayesian approach to compute the joint posterior between unimodal experts, modelling the dependence between them. Experim... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful and detailed comments. We will respond to your concerns and questions point by point.
## Confusion about the concept of sub-sampling modalities
Thank you for pointing this out. In the paper, we use the concept of sub-sampling to refer to ELBO sub-sampling and to the ... | Summary: The paper introduces a new method for aggregating multimodal expert distributions in Variational Autoencoders (VAEs) by incorporating the dependence between experts, which has traditionally been ignored in models like the product of experts (PoE) and mixture of experts (MoE). This method, called Consensus of ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed comments. We will address your concerns and questions point by point.
## Experiments and analysis on edge cases
We agree that analyzing CoDE-VAE on edge cases helps to understand its behavior, and makes our research more robust. We believe that the exper... | Summary: This paper introduces the Consensus of Dependent Experts (CoDE) in the context of multimodal learning with Variational Autoencoders (VAEs). Current approaches for this task, such as: (i) the product of experts; or (ii) the mixture of experts assume cross-modal independence which is restrictive. Towards this en... | Rebuttal 1:
Rebuttal: Thank you for your careful and comprehensive comments. We will address your concerns and questions, point by point:
## How accurately is ELBO minimized:
We are not completely sure if we follow your concern. If the comment refers to how the ELBO is maximized (the loss), we train the CoDE-VAE mode... | null | null | null | null | null | null | null | null |
Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective | Accept (poster) | Summary: The paper proposes PromptQuine, an automated prompt optimization strategy that prunes a given prompt using evolutionary search to improve the performance at a given task. The method outperforms existing methods when validated on classification, multi-choice question answering and reasoning datasets across a wi... | Rebuttal 1:
Rebuttal: We greatly appreciate Reviewer e5jB for suggestions on our experimental setups. We are delighted that you acknowledge the **richness in detail** of our study. We address your concerns below:
Anonymous link (AL) for several tables: https://anonymous.4open.science/r/ughj/e5jB/README.md
>Recap: Clai... | Summary: The paper introduces a novel prompt design paradigm that challenges the conventional approach of using well-crafted natural language prompts for large language models. The authors demonstrate that pruning random demonstrations into seemingly incoherent "gibberish" can significantly improve performance across a... | Rebuttal 1:
Rebuttal: We are *very grateful* to Reviewer 8j6A for their highly positive comments on our paper. Thanks for acknowledging our efforts on making this paper *empirically rigor* and the **Significance, Creativity and Originality** of this work towards general *prompt tuning and ICL*. We will **make every eff... | Summary: The paper applies evolutionary algorithms to the paradigm of LLM prompt pruning, introducing a new algorithm called PromptQuine. PromptQuine autonomously searches for better pruning strategies through an iterative process of mutation and selection, inspired by biological self-replication and evolutionary dynam... | Rebuttal 1:
Rebuttal: We want to express our sincere thanks to Reviewer KRwQ for their detailed tips to improve our writing. **This is invaluable, and we do learn a lot.** We are also grateful for viewing our work as **significant contribution** by presenting *interesting results* to the community. Here is the Table/Fi... | Summary: This paper introduces an evolutionary method called PromptQuine for optimizing few-shot prompts by pruning them. They show that their optimized prompts outperform the original few-shot prompts as well as the RLPrompt baseline on a held-out set of test examples across a wide range of standard language model ben... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer KfU1 for **extremely detailed review** on many tech details. We are glad that the reviewer appreciates the *contributions of our insights towards prompting and interpretability*. We address your questions below:
Anonymous link (AL) for tables/data: https://anonymo... | null | null | null | null | null | null |
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models | Accept (poster) | Summary: This work investigates the extent to which task-specific fine-tuning can help VLMs to overcome limitations in two psychologically inspired domains, with a special emphasis on the extent to which benefits of fine-tuning generalize between tasks and across variations within a task.
Claims And Evidence: The expe... | Rebuttal 1:
Rebuttal: Dear reviewer 3bXm, thank you very much for your comments. We are happy to hear that our experiments “very clearly demonstrate that task-specific fine-tuning leads to brittle improvements”, this was the main point we were trying to convey. We also appreciate your assessment that our “methods and e... | Summary: This paper investigates whether fine-tuning models on intuitive physics and causal reasoning improves performance within these specific domains. The authors conclude that such fine-tuning does not enhance performance on other visual characteristics or tasks in different cognitive domains.
Claims And Evidence:... | Rebuttal 1:
Rebuttal: Dear reviewer uVR9, thank you very much for your thorough review. We appreciate that you find our topic “intriguing” and our experimental designs “sound and valid”. In the following we discuss how we have remedied the concerns that you have raised. Your comments have, in our view, considerably str... | Summary: This is an interesting piece of work that seems to be among the first to investigate the following: fine-tuning is a widespread approach to improving LLM performance in the text domain, but for domains such as intuitive physics and causal reasoning, which are not text-related and not really purely visual capab... | Rebuttal 1:
Rebuttal: Dear reviewer mM6z, thank you very much for your feedback. We appreciate that you think our work is interesting and that the “designs and analyses were generally sound”. Furthermore, we are glad to hear that you agree that our work “does shed some light on the extent to which [vision finetuning] w... | Summary: This paper explores the limitations of Vision-Language Models (VLMs) in causal understanding of the physical world — a problem that is quite interesting to the community. The authors examine the potential of fine-tuning (FT) as a method to improve performance on intuitive physics and causal reasoning tasks. Th... | Rebuttal 1:
Rebuttal: Dear reviewer fjkm, thank you very much for your comments. We appreciate that you think the findings are “very interesting” and that you agree that our “method and evaluation criteria make sense”. In the following we discuss the specific concerns that you raised and how we have sought to remedy th... | null | null | null | null | null | null |
Rethinking the Bias of Foundation Model under Long-tailed Distribution | Accept (poster) | Summary: This paper addresses the challenge of learning on long-tail data (and the bias of foundation models). The author defines the imbalance problem as parameter imbalance and data imbalance. They propose a backdoor adjustment method to address the imbalance problem. Experiments conducted on different long-tailed da... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. Below, we summarize your points in quotes, followed by our corresponding replies.
> How to formulate incomplete semantic factors and the question of their maximum number.
The incomplete semantic factor ($C$) represents the semantic region in the image that ... | Summary: This paper examines the inherent biases introduced by the imbalanced training data used to pre-train foundation models, and how these biases affect downstream long-tailed learning tasks. The authors find that during fine-tuning, parameter imbalance (the imbalance in the pre-trained model parameters) plays a mo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback. In the following, your questions are summarized in quotes, followed by our point-by-point responses.
> Could the improvement be purely due to randomness?
To ensure that the observed performance improvement is attributable to the advantages of our... | Summary: This paper studies the impact of foundation models' biases—trained on disproportionately distributed data—on downstream tasks with imbalanced labels. The authors characterize two types of imbalance: (1) parameter imbalance, which has its roots in the pre-training stage, and (2) data imbalance, which exists in ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and acknowledgement of our work! In the following, we summarize a series of works on Invariant Risk Minimization (IRM) to reduce bias and briefly introduce the differences between our work and existing studies.
Invariant Risk Minimization (IRM) aims to enhance... | null | null | null | null | null | null | null | null |
Black-Box Adversarial Attacks on LLM-Based Code Completion | Accept (poster) | Summary: The authors propose INSEC, a black-box attack that craft a universal perturbation attached to code that, once submitted to LLMs, they include unsafe functionalities that can be later exploited by an attacker.
This universal perturbation is computed on a training set, and it is generated through a n heuristic-b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall positive review and discuss their raised questions below. We will gladly incorporate their feedback into the next revision of the paper.
### **Q1: Do you expect the results to change with different static analyzers? Is using CodeQL an important choice for t... | Summary: The paper introduces INSEC, a novel black-box adversarial attack that manipulates LLM-based code completion engines to generate vulnerable code while maintaining functional correctness. The attack works by inserting a specially crafted comment string before the completion cursor, which is derived through a que... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful remarks. We briefly answer the raised questions below and will incorporate all feedback into our next revision.
### **Q1: How does your work differ from previous work that attacks Code LLMs through perturbed inputs?**
We thank the reviewer for their ref... | Summary: The authors propose INSEC, a black box attack on code infilling models via adding comments right before the location of code completion that contain an adversarially optimized string. The goal of the attack is to produce functioning code that contains security vulnerabilities. The adversarial comment is initia... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's critical assessment and answer their questions below. We will incorporate all feedback.
### **Q1: Can you adjust your use of certain adjectives such as "realistic" and "practical"?**
Yes. We thank the reviewer for pointing this out! We will revise the paper to ... | Summary: The paper introduces INSEC, a black-box attack on LLM code completion, to bias these LLMs to generate insecure code at a higher rate. The attack works by injecting an attack string as a short comment into the completion input, with the comment created through a query-based optimization procedure. INSEC was eva... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and discuss their questions below. We will incorporate their feedback into the next revision of the paper.
### **Q1: Can you please clarify the evaluation setting?**
We highlight that we have two separate datasets for the evaluation of vulnerab... | null | null | null | null | null | null |
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks | Accept (oral) | Summary: The paper introduces IT-Bench, a specialized benchmarking framework designed to evaluate AI agents on real-world IT automation tasks across three key domains: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). Built from 94 scenarios derived from a... | Rebuttal 1:
Rebuttal: **Q1. Can IT-Bench incorporate resilience testing (e.g., telemetry noise) to better simulate production unpredictability?**
> Yes! We already incorporate a few resilience testing tools like Chaos Mesh in ITBench, which can be used to evaluate agentic technologies under different resilience testin... | Summary: This paper is a benchmark paper. It evaluates the recent LLM agent systems in three IT domains: (1) Site Reliability Engineering (SRE),
(2) Compliance and Security Operations (CISO), and (3) Financial Operations (FinOps). The main contribution of this paper is preparing three benchmarks and thoroughly evaluati... | Rebuttal 1:
Rebuttal: **Q1. unique challenge of IT tasks compared with the broader SWE tasks**
> IT tasks are more diverse than SWE. Consider SRE, closer to SWE than FinOps/CISO. SRE involves distributed systems (multi-machine, full stack: app, platform, OS, hardware & their integrations). SWE focuses on single program... | Summary: This paper presents IT-Bench, a framework that benchmarks AI agents for IT automation across roles including Site Reliability Engineering, Compliance and Security Operations and Financial Operations. It offers 94 real-world scenarios with automated, partial scoring evaluation and a leaderboard to ensure reprod... | Rebuttal 1:
Rebuttal: **Q1.1 Complexity and infrastructure demands may hinder accessibility**
>The framework’s complexity is abstracted from the agent interface, which is designed for accessibility, similar in principle to SWE-agent. AI researchers in the broader community have been able to use ITBench. Environment set... | null | null | null | null | null | null | null | null |
Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations | Accept (poster) | Summary: This paper studies a new variant of strategic classification applied to automated hiring decisions when applicants use large language models (LLMs) to improve (i.e., “manipulate”) their resumes. The authors observe that LLM-based enhancements can blur the line between skilled and unskilled applicants, especial... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback on our work. In particular, we appreciate the suggestion to connect the work to human-in-the-loop hiring and “AI detection” strategies, and will discuss them in the next version of the paper.
We want to highlight that our main contribut... | Summary: * This paper proposes and investigates a theoretical model for LLM strategic manipulations in the job application market, motivated by empirical observations.
* The model is motivated by three empirical observations: (1) LLMs tend to improve the score of a resume in an automated ranking system, (2) higher-cost... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed comments and feedback on our work. Here we will focus on answering questions from the reviewer:
1. **Practical scenario of X and Y independence**: Just to clarify our assumptions, our paper builds on existing fairness literature and assumes that “X and G are ... | Summary: The paper explores challenges of fairness and accuracy in hiring when job seekers use generative AI tools to enhance resumes. It proposes a "two-ticket" scheme, where employers also manipulate resumes using AI. The study demonstrates, theoretically and empirically, that this approach improves fairness and accu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful summary and comments on our paper. We address the questions below:
1. **Technical/Logistical Challenges**: In the real world, there may be practical challenges in implementing our “two-ticket” scheme. A major challenge is for companies to decide which mode... | Summary: The paper considers the problem of hiring in the scenario when applicants use LLMs to assist in CV writing (as well as hirers can have their own LLMs). This potentially can lead to unfair and inaccurate hiring, if, say some applicants use paid version of LLM while others do not. To mitigate this, the authors p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and for reading our paper (even the appendix!). We will answer the questions below:
> Does the framework hold if manipulations apply to style features?
Our framework considers manipulations made to style features rather than fundamental featur... | null | null | null | null | null | null |
On Learning Parallel Pancakes with Mostly Uniform Weights | Accept (spotlight poster) | Summary: This paper is concerned with learning mixtures of $k$ Guassians, when given i.i.d samples from the mixture. When the mixture weights and covariances of the individual components are unknown and arbitrary, the best-known algorithm from the literature (due to Bakshi et al. 2022) has sample complexity $d^{O(k)}$.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive assessment of our work. We respond to the individual questions below:
(**Difference between Bakshi et al. 2022 and Diakonikolas et al. 2017**) Yes, the reviewer is correct that the algorithm from Bakshi et al. 2022 applies to all GMMs, while in th... | Summary: The paper studies a hypothesis testing problem where the main task is to distinguish (with as few as possible samples) between a standard Gaussian $N(0,I_d)$, and a "parallel pancakes" distribution. This distribution is characterized by k discrete mean points along an unknown line in d dimensions. Now the dist... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for the positive assessment of our work and their thorough reading. When we refer to matching the $m$ moments, we always mean the first $m$ moments. We will ensure to clarify this whenever it is not currently explicit. The other points raised are typos, and we will fix th... | Summary: This paper studies the problem of learning mixtures of Gaussians where each component in the mixture has a shared covariance.
First, an SQ lower bound is proved, matching a recent positive algorithmic result and indicating that it likely cannot be improved. It is shown that even in the case when the mixture i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and their positive assessment of our work. | Summary: This paper studies the hypothesis-testing problem of parallel Gaussian pancakes, specifically under structural assumptions on the component weights. The goal is to distinguish between the standard gaussian and the k gaussian pancakes with collinear centers and common covariance. For learning the general mixtur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and their positive assessment of our work. We will fix the typos pointed out in the final version. We respond to their question below:
(**Is the $\log(k)/w_{\min}$ term in the sample complexity necessary?**) This term corresponds to (roughly) the number of s... | null | null | null | null | null | null |
Leveraging Offline Data in Linear Latent Contextual Bandits | Accept (poster) | Summary: This paper introduces a linear version of latent bandit models, where the reward for user u and action a of feature $\phi_{u,a}$ at step t is $Y_{u,a} = \phi_{u,a}U\theta+\varepsilon_t$ where $\varepsilon_t$ is an iid subgaussian noise, U an unitary matrix and $\theta$ a low-dimensional latent vector. U and $\... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for their kind and constructive comments. We are excited to hear that the reviewer highlights so many strengths of our work, including:
1. The strength of our theoretical contributions.
2. Our problem formulation.
3. Our novel and interesting algorithmic contri... | Summary: This paper explores linear latent contextual bandit problems and how offline data can be used to speed up online learning. The authors introduce an offline algorithm that learns a low-dimensional latent subspace with provable guarantees. Building on this, they propose an online algorithm that achieves minimax-... | Rebuttal 1:
Rebuttal: Thank you for your review and confidence in our paper! We are grateful for your appreciation of:
1. The clarity of our claims and the strength of the theoretical and empirical support for them.
2. The expansion of the scope of linear bandits to include latent states and hybrid offline-online learn... | Summary: In this paper, the authors study the linear latent contextual bandit problem. They consider a setting in which the latent reward vectors lie within a low-dimensional subspace. An offline dataset from tasks whose hidden reward vectors share the same subspace is assumed to be available. They first present an alg... | Rebuttal 1:
Rebuttal: Thank you for your comments. We appreciate that you recognize:
1. The clarity and veracity of our theorems and proofs.
2. The application of our algorithms to both synthetic and real data.
3. Our contribution to the problem of leveraging offline data in bandits.
We address your qualms below. **I... | Summary: This paper studies the setting of _linear latent contextual bandits_. If you are given multiple trajectory data under some unknown behavior policy, with possibly different latent states for each trajectory, how do you efficiently use it in an online setting? This paper proposes three algorithms and their analy... | Rebuttal 1:
Rebuttal: We are grateful for your review and your confidence in our paper! We appreciate that you enjoyed our:
1. Clarity and thoroughness of evidence.
2. Presentation of intuition and writing quality.
3. Demonstration of practical utility through experiments.
4. Generality over existing work in leveraging... | null | null | null | null | null | null |
Behavior-agnostic Task Inference for Robust Offline In-context Reinforcement Learning | Accept (poster) | Summary: This work analyzes the shortcomings of existing In-context Reinforcement Learning (ICRL) methods, pointing out their inability to handle context shift scenarios. The authors theoretically analyze the necessity of maximizing the true mutual information between context representation and task indices. Building o... | Rebuttal 1:
Rebuttal: Thank you for your review! We answer your questions below.
> Q1. Types of environments.
R1: We use a set of MuJoCo environments that is standard in the field of meta-RL and consistent with our baselines. To further demonstrate the generality of BATI, we have additionally conducted a preliminary ... | Summary: Authors propose Behavior-agnostic Task Inference (BATI) approach for meta RL problems which is claimed to be more robust to noisy dynamics compared to previous methods like UNICORN or CSRO and which works at the same level in noise-free cases.
Claims And Evidence: Claims are supported by the evidences. But ex... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and the detailed review! We address your concerns below. **Due to space limits, all the figures and tables referred below are available anonymously [on this website](https://sites.google.com/view/bati-icrl).**
> Q1. Experiment protocol of BATI and... | Summary: The paper introduces Behavior-Agnostic Task Inference (BATI) to improve offline in-context reinforcement learning (ICRL) under distribution shifts. BATI, a model-based maximum-likelihood approach, infers task representations robustly by focusing on environmental dynamics. Results show BATI outperforms existing... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We're encouraged by the overall positive assessment that our paper `"is generally well-written"` and `"addresses a critical challenge in ICRL"`. Below, we address the main concerns raised regarding the generalizability of our method beyond MuJ... | Summary: The authors propose a modification to the way offline context-based meta-RL methods supervise task identification, which they call BATI. The core idea is to remove the correlation between the task estimate and the behavior of the policy collecting the context. That way, the test-time context policy can be sign... | Rebuttal 1:
Rebuttal: Thank you for your kind words and insightful review! We are happy to answer your questions below.
> Q1. Training contexts generated by adaptive policies.
R1: The result would likely depend on the exact behavior of the adaptive policy. For example, if the adaptive policy is bad, it may randomly w... | null | null | null | null | null | null |
Geometric Hyena Networks for Large-scale Equivariant Learning | Accept (spotlight poster) | Summary: This paper introduces an SE(3)-equivariant extension of the Hyena model (Poli et al. 2023) which employs long convolutions evaluated in the Fourier domain. This Geometric Hyena model is used for property predictions of large biological molecules. The authors show that their model outperforms transformer models... | Rebuttal 1:
Rebuttal: ## Computational and performance benefits:
We appreciate the reviewer acknowledging the runtime and memory benefits of our method, as computational efficiency is the main purpose of our work.
Second, we believe there may be a misunderstanding regarding performance improvements. G-Hyena signific... | Summary: The authors introduce a novel equivariant model designed for modeling long geometric sequences. To ensure compatibility with higher-order geometric tensors in global convolution, they propose an equivariant global convolution specifically for vectors. Additionally, they demonstrate that these computations can ... | Rebuttal 1:
Rebuttal: ## Application focus of Geometric Hyena:
Regarding the generalizability of our method to arbitrary geometric graphs, we refer the reviewer to the Limitations section (lines 424–439R), where we explicitly discuss the limitations of G-Hyena for point clouds and emphasize that our method is best suit... | Summary: This paper presents a novel equivariant SO(3) neural network for processing geometric graphs with sequence structure and invariant node features. The network a geometric version of Hyena and implements equivariant long convolution in fourier space, allowing for global information flow with subquadratic comple... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback on contributions of our paper, highlighting the novelty ("the extension is non-trivial and the method is clearly novel") of our method and its experimental validation ("the authors consider a strong set of experiments to test their methods").
## Non-... | null | null | null | null | null | null | null | null |
Fair Clustering via Alignment | Accept (poster) | Summary: The paper introduces an in-processing fair clustering approach that matches two instances from different protected groups and assigns them to the same cluster. The approach directly minimizes the clustering cost with respect to both the matching map and cluster centers simultaneously. The proposed method is th... | Rebuttal 1:
Rebuttal: *To reviewer Nujs: We sincerely appreciate your review and thank you for the opportunity to improve our work. Please refer to our point-by-point responses below.*
------
### Claims And Evidence
> 1: However, in the experiments, the authors do not compare the results of proposed method with the ... | Summary: This paper introduces a fair clustering method called Fair Clustering via Alignment (FCA), which aims to balance the trade-off between fairness and clustering utility. The authors propose a decomposition of the fair k-means clustering objective into two components: the transport cost and the clustering cost. T... | Rebuttal 1:
Rebuttal: *To reviewer 4J8b: We sincerely appreciate your review and the opportunity to improve our work. Please refer to our point-by-point responses below.*
------
### Weaknesses
> 1: The computational complexity ...
- **Section 4.3 introduces a partitioning technique to reduce the computational complex... | Summary: In group fair clustering, a clustering objective is optimized in light of a group fairness constraint. If each data point is assigned some class, we require the proportions of these classes in each cluster to be the same as, or close to, their proportions out of the total dataset.
This work considers the prob... | Rebuttal 1:
Rebuttal: *To reviewer AM68: We sincerely appreciate your review and thank you for the opportunity to improve our work. Please refer to our point-by-point responses below.*
------
### Weaknesses
> 1: Extremely notationally dense ...
- Answer:
- When preparing this work, we initially considered summ... | Summary: The paper is focused on fair clustering. In particular, it suggests a new method based on decomposing the clustering cost which claims to have higher flexibility than prior methods in trading the clustering cost and fairness violations. Theoretical analysis and experiments comparing to baselines are conducted ... | Rebuttal 1:
Rebuttal: *To reviewer 1gG2: We sincerely appreciate your review and the opportunity to improve our work. Please refer to our point-by-point responses below.*
------
### Claims And Evidence
> 1: I don't find ...
- First, we would like to highlight our main contributions:
- A novel reformulation of the ... | null | null | null | null | null | null |
BoxLM: Unifying Structures and Semantics of Medical Concepts for Diagnosis Prediction in Healthcare | Accept (poster) | Summary: This paper introduces BoxLM, a novel framework for diagnosis prediction in healthcare that aims to unify the semantic understanding of medical concepts with their underlying structural relationships. This approach integrates ontology-driven hierarchies and EHR-driven visit patterns with semantic embeddings fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive evaluations and many detailed suggestions. In the following, we focus on several of the main issues to provide our feedback:
> **Reviewer yPUD.W1:** The difference between BoxLM and BoxCare.
As described in Section 2, BoxCare only uses box embedding... | Summary: In this paper, the authors focus on a critical task: diagnosis prediction. They introduce a unique approach, the BoxLM representation, to represent Electronic Health Records (EHR) and diseases. While the paper is well-written and easy to follow, it has two significant shortcomings: 1. The evaluation metric use... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall summary. As for the several weaknesses and questions you mentioned, our responses are listed as follows:
> **Reviewer UQNn.W1:** The evaluation metric suggests including recall.
Following [2-6], we adopt visit-level Precision@k (P@k) and code-level Accuracy... | Summary: The paper proposes BoxLM, a framework unifying structural (ontology and EHR hierarchies) and semantic (language model embeddings) representations of medical concepts using box embeddings. Key contributions include a structure-semantic fusion mechanism, an evolve-and-memorize patient box learning module for tem... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments on our paper. In response to the shortcomings mentioned, we provide the following answers to several questions:
> **Reviewer skUY.W1:** Adding quantitative metrics for interpretability.
In our study, CCS (Clinical Classification Software) codes... | null | null | null | null | null | null | null | null |
Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making | Accept (poster) | Summary: This paper studies the attribution of counterfactual outcome to agent actions and states, that are total agent-specific effect and reverse state-specific effect. Moreover, it futher decompose the total agent-specific effect into individual agent effect, and the reverse state-specific
effect (r-SSE) into r-SSE-... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Please find below our response to your comments and questions.
## Response to Comments and Questions
**Proof of Theorem 3.3.** We respectfully disagree with your concern regarding the correctness of the proof of Theorem 3.3. While we understand that the resu... | Summary: The paper proposes a causal explanation formula for multi-agent Markov Decision Processes (MMDPs). It uses Structural Causal Models (SCMs) to decomposes the total counterfactual effect of an agent's action by attributing to each agent and state variable a score that results from their respective contributions ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and positive score. We are glad to see that you find our paper well-written and addressing significant issues of AI systems. We are also happy to hear that you find our method insightful and sensible, and our experimental setup sound and clear. Please find belo... | Summary: This paper focuses on the causal analysis of decision-making in multi-agent cooperative frameworks, specifically the decomposition and attribution of counterfactual effects in the decision-making process of agents. The key contribution is the decomposition of the total counterfactual effect into two parts: the... | Rebuttal 1:
Rebuttal: Thank you very much for your positive score and your kind words about our work. We are glad to hear that you find our approach well-supported, and appreciate its novelty, intuitiveness, and the comprehensive causal analysis it provides for multi-agent decision-making. We are also happy to see that... | Summary: This paper proposes a novel decomposition of counterfactual effects in multi-agent sequential decision-making settings. Building upon prior work on agent-specific counterfactual effects (cf-ASE), the authors present a bi-level decomposition separating the impact of an agent’s action into (i) how it affects the... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We are glad to see that you find our proposed method clear, logically appropriate and enhancing the interpretability of decision-making outcomes. We are also happy to see that you find our experimental testbed well-suited. Please find below our response to you... | null | null | null | null | null | null |
A Proximal Operator for Inducing 2:4-Sparsity | Reject | Summary: This paper proposes to get 2:4 weight sparsity gradually using the proximal gradient method on a per-matrix level for pruning large language models (LLMs) after pretraining.
1) Firstly, a special regularizer with 2:4 sparsity null space is proposed, and the authors show that we can control the structured spars... | Rebuttal 1:
Rebuttal: Thank you very much for thoroughly checking our theoretical claims and helping to improve our presentation. We will carefully take your considerations into account and will revise Section 3.3 around Corollary 6 to ensure it is easier to follow.
We want to briefly reply to some of your comments a... | Summary: The proposed method solves the complex proximal operator for 2:4 sparsity by optimized masked gradient updating. The theoretical analysis provides clear support for the mechanism of the proposed method. The authors have conduct extensive experiments to validate the effectivenss of the proposed method.
Claims... | Rebuttal 1:
Rebuttal: Thank you very much for checking the correctness of all proofs for our theoretical claims and your positive assessment. We want to briefly answer two comments:
> It would be better to show some real speedup for 2:4 sparsity in LLMs.
We kindly ask you to check our response to reviewer 8YCw where ... | Summary: This paper proposes a proximal operator to improve the one-shot N:M weight pruning for large language models. The paper finds better sparsity masks in trained models by minimizing a regularizer jointly with local squared loss though deriving the proximal operator. Besides the algorithm for better masks, the pa... | Rebuttal 1:
Rebuttal: Thank you very much for checking all our theoretical statements and the review of our paper. Below we provide a discussion on other optimization objectives as suggested in your review and some data and considerations on speedups.
### Optimization objectives beyond squared loss
First we recall the... | Summary: The paper presents a post-training pruning method to induce N:M sparsity. The two main innovations of this article are:
1) It proposed a Gradient Descent approach that can be implemented to any post-training pruning method to compensate the pruning loss.
2) Combining with the proposed Regularization, this ar... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and confirming that our theoretical claims and the proposed method make sense. We understand that your reluctance comes from missing ablations on *calibration samples* as well as the unconvincing *performance* on the current 70B scale Llama models. We provide a ne... | null | null | null | null | null | null |
Elucidating the Design Space of Multimodal Protein Language Models | Accept (spotlight poster) | Summary: The paper discusses the design space of multimodal protein language models through the lens of discretizing protein structures. The paper attempts to address a key challenge with information loss of fine-grained structural details upon tokenization. Multimer structures are considered, and BIT-based modeling is... | Rebuttal 1:
Rebuttal: Many thanks for your comments that have greatly improved our manuscript! We address your concerns as below. We sincerely thank you once again and welcome any further feedback!
> Q1: Can the authors comment on any simple baselines or alternative approaches they pursued to resolve the identified pr... | Summary: The manuscript systematically explores the design space of multimodal PLMs, aiming to identify and address their existing limitations. The authors highlight tokenization loss and inaccurate structure token predictions as major bottlenecks in structure prediction performance. To mitigate these issues, they prop... | Rebuttal 1:
Rebuttal: Many thanks for your comments that have greatly improved our manuscript! We address your concerns as below. We sincerely thank you once again and welcome any further feedback!
> W1: The current manuscript does not address structure-conditioned protein generation or structure-aware protein predicti... | Summary: This paper performs an exploration of how to improve multimodal protein language models that jointly model both protein sequences and structures. The paper identifies limitations in the existing literature of token-based multimodal PLMs and propose (and explore) many design choices for such PLMs. The paper ide... | Rebuttal 1:
Rebuttal: Many thanks for your comments that have greatly improved our manuscript! We address your concerns as below. We sincerely thank you once again and welcome any further feedback!
> The bit-level and index-level evaluation is really stark. Is there any broader intuition behind this that you could che... | Summary: This paper aims to systematically improve current multimodal protein language models in the following respects: (1) generative modeling, where the authors argue that structural information loss caused by index-based structure tokenization cannot be resolved by improving reconstruction accuracy, and opt for a f... | Rebuttal 1:
Rebuttal: Many thanks for your comments that have greatly improved our manuscript! We address your concerns as below. We sincerely thank you once again and welcome any further feedback!
> W1: the paper would benefit from a more structured presentation of its contributions (e.g. a taxonomy)
Thank you for ... | null | null | null | null | null | null |
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models | Accept (poster) | Summary: This paper introduces a new offline reinforcement learning approach for LLM alignment, motivated by the lack of statistical consistency in the traditional BT model, which underpins popular offline algorithms such as DPO. The author proposes directly optimizing the density ratio using a Bregman divergence loss ... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on our paper. We address your points below.
1. **Performance concerns:**
The reviewer expressed concern that DDRO does not demonstrate compelling advantages over existing models, potentially limiting its practical applicability. Regarding the magnitude ... | Summary: This paper introduces Direct Density Ratio Optimization (DDRO), a novel alignment method for large language models (LLMs) that addresses the statistical inconsistency of existing approaches reliant on restrictive preference models (e.g., Bradley-Terry). By directly estimating the density ratio between preferre... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing valuable feedback. We address your points below.
1. **Limited improvement and average results:**
The reviewer observed that the empirical improvement of DDRO over KTO seems limited and suggested providing average results across datasets. Regar... | Summary: This paper introduces Direct Density Ratio Optimization (DDRO), a method for aligning LLMs by directly estimating the density ratio between preferred and unpreferred output distributions. DDRO minimizes a Bregman divergence-based loss, eliminating the need for explicit preference modeling while ensuring statis... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions regarding our work on DDRO. We appreciate the careful reading and valuable feedback.
1. **Handling neutral data:**
The reviewer asked if DDRO can handle "neutral" labels, which might arise when annotators evaluate single responses withou... | Summary: This paper focuses on LLM alignment, aiming to address two key limitations of existing direct preference learning methods such as DPO: (1) Existing work requires a preference model (e.g., the Bradley-Terry model), which may not generalize well to capture complex human preferences that do not fit these models; ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive comments on our paper. We appreciate the opportunity to address your concerns and clarify aspects of our work.
1. **Is the density ratio $g$ an implicit preference model?**
We thank the reviewer for this insightful question. The density... | null | null | null | null | null | null |
Distillation of Discrete Diffusion through Dimensional Correlations | Accept (poster) | Summary: This paper focuses on an important research question of distilling discrete diffusion models, which poses unique challenges due to the necessity of modeling the joint distribution of multiple discrete states grows with a combinational complexity. This paper proposes Di4C, a principled model agnostic approach f... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of the paper. Let us answer your questions. We will also correct typos you have pointed out.
### [q-1] Why we chose SDTT model as teacher
> Why do you opt to apply your method on top of another specific distilled model? Does the proposed method work well on... | Summary: This paper studies the distillation problem for discrete diffusion models. The authors identify a key challenge in capturing dimensional dependencies and provide theoretical analyses to support their findings. To address this, they propose a mixture student model with tailored loss functions to facilitate dist... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive comments. Let us respond to your questions.
### [d-1] On the reference distributions
> the method relies on reference distributions for $r_\delta$ and $r_t$, which must either be sampled from real training data or approximated using multiple teacher steps.... | Summary: The paper proposed a improved model for distilling discrete diffusion models(DDMs). The key idea is that traditional DDMs break apart the latent distributions into product of marginal distributions, while the proposed model represent the latent distributions into products of bi-dimension distribution pairs, wh... | Rebuttal 1:
Rebuttal: Thank you for your review. Let us reply to your comments.
### [1-1] Added metrics beyond FID/IS
> the authors mainly compare their model with the teacher model using FID/IS scores, which is inherited limited
We have additionally computed precision and recall metrics for the ImageNet experiment ... | null | null | null | null | null | null | null | null |
WorldSimBench: Towards Video Generation Models as World Simulators | Accept (poster) | Summary: This paper introduces WorldSimBench, a benchmark for evaluating video predictive models from an embodied perspective, in contrast to previous approaches focused on generative evaluation. It features Explicit Perceptual Evaluation and Implicit Manipulative Evaluation, combining human preference assessments from... | Rebuttal 1:
Rebuttal: Thank you for your constructive and thoughtful comments. They were indeed helpful in improving the paper. We take this opportunity to address your concerns:
>Hierarchical Structure of Modalities
The hierarchical structure we propose is not based on semantic abstraction, but on the practical diffi... | Summary: This paper proposes a framework that can be used to evaluate world simulators, such as video generation models. the framework is divided into a couple of components, a perceptual evaluation, and an embodied evaluation. the perceptual one uses a model trained on human collected data to score the results. the em... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper：
- **Inspiring for the Community**: *"I believe this paper will have some impact to a sizable audience in computer vision and ML communities."*
- **Comprehensive Literature Review**: *"References are adequate."*
---
>Supplementary Material
Our supple... | Summary: The lack of categorization based on inherent characteristics hinders predictive model development, and existing benchmarks fail to evaluate highly embodied models effectively. To address this, this paper introduces WorldSimBench, a dual evaluation framework for World Simulators. It includes Explicit Perceptual... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper：
- **Inspiring and Novelty**: *"It provides key insights to advance video generation models and strengthen their role in embodied AI."*
- **Reasonable Evaluation Criteria**: *"These benchmarks provide a comprehensive assessment of a simulator’s effectiv... | Summary: This paper proposes WorldSimBench, a benchmark used to evaluate the world simulation performance of video generative models. This paper investigates several video valuation benchmarks and introduces a hierarchy for classifying video models. This paper evaluates several video generative models through proposed ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper：
- **Solid Experiment**: *"This paper includes extensive empirical results to support the main claim made in this paper."*
- **Novelty and Soundness**: *"This paper introduces a human preference evaluator and video-to-action evaluation metrics to evalua... | null | null | null | null | null | null |
History-Guided Video Diffusion | Accept (poster) | Summary: This paper delves into video diffusion models, aiming to extend classifier-free guidance (CFG) to video diffusion with variable-length history frames. The authors identify two key challenges: architectures supporting only fixed-size conditioning and poor performance of CFG-style history dropout. To tackle thes... | Rebuttal 1:
Rebuttal: Thank you for finding our work significant and effective. Below, we address your concerns with further explanation, additional ablations, and results of fine-tuning a video foundation model into DFoT.
> ### **Q1. Practical implications of theoretical results (ELBO)**
Thank you for your insightfu... | Summary: The paper introduces the Diffusion Forcing Transformer (DFoT), a video diffusion architecture that extends diffusion forcing with a theoretically grounded training objective that enables conditioning on a flexible number of history frames. On top of diffusion forcing using transformer, the authors introduce Hi... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on the strengths of our method, theoretical justification, and extensive analysis. Below, we address questions on sampling efficiency and scalability/generalizability of DFoT, including new results from fine-tuning a 1.3B text-to-video foundation model (see **[... | Summary: Classifier-free guidance (CFG) greatly improves conditional generation in diffusion models, but applying it to video diffusion—where the number of context frames can vary—introduces significant challenges. Existing architectures often restrict conditioning to a fixed size, and CFG-style history dropout is inef... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on the novelty and theoretical support of our method, and extensive experimental results. Below, we address your questions by presenting enhanced long video generation achieved through fine-tuning a 1.3B text-to-video foundation model and providing an ablative ... | Summary: This paper regards the noise in the diffusion process as a form of masking, integrating history frames and generated frames into a unified Diffusion Forcing Transformer (DFoT) framework. By combining different masking strategies for history information, the paper proposes several History Guidance (HG) methods,... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on our method's innovation, theoretical support, and evaluation. Below, we address concerns on long video generation and present new results from fine-tuning a 1.3B text-to-video foundation model to DFoT.
> ### **Q1. Advantage of our method in ultra-long video... | null | null | null | null | null | null |
Causal Discovery from Conditionally Stationary Time Series | Accept (poster) | Summary: This paper extends the existing work in time series causal discovery from stationary data to conditionally stationary time series data, which is stationary if conditioned on the latent states. The authors propose a conditional summary graph to represent the causal structure and give the identifiability results... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s feedback. Below, we address key concerns.
> There is room for the clarity/simplification of the notations.
We appreciate this point and will revise the manuscript for clarity.
- Subscripts $\psi$ and $\phi$ denote parameters of the generative model and vari... | Summary: This paper introduces State-Dependent Causal Inference (SDCI), an approach for causal discovery building on VAE-based approaches in nonstationary time series characterized. SDCI leverages a "conditional summary graph" to compactly represent state-dependent causal structures and establishes identifiability guar... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful comments. Below, we address the key concerns raised in the review.
> It is unclear how the proposed method is different from existing methods...
While prior works [1-3] also consider discrete latent variables for modeling nonstationary time serie... | Summary: This paper presents a new framework for solving the causal discovery problem in non-stationary sequences. This paper makes certain assumptions under the non-stationary condition, and then proposes the new method Conditional summary graph for representing causality, and the method for conducting causal discover... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s time and thoughtful feedback. We are glad they find our framework innovative and acknowledge its significance in handling nonstationary time series. Below, we address the key concerns raised in the review.
> ... if the actual data cannot meet the premise, may... | null | null | null | null | null | null | null | null |
Clustering Properties of Self-Supervised Learning | Accept (poster) | Summary: This paper studies the clustering properties of self-supervised learning. This paper finds that the encoder's output exhibits superior and more stable clustering properties than other components. Based on insight, this paper proposes a novel positive feedback method to improve the representation ability furthe... | Rebuttal 1:
Rebuttal: Dear Reviewer kGg9,
We sincerely thanks for your time and the valuable suggestions to improve this paper. Here we address each of the comments in detail.
**Why encoding and embedding have different clustering properties**: Thanks for this constructive question. Due to word count limitations, we ... | Summary: The paper presents a new self-supervised learning method that encourages clustering in the output "embedding" layer based on clustering found in the earlier "encoding" layer used as representations for downstream tasks.
The paper analyses clustering metrics on representations extracted at different layers of... | Rebuttal 1:
Rebuttal: Dear Reviewer PEEd,
We would like to begin by sincerely thanking you for the insightful and valuable comments. Below, we address each of the comments in detail.
**Relation to Broader...**:
We have revisited these papers, and will cite and discuss them in the new manuscript.
- We will further i... | Summary: This paper investigates the clustering properties inherent in self-supervised learning (SSL) through joint embedding architectures. The authors empirically demonstrate that encoder outputs (encodings) exhibit superior clustering quality compared to projector embeddings, as measured by silhouette coefficients a... | Rebuttal 1:
Rebuttal: Dear Reviewer 69BS,
We sincerely thanks for your time and the valuable feedback for this paper.
**Why encodings inherently possess better clustering capability**:
We would like to thank the reviewers for their insightful discussion regarding the distinction between encoding and embedding. To th... | null | null | null | null | null | null | null | null |
SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding | Accept (poster) | Summary: This paper proposes a privacy-preserving retrieval and aggregation method, SecEmb, that can be applied to federated recommender systems that leverage sparse embeddings. Likewise, due to the latent representation, SecEmb achieves faster computational & communication times versus standard (uncompressed) FedRec p... | Rebuttal 1:
Rebuttal: Thanks for your insightful comment and recognition of our work. Hope our response below could address your concerns.
*Q1*: I will mention that the Theorems provided and proven in the supplementary material should be summarized in the main body.
*Ans*: Thanks for your suggestion. We will move The... | Summary: In the context of federated recommendation systems, this paper proposed the SecEmb protocol based on the Functional Secret Sharing algorithm and its coding property, which combines on-device request and update consistency on item indices. The protocol allows the client-side to only download and upload the embe... | Rebuttal 1:
Rebuttal: Thanks for your valuable comment and suggestions. Hope our response below could address your concerns.
*Q1*: The criteria for dataset splitting should be added.
*Ans*: We randomly split the ratings **for each user** into training and testing set.
*Q2*: More large-scale datasets are desired & Ad... | Summary: This paper proposes a lossless and efficient federated recommendation training protocol to address the challenge of balancing efficiency and privacy in federated recommendation systems.
Additionally, it explores the use of row-wise sparsity in embedding matrices to optimize computational load.
Extensive ex... | Rebuttal 1:
Rebuttal: Thanks for your insightful comment and valuable suggestions. Hope our response below could address your concerns.
*Q1*: The evaluation criterion for utility in this paper is relatively outdated.
*Ans*: Our paper focuses on the rating prediction task, and thus utilizes RMSE to measure the discrep... | Summary: This paper introduces a secure federated recommender system, tailored to the case where user data is sparsely presented. Conventional federated secure aggregation methods suffer from unnecessary communication overhead. Thus, the authors use function secure sharing and propose two modules (embedding retrieval m... | Rebuttal 1:
Rebuttal: Thanks for your insightful comment and positive rating of our paper. Hope our response below could address your concerns.
*Q1*: Elaboration on "only the embeddings for interacted items are relevant for model updates"
*Ans*: It works for recommender systems (RS) based on item embeddings, as long ... | null | null | null | null | null | null |
Temporal Difference Flows | Accept (oral) | Summary: This paper studies the problem of learning generative horizon models (GHMs), which are generative models of the successor measure of a policy. That is, GHMs are models capable of generating samples from the discounted distribution of future states visited by a policy at any given time step $t$. In particular, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and thoughtful evaluation, and for recognizing the clarity, rigor, and contributions of the work—both theoretical and empirical. Your careful reading and constructive suggestions are greatly valued.
> **Number of seeds and statistical reporting**
Thank you... | Summary: This paper introduces a flow-matching and diffusion-based generative modeling framework to learn accurate Geometric Horizon Models by integrating temporal difference structure in the learning objective and sampling process. The proposed TD$^2$-DD and TD$^2$-CFM achieve low modeling errors in both success measu... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and thoughtful evaluation. We appreciate the recognition of our theoretical contributions, the robustness of TD²-CFM and TD²-DD in long-horizon modeling, and the strong planning performance demonstrated through Generalized Policy Improvement (GPI)... | Summary: This paper introduces TD-flow, a novel approach to using the Bellman equation on probability path with flow(score)-matching techniques for generative models. The approach outperforms the existing ones in terms of more accurate generation over extended horizons. TD-flow is validated across diverse experiments a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and encouraging feedback. We're especially glad to hear that the paper was clear and accessible, even for readers less familiar with generative modeling.
> **What benefits does TD bring to flow/score matching?**
This is a great question, and we appreciat... | Summary: This paper introduces a new family of algorithms–TD-CFM, Coupled TD-CFM, and TD^2-CFM which propose a Flow-matching-based generative modeling framework to learn to sample from the discounted successor measure. Learning the successor measure rather than predicting long term rollouts prevents the usual accumulat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback, and we’re glad that the novelty and potential of TD-Flow and its variants were appreciated. We respond below to the key points raised and will revise the paper accordingly to better highlight these aspects.
> **Comparison to di... | null | null | null | null | null | null |
Online Sparsification of Bipartite-Like Clusters in Graphs | Accept (poster) | Summary: The paper studies graph sparsifiers that preserve bipartite-like communities in undirected and directed graphs. The notion of communities is formalized via the bipartiteness ratio and then generalized to $k$ communities in the standard way through $k$-way partitions.
For undirected graphs, the novel sparsifie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and detailed comments. Here is our response to the raised questions:
**Response to _Other Comments Or Suggestions_:**
> In Theorems 1 and 2, there are no bounds on the size of the sparsifier. In some sense, they are implicit in the running time... | Summary: In this paper, the authors study study bipartite-like clusters and present efficient and online algorithms that find such clusters in both undirected graphs and directed ones. Experiments on real and synthetic graphs demonstrates that the proposed algorithm can speedup the existing algorithm.
Claims And Evide... | Rebuttal 1:
Rebuttal: We thank the reviewer's work and the report. Here is our response to the raised questions:
**Response to _Claims And Evidence_**:
>The title of the paper is "Finding Bipartite-like Clusters on the Fly", however, the proposed techniques seems like sparsifier regarding bipartite-like clustering. A... | Summary: The paper propose efficient and online algorithms to detect bipartite-like clusters for both directed and undirected graphs. They both are graph sparsifiers that can sparsify the graph into $\tilde{O}(n)$ edges while preserving the bipartite clusters with high probability.
Claims And Evidence: The claims are ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and detailed comments. Here is our response to the raised questions:
**Response to _Experimental Designs Or Analyses_**:
>The speedup is not obvious in figure 3, and the running time fluctuates around 1750 nodes. Some explanations here could be... | Summary: This paper studies the problem of finding bipartite-like clusters in both directed and undirected graphs. The authors propose a novel graph sparsification algorithm that can be implemented online and preserves the structure of bipartite-like clusters. The main findings are theoretical results proving that the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and such detailed report. Here is our response to the raised questions:
**Response to _Other Comments Or Suggestions:_**
>Consider explicitly mentioning what do you mean by online in the paper.
Online in our setting is more relevant to online algorithms... | null | null | null | null | null | null |
Gradient Inversion of Multimodal Models | Accept (poster) | Summary: This paper studies gradient inversion (GI) attacks specifically for multi-modal Document Visual Question Answering (DQA) models and proposes GI-DQA, a novel method for reconstructing private document content from gradients. The empirical experiments demonstrate that their approach exposes critical privacy vuln... | Rebuttal 1:
Rebuttal: We thank the reviewer for his time, effort, and valuable suggestions.
Q1. "Tab. 2 cumulative.." + Q3."..channel and pixel-level TV.."
A1. We followed the standard practice of prior works such as GradViT (Hatamizadeh et al., 2022) and GradInversion (Yin et al., 2021), which apply priors increment... | Summary: The paper explores gradient inversion attacks targeting multi-modal Document Visual Question Answering (DQA) models in the context of federated learning and propose GI-DQA a novel method that reconstructs private document content from gradients. The approach seems to expose privacy vulnerabilities.
Claims And... | Rebuttal 1:
Rebuttal: We thank the reviewer for his time, effort, and valuable suggestions.
Q1. "access to a template.."
A1. While the use of a template may appear simplified at first glance, we argue that this setup is practically grounded and reflects real-world scenarios. In many document-based applications, such ... | Summary: The paper proposes a gradient inversion attack targeting multi-modal DQA models in Federated Learning (FL) setups: GI-DQA.
In DQA models, the input consists of both a document and its corresponding question, while the output is the target answer. GI-DQA first employs existing methods to reconstruct question-... | Rebuttal 1:
Rebuttal: We thank the reviewer for his time, effort, and valuable suggestions.
Q1. "found [r1].."
A1. We thank the reviewer for pointing out this work. [r1] explores gradient inversion in multi-modal FL using synthetic image-text pairs processed by separate, modality-specific models trained independently... | Summary: This paper presents a novel approach to gradient inversion attacks (GI-DQA) on multi-modal models specifically targeting extraction of textual information in Document Question Answering (DQA) tasks. The authors demonstrate why gradient inversion attacks designed for targeting unimodal models trained for image ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his time, effort, and valuable suggestions.
Q1. "..confidence intervals.."
A1. Our original submission reported mean values, as we observed low variance across runs, confirming that our method is stable and consistently outperforms baseline methods.
We will include the ... | null | null | null | null | null | null |
Lean and Mean Adaptive Optimization via Subset-Norm and Subspace-Momentum with Convergence Guarantees | Accept (poster) | Summary: This paper introduces two complementary adaptive optimization techniques that reduce the memory footprint of optimizer states while accelerating LLM large-scale neural network training.
- Subset-Norm (SN): A generalization of AdaGrad-Norm and AdaGrad-Coordinate that shares step sizes across subsets of paramet... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful consideration and constructive feedback. Below, we clarify points raised and address specific concerns.
___
> No discussion of quantization-based approaches like 8-bit Adam
Due to space limit, we cite and discuss quantization approaches (and more) in the relat... | Summary: This paper proposes two modifications to AdaGrad: 1) instead of coordinate wise adaptive learning rate, subset norm adaptative learning rate can provide adaptive learning rate scaling for different subsets of parameters 2) similar to GaLore, it keeps momentum in low rank and recovers momentum by up-projection,... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful consideration and constructive feedback. Below, we clarify points raised and address specific concerns.
____
> Theorem 3.1 is highly dependent on the variance of the stochastic noise
- The reviewer is right that the bounds depend on the noise and this is stand... | Summary: This work studies two modifications to widespread adaptive optimization meta-algorithm procedures, with the goal of reducing the memory footprint of adaptive optimization while simultaneously improving performance.
The first modification is referred to as adaptive subset-norm (SN) stepsizes, which is similar ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful consideration and constructive feedback. Below, we clarify points raised and address specific concerns.
___
> Question 1 and 2
We answer question 1 and 2 of the reviewer together as they are related:
- The reviewer is correct in that arbitrarily increasing the... | Summary: This paper proposes two techniques, Subset-Norm (SN) and Subspace-Momentum (SM), to reduce the memory footprint of adaptive optimization methods (like Adam and AdaGrad) when training large deep learning models, particularly large language models (LLMs). Subset-Norm reduces the memory of the adaptive step-size ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful consideration and constructive feedback. Below, we clarify points raised and address specific concerns.
___
> the "why" behind the performance improvement of SM is not fully explained.
This is a limitation that is general to the class of algorithms that utili... | null | null | null | null | null | null |
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search | Accept (poster) | Summary: This paper propose a post-training framework, incluing two stages: (1) the chain-of-Action-thought fine-tuning, i.e., format fine-tuning; (2) self-improvement RL, i.e., iterative distillation and RL. For first stage, using Qwen-2.5-Math-Instruct and Llama-3.1-70B-Instruct constructs a multi-agent data synthes... | Rebuttal 1:
Rebuttal: **1. Over-claim about "without external guidance".**
We would like to clarify that **“external guidance” specifically refers to guidance provided by another LLM verifier at inference time (see Abstract, lines 18-19)**. Many existing LLM reasoning methods rely on extensive sampling and guidance fr... | Summary: The paper presents a method called Satori, which could enhance the reasoning abilities of LLMs. It does this through Chain-of-Action-Thought (COAT), a system that adds special “meta-action” tokens (like ``<|reflect|>`` and ``<|explore|>``) to regular chain-of-thought prompts. These tokens let the model pause t... | Rebuttal 1:
Rebuttal: **1. R1 uses a cold-start RL to develop reflection skills on its own (the “Aha moment”), but the paper relies on a specialized dataset to teach reflection-like abilities.**
We would like to respectfully clarify several important points:
- **R1 is a concurrent work and comparison should not be r... | Summary: This work studies the problem of post-training LLMs for self-reflection and self-exploration capabilities. A training scheme termed COAT is proposed, which consists of two stages: (1) a small-scale SFT to initiate COAT reasoning format; (2) a large-scale RL finetuning stage to further enhance the self-reflecti... | Rebuttal 1:
Rebuttal: **Compare with more naive approaches (e.g., the ones adopted in DeepSeek-R1 with only RL finetuning).**
We appreciate the reviewers' interest in the comparison between our method and the RL-only approach (r1-zero). As noted in our response to reviewer WTyn, r1 is a concurrent work, and such compa... | Summary: In this paper, the authors proposes Satori as a framework for LLM reasoning. It is a two-stage framework, including Format Tuning and Self-improvement, to enhance LLM reasoning capabilities. The core contribution of this paper is the Chain-of-Action-Thought (COAT) framework, which structures LLM reasoning with... | Rebuttal 1:
Rebuttal: **1. Ablations and analysis are limited: Some design choices (meta-actions, RAE parameters, reward function) could be more thoroughly justified and explored.**
We thank the reviewer for the suggestion. **We have conducted additional ablation studies to further analyze our design choices at** http... | null | null | null | null | null | null |
DeepCrossAttention: Supercharging Transformer Residual Connections | Accept (poster) | Summary: The authors propose DeepCrossAttention (DCA), a novel method that enhances residual connections in transformer architectures. In standard transformers, if we denote the input and output of the i'th block as $x_i$ and $y_i$ respectively, vanilla residual connections simply use $x_i = \sum_{j < i} y_j$. DCA inst... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our manuscript. We address the two concerns raised by the reviewer below.
> Experimental validation is limited to language modeling tasks; testing on other modalities (vision, audio) would strengthen the paper's claims about the method's general... | Summary: The authors introduce learnable residual connections to improve over standard residual connections used in ResNets and transformers. They highlight that simple residual connections struggle to recover the input (learn the identity function) on toy examples and their proposed learnable residual connections can ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful feedback on our manuscript. We address the three questions raised below.
> Most efficiency gains seem to occur by using the first and last-k layer outputs in the GRN, for k=2. Moreover the perplexity gains from increasing k further are limited. ... | Summary: The paper introduces DeepCrossAttention (DCA), a new mechanism that stores and uses intermediate features of transformers. DCA enables learnable, input-dependent weights to mix preceding intermediate features, enhancing the model's representation power. The authors also provides theoretical justifications rega... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our manuscript. We hope that our responses adequately address the reviewer's concerns. We believe these clarifications and additional results strengthen the manuscript.
> Could the authors provide analysis regarding the memory usage during forwa... | null | null | null | null | null | null | null | null |
One-Pass Feature Evolvable Learning with Theoretical Guarantees | Accept (poster) | Summary: This work focuses on the online learning scenario with a special assumption of the environment: the feature space evolves, where old features vanish and new features emerge during the process of online learning. This work considers to characterize the feature relationship via kernel function, and proposes the ... | Rebuttal 1:
Rebuttal: [Q1] …whether it is necessary to adopt random Fourier features, or are there other kernel approximation methods that are compatible with the OPFES approach?
[A1] We will clarify that we take random Fourier features to avoid storing the entire and partial data, since kernel function $k(x_1,x_2)$ i... | Summary: This paper tackles online learning in feature-evolving streams where old features vanish and new ones emerge. They propose OPFES, a one-pass algorithm that processes data without storage. It combines online kernel learning with random Fourier features to adaptively capture evolving data patterns and introduces... | Rebuttal 1:
Rebuttal: [Q1] The scenario of the OPFES algorithm that can be applied is a little bit limited. The feature space can only change once during the whole learning process.
[A1] We will clarify that this work focuses on one feature evolution as in [Hou et al., 2021], and we can consider multiple feature evolu... | Summary: This work proposes "One-Pass Feature Evolvable Learning" (i.e. OPFES), this is a method for handling streaming data where old features vanish and new features emerge. The core contribution is the kernel ortho-mapping discrepancy as $E\left(S\_n, K^{(1)}, K^{(2)}\right)=\min \_{U \in \mathrm{U}\_n} \frac{1}{\sq... | Rebuttal 1:
Rebuttal: [Q1] The KOM discrepancy can be understood as a measure of the difference between two sets of kernel mappings…Orthogonal Procrustes Problem ... KOM is superior to kernel alignment and $\ell_2$ distance…no real theoretical argument …
[A1] We will clarify that the KOM discrepancy presents the first... | Summary: The paper focuses on "feature evolvable learning" -- a setting in which features are being learned during a data stream, with new features learnt over time. The goal is not to retrain the model from scratch but to transfer from an older feature space to a new feature space. Similar problems have been studied i... | Rebuttal 1:
Rebuttal: [Q1] One main weakness of the paper is that it does not leverage the power of deep learning … a follow-up work could consider the Neural Tangent Kernel framework that has been developed for the approximate analysis of neural networks…
[A1] We will clarify that this work tries to answer two fundam... | Summary: This paper tackles the problem of feature evolvable learning in streaming data settings, where features may vanish and emerge over time—a scenario that arises in applications like sensor networks or dynamic monitoring systems. The authors propose a new metric, the Kernel Ortho-Mapping (KOM) discrepancy, which ... | null | null | null | null | |
BARNN: A Bayesian Autoregressive and Recurrent Neural Network | Accept (poster) | Summary: This paper introduces a Bayesian version of an RNN by modeling the time-dependent weights as a reparameterized random variable, where the posterior is learned in an amortized way using a time-dependent ELBO. A specific variational posterior parameterization is provided, where each layer’s weights are parameter... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their time and valuable feedback. In particular, we are delighted that the reviewer believes our work *is of pragmatic interest due to its broad applicability in uncertainty-aware sequence modeling*, and that their appreciate the experiment section. We answer the que... | Summary: This paper addresses the problem of uncertainty quantification with autoregressive and recurrent neural neworks. Variational Bayes method is applied to infer the posterior distribution of network parameters, and further techniques are developed using variational dropout methods. Applications are made to PDE so... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in making constructive comments and providing us with additional references. We are glad that the reviewer finds our experiments *nice* and acknowledges that *the choices of applications are real-world driven*. We answer below the questions raised by th... | Summary: This article proposes a new Bayesian recurrent neural network, mainly by introducing variational dropout into recurrent neural networks. Experiments can prove the model's ability to quantify uncertainty.
Claims And Evidence: This paper builds a variational Bayesian autoregressive and recurrent neural network.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments, and we appreciate that the reviewer finds the paper *well written* with the experiments *sufficient to demonstrate the validity of the model*. We address the reviewer's comments point by point below in the hope of clarifying some misunderstandings... | Summary: This paper introduces BARNN, a framework designed to turn any autoregressive or recurrent deep learning model into a Bayesian version with minimal modifications. The authors propose jointly modeling both the states (e.g., tokens, PDE solutions, molecule strings) and the model’s weights as they evolve in time. ... | Rebuttal 1:
Rebuttal: We are delighted that the reviewer finds our approach *elegant and general, allowing flexible application across domains* and the tVAMP prior and time-dependent dropout mechanics *novel and easy to implement*. We appreciate that the reviewer considers that our *claims are generally supported by we... | null | null | null | null | null | null |
Unpaired Point Cloud Completion via Unbalanced Optimal Transport | Accept (poster) | Summary: This paper proposes UOT-UPC, a novel approach to unpaired point cloud completion using the Unbalanced Optimal Transport framework. The model formulates the completion task as an optimal transport problem and trains a neural network-based Neural OT map to learn the transport mapping from incomplete to complete ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our manuscript and providing valuable feedback. Moreover, we appreciate the reviewer for considering our work addresses "limitations in existing heuristic-driven methods" by "bridging OT theory and unpaired point cloud completion". We hope our ... | Summary: This paper studies the problem of reconstructing 3d objects from partial observations, an important problem in real-world graphics applications. While some approaches to this problem consider settings where a large dataset of partial and full observations of the same objects are available, this work focuses on... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our manuscript and providing valuable feedback. We are especially grateful for the reviewer’s recognition of our contribution, noting that “this paper pushes forward the state of the art of point cloud completion by considering the unpaired pro... | Summary: This paper introduces Unbalanced Optimal Transport Map for Unpaired Point Cloud Completion (UOT-UPC), which is a novel point cloud completion approach that uses unpaired point clouds during training. Unlike previous approaches that have formulated the point cloud completion task as an optimal transport problem... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our manuscript and providing valuable feedback. Moreover, we appreciate the reviewer for considering "the most important contribution is that this paper is the first to bring UOT to unsupervised point cloud completion". We hope our responses to... | null | null | null | null | null | null | null | null |
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation | Accept (poster) | Summary: This work presents a loss function for semantic segmentation based on the steerable pyramid decomposition of images, *i.e.*, wavelet transform. The insight is that the steerable pyramid preserves structural similarity while capturing multi-scale features across multiple orientations. The proposed CWMI consists... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and the helpful list of recent models and loss functions for comparison. In response, we have conducted additional experiments using Vision Mamba UNet (VM-Unet, Ruan et al, 2024), a segmentation architecture based on the recently proposed MedMamba ... | Summary: This paper proposed a complex wavelet-based loss function and proved its effectiveness in several segmentation tasks.
## update after rebuttal”
After I review the author's response and other reviewers' comments, I raise my score to 2 to thank the authors for their efforts. No higher score because the contrib... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the opportunity to clarify our motivation and contributions. We respectfully disagree with the assessment that the proposed CWMI loss function is incremental and provides only marginal improvements over traditional losses like L1 or L2. Below, we address ... | Summary: This paper introduces Complex Wavelet Mutual Information (CWMI) loss for semantic segmentation tasks. The proposed method employs a complex steerable pyramid to perform multi-scale and multi-orientation wavelet decomposition on both prediction and label images, computing mutual information in each subband to e... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We especially appreciate the recognition of the novelty and practical value of our proposed CWMI loss. Your comments helped us refine the manuscript and gain deeper insights into both theoretical and empirical aspects of... | null | null | null | null | null | null | null | null |
Meta Optimality for Demographic Parity Constrained Regression via Post-Processing | Accept (poster) | Summary: This paper considers fair regression with respect to statistical parity under the attribute-aware setting. The main focus/contribution is on obtaining a minimax rate for learning fair regressors:
1. Taking the post-processing perspective/approach to achieving fairness, and leveraging the error decomposition r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed feedback. Here, we address the main concerns:
W1:
A pertinent example is when $f^*_{\mu,s}$ is a composition of multiple functions, i.e., $f^*_{\mu,s} = g_q \circ ... \circ g_0$, within the Holder class as used by Schmidt-Hieber (2020), and $\vartheta^*_{\mu... | Summary: This paper studied the fair regression problem with demographic parity as a constraint. It claimed that existing minimax optimal regression algorithms are coupled with data generation methods, and proposed meta-theorems to validate the fair minimax optimality. Then they demonstrated that the optimal regression... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comprehensive feedback and would like to address their main concerns:
W1: Rawlsian Fairness
We wish to clarify that Rawlsian fairness is fundamentally different from equality-based fairness concepts like demographic parity and equalized odds. Rawlsian fairness focuse... | Summary: This paper investigates the theoretical properties of fair regression problems by leveraging optimal transport techniques. It provides important theoretical bounds in the context of fair regression, and designs regression algorithm matching the upper bound.
Claims And Evidence: Yes. The overall structure of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and for recognizing the importance of our theoretical contributions in fair regression. | null | null | null | null | null | null | null | null |
Preference Learning for AI Alignment: a Causal Perspective | Accept (poster) | Summary: This paper proposes a causal framework for preference learning in the context of aligning LLMs with human values. The authors argue that relying solely on observational data can lead to reward functions that pick up spurious correlations rather than true causal drivers of user preferences. To address this, the... | Rebuttal 1:
Rebuttal: Thank you for your review and for engaging with the core ideas of our work. We appreciate your time and feedback, and we’d like to address your concerns in hopes of clarifying our contributions and positioning.
**The semi-synthetic design** We appreciate the reviewer’s attention to our experiment... | Summary: - This paper introduces a causal framework for preference learning in AI alignment, specifically focusing on reward models trained on LLM prompts and response pairs. The authors frame prompt-response-response tuples as treatment variables, with latent rewards for each prompt-response combination serving as med... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful review. We appreciate the overall positive evaluation of our work. Below we address your comments and concerns:
**The Multihead architecture** We appreciate your suggestion, **ACTION:** we clarify how the Multihead architecture incorporates the causal st... | Summary: This paper uses a causal framework to articulate several assumptions commonly made when reward modeling from preference data. Namely, users are modeled as having implicit rewards which they assign to each response: hence, a preference label is formalized as being a function of these two potential rewards. The ... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful evaluation of our work. We appreciate the time and effort you put into assessing our paper. Below, we address your questions and comments point by point.
**Presentation of assumptions** We acknowledge your concern regarding the potential misinterpretatio... | Summary: This paper introduces a causal framework for preference learning in AI alignment, aiming to improve the robustness of reward models. Reward modeling from preference data is a crucial step in aligning large language models (LLMs) with human values. The authors propose integrating causality into reward modeling,... | Rebuttal 1:
Rebuttal: Thank you for the time taken to evaluate our work. We your recognition of our work as a meaningful addition to the broader literature. Below we address your comments:
**Presentation.** In the camera-ready version, we will use the additional space to clarify key intuitions, especially for readers ... | null | null | null | null | null | null |
ML$^2$-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning | Accept (poster) | Summary: This paper proposes a lightweight graph contrastive learning (GCL) framework that integrates manifold learning theory, i.e., Manifold Learning Inspired Lightweight Graph Contrastive Learning (ML^2-GCL), aiming to optimize embedding representations through geometric structural constraints while reducing the com... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback and careful reading. Below, we will provide a point-by-point response.
W1: See Q of **Reviewer m6R5**.
**W2: This may be caused by software incompatibility. You can try Adobe Acrobat 10.0 to open the PDF file.**
W3: We will modify this i... | Summary: Recent years have witnessed a phenomenon that graph contrastive learning faces the balance between effectiveness and efficiency. In spite of its popularity and success, several potential risks including underlying semantic disturbance brought by augmentation strategies, failure of GCN in capturing long-range d... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback and attention. Here, we will provide a point-by-point response.
W1: Thank you for your attention. In fact, the proposed novel contrastive loss can be regarded as personalized graph embedding, where positive pairs with larger weights should ... | Summary: This paper explores an effective and lightweight graph contrastive learning method called ML^2-GCL, highlighting the need for a deeper understanding of graph contrastive learning methods from a manifold learning perspective. ML^2-GCL recovers global nonlinear structure from locally linear fits with closed-form... | Rebuttal 1:
Rebuttal: Thanks for the reviewer’s careful reading and valuable suggestion. In the following, we will provide a point-by-point response.
Wa: In future, we will investigate deep integration mechanisms between manifold learning and dynamic graph structures to address nonlinear evolution patterns in temporal... | Summary: As a mainstream and representative unsupervised learning method, contrastive learning has achieved great success in the field of computer vision. Inspired by such achievements, graph contrastive learning (GCL) has attracted much interests in the past few years. Despite its excellent performance, GCL suffers fr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback and valuable comments. Below, we will provide a point-by-point response to each comment.
W1: We have briefly discussed this in Introduction. Here, we will give a detailed discussion.
**Similarities**
**1. Consistency in Core Objectives**... | null | null | null | null | null | null |
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning | Accept (poster) | Summary: This paper studies transfer learning where one aims to learn a good policy for a target domain with data collected from multiple source domains. And they consider the distributed and decentralized setting, where one central server can only access partial data from source domains. The authors apply the principl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and feedback. Please refer to the link https://anonymous.4open.science/r/ICML-2663/README.md for our newly conducted experiments.
**Negative transfer**
Our initial environments were relatively simple, so negative transfer was not evident. To support ou... | Summary: The paper introduces a novel pessimism-based transfer learning framework to address critical challenges in zero-shot transfer RL. The authors propose constructing conservative proxies—via robust Bellman operators and novel aggregation schemes (both averaged and minimal pessimism operators)—that yield lower bou... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and feedback, and appreciate the reviewer identifying our contribution. Please refer to the link https://anonymous.4open.science/r/ICML-2663/README.md for our newly conducted experiments.
**Hyperparameter sensitivity; Benchmark like contextual MDPs.**
We appre... | Summary: This paper studies zero-shot transfer reinforcement learning. The authors incorporate a pessimism principle into transfer learning to serve as a lower bound to conservatively estimate the target domain’s performance. The authors propose and analyze two types of conservative estimates, rigorously characterizing... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and feedback, and appreciate the reviewer identifying our contribution. Please refer to the link https://anonymous.4open.science/r/ICML-2663/README.md for our newly conducted experiments.
**Upper bound on $\max_\pi\zeta^\pi$.**
We first clarify that $\|\zeta\... | Summary: The paper introduces a novel framework for zero-shot transfer reinforcement learning (RL) based on the pessimism principle. The key idea is to construct a conservative proxy for the target domain's performance, ensuring that the transferred policy achieves a robust lower bound on performance while avoiding neg... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and feedback, and appreciate the reviewer identifying our contribution and novelty. Please refer to the link https://anonymous.4open.science/r/ICML-2663/README.md for our newly conducted experiments.
**Other relevant robust RL approaches**
We thank the review... | null | null | null | null | null | null |
Subobject-level Image Tokenization | Accept (poster) | Summary: This paper presents a method to encode an image at sub-object level. Specifically, it first detects edges and boundaries in the image with a small model, then utilizes the watershed algorithm to segment the image into sub-object parts. The authors conduct both intrinsic evaluations to validate the segment qual... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We realize that there are some concerns that arise from the interplay between token segmentation and token embedding. Therefore, before addressing your comments point-by-point, we first clarify how and why we disentangle these two components in our paper:
... | Summary: Tokenization is an important step for any transformer-based model. For the vision transformers, it is often performed on a patch-level, where locally neighboring parts of the image are tokenized together in the form of small square patches. However, patch-based tokenization is not adaptive, that is it tokenize... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and comprehensive review. We sincerely appreciate the considerable effort and depth of analysis you provided! Below, let us address each of your concerns point-by-point and outline concrete steps we will take to improve the final manuscript:
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### 1. **VLM... | Summary: This paper proposes sub object-level image tokenization, which tokenize image based on the morphological structure of the image. Compared to other potential subobject tokenizers, EPOC improves efficiency. Experiments on multiple VLMs demonstrate the advantages of the subobject tokenizer.
## update after rebut... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and thoughtful questions. Your recognition of the strengths of our method is greatly appreciated. Below are our clarifications on your valuable questions:
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### 1.**Extrinsic evaluations on caption data rather than general VQA** (in “Methods And Evaluation... | Summary: This paper introduces **Subobject-level Image Tokenization**, a novel adaptive image tokenization strategy inspired by subword tokenization in NLP. Previous patch-based image tokenization methods suffer from inefficiencies and polysemanticity. To address these limitations, the paper proposes a new tokenizer ca... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review. Below, we provide detailed responses addressing your specific questions and suggestions:
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### 1. **Formulating Token Monosemanticity Score** (Question 1)
Yes. Below is the explicit formal definition, which will be added to the final version:
... | null | null | null | null | null | null |
Multi-agent Architecture Search via Agentic Supernet | Accept (oral) | Summary: This paper introduces the concept of “agentic supernet”, which transforms the automatic LLM-based multi-agents design paradigm from a static, one-size-fit-all approach to a dynamitic and adaptive framework. Their MaAS framework samples components from the supernet to assemble appropriate multi-agent systems ac... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful comments and thorough understanding of our paper! Here we give point-by-point responses to your comments and describe the revisions we made to address them.
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>**`Weakness 1`: Clarification on parameter $\phi$** The paper lacks clarity regarding the param... | Summary: This paper introduces MAAS (Multi-agent Architecture Search), an innovative framework for automating the design of multi-agent systems powered by Large Language Models (LLMs). MaAS addresses the limitations of existing methods that seek to identify a single, static, and complex multi-agent architecture, which ... | Rebuttal 1:
Rebuttal: We would like to express our sincere respect for your insightful review! In response to your comments, we have carefully prepared a point-by-point reply:
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>**`Essential References Not Discussed`**
Thank you for the valuable supplement! We have added this important citation in our revised manu... | Summary: The paper proposed a novel automated multi-agent framework through agentic supernet (MaAS), which both delivery satisfactory performance and resource allocation efficiency for user queries across different domains. The framework was comprehensively evaluated on six benchmark tasks with comparison to about 15 b... | Rebuttal 1:
Rebuttal: Sincere thanks for the thoughtful and constructive reviews of our manuscript! Based on your questions and recommendations, we give point-by-point responses to your comments.
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>**`Weakness 1: Lack of variance estimation`**
Thank you for your insightful suggestion! In fact, all results in Table... | Summary: This paper introduces a novel mechanism called the "Agentic Supernet" to enable dynamic inference within multi-agent systems. Unlike traditional fixed agentic systems, the supernet and its subnet agents, which are instantiated through parameterized sampling, allowing for adaptive inference across a variety of ... | Rebuttal 1:
Rebuttal: >**`Weakness 1: Insufficient Clarity in Presentation`**
Thank you for the insightful comment! Each layer shares the same set of operators, except for the first layer, where the early-exit operator is excluded. The operators in each layer produce outputs in parallel, which are then concatenated an... | null | null | null | null | null | null |
An analytic theory of creativity in convolutional diffusion models | Accept (oral) | Summary: The paper proposes a formula to predict images generated by convolutional diffusion models. The analysis suggests that biases in convolutional neural networks—such as locality and translational equivariance—prevent diffusion models from learning a perfect score function, encouraging them to generate samples th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. Below, we hope to carefully address a few of their concerns:
>The evidence is promising in the cases of MNIST and FashionMNIST. However, the evaluation based on CIFAR-10 is less conclusive. This is still acceptable because the ResNet-based... | Summary: This work proposes that biases of translation-equivariance and locality are sufficient to explain novel image generation in fully convolutional diffusion generative models. It does so by showing that a closed-form score model subject to those constraints qualitatively recapitulates the images generated by trai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their highly detailed feedback and insightful suggestions. Below, we carefully address several of the points that they raised.
>I would encourage the authors to reconsider their use of the term "creative", as I think "novel" or "original" would be somewhat more precise, ... | Summary: This paper develops a theory for why convolutional diffusion models fail to learn the ideal score function. It is theorized that this is due to locality from small receptive fields and translational equivariance. Under these assumptions, an optimal minimum MSE approximation to the ideal score function is deriv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and suggestions. We address some of these below:
>How important is each constraint in the analytic solution? […] Is it possible to ablate each constraint and observe the correlation with the diffusion samples?
An equivariant score machine on its ... | Summary: This paper presents an analytic theory of generalization in convolutional diffusion models. It identifies that, given a finite empirical dataset, the optimal score function produces a perfect reverse diffusion process, leading to replicas of training samples. The paper then hypothesizes that the creativity of ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and the thoughtful suggestions that they made for our paper. Below we address some of these comments and suggestions.
>Perhaps one additional aspect could be explored: in real scenarios, two factors influence the final generation of diffusion models—the induc... | null | null | null | null | null | null |
BaxBench: Can LLMs Generate Correct and Secure Backends? | Accept (spotlight poster) | Summary: This paper introduces BAXBENCH, a novel benchmark for evaluating large language models' (LLMs) capabilities in generating correct and secure backend applications. The benchmark consists of 392 tasks spanning 28 scenarios implemented across 14 popular backend frameworks in 6 programming languages. BAXBENCH eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and overall positive assessment of our paper. We address their questions below.
**Q1: Can you discuss how SWE Lancer relates to BaxBench?**
We thank the reviewer for pointing us to SWE Lancer and we will gladly add a discussion on it in the next ... | Summary: This paper introduces BaxBench, a benchmark for evaluating LLMs' ability in generating functionally correct and secure backends.
It evaluates LLMs in 28 scenarios and 14 frameworks and show that generating secure and correct backends is still challenging.
Claims And Evidence: Yes, I find the claims in the sub... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and overall positive review, and address their questions below.
**Q1: Do the scenarios provide sufficient semantic diversity?**
Yes. The programs have to handle files, databases, access controls, OS commands, and external binaries (e.g., compilers, png,... | Summary: This paper introduces a benchmark for assessing large language models' abilities to generate application backend code. The benchmark comprises 28 distinct scenarios across 14 popular backend frameworks that specify application requirements, API specifications, environment instructions, and database needs. Eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and constructive review and address their questions below.
**Q1: Can you evaluate coding agents?**
Upon the reviewer’s request, we tested the most advanced open-source general coding agent, OpenHands (OH) powered by GPT-4o and Claude 3.5 Sonnet.
We use... | Summary: This paper introduces BAXBENCH, a benchmark to evaluate LLM-based generation of correct and secure backend applications. Functionality of generated code is validated through testing, while security is evaluated through end-to-end exploits. The authors evaluated 10 LLMs on BAXBENCH and found that even the best ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review and overall positive assessment. We address their questions below.
**Q1: How can models be improved on BaxBench?**
To understand functionality challenges in BaxBench, we manually investigate 20 incorrect programs generated by OpenAI o1, and find ... | null | null | null | null | null | null |
Ensemble Learned Bloom Filters: Two Oracles are Better than One | Accept (poster) | Summary: This paper introduces Ensemble Learned Bloom Filters (ELBF), an approach to improving the performance of Learned Bloom Filters (LBF) by leveraging multiple learning oracles of smaller size instead of a single large oracle. The authors formulate the ELBF design as a combinatorial optimization problem: given a p... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. They are mostly insightful and make us step backwards to rethink several design issues of our algorithm. Please find our response below.
**Answer to Q1.**
- **Performance of ELBF w.r.t. number of oracles $n_o$:** Theoretically (worst-case complexity bound... | Summary: This paper studies Learned Bloom Filters (LBF), which enhance traditional Bloom Filters with a learned model (oracle) as a pre-filter. A key challenge in single-oracle LBFs is that the oracle’s size can become a bottleneck when the overall space budget is limited. To address this, the authors propose an ensemb... | Rebuttal 1:
Rebuttal: We sincerely thank you for the positive feedback. We envision at least two future research directions related to this work: (1) developing theoretically proven algorithms for the correlated case, either exact or approxiation algorithms, ideally with low complexity, (2) extending our idea of orches... | Summary: This problem examines whether combining multiple learned oracles can generate a system of lower false positive. In the first case, where each learned oracle is paired with a separate filter, the authors provide theoretical analysis to formulate the problem as a knapsack problem and use dynamic programming to s... | Rebuttal 1:
Rebuttal: We sincerely thank you for the positive feedback. Our greedy algorithm developed in Section 4 is tested and evaluated in our primary experiments in Section 6 on Experiments, where it outperforms baselines under various memory constraints. More in-depth experiments are presented in Appendix due to ... | null | null | null | null | null | null | null | null |
Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting | Accept (poster) | Summary: This paper focuses on improving the perceptual quality of 3DGS by 1) extracting edges to represent perceptual sensitivity and embedding it into the primitives for supervision; 2) introducing additional densification strategy based on the sensitivity. Experiments show that the proposed method achieves SOTA perf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:
**Q1. Interpretable Visualizations**
Thank you for your suggestion. Since our method is designed to optimize ... | Summary: This paper proposes a 3D Gaussian Splatting (3DGS) optimization method that leverages additional perceptual sensitivity. By incorporating visual sensitivity, specifically edge response, the approach enables more fine-grained optimization of Gaussian representations. The perceptual sensitivity-adaptive densific... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:
**Q1. Discussion of 3DGS-MCMC**
Eq. 9 in 3DGS-MCMC aims to **maintain** the opacity of a spatial region befor... | Summary: The paper introduces Perceptual-GS, a method to improve 3D Gaussian Splatting (3DGS) for novel view synthesis by integrating a perceptual-sensitivity mechanism during training. Concretely, the authors compute gradient-based sensitivity maps to model human perception of local structures, then employ a dual-bran... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:
**Q1. Concerns about Engineering-driven Incremental Improvements**
To the best of our knowledge, our work is ... | Summary: Perceptual-GS addresses a core limitation of 3D Gaussian Splatting (3DGS) for novel view synthesis by adaptively distributing Gaussian primitives based on human perceptual sensitivity. Traditional 3DGS methods suffer from either insufficient coverage in visually important areas or over-densification in simpler... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:
**Q1. Concerns about the Generalizability**
Thank you for the valuable suggestion. To further demonstrate the... | null | null | null | null | null | null |
Learn Beneficial Noise as Graph Augmentation | Accept (poster) | Summary: The paper proposes a graph contrastive learning method called Positive-incentive Noise driven Graph Data Augmentation (PiNGDA), which makes the model learn to generate perturbations that benefit the training. Comprehensive experiments are conducted to evaluate the performance of the method.
Claims And Evidenc... | Rebuttal 1:
Rebuttal: **Response to Reviewer iJqw**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1&W2:** As is mentioned in 'Experimental Designs or Analyses', the detailed hyperparameter of the training is not listed. Moreover, as is mentioned... | Summary: This paper proposes a framework named Positive-incentive Noise driven Graph Data Augmentation (PiNGDA). It theoretically analyzes the drawbacks of the existing data augmentation in GCL and leverages a π-noise generator to learn beneficial noise as the augmentations for GCL. Meanwhile, they also design a differ... | Rebuttal 1:
Rebuttal: **Response to Reviewer J7B2**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1&2&3&4:** The relationships between the proposed model and the learnable methods are not discussed in Section 1. The graph contrastive learning ta... | Summary: This paper proposes a graph data augmentation method based on beneficial noise. The noise generator learns the optimal perturbation of graph structure and node features to solve the problem of insufficient stability of traditional data augmentation strategies in graph contrastive learning. Experimental verific... | Rebuttal 1:
Rebuttal: **Response to Reviewer smN2**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1&Q1:** The theoretical analysis in Section 3.3 is interesting, but the definition of task entropy are unclear.The theoretical analysis in Section ... | Summary: This paper proposes a novel method called PiNGDA for addressing the instability of traditional heuristic augmentation techniques in graph contrastive learning (GCL). The authors introduce the concept of π-noise, which is beneficial noise that reduces task complexity, and design a trainable noise generator to p... | Rebuttal 1:
Rebuttal: **Response to Reviewer LdjD**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1:** Innovation of application of $\pi$-noise to GCL.
**Reply:** Although $\pi$-noise has been explored in other fields, its adaptation for graph ... | Summary: This paper proposes a graph contrastive learning (GCL) methods, namely PINGDA, with a novel learnable graph augmentation. The learnable augmentation follows a new information theory framework, namely positive-incentive noise. The authors propose to view all augmentations as “noise” and thus design a new algori... | Rebuttal 1:
Rebuttal: **Response to Reviewer 65ie**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1:** The introduction of positive-incentive noise and the discussions with the existing papers of noise are not enough, especially in the main pape... | Summary: This work introduces the PiNGDA method, designed to enhance graph data augmentation through the incorporation of beneficial noise. The paper also introduces the concept of task entropy, offering a fresh lens through which to comprehend the objective function of contrastive learning. Practical results demonstra... | Rebuttal 1:
Rebuttal: **Response to Reviewer PRCX**
We greatly thank you for the detailed and valuable comments. Please find our responses to the comments as follows:
>**W1:** The depth of the discussion of the experimental results can be further improved, such as the explanation of certain experimental phenomena and... | null | null |
How to Synthesize Text Data without Model Collapse? | Accept (poster) | Summary: This paper introduces a novel approach to generating semi-synthetic text data to address the issue of model collapse when trained with synthetic data. The method is supported by a solid theoretical framework under a simplified linear model setting. Extensive experiments validate the effectiveness of the approa... | Rebuttal 1:
Rebuttal: We are grateful for your positive feedback and insightful comments. Below, we give detailed responses to your questions.
> [Q1] : The solution authors provided is to use semi-synthetic data instead, which seems deviated from what the title suggests.
We will revise the wording and provide furth... | Summary: The paper is twofold: The first part of the paper focuses on the effects of mixing real and synthetic data and what the authors call non-iterative model collapse. The second part of the paper proposes ToEdit, a method to adjust synthetic text data by resampling those tokens that have high probability to be gen... | Rebuttal 1:
Rebuttal: We are grateful for your enthusiastic feedback. Below, we give detailed responses to your questions.
> [Q1] : References Not Discussed
We will include a discussion in the revised version as follows:
[1] demonstrates that without enough fresh real images, future generative models will graduall... | Summary: This paper investigates the issue of model collapse. Model collapse happens when training models on synthetic data cause performance degradations or, in some scenarios, complete model breakdown. The authors discuss the negative correlation between the proportion of synthetic data and model performance, even wi... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. In the following, we will address your concerns accordingly.
> [Q1] : References Not Discussed
We will include a discussion on all the provided references as follows:
[1] develops a rigorous framework to demonstrate the importance of real data in maintainin... | Summary: The issue that is addressed by this paper, using synthetic data during pretraining, is a very important and timely one. Going forward, pretraining will use a higher, and eventually dominant, proportion of synthetic data. The main findings are in 3.2, the three failure modes of Cosmopedia, when evaluated using ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your critical feedback and valuable suggestions. Below, we will strive to address your concerns and refine our paper accordingly.
> [Q1] : I think the problem with "synthetic data" is with a particular synthetic data (Cosmopedia) only. This paper can lead to a false imp... | null | null | null | null | null | null |
Safely Learning Optimal Auctions: A Testable Learning Framework for Mechanism Design | Accept (poster) | Summary: The authors study a variant of Rubinfeld and Vasilyan's *testable learning* in mechanism design, and give a concrete tester-learner for basic auction settings.
Many classical results in mechanism design, e.g. Myerson's Optimal Mechanism for auctions, require the underlying distribution over valuations be *reg... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our paper. We appreciate the constructive feedback and comments.
We would like to address your concern that our relaxed version of the completeness guarantee is too weak for what we wish to show. We agree that the completeness condition as mistakenly written in a ... | Summary: Summary:
The paper proposes a framework for testably learning revenue-optimal auctions. In this setting, an auction designer has access to m samples (bids) and aims to design a DSIC and IR auction that maximizes revenue. Unlike prior work, which typically assumes conditions like regularity or MHR without test... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our paper and providing helpful feedback and comments. We would first like to address your presentation concerns. We are committed to improving the preliminaries by ensuring that the definitions are introduced in the proper order. We will also improve the “m notatio... | Summary: This paper considers auctions with possibly regular or near regular distributions, and considers a two step process of a) testing for regularity, and b) designing an approximately optimal auction if the distribution tests as near regular. Regularity is a key distributional assumption in auction theory: it stat... | Rebuttal 1:
Rebuttal: Thank you for reading our paper and appreciating our results. We would first like to address the importance of our work in the context of Roughgarden and Schrijvers (2016) and, more generally, Guo et al. (2019) [2]. Both of these papers provide sample complexity results for learning optimal auctio... | null | null | null | null | null | null | null | null |
Context is Key: A Benchmark for Forecasting with Essential Textual Information | Accept (poster) | Summary: This paper introduces the "Context is Key" benchmark to evaluate the capability of models in leveraging textual information for time series forecasting. By designing 71 tasks and evaluating them with both human and large language model (LLM) annotators, the study confirms the significant role of contextual inf... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful response. We appreciate your recognition of our work as addressing a gap in the field, offering valuable insights for future developments, and providing clear and convincing findings. We address your concerns below and are happy to clarify any further points.
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#... | Summary: The paper introduces Context is Key (CiK), which is a benchmark aiming to evaluate forecasting models’ ability to integrate both numerical time-series data and essential textual context. Unlike traditional forecasting benchmarks that rely solely on numerical data, CiK explicitly requires models to process and ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful response. We are grateful that you highlighted the thoroughness of our experiments, the value of the RCRPS metric, and the quality of the writing. We are pleased that you see the CiK benchmark as filling a critical gap in the literature, with real-world implications a... | Summary: This paper introduces "Context is Key" (CiK), a benchmark for time-series forecasting models that incorporate textual context alongside numerical data. The authors design 71 tasks across seven domains where textual information is essential for accurate forecasting, propose a Region of Interest CRPS (RCRPS) eva... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. We appreciate the recognition of our contributions, including the comprehensive empirical evaluations, task diversity and quality, the soundness of the proposed metric, and the thoroughness in handling data contaminations. Below, we clarify several points rais... | null | null | null | null | null | null | null | null |
Radio: Rate–Distortion Optimization for Large Language Model Compression | Accept (poster) | Summary: The authors propose to utilize rate-distortion theory to guide the allocation when quantizing LLM. Through extensive tests on benchmark datasets and pre-trained models, it is shown that the proposed approach can provide improvement on the state of the art when quantizing LLMs for 3 or 4 bits per parameter on a... | Rebuttal 1:
Rebuttal: Thank you, especially for initiating a discussion around water-filling.
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*1. For the first claim, …, unclear if the key factor that enables the performance is actually the R-D theory formulation, or a better estimation of the stats ...*
The manuscript already provides the brakdown of performa... | Summary: This paper proposed a rate-distortion optimization framework, Radio, for compressing large language models (LLMs) via quantization. Specifically, this paper formulate quantization as distortion problem and try to minimize it by assigning bit depth to weight groups.
Claims And Evidence: Most claims are support... | Rebuttal 1:
Rebuttal: Thank you, especially for checking the derivation of (5)!
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*1. However, I wonder whether the time cost increases exponentially as the model size grows. How does Radio’s quantization time scale with model size (e.g., OPT-175B), and how does it compare to GPTQ’s runtime?*
Thank you for raising ... | Summary: The paper
* formulates a rate-distortion theoretic framework for quantization of LLMs
* designs an algorithm to solve the optimization (for model compression) resulting from that framework
* runs the model compression method on various models to show the aspects
## update after rebuttal
My assessment won't ch... | Rebuttal 1:
Rebuttal: Thank you, especially for checking through the individual equations.
____
*1. Perplexity across optimization iterations (Figure 4) is not very well explained.*
Thank you for pointing this out. Figure 4 shows how quantized model accuracy (in terms of perplexity) improves as more gradient variances... | Summary: The paper focuses on the problem of compressing Large Language Models (LLMs) for efficient deployment on resource-limited devices. It proposes Channel-Wise Mixed-Precision Quantization (CMPQ), a novel mixed-precision quantization method. CMPQ allocates quantization precision in a channel-wise pattern based on ... | Rebuttal 1:
Rebuttal: Thank you! First, we kindly clarify that our work is entitled “Radio”, not “CMPQ”. CMPQ appears to refer to a prior work by different authors.
____
*1. The introduction of multiple components such as channel-wise quantization, non-uniform quantization, and two types of outlier protection may make... | null | null | null | null | null | null |
LASER: Attention with Exponential Transformation | Accept (poster) | Summary: This paper addresses the problem of vanishing gradients in standard dot-product attention in transformers. The authors show mathematically how this problem arises in the Jacobian of the attention function during backpropagation. Next, a new technique called LASER (Logarithm of Summed Exponentials of Representa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments and suggestions.
### Addressing Other Comments or Suggestions
**Comment 2:**
Unfortunately, this is a typo — it is supposed to be `exp(v_1) - exp(v_2) >> 0`. While `v_1 - v_2` might not be significantly different, the difference between `exp(v_1)` and `ex... | Summary: This paper studies the problem of Gradient saturation in softmax in the attention architecture. Thus, it proposes LASER, a log-sum-exp structure to replace the original dot-product formulation in attention with Log-Weighted-Sum-Exp Trick. Extensive experiments on different benchmarks and models, with in-depth ... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s helpful observations.
> I'm curious about why LASER is stabler than vanilla attention mechanism at training time, since the author mentioned that LASER introduces larger gradient norm, intuitively this may cause fluctuation in training. Can the author provide some ana... | Summary: The papers studies the gradients during backpropagation of the standard softmax dot product attention within transformers. The authors' key insight is that these gradients can be extremely small, leading to poor gradient signal propagation, which in turn leads to sub-optimal learning. They then suggest a modif... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback.
> **Reviewer:** The authors simply show that gradients saturate in the attention gradients during backpropagation and then show that by applying an exponential on the values and a logarithm on the softmax probabilities one can produce a gradient t... | Summary: This paper introduces LASER (LogArithm of Summed Exponentials of Representations), a novel attention mechanism for Transformers. The researchers found that in the standard attention mechanism, the gradients backpropagated through the softmax operation can be small, which may lead to inefficient learning of par... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive comments.
> The paper mainly focuses on models with a certain scale of parameters, and it's not clear how well it will perform in models with extremely small or large numbers of parameters.
We trained a model with **7.7B parameters scaling up by 3.... | null | null | null | null | null | null |
On the Local Complexity of Linear Regions in Deep ReLU Networks | Accept (poster) | Summary: This paper suggests a new measure for analyzing feedforward neural networks based on the density of points where the model’s gradient is discontinuous (“kinks”) near training samples, which they term Local Complexity (LC). It builds opton prior findings that neural networks tend to form fewer linear regions, e... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and detailed comments which have helped us improve our paper. We have addressed your thorough list of minor errata for the camera-ready version. Below we address each major point raised.
> The LC term was previously introduced in a paper ... Th... | Summary: The authors introduce two novel metrics, Local Complexity (LC) and Local Rank (LR), to analyze the structure of linear regions in ReLU networks. These metrics provide insights into the relationship between network complexity, feature representations, adversarial robustness, and representation cost. Theoretical... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and detailed comments which have helped us improve our paper, and their positive review. We have addressed your minor errata for the camera-ready version.
> The method for estimating LC via bias perturbations could be better motivated, particu... | Summary: The paper studies ReLU networks and proposes local complexity that estimates the average density of gradient discontinuities under an input distribution and a parameter set. Three theorems are provided. First, it is shown that local complexity can be estimated by gradients at each neuron. Second, it is establi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and detailed comments. We have addressed minor errata for the camera-ready version.
> It is not clear to the reader why the local rank is defined through the Jacobian. What is its relation to the rank of the weight matrix? ... Would this direct... | null | null | null | null | null | null | null | null |
Fraud-Proof Revenue Division on Subscription Platforms | Accept (poster) | Summary: The paper "Fraud-Proof Revenue Division on Subscription Platforms" addresses the problem of revenue distribution on subscription-based platforms, particularly in the context of music streaming services. The authors formalize three types of manipulation-resistance axioms—fraud-proofness, bribery-proofness, and ... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our response below.
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> Comparison with Other Mechanisms: The paper compares ScaledUserProp with GlobalProp, UserProp, and UserEQ. Are there other mechanisms in the literature that the authors considered but did not include in their analysis? If so, why we... | Summary: The authors propose a mechanism-design framework to counter manipulation in subscription-based streaming platforms. They formally define three types of fraudulent behaviors and illustrate how the commonly used “global proportion” revenue rule is highly vulnerable. To address this, they introduce some new axiom... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our responses below.
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> Main question: can you convince me that there’s an audience for this work at ICML? ... This led me to set my score as "Weak Reject," despite the paper's clarity of presentation and interesting problem, but I am open to being shown ... | Summary: This paper examines fraud-proof mechanisms in subscription platforms. Specifically, the authors define a set of axioms covering fundamental properties, protection against strategic manipulation, and fairness in revenue division mechanisms. They analyze commonly adopted mechanisms and verify which axioms they s... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our responses below.
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> “In the axioms of fraud-proofness, the final constraint is given as $\phi_{I'}(\hat{C}) - \phi_{I}(\hat{C}) \leq \hat{n}$. Why is the right-hand side not generalized to a more flexible form, such as $\hat{n} \cdot A$, where $A$ is ... | Summary: The paper explores fraud-proof revenue division on subscription platforms like Spotify and Apple Music, where users pay a fixed fee for unlimited access, and creators are compensated based on engagement. Current revenue-sharing rules, like GLOBALPROP (proportional to total streams), are vulnerable to manipulat... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our responses below.
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> The claim that “SCALEDUSERPROP (SUP) is the fairest alternative” is not fully substantiated. ... Additionally, the paper does not address how the proposed fairness notion might impact the platform’s long-term ecosystem, including i... | null | null | null | null | null | null |
Minerva: A Programmable Memory Test Benchmark for Language Models | Accept (poster) | Summary: Minerva introduces a thorough evaluation set to test the memory abilities of different LLMs. The evaluation set is generated with parametric programs and covers a wide breadth of different memory skills such as information retrieval and localization, processing, content transfer and structural awareness. Test... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and positive assessment of our paper. Below, we address your main concerns:
**Use of in-context examples**
We intentionally did not include in-context learning examples, as our goal is to evaluate models' inherent memory capabilities, rather than their ab... | Summary: Paper presents a new benchmark for evaluating LLMs' long-context problem-solving abilities. The proposed benchmark include atomic tasks (searching, recalling, editing, matching, etc) that evaluate models on tasks that go beyond those commonly explored (passkey, key-value, needle in the haystack). Experiments p... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive assessment of our work and thoughtful feedback.
One key takeaway from our experiments is that different models exhibit high variance across atomic memory tasks, reinforcing the need for a diverse evaluation suite. In addition, we found that a major ... | Summary: This paper presents a framework for automatically generating a broad set of tests to evaluate LLMs' memory usage. Going beyond simple search, the benchmark assesses tasks like recalling, editing, matching, and tracking information across distinct data blocks. Experiments reveal that while models handle basic r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments and the opportunity to clarify our contributions.
**Position of the paper and fitness with ICML**
The reviewer raises the concern that our paper does not introduce new theoretical concepts. The paper does not aim to be theoretical. The nature of memory analy... | Summary: This paper introduces a framework for systematically evaluating the memory utilization capabilities of language models. Expanding beyond conventional memory tests—such as passkey retrieval, key-value lookup, and needle-in-the-haystack search—the proposed framework assesses models on a broader range of atomic t... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback and the chance to clarify our contributions.
**Insights from the benchmark**
Our main goal is not to rank known models, but rather to introduce tests to evaluate different functionalities needed for LLM (agents). Any model can be (1) tested using our benchm... | null | null | null | null | null | null |
Synonymous Variational Inference for Perceptual Image Compression | Accept (poster) | Summary: This paper proposes a novel framework for perceptual image compression based on synonymous variational inference (SVI). Specifically, the paper introduces a method to analyze the optimization direction of perceptual image compression using semantic information theory. A new image compression scheme called Syno... | Rebuttal 1:
Rebuttal: Dear Reviewer KhzF,
Thank you for recognizing our work, especially **the originality and novelty of our SVI theory analysis for perceptual image compression** and our **well-structured and clearly presented**.
Your concern centers on our unsatisfactory experimental results, which is also a conce... | Summary: This paper presents a novel perspective to analyze the perceptual image compression problem, which is based on the notion of synonymy in semantic information theory, which suggests that images with perceptual similarity constitute a synonymous set. Based on this, the authors propose a synonymous variational in... | Rebuttal 1:
Rebuttal: Dear Reviewer 7ghf,
We sincerely appreciate your high regard for our work, especially your recognition of our work including:
- **New theoretical perspective on perceptual image compression**
- **Mathematically supported unified theory**, which can **extends and unifies the existing RD and RD... | Summary: This paper introduces a novel progressive training approach for image compression, which focuses on both improving image quality and maintaining semantic consistency during compression. By using synonymous latent representations, the model progressively decodes and recovers image details, ensuring high-quality... | Rebuttal 1:
Rebuttal: Dear Reviewer VZKv,
We sincerely appreciate your recognition of our SVI analysis, especially your understanding of **semantic consistency** and the **crucial potential in multi-modal compression tasks of our SVI theory**.
Your concerns are essential for refining our paper and guiding future rese... | Summary: This paper is about perceptual image compression, where previous works measure the perceptual quality by calculate certain divergence distance between the source distribution and the reconstructed distribution.
This paper is inspired by a recent advancements in semantic information theory (Niu & Zhang, 202... | Rebuttal 1:
Rebuttal: Dear Reviewer uGiv,
Thank you for recognizing our contributions, especially the viewpoint that **our SVI theory will provide important and new insight for learned image compression community**.
We believe that your questions and suggestions are crucial for improving our work. Below are our respo... | Summary: This paper proposes synonymous variational inference and introduces synonymous image compression. It is based on the observation that a given image to be encoded has a set of synonymous images that share the same semantic meaning. Instead of optimizing the variational distribution at the pixel level, we optimi... | Rebuttal 1:
Rebuttal: Dear Reviewer 1wA9,
Thank you for recognizing our work, especially our proposed analysis theory, i.e., **synonymous variational inference (SVI)**, as **a good contribution to the area of perceptual image compression**.
We think that your questions are valuable for improving our work. Below are o... | null | null | null | null |
Task Generalization with Autoregressive Compositional Structure: Can Learning from $D$ Tasks Generalize to $D^T$ Tasks? | Accept (poster) | Summary: This paper demonstrates that for Boolean tasks with an AutoRegressive Compositional Structure (e.g., sparse parity), using chain-of-thought (CoT) to break down the tasks into simpler sub-problems—while predicting the outcomes of intermediate steps—significantly improves generalization in the GPT-2 model.
Clai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Below, we address each point in detail.
> **(1) Tasks that were previously unlearnable without CoT (2) The method also appears to have an advantage...**
We thank the reviewer for raising this question regarding whether the difference betwee... | Summary: This paper presents the simple but useful and intuitive idea that *chain-of-thought generation reduces the theoretical complexity of compositional problems, and therefore leads to theoretically and empirically less required samples for strong generalisation* to unseen functions. Specifically, prior work has sh... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's support and valuable suggestions. Below, we address each point in detail.
> The sequential translation task is not a real-world language task.
Thank you for pointing this out. We agree the translation example is synthetic and will clarify that our goal was ... | Summary: This paper investigates when models trained on a small set of tasks can generalize to a much larger task family. The authors approach this through the lens of "autoregressive compositional structure" (ARC), where tasks are composed of T sequential operations, each chosen from D possible subtasks, creating a to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and valuable suggestions. Below, we address each point in detail.
>Dependency on $d$ and $k$ in Experiments
We agree that a broader range of $d$ and $k$ provides additional support for our findings. However, both $d$ and $k$ appear in the base and exponen... | Summary: This paper investigates task generalization in large language models (LLMs) through the lens of AutoRegressive Compositional (ARC) structure. The central question explored is: When can learning from a small set of tasks enable generalization to a much larger task family? The authors propose that LLMs, particul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive review. Below, we address each point in detail.
> Scope is limited to synthetic tasks
Our goal is to establish a foundation where the compositional structure of tasks is clearly defined and generalization to unseen tasks is fully controll... | null | null | null | null | null | null |
FrameBridge: Improving Image-to-Video Generation with Bridge Models | Accept (poster) | Summary: This paper introduces FrameBridge, a novel approach to improve image-to-video (I2V) generation using diffusion models. The authors address the mismatch between the noise-to-data generation process of traditional diffusion models and the I2V task, which can lead to suboptimal results. FrameBridge proposes a bri... | Rebuttal 1:
Rebuttal: Dear Reviewer 2c9b,
We sincerely appreciate your recognition of the strengths and effectiveness of our work and the valuable suggestions to help us improve that. We are happy to have a discussion and hope it could address your concerns. Tables are provided in https://framebridge-icml.github.io/re... | Summary: This paper introduces FrameBridge, reformulating the image-to-video task as data-to-data generation through a bridge model. Different from image-to-video as first frame condition (i.e., noise-to-data generation), data-to-data generation achieves better consistency and can well preserve information in the first... | Rebuttal 1:
Rebuttal: Dear Reviewer g6Xo,
We sincerely appreciate your acknowledgement of the strengths of our work and instructive suggestions to help us improve that. We hope the following discussions could address your concerns and questions. Tables are provided in https://framebridge-icml.github.io/rebuttal-demo-p... | Summary: This paper proposes a bridge model-based image-to-video generation model. It first formulates image-to-video generation as data-to-data generation instead of noise-to-data generation. Under this formulation, the generation should be easier because it starts from a strong prior of the image instead of the Gauss... | Rebuttal 1:
Rebuttal: Dear Reviewer WAVy,
We sincerely appreciate your recognition of the strengths of our work and providing valuable suggestions to help us improve it. We hope the following discussions can address your concerns. Tables and demos are provided in https://framebridge-icml.github.io/rebuttal-demo-page/.... | Summary: This work focuses on the mismatching issue of diffusion models and I2V generation tasks, and propose FrameBridge, which build a data-to-data generation process with bridge model, making the generation procedure more in line with the frame-to-frames nature of I2V task. For fine-tuning scenario, a SNR-Aligned Fi... | Rebuttal 1:
Rebuttal: Dear Reviewer fXNN,
We sincerely appreciate your acknowledgement of our methods and proposed techniques as "well-motivated", "practical and effective". We are happy to engage in a thorough discussion and hope it will address your concerns and questions. Tables and demos are provided in https://fr... | null | null | null | null | null | null |
Explicit Preference Optimization: No Need for an Implicit Reward Model | Accept (poster) | Summary: The paper presents a new objective to use for preference optimization that replaces DPO-like objectives. The new objectives (EXPO) are designed to address two issues with DPO-like objectives, (1) shifting the learned policy away from the reference policy when the reference policy closely matches the optimal po... | Rebuttal 1:
Rebuttal: We are appreciative of the reviewer's helpful comments.
**Comment:**
*For the right $\lambda$, QPO can closely match EXPO ... results of Figure 4 support this ... so how beneficial is EXPO in real-world cases with tuned $\lambda$.*
**Response:**
Actually, there are no values of the QPO hyperpara... | Summary: This paper proposes a new direct preference optimization (DPO) method. The authors first formulate quasi-convex generalizations to unify some of existing DPO based methods. Then, they identify two limitations of existing DPO based methods under this formulation. One limitation is the failure to preserve optima... | Rebuttal 1:
Rebuttal: We appreciate the constructive comments, and address the main points as follows (grouping where appropriate).
**Comment:**
*First/Main limitation: I am not convinced by the first limitation of existing approaches. The considered setting seems a bit synthetic to me as it only considers two types ... | Summary: This paper works broadly on offline preference optimization methods, the most canonical of which is DPO, discusses a common weakness shared by all of these methods, and proposes a method that fixes this problem. The paper argues that DPO's uniform regularization to the reference policy creates problems: assume... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out that our paper is clear, based on convincing evidence, and supported by interesting and relevant theory. The reviewer also provided many constructive comments; with limited space to reply, we prioritize the main critiques as follows:
**Comment:**
*Methodolo... | Summary: This paper proposes a framework for aligning language models with human preferences without relying on implicit reward models. EXPO addresses limitations of existing methods like DPO and IPO, which suffer from suboptimal regularization and interpolation issues. The authors propose two variants: a compositional... | Rebuttal 1:
Rebuttal: Thanks for checking many of the technical details of our paper, including proof and empirical materials, while acknowledging the novelty of our approach in addressing underappreciated limitations in prior preference optimization methods. We respond to points of critique as follows.
**Comment:**
... | null | null | null | null | null | null |
Optimal Decision Tree Pruning Revisited: Algorithms and Complexity | Accept (poster) | Summary: The authors investigate the computational complexity of pruning a decision tree.
More specifically the focus is on the algorithmic optimization of two pruning techniques: replacement (removing a subtree and assigning to the root the majority class in the leaves) and raising (removing the subtree rooted at an ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Indeed, we agree that $n^{2d}$ running time would not scale to practical data and this is in particular why we need more precise complexity and running time analyses such as those we provide here. To add to these considerations, note that the implemented algorithm inste... | Summary: Decision trees are widely used for tabular datasets. The authors conduct a comprehensive analysis of decision tree pruning operations, including subtree replacement and subtree raising. The paper provides a theoretical analysis of the complexity of each pruning operation, showing that optimal subtree replaceme... | Rebuttal 1:
Rebuttal: Thank you for your review and the insightful questions!
1) About the concern about the practical value of using the optimal strategy:
Prior to our work there was no evidence how good these heuristics work in practice, that is, whether they are close to the optimum, or whether they can be outperfo... | Summary: This manuscript analyzes the complexity gap between two tree pruning strategies: subtree replacement and subtree raising. The former is polynomially solvable, whereas the latter is NP-complete. This paper identifies the key parameters that can bridge the gap between these strategies and analyze their impact, p... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Allow us to respond to all your concerns:
Presentation:
- Theorems in Section 4 and 5: Note that all of these theorems correspond either to different combinations of parameters or different subsets of parameters that are fixed to at most some constant value. We have... | Summary: The submission at hand aims at understanding the parameterized complexity of problems relating to editing decision trees to conform to a data set up to a bounded number of errors. The considered operations are either of raising and replacing and the considered parameters are numbers given on input, relate to t... | Rebuttal 1:
Rebuttal: Thank you for the review and the helpful feedback!
We now make clear in the introduction that we focus on binary trees and incorporated the comment about line 417.
> Which of these results can be adapted when allowing both replacing and raising?
That’s an insightful question. While the deta... | null | null | null | null | null | null |
The Batch Complexity of Bandit Pure Exploration | Accept (poster) | Summary: The authors derive an instance-dependent lower bound on the batch complexity required by any $\delta$-correct pure exploration algorithm. This lower bound is expressed as a function of the instance’s complexity, $T^\star(\mu)$, and shows that as sample efficiency improves, the minimal number of batches requir... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful suggestions.
The suggested paper, ``Optimal $\delta$-correct best arm selection for heavy tailed distributions'', contains a BAI algorithm for a different class of non-parametric distributions (given by a moment constraint).
They also use batches in a Tr... | Summary: The paper investigates the batch complexity of fixed-confidence pure exploration problems in stochastic multi-armed bandits (MAB), where the algorithm is allowed to change its sampling strategy only across batches. The authors derive novel instance-dependent lower bounds on the number of batches required by an... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for their helpful comments.
Firstly, we'd like to redirect the reviewer to the two last paragraphs of our response to reviewer yvDd: the bound on the sample complexity in Theorem 3.11 was not accurate in the regime of big $T_0$. The parameter $T_0$ impacts the size... | Summary: This work studies the batch complexity in the bandit pure exploration including best arm identification, top-k arm identification, and thresholding bandit problems. The paper begins by establishing a lower bound on the number of batches required for an algorithm that is $\delta$-correct for any Gaussian instan... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insights and corrections.
Thank you for pointing out a few unclear passages.
In Assumption 2.2, $y$ (in normal font) is a scalar, and $\bf{y}$ (in bold) is the vector $(y,y,...,y)$.
We will make sure to clarify this.
In Algorithm 1, we indeed did not detail whe... | Summary: The paper investigates pure exploration problems with a specific focus on batch complexity.
First, the authors establish a theoretical lower bound for batch complexity and characterize it in relation to sample complexity, which is well understood from previous work. They then propose the PET algorithm, which ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions.
The inequality on line 94 comes from Donsker-Varadhan duality, and can more precisely be found in Lemma 2 of Wang 2021, upper bounding the difference of means by the square root of the KL divergence for two sub-Gaussian variables.
Lemma 3.1 i... | null | null | null | null | null | null |
Random Feature Representation Boosting | Accept (poster) | Summary: This paper introduces a Random Feature Representation Boosting method which uses random features at each layer and iteratively optimizes the functional gradient of the network representation. Experiments shows it outperforms traditional RFNNs and end-to-end trained MLP ResNets.
Claims And Evidence: My questio... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work and for your comments. Below, we address your questions and clarify the novelty and key differences of our work.
**Q: What are the main differences between Random Feature Representation Boosting and existing Functional Boosting? Are dense Random Fe... | Summary: This paper studies an area of "Extreeme learning" where the weights of a non-linear transformation are set without any gradient calculations. In this instances, a Residual Network connection is used to derive the math for setting the procedure in a deep randomized network for MSE prediction problems specifical... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and thoughtful points raised, especially the positive feedback regarding experimental rigour. We address your points below.
**Are results MSE only?:** We apologize if this was unclear. Regression tasks are reported in Table 1 and Figure 2, while classification... | Summary: The paper introduces Random Feature Representation Boosting (RFRBoost), a novel approach that combines random feature neural networks (RFNNs) and gradient boosting theory to construct deep residual neural network models. The main idea is to build a deep ResNet structure using random feature layers that explici... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough and positive assessment of our work and for your valuable comments. Below, we address your questions:
**Empirical validation primarily focuses on tabular data:** The main question we set out to answer in our paper was whether one could build deep random fe... | Summary: This paper studies the problem of boosting random features and its connection to ResNets. At stage $t$, a new group of random features $f_t = f_t(\Phi_{t-1})$ are generated and added to the previous stage's features $\Phi_t = \Phi_{t-1} + \Delta_t f_t$ where the matrix $\Delta_t$ gets optimized and the readout... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer fxoT for their careful reading and constructive feedback. Below, we provide detailed responses to each of the points raised.
**On Critical Difference Diagrams:** To facilitate a meaningful comparison across all baseline models and RFRBoost variants, we use the Wilcoxon... | null | null | null | null | null | null |
Automated Hypothesis Validation with Agentic Sequential Falsifications | Accept (poster) | Summary: This paper presents POPPER, an agent framework inspired by Karl Popper's principle of falsification that can automatedly validate hypotheses statistically rigorously. POPPER validate a hypothesis through conducting analyses or experiments for each sub-hypothesis, calculating p-values and e-values, and determin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We respond to the specific comments below:
> **“Can authors conduct analysis on how POPPER outperforms the baselines?”**
We appreciate the reviewer’s insightful suggestion. Following the reviewer’s recommendation, we conducted a detailed manual ... | Summary: The paper introduces POPPER, a framework for using AI agents to perform hypothesis validation. Given a hypothesis, the system designs and executes a series of falsification experiments, and uses statistical methods for accumulating evidence until the hypothesis can be accepted or rejected with a final p-value.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We address each point in detail below:
> **“The claim "POPPER compares with human experts" should be appropriately caveated with limitations.”**
We appreciate the reviewer highlighting this important point. We agree and will explicitly note the ... | Summary: The paper introduces a framework called Popper, which leverages Large Language Models to validate hypotheses specified in natural language. The proposed framework makes use of two LLM agents; one decomposes the hypothesis of interest into smaller sub-hypotheses and proposes experiments to test them, and anothe... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's thoughtful feedback and acknowledgment of our work's value in falsifying natural-language hypotheses. Below, we respond in detail to the specific points raised:
> **"Proposed framework claims to design and execute any type of experiments (laboratory, simulatio... | Summary: Manuscript provides a contribution to the automated scientist literature. Premise is that free-form hypothesis positing and testing needs to be accomplished at scale and this necessitates automation. This task is accomplished using agentic/LLM flows which break-down a hypothesis into sub-hypotheses. Sub-hypoth... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and for recognizing our efforts to introduce rigor in the context of LLMs as bold and commendable. We address the thoughtful suggestions raised by the reviewer in detail below:
> **"Tune down the claim and specify the assumptions"**
W... | null | null | null | null | null | null |
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions | Accept (poster) | Summary: This paper proposes Annealing Flow, a method based on continuous normalizing flow to sample from high dimensional multi-modal distribution. The authors provide a efficient training and sampling algorithm, which can be also applied to Monte-Carlo estimation. Various experiments are conducted to verify the effic... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable comments and suggestions! Please see below for our response to your concerns.
> Summary of Review: The paper lacks novelty and misses key related works. [1] proposes an annealed NF using L2 distance. [2] employs kernel methods instead of neural ne... | Summary: This paper proposed a new flow-based sampler from continuous target density functions via combining several ideas.
More specifically, they introduce a method that they call Annealing Flow (AF) by using continuous normalizing flows trained with dynamic optimal transport objective function. They use Wasserstein ... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review and thoughtful comments! We address your concerns point by point below:
> It is hard for me to convince myself that the proposed density ratio estimation (Section 5.2) will perform better than the formula in line 289.
Thank you for your detailed observ... | Summary: The paper proposes a new method to learn a vector field v(x,t) such that the neural ODE dx = v(x,t) dt approximates the optimal transport between two given distributions p and q. This is laudable, because scalable and accurate optimal transport solvers in high dimensions are still an important research topic. ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and careful attention to our mathematical developments! We acknowledge that some symbols lacked rigor and appreciate you pointing them out! Please see below for our response to your concerns on both math rigor and experimental comparisons.
> The paper defines ... | Summary: The authors devise a new technique for sampling from high-dimensional multi-modal distributions. The assumed setting is that we are given an unnormalized analytical form of the density we wish to sample from. The proposed technique, dubbed Annealing Flow, is based on a continuous normalizing flow guided by an ... | Rebuttal 1:
Rebuttal: Thank you sincerely for your time and thoughtful review! Below, we respond to each of your questions in detail. We also provide a summary of our key contributions and the representativeness of our comparison experiments at the end of this response.
> Nonetheless, the writing could be made clearer... | null | null | null | null | null | null |
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures | Accept (poster) | Summary: This work studies the convergence and training dynamics of transformers for in-context classification tasks of Gaussian mixture data. The results show that a single-layer transformer trained by gradient descent converges to the global optimal at a linear rate. A quantification of how the training and testing p... | Rebuttal 1:
Rebuttal: >The technical contribution beyond [Zhang et al., 2023a] is not clear to me. You consider the classification problem with a different loss function. But why is it challenging enough compared with [Zhang et al., 2023a]. It is better to include related discussions in the paper.
Compared to [1], we ... | Summary: This paper studies the in-context learning (ICL) capabilities of the transformer model. In particular, this paper shows that one layer of linear attention mechanism, after pre-training through gradient descent, can implement classification of Gaussian mixture data. The main results of this paper are the conver... | Rebuttal 1:
Rebuttal: >I suggest the author to condense Section 4. Remarks E.1 and G.2 should be included as part of the main body.
Thanks for your suggestions. We will modify them in the revised paper.
>It is okay to assume homo-scedasticity (same $\Lambda$ for both classes), but the assumption on the means having t... | Summary: This paper looks at ICL for classification using Gaussian mixtures (same covariance across classes but different means) by trained transformers. They show that under the condition that the training and test data come from the same covariate covariance distribution, using linear attention can provably work with... | Rebuttal 1:
Rebuttal: We conducted additional experiments and uploaded code in https://anonymous.4open.science/r/In-Context-Classification-of-Gaussian-Mixtures-2374
>Fundamentally, I felt that the lack of serious comparison to just the linear-regression approach is a big weakness. Treating classification as linear reg... | Summary: In this paper, the authors provide a theoretical analysis of in-context learning of linear classification tasks on the Gaussian mixture models. By assuming a simplified linear self-attention structure and fixing some parameters during the whole training, the authors prove that linear attention can converge to ... | Rebuttal 1:
Rebuttal: >By assuming an over-simplified attention structure...
Setting some parameters to fixed values and considering spare form parameters is commonly used in ICL theory papers [7,8,9,10], and we adopt a similar parameterization as in [7,8,10]. Even for this simplified structure, our analysis for the c... | null | null | null | null | null | null |
Understanding Generalization in Quantum Machine Learning with Margins | Accept (poster) | Summary: The authors address generalization in quantum machine learning by introducing a margin-based framework. The authors critique traditional uniform generalization bounds, which have been shown to be ineffective in both classical and quantum settings, and propose margin-based generalization bounds as a more reliab... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 5bgA for insightful comments. Below, we address each point directly and explain how we plan to incorporate your suggestions.
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### 1) **Limited experiments; broader testing is needed for statistically robust validation**
To strengthen empirical validation, we have no... | Summary: This paper establish a margin-based generalization bound for multiclass classification with Quantum Neural Networks, adapting techniques from classical neural networks to the quantum domain. This approach interprets quantum measurements as nonlinear activations and extends matrix covering techniques to complex... | Rebuttal 1:
Rebuttal: We thank Reviewer rwMH for thoughtful evaluation.
---
### 1) **Experiments on small qubit systems**
We agree that our experiments were conducted on relatively small quantum systems (8-qubit QCNNs). While it is possible to increase the number of qubits by one or two, we chose 8 qubits as a practic... | Summary: The manuscript describes a theoretical and experimental analysis of quantum machine learning models, with the focus on generalization bounds. The authors build on prior quantum machine learning results indicating vacuity of bounds based on parameter count or other measures of complexity of the hypothesis space... | Rebuttal 1:
Rebuttal: We thank Reviewer uadb for thorough evaluation and insightful suggestions.
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### 1) **Experimental Scope and Additional Results**
We acknowledge the reviewer's concern regarding the breadth of empirical evidence. To address this directly, we conducted extensive additional experiments on two can... | Summary: This paper provides generalization error upper bounds for parameterized quantum neural networks using arguments from Bartlett et al. (2017) 's construction.
Claims And Evidence: I find all the claims in the paper to be reasonable. What helps this work is that there is a long line of work in deriving generaliz... | Rebuttal 1:
Rebuttal: We thank Reviewer tcJi for constructive feedback.
We understand the concern that our theoretical contribution could appear incremental, given the established history of margin-based bounds in classical ML. However, our work is the first to systematically extend this theoretical framework to QNNs... | null | null | null | null | null | null |
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think | Accept (poster) | Summary: This paper investigates continual learning for deep neural network when the gradient is not accessible -- instead of using backpropagation methods (first order methods (FO)), the gradient is approximated by forward pass methods (zeroth-order optimization (ZO)). The paper present ZeroFlow benchmark, where diffe... | Rebuttal 1:
Rebuttal: **Q1: Caption of Figure 4**
Thanks for pointing out! We've corrected it.
**Q2: Missing Quotes and Descriptions**
We've included quotes (EASE, CVPR-24 and APER, IJCV-24) and use one sentence to describe them.
**Q3: Longer task sequences**
As shown below, we evaluated the results on a task s... | Summary: This submission presents a novel benchmark, ZeroFlow, for evaluating overcoming catastrophic forgetting under a gradient ban. The key insight is that forward pass optimization alone can also mitigate forgetting, which challenges the conventional reliance on backpropagation-based optimization. The study evaluat... | Rebuttal 1:
Rebuttal: **Q1: More Discussion of Enhancements**
Indeed, as you note, Enhancement 3 has a potential acceleration advantage, which benefits from the reduction in average queries. More analysis see Enhancement 3 in our response 3 to Review HREV. Thank you for your suggestion, which effectively improves our ... | Summary: Claimed contributions:
- Contrib 1: benchmark (called ZeroFlow) of continual learning using two previously published strategies: EASE and APER, but only using zero order estimation of descent directions, on vision tasks
- Contrib 2: insights into the role of forward pass in managing task conflict, and trade-... | Rebuttal 1:
Rebuttal: **Q1: Extensions to Contrib 1**
We extended the experimental scope to enhance Contrib 1. In detail, we evaluated ZeroFlow on extra strategies: memory replay CL and VLM-CL (see **Q5/7 of Reviewer see4**).
**Q2: Explanation of Contrib 2**
In Section 3.2, ZeroFlow examines how ZO optimization help... | Summary: The paper explores the challenge of catastrophic forgetting in continual learning under a gradient ban setting, where gradients information is unavailable. To address this, the authors investigate zero-order optimization methods, which rely only on forward passes without requiring backpropagation. Their key fi... | Rebuttal 1:
Rebuttal: **Q1: Justification of Datasets**
We follow the typical dataset setup to perform all evaluation [1,2]. In general, any overlap has been properly accounted for and domain gap is further considered (e.g. ImageNet-A and OmniBench are acknowledged to have large domain gap with ImageNet, please refer ... | null | null | null | null | null | null |
RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers | Accept (poster) | Summary: This paper proposes RePaViT, a method for accelerating Vision Transformers (ViTs) through structural reparameterization of the feedforward network layers. Specifically, this paper argues that the computation costs of FFN layers cannot be ignored, thus a structural reparameterization method on FFN layers are de... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 8Sgy's recognition of our method's high performance and would like to address the concerns raised:
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__C1: Table 2 tries to show the advantages of RePaViT comparing with pruning methods. However, the number of model parameters of most of pruning methods are not provided,... | Summary: This paper proposes a novel structural reparameterization method -- RePaViT that targets the feedforward network (FFN) layers of Vision Transformers (ViTs) to accelerate inference. The key idea is a channel idle mechanism—during training, only a subset of FFN channels are activated (with the others kept “idle”... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer d1Yn's detailed and careful review comments. We thank Reviewer d1Yn for pointing out the strengths of our work, including
* clear and understandable presentation
* solid and comprehensive experiments
* high performance
* and novel from existing methods.
We wou... | Summary: The paper introduces RePaViT, a method for accelerating Vision Transformers by applying structural reparameterization specifically to FFN layers. The key observation is that FFN layers significantly contribute to ViT inference latency, especially as the model scales. To address this, the authors propose a "cha... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer NyjX for the recognition of our work, especially on
* clear novelty
* significant real-world application potential
* and thorough and convincing experiments.
We would like to answer and clarify the questions as below:
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___Q1: What motivated the default ch... | null | null | null | null | null | null | null | null |
High Dynamic Range Novel View Synthesis with Single Exposure | Accept (poster) | Summary: The paper introduces Mono-HDR-3D, a framework for High Dynamic Range Novel View Synthesis (HDR-NVS) that operates effectively with only single-exposure Low Dynamic Range (LDR) images during training. The approach addresses limitations of previous multi-exposure methods by proposing a meta-algorithm that includ... | Rebuttal 1:
Rebuttal: ## Reviewer ZUxK
**Q1: While it may be relatively straightforward to outperform HDR-GS and HDR-NeRF when tailoring the design to a specific setting, the real challenge lies in demonstrating that the method can also surpass these baselines under their own conditions.**
Great point! As suggested, ... | Summary: This paper studies the high dynamic range novel view synthesis problem with only single-exposure LDR images given.
The authors propose a generic framework, Mono-HDR-3D, that learns to capture the underlying camera imaging process for bridging LDR and HDR space effectively under the challenging single exposure... | Rebuttal 1:
Rebuttal: ## Reviewer nprx
**Q1: How to validate the two MLPs L2H-CC and H2L-CC decompose the camera imaging process. There are no supervision in the loss function to ensure this part.**
Great question! It is exaclty due to no such supersion that makes the problem extremely challenging. It is the architec... | Summary: This paper introduces Mono-HDR-3D, a novel single-exposure HDR-NVS approach that reconstructs 3D HDR scenes in NeRF or 3DGS using only LDR images, eliminating the need for multi-exposure inputs. The method comprises two modules based on LDR image formation principle, which is LDR-to-HDR module that predicts HD... | Rebuttal 1:
Rebuttal: ## Reviewer yk8h
**Q1: I question the necessity of H2L module: what happens if you render the image in HDR, and simply convert it to LDR with existing modules or analytic method, instead of approximating it with MLP? Is this case not possible because there are no module which support backpropagat... | Summary: This paper proposes a novel method for HDR scene novel view rendering with single-exposure LDR images. The approach involves two key components: an LDR-to-HDR (L2H) converter and an HDR-to-LDR (H2L) converter, both designed based on the camera imaging process. The L2H module first converts LDR images into HDR ... | Rebuttal 1:
Rebuttal: ## Reviewer dY8v
**Q1: The proposed L2H-CC converts an LDR model to an HDR model, however, after reading the paper, I'm not completely clear how its design prevents the module from learning a trivial solution of mapping to an LDR instead of HDR mode.**
Let us summarize the key features of our met... | null | null | null | null | null | null |
ENSUR: Equitable and Statistically Unbiased Recommendation | Accept (poster) | Summary: This paper introduces ENSUR (Equitable and Statistically Unbiased Recommendation), a novel framework aimed at ensuring confidence and fairness in recommender systems. The authors propose a dynamic method for generating prediction sets that guarantee:
1. A user-predefined confidence level (e.g., 90%) for includ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their encouraging feedback and for appreciating the relevance, rigor, and practical efficiency of our framework. Below, we address the questions:
a) ENSUR's performance under significant group size imbalance:
While the empirical results presented in the pap... | Summary: The paper introduces ENSUR (Equitable and Statistically Unbiased Recommendation), a framework designed to enhance fairness and confidence in recommender systems. The core idea is to generate dynamic prediction sets that (1) ensure a high-confidence inclusion of the true item, (2) guarantee fairness across dive... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and for recognizing our framework's novelty, theoretical rigor, and practical efficiency. We appreciate their insightful comments and questions and address them below:
a) Adaptation of ENSUR when fairness groups are inferred dynamically:
We t... | Summary: This paper proposes a novel and reliable framework called Equitable and Statistically Unbiased Recommendation (ENSUR)) to dynamically generate prediction sets for users across various groups. This paper further designs an efficient algorithm named Guaranteed User Fairness Algorithm (GUFA) to optimize the propo... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their encouraging and supportive feedback. We are glad they found our paper well-written, rigorous, and efficient. Below, we aim to address their insightful queries and suggestions:
a) Technical Innovation:
Our technical innovation lies in ENSUR being a uni... | Summary: The paper introduces a comprehensive framework named ENSUR (Ensuring Statistical Fairness and Confidence in Recommendation Systems), which is designed to statistically ensure both fairness and confidence in the outcomes generated by recommendation systems. The authors propose that by utilizing two key componen... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for their positive and encouraging feedback. We sincerely appreciate their recognition of our framework's rigor, versatility, and substantial empirical validation.
a) Hyperparameter Selection and Tuning:
We acknowledge a detailed explanation presenting hyperpar... | null | null | null | null | null | null |
Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation | Accept (poster) | Summary: This work focuses on building and evaluating models for uplift modeling. The work finds a critical limitation in existing evaluation metrics, as many of these models do not weigh negative outcomes enough. The work finds that this lead to biased evaluations due to incorrect orderings between persuadable and sle... | Rebuttal 1:
Rebuttal: **References &Weaknesses 1:** I would appreciate more discussion on [1].
**Response 1:** Thank you for your concern. As far as we know, TOC/AUTOC can be understood as introducing a threshold $u$ and a logarithmic function to the conventional uplift and Qini curves, as shown in the following formu... | Summary: This paper proposes PTONet, a new uplift model that integrates the Principled Uplift Loss (PUL) to improve CATE ranking accuracy, outperforming existing models in experiments on simulated and real-world datasets.
Claims And Evidence: The paper effectively presents its claims and supports them with clear evide... | Rebuttal 1:
Rebuttal: Thank you for your feedback. If you have any additional concerns or questions, we would be happy to answer them. If you have no additional concerns, we would appreciate you considering increasing your recommendation score.
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Rebuttal Comment 1.1:
Comment: I confirm that I have read the author'... | Summary: In an RCT with two groups, treatment and control, an uplift model is supposed to rank four types of units - treatment positive, treatment negative, control positive and control negative in alignment with their CATE (which is unobservable). This paper claims that existing evaluation metrics such as uplift curve... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We will address each of your concerns one by one.
**Claims&Methods:** ... explanation for the claim that equation 12 handles treatment assignment bias. ... clear citations should be added.
**Response 1:** Thank you for your suggestion. We will revise the cit... | Summary: This paper reveals the limitations of previous uplift and Qini curves in evaluating uplift models, demonstrating their susceptibility to manipulation by suboptimal ranking strategies that can artificially enhance the performance of biased models. To address this, the authors introduce the Principled Uplift Cur... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will address each of your concerns one by one.
**Weak 1:** A key concern is that the approach does not improve the worst-case scenario, ... conventional metrics at least ensure that PE^{TP} is ranked no lower than second place. ..., it also introduces a tradeoff in... | Summary: This paper proposes a new evaluation metric, the Principled Uplift Curve (PUC), which assigns equal importance to individuals with positive and negative outcomes and offers an unbiased evaluation of uplift models. The authors derive a new loss function with a new model architecture to reduce bias during
uplift... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We will address your concerns and questions one by one.
**Cons:** The proposed evaluation metric could be discussed in more detail since it is an essential part of the proposed method.
**Response 1:** Thank you for your concern. Based on your suggestion, we ... | null | null | null | null |
Extracting Rare Dependence Patterns via Adaptive Sample Reweighting | Accept (poster) | Summary: This paper tackles independence testing, when there is rare dependence. Rare dependence is defined as the case when most of the data points exhibit independent behaviour between two variables, but a subset exhibits dependence. The authors propose to solve this problem by augmenting the dataset with weights tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. Below we address all raised points grouped by topic. All figures and tables are available at https://tinyurl.com/mwafx6kh.
- **Rare dependence in reality** (Claims & Methods & W1)
We clarify that we have evaluated our method on a... | Summary: This paper considers the discovery of the dependence pattern in a specific small region, coined "rare dependence". The authors proposed a reweighting (importance sampling) -based approach and presented several statistical properties. They also demonstrated several applications, e.g., causal discovery, on synth... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive comments and helpful feedback. Please see below for our response. All figures and tables are available at https://tinyurl.com/mwafx6kh.
- **On kernel choice** (Comments & Q1)
Indeed, RHSIC/RKCIT performance depends on kernel choice. Our theoretical clai... | Summary: Existing conditional independence testing methods suffer to detect dependencies that occur in a small regions of the data which is referred to as rare dependence. This work aims to resolve this issue by proposing a kernel-based independence testing with an importance reweighting, which assigns higher weight t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive feedback. Please see below for our response. All figures and tables are available at https://tinyurl.com/mwafx6kh.
- **On Motivation** (Reference & W1)
We thank the reviewer for this valuable suggestion. We agree that the motivation can ... | Summary: This paper proposes the use of adaptive sample weighting to detect rate dependencies in data. The key idea is to incorporate weights for data points by formulating an objective problem that maximizes the reweighted HSIC along with regularization terms. Asymptotic hypothesis test guarantees for the resulting re... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive comments and helpful feedback. Please see below for our response. All figures and tables are available at https://tinyurl.com/mwafx6kh.
- **(Weakness & Reference)**
Thank you for your comments and for pointing out the work by Zhang et al. (2023), which w... | null | null | null | null | null | null |
Achieve Performatively Optimal Policy for Performative Reinforcement Learning | Reject | Summary: - **Proposed Algorithm:** This work introduces a zeroth-order performative policy gradient (0-PPG) algorithm that converges to the PO policy with polynomial computational complexity under mild conditions.
- **Key Theoretical Properties:**
- When the policy regularizer dominates the environmental shift, the v... | Rebuttal 1:
Rebuttal: **Clarification on Existing Work:** Could you specify why previous research in performative RL has focused solely on the PS policy?
**A:** Great question. There are two reasons. First, the method to obtain a performatively stable (PS) policy is more straightforward to think of than to obtain a pe... | Summary: The paper studies the problem of performative reinforcement learning, where the choice of policy actively influences the dynamics in the environments (transitions) as well as the rewards.
The authors introduce the first algorithm which provably converges to the performatively optimal (not stable policy) unde... | Rebuttal 1:
Rebuttal: **Essential References Not Discussed:** The relevant literature is appropriately cited. It might be nice to tell a bit of this story above around how their results contribute to the broader literature on performative prediction, but this is really up to the authors.
**A:** Thank you very much for... | Summary: This paper proposes an algorithm to compute performatively optimal policies, i.e. policies maximizing the expected sum of rewards in an MDP-like environment where the transition and reward functions are dependent on the policy that is executed. The algorithm consists in iteratively building an ascent direction... | Rebuttal 1:
Rebuttal: **Claims And Evidence (1):** Why there would not be an analytical form to the gradient?
**A:** Good question. I later found this gradient can be computed by chain rule, but involves the unknown $\nabla_{\pi}p_{\pi}(s'|s,a)$ and $\nabla_{\pi}r_{\pi}(s,a)$. We will revise this claim.
**Claims And ... | null | null | null | null | null | null | null | null |
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios | Accept (spotlight poster) | Summary: In this paper, the authors propose a novel multi-view clustering method AIRMVC for noisy scenarios. They formulate the noisy identification as the anomaly problem. Besides, a noise-robust contrastive loss is designed to enhance the model performance. Experiments on six datasets show the effectiveness of the pr... | Rebuttal 1:
Rebuttal: **Explanation for projector and classifier:** Thanks. We perform feature mapping and transformation in the latent space using a projector, and the sample predictions are obtained through a classifier. The projector and classifier are shared across different views. We will include the corresponding... | Summary: This paper addresses the challenge of noisy data in multi-view clustering by proposing a method called AIRMVC. Specifically, AIRMVC first formulates noise identification and employs a Gaussian Mixture Model (GMM) to achieve this. It then introduces a hybrid rectification strategy with an interpolation mechanis... | Rebuttal 1:
Rebuttal: **Additional experiments:** Thanks. Following your suggestions, with NVIDIA A6000 GPU we conduct experiments on CIFAR10 dataset, which contains 60,000 samples, 4 views and 10 classes. Besides, YouTube is a comprehensive video platform. We extract facial images from videos as a real-world data sour... | Summary: The paper considers the problem of multi-view clustering in the presence of noise. In particular, a new approach is proposed that aims to detect noisy samples, characterized as outliers, and to rectify them based on the assumption that the first view is noise-free. In addition, the construction of the pairs in... | Rebuttal 1:
Rebuttal: **Explanation for adding noise:** Thanks. Different with the noisy alignment, we simulate noisy scenarios by injecting standard Gaussian noise to the original views, excluding the first view. Specifically, we generate random Gaussian noise with the same shape as the view and inject it into the ori... | Summary: To mitigate the impact of noisy data on multi-view clustering models, this paper proposes a method capable of automatically identifying and correcting noise. Specifically, the authors reformulate noise identification as an anomaly detection problem. Then, they design a hybrid correction strategy to enhance mod... | Rebuttal 1:
Rebuttal: **Experiments of time and space cost:** Thanks. Following your suggestion, we conducted time and space complexity experiments on the six used datasets with 10% noisy ratio. Specifically, we measure the training time per epoch for all baselines using seconds as the evaluation metric. The space cost... | null | null | null | null | null | null |
A Scalable Solver for 2p0s Differential Games with One-Sided Payoff Information and Continuous Actions, States, and Time | Reject | Summary: This paper investigates how to solve 2-player zero-sum EFGs with continuous state and action set, when one of the players has perfect information. They also justify the result with experiments.
Claims And Evidence: It is not clear to me since the proof sketch of the main result Theorem 4.1 is very short and c... | Rebuttal 1:
Rebuttal: **[Q3.1]** It is not clear to me since the proof sketch of the main result Theorem 4.1 is very short and confusing. Moreover, I did not find the definition of I+1-atomic in the paper.
**[A3.1]** Here is an extended explanation of Thm. 4.1 which we prove in detail in App. B (and more explanation i... | Summary: This work tackles an imperfect-information extensive-form games (IIEFGs) often struggle with continuous action and state spaces and continuous time. This paper addresses the scalability challenges for 2p0s game with one-sided information. By showing an atomicity property and using the same the authors show tha... | Rebuttal 1:
Rebuttal: **[Q2.0]** From what I gather, inspired by structural results from prior work, the authors study computational issues in implementing these.
**[A2.0]** Thank you. We would like to highlight that to the authors’ best knowledge, this is the first paper that concretely explained the atomic nature of... | Summary: This paper focuses on two-player zero-sum differential games with continuous actions, states, and time. The author first proves that equilibria of these games can be computed by solving a dynamic programming problem with a discrete-time approximation. It is then shown that the complexity of this dynamic progra... | Rebuttal 1:
Rebuttal: **[Q1.1]** The benefit of multigrid in addressing the fine time-discretization issue is not fully clear.
**[A1.1]** Please refer to our response [**[A2.3]** to **[Q2.3]** raised by R2 (tRrx)](https://openreview.net/forum?id=iDnwpbn20h¬eId=vlkPNnyOPK)
**[Q1.2]** Why is the optimal solution in... | null | null | null | null | null | null | null | null |
Test-Time Learning for Large Language Models | Accept (poster) | Summary: The paper introduces Test-Time Learning (TTL) paradigm for Large Language Models (LLMs), termed TLM, designed to dynamically adapt LLMs to target domains using only unlabeled test data during inference. The authors propose three key components: (1) an input perplexity minimization objective, based on empirical... | Rebuttal 1:
Rebuttal: We are deeply grateful for your thoughtful and encouraging feedback. Your recognition of our motivation and the thoroughness of our experiments is truly inspiring. Our detailed responses are as follows:
>Q1. One experiment question is that how does your methods compared with other adapting method... | Summary: This paper proposes a novel Test-Time Learning (TTL) method that assigns weights to different samples based on input perplexity and employs LoRA for model adaptation. Experimental results demonstrate that, compared with existing TTL methods, the proposed approach achieves superior performance across multiple d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments and detailed suggestions. Responses are below:
>Q1. Regarding the claim of "Reducing Output Perplexity through Input Perplexity Minimization," the theoretical justification provided is somewhat unclear.
**A1.** We appreciate your feedback. We cl... | Summary: In this paper, the authors propose a new Test-Time Learning (TTL) approach called Test-Time Learning for LLMs (TLM) that uses unlabeled test data to address distribution shifts arising from specialized domains. They highlight three main contributions:
1. Self-Supervised Perplexity Minimization: The authors se... | Rebuttal 1:
Rebuttal: We sincerely appreciate your high level of encouragement for our work. Your recognition of "provides **a solid rationale for the necessity of TLM** as a solution", "the paper **strongly justifies the efficacy of perplexity-based optimization**", and "AdaptEval appears likely to **serve as a unifie... | Summary: This paper proposes a test-time learning scheme for LLMs that minimizes input perplexity (instead of entropy in Test-Time Adaptation) for unlabeled data at test time for better domain adaptation. In addition, the authors draw an insight showing that high-perplexity inputs are more informative for optimization ... | Rebuttal 1:
Rebuttal: >Q1. The claim "This improved representation ..., thereby reducing conditional output PPL" is not properly justified either theoretically or empirically.
**A1.** We thank the reviewer for this important observation. We agree that the original statement regarding conditional output PPL reduction r... | null | null | null | null | null | null |
Tractable Transformers for Flexible Conditional Generation | Accept (poster) | Summary: This paper introduces Tracformer, a Transformer-based generative model designed for flexible and robust conditional generation tasks. Tracformer incorporates a sparse multi-scope attention mechanism to capture both local and global contextual information efficiently. Empirical results demonstrate that Tracform... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and for recognizing the potential of Tracformers in conditional generation tasks.
> Some abbreviations are repeatedly defined throughout the paper, which leads to unnecessary redundancy.
We appreciate the reviewer’s feedback and will revise t... | Summary: This paper explores why non-autoregressive (NAR) generative models often underperform in conditional tasks, despite strong unconditional performance. The authors introduce Tractable Transformers (TracFormer), which factorize conditional queries to handle partial inputs flexibly while leveraging both local and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and for acknowledging that the paper addresses a clearly defined and significant problem.
> At line 630 in Appendix A, are you referring to Decoder instead of Encoder?
We thank the reviewer for pointing out this typo, and the paragraph title ... | Summary: This paper proposes a novel model architectural modification of Transformers and demonstrate that the proposed model outperforms baselines on non-autoregressive (NAR) conditional generation tasks, especially when the mask pattern at inference is different from the mask pattern at training.
Claims And Evidence... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and for recognizing the novelty and clarity of our work.
> I understand that the proposed model is smaller, but more experiments (e.g. slightly scaling it up to match baseline model sizes) may be needed to more convincingly demonstrate its cap... | Summary: This paper is motivated by the fact that Non Auto-regressive (NAR) generative models do not work very well for conditional generation. The paper proposes Tracformers, a transformer-based architecture robust for conditional generation in the more difficult NAR setting: this is done through using *multiple conte... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and for recognizing Tracformers as a promising architecture for addressing the challenges NAR models face in conditional generation.
> The abstract is quite unclear in retrospect; terms that are well defined later (conditional probability quer... | null | null | null | null | null | null |
Primal-Dual Neural Algorithmic Reasoning | Accept (spotlight poster) | Summary: This work proposed a framework PDNAR, that lies within the neural algorithmic reasoning framework, uses a bipartite MPNN to simulate the primal dual algorithm on solving minimum hitting set problem and its extensions.
Claims And Evidence: They provided theoretical proof that MPNN can simulate the algorithm.
... | Rebuttal 1:
Rebuttal: Thank you for your support and highlighting the strengths of our paper, which we summarize below.
- Paper: **well-written** and **easy to follow**
- Problem definition: **solid**
- Novelty: **significant**
- Benchmark datasets: **good**
- Empirical results: **strong**
We address your questions... | Summary: The authors present a neural architecture adopting the primal-dual framework, as studied in algorithm design especially for the approximation of NP-hard problems. Using the minimum hitting set as the primary case study, the authors prove the proposed architecture satisfies the requirement of algorithmic align... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time carefully **reading our main paper and appendices** and for **strongly supporting** our paper! Thank you for highlighting our experiments and discussion in the appendix as comprehensive and helpful.
We answer your questions in the following:
> I ... | Summary: This paper presents a general NAR framework based on the primal-dual paradigm, aiming to solve NP-hard problems that traditional NAR methods struggle with by mimicking approximation algorithms. The authors provide a detailed model description, theoretical justifications, and empirical validation on three NP-ha... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper, which we summarize below.
- Paper: **novel** and **well-written**
- Motivation: **meaningful** and **valuable**
- Theoretical support: **solid**
- Writing: **clear**, **well-structured and well-organized**
We address your questions in the follow... | Summary: The authors propose Primal-Dual Neural Algorithmic Reasoning (PDNAR), for training neural networks to simulate classical approximation algorithms. The core idea is to leverage primal-dual paradigm, by representing primal and dual variables as a bipartite graph and parameterzing by GNN. Optimal solutions from... | Rebuttal 1:
Rebuttal: Thank you for your time reading our paper and providing valuable feedback!
We appreciate your **positive** acknowledgement of our **empirical design**, **results**, and **application**.
We address your questions in the following:
> Are there any failure cases? What kind of scenarios should us... | null | null | null | null | null | null |
Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning | Accept (poster) | Summary: This paper presents a Byzantine-Resilient federal low-rank matrix completion algorithm, which aims to recover the decomposition of the ground truth matrix $X^\star=UB$ from its entrywise measurements. The proposed algorithm works by minimizing $U$ and $B$ alternatively under the settings of federated learning.... | Rebuttal 1:
Rebuttal: We would like to express our sincerest gratitude for your diligent study of our work and the thoughtful reviews. We will now answer your questions and concerns.
* We have strengthened the experiments by adding results for GM and for the LRCS problem. We have also evaluated our algorithm on real-wo... | Summary: This paper considers multiple versions of low-rank matrix completion problem under different observation models, in a federated learning scenario. In this scenario, the columns of the observed matrix are distributed among multiple clients. The paper proposes a Byzantine-resilient algorithms that may use two di... | Rebuttal 1:
Rebuttal: We would like to express our sincerest gratitude for your diligent study of our work and the thoughtful reviews. We will now answer your questions and concerns.
* We have strengthened the experiments by evaluating our algorithm on real-world MovieLens 1M dataset (Harper & Konstan, 2015). The datas... | Summary: This paper proposes a provably secure sample and communication-efficient federated alternating minimization-based algorithms Krum-AltGDminv and GM-AltGDmin for low-rank matrix completion (LMRC) problems that are resilient to Byzantine attacks. They then extend their analysis to show how a simple modification o... | Rebuttal 1:
Rebuttal: We would like to express our sincerest gratitude for your diligent study of our work and the thoughtful reviews. We will now answer your questions and concerns.
* We have strengthened the experiments by adding results for GM and for the LRCS problem. We have now also evaluated our algorithm on the... | Summary: This paper mainly focuses on the low-rank matrix completion problem, under the setting of federated (centralized) learning with Byzantine attacks. The authors propose an algorithm designed to be resilient to the attacks by employing robust aggregators such as Krum or the Geometric Median. Theoretical analysis ... | Rebuttal 1:
Rebuttal: We would like to express our sincerest gratitude for your diligent study of our work and the thoughtful reviews. We will now answer your questions and concerns.
* Figure 1 of our paper shows that our Algorithm 1, Byz-AltGDmin-LRMC, converges for the federated LRMC problem even when $40\\%$ of the ... | null | null | null | null | null | null |
Low-Rank Thinning | Accept (poster) | Summary: This paper proposes Low-Rank Thinning, a method for selecting representative data points using sub-Gaussian thinning with improved efficiency. By leveraging low-rank structures, it enhances dataset summarization capability. Theoretical guarantees and empirical results demonstrate its ability to reduce computat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback and are delighted that the reviewer found our theoretical guarantees strong, our methods practical, and our applications diverse and of benefit to the community.
**A new application:** We presented three vignettes with three diverse... | Summary: This paper focuses on the thinning problem and proposes a low-rank method to simplify datasets with a limited number of data points, based on the analysis of sub-Gaussian thinning. The proposed low-rank analysis generalizes sub-Gaussian thinning to any distribution and kernel, ensuring high-quality compression... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback and are glad that the reviewer found our framework “comprehensive” and our speed improvements “significant.”
We apologize for any inaccessibility in our presentation. We aimed to showcase the broad applicability of theory developed ... | Summary: This paper introduces a novel theoretical analysis of "thinning algorithms" that adapt effectively to low-rank structures present in data. The authors develop a theoretical upper bound on the discrepancy metrics (Theorem 1) applicable to any kernel and any data distribution, thereby demonstrating that thinning... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback and are delighted that the reviewer found our methods broadly applicable, our experiments extensive and convincing, and our contributions novel and clearly positioned within the literature.
**Summaries:** Following the reviewer’s ad... | Summary: This paper presents a new analysis of sub-Gaussian thinning algorithms based on a low-rank assumption. It provides theoretical guarantees that apply to many data distributions and kernel functions, in contrast to previous work with more limited applicability and poor dimension dependence. The key insight is th... | Rebuttal 1:
Rebuttal: We thank the reviewer for championing this submission and are delighted that the reviewer found the paper “clear and easy to follow,” the theoretical results “solid and comprehensive,” and the “wealth and diversity of application quite impressive.”
Thank you also for the interesting question conc... | null | null | null | null | null | null |
Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation | Accept (poster) | Summary: To address the issue of suboptimal OOD detection performance during the later stages of training, this paper proposes Progressive Self-Knowledge Distillation (PSKD) framework. PSKD strengthens the OOD detection capability by leveraging self-provided uncertainty embedded targets. PSKD is orthogonal to most exis... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful feedback.
**Comment 1. Statistical Tests for Comparison:**
Thank you for your valuable suggestion. The discussion on statistical tests for result comparison will be included in the final version. Here, we report the statistical test results usin... | Summary: This paper proposes Progressive Self-Knowledge Distillation (PSKD), a framework that leverages self-distillation and dynamic teacher selection to enhance a model’s intrinsic OOD detection capability. PSKD uses pseudo-outlier samples generated through rotation, distortion, and Gaussian noise to iteratively refi... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful feedback.
**Comment 1. Analysis of Training Stability:**
Tables 6-11 in Appendix C of the original paper report the performance of PSKD across multiple independent training runs on various benchmarks. The results show that PSKD exhibits a stand... | Summary: This paper concerns the out-of-distribution (OOD) detection task. Recent work shows that memorizing atypical samples during later stages of training can hurt OOD detection, while strategies for forgetting them show promising improvements. However, directly forgetting atypical samples sacrifices ID generalizati... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful feedback.
**Comment 1. Analysis of Performance Disparities:**
The performance improvement of PSKD is inherently dependent on the model's intrinsic OOD capability relative to the training data. Compared to the small-scale CIFAR dataset, the large... | Summary: This paper proposes Progressive Self-Knowledge Distillation (PSKD), a framework to enhance out-of-distribution (OOD) detection by leveraging uncertainty-embedded targets from a self-selected teacher model. The authors argue that models tend to memorize atypical samples during later training stages, harming OOD... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful feedback.
**Comment 1. Teacher Selection with Realistic Outlier:**
To ensure a fair comparison, we intentionally avoid using realistic OOD data as auxiliary information following the setting of [1]. To further explore this, we introduce realist... | null | null | null | null | null | null |
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach | Accept (poster) | Summary: This paper introduces an information-theoretic framework to analyze and understand the performance of Multimodal Large Language Models (MLLMs) under distribution shifts, which occur when the evaluation data differs from the instruction tuning distribution. The authors propose the concept of Effective Mutual In... | Rebuttal 1:
Rebuttal: > _A1. Legend issue in Fig. 1. and non-monotonic performance trend in text shifts_.
Thank you for pointing out the visualization issue—we will revise the legend in Fig. 1 to improve clarity and avoid confusion!
* Regarding the non-monotonic trend in win rate under text shifts, this behavior aris... | Summary: This paper introduces Effective Mutual Information (EMI), a novel metric for quantifying the relevance between input queries and model responses in MLLMs. Unlike standard Mutual Information, EMI removes domain-dependent components, making it a more generalizable measure especially in OOD settings. The authors ... | Rebuttal 1:
Rebuttal: > _A1. Effect of modality fusion/interaction on generalization_.
* MLLMs commonly undergo a modality alignment phase during training, which may affect generalization, and it is known that modality fusion can reduce the sample complexity to improve generalization [1]!
* As noted in `line 250-254` ... | Summary: I struggled to understand the paper so take this with a grain of salt:
The authors suggest there is a risk involved in using multimodal models (language models conditioned on visual input) and suggest a way to measure this is using a difference of mutual information between a model and the distribution of the... | Rebuttal 1:
Rebuttal: We appreciate sTSP's effort to read our paper and provide comments. Here is a notation table and our responses.
|Var.|Def.|
|-|-|
|$X_t=(X_{t,1},...,X_{t,L_t})$ where $X_{t,i}\in V$|a random variable (r.v.) of a text input sequence with length $L_t$ of tokens in vocabulary $V$|
|$X_v=(X_{v,1},...... | Summary: The paper proposes an information-theoretic framework to analyze the performance of multimodal large language models (MLLMs) under distribution shifts. It introduces Effective Mutual Information (EMI) to quantify the relevance between input queries and model responses. The authors also derive an upper bound fo... | Rebuttal 1:
Rebuttal: > _A1. Applicability on SOTA MLLMs._
Thanks for the suggestion! Following your comment, **we additionally conduct the full evaluation with`Qwen2.5-VL-7B-Instruct` and `InternVL2.5-7B`**.
Specifically, we first evaluate the official release of `Qwen2.5-VL-7B-Instruct` and `InternVL2.5-7B` models ... | null | null | null | null | null | null |
What Has a Foundation Model Found? Inductive Bias Reveals World Models | Accept (poster) | Summary: This paper proposes methods for evaluating if the predictions made by foundation models following fine-tuning on new tasks are compatible with a reference world model. The authors introduce two metrics; inductive bias towards respecting state and inductive bias towards distinguishing state, which aim to measu... | Rebuttal 1:
Rebuttal: Thank you for your careful and insightful review.
> _I will increase my score if the Framework section is sufficiently improved, and if the language is toned down a bit re: the results showing that the foundation model has learned a world model (rather than, to what degree its predictions are co... | Summary: In this paper, the authors focus on the problem of understanding the generalization capabilities of foundational models. For example, can a foundational model truly develop an inductive bias towards Newtonian mechanics or the rules of a board game? The authors test this question, developing a framework for tes... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper. We're glad that you appreciated our paper and findings, and that our results gave you "a deeper understanding [of] why foundation models fail to generalize".
> _The proposed metrics (R-IB and D-IB), while needed for a comprehensive evaluation, are... | Summary: This paper investigates whether foundation models trained via next-token prediction learn "world models". The paper uses synthetic tasks to measure whether such models are able to generalize from their training tasks to other tasks drawn from the same distribution. The evaluation is performed using two metrics... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper. We respond to your comments and describe new results below; to summarize the new results, we've added:
- New experiments showing robustness to predictor in R-IB and D-IB calculations
- A more intuitive explanation of our framework
- Clarification an... | null | null | null | null | null | null | null | null |
Deep Electromagnetic Structure Design Under Limited Evaluation Budgets | Accept (poster) | Summary: The authors present Progressive Quadtree-based Search (PQS), a novel method for electromagnetic structure (EMS) design under limited evaluation budgets. PQS leverages a quadtree-based hierarchical representation to mitigate the curse of dimensionality inherent in conventional pixel-wise optimization. In additi... | Rebuttal 1:
Rebuttal: >Q1. The CSS mechanism relies on assessing the consistency of historical predictors. However, there is only one predictor available at t=0.
**A1.** Thank you for your insightful comment. We actually train multiple predictors at t=0 rather than relying on a single one. These predictors are then u... | Summary: This paper proposes a novel method for electromagnetic structure (EMS) design under limited computational budgets, called Progressive Quadtree-based Search (PQS). PQS employs a Quadtree-based Search Strategy (QSS) to progressively explore the high-dimensional EMS design space and incorporates a Consistency-bas... | Rebuttal 1:
Rebuttal: > Q1. PQS should be compared with SOTA surrogate-assisted evolutionary algorithms (SAEAs).
**A1.** Thank you for your constructive suggestion. We compared our method with TS-DDEO[1] and SAHSO[2] in Tables 1-3. PQS outperforms them by 3.14-3.60 and 9.63-10.99 and achieves significantly higher rob... | Summary: # I have no experience in this field so I am not capable of completing a sound review. I acknowledge that some of the comment are generated with the assistance of LLM, and some parts are left blank.
The paper proposes Progressive Quadtree-based Search (PQS) for electromagnetic structure (EMS) design under limi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and valuable feedback. We acknowledge your concerns and would like to clarify our motivations and technical contributions.
**1. Significance of the Task & Motivation**
Electromagnetic structure (EMS) design is fundamental to modern antenna/material development, ... | null | null | null | null | null | null | null | null |
On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback | Accept (poster) | Summary: The authors mathematically analyze the feedback loop created by affinity bias in hiring processes and propose strategies to mitigate its effects. They introduce affinity bandits, a model where biased feedback evolves based on the selection frequency of each arm. Their algorithm operates without prior knowledge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback on our paper. We respond to the main points and questions below. Please let us know if you require any further clarifications. We hope that the reviewer will consider raising their score if we have sufficiently addressed their concerns.
> Given th... | Summary: This paper examines how affinity bias influences feedback loops and impacts decision-making in multi-armed bandit problems. The novel formulation assumes biased reward values, where the bias toward an arm depends on the fraction of arms with the same set of trials. The authors establish a new lower bound that ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback on our paper. We respond to the main points and questions below. Please let us know if you require any further clarifications.
> The authors should discuss the connection to rising bandits, another important category of non-stationary MAB problem... | Summary: Motivated by affinity biases arising from many decision-making systems, including hiring, the authors *introduced* a stochastic bandit framework that could model the hiring process (affinity bandits setting). In this model, we have $n$ rounds of hiring, and in each arm $\in [K]$ corresponds to a group, and the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback on our paper. We respond to the main points and questions below. Please let us know if you require any further clarifications.
> It is not clear whether the biased feedback model used in this paper (Assumption 2.1) is a new model of perception, o... | Summary: This work studies a new setting called affinity bandits, which extends the non-stationary bandits setting to capture the affinity biases. They motivate the setting with a hiring feedback loop, where people tend to hire someone with similar features. They made assumptions of the feedback bias in Assumption 2.1 ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback on our paper. We respond to the main points and questions below. Please let us know if you require any further clarifications. We hope that the reviewer will consider raising their score if we have sufficiently addressed their concerns.
> The main ... | null | null | null | null | null | null |
Human-Aligned Image Models Improve Visual Decoding from the Brain | Accept (poster) | Summary: The authors tackle the task of image decoding from brain activity (EEG and MEG). On this task, most of the existing papers map brain data to pretrained vision encoders such as CLIP and DINO. The authors propose to analyze the role of these vision encoders and show that aligning them with human perception boost... | Rebuttal 1:
Rebuttal: We thank Reviewer KJCA for their constructive review and insightful suggestions. We agree that evaluating the impact of human-aligned embeddings across a broader range of vision tasks would be highly valuable. In this work, we focused on image retrieval as a direct and interpretable proxy for brai... | Summary: This paper explores the problem of decoding visual images from the brain by replacing the visual encoder with a human-aligned visual encoder. The experiments demonstrate that the use of human-aligned visual encoder effectively improves the brain-image retrieval performance.
Claims And Evidence: See Other Stre... | Rebuttal 1:
Rebuttal: We thank Reviewer q6J2 for their thoughtful review. Please find our responses to your concerns and suggestions below.
**Regarding the image reconstruction task:**
Thank you for highlighting this important point. We agree that image reconstruction is a key aspect of brain decoding and would be an... | Summary: This paper compares the image identification (decoding) performance of human-aligned image embedding models and their unaligned counterparts. The authors find that human-aligned models generally performed better than the unaligned models in EEG and MEG, after evaluating several different base image encoders an... | Rebuttal 1:
Rebuttal: We thank Reviewer 9vzB for their careful review and constructive suggestions. We appreciate your thoughtful engagement with our work.
Your concern about the image encoders differing across alignment methods is valid. However, we would like to clarify that our comparisons are always made within th... | Summary: The paper explores the application of human-aligned image encoders to enhance the decoding of visual information from brain signals, specifically EEG and MEG data. The authors propose that image encoders fine-tuned to align with human perceptual similarity judgments improve the mapping of brain activity to vis... | Rebuttal 1:
Rebuttal: We thank reviewer ewMG for their thorough review and constructive feedback. Below, we provide our response to your questions as well as some clarifications on the points you raised.
**Regarding the image reconstruction task:**
Thank you for this thoughtful comment. We agree that visual decoding... | null | null | null | null | null | null |
FloE: On-the-Fly MoE Inference on Memory-constrained GPU | Accept (poster) | Summary: Mixture-of-experts have become a popular way to scale up the transformer models these days, but the large model scale creates challenge when deploying the model under limited resources.This paper introduces FloE, an inference system designed for on-the-fly MoE inference on consumer-grade GPUs. FloE integrates ... | Rebuttal 1:
Rebuttal: Dear reviewer ZXAh,
We sincerely appreciate your recognition and valuable suggestions. Below, we summarize and respond to the **issues**, **suggestions**, and **questions** you raised.
**`[Minor Issue 1]` The absence of Mixtral-GPU performance results in Section 3.2. While HQQ Int2 seems to refl... | Summary: The paper introduces an on-the-fly inference system (called FloE) for Mixture of Experts models on memory-constrained GPUs. The utilization of the limited GPU memory is optimized by a hybrid compression scheme, especially focused on intra-expert sparsity - while still utilizing inter-expert sparsity. When the ... | Rebuttal 1:
Rebuttal: Dear reviewer bhnT：
Thank you for recognizing the importance of the research problem, as well as the soundness of our methodology and experiments. Below, we address the key concerns regarding **MMLU performance** and **comparisons with non-compressed baselines**:
**1. FloE achieves competitive p... | Summary: The paper introduces FloE which is an inference system to run MoE models on memory constrained GPUs. FloE includes various techniques (1) compression coupled with (2) dual predictors.
(1) the work suggests that there is an internal sparsity in experts that can be set to zero during inference by using magnitude... | Rebuttal 1:
Rebuttal: Dear Reviewer u26i,
We sincerely thank you for your thorough and thoughtful review of our submission. Your feedback is invaluable, and we will provide a concise response to the knowledge background and GPU utilization concerns you raised.
**`[Minor issue]` The paper is very difficult to read as ... | Summary: The paper presents FloE, a system for on-the-fly inference of Mixture-of-Experts (MoE) models on memory-constrained GPUs. It addresses the challenge of high memory and I/O overhead in MoE inference by introducing a hybrid compression strategy, which combines contextual sparsification of gate and down projectio... | Rebuttal 1:
Rebuttal: Dear reviewer oREx,
Thanks for your careful review. We summarize and address your key concerns as follows:
**`[Suggestion 1]` A theoretical analysis of how expert sparsification and quantization affect routing distributions and model expressiveness.**
**`[Response]`** In **Appendix B**, we pr... | null | null | null | null | null | null |
Divide and Conquer: Learning Label Distribution with Subtasks | Accept (poster) | Summary: The paper proposes a new plug-in method for label distribution learning, based on auxiliary tasks derived from the original dataset label distribution. The subtasks are defined by a label mask optimized before the optimization procedure, encouraging diversity between the subtasks and alignment with the origina... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! We have provided point-by-point responses to your questions below.
**Comment 1:** Why do the authors define the diversity as in Definition 4.3, instead of the cosine similarity used in Equation 1?
**Response:** In **Definition 4.3**, we use $\bar{\boldsymb... | Summary: The authors introduce S-LDL, a novel label distribution learning (LDL) method that generates/utilizes label distribution subtasks. This method can seamlessly integrate with existing LDL methods without any prior/expert knowledge, and is suitable for some derived tasks. The paper also conducts analyses and expe... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! Responses to your concerns are as follows.
**Comment 1:** There are limited performance improvements in some cases, particularly with the Yeast_ series datasets.
**Response:** For LDL, even small changes in metrics can indicate significant performance impr... | Summary: This paper studies the Label distribution Learning (LDL) problem. It first claims the disadvantages of existing works, pointing out their contradiction between auxiliary tasks and the generalizability. To mitigate this issue, the authors propose a new method S-LDL, which generates subtask label distributions t... | Rebuttal 1:
Rebuttal: Many thanks for your precious comments! We have provided point-by-point responses to your questions below.
**Comment 1:** It is better to provide some evidence that existing methods cannot deal with the contradiction.
**Response:** In **Section 1 & 2**, we have provided such evidence. On the one... | Summary: This paper investigates the problem setting of Label Distribution Learning (LDL). In particular, the authors propose the concept of leveraging subtasks to facilitate learning.
## update after rebuttal
I reviewed the rebuttal and further responses. I am still not fully convinced. I did not have more time to ch... | Rebuttal 1:
Rebuttal: Many thanks for your comments! Responses to your questions are as follows.
**Comment 1:** How does it (LDL model) differ from a typical classification network?
**Response:** Although typical classification networks can formally produce outputs similar to label distribution learning (LDL) through... | null | null | null | null | null | null |
Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation | Accept (poster) | Summary: This paper introduces the Counterfactual Voting Adjustment (CVA) framework, designed to address biases in online voting systems that distort information quality assessment. Specifically, CVA targets position bias (where content appearing higher receives more votes) and herding bias (where visible prior votes i... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive evaluation and recognition of our work’s strengths and its practical value. The reviewer raised several crucial questions, to which we respond below.
**a. Re “unobserved confounders”:**
Among various factors influencing helpfulness evaluation, this paper spe... | Summary: This paper proposes Counterfactual Voting Adjustment (CVA), a causal inference framework to mitigate position bias (content visibility due to ranking) and herding bias (influence of prior votes) in online voting systems. By modeling counterfactual scenarios where votes are cast under neutral visibility and bal... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive evaluation and recognition of our work’s strengths. Below we respond to the questions/comments:
**a. Regarding the ground truth (proxy reliance),**
i. For human judgments, due to the different topics discussed in different communities, to hire specific group... | Summary: This paper develops a model of voting (for example, StackOverflow up/down votes) that measures and removes the effect of position bias (voters are more likely to vote on already highly-ranked items) and herding bias (voters are likely to agree with the existing consensus). This allows a better estimate of item... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive evaluation and recognition of our work’s strengths and its practical value. Below we respond to the questions and comments:
**Re the comparison with the existing model:**
The proposed CVA shows three main advantages compared to existing CVP: Firstly, the CVP... | null | null | null | null | null | null | null | null |
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models | Accept (poster) | Summary: The manuscript studies the problem of intervention generalization in additive causal models. Specifically, the authors consider a data generating process in which an outcome variable $Y$ is an additive function of actions $A_1, \dots, A_K$ and unobserved confounders $C_1, \dots, C_K$. Given some set of observa... | Rebuttal 1:
Rebuttal: # Response to Reviewer 2XuD
We thank the reviewer for the valuable comments and the pointers to Caroline Uhler’s lab related research. We would like to address some of the reviewer’s concerns.
On the baselines: we agree with the reviewer that using the purely observational data the pooled data m... | Summary: This paper presents a novel construction for identifiability of joint interventional effect from single variable intervention results, with additive assumptions placed on the causal mechanism. The method is tested on synthetic data and demonstrated effectiveness and on pare performance when compare with a mode... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your thoughtful comments. We appreciate your feedback and would like to address your concerns below.
## On the need for parametric identification
From g-ID theory [1], we know that our problem setting is non-identifiable in the non-parame... | Summary: The paper proposes a method for estimating joint interventional effects using observational data and single-variable interventions within nonlinear additive models. It establishes identifiability results, showing that joint effects are recoverable without direct joint interventional data. The authors validate ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 9nyV
Thank you for your thoughtful review and for engaging with our work. We appreciate your careful reading and feedback.
We would like to address your primary concern regarding the novelty of our approach and the necessity of our assumptions:
You mentioned that Assumptio... | Summary: This paper addresses the challenge of unobserved confounding in multi-treatment (joint intervention) causal inference. It introduces a method leveraging observational data and experimental data where interventions are applied to only one variable at a time, under the assumption of a nonlinear additive outcome ... | Rebuttal 1:
Rebuttal: # Response to Reviewer dotZ
Thank you for your thoughtful review and constructive feedback on our paper. We appreciate the time you've invested and would like to address your key concerns.
## On the strong assumptions of our method
We acknowledge that our model makes strong assumptions, particu... | null | null | null | null | null | null |
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning | Accept (poster) | Summary: This paper propose a method to learn ODE based dynamical systems from observed sequences. The main feature of the proposed method which sets it apart from prior work is that it can learn invariant functions that can be reused across different environments, effectively disentangling general and reusable functio... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. We will address them point by point below.
## Drastic and non-linear changes across function environments
> What would happen if changes across function environments were more drastic and non-linear? For example, changing f(theta) = sin(theta) to... | Summary: This paper proposes a method for discovering invariant functions underlying dynamical systems governed by Ordinary Differential Equations (ODEs). The key claim is that different environments modify the observed system, but an invariant function can be disentangled and recovered to represent the core governing ... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your feedback and would like to address your concerns point by point.
## Definition and identifiability of invariant function
> The definition of an invariant function is vague...
We thank the reviewer for raising this point.
- **Rigorous definition**: ... | Summary: This paper introduces an approach to learning invariant functions in dynamical systems (governed by ODEs) in different environments. The authors propose a method called Disentanglement of Invariant Functions (DIF), which aims to discover intrinsic dynamics across multiple environments while avoiding environmen... | Rebuttal 1:
Rebuttal: We appreciate your feedback and the acknowledgment of the novelty and effectiveness. Below, we address your concerns point by point.
## Limited experimental setup
Thank you for raising this concern. Our work represents the first step in invariant function learning, introducing a new paradigm for... | Summary: The authors proposed the task of learning invariant functions in dynamical systems: considering the ODE equation $\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}t} = f(\mathbf{x})$ of the system evolution as a combination of the invariant function $f_c$ corresponding to the inherent properties of the system itself and ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad you recognized our theoretical framework and experimental validation. We address your concerns point-by-point as follows.
## Practicability in real-world scenarios
Thank you for this question. While we focus on establishing theoretical foundatio... | null | null | null | null | null | null |
The impact of uncertainty on regularized learning in games | Accept (poster) | Summary: The paper examines the behavior of the stochastic FTRL dynamics in games, in which the usual FTRL algorithm is randomly perturbed. It provides a general characterization: every player reaches an almost pure strategy in finite time. This stands in contrast to the deterministic setting. A consequence of the prev... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time, input, and overall appreciation, both in terms of results and presentation! We reply to your remarks and questions below:
> Theorems 2 and 3 mirror some existing results from the deterministic setting, but are still interesting and new.
Indeed, Theorems 2... | Summary: This work investigates the impacts of the noises on the observed payoffs when applying continuous-time FTRL for game solving. The main conclusions are that every player will reach an almost pure strategy in finite time and the limit of the dynamics of the continuous-time FTRL will converge to the pure Nash equ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time, input, and positive evaluation! We reply to your remarks and questions below:
> I would be curious about proof idea and basic techniques that lead to the main results (…) I'm a bit surprised that these results weren't discovered in previous work. Therefore... | Summary: This paper investigates the impact of uncertainty on the dynamics of the Follow-The-Regularized-Leader (FTRL) algorithm. The author first shows that under uncertainty, the FTRL algorithm approaches a pure strategy. Then, the author demonstrates that pure strategies are the only possible limit points of the sto... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time, input, and positive evaluation! We reply to your remarks and questions below:
>I am curious about the difference between Corollary 3, Theorem 3, and the results presented by [19]. Do Corollary 3 and Theorem 3 generalize their results to broader settings?
... | Summary: The authors consider the continuous time limit of the FTRL dynamics under noisy observations of game payoff matrixes and prove that, unlike in the noiseless case, the dynamics converge to pure Nash, along with several other results.
## Update After Rebuttal
The authors answered my questions quite well -- I mi... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time, positive evaluation and encouraging words! We reply to your remarks and questions below:
> I'm mostly familiar with the discretized, discrete-time version of FTRL. Could you comment on how your results would transfer to this setting? Like, with noisy payof... | null | null | null | null | null | null |
Graph Minimum Factor Distance and Its Application to Large-Scale Graph Data Clustering | Accept (poster) | Summary: The work proposed a distance metric MMFD between graphs through comparing distributions and showed that MMFD is a pseudo metric and has a closed-form solution. The work also proposed several variants of MMFD and analyzed the properties theoretically. The proposed methods were compared with graph kernels, graph... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your recognition of our work. Our responses to your comments are as follows.
**To Q1:**
In a dataset, the graphs belonging to the same cluster are usually similar to each other. Therefore, for a pair of very similar graphs, denoted as $G_1$ and $G_2$, we c... | Summary: The paper studies graph distance and graph clustering. It introduced a minimum mean factor distance (MMFD) between graphs and its extensions such as low-rank MMFD, MMFD-KM, and MFD.Theses methods outperformed many baselines on large-scale graph datasets such as REDDIT-5K.
Claims And Evidence: The claims are s... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We are grateful for your acknowledgment of our work. Our responses to your comments are as follows.
**To W1:**
Thanks for pointing it out. Actually, we have the time cost comparison on much larger datasets (e.g. graphs with 10000 nodes). They are in Table 8 of Appendix C4. By the... | Summary: This paper introduces a new measurement, named MMFD, for comparing and clustering graph data. By considering the adjacency matrix of graph as a kernel matrix, MMFD transforms the graph comparison problem into distribution comparison.
This paper then proposes a low-rank approximation and a generalized version ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
It is our great honor to receive your positive assessment. Our responses to your questions are as follows.
**To Q1:**
Yes. Take $G_1$ and $G_3$ in Table 2 of our paper as an example, where $\text{MMFD}(G_1,G_3)=0.1598$. We add one additional node to $G_1$ and the additional node ... | Summary: The authors study the clustering problem for graph data. They propose a new measure between two graphs called minimum mean factor distance (MMFD) that serves as a kernel function in graph data clustering. MMFD measure the minimum distance (through rotation by a real orthonormal matrix) of the mean vectors of $... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your insightful comments. They improved our work a lot.
**To W1:**
Previously, we focused on deriving from Equation (12) and overlooked Equation (11). Your advice has helped us make Section 2.3 more concise, thereby freeing up more space to supplement othe... | null | null | null | null | null | null |
Weakly Supervised Anomaly Detection via Dual-Tailed Kernel | Accept (poster) | Summary: This paper proposes Weakly Supervised Anomaly Detection via Dual-Tailed Kernel (WSAD-DT), which uses two centroids, one for normal samples and one for anomalies. It uses a light-tailed kernel for normal samples, and heavy-tailed kernel for abnormal samples. To prevent degenerate ``all-points-collapse’’ solutio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**Usage of Lemma 4.1 in the main paper.**
Lemma 4.1 is the formal stepping-stone for showing that a single kernel cannot serve as both a strictly light-tailed and a heavy-tailed function. By demonstrating $\lim_{d\to\infty} \kappa_{\mathrm{light}... | Summary: This paper proposes a method to improve anomaly detection performance by utilizing two kernel functions in the weakly supervised anomaly detection problem, where only unlabeled data and a small amount of labeled anomaly data are available. The effectiveness of the proposed method is validated through experimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback.
**Q1**
From a theoretical standpoint, WSAD-DT applies margin-based reasoning to handle both partial contamination in the unlabeled set and extremely limited anomaly labels. Assigning each class its own center and using a dual-tailed kernel—light-ta... | Summary: This paper proposes a weakly supervised anomaly detector WSAD-DT via a dual-tailed kernel that can clearly distinguish anomalies from normal samples under weak supervision. Moreover, an ensemble strategy was devised to divide the unlabeled data into distinct subsets. Meanwhile, the limited labeled anomalies we... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their perceptive review.
**Q1**
We adopt the 70/30 train/test split protocol following the approach used by AdBench (Han et al., 2022) [1], and this does not contradict the principle of weak supervision. What determines the “weakness” here is not how much of... | Summary: The paper proposes WSAD-DT, a weakly supervised anomaly detection framework that employs dual-tailed kernels (light-tailed for in-class compactness, heavy-tailed for out-of-class separation) and an ensemble strategy to address limited labeled anomalies. Empirical results on AdBench datasets show state-of-the-a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**Ablation stay extreme case(e.g., 0.1%) **
We have conducted additional experiments with a 0.1\% fraction of labeled anomalies. Table https://anonymous.4open.science/r/weakly_anomaly_detection/Table1.png summarizes AUC-ROC results under this ex... | null | null | null | null | null | null |
Instance Correlation Graph-based Naive Bayes | Accept (spotlight poster) | Summary: The authors propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB), which can work with numerical attributes and utilize the correlations among instances. The average classification accuracy of ICGNB on 24 datasets is higher than the best competitors.
Claims And Evidence: The re... | Rebuttal 1:
Rebuttal: **Questions For Authors:** How about the performance if other graph convolution functions are used?
**Author Response:** Thanks for your valuable comments. In addition to the graph convolution function used in VGAE, some other graph convolution functions in GraphSAGE and GAT can also be used. To ... | Summary: This paper proposes a novel instance correlation graph (ICG) based Naïve Bayes classification framework. It first constructs an ICG from original attributes, and then employs a variational graph autoencoder (VGAE) to generate embeddings based on both ICG and original attributes. Extensive experiments have been... | Rebuttal 1:
Rebuttal: **Q1:** Constructing a full connection graph of instances based on Euclidean distance is time consuming and usually introduces noisy edges, how to address these issues?
**Author Response to Q1:** Thanks for your valuable comments. In our paper, to construct the instance correlation graph (ICG... | Summary: This paper presents ICGNB, an enhanced Naïve Bayes method based on the Instance Correlation Graph (ICG). ICGNB leverages ICG to capture instance correlations, employs a Variational Graph Autoencoder (VGAE) to generate new attributes, and optimizes attribute weighting using Conditional Log-Likelihood (CLL). Exp... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. We sincerely appreciate the time and effort you have dedicated to reviewing our work. Below, we provide detailed responses to each of your concerns.
**Author Response to Computational Complexity:** We complement the time complexity analysis as follo... | Summary: The paper introduces **Instance Correlation Graph-based Naïve Bayes (ICGNB)**, a novel enhancement of the **Gaussian Naïve Bayes (GNB)** classifier. Traditional Naïve Bayes methods assume conditional independence among attributes, limiting their effectiveness, especially for numerical data. The proposed **ICGN... | null | null | null | null | null | null | |
Private Model Personalization Revisited | Accept (poster) | Summary: The paper addresses the problem of model personalization under user-level differential privacy (DP) in the federated learning setting. The authors propose a novel private federated learning algorithm based on the FedRep framework, which learns a shared low-dimensional embedding and user-specific local models w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments. We respectfully disagree with several conclusions and clarify key contributions and motivations below.
**On the Practical Significance of the Low-Dimensional Embedding Assumption**:\
The *low-dimensional* shared representation assumption is widel... | Summary: The authors present a novel technique to unlock differently private personalised models in the shared representation framework in a federated setting.
Claims And Evidence: The claims are sufficient and well supported.
Methods And Evaluation Criteria: While the paper is focused on theory the experiments are r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback.
**On the scope of the experimental evaluation**: We would like to emphasize that the primary contribution of our work is theoretical, and the experimental section is designed to validate our theoretical findings and demonstrate the concrete adva... | Summary: The authors study model personalization under user-level differential privacy in a shared representation framework for the federated learning setting. Specifically, there are $n$ users, and the data for user $i\in[n]$ is generated using $y = x^Tw^\star_i + \zeta$ where $\zeta$ is sub-gaussian noise and $w_i^\s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive comments and for their appreciation of our work.
**On the scope of the experimental evaluation**: We would like to emphasize that the primary contribution of our work is theoretical, and the experimental section is designed to validate o... | null | null | null | null | null | null | null | null |
PRIME: Deep Imbalanced Regression with Proxies | Accept (poster) | Summary: The paper introduces PRIME, a novel representation learning method for deep imbalanced regression tasks. PRIME leverages synthetic reference points called "proxies" to guide the learning of balanced and well-ordered feature representations, even for minority samples. Unlike previous methods that rely solely on... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and the opportunity to clarify our claim regarding state-of-the-art (SOTA) performance. Below, we provide detailed comparisons with VIR and IM-Context, along with additional experiments to ensure a fair evaluation.
## 1. Comparison with VIR
Our claim of achiev... | Summary: This paper presents PRIME, a method for handling regression tasks with imbalanced data distributions. PRIME introduces synthetic proxies as reference points that uniformly represent the continuous target space, aiming to mitigate representation collapse toward majority-target regions. The method uses two main ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments. We have addressed all points and will revise the manuscript accordingly.
## 1. Impact of hyperparameters
We have already provided detailed analyses of the impact of each hyperparameter ($\lambda_p$, $\lambda_a$, $\tau_f$, $\tau_t$, and $\alpha$) in Tables 16–... | Summary: The authors propose a novel method for imbalanced regression, leveraging learnable proxies as global reference points to achieve a balanced and well-structured feature distribution and aligning sample features with these proxies. Extensive experiments are conducted on multiple benchmarks, yielding strong and i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and insightful comments. We have addressed all points and will revise the manuscript accordingly.
## 1. Ablation on non-learnable proxy
As noted by the reviewer, PRIME is flexible with respect to how proxies are constructed—proxies can be either learnable... | Summary: For Deep Imbalanced Regression (DIR), the authors propose Proxy-based
Representation learning for IMbalanced rEgression (PRIME).
They generate synthetic proxies in the feature space and align
instances to the proxies. The proxies are distributed uniformly
across the target values. While the corresponding ta... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We have addressed all points and will revise the manuscript accordingly.
## 1. PRIME’s effectiveness in the Few category
We would like to point out that PRIME alone achieves state-of-the-art performance in the Few category on three out of four d... | null | null | null | null | null | null |
Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression | Accept (poster) | Summary: This paper proposes the Low-Rank Tensor Transitions (LoRT) framework to address various shift problems and decentralized data management. LoRT employs a novel fusion regularizer to enforce low-tubal-rank solutions, enabling effective integration. Its two-step refinement process mitigates model shifts and ensur... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and thoughtful feedback.
We are encouraged that the reviewer finds our theoretical analysis and experimental design sound, and recognizes the potential of the proposed LoRT frameworks for addressing distribution shifts in tensor regression. Belo... | Summary: This paper addresses the challenge of data scarcity in the target task within Tensor Regression. The authors propose a novel transfer learning framework called Low-Rank Tensor Transitions (LoRT) to tackle this issue. LoRT employs a two-stage adaptation process to mitigate model shift and enhance adaptation to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We appreciate the recognition of our theoretical framework, decentralized extension, and empirical evaluations. Below, we address the main concerns:
> **Concern 1:** Conducting evaluations on real-world problems where Tensor Comp... | Summary: In this paper, the authors propose a novel method called Low-Rank Tensor Transitions (LoRT), designed to address issues such as model shift, covariate shift, and decentralized data management in tensor regression for transfer learning. Experimental results demonstrate that LoRT significantly outperforms tradit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and recognition of our contributions to robust tensor regression under distribution shift. We address the reviewer’s key concerns below.
> **Novelty:** The core idea of LoRT lacks significant novelty and seems to be an incremental improvement.
... | Summary: This paper on Low-Rank Tensor Transitions proposes a new tensor regression framework focusing on transferable tensor learning and decentralized data management.
This work aims to address three challenges. Working with limited sample sizes,
shifting modes as well as covariance shift.
To tackle these challeng... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and recognition of our theoretical framework, analysis, algorithmic design, and empirical results. Below we address the specific concerns.
> Hardware implementation for distributed setting
Current implementation focuses on validating the theory, but D-LoR... | null | null | null | null | null | null |
Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments | Accept (poster) | Summary: The paper introduces Craftium, a new platform for creating rich 3D environments that balance flexibility and computational efficiency. This design allows researchers to develop complex environments with minimal code. Notably, Craftium supports both single-agent and multi-agent scenarios in the same rich 3D wor... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comments on Craftium's novelty and its role in addressing a clear need in the RL/AI research community. We also thank the reviewer for the valuable feedback and references. Below, we address the concern regarding the parallel environment benchmark and respond to the qu... | Summary: The paper introduces Craftium, a flexible, efficient, and user-friendly framework for creating rich 3D environments for single- and multi-agent research in autonomous systems, such as reinforcement learning (RL), embodied AI, and multi-agent reinforcement learning (MARL). It addresses the trade-off between com... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer acknowledges Craftium's role in bridging the gap between computationally efficient yet simple environments and those that are rich and flexible but computationally costly, as well as its logical, pragmatic, and well-justified design.
We agree that an ablation stu... | Summary: The paper presents Craftium, a highly customizable and efficient multi-agent 3D environment tailored to RL research. Craftium builds on the open-source game engine environment Luanti. It can be used to easily build rich 3D environments with numerous possibilities. Benchmarks against other environments show the... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer recognizes Craftium as an important contribution and finds the paper well-written with well-supported claims.
We also appreciate the reviewer for suggesting a valuable reference that strengthens our coverage of realistic simulators. We will include ThreeDWorld in ... | Summary: The paper introduces Craftium, a new platform for creating 3D environments aimed at reinforcement learning and multi-agent research.
Claims And Evidence: Claim 1: High Flexibility and Rich Features. The authors assert that Craftium allows “nearly limitless” possibilities for creating custom environments, in c... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and the time spent evaluating our paper. Below, we address the concerns raised.
**[Incremental contribution]**
While Craftium's individual components have existed separately, no prior framework combines ease of use, high efficiency, multi-agent su... | null | null | null | null | null | null |
Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning | Accept (poster) | Summary: This paper introduces SDQC, a method designed to address the OOD problem in safe offline RL. By decoupling states into two independent representations, SDQC improves decision-making, safety, and generalization when facing unseen states. The method employs Q-supervised contrastive learning to guide the learning... | Rebuttal 1:
Rebuttal: We thank Reviewer y1rM for providing the positive feedback and recognizing the importance of our work. Please see our response to your questions below.
## (Weakness/Question1) More Generalization Experiments on "Button"
We would like to clarify why we did not conduct generalization experiments o... | Summary: This paper proposes a new safe offline RL algorithm called SDQC, the key idea of the algorithm is to decouple observations into reward and cost-related representations, and use them in a value & policy learning framework similar to the one proposed in FISOR. The key intuition is that the values related to rewa... | Rebuttal 1:
Rebuttal: We thank Reviewer JBF8 for the valuable suggestions. We would like to address your concerns point by point below.
## (Method1) Potential instability for Eq. 5
In Eq. 5, the exponential coefficient $\Gamma(s,\tilde{s})$ is a detached soft measure introduced in [1] that helps stabilize training.
Reg... | Summary: This paper introduces a framework "State Decoupling with Q-Supervised Contrastive Representation" (SDQC) to improve safe offline reinforcement learning. They do this by using two distinct state representations (the "decoupling"), one being for rewards and one for costs, and learn these using contrastive object... | Rebuttal 1:
Rebuttal: We acknowledge Reviewer zA7P for appreciating the significance and novelty of our work. Below, we respond to the your questions point by point.
## (References) Missed discussing on relevant references
The suggested papers are indeed highly relevant to our work. They represent extensions of bisim... | Summary: This paper introduces State Decoupling with Q-supervised Contrastive representation (SDQC), a framework that decouples the global observations of an RL agent into reward- and cost-related representations to deal with OOD data and improve generalisation.
Claims And Evidence: 1) The authors claim that "Safe off... | Rebuttal 1:
Rebuttal: We sincerely acknowledge Reviewer cp6m for providing the insightful feedback. Please see our response to your concerns below.
## (Claim1) Motivation: How are safe offline datasets collected?
In offline RL, datasets are typically collected from past human experiences. For example, thousands of car ... | null | null | null | null | null | null |
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