title stringlengths 15 163 | paper_decision stringclasses 4
values | review_1 stringlengths 853 32.6k | rebuttals_1 stringlengths 0 15.1k | review_2 stringlengths 1.03k 35.6k | rebuttals_2 stringlengths 0 15.1k | review_3 stringlengths 807 27.4k ⌀ | rebuttals_3 stringlengths 0 15k ⌀ | review_4 stringlengths 780 22.2k ⌀ | rebuttals_4 stringlengths 0 15.1k ⌀ | review_5 stringclasses 171
values | rebuttals_5 stringclasses 166
values | review_6 stringclasses 25
values | rebuttals_6 stringclasses 24
values | review_7 stringclasses 4
values | rebuttals_7 stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability | Accept (poster) | Summary: This paper introduces SAEBench, a new benchmark to evaluate sparse autoencoders (SAEs). The authors point out that current SAE research may over-index on sparsity-recontruction tradeoffs, a metric that may not be a good proxy in practice. SAEBench covers seven metrics covering interpretability, feature separat... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We address each of your points below and will incorporate corresponding clarifications and improvements in the camera-ready version, if accepted.
**Metric Definitions**
We appreciate your emphasis on clear metric definitions. We already prov... | Summary: The paper introduces SAEBench, to evaluate SAE, of course, on various design choices. It proposes a unified evaluation suite that uses diverse metrics—concept detection, automated interpretability, reconstruction fidelity, and feature disentanglement—to assess SAE performance. The authors train and benchmark o... | Rebuttal 1:
Rebuttal: We thank Reviewer 55S8 for their thoughtful and detailed review. We’re glad you found the evaluation criteria sound and appreciated the comprehensive comparisons across sparse autoencoder (SAE) variants. First, we highlight the addition of RAVEL, a metric for feature disentanglement and model edit... | Summary: The paper introduces SAEBench, a new benchmarking framework for sparse autoencoders (SAEs) in language model interpretability. The authors identify limitation in existing evaluation approaches, which often rely solely on unsupervised metrics like the sparsity-fidelity tradeoff with limited practical relevance.... | Rebuttal 1:
Rebuttal: We thank Reviewer HzWd for their thoughtful and encouraging review. We’re glad you found the benchmark design, breadth of evaluations, and insights on SAE scaling and architecture choices valuable. First, we highlight the addition of RAVEL, a metric for feature disentanglement and model editing, t... | Summary: This paper introduces a benchmark framework for evaluating SAEs. SAEBench introduces a comprehensive evaluation suite with seven metrics across four capabilities:
- Concept Detection: Measuring how precisely latents correspond to concepts
- Interpretability: Evaluating human-understandability of latents
- Rec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and are glad that the benchmark’s goals and structure came through clearly. First, we highlight the addition of RAVEL, a metric for feature disentanglement and model editing, to our evaluation suite and invite you to compare SAE architectures in ou... | null | null | null | null | null | null |
Policy Design for Two-sided Platforms with Participation Dynamics | Accept (poster) | Summary: This paper studies the effect of matching policies in two-sided platforms, taking the evolution of both viewer and provider sides into consideration.
The authors show that myopic matching policies are only optimal in strong assumptions and appeared to be suboptimal at other cases.
The authors propose a new mat... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and efforts on the review. We respond to the key comments and questions below.
---
**(factual discussion)**
---
> **W3 [polarized equilibrium]**
We clarify the reasoning behind the paragraph below Proposition 2 step by step. First, consider ... | Summary: In this paper, the authors formulated and studied the dynamics of population effects on two-sided platforms, where viewer and provider populations evolve based on certain rules. Theoretically, the authors show that the myopic-greedy policy can fail to perform well when the population effects are heterogeneous ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and the acknowledgment of the contributions. We respond to the key comments and questions below.
---
> **Could some related works that model the population departure be included as baselines?**
Thank you for the great point. Unfortunatel... | Summary: The paper models the dynamics of a two-sided platform with viewers and providers. Viewers receive satisfaction from watching content assigned to them from providers that they like, and providers receive exposure from having their content assigned to viewers. The population of both the viewers and providers are... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and the acknowledgment of the contributions. We respond to the key comments and questions below.
---
> **I am a bit wary about some of their claims**, though. For example, they state, after Theorem 3 and Proposition 3, (overstatement)
Th... | Summary: This paper proposes and analyzes a theoretical model of population effects of the (e.g. recommendation) policies for two-sided platforms, where there are consumers and producers. The basic intuition is that a recommendation system influences both the short-term satisfaction of the content consumer but also the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and effort on the review. We respond to the key comments and questions below.
---
**(factual discussion)**
---
> **there is a lack of theoretical results in support of their proposed look-ahead policy**
Thank you for the valuable feedback. We... | null | null | null | null | null | null |
Direct Motion Models for Assessing Generated Videos | Accept (poster) | Summary: This paper proposes TRAJAN, an architecture designed to obtain dense high-level motion features using tracks predicted by the BootsTAPIR model. The authors demonstrate that these extracted features can effectively measure pairwise distances between videos in terms of motion, as well as evaluate the temporal di... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and feedback.
> there appears to be a contradiction between interpretations of Figure 5.
TRAJAN is sensitive to differences in motion between videos (whether they be from a camera, or from an object). In Figure 5(a), the generated and reference video mot... | Summary: This paper proposes TRAJAN, a novel motion-focused evaluation framework for assessing the quality of generated videos. Unlike traditional metrics like FVD that emphasize appearance, TRAJAN uses auto-encoded point tracks to assess temporally extended motion features. It supports distribution-level, video-pair, ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and feedback. We were pleased to see that you found our approach “methodologically sound and well-motivated” and that “All claims are thoroughly validated by extensive empirical evaluation”.
You also mentioned how “The paper [...] provides strong conceptua... | Summary: The authors propose a new video evaluation metric using point tracks instead of pixel reconstruction or recognition features, which can evaluate temporal consistency.
Claims And Evidence: The claims seem to be supported well in this paper.
Methods And Evaluation Criteria: The evaluation makes sense that demo... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and feedback. Please see [https://sites.google.com/view/trajan-videos-anonymous/home](https://sites.google.com/corp/view/trajan-videos-anonymous/home) for additional experiments on score interpretation.
> “The corrupted images as demonstrated in figure.4 d... | null | null | null | null | null | null | null | null |
Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing | Accept (poster) | Summary: This work aims to design peptide sequence based on observed mass spectra, addressing the issue of missing fragmentation. This problem is due to the incomplete fragmentation of precursor peptides or inherent limitations within tandem mass spectrometer. The author design a bipartite matching algorithm to impute ... | Rebuttal 1:
Rebuttal: We acknowledge with gratitude for the reviewer's appreciation that our work's motivation is quite clear and supported by the experimental results, as well as the recognition that the design of proposed algorithm is generally reasonable. We address the reviewer's concerns in detail below.
**Q1: Th... | Summary: This paper proposes LIPNovo, which is devised to compensate for missing fragmentation information within observed spectra before executing the final peptide prediction.
Claims And Evidence: The claims made in the submission are supported by clear and convincing evidence
Methods And Evaluation Criteria: The p... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the recognition that the proposed method is novel, and the evaluation is fair, which addresses the key issue of missing fragments in de novo peptide sequencing and can advance progress in this field. We address the reviewer's question as follows.
**Q: It i... | Summary: This paper presents a novel computational paradigm called **LIPNovo** for **de novo peptide sequencing**, addressing the problem of missing fragmentation information commonly encountered in mass spectrometry data. Unlike existing methods that rely on incomplete spectra, LIPNovo performs latent space imputation... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback on our work's novelty and the recognition that our method significantly improves the robustness of the model, achieves SOTA performance, and demonstrates generalization and efficiency. Our responses to the concerns are as follows.
**Q1: Limitations of theoretical ... | null | null | null | null | null | null | null | null |
Automated Benchmark Generation for Repository-Level Coding Tasks | Accept (poster) | Summary: This works presents SETUPAGENT - an automated LLM-based approach to generate repository-level coding benchmarks from a list of github repos. It enables creation of larger and diverse benchmarks suitable for evaluating software engineering agents, and automates steps such as dependencies setup, test execution, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable and positive review, highlighting both the novelty of our work and the value it brings to the community. Below, we address their remaining questions:
**Can you extend the evaluation of the created benchmarks?**
We have added three additional agents/metho... | Summary: The paper addresses the problem of automatically creating repository-level execution benchmarks for software engineering tasks. The authors first describe SetupAgent, an LLM-powered agentic framework to setup the execution environment for any Python repository. This is then used to create SWA- and SWEE-Bench, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review, acknowledging the value of our contribution to the community and the quality of our analysis. Below, we address their remaining concerns.
**Can you evaluate the presence and correctness of fail-to-pass tests more rigorously?**
We first want to hi... | Summary: This paper introduces SETUPAGENT, a system for automatically generating repository-level benchmarks for code agents by setting up historically accurate execution environments. It extracts installation and testing commands from GitHub repositories using LLMs, iteratively refines them based on execution feedback... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback, highlighting the depth of our analysis, the scalability of our approach, and the value of our work for the community. Below, we address their remaining questions and concerns:
**Can you conduct a manual review of generated tasks to assess their q... | Summary: To achieve the automatic generation of challenging and realistic repository-level coding benchmarks, this work proposes an LLM-driven method, SETUPAGENT, to automate the extraction of valid information from complex real-world repositories, ensuring the correct setup of the environment for perfectly reproducing... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and positive feedback, recognizing the relevance and quality of the benchmarks we created. Below, we address their remaining questions:
**Can you assess the repeatability of SetupAgent and what it implies for the generated benchmarks?**
First, we wa... | null | null | null | null | null | null |
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning | Accept (poster) | Summary: This paper presents a two-stage learning-to-defer (L2D) approach for the multi-task setting involving both classification and regression. The authors provide theoretical justification in the form of consistency bounds, as well as empirical justification in the form of validation on (multi-task) object detectio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and constructive feedback. We are pleased that they found the paper well-written, the empirical setup appropriate, and the contributions clearly positioned in relation to prior work.
Below, we provide clarifications on several points raised in the review.
... | Summary: The paper presents a new application of learning to defer to the context of multitask, where the task's target consists of both a regression and a classification task. The authors provide a theoretical analysis of two-stage L2D, showing that the proposed surrogate loss is both Bayes-consistent and $\mathcal{G}... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback. We appreciate their positive assessment of the strength of our theoretical and empirical contributions.
Below, we address the potential connection to coverage constraints and provide further details on specific aspects of our experi... | Summary: The paper developed a Two-Stage Learning-to-Defer framework for multi-task problems, enabling joint classification and regression. This framework features a novel two-stage surrogate loss family that is both $(\mathcal{G}, \mathcal{R})$-consistent and Bayes-consistent for cross-entropy-based surrogates. The au... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and thoughtful comments. We are pleased that they found our framework well-presented and the theoretical analysis rigorous. In the following, we clarify the technical challenges that arise in the multi-task setting.
First, we emphasize that **our p... | Summary: In this paper, the authors analyze the learning-to-differ (L2D) problem in the two-staged multi-task (classification and regression) setting. The paper introduces the pointwise Bayes rejector for the mult-task deferral and introduces a surrogate differal loss that is Bayes consistent. The paper further provide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful feedback. We are glad that they found our theoretical contributions novel, the paper well-motivated, and the analysis of the two-stage multi-task Learning-to-Defer setting valuable to the literature. Please find some clarification below:
> I... | null | null | null | null | null | null |
MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections | Accept (poster) | Summary: The paper proposes Multiway Dynamic Dense (MUDD) connections to improve cross-layer information flow in Transformers. By dynamically computing per-position weights for query, key, value, and residual streams, MUDD mitigates representation collapse and enhances depth efficiency.
Claims And Evidence: Most claim... | Rebuttal 1:
Rebuttal: Thanks!
> it lacks comparisons to alternative cross-layer architectures like OmniNet (Tay et al., 2021a) or MoE-based models (Fedus et al., 2022).
**vs. OmniNet**
We've already compared MUDD connections with OmniNet briefly in Related Work. While both aim at promoting information flow across th... | Summary: The paper proposes a new method for aggregating information from previous layers that improves information flow in transformers. Specifically, the authors combine two methods for the aggregation: gated, token-specific weighting and separate streams for inputs to the attention (queries, keys, values, and residu... | Rebuttal 1:
Rebuttal: Thanks! (mem = memory, backprop = backpropagating)
>mem usage and wall-clock time for main experiments
Peak activation mem usage for training a transformer in float16 with L layers, N heads, seq length T, batch size B and model dim D occurs at the beginning of backprop and is composed of two par... | Summary: The paper proposes Multiway Dynamic Dense (MUDD) connections to enhance cross-layer information flow in Transformers by augmenting residual connections with dynamic, multi-headed dense aggregations. Key innovations include decoupling the query, key, value, and residual streams of each Transformer block, enabli... | Rebuttal 1:
Rebuttal: Thanks!
>Limited discussion on other dynamic weight methods (e.g., Llama 3’s routing) or global memory approaches (OmniNet).
Although we don't know what you mean by "Llama 3's routing", we do a comparison with another representative dynamic weight method MoE, and a global memory approach OmniNet... | null | null | null | null | null | null | null | null |
Neurosymbolic World Models for Sequential Decision Making | Accept (poster) | Summary: The paper presents SWMPO, a framework for learning neurosymbolic Finite State Machines (FSMs) to model environmental structures for policy optimization. The key contributions are an unsupervised learning algorithm for training modular world-model primitives from low-level continuous observations, a state-machi... | Rebuttal 1:
Rebuttal: We thank Reviewer **pyge** for their thoughtful comments and are committed to incorporating your feedback into the manuscript.
> **Note:** Since our submission, we have demonstrated stronger RL performance (see response to **B4Kd**).
Please excuse our brevity due to the character limit.
## Mode... | Summary: In the setting of POMDP, the paper introduces SWMPO, a framework based on a Markov Decision space model in which each transition can be characterized by mode (FSM). That is, at each $t$, the transition occurs by
$(o_t, a_t) \mapsto o_{t+1} = f(o_t, a_t | M_t) $ where $M_t is the mode at time $t$.
The mode ... | Rebuttal 1:
Rebuttal: We thank reviewer **B4Kd** for their thoughtful comments and appreciate your recognition of SWMPO’s extension of FSM-based modeling to non-linear continuous domains and of its practical competitiveness. We are committed to incorporating your feedback into the final version of the manuscript.
> **... | Summary: The paper presents Structured World Modeling for Policy Optimization (SWMPO), a framework for unsupervised learning of neurosymbolic Finite State Machines (FSM) that capture environmental structure for policy optimization. The method operates in two main stages: (1) learning local “world-model primitives” that... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer **bZQ6** for their thoughtful comments, and we are committed to incorporating your feedback into the final manuscript.
> **Note:** Since our initial submission, we have demonstrated stronger RL performance of SWMPO over baselines (see response to **B4Kd**).
Please exc... | null | null | null | null | null | null | null | null |
EPIC: Efficient Position-Independent Caching for Serving Large Language Models | Accept (poster) | Summary: Existing work accelerates LLM workload by reusing the KV cache of a text chunk when it is the prefix of the request. Existing work break the "prefix" limitation by dynamically finding and computing the attention for a subset of tokens. This work further accelerate existing work by only performing recomputation... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address the questions and confusion below.
> Key assumptions — attention sink.
The attention sink phenomenon is well-studied in attention sparsity research. For example, StreamingLLM [1] and DuoAttention [3] discover that only a few useful tokens receive... | Summary: The paper introduces a context caching method with limited recomputation called LegoLink. Prior work either fully recomputes KV caches for new inference calls (e.g. full re-encoding) or uses dynamic recomputation to recompute a fraction of total cached values (i.e. CacheBlend). To further reduce cost, LegoLink... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address the review questions below.
> Missing related work.
Thank you for pointing this out. We will include a subsection discussing RAG applications and their connection to context caching. This addition will clarify PICI's positioning without modifying ... | Summary: The authors propose a system of context caching which can store pre-computed key value stores for multiple documents. The proper caches are then retrieved given a user query. The key insight is that only a fixed number of initial tokens needs to be recomputed in order to avoid shifting the attention score dist... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address the review questions below.
> More details (overall workflow, cache hit def, indexing, cache swap in/out, too-large cache) of PICI system in the main text instead of the appendix.
Due to page limits, we prioritized the attention sink effect in PIC... | Summary: This paper introduces PICI, an efficient position-independent context caching system for serving large language models. The system pre-computes the KV caches of unchanged contents and splits them into blocks. The incorporated method, LegoLink, leverages the static attention sparsity of each block, eliminating... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address the review questions below.
> More details (overall workflow, cache hit def, too-large cache) of PICI system in the main text instead of the appendix.
Due to page limits, we prioritized the attention sink effect in PIC and our simple yet effective... | null | null | null | null | null | null |
Combinatorial Reinforcement Learning with Preference Feedback | Accept (poster) | Summary: This paper studies combinatorial reinforcement learning (Combinatorial RL) under Multinomial Logit (MNL) preference feedback, where the agent selects a subset of items (an "assortment") at each step, and user choices follow an MNL model. Unlike traditional MNL bandits, which optimize single-step rewards, this ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! Below, we provide our responses to your comments and concerns.
---
### **Experiment**
- **Additional real-world data experiments:**
In response to the reviewer’s suggestion, we conducted experiments on the real-world MovieLens dataset (Harper & Kons... | Summary: The paper addresses the problem of combinatorial reinforcement learning with preference feedback, where a learning agent offers an assortment of multiple items (an action) to a user, whose preferences follow a multinomial logistic model. This framework is particularly relevant for applications like recommender... | Rebuttal 1:
Rebuttal: We appreciate your time to review our paper and your valuable feedback.
It seems that your main concern is the lack of empirical validation for the proposed algorithm. However, we would like to emphasize that this is a theoretical paper, submitted to the theory category. Our work introduces a ne... | Summary: This paper considers the combinatorial RL with a MNL preference distribution, where given an combinatorial action(assortment), the final action is sampled from a linear MNL model. In this setting, the learner needs to estimate both the underlying MNL model parameter and the transition dynamics, as in the stand... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive support and recognition of the value of our work! We truly hope this research helps to shed light on a new direction for the RL community, particularly in the area of combinatorial RL. Please don’t hesitate to reach out if you have any further questions. | Summary: This paper studies a combinatorial reinforcement learning setting in which an agent repeatedly offers a subset (assortment) of items and observes the user’s choice according to a multinomial logistic (MNL) model. Key challenges include (1) learning long-term (multi-step) item values rather than merely single-s... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the value of our work and providing a positive evaluation! We will address your questions below.
---
### **Additional real-world data experiments**
As per the reviewer’s request for real-world experiments, we have additionally conducted experiments on the large-scale,... | null | null | null | null | null | null |
Stream-level Flow Matching with Gaussian Processes | Accept (poster) | Summary: This paper extends Conditional Flow Matching by introducing Gaussian processes to model latent "streams" connecting source and target distributions. Key contributions include: (1) a generalized CFM framework using GP streams while maintaining simulation-free training; (2) demonstrating reduced variance in vect... | Rebuttal 1:
Rebuttal: Thanks a lot to the reviewer for their positive comments, and thanks for suggestions on writing. We will update our manuscript accordingly, and add more details in the experiments (e.g. compare to more methods besides CFM variants) whenever possible. Here, we clarified some specific points…
1. > ... | Summary: The paper proposes a novel flow matching method that incorporates **stochastic** bridges instead of **deterministic** bridges, which are typically used in the flow matching framework. In the context of generative modeling, flow matching (FM) is used to train neural ODEs with an initial distribution so that the... | Rebuttal 1:
Rebuttal: Thanks a lot to the reviewer for their positive comments, and thanks for suggestions on writing. We will update our manuscript accordingly, and add more details in the experiments whenever possible. Here, we clarified some specific points…
1. > However, in comparison to this widely adopted approa... | Summary: This paper proposes a generalization of conditional flow matching (CFM) models using Gaussian process (GP) streams. While CFM uses two endpoints as condition, GP stream defines a GP over time that connects 2 or more points from $t=0$ to $t=1$, providing more controls over the mean and variance of the path (thu... | Rebuttal 1:
Rebuttal: Thanks a lot to the reviewer for their positive comments, and thanks for suggestions on writing. We will update our manuscript accordingly. Here, we clarified some specific points…
1. > Some aspects of the experimental setup are unclear.
The details of GP construction can be found in Appendix C ... | Summary: The paper introduces stream-level flow matching with Gaussian processes (GP-CFM), which extends conditional flow matching to matching streams, i.e. latent stochastic paths that connect the source and target end points using Gaussian processes. The proposed framework naturally allows to include correlated obser... | Rebuttal 1:
Rebuttal: Thanks a lot to the reviewer for their positive comments. Here, we clarified some specific points…
1. > However, the authors do not directly access estimator variance and I don't see how the improvement in average Wasserstein-2 distance (or FID) can be directly attributed to a lower (per stream) ... | null | null | null | null | null | null |
Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence | Accept (poster) | Summary: This paper studies the information-theoretic generalization bounds within the conditional mutual information (CMI) framework by introducing a new information measure called binary Jensen-Shannon (JS) divergence. Specifically, the paper begins with a cleverly designed lemma that builds a relationship between bi... | Rebuttal 1:
Rebuttal: Dear Reviewer PSQE, thank you for your kind words and insightful comments! We address your questions below:
---
**On the Optimal Convex Comparator**
We appreciate you highlighting this work. As stated in Theorem 4 of that paper, the Cramér function is defined as the convex conjugate of the CGF ... | Summary: This paper investigates the question of tightness in mutual information generalisation bounds. Authors propose exactly tight generalisation bounds based on the binary Jensen-Shannon divergence. They show that their results are also tighter than various existing bounds and successfully involve the impact of a s... | Rebuttal 1:
Rebuttal: Dear reviewer S7W7, Thanks for your valuable comments! We are addressing your questions as follows:
---
**Redefinition of $L_n$ and $L_\mu$**
You are correct that $L_n$ and $L_\mu$ are defined in both Sections 2.1 and 2.2. These two definitions are actually equivalent but expressed using differ... | Summary: This paper introduces a novel framework for deriving *exactly tight* information-theoretic generalization bounds in machine learning using the binary Jensen-Shannon (JS) divergence. By leveraging a binarization technique for loss variables and supersample frameworks, the authors propose hypothesis-based and pr... | Rebuttal 1:
Rebuttal: Dear Reviewer 537o, Thank you for your thoughtful comments and questions! We address them below:
---
**Assumption in Corollary 3.12**
We agree that the assumption in Corollary 3.12 is stronger than Assumption 3.7. This limitation is acknowledged in Section 5, and we leave the task of relaxing t... | Summary: The paper discusses tight information theoretic bounds for the generalization error. The bound is general and can be applied to any machine learning model. This line of work is based on the seminal works of Xu and Raginsky (2017) and follow-up works that use the mutual information between training data and the... | Rebuttal 1:
Rebuttal: Dear reviewer o7Wh, thanks for your thorough reading and constructive questions! We are addressing your questions as follows:
---
**On the Nature and Significance of Information-Theoretic Results**
This is an insightful and important question involving many works studying information-theoretic ... | null | null | null | null | null | null |
Telling Peer Direct Effects from Indirect Effects in Observational Network Data | Accept (poster) | Summary: Estimating causal effects in observational network data is challenging due to peer interactions. Existing methods struggle to distinguish different types of peer effects. To address this, the proposed approach defines a general setting that considers peer direct effects, peer indirect effects, and individual t... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and the high recognition of the value and importance of our work.
**Comment:** *Baseline models details needed: Which peer and self-treatment effects do they evaluate?*
In our simulation, the baseline models provide estimates of the ove... | Summary: This paper studies the causal effect estimation problem without SUTVA assumption. Specifically, the authors identify the overlooked problem that existing methods cannot distinguish between peer (in)direct effects and self-treatment effects. The authors propose a method called gDIS to estimate these estimands i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Detailed responses to each specific comment are provided below.
**Weakness 1:** *Lack of comparison with PIE/PDE estimators.*
The PIE/PDE estimators cited in our introduction (e.g., VanderWeele et al., Shpitser et al.) are designed for One-to-One ... | Summary: This paper focuses on differentiating between various types of causal effects in network data: peer-direct effects (PDE), peer-indirect effects (PIE), and self-treatment effects (STE). The authors propose a general setting to identify and estimate these effects, with theoretical identification conditions and p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and recognition of our work's importance.
**W1:** Network unconfoundedness assumption... more discussion needed
We plan to explore: a) Instrumental Variable methods [1] to introduce variables influencing treatment but not outcomes; b) Hidden co... | Summary: The paper addresses the challenge of estimating treatment effects in observational network data with network interference. The authors propose a framework to decompose peer effects into direct and indirect peer effects and provide theoretical analyses of the identification conditions. Additionally, the paper i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Detailed responses to each specific comment are provided below.
**W1:** *The effectiveness of the proposed approach depends on strong assumptions, which may be challenging to satisfy in real-world observational data.*
Our method is based on three ... | null | null | null | null | null | null |
MemFreezing: A Novel Adversarial Attack on Temporal Graph Neural Networks under Limited Future Knowledge | Accept (poster) | Summary: The authors propose MemFreezing, an adversarial attack on temporal graph neural networks (TGNNs) that poisons TGNNs' recurrent neural network memory without knowledge about the future. For this, MemFreezing injects fake nodes into the graph that put their connected nodes into a "fronzen" state. Which means tha... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the positive feedback and valuable comments. In response, we added more discussion on data crawling, adversarial training, and victim node selection, and we will also revise our paper accordingly.**
---
## **Q1. Data Crawling**
Thank you for the valuable qu... | Summary: The paper studies adversarial attacks on temporal graph neural networks and proposes an effective approach to generate attacks that can persist over future timesteps. They consider an online adversarial attack setting and add fake nodes with carefully crafted memory representations at each timestep such that t... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the valuable comments and insights from the reviewer. In response, we carefully respond to the reviewer’s questions and will also revise the paper accordingly.**
---
## **Q1. Generalizability to non-memory-based TGNNs**
We acknowledge that MemFreezing primarily target... | Summary: The paper introduces MemFreezing, a novel adversarial attack framework designed to disrupt temporal graph neural networks (TGNNs) under realistic constraints where attackers have limited knowledge of future graph changes. The core idea is to strategically freeze node memories in TGNNs, rendering them unrespons... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the positive feedback and valuable comments. In response, we clarify the rationale behind the future simulation choices, discuss more block-box attack setups, and clarify the observation on stable states and attack budget. We will also carefully revise the pap... | Summary: The method makes use of a design component (node memory states) of recent temporal graph neural networks to disturb model predictions in future unseen time steps. This is done by selecting high-degree nodes (referred to as root nodes) and their 2 neighbours with the highest node degrees (referred to as support... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the positive feedback and valuable comments. In response, we exemplify the cross-freezing in a social media case, discuss its potential defenses, and clarify the assumption of similar ideal stable states among nodes. We will also carefully revise the paper fol... | null | null | null | null | null | null |
Retraining with Predicted Hard Labels Provably Increases Model Accuracy | Accept (poster) | Summary: This paper investigates the benefits of retraining a model using its own predicted hard labels in scenarios where training data contains noisy labels.
There are two strategies for retraining the model:
- *Full Retraining:* The model is retrained on the entire dataset using its own predicted hard labels.
- *... | Rebuttal 1:
Rebuttal: Thanks for your review and questions! We address your concerns below.
**Other Strengths And Weaknesses:**
**1. "First, the theoretical analysis is…remains questionable."**:
* We agree that our analysis on linear models for binary classification will not fully explain what happens in the case of... | Summary: The authors theoretically analyze retraining in a linearly separable binary classification problem and show that it can improve the model accuracy with respect to the initial training in presence of label noise. They show that retraining is particularly helpful with high levels of label noise. Then, the paper ... | Rebuttal 1:
Rebuttal: Thanks for your review and great questions! We address your questions/concerns below.
**Claims And Evidence:**
1. We will clarify "binary" in the abstract.
2. Here we simply meant a setting where we have labels for all samples - to distinguish it from the setting of self-training where we are *n... | Summary: The paper gives a theoretical treatment on when learning with predicted hard label is beneficial than learning with original noisy label.
Claims And Evidence: Yes, the claims were proved.
Methods And Evaluation Criteria: Overall makes sense to me. Though not quite sure why consider the "label DP" setup, seem... | Rebuttal 1:
Rebuttal: Thanks for the review and great questions!
**(A) Label DP setting.** We focused on this because it’s not clear how to apply existing noise-robust techniques on top of existing label DP mechanisms, while retraining is a simple post-processing step. For e.g., as mentioned in lines 365-366 right col... | null | null | null | null | null | null | null | null |
Linear Mode Connectivity between Multiple Models modulo Permutation Symmetries | Accept (poster) | Summary: The authors observe the linear mode connectivity hypothesis as proposed before has only been confirmed between two independent models, and propose an algorithm to merge multiple models such that the test loss doesn’t meaningfully change but the model has an approximately flat global minima.
Claims And Evidenc... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments and for taking the time to carefully read our paper.
> The contribution seems important for the larger literature on linear mode connectivity, but this isn't particularly well motivated in the paper itself. The STE-MM algorithm seems novel, but bec... | Summary: Prior work showed that linear mode connectivity can be achieved between two independently trained neural networks by applying an appropriate parameter permutation, suggesting that SGD-trained models converge to a shared low-loss basin under permutation symmetries. This paper extends their analysis to multiple ... | Rebuttal 1:
Rebuttal: We are grateful for your valuable comments and for your close reading of the paper.
> I recommend that the authors extend their approach to more challenging datasets, such as CIFAR-100 and ImageNet, to further validate its effectiveness.
Thank you for your suggestion. To test our method on more ... | Summary: This paper investigates permutation-based methods for merging multiple models. The literature mainly focused on merging pairs of models, and this paper shows that these methods fail to transfer multiple models into the same basin. Then they introduce a method for merging multiple methods and find multiple perm... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and the effort you put into reviewing our work.
> The algorithm explanation could be improved. I suggest the authors expand and clarify the reason for dummy variables and how the algorithm works in general by following the explanation of the seminal paper (A... | Summary: This paper focuses on the linear mode connectivity between neural networks (NNs) trained using stochastic gradient descent (SGD). First, it shows that existing permutation search methods perform poorly when more than two models are involved. To address this issue, the authors propose a novel search method, the... | Rebuttal 1:
Rebuttal: Thank you for reading our paper carefully and for your constructive comments.
> However, other merging methods, such as TIES merging and DARE, are also widely used and have been shown to be effective. Additionally, due to computational cost considerations, simple averaging remains a common approa... | null | null | null | null | null | null |
R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models | Accept (poster) | Summary: The paper introduces R*, an efficient framework for automatic reward function generation in reinforcement learning. R* addresses the challenge of designing high-quality reward functions by leveraging LLMs through two key components: reward structure evolution and parameter alignment optimization. The framework... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. For the reviewer's questions, we will respond to them one by one as follows.
1. **[How are the parents selected, and how is it determined which reward module to insert into another parent?]**
We maintain a buffer of reward functions with a fix... | Summary: This paper proposes a new method for designing reward functions with LLMs, R*. R* uses LLMs to generate modular reward function components, and maintains a population of reward functions. These population of reward functions are evaluated based on how well they guide the agent to the sparse reward. Based on th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for a positive assessment of our work. For the reviewer's questions, we will respond to them one by one as follows.
1. **[they do not discuss Eureka in depth, so it is really hard to estimate the novelty and contribution of this work. ]**
E... | Summary: This paper introduces R* which designs reward function by utilizing LLMs to construct a set of reward functions, 'evaluating' these rewards by training PPO agents to maximize the rewards, and then improving the reward functions based on voting mechanism followed by preference-based learning. Ablation study sho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. For the reviewer's questions, we will respond to them one by one as follows.
---
1. **[The performance of PPO with oracle reward seems very low to me -- it's much lower than the performance reported in Eureka tasks. Maybe I'm missing something... | Summary: This paper proposes LLM-based reward function generation to train models for tasks such as robotic hand manipulation.
The presented method can be decomposed into:
1. generation of modular reward functions using an LLM
2. augmentation with new reward functions based on modular mixing of the functions from step... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for a positive assessment of our work. For the reviewer's questions, we will respond to them one by one as follows.
1. **[The novelty of the proposed ideas is low, and seem to be a collection of multiple ad-hoc steps]**
Our work focuses on ... | null | null | null | null | null | null |
Diverging Preferences: When do Annotators Disagree and do Models Know? | Accept (poster) | Summary: This paper investigates when and why human annotators disagree, identifying 4 broad sources of preference divergence (task underspecification, response style, refusals, errors) covering >30% of responses in RLHF datasets. They then explore how divergent preferences impact LLM training (with reward modelling) a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions!
**Q1: The authors might consider engaging further with classic literature on social choice and voting theory.**
Thank you for this suggestion! We agree that Arrow's impossibility theorem and social choice theory is relevant to our work, parti... | Summary: - This paper introduces two datasets consisting of annotations for potential reasons for disagreements in preferences and derives a taxonomy from these annotations.
- The paper also studies the distribution of rewards across different modeling techniques.
- The paper also compares single versus distributional ... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We address each comment below. Please let us know if you have any remaining questions or concerns.
**Clarifying Dataset Collection and Release**
HelpSteer2 and MultiPref are cited. Note that MultiPref was made public prior to the publication documenting it, thus we co... | Summary: The authors examine diverging preferences in human-labeled datasets and present a taxonomy of disagreement sources. They show most disagreements stem from task underspecification and response style, not annotator errors, challenging the view that disagreements are mere noise. Standard reward modeling methods, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive feedback.
**Q: Can you clarify the rationale behind selecting the specific values used to map the reward gap to various annotator preferences?**
Appendix A provides more details on how we select these hyperparameters (we select the best performin... | Summary: This paper investigates diverging preferences in human-labeled datasets used for reward modeling and language model evaluations. The authors develop a taxonomy of disagreement sources. Through empirical analysis of HelpSteer2 and MultiPref, they find that disagreements are not random noise but stem from system... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive feedback!
We would like to address and clarify the following points in the review:
**W1: Contribution List + Connection between taxonomy and reward modeling**
We agree that our contributions and their connections would be more clear. In our revisi... | null | null | null | null | null | null |
Fixing Value Function Decomposition for Multi-Agent Reinforcement Learning | Reject | Summary: The paper studies the individual-global max (IGM) principle in model-based multi-agent reinforcement learning (MARL). They introduce a novel characterization of function classes of value function approximators, referred to as IGM-complete. They show the equivalence between this class and a parameterization of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough feedback; we will use it to improve the clarity of the submission.
# Direct Questions
## Re: WQMIX
See our response to Reviewer `5gNj`.
We will clarify this comment in the paper.
# Other Comments
## Re: QPLEX statement
The corresponding statement for QP... | Summary: In this paper, the authors address the problem of cooperative multi-agent reinforcement learning with value function decomposition method. They propose a class of decomposition function that are complete with respect to the IGM principles (the max of individual value functions matches the max of the joint valu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
# Direct Questions
## Re: WQMIX
The WQMIX theory [1] explicitly assumes fully-observable control (MMDP), and makes assumptions that do not hold for Dec-POMDPs, e.g., that decentralized policies can achieve the same optimal behavior as centralized polici... | Summary: The paper bridges theory and practice by proposing QFIX , a minimalist yet powerful framework for IGM-complete value decomposition. By extending prior methods with a simple fixing mechanism, QFIX achieves superior performance, stability, and scalability, setting a new standard for cooperative MARL algorithms.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and positive feedback.
# Direct Questions
## Re: Reason for the Improvement and Model Size
We believe that the empirical results combined with the model sizes of Table 1 provide a compelling argument that the performance of Q+FIX is driven from its mix... | null | null | null | null | null | null | null | null |
Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer | Accept (poster) | Summary: This paper studies the Massive Multi-Task Reinforcement Learning problem and proposes M3DT. When the number of tasks is large, it shows great performance improvement compared with former methods.
Claims And Evidence: In section 3.1, the authors propose an insight "Reducing the learning task number, particular... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback and questions that will surely turn our paper into a better shape.
>Q1. The logic in insight "Reducing ..." is not smooth. Fig.2 does not tell us reducing 160 tasks to a small number of groups can enhance the performance. First, the per... | Summary: Transformer-based models have recently shown success in offline reinforcement learning by framing the problem as a sequence modeling problem. Moreover, offline multi-task reinforcement learning (MTRL) has benefited from the high capacity of these models for solving complex and diverse tasks. Nevertheless, as t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work. We offer our responses to address your concerns as follows and will supplement these details and correct the typo in the revised manuscript.
>Q1. The limitations are not well-discussed. It is important to understand how to fix some limitations... | Summary: This paper introduces M3DT, a novel mixture‐of-experts (MoE) extension of the Decision Transformer designed to tackle the scalability challenges in massive multi-task reinforcement learning. The method leverages task grouping, dedicated expert modules, and a three-stage training mechanism to reduce gradient co... | Rebuttal 1:
Rebuttal: We are greatly appreciative of your recognition of our work. We offer our responses to address your concerns as follows and will address the formatting issues in Other Comments in the revised manuscript.
>Q1. how “massive” these benchmarks are.
A1. Current standard MTRL algorithms typically handl... | Summary: The authors study the problem of distilling a large multi-task offline RL dataset into a single policy via Prompt Decision Transformer (DT). The authors first study the scalability of Prompt DT with respect to both tasks and model size. These experiments provide a (very clear) demonstration of the theory that ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough reading of our manuscript and your valuable comments regarding out task setup and method. These comments have helped us realize that we inadvertently omitted several critical details in our original manuscript. We believe that supplementing and clari... | null | null | null | null | null | null |
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift | Accept (poster) | Summary: The paper introduces **Diversified Prototypical Ensemble (DPE)** to enhance robustness against subpopulation shifts. The method trains multiple diverse prototypes per class on top of a frozen feature extractor and enforces feature diversity through **inter-prototype similarity (IPS) loss**. By restructuring th... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive feedback on our submission. We appreciate the time you have dedicated to evaluating our work, and we are pleased that you recognize the strength of our method in improving worst-group accuracy (WGA) under subpopulation shifts. Your concerns are
- (1) U... | Summary: This paper studies the subpopulation shifting problem. To alleviate the issue, motivated by the idea of ensemble learning, the author proposes using a mixture of diversified prototypical classifiers over the feature prototypes of the subpopulations to classify different subpopulations correctly. Extensive expe... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive review. We appreciate your recognition of our well-motivated method, the novel application of prototype-based ensemble for subpopulation shift, and the thorough experimental validation.
Your main concerns include
- (1) lack of ablation on the number of... | Summary: This paper tackles the problem of subpopulation shift in machine learning, where the proportions of different subgroups within a dataset change between training and testing. The authors propose a novel method called Diversified Prototypical Ensemble (DPE) to improve robustness to such shifts. DPE combines prot... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate your recognition of the novelty and relevance of DPE, its effective combination of prototypical networks and ensemble diversification, and its strong empirical performance on worst-group accuracy across standard benchmarks.
Your main concerns incl... | Summary: This paper introduces the Diversified Prototypical Ensemble (DPE) to improve the robustness of machine learning models to subpopulation shifts. It replaces the standard linear classification layer with an ensemble of distance‐based prototypical classifiers. A two-stage training scheme is used: first use ERM to... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our well-organized presentation, the strong empirical performance of DPE on worst-group accuracy, and its relevance to prototypical networks and subpopulation shift.
You raised key concerns regarding:
- (1) ablation on prot... | null | null | null | null | null | null |
Identifying key amino acid types that distinguish paralogous proteins using Shapley value based feature subset selection | Reject | Summary: When understanding the evolution of natural proteins, it can be helpful to distinguish related families where the sequences are similar but they perform different function. These are assumed to have diverged during evolution. A useful part of this workflow is to identify key amino acids that distinguish the tw... | Rebuttal 1:
Rebuttal: ## 1. How is the AFS, irrespective of where they occur in the sequence, helpful for understanding the distinction between the families?
The AFS is a data-driven prediction of the amino acid types that may play a role in the functional difference between paralogous proteins. Post-hoc computing the... | Summary: The authors introduce a Shapley-value based approach to identify key amino acids distinguishing paralogous proteins (P and Q). By utilizing a Shapley-based SVM classifier, they define amino acid feature subsets (AFS) for each protein: AFS(P) and AFS(Q). They train an SVM to differentiate these subsets and vali... | Rebuttal 1:
Rebuttal: ## 1. Could Shapley value cutoff be tuned based on prior knowledge of amino acids? E.g., when $x_i^{AAC}=0$ for some amino acid $i$?
The cutoff, in principle, can be user-defined as it is only used to select the top-ranked features. The efficiency axiom based cutoff, i.e $\sum_i \phi_i/n$ , selec... | Summary: This article deals with a biological problem: distinguishing paralogous proteins. It is addressed as a set of binary pattern classification problems.The method designed to solve them is a pattern extraction method. It consists in identifying the amino acids characterizing the paralogs. This extraction is the r... | Rebuttal 1:
Rebuttal: ## 1. Why not address the problem as multi-category pattern classification, eg, using multi-class SVM with a string kernel?
Classification of proteins is not our task. Our learning task is identifying the amino acid types (feature subset) that play a role in the functional difference of a given pa... | Summary: Manuscript proposes a method for identifying which amino acid types are discriminative between paralogous families’ sequences. The selected set of amino acid types, 5-10 amino acid out of 20, are called amino acid feature subset (AFS). Feature relevance is determined using Shapley values. Biological relevance... | Rebuttal 1:
Rebuttal: ## 1. Figure E7?
Figure E7 is present on page 22, Appendix Sec E.1. It shows the alignment of sequences after 3-D structure alignment. The structural superimposition is not shown in the figure. The AFS amino acids are in bold in the alignment, and the contact points of hemoglobin tetramer are hig... | null | null | null | null | null | null |
All-Purpose Mean Estimation over R: Optimal Sub-Gaussianity with Outlier Robustness and Low Moments Performance | Accept (oral) | Summary: Mean estimation is the following simple task: given samples from a probability distribution D on R, estimate the mean mu of D. Although mean estimation has been studied since the dawn of time, various elementary and fundamental questions about mean estimation are still being tackled.
A natural goal is to obt... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work! | Summary: This paper considers the problem of designing "all-purpose"mean estimation algorithms that can be applied to a variety of scenarios. To be specific, the authors consider the estimator by Lee & Valiant (2022). This algorithm, is previously shown to be optimal in the standard finite variance and i.i.d. setting. ... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper. We answer your questions below.
**Beyond 1-d**: Currently, even the construction of a 2-d mean estimator achieving the analogous guarantees of Fact 1.1 (i.e. with sharp constants) is an *open problem* in the field, so no such estimator is known at ... | Summary: This paper concerns the problem of estimating the mean of i.i.d. real-valued samples.
The authors studies an estimator due to Lee & Valiant 2022 and show that this estimator enjoys several properties not known before.
These include
- optimal robustness against adversarial outliers
- optimal accuracy for heav... | Rebuttal 1:
Rebuttal: Thank you for your positive review and questions. Here, we answer the questions raised.
**Thm 2.2 vs 2.3**: The difference is indeed subtle, but neither theorem implies the other, and since different readers might consider one or the other "more natural", we included both, to avoid leaving reader... | Summary: This paper can be seen as a follow up for the seminal work of Lee & Valiant (2022), which proposed an optimal mean estimator for distributions over the set of real values, i.e. $\mathbb{R}$. Based on that, this paper further shows that the mean estimator of Lee & Valiant (2022) is ``all-purpose'', that is, it ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. To answer your question regarding high(er)-dimensional mean estimation: currently, even the construction of a 2-d mean estimator achieving the analogous guarantees of Fact 1.1 (i.e. with sharp constants) is an *open problem* in the field, so no such est... | null | null | null | null | null | null |
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse | Accept (poster) | Summary: The authors focus on the question of generating global counterfactual explanations. They introduce the problem from a theoretical perspective. They introduce the problem of FCR and FC which correspond to Finding Common Recourse and Finding Counterfactual problems, respectively. They introduce these problems fr... | Rebuttal 1:
Rebuttal: >Q1: validity and sparsity metrics are missing.
**Answer:** These two metrics have been considered, but under a different form in the definition of common recourse (in page 2 of our paper).
**Validity:** It measures how often the recourse suggestion changes the model’s prediction. In our definit... | Summary: In this study, the authors have formalized the problem of generating global counterfactual explanations for Graph Neural Networks (GNNs) with common recourse. Considering the NP-hard nature of the FCR and FC problems, the authors have developed COMRECGC, a method specifically designed to extract high-quality c... | Rebuttal 1:
Rebuttal: Thank you for your strong support and encouragement!
>Q1: Testing the approach on different GNN architecture.
**Answer:** We provide the following **additional experiments**:
- **Experiments on different GNN architectures:** GAT, GraphSAGE and GIN on the datasets of the paper for solving the FC... | Summary: The paper introduces COMRECGC, a framework for generating global counterfactual explanations for graph neural networks through common recourse. Unlike local counterfactual explanations which are instance-specific, this approach seeks to find a small set of transformations (recourse) that can convert multiple "... | Rebuttal 1:
Rebuttal: >Q1: Some part of the analysis of the FCR problem is missing.
**Answer:** We will add the following in Appendix B.2:
Let us define $f$ as the function that associates to a set of common recourse its total coverage. We prove that $f$ is submodular:
Let $A \subseteq B$ be sets of recourse, and let... | Summary: This paper designs an algorithm COMRECGC for global graph counterfactual explanation (CE). It considers the finding common recourse (FCR) explanation to address the limitations of existing graph CE methods (relying on experts for recourse directions; separating the process of funding recourse direction from da... | Rebuttal 1:
Rebuttal: Thank you for your remarks and we appreciate the support for our work.
>Q1: Could you include additional evaluations on efficiency, particularly in terms of time and space complexity?
**Answer:** We make the following notes:
* **Time complexity** We have provided the time complexity analysis of ... | null | null | null | null | null | null |
Adaptive Sample Sharing for Multi Agent Linear Bandits | Accept (poster) | Summary: The paper studies an adaptive sample sharing problem for multi-agent linear bandits, where agents' true parameters may be different. The authors propose a separation test, which detects the stopping time for beneficial collaboration. The authors provide both the separation time upper-bound and cumulative pseud... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive and constructive comments. Below, we address your concerns and provide detailed answers to your questions.
1/ The orange ellipsoid (representing the collaborative estimation) corresponds to a reduced estimation variance, as it yields a smalle... | Summary: This paper considers the problem of multi-agent linear bandits in a collaborative setting where the aim is to maximise the cumulative reward across all agents. In this problem each agent has the same (static) set of arms but has a different parameter. The idea is if the parameters of two agents are close enoug... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their very positive and insightful comments. Thank you very much for your kind appreciation of the paper's results.
Thank you for the suggestion regarding the structure of the paper; we will revise it, as we agree it improves the overall logical flow.
Addi... | Summary: This paper studies the collaboration of multiple heterogeneous agents in addressing a linear bandit problem. It proposes an adaptive sample approach to dynamically determine whether agent pairs should cooperate (utilize observations). Based on the technique, the paper proposes the BASS algorithm to address the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive and constructive comments. Below, we address your concerns and provide detailed answers to your questions.
1/ Thank you for the remark; we will revise the structure of our paper, as we agree it improves the overall logical flow.
2/ There is ... | Summary: This paper considers a multi-agent linear bandit problem in which each agent seeks to estimate its own linear parameter (so that they can minimize regret) while all agents select arms from a shared set. In this setting, agents are allowed to share reward observations with other agents to reduce the uncertainty... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive and constructive comments. Below, we address your concerns and provide detailed answers to your questions.
1/ We will add a specific section in the appendix, featuring a flow-chart, where we will better expand the description of the algorithm... | null | null | null | null | null | null |
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation | Accept (spotlight poster) | Summary: The paper first reveals that Integrated Gradients (IG) effectively captures important points but has been underestimated in previous research due to traditional evaluation metrics' inability to consider feature importance's directional information. Therefore, the authors propose novel evaluation metrics, CPD a... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable feedback and have responded to each of your questions below.
---
[Q1] Random retention strategy to mitigate OOD.
[A1] Thank you for this comment. Integrated Gradients (IG) often interpolates through out-of-distribution (OOD) regions. Our proposed random reten... | Summary: The paper addresses the explainable AI (XAI) issue in time series. It proposes CPD and CPP evaluation metrics, discovers that traditional Integrated Gradients (IG) performs well, and then presents the TIMING method. Experiments show that TIMING outperforms baseline methods in multiple aspects.
Claims And Evid... | Rebuttal 1:
Rebuttal: We sincerely thank you for your helpful feedback and have addressed each of your comments below.
---
[Q1] Insufficient evidence for TIMING's segment-based masking superiority & comparison with complex masking.
[A1] Our results across real-world (Table 2, 3) and synthetic (Table 4) datasets show... | Summary: This paper proposes an improved version of the integrated gradient for time series tasks. The paper also challenges previous metrics for evaluating time series explainability and accordingly proposes two improved metrics for better evaluation. Overall, I think it is a good paper.
## update after rebuttal
Tha... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful insights and have addressed each of your comments individually below.
---
[Q1] Clarification on Figure 1. (particularly its y-axis)
[A1] Figure 1 has two components. The upper portion shows ground truth signed attributions on the y-axis. Two XAI methods estimate a... | Summary: The authors introduce Temporality-Aware Integrated Gradients which addresses the reliability issues of naive IG in the time series setting by applying a random retraining to partially retain certain data points with a segment-based mask. The theoretical properties of this approach are explored and comprehensiv... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful feedback. Below, we address each comment individually.
---
[Q1] C3. Theoretical analysis of time/memory complexity.
[A1] As shown in Fig. 4, TIMING demonstrates high efficiency compared to baselines like LIME, FO, AFO, and modern time series XAI method... | null | null | null | null | null | null |
µnit Scaling: Simple and Scalable FP8 LLM Training | Accept (poster) | Summary: The paper introduces a new 8-bit training method for LLMs, by keeping all tensors close to unit variance. The method has fewer hyper-parameters than earlier methods, and is more straightforward to implement. It also enables more accurate hyperparameter transfer from small models to large ones.
Claims And Ev... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback on our work and are glad that they find the method’s theoretical basis and empirical results rigorous and compelling.
## Training duration
> Only thing to make this claim stronger would be to train for more steps. Training steps are a bit short, b... | Summary: This paper presents µnit Scaling(µS), a straightforward and scalable FP8 training method. It addresses the root causes of numerical instability in conventional transformer blocks and proposes effective solutions to mitigate these issues. µS approach incorporates Square-root Softmax Attention and Post-Branch-La... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments on our work, and are glad that the ideas we presented are clear.
## Ablating components of µS
> Are there ablation experiments on the role of different components of µS?
Most interventions in µS are **uniquely determined by simple math and the design goals**... | Summary: This paper introduces µnit Scaling (µS), a method for efficient FP8 training of large language models without requiring dynamic scaling factors or extensive hyperparameter tuning. µS builds on Unit Scaling to maintain unit variance in weights, activations, and gradients, ensuring stable low-precision training.... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s helpful feedback and questions.
## Novelty of µS
> The novelty is limited. The proposed µnit Scaling (µS) scheme combines previously published µP and Unit Scaling.
While µS does build on ideas from both of these methods, µS involves modifications that are not... | null | null | null | null | null | null | null | null |
An efficient implementation for solving the all pairs minimax path problem in an undirected dense graph | Reject | Summary: This paper considers the all pairs minimum bottleneck edge problem. For this problem, given a weighted undirected graph $ G $ with weights $ w $, the goal is to compute:
$$
d_{bot}(s, t) = \min_{p\in \mathcal{P}(s, t)} \max_{e\in p} w(e)
$$
for all pairs $ (s, t) \in V(G) \times V(G) $, where $ \mathcal{P}(s... | Rebuttal 1:
Rebuttal: I am very glad there finally appears a reviewer challenging the contributions of the paper with real and solid code, not by cheap talking. Thank you!
I have tested Reviewer t5C9's code, it is indeed faster. However, the acceleration is due to the use of Numba JIT (Just-In-Time Compilation). It ... | Summary: This paper presents an implementation of an existing algorithm (Liu, 2023) for the all pairs minimax path problem for undirected dense graphs.
Claims And Evidence: As claimed, this paper provides an implementation of the algorithm proposed in (Liu, 2023).
Methods And Evaluation Criteria: There is no problem.... | Rebuttal 1:
Rebuttal: Question:
I am not sure why this paper includes the proof. If the authors of this paper think that the explanation of the correctness in (Liu, 2023) is wrong, this paper should explain which part of (Liu, 2023) is incorrect.
Response:
The (Liu, 2023) paper did not include a proof of correctness... | Summary: In this paper the minimax path problem is studied. The input consists of an undirected graph and the goal is to compute a minimax path between all pairs of vertices. For this, in an s-t path the edge with highest weight is the bottleneck edge. In other words, the goal is to compute a path with the lowest bottl... | Rebuttal 1:
Rebuttal: Thanks for the evaluation. "The first O(n^2) implementation for calculating the APPD matrix" and "theoretical proof of correctness" are not little contributions, for a fundamental problem of minimax path problem or widest path problem in graph theory.
Question:
footnote 3 violates double blind ... | Summary: The paper studies, given a graph G, the all pairs shortest minimax path problem. Here, the cost of the path between two nodes u and v of the graph is simply the edge with the largest cost and the minmax path is simply the smallest cost path between u and v. It is well-known that the path between two nodes in t... | Rebuttal 1:
Rebuttal: Contributions of the paper:
1. It provides the first code implementation for solving the all pairs minimax path problem or widest path problem in an undirected dense graph, in $O(n^2)$ time.
2. It provides the fastest code implementation for solving the all pairs minimax path problem or widest... | null | null | null | null | null | null |
Rényi Neural Processes | Accept (oral) | Summary: This paper identifies an important issue in Neural Processes (NPs) where prior misspecification can appear from the fact that the conditional prior and posterior share parameters, which can lead to and degrade uncertainty estimates. The authors propose Rényi Neural Processes (RNPs) as a solution, replacing th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the effectiveness and theoretical rigor of our work. We appreciate their effort in helping us improve the efficiency and practicality of the work.
# Computational costs.
As shown in Supp Table 7 of the paper, we have already compared the wall clock time b... | Summary: The paper presents a novel approach for training neural processes. By replacing the conventional KL divergence with the Renyi divergence, this allows the model to adapt when confronted with a misspecified prior, therefore enabling more robust inference. This paradigm is somewhat analogous to the utilisation of... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's efforts in helping us refine the details and acknowledge the original literature. We agree that a rigorous analysis and self-sufficient figures would strengthen the soundness of our work.
# Significance tests in Table 1 and 2.
All the results presented in the paper ... | Summary: The paper introduces Rényi Neural Processes (RNPs), a modification of Neural Processes (NPs) that replaces the standard Kullback-Leibler (KL) divergence with the Rényi divergence to mitigate prior misspecification. The authors argue that parameter coupling between the prior and posterior in traditional NPs lea... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our "well-motivated theoretical derivations" and "strong empirical results".
# Incremental novelty
We would like to clarify to the reviewer that our work goes beyond using RD for prior misspecification. We are the first to identify prior misspecification in ... | Summary: The paper replaces the Kullback–Leibler (KL) divergence in the standard neural processes (NPs) with the Renyi divergence to mitigate the issue of prior misspecification. The proposed Renyi neural process (RNP) has a tuning parameter $\alpha>0$ that penalizes the misspecified prior and unifies the variational i... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our innovation of unifying the objectives and carefully reviewing details including supplementary materials and numerical results.
# Additional evaluation metrics.
We have additionally reported the relative errors for two baseline methods NP and ANP on th... | null | null | null | null | null | null |
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach | Accept (poster) | Summary: This paper proposes a general data-centric directed graph online knowledge distillation framework called EDEN. The framework achieves data-centric machine learning, guided by the proposed hierarchical encoding theory for the graph-structured data. The paper conducts experiments to validate the efficacy of the ... | Rebuttal 1:
Rebuttal: **Q1: Claims And Evidence**
We sincerely apologize for the insufficient explanation in our initial submission, which may have caused confusion. We kindly ask you to refer to our response to Reviewer RNnj Q2, where we provided an example of the hierarchical structure of directed graphs in the real... | Summary: This paper introduces entropy-driven digraph knowledge distillation (EDEN), a data-centric framework for representation learning on directed graphs. EDEN addresses the limitations of current directed Graph Neural Networks by leveraging directed structural measurements to construct a hierarchical knowledge t... | Rebuttal 1:
Rebuttal: Due to the word limit imposed by the new regulations of ICML 2025 rebuttal, we have not provided detailed references, but we will gladly supply them in our subsequent discussions if needed.
**Q1: Claims And Evidence**
We sincerely apologize for any concerns that may have arisen. We kindly refer ... | Summary: This paper focuses on data-efficient representation learning for directed graphs and presents a novel online knowledge distillation framework based on a hierarchical tree structure. Leveraging this framework, the authors introduce EDEN, a new method that can be employed as a plug-and-play module to improve per... | Rebuttal 1:
Rebuttal: **W1: Enhanced Explanations for Accessibility**
We appreciate your in-depth feedback and acknowledge that certain sections, particularly the formula interpretations, require background knowledge, which may pose challenges for readers. To improve readability, we will incorporate more intuitive exp... | Summary: The author proposes a complex but effective method called EDEN, which tailored for the directed graph, specifically, it frist build a coarse-gradined Hierarchical Knowledge Tree (HKT), then, it refine the HKT with knowledge flow in the HKT. The method is widely adopted in the 14 graphs and the results valid th... | Rebuttal 1:
Rebuttal: Due to the word limit imposed by the new regulations of ICML 2025 rebuttal, we have not provided detailed references, but we will gladly supply them in our subsequent discussions if needed.
**Q1: Relation To Broader Scientific Literature**
I sincerely apologize for any confusion that may have be... | null | null | null | null | null | null |
Distributed Event-Based Learning via ADMM | Accept (poster) | Summary: This paper introduces an event-triggered distributed learning method using ADMM to reduce communication in federated learning (FL) while handling non-i.i.d. data distributions.
The key contributions claimed include:
- A communication-efficient approach that reduces the number of message exchanges using an ev... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their thorough review and constructive feedback.
Below, we address the specific points raised.
- **Justification of Experiment Design and Generalization:**
The point of our manuscript is to present a distributed optimization/learning algorithm that is both communi... | Summary: The authors present an event-based distributed agent framework that leverages a relaxation of Alternating Direction Methods of Multipliers (ADMM) to provide a framework with a reduced communication cost.
Claims And Evidence: The authors make sufficient claims to support their idea and also list its limitation... | Rebuttal 1:
Rebuttal: We appreciate the time and effort invested in evaluating our work. Below, we address each of the points raised.
- **Robustness to Biased/Shifting Distributions:**
Our experiments already use heterogeneous datasets with inherent bias and distribution shifts among agents (e.g., each agent having o... | Summary: This paper proposed an ADMM style algorithm with event-triggered communication for minimizing the sum of a smooth possibly nonconvex function and a closed proper convex convex regularizer subject to linear equality constraints. This general problem formulation subsumes the typical consensus optimization framew... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and recognition of our work’s strengths. We reviewed (Hadjicostis et al. 2016), and their approach using running sums offers an interesting alternative to periodic synchronization. While our method ensures strong theoretical guarantees, explori... | null | null | null | null | null | null | null | null |
Policy-labeled Preference Learning: Is Preference Enough for RLHF? | Accept (spotlight poster) | Summary: This paper introduces PPL, a new method for learning policies from human preferences based on the regret based model of preferences. The authors note a key distinction between the regret model used in prior work (CPL by hejna et al.) and propose an improvement upon it to consider the current policy. The author... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. If any of our responses fail to address the intent of your questions or if you have remaining concerns, please let us know.
**1. Final practical implementa... | Summary: The paper proposes Policy-labeled Preference Learning (PPL) to mitigate what it calls “likelihood mismatch” in RLHF. The authors illustrate how policy labels (i.e., knowledge of which behavior policy generated which trajectory) can help disentangle suboptimal policy actions from stochasticity in the environmen... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. If any of our responses fail to address the intent of your questions or if you have remaining concerns, please let us know.
**1. Paper position disconnecti... | Summary: The paper proposes a novel approach to preference-based RL, grounded in regret-based modeling of human preferences. Unlike prior work that also uses regret-based modeling, the paper explicitly labels the behavior policy. This, as argued by the authors, is important in order to resolve the likelihood mismatch ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and thorough evaluation of our paper. We have organized our responses to your comments below. If any of our responses fail to address the intent of your questions or if you have remaining concerns, please let us know.
**1. Generalization to other do... | Summary: This paper proposes an algorithm called policy-labeled preference learning where the preferences are assumed to be formed by a regret-based metric and the preference data are labeled by the policy that generated the trajectories. The paper did a very good job explaining why the regret-based preference model ma... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper. We have organized our responses to your comments below. If any of our reconstructed responses miss the intent of your questions or if there are remaining concerns, please let us know so we can address them.
**1. Difference be... | null | null | null | null | null | null |
Robust Sparsification via Sensitivity | Accept (poster) | Summary: The paper proposes a general framework for constructing ε-coresets for robust optimization problems of the form $\min_{x \in \mathbb{R}^d} F(x) = \sum_{i=1}^n F_i(x)$, where the robust version $F^{(m)}(x)$ aggregates all but the $m$ largest values of $F_i(x)$. This formulation is motivated by the need to handl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. Below are our responses.
Not very new problem. For example, the outlier problem as proposed in Question 1.1 has been commonly studied, which can be
traced back to [RL87].
We agree that robust estimation [RL87] is classical work in the field. Howev... | Summary: This work studies the coreset for robust optimization problems, where the loss function is defined to allow the removal of the highest $m$ costs. Research on robust coreset is relatively limited compared to the vanilla version. For functions with total sensitivity $T$, using the vanilla coreset algorithm and s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. Below are our responses.
[1] also explores the robust coreset within a broader context. And their robust coreset also consists of a vanilla
coreset and a sampling-based component of size $O(m/\varepsilon)$ (can be improved with bounded doubling di... | Summary: This paper studies a robust version of the $\epsilon$-coreset construction for a function class $\mathcal{F}$. Specifically, if we assume that there are $m$ outliers in $\mathcal{F}$, the goal is to construct a coreset such that it will always be an $\epsilon$-coreset even if we remove up to $m$ largest functi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. Below are our responses.
This is a large running time when m is large, and may prohibit realistic application of the algorithm to practical problems.
We acknowledge the exponential dependence on $m$. However, many existing robust algorithms, includin... | Summary: This manuscript shows that two simple conditions are sufficient for the existence of a small coreset for$F(m)$: $F(x)$ has a small vanilla coreset and has bounded total sensitivity. Then develops a general framework for constructing ε-coresets for several robust problems. Experiments on real-word datasets demo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. Below are our responses.
this scheme is only compared with uniform sampling. I understand that core sets are carefully designed preferential
sampling. So, are the results of such sampling better than those of random sampling?
We compared against ... | null | null | null | null | null | null |
INRFlow: Flow Matching for INRs in Ambient Space | Accept (poster) | Summary: Current flow matching (FM) methods are usually trained in two-stage paradigm, which sets obstacles for unifying models across data domains. To deal with this, this paper introduces INRFlow. In the proposed method, they estimate the map from coordinate to value via FM in a pointwise manner. To further model spa... | Rebuttal 1:
Rebuttal: 1. There may raise some concerns on novolties, as the proposed method is essentially combination of INR with flow matching, and using attention to handle latent variables for context information. Although the architecture is absolutely new and novel, the proposed method can solve problem well. The... | Summary: This paper presents INRFlow, a novel domain-agnostic generative model that operates in ambient space, eliminating the need for hand-crafted data compressors in different domains. The key innovation is a conditionally independent point-wise training objective, allowing INRFlow to model continuous coordinate-val... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging INRFlow’s competitive experimental performance and flexibility in inference. We also appreciate your thoughtful comments which help substantially improve the quality of our work. Please find point-by-point response to your questions below.
1. How are spatia... | Summary: # Update
The authors have adequately addressed my concerns, and I expect that they incorporate:
1. more explanation on how to consistency generate images at different resolutions, and
2. explanation on the difference between their proposed methods and other function-space methods I mentioned
to the final v... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments which helped substantially improve the clarity of the submission. Please find point-by-point response to your questions below.
1. Clarification of how upsampling in Figure 4 is conducted.
- We agree with the reviewer that description of these ... | Summary: This paper proposes INRFlow, a novel domain-agnostic approach to learn flow matching in ambient space without the need of a pretrained domain-specific encoder. INRFlow has been evaluated on three tasks: image-to-image generation, image-to-3D point cloud generation, and protein folding. The effectiveness has be... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the strength of INRFlow as a domain-agnostic flow matching model and thoughtful comments. Please find point-by-point response to your questions below.
1. How do you select the pseudo coordinates?[...]Can you do some ablation studies on this?
- In image d... | null | null | null | null | null | null |
Cost-efficient Collaboration between On-device and Cloud Language Models | Accept (poster) | Summary: The paper presents a setting where a small model having access to local data collaborates with a state-of-the-art LLM cloud-hosted (without access to the data) to solve real tasks. To improve over an initial naive protocol (with back and forth chats between the two models), the paper introduces Minions, where ... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback! We include a Common Response, followed by an Individual Response.
Please see the revised paper at this anonymous link: https://storage.googleapis.com/anonymous-files/minions.pdf
## Common Response
We appreciate the positive feedback from all the reviewers:
... | Summary: This paper presents MINION and MINIONS, novel frameworks for cost-efficient collaboration between small on-device and cloud-based language models. MINION enables asymmetric collaborative communication between LocalLM (Reading) and RemoteLM (Reasoning), achieving a 30.4× cost reduction while recovering 87% of r... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback! We include a Common Response, followed by an Individual Response.
Please see the revised paper at this anonymous link: https://storage.googleapis.com/anonymous-files/minions.pdf
## Common Response
We appreciate the positive feedback from all the reviewers:
... | Summary: The paper proposes an agentic pattern for collaborative modeling between a cloud-based large LM and a client-side small LM to reduce cloud inference costs. The authors propose two approaches:
1. MINION: A simple communication protocol where the small model summarizes and interacts with the cloud model. However... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback! We include a Common Response, followed by an Individual Response.
Please see the revised paper at this anonymous link: https://storage.googleapis.com/anonymous-files/minions.pdf
## Common Response
We appreciate the positive feedback from all the reviewers:
... | null | null | null | null | null | null | null | null |
MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs | Accept (poster) | Summary: This paper performs model merging using multi-objective evolutionary search that yields Pareto optimal solutions. The fitness function of the evolutionary algorithm requires one to evaluate a given model’s performance several times. The authors propose using a performance estimator using item-response theory (... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. We appreciate your insights and will do our best to address your concerns within the space constraints of the rebuttal.
### Methods and evaluation criteria
We report Spearman rank correlations (higher is better) using the Figure 3 setup. Due t... | Summary: This paper proposes an efficient evolutionary model merging framework to achieve multilingual model merging and cross-language knowledge transfer, and conducts a large number of experiments and theoretical analysis to verify the effectiveness of the method.
Claims And Evidence: YES
Methods And Evaluation Cri... | Rebuttal 1:
Rebuttal: Thank you for your thorough evaluation of our work and for highlighting areas in need of clarification. We appreciate the opportunity to provide more detail on our key contributions relative to [1], our assumptions regarding the availability of a validation set, and our comparisons with in-context... | Summary: This paper introduces MERGE3, a framework for efficient evolutionary model merging on consumer-grade GPUs. The method addresses computational bottlenecks in evolutionary merging by: (1) extracting a reduced dataset for evaluation, (2) estimating model abilities using Item Response Theory (IRT), and (3) evolvin... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful and detailed review. We are glad you find our framework coherent, our theoretical underpinnings solid, and our empirical results convincing. Below, we address your specific points and questions.
**Limited analysis of hyperparameters:** The main hyperparameter in ... | Summary: The authors present a framework for efficient evolutionary merging of language models for creating models with strong multi-task and/or cross-lingual task performance from a library of existing fine-tuned models without additional fine-tuning. In MERGE$^3$, the critical efficiency benefit comes from Extracting... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review. Your feedback helped us identify key areas for clarification. Below, we respond to each point.
**Claims And Evidence**
- In the revision, we’ll replace “while preserving performance” with: 50× compute reduction with ~86% accuracy retained (e.g., ... | null | null | null | null | null | null |
STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings | Accept (poster) | Summary: Interesting topic and good experimental design choices. However the work lacks empirical evidence that watermarking is what makes the method strong.
Claims And Evidence: Good: LLM rephrasing enables reliable statistical tests for dataset membership.
Bad: The fact that it works better with green/red watermar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work. We respond to the raised concerns below.
### Re: Experimental Designs
> E1: Main limitation is that the emphasis is mostly on protecting benchmarks, while the method makes more sense for other types of texts
We would like to clari... | Summary: The paper proposes a framework, called Stamp, for detecting dataset membership (infering whether a dataset was included in the pretraining dataset of an LLM).
The framework consists of generating multiple watermarked rephrases of the content, with a distinct watermark embedded in each rephrasing. One version... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and are happy to see that the reviewer enjoyed our writing and found our method novel. We respond to the reviewer’s comments and questions below.
### Re: Weaknesses
> W1: Scaling to real-world pretrained models.
We acknowledge the reviewer's c... | Summary: The authors propose a method for dataset membership inference based on generating one public paraphrase of specific content and several private ones, then using a perplexity-based statistical test for detecting whether the dataset was part of the training set.
### Update after rebuttal: Thank you for addressi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We respond to the reviewer’s comments and questions below.
### Re: Weaknesses
> W1: If the goal is to detect copyrighted samples, it seems like a strong assumption to presume that the "defender" has a relatively large dataset of copyrighted samples. It c... | Summary: This paper presents STAMP, a framework that helps content creators detect whether their content (e.g., benchmark test sets, blog articles, research abstracts) has been used without authorization in the pretraining of large language models. The key idea is to release watermarked rephrasings of the content, whic... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and feedback. We are happy to see that they appreciate the robust detectability that our method offers, and find the paper easy to follow. We discuss their concerns below:
### Re: Important baselines not discussed (ER1)
Thanks for sharing these... | null | null | null | null | null | null |
OmniAudio: Generating Spatial Audio from 360-Degree Video | Accept (poster) | Summary: This work created Sphere360, a real-world dataset for realistic 3D audio reproduction. An efficient sem-automated pipeline for collecting and cleaning paired video-audio data is established. The challenges of the created task are clearly described. The demos are interesting. Code and datasets will be made publ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our demo page and our dataset and all your valuable feedback. We plan to open-source the codebase in April 2025 to facilitate community-driven improvements in this direction and welcome the reviewer's specific recommendations on cutting-edge techniques w... | Summary: This paper addresses a novel task called 360V2SA, which involves generating First-order Ambisonics (FOA) spatial audio from 360-degree videos. To tackle this challenge, the authors introduce a new dataset called Sphere360, containing more than 100k clips of real-world 360-degree videos paired with their FOA au... | Rebuttal 1:
Rebuttal: Thank you for recognizing our motivation and demo, as well as acknowledging our experimental validations. We hope our response addresses all your concerns and questions.
## Updated Table 2 and Inference Latency
We sincerely apologize for the typos in Table 2 in the submission. Please check our *... | Summary: This paper addresses an interesting problem of generating spatial audio from panoramic videos. They first propose a real-world dataset, Sphere360, for 360 videos and their spatial audios. They also propose an effective training strategy combining coarse-to-fine pre-training and dual-branch video encoding for ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the strong motivation and exceptional quality of our paper. We hope our response thoroughly addresses your concerns and questions.
## Updated Table 2 and Inference Latency
We sincerely apologize for the typos in Table 2 in the submission. Please check ... | Summary: This paper proposes the task of generating spatial audio from 360-degree videos. To support this task, the authors construct a dataset named Sphere360, comprising curated real-world 360-degree videos collected from YouTube. Leveraging this dataset, the authors introduce the AudioSpace model, which employs self... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and detailed suggestions. We hope our response below fully resolves your concerns and questions.
## Claims that AudioSpace achieves SOTA performance
We sincerely apologize for the two typos in Table 2 main results and typos and boldfacing errors ... | null | null | null | null | null | null |
Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning | Accept (poster) | Summary: This paper proposes a few-shot learning framework based on a spiking neural network (SNN), combining a self-feature extraction module and a cross-feature comparison module to optimize feature representation and reduce energy consumption. The paper enhances the generalization and noise resistance of the model b... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful suggestions.
**Q1:The experimental part lacks the robustness analysis of the model under different datasets such as event-based neuromorphic datasets.**
R1:We are very grateful for this valuable suggestion. We supplemented the performance of the model on d... | Summary: This paper proposes a few-shot learning framework based on spiking neural networks (SNNs), which combines a self-feature extraction module and a cross-feature comparison module to significantly improve classification performance and reduce energy consumption. This method is innovative in using SNNs for efficie... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful suggestions.
**Q1:The choice of the optimal λ coefficient lacks a detailed experimental basis or theoretical explanation.**
R1:We appreciate this insightful feedback. We supplement the detailed experimental basis from three aspects:
1.Empirical Evidence ... | Summary: The paper proposes a few-shot learning framework based on SNNs, which combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption.
Claims And Evidence: Please refer to Other Strengths and Weaknesses.
Methods And Evaluation Crit... | Rebuttal 1:
Rebuttal: We sincerely appreciate the insightful feedback, which has helped us clarify our contributions and identify areas for improvement. Below, we address each concern point-by-point:
**Q1:Design motivation and theoretical analysis.**
R1:Our paper introduces a novel framework, SSCF (Spiking Self-Cross... | Summary: This paper focuses on leveraging spiking neural networks (SNNs) for few-shot learning (FSL) to enhance generalization ability and energy efficiency. The proposed method combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption.... | Rebuttal 1:
Rebuttal: We sincerely appreciate the insightful feedback, which has helped us clarify our contributions and identify areas for improvement.
**Q1:How to tackles the challenges of SNN’s few shot learning? Simply adds two simple attention-like modules ?**
R1:We appreciate this critique and clarify that our ... | null | null | null | null | null | null |
Active Treatment Effect Estimation via Limited Samples | Accept (poster) | Summary: The paper proposes a new active learning strategy for experimental design and an accompanying ATE estimator, derived from literature on estimators with finite sample guarantees. This is especially relevant to cases where experimental sampling must be constrained due to cost or other concerns. Additionally, the... | Rebuttal 1:
Rebuttal: Thanks for your advice! Here is our response point by point:
> **Q1: The proof of Lemma 4.4 borrows notation from Ghadiri et al. without defining it or referencing that paper, making it hard to follow without previously reading the other paper. In particular, $2 y=t+Z \bigodot \mu$. Also, it does... | Summary: Experimental design for estimating treatment effects does not generally have strong finite-sample guarantees, especially as the dimensionality of the covariates grows. Recent works implement experimental design based on leverage scores. This work proposes an alternative approach called IRD, which helps achieve... | Rebuttal 1:
Rebuttal: Thanks for your review and comments! Here are the responses to all of your questions.
> **Q1: It would be helpful to see results as a function of covariate dimensionality.**
Thanks for your suggestion! We provide the following supplementary experiments on the performance differences as d varies w... | Summary: The authors considered the problem of estimating the causal effect in an active sampling framework. In particular, they proposed a method called RWAS which attains the sample query of $O(d/\epsilon)$ to achieve $\epsilon$-approximation error where $d$ is the number of covariates. Moreover, they also provided a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable questions! We address your questions point-to-point in the following.
> **Q1: However, the authors should provide more explanation or context about the proposed method.**
1. **Insightful motivation**.
**Motivating example**. Suppose $X$ is $n \... | Summary: This paper developed a finite-sample estimator with sample complexity analysis for causal effect estimation. The paper demonstrated the near-optimality of the sample size, and further extended the framework to social networks. Numerical experiments with simulated and real-world data supported the effectiveness... | Rebuttal 1:
Rebuttal: ### **Acknowledgement and General Response to `RW kg3L`, `RW PucS` , `RW RneH`, `RW fVjN`** ###
Thrilled to receive such a positive reception from dear reviewers! Also, sincerely thank all reviewers for their insightful suggestions! In this general response, we carefully synthesized the reviewers’... | null | null | null | null | null | null |
NeuronTune: Towards Self-Guided Spurious Bias Mitigation | Accept (poster) | Summary: The paper proposes NeuronTune, a method for mitigating spurious correlations in neural networks by intervening directly in the latent space at the neuron (feature-dimension) level. The work addresses the common challenge that many “robustness” or “debiasing” approaches rely on knowing or inferring spurious att... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback on our submission. We provide our responses to your questions below.
## Essential References Not Discussed
- **Comparison with NeuroInspect**: NeuroInspect identifies neurons responsible for mistakes from the counterfactual explanation perspective a... | Summary: The authors propose to improve OOD generalization by identifying neurons that contribute significantly to misclassification on the validation set, and then retraining the output layer while setting those neurons to zero. Relative to ERM, this simple idea trades off in-distribution accuracy for significant impr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and for recognizing both the simplicity and effectiveness of our approach. We appreciate your valuable feedback and are glad that our efforts to explain the model selection strategies were helpful.
## Regarding the subpopulation shift interpretation
We app... | Summary: The work proposes a bias-unaware post-hoc model debiasing method. The approach
is based on the observation that, in a setting where a high majority of
samples, but not all, contain a biased attribute, the neurons in the
penultimate layer that are affected by the spurious attribute exhibit a
different behavior ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback on our submission. Here are our responses to address your questions.
## Evaluation Criteria
- **Usefulness of Accuracy Gap**: We hope to explicitly provide readers with a direct view on the numeric difference between worst-group accuracy ... | null | null | null | null | null | null | null | null |
Validating Mechanistic Interpretations: An Axiomatic Approach | Accept (poster) | Summary: They propose a framework to quantify the effectiveness of a total decomposition of a model into sequential interpretable components. They find that this framework is able to validate (by showing a high probability in each of the equations of their axioms) explanations they create for the model components of a ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments, we respond in detail below.
## Q1. More experiments are required to evaluate the framework by analyzing practical foundation models using the axioms from B.1
We sketch how the circuit for IOI in GPT-2 [1] may be expressed and analyzed in our framework in the ... | Summary: This paper introduces a set of axioms aimed at formalizing mechanistic interpretability for neural networks, inspired by abstract interpretation concepts in program analysis. The authors define mechanistic interpretations as human-interpretable programs that approximately replicate the computations of neural n... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, we respond in detail below.
## Q1. Case studies are too simple; it is unclear whether the approach is applicable to larger models
We note that our framework is already compatible with larger models and more complex architectures, and we emphasize that our ... | Summary: This paper is a first effort to draw a parallel between mechanistic interpretability and abstract interpretation from programming language theory. This is a natural analogy, and its very exciting to see contact between these two areas. The authors introduce four axioms for how an abstraction interpretation of ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments, and for your strong support for our work! We respond in detail below.
## Q1. What happens when only some of the axioms are adhered to?
In appendices D and E, we include a discussion of what happens when we consider component axioms (Axioms 2 and 4) alone; in... | Summary: The paper introduces a formal framework for assessing mechanistic interpretations of neural networks. The authors propose a set of axioms inspired by abstract interpretation from program analysis to systematically evaluate whether a given mechanistic interpretation accurately captures a model’s internal comput... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, we respond in detail below.
## Q1. It is unclear how well this approach generalizes to larger models
We emphasize that our key contribution is our framework (i.e., axioms) for evaluation of mechanistic interpretations and not particular techniques to deriv... | null | null | null | null | null | null |
Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks | Accept (poster) | Summary: This is a theoretical paper seeking to explain the phenomenon that in some situations overparameterized models, once the number of noisy training samples is large, fail to interpolate the training data and instead converge to a solution that minimizes the test loss. This was observed with in-context learning a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful feedback.
> The gap between the theoretical setting in this work and the empirically observed phenomenon with in-context learning and diffusion modes is large, and it is not yet clear whether and how the properties of the training dynamics identif... | Summary: - In the context of two-layer ReLU networks, the paper theoretically explores the issue where trained models got stuck in spurious local minima of the training loss as the number of training samples exceed a certain threshold.
- It is demonstrated that networks might converge towards simpler solutions rather... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful feedback.
> the theorems and propositions could be rephrased in a more accessible and less mathematically rigorous manner to improve clarity
We we will enhance the clarity of our results in the revised version. Specifically, we will include an i... | Summary: The paper theoretically studies how overparameterized networks converge to simpler generalizing solutions (as opposed to interpolating training data) when there are sufficiently many training samples. They do this by studying early alignment, where networks align their weights to the directions of the data ear... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful feedback.
> Atanasov et al. (2021) seems particularly relevant since they also study early alignment and how it relates to feature learning.
Thank you for pointing out this reference. We will include it in the revised version, as it provides valu... | Summary: This paper studies the simplicity bias in regression to a two-layer ReLU network using gradient flow. The author shows that despite overparameterization, the network may converge toward simpler solutions rather than merely interpolating the data, leading to a drastic improvement in test loss.
Claims And Evide... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful feedback.
> In lines 72 and 73, is there any reference to support this claim?
Yes, this phenomenon is extensively discussed by Raventos et al. (2024). In particular, Figure 4 and the discussion at the bottom of page 7 suggest that the model does ... | null | null | null | null | null | null |
Persistent Topological Features in Large Language Models | Accept (poster) | Summary: The authors of this paper explore the applicability of Zigzag filtrations in the Persistent Homology framework for feature extraction in LLM analysis. They propose building filtration on top of simplicial complexes defined by kNN-neighborhoods instead of proximity-induced cliques more commonly used in the pers... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. They recognize the novel approach of using zigzag persistence in the context of interpretability of NNs and the clarity of presentation. Additionally, they raise a few points which we address element-wise below:
> Claim of “Identification of Phases... | Summary: The paper tackles the problem of understanding how LLMs work by looking at how layers sequentially transform prompts. Unlike current art that only provides static views of internal representations, the paper uses zig-zag persistence across layers obtained from simplicial complexes built using kNN. Based on the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. They recognize the novelty of our approach to analyzing LLMs and its importance in enhancing understanding of these models. They also acknowledge a proper review of existing literature.
As for feedback on points to improve, we reply element-wis... | Summary: The authors introduce the concept of Zigzag persistence from topological data analysis to understand how features evolve through layers. The authors aim to offer a statistical perspective on how prompts are rearranged and their relative positions changed in the representation space, providing insights into the... | Rebuttal 1:
Rebuttal: Reply to zXjm
We thank the reviewer for their useful feedback. They highlight the novelty of using zigzag persistence from topological data analysis to understand feature evolution in LLMs, appreciating the accessible presentation. They raise a few concerns, which we address below:
> My biggest ... | Summary: This work introduces a framework for applying the topological descriptor zigzag persistence to analyze the internal representations of large language models (LLM). The experiments are conducted to evaluate the LLM models (Llama2-7B, Llama3-8B, Mistral 7B, and Pythia 6.9B), which demonstrates the effectiveness ... | Rebuttal 1:
Rebuttal: Reply to 4Kx7
We thank reviewer 4Kx7 for their useful feedback. While recognizing the novelty of our approach in combining zigzag persistence for LLM interpretability, the reviewer correctly points out that our work should have included a broader discussion of similar TDA approaches. We agree wit... | null | null | null | null | null | null |
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment | Accept (poster) | Summary: This paper explores whether GPT models, designed for next-token prediction, implicitly learn a causal world model from which sequences are generated. The authors derive a causal interpretation of the GPT attention mechanism and suggest that GPT models can be used for zero-shot causal structure learning with a ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough review and for the clear and detailed suggestions. We believe these suggestions further highlight the significance of the causality-based approach presented in the paper compared to correlation-based approaches.
# Answer to questions
## Re Question 1 and 2... | Summary: N/A
Claims And Evidence: N/A
Methods And Evaluation Criteria: N/A
Theoretical Claims: N/A
Experimental Designs Or Analyses: N/A
Supplementary Material: N/A
Relation To Broader Scientific Literature: N/A
Essential References Not Discussed: N/A
Other Strengths And Weaknesses: N/A
Other Comments Or Sugge... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and your perspective on the paper. We value your feedback and believe the following answers your concerns.
* Re first point. We would like to clarify that autoregressive generation is not inherently causal. Often the attention in GPT is called 'causal' but i... | Summary: The paper investigates whether a GPT trained for next-token prediction implicitly learns a causal world model, using an interpretation of the attention matrix as encoding a linear Gaussian SCM, first proposed in Rohekar et al. (2024). The introduce a causal discovery method for learning partially oriented caus... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback, insights, and important questions. Addressing your review improves the over clarity of the paper and emphasizes the significance of the contribution.
# Re Questions for Authors
1. The relation $\mathbf{D}^{-1}\mathbf{A} = (\mathbf{I}-\mathbf{G})^{-1}$ is no... | Summary: The work explores whether or not GPT style models learn a causal world model implicitly without explicitly being trained to do so by using the predict the next token objective. This is done in Othello and chess, and the theoretical formalization paired with the empirical results strongly suggest that GPT style... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and suggestions for improvement. Your suggestions will improve the clarity and emphasize the significance of the proposed approach and findings presented in the paper.
We also thank you for the many suggestions for future work.
**Re Other Comments or Suggestions*... | null | null | null | null | null | null |
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer | Accept (poster) | Summary: This paper introduces APT, extending on PFNs and TabPFN, which leverages adversarial synthetic data agents for pretraining and incorporates a mixture block architecture to handle classification tasks with an arbitrary number of classes, addressing the class size limitation.
Claims And Evidence: No, the author... | Rebuttal 1:
Rebuttal: We thank reviewers for their valuable feedback and comments. We see that the reviewer’s main concern lies in the confusion about zero-shot meta-learning, so first and foremost, we want to address this concern and try our best to provide the reviewer a clear picture.
---
Weakness 1 & 2 (Question ... | Summary: This work introduces APT, a zero-shot meta-learning model for tabular prediction, pre-trained with adversarial synthetic data agents. It improves TabPFN, removes class size limitations via a mixture block architecture, and matches SOTA GBDTs on small tabular tasks. While enhancing performance in classification... | Rebuttal 1:
Rebuttal: We thank the reviewer for their clear and valuable feedback on our work, and we address your questions and comments as follows:
---
“Other Comments Or Suggestions” & Question 1:
**Response**: Thank the reviewer for the suggestion. We added citation to TabPFN v2 [Nature'25] per your comment, but... | Summary: The paper introduces an Adversarially Pre-trained Transformer (APT) for zero-shot meta-learning on tabular prediction tasks. APT is pre-trained using adversarial synthetic data agents that continuously generate challenging datasets, enabling the model to generalize to unseen tasks without requiring real-world ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their succinct and extremely clear examination of our work.
In light of your feedback, we have added the following paragraphs:
---
Weakness 1:
**Response:** *The limitations are imposed on PFNs by the transformer architecture’s quadratic computation scaling. However, ... | Summary: This paper introduces the Adversarially Pre-trained Transformer (APT), which is a novel zero-shot meta-learning method for tabular data prediction tasks. By employing adversarial synthetic data agents and a mixture block architecture, APT addresses key limitations in prior tabular learning methods, particularl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind and detailed examination of our work.
In particular, the reviewer raises many profound questions about PFNs in general -- many of which our paper does not resolve on its own. Resolving the full gamut of these questions is a task for the entire research communi... | null | null | null | null | null | null |
Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension | Accept (poster) | Summary: The paper considers the problem of weak-to-strong generalization, which is the study of how the performance of a strong model trained on pseudo labels of a weaker model generalize. More formally the following setup is consider. Two features transforms, $\phi_s$ the strong model and $\phi_w$ the weak model, are... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive suggestions and supportive feedback. We are glad that they found our paper well-written and provided novel perspectives on W2S. Since we could not include figures in OpenReview rebuttal, we will synopsize our new experiments in text and present the form... | Summary: The paper studies weak-to-strong generalization in ridgeless regression with (sub-)Gaussian features. It reveals that weak-to-strong generalization arises from the discrepancy between the weak model's features and the strong model's features.
Claims And Evidence: The main theorem statement is clearly presente... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and suggestions. We are glad that they found our paper well-written and provides novel understanding for W2S. Since we could not include figures in OpenReview rebuttal, we will synopsize our new experiments in text and present the formal results in an anonymous... | Summary: This paper theoretically investigates the weak-to-strong (W2S) generalization phenomenon in the setting of ridgeless regression. From a bias-variance decomposition perspective, the authors utilize the intrinsic dimensionality of fine-tuned models to analyze the generalization performance of the weak teacher mo... | Rebuttal 1:
Rebuttal: We appreciate the constructive suggestions from the reviewer. We are glad that they found our paper well-presented and our perspective novel. Since we could not include figures in OpenReview rebuttal, we will synopsize our new experiments in text and present the formal results in an anonymous URL:... | Summary: This paper presents a theoretical analysis of weak-to-strong (W2S) generalization, a recently observed phenomenon where a strong student model outperforms a weak teacher model when trained on the teacher's pseudo-labels. The authors provide a variance reduction perspective by leveraging the concept of intrinsi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and suggestions. We are glad that they found this work well-presented and provided a good understanding of W2S. Since we could not include figures in the OpenReview rebuttal, we will synopsize our new experiments in text and present the formal results in an ano... | null | null | null | null | null | null |
Self-Play $Q$-Learners Can Provably Collude in the Iterated Prisoner's Dilemma | Accept (poster) | Summary: In this work, The authors prove that multi-agent Q-learners playing the iterated prisoner’s dilemma can learn to collude. The complexity of the cooperative multi-agent setting yields multiple fixed-point policies for Q-learning: the main technical contribution of this work is to characterize the convergence t... | Rebuttal 1:
Rebuttal: Authors would like to thank Reviewer LKws for their in-depth comments and very insightful review.
> 1 In recent years, there has been significant progress in algorithmic collusion research, but the authors cited less relevant work from the past year in their literature review. It is recommended t... | Summary: This paper studies whether Q-learning algorithm with self-play and one-step memory can lead to collusion in iterated prisoner's dilemma game. The authors characterize the conditions on the initializations, rewards, and discount factor to guarantee that the agents would shift from always defect to Pavlov strate... | Rebuttal 1:
Rebuttal: > Such an approach leads to the claimed Bellman equation (2), which depends on opponent policy \pi^2. This causes ambiguity due to its dependence on opponent policy.
We are confused by this statement, as dependence on the opponent's strategy is inherent to multi-agent games—the $Q$-values necess... | Summary: The paper studies Q-learning in the Iterated Prisoner’s Dilemma with memory 1. It shows by formal proof that under some condition, Q-learning results in the so-called Pavlov strategy, which forms a cooperative equilibrium. The paper also conducts some experiments, including experiments with Deep Q-learning on ... | Rebuttal 1:
Rebuttal: Authors would like to thank Reviewer hUYk for their in-depth comments, which have significantly improved the updated version of the manuscript.
> I’d like to know what the relevant differences are to this paper. E.g., is it the slightly increased complexity of the environment? Or not using self-p... | Summary: This paper shows that in the Prisoner's dilemma, Q-learning agents can learn to collude to a collaborative policy.
The authors clearly identify the underlying assumption to such behaviours.
Claims And Evidence: The main contributions of the paper is to prove that both with (Theorem 3.3) and without exploratio... | Rebuttal 1:
Rebuttal: Authors would like to thank Reviewer LKws for their in-depth comments and very insightful review.
> 1 - The authors prove the convergence results for the case of one-step memory. How is this case motivated?
The primary motivation for this work stems from the findings of Banchio and Mantegazza (... | null | null | null | null | null | null |
OmniBal: Towards Fast Instruction-Tuning for Vision-Language Models via Omniverse Computation Balance | Accept (poster) | Summary: ## update after rebuttal:
Score updated to 3
This paper suggest that during distributed training of vision-language models, there are computation imbalance due to model architectures, data types and how the mini batches are constructed inside and across devices. This paper proposes a novel method of mini-bat... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Figures and tables are shown at https://anonymous.4open.science/r/O-A/O.pdf.
*Q1: Not sure I can distinguish between evidence 1 (line 041) and evidence 3 (line 047):*
"(1) Varying input sizes of LLM and VIT cause imbalance computational loads across training ite... | Summary: This work focuses on addressing imbalanced computational loads in large-scale 3D parallel training of vision-language models by rebalancing across data, model, and memory dimensions. The authors conduct experiments on various models, datasets, and hardware platforms to demonstrate the speed-up ratio for vision... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Figures and tables are shown at https://anonymous.4open.science/r/O-A/O.pdf.
*Q1: The claim that "vision-language instruct-tuning models have recently made significant progress due to their more comprehensive understanding of the world" is unclear. The statement impl... | Summary: This paper identifies a significant computational imbalance issue in large-scale distributed training for Vision-Language Models (VLMs) due to heterogeneity in vision and language components. To tackle this, the authors propose OmniBal, a comprehensive framework that balances computation across three dimension... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Figures and tables are shown at https://anonymous.4open.science/r/O-A/O.pdf.
*Q1: what are the major challenges that prevent applying those approaches to VLMs?*:
**Major Challenges:**
- **Data Level:**
Simple packing strategies for LLMs lead to a severe imbalance i... | Summary: The paper addresses the causes of computational imbalance in VLM training, including aspects of data, model, and memory, and introduces OmniBal, a training framework designed for improving training efficiency of VLMs. OmniBal is basically comprised of three algorithms, balancing batch sizes, model partitions, ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Figures and tables are shown at https://anonymous.4open.science/r/O-A/O.pdf.
*Q1: In section 3 the paper mentions the differences between the training of VLMs and LLMs. However, it needs more solid explanations and explicit data to tell: (i) Whether present strategi... | Summary: This paper focuses on the large-scale distributed training of multimodal large language models, and propose a omniverse computation strategy to manage vision-language data distribution and the training memory optimization.
Although the studied problem is significant in the development of multimodal large lan... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback.
*Q1: ICML scope concerns*
**A1:** We believe our paper is within the scope of ICML.
**According to the ICML 2025 Call for Papers, topics of interest include (but are not limited to):
"Machine Learning Systems (improved implementation and scalability, har... | null | null | null | null |
Robust Spatio-Temporal Centralized Interaction for OOD Learning | Accept (poster) | Summary: This authors introduce a Spatio-Temporal OOD Processor, a framework for out-of-distribution learning in spatiotemporal graph convolutional networks. As stated, the traditional methods rely on node to node message interactions, which degrade performance in OOD settings. The proposed method addresses such limita... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We provide figures and tables at an anonymous link ( https://anonymous.4open.science/r/reb_/5Pwy.pdf ). **The "#" before a title means that complete tables in the section are available in the anonymous link.**
> \# Meta-learning
We introduce meta-learning bas... | Summary: The authors introduce a novel spatio-temporal interaction mechanism called STOP, which is tailored to enhance the sensitivity of the conventional node-to-node messaging mechanism favored by existing models to spatiotemporal shift. Key elements of STOP include the centralized message mechanism, the message pert... | Rebuttal 1:
Rebuttal: Thank you sincerely for your appreciation and thoughtful comments regarding the paper. Your feedback is invaluable in enhancing the quality of the manuscript.
> **W1. Visualization Cases**
Due to the rebuttal policy restrictions of ICML, we were only able to include some prediction visualization... | Summary: The spatiotemporal messaging mechanism utilized in STGCN exhibits inherent sensitivity to spatiotemporal variations. To address these limitations, the authors introduce a centralized messaging architecture integrated with a message perturbation mechanism and DRO optimization. Extensive experimental evaluations... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable time and effort, which are crucial for improving the quality of our manuscript.
> **W1. General Graph OOD Learning Discussion**
In the field of graph representation learning, researchers have focused on modifying model architectures to enhance the representati... | Summary: The paper introduces a spatiotemporal interaction mechanism called STOP. STOP includes centralized message passing mechanisms with message perturbation and DRO to enhance stability for spatiotemporal sifts. Through extensive experiments, STOP's robustness to spatiotemporal shifts is effectively demonstrated.
... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation and comments on the paper. Your comments are crucial to improving the quality of the manuscript.
> **W1 & Q2. Efficiency Analysis**
We sincerely apologize for any misunderstanding caused. Below, we report the efficiency comparison between the proposed m... | null | null | null | null | null | null |
Finite-Time Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents | Accept (poster) | Summary: This paper studies the convergence problem of the stochastic Stackelberg games. Specifically, this paper makes the following contributions. It analyzes different scenarios that reflect the decision-maker's ability to reason about the agents' behavior via different estimates of how it could impact the gradient.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. Below we respond to the topics you brought up in sections of the review.
**Other Strengths And Weaknesses:**
*Strength. The theoretical analysis is thorough and solid. For example, in section 4, the paper conducts a theoretical analysis for the ... | Summary: The paper studies the stochastic Stackelberg game and characterize the complex dynamics between the decision maker and learning agents. The drift, noise and optimization errors of the learning agents are decoupled and algorithms are proposed to control each components.
Claims And Evidence: Yes
Methods And Ev... | Rebuttal 1:
Rebuttal: Thanks for the review. Given space constraints, we focus on related literature and technical novelties.
**Referenced work**:
The results don't exist in prior works, nor do they easily extend from analysis in referenced papers.
- ZhaoEtAl2023, YuChen2024, different in game structure and theoreti... | Summary: This paper focuses on learning in Stackelberg games under the setting where the agents might learn their equilibrium gradually. First, the author provides the equilibrium tracking error for the learning agents for a given sequence of the decision-maker’s actions. Then, a learning algorithm for the decision-mak... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper, and pointing out some of the novelties in your review. Below we focus on the key queries in your review which we believe should address your concerns. **Please let us know if there are further clarifications**.
**Theoretical Claims:** We label... | Summary: This paper proposes an algorithm for a game in which there is one leader and $n$ followers. The leader chooses an action and the followers play a Nash equilibrium which is influenced by the leader's action. The solution concept is that of a Stackelberg equilibrium. The game is static but is repeated during th... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. Below we respond to your questions. Please let us know if this has clarified things.
**Questions For Authors:**
1. *Can you comment on the difference between Definition 4.2 and the notion of Stackelberg equilibrium on page 4 (left column)? How i... | Summary: The paper explores a learning problem in the context of Stackelberg games with single leader (decision-maker) and multiple followers (agents). The authors consider a setting where both the agents and the decision-maker are learning. The agents are learning, for any action taken by the decision-maker, the Nash ... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper, and acknowledging some of the strengths of the paper.
**Qs for Authors:**
**Re1.**: No, the decision-maker **(DM)** doesn't know the algorithm or $\mathcal{D}\_e$ *a priori*.
- **Algorithm**: In Line 396, DM doesn't know $\mathcal{A}$. From the environment, it r... | null | null | null | null |
Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning | Accept (poster) | Summary: - This paper proposes a unified framework (UCGS) for solving 4 different abstract visual reasoning tasks with a single deep network architecture.
- The already existing abstract visual reasoning tasks are based on problem panels consisting of several images showing simple visual concepts following different a... | Rebuttal 1:
Rebuttal: Thanks for the constructive suggestions. The detailed responses to the reviewer's comments are as follows.
**Q1: Some statements could be misunderstood and the paper would benefit from a more precise formulation.**
Thank you for pointing out the inaccurate statements. We will carefully check the... | Summary: This paper presents a method for solving abstract visual reasoning tasks that aims to unify previous methods for different types of tasks (e.g. matrix reasoning vs. odd-one-out) and different modes for solving problems (e.g. classification vs. generation). The method is evaluated on various abstract visual rea... | Rebuttal 1:
Rebuttal: Thanks for the constructive suggestions. The detailed responses to the reviewer's comments are as follows.
**Q1: Comparison and discussion to RPM solvers and general-purpose systems**
We conducted additional experiments on the general-purpose systems GPT-4V and GPT-4o and classic task-specific R... | Summary: The authors transform a series of different classical abstract visual reasoning tasks by making them all into a task of generating one missing data panel given the remaining set of example panels instantiating a visual concept, which is captured with conditional generative models. Using a new architecture base... | Rebuttal 1:
Rebuttal: Thanks for the constructive suggestions. The detailed responses to the reviewer's comments are as follows.
**Q1: Concerns about the formulation and experiments of Bongard problems**
Similar to the task-specific solvers, UCGS is a framework designed to solve abstract visual reasoning (AVR) tasks ... | null | null | null | null | null | null | null | null |
Simple Randomized Rounding for Max-Min Eigenvalue Augmentation | Accept (poster) | Summary: This paper researches the $\textbf{max-min eigenvalue augmentation}$ problem: given symmetric PSD matrices $M, A_1, \cdots, A_m \in \mathbb{R}^{n \times n}$ and a positive integer $k < m$, the goal is to solve the following optimization problem $$\max_{z \in \\{0, 1\\}^m, \\|z\\|_0 \le k} \lambda\_{\min} \left... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our manuscript! Below we include our responses for each relevant section.
$\text{Other Strengths And Weaknesses:\}$
1. Yes, the optimal increase might not always be sufficiently large in practice. Although, if it is not sufficiently large, then ... | Summary: In this work, the authors provided a new algorithm for the max-min eigenvalue augmentation problem. The method is able to achieve a constant approximation to the optimal value with a constant probability, given that the augmentation improvement is sufficiently large. The results are established by proving an e... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our manuscript! Below we include our responses for each relevant section.
$\text{Other Comments or Suggestions:\}$
1. Yes, nice catch, we should have 1 instead of j here; we can make this update.
2. We agree that the exposition would benefit fr... | Summary: This paper studies the max-min eigenvalue augmentation problem: choose a subset of at most $k$ PSD matrices $A_i$ to augment a PSD matrix $M$ to maximize the minimum eigenvalue of $M + \sum_i A_i$.
This problem generalizes the Bayesian E-optimal design (where certain experiments need to run together) and maxi... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our manuscript! Below we include our responses for each relevant section.
${\bf \text{Questions For Authors:}}$
We left the lower bound as a direction for future work, but since our original submission, we have realized that it is tight and woul... | Summary: This paper presents a matrix concentration on the minimum eigenvalue of the sum of PSD matrices, where the lower bound is parameterized via a generalization of the intrinsic dimension of the expected sum of the matrices. This complements the existing upper bounds e.g. from Joel Tropp's monograph on matrix conc... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our manuscript! Below we include our responses for each relevant section. We focus most of our response on your main line of inquiry.
${\bf \text{Theoretical Claims/Other Strengths and Weaknesses:}}$
These are greats points. In what follows, w... | null | null | null | null | null | null |
DiffAdvMAP: Flexible Diffusion-Based Framework for Generating Natural Unrestricted Adversarial Examples | Accept (poster) | Summary: In this paper, the authors propose a flexible framework named DiffAdvMAP to facilitate the effective and natural generation of unrestricted adversarial examples (UAEs). The framework is based on the posterior distribution with two constraints of adversary and reconstruction, which is eventually optimized with ... | Rebuttal 1:
Rebuttal: Thank you for your reviews and kindly reminder. According to your opinions, we add the theoretical explanation of the effetviness of DiffAdvMAP, and explain the reason for the difference of transfer ASRs in Sec. 4.2 and Sec. 4.3, the responses are as follows. We wish you can improve the score afte... | Summary: This paper introduces Diffusion-based Adversarial Maximum a Posteriori (DiffAdvMAP), a framework that generates unrestricted UAEs by sampling from posterior distributions. It leverages a diffusion model to approximate the prior distribution of real data and incorporates adversarial and reconstruction constrain... | Rebuttal 1:
Rebuttal: We are sorry that maybe because our writing is not clear enough, you have some misunderstandings about the innovation of our paper. Our responses are as follows, and we hope you can improve the score after reading our responses and paper carefully.
1. DiffAdvMAP is a flexible framework that can be... | Summary: This paper proposes DiffAdvMAP, a Bayesian-based framework for generating universal adversarial examples (UAEs) by approximating their posterior distribution. Unlike existing diffusion-based methods, which struggle with low naturalness and limited effectiveness, DiffAdvMAP leverages adversarial and reconstruct... | Rebuttal 1:
Rebuttal: Thank you for the reviews and kindly suggestion. According to your opinions, we add experiments on another dataset Celeba-HQ, the experimental results are satisfactory. We wish you can improve the score after reading our response.\
1. Dataset Diversity: We use the ImageNet-compatible dataset to ev... | Summary: This paper introduces DiffAdvMAP, a flexible diffusion-based framework for generating Universal Adversarial Examples (UAEs) under various attack conditions. By approximating the posterior distribution of UAEs using pre-trained diffusion models, DiffAdvMAP generates more natural UAEs compared to existing diffus... | Rebuttal 1:
Rebuttal: Thank you for the reviews and kindly reminder. We added the theoretical explanation and the experiments on CLIPure as follows. We wish you can improve the score after reading the response.
1. Efficiency: The main motivation of the paper is to develop a framework that achieves superior naturalness ... | null | null | null | null | null | null |
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents | Accept (poster) | Summary: This paper conducted and analyzed an experiment of "predator and prey" by mice and a robotic agent to learn the behavior patterns of biological self-preservation instinct, and designed and evaluated the simulation of reinforcement learning agents and the same "predator" robotic agent. The paper implemented neg... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and valuable feedback!
We would like to address several key points:
**Q: Motivation for Risk-Awareness in RL**
A: Our paper aims not to improve RL performance but to understand differences between biological and artificial learning. This basic research... | Summary: This paper compares the learned behaviors of biological mice and simulated RL agents in a predator-avoidance maze environment. The authors highlight the disparities in behavior and propose two mechanisms to bridge the gap between a TD-MPC2-trained simulated agent and biological mice. Finally, they compare the ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and valuable feedback. Below we address each point systematically:
**Q: Regarding algorithm selection and comparison**
A: We experimented with DQN and SAC alongside TD-MPC2 but focused on TD-MPC2 because: (1) Model-based approaches together with TD... | Summary: This paper investigates the behavioral differences between biological mice and reinforcement learning (RL) agents in a predator-avoidance maze environment. The authors find that RL agents lack preservation instincts, often taking risky, efficiency-driven paths without assessing potential threats, in contrast t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. Regarding the two main concerns raised:
**Q: On generalization across environments: The experiments are conducted in Cellworld Gymnasium, raising questions about whether the proposed mechanisms would generalize to other, more diverse environments ... | Summary: The authors devise several RL agents for a navigation task that involves reaching a goal while avoiding a predator. The task is closely modeled after an actual biological experiment with real mice. Based on the observation of systematic behavioral differences between biological and artificial agents in this pr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough feedback and would like to address several key points:
**Q: Why PTSB and VP-TDMPC2**
A: Our PTSB models rodents' responses to adverse stimuli. Even with mild aversive stimuli (air puffs), mice show pronounced fear responses after few exposures, exhibiting... | null | null | null | null | null | null |
DIS-CO: Discovering Copyrighted Content in VLMs Training Data | Accept (poster) | Summary: The paper focuses on an important and timely problem, i.e., how to discover the copyrighted content in the training data of VLMs. Specifically, the paper’s contribution includes (1) a new benchmark, MovieTection, (2) a new method DIS-Co, (3) comprehensive experiments, and (4) some new discoveries.
## Update a... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate the time and effort invested in reviewing our paper. Below, we clarify your comments.
> Why do you think using copyrighted material in the training data is copyright infringement rather than fair use?
>
If the paper gave the impression that we consider all copyrigh... | Summary: The author proposes a copyright detection method for VLM training data based on free-text generation, where movie frames are input into the model to generate corresponding titles, allowing for the detection of whether the model has memorized copyrighted content. The main innovations include: 1) the constructio... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate the time and effort dedicated to evaluating our paper. We understand the concerns raised and below, we address each point in detail:
> **Q1.1** How applicable and universal is the proposed mechanism?
>
We acknowledge that MovieTection’s focus on box-office hits may... | Summary: This paper introduces DIS-CO, a new method to check if large vision-language models (VLMs) were trained on copyrighted material. the authors use the idea that a model will "remember" specific content if it has seen it before. In this work, the model is asked to name a movie from a single frame or caption. The ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We greatly appreciate the time and effort you invested in reviewing our paper. Below, we provide a response to your question.
> My question to this paper is whether if this paradigm can be applied to other copyright content as well beyond just movie, which also states in the metho... | Summary: This paper investigates the challenge of verifying whether copyrighted content was used to train large vision-language models (VLMs) without direct access to their training data. The authors introduce DIS-CO, a novel approach that leverages the hypothesis that VLMs can recognize images from their training corp... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your valuable feedback and comments. Below, we address each of your questions.
> **W1.** Lack of formal mathematical analysis.
>
Here, we present our mathematical analysis to support our intuition on why free-form completions (FF) are a more robust indica... | null | null | null | null | null | null |
Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms? | Accept (poster) | Summary: The paper studies group strategies in content creation games: a game framework in which individual creators in a recommender system can form groups. This can lead to interesting scenarios, such as a creator deviating from its strategy in the "vanilla" Nash equilibrium, even though this reduces its individual u... | Rebuttal 1:
Rebuttal: >For the Bandit C3 game, to get $PNE(G, s)$ you initialize all creators in the group $c$ with $s_c$, wlog say these are 1 to $n_c$; now from $n_c$ to $n$ you follow Algorithm 1?
We initialize the group of creators with $s_c = (s_1, s_2, \dots, s_{n_c})$, which are not necessarily identical across... | Summary: This paper investigates group strategic behaviors among content creators in recommendation systems. Specifically, the authors assume that creators within a group can strategically deviate to maximize their collective reward. Using bandit C3 games, they theoretically demonstrate that user welfare can suffer sig... | Rebuttal 1:
Rebuttal: >One major issue is the definition of group deviations.
As we discussed in the paper (Lines 270-274), suppose that all creators have reached an equilibrium, and then some creators decide to form a group. Joining such a group becomes a dominant strategy because the group utility is at least as hi... | Summary: The paper studies how strategic behaviors of a group of creators can affect user welfare with game-theoretic analysis. In particular, they adopt the content creation competition (CCC) game-theoretic framework introduced in prior works, where users and content creators both derive utilities from the recommender... | Rebuttal 1:
Rebuttal: >The paper focuses a bit too much on settings that are not algined with real-world applications. For instance, I think content creators are rewarded for engagement, but the paper focuses a lot on the setting where creators are rewarded for exposure.
As mentioned in the paper (Lines 144-148), the ... | Summary: The paper is attempting to answer : How do group strategies among content creators impact recommendation systems, specifically focusing on content distribution and user welfare? The paper examines how content creator groups impact recommendation systems, contrasting with individual creator behavior. Particular... | Rebuttal 1:
Rebuttal: >The paper primarily uses synthetic data for simulations. Including empirical validation with real-world data from online platforms would further strengthen the claims and demonstrate the practical relevance of the findings.
Thank you for the thoughtful suggestion. We will further strengthen the ... | null | null | null | null | null | null |
Improving Generalization with Flat Hilbert Bayesian Inference | Accept (poster) | Summary: This paper proposes a sharpness-aware version of stein variational gradient descent and applies it to neural network fine tunings.
The goal is to learn a map (in a RKHS) that transforms from the reference distribution to the true posterior by minimizing the KL divergence.
To motivate the sharpness-aware optimi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful response. As rightly pointed out, we’ve acknowledged in the *Limitations* section that FHBI, like other particle-based Bayesian inference methods, shares the common drawback of requiring multiple models to be retained during training. This makes it less prac... | Summary: The paper introduces a novel Bayesian inference method designed to improve generalization by leveraging functional sharpness-aware particle sampling in RKHS. The key innovation lies in combining sharpness aware minimization (SAM) with SVGD in infinite-dimensional RKHS to form the proposed FHBI. The authors ext... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback. We would like to address the concerns as follows:
+ **Regarding the relationship between FHBI and SAM:** As discussed at the end of Section 4, FHBI is a generalization of SAM with multiple model particles. This property is reflected more clearly in the upda... | Summary: The paper introduces Flat Hilbert Bayesian Inference (FHBI), a novel algorithm designed to enhance generalization in Bayesian inference by extending principles from finite-dimensional Euclidean spaces to infinite-dimensional reproducing kernel Hilbert spaces (RKHS). FHBI employs an iterative two-step procedure... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and would like to address the concerns as follows:
+ **Memory consumption and scalability with larger models:** All experiments were conducted using a single Tesla V100 GPU with 4 model particles. The memory usage and training time for the CIFAR... | null | null | null | null | null | null | null | null |
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting | Accept (poster) | Summary: This paper proposes a novel long-short-term representation modeling approach for handling non-stationarity in multivariate time series. For short-term representation, the integrated attention mechanism is employed to model temporal dependencies. For long-term representation, the cointegrated attention mechanis... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and valuable insights into our work. Below, we address your concerns:
**Q1:** The loss function lacks sufficient discussion on its contribution to performance improvements.
**A1:**
We conducted additional experiments where TimeBridge was trained using the *... | Summary: This study focuses on the problem of time series forecasting (TSF) by addressing the dual challenge of integrating both long-term and short-term relationships. The proposed TimeBridge employs a dual-attention framework: initial patch-level integrated attention for short-term pattern analysis and cointegrated a... | Rebuttal 1:
Rebuttal: Thank you for your feedback and insightful comments on our work. Below, we address each of your concerns and questions:
**Q1:** As indicated in Table 6, the sequential selection strategy of CI and CD significantly impacts prediction performance on Electricity and Traffic datasets, yet this phenom... | Summary: This paper introduces TimeBridge, a methodological framework addressing non-stationarity in long-term time series forecasting. The proposed approach is structured around two core mechanisms: Integrated Attention, which seeks to mitigate short-term non-stationarity by stabilizing localized variations, and Coint... | Rebuttal 1:
Rebuttal: Thank you for your feedback and insightful comments on our work. Below, we address your questions:
**Q1:** It omits direct comparisons with seasonal-trend decomposition methods.
**A1:** We add comparison with seasonal-trend decomposition methods (Autoformer [1] and TDformer [2]). The table below... | Summary: This paper introduces a new framework to tackle the challenges posed by non-stationarity in multivariate time series forecasting. It addresses both short-term fluctuations and long-term trends by employing two specialized attention mechanisms, i.e., Integrated Attention and Cointegrated Attention. The framewor... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments on our work. Below, we address each of your questions:
**Q1:** A more detailed theoretical exploration could strengthen the framework's validity.
**A1:** Thank you for your valuable comments. Below we provide a concise theoretical explanation grounded in class... | null | null | null | null | null | null |
G-Adaptivity: optimised graph-based mesh relocation for finite element methods | Accept (spotlight poster) | Summary: This paper proposes to apply graph neural network (GNN) architectures for mesh relocation in finite element methods (FEM). The technical contributions involve a novel training pipeline and improved network structures together with appropriate loss functions. Experiments demonstrate the effectiveness and potent... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and recognition of the importance and potential impact of our work. Your comments encouraged us to **test our model at significantly larger scales, including 3D simulations**. We hope the [additional experiments](https://imgur.com/a/rOdOAA0) we performed adequa... | Summary: This paper presents a novel approach to mesh relocation (r-adaptivity) in finite element methods (FEMs). Traditional r-adaptive methods optimize mesh geometry by solving additional meshing PDEs, which are computationally expensive. Recent machine learning (ML) methods focus on learning surrogates for these cla... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our manuscript and for your positive comments and questions. Below we provide further clarifications on the **role of Firedrake, hyperparameter choices** and **generalisability of our approach**. We hope the results of the additional experiments and our respons... | Summary: This paper focuses on using graph neural network to predict deformation of the computational domain in order to reduce error of solution obtained by finite element (FE) method. The philosophy follows [1]. The contribution of this paper is three-fold. 1. The authors proposed a new design on the model architectu... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our manuscript and for your valuable comments and questions, following which we have performed several additional numerical experiments. **We appreciate your suggestion to benchmark more closely against UM2N, and we have now conducted extensive new experiments,... | Summary: In this work, a GNN-based mesh relocation method is proposed by directly minimizing the finite element solution error. A diffusion-based GNN-deformer is applied which can reduce mesh tangling. Experiments show the proposed method achieves lower solution error, on Poisson's, Burgers', and Navier-Stokes equation... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our manuscript and for your valuable comments and questions, particularly for encouraging us to perform **experiments on more complex geometries and larger scale problems,** and to provide **clarification on our theoretical results and the use of ML-based r-ada... | null | null | null | null | null | null |
Learning with Selectively Labeled Data from Multiple Decision-makers | Accept (poster) | Summary: The paper tackles the problem of correctly quantifying classification risk in the selectively labeled data setting. In this setting, true outcomes are only observed for samples that receive a certain classification/decision (e.g., default outcomes are only observed for those who are given the loan). To address... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. We now address each of your comments below.
- Evidence of (Quasi-)randomly Assigned Heterogeneous Decision-Makers: The selective label literature has mainly considered two examples for randomly assigned heterogeneous decision-makers:
1. Judicial Decision-Ma... | Summary: This paper studies multiclass classification with selectively labeled data, where label distribution is biased due to historical decision-making. By leveraging variations in decision rules across multiple decision-makers, the authors apply an instrumental variable (IV) framework to establish necessary and suff... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and your references. Our work indeed builds on these previous literature and is therefore closely related to them. However, our work is not “a special case of these prior works”. Instead, our work significantly generalizes these existing literature an... | Summary: This paper focuses on the problem setting of classification with selective labeled data, that is, the labeled data at hand can be biased because of decision-making in the past. This paper defines the problem mathematically and solves this problem from the perspective of the instrumental variable (IV) framework... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments and positive feedback for our work.
- Computational Cost: The computational cost of our method is higher than vanilla method which directly learn a classifier from the observed (selectively labeled) data. That is because our method consists of ... | null | null | null | null | null | null | null | null |
Geometric Median (GM) Matching for Robust k-Subset Selection from Noisy Data | Accept (poster) | Summary: The paper proposes the use of the Geometric Median to robustly identify subsets of a dataset that best represents the full dataset. The main goal is to reduce sensitivity of the selection algorithm to outliers in the corrupted data, a problem that traditional subset selectors that rely on the empirical mean su... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed feedback. We are encouraged that the reviewer finds the theoretical claims well supported, the empirical design appropriate, and the core contribution valuable.
Below, we address the specific points raised:
**Q1: Performance at Higher Corru... | Summary: The paper introduces Geometric Median (GM) Matching, a novel approach for robust k-subset selection from noisy datasets. The key contribution is replacing the empirical mean, which is sensitive to outliers, with the Geometric Median (GM), a robust estimator with an optimal breakdown point of 1/2. The GM Matchi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed, thoughtful, and encouraging feedback. We are pleased that you found our theoretical results, empirical validation, and overall framing of GM Matching to be strong contributions. We respond to your key points below:
**Q1. Missing Related Work**
T... | Summary: This paper proposes a dataset pruning method with subset selection. The proposed method utilizes the geometric median moment matching allowing a small amount of an approximation error. Also, the authors provide the theoretical guarantee that the proposed geometric median moment matching leads to a good approxi... | Rebuttal 1:
Rebuttal: **Q1. The terminology k-subset selection.**
We appreciate the reviewer’s observation regarding potential ambiguity in the term k-subset selection. Our usage follows common conventions in the subset selection and coreset literature, where k typically denotes the size of the selected subset—either ... | null | null | null | null | null | null | null | null |
Inductive Moment Matching | Accept (oral) | Summary: # Update
I gave the score of 4. My complaints are minor, and the authors have addressed them in the rebuttal. I'm comformtable with the paper being published in the form close what it is right now. So, I decided to not change my evaluation.
# Old Summary
This paper presents moment matching self distillation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insights and suggestions. We would like to first refer the reviewer to the **Overall Update** section in our rebuttal for reviewer SxrL for important updates to the paper. And we would like to address the concerns below.
> Claims on (4) training stability and (5) not... | Summary: The paper introduces Moment Matching Self-Distillation (MMSD), a novel framework for training few-step generative models from scratch. MMSD offers a single-stage training procedure that avoids the need for pre-training or optimizing two networks. It leverages self-consistent interpolants to match the moments o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insights and suggestions. We would like to first refer the reviewer to the **Overall Update** section in our rebuttal for reviewer SxrL for important updates to the paper. And we would like to address the concerns below.
> The rationality of classifier-free guidan... | Summary: This paper introduces a novel generative model enabling the few-step generation of high-quality photorealistic images. The approach builds on prior work in consistency trajectory models [1] and flow map matching [2], where a generator learns to move a noisy image from one timestep to another. However, unlike [... | Rebuttal 1:
Rebuttal: # Overall Update:
\
We thank all reviewers for their insights and helpful suggestions. We would like to announce several important updates.
- To distinguish our method from other distillation-based post-training techniques, we change our title and model name from **“Moment Matching Self-Distillat... | null | null | null | null | null | null | null | null |
Deep Reinforcement Learning from Hierarchical Preference Design | Accept (poster) | Summary: This paper proposes HERON, a novel hierarchical reward design framework for reinforcement learning (RL) that leverages the hierarchical structures of feedback signals to ease the reward design process. HERON constructs a decision tree based on the importance ranking of feedback signals to compare RL trajectori... | Rebuttal 1:
Rebuttal: Dear Reviewer Q7xT,
Thank you for the insightful review! We are glad you appreciate our work, and present our rebuttal to your review below.
## **Experimental Designs Or Analyses**
**The experiments could benefit from additional ablation studies to further isolate the impact of specific compon... | Summary: This paper introduces HERON, a decision-tree-based approach for reward design in reinforcement learning. In particular, the authors leverage human expertise to define a hierarchy of feedback signals. The authors employ that hierarchy to compare trajectories, collecting a dataset (with a policy model) and learn... | Rebuttal 1:
Rebuttal: Dear Reviewer zX5S,
Thank you for your detailed review and insightful suggestions! We are glad you think our work is novel. We provide our response to your review below. We shorten some questions to save space.
## **Claims and Evidence**
**The authors hypothesize that HERON's reward function "m... | Summary: The paper proposes HERON, a hierarchical preference-based RL framework that leverages the hierarchical importance of feedback signals to design reward functions. HERON constructs a decision tree based on human-provided importance rankings of feedback signals to compare trajectories and train a preference-based... | Rebuttal 1:
Rebuttal: Dear Reviewer 7gbW,
Thank you for your detailed review and suggestions to improve our work. First and foremost we would like to address your review of our claims, in which you say we claim “HERON universally eases reward design”. Nowhere in the paper do we make this claim, and in fact we consiste... | Summary: In this work, the authors propose a novel hierarchical reward design framework tailored for environments that require the integration of multiple feedback signals. The framework is motivated by the observation that these signals often contribute unequally to the overall reward and that there is a hierarchy in ... | Rebuttal 1:
Rebuttal: Dear Reviewer uDMx,
Thank you for your thoughtful and detailed review of our paper. We are glad you appreciate the novelty of our approach as well as our experimental analysis. We provide our rebuttal to your critiques below. We enumerate your questions and comments to save characters.
## **Expe... | null | null | null | null | null | null |
Steering Protein Language Models | Accept (poster) | Summary: This paper presents a method to control the output of protein language models, inspired by the Activation Steering approach in the LLM domain, allowing the generated sequences to exhibit a given property. This method does not require retraining the protein language model and can be directly applied during the ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing valuable feedback. We detail our response below point by point. Please kindly let us know whether you have any further concerns.
----
**Q1**: "the evaluation may exhibit a certain level of **circular dependency**. ... This could potentially be consid... | Summary: This paper introduces a method for steering Protein Language Models (PLMs) towards outputs with desirable properties. It is based on a technique called 'Activation Steering' used for LLMs, where internal activation vectors of LLMs are modified to shift them towards desired behaviour.
The authors show how thi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive feedback. We have provided a detailed explanation for your concerns as follows. Please feel free to let us know if you have any additional concerns or questions.
**W2: Assumptions .. continuity, linearity...Theoretical grounding**
- Our method is based ... | Summary: This paper adapts activation steering techniques from LLMs to Protein Language Models (PLMs) to guide protein generation toward desired properties without retraining. It introduces an Activation Steering-based Protein Optimization (ASPO) framework that outperforms existing methods on thermostability, solubilit... | Rebuttal 1:
Rebuttal: We appreciate very much your constructive comments on our paper. We have provided a detailed explanation for your questions as follows. Please feel free to let us know if you have any additional concerns or questions.
----
**Q1: "How do you deal with multi-property optimization?"**
Thank you fo... | Summary: This paper introduces activation steering, which is a technique adapted from large language models, to control protein language models for generating and optimizing protein sequences with targeted properties (e.g., thermostability, solubility, fluorescence).
The method modifies internal model activations usin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing valuable feedback. We detail our response below point by point.
**W1: the ESM3 model ... generate a full sequence from all-mask states**
We conduct experiments on full sequence generation using ESM3, maintaining the same settings as described in Sec... | null | null | null | null | null | null |
Prediction models that learn to avoid missing values | Accept (spotlight poster) | Summary: The authors introduce missingness-avoiding (MA) machine learning, a framework for altering model training to avoid reliance on missing features. Through experiments on decision trees, lasso, and tree ensembles, they show that MA can reduce reliance on missing-value features with only a minor hit to predictive ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and appreciate their recognition of the clear explanations and sensible evaluation criteria. We respond to specific comments below.
**Re: real-world importance of reducing missingness reliance**
Thanks for raising this point—we understand its imp... | Summary: This manuscript focuses on improving the reliability of machine learning methods when encounter missing value at inference time. A novel framework termed Missingness-Avoiding learning is proposed to reduce the models' reliance of missing values for decision trees, sparse linear models and ensemble methods. Spe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and appreciate their recognition of our work addressing a practically important yet underexplored challenge in the healthcare domain—enhancing model reliability under test-time missingness. We address the specific comments below.
**Re: MA learni... | Summary: This work introduces a generic framework for encouraging models to avoid accessing missing values through regularization. Specific implementations of this framework for Lasso, greedy decision trees, and tree ensembles are introduced. A thorough discussion of the settings in which missingness can and cannot saf... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and valuable feedback, including their appreciation of our methods, empirical effectiveness, and the helpful discussion. We have addressed all suggestions to improve clarity and respond to specific comments below.
**Re: motivation of avoiding missing val... | Summary: The paper proposes a novel framework – “missingness-avoiding” (MA) machine learning – designed to mitigate the impact of missing data during prediction. The core idea is to train models that inherently minimize reliance on features with missing values at test time. This is achieved by incorporating classifier-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and appreciate their recognition of our well-motivated and novel MA framework. We have addressed all suggestions to improve clarity and respond to specific comments below.
**Re: relevance of MA learning**
We would like to direct the reviewer to... | null | null | null | null | null | null |
Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting | Accept (poster) | Summary: This paper proposes the Robust Spatio-Temporal Information Bottleneck (RSTIB) principle to enhance the robustness of spatio-temporal prediction models to noise interference. The authors introduce RSTIB-MLP, a multi-layer perceptron (MLP) based implementation that achieves state-of-the-art performance in the fa... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive comments. We provide a point-by-point response as follows.
> ### **Re: Claims And Evidence & Experimental Designs Or Analyses & W1 & W2 & C2 & Q1**
Due to space limits, please refer to **Re: W1** in the Rebuttal for Reviewer **ZfWm**.
> ###... | Summary: This paper introduces a novel MLP training method based on the Information Bottleneck principle, termed RSTIB-MLP, designed to address the balance between model efficiency and robustness. By analyzing the dual noise effect in STgraph data, authors propose the Robust Spatiotemporal Information Bottleneck (RSTIB... | Rebuttal 1:
Rebuttal: > ### **Re: Supplementary Material**
We are sincerely sorry for the typos, and will correct them in our final version.
P16: "illustrated in Fig.7(a)"; "represented in Fig.7(b)".
> ### **Re: Essential References Not Discussed**
Appendix K.7 has examined such scenarios where transformer-based ... | Summary: The authors disclose the dual noise effect behind the spatial-temporal data noise, and propose theoretically-grounded principle termed Robust Spatial-Temporal Information Bottleneck (RSTIB) principle, which preserves wide potentials for enhancing the robustness of different types of models. Comprehensive exper... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive comments. We provide a point-by-point response as follows.
> ### **Re: W1**
Please note that the derivation of the RSTIB principle and the implementation of RSTIB-MLP do not make assumptions regarding the noise type. AWGN is employed in our ... | Summary: "This paper theoretically motivates and implements a novel regularization technique for training MLP-based models in spatiotemporal forecasting. The MLP models are distilled from state-of-the-art architectures that leverage graph neural networks. KL divergence terms between the data (input, output, encoded) an... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive comments. We provide a point-by-point response as follows.
> ### **Re: Experimental Designs Or Analyses**
- **"What are the teacher model(s) used in the experiments in Table 2?"**
Teacher model selection settings are described below Figure ... | null | null | null | null | null | null |
On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving | Accept (poster) | Summary: This work intends to reduce LLM inference complexity without unacceptable serving quality degradation. The contributions are two folds: (1) dual-state linear attention (DSLA): a variant of gated linear attention with two hidden states for history and recency (2) DSLA-Serve: an online adaptive distillation fram... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive comments! Please see below.
**A1. Serving system details**
We implemented our inference system on top of the DeepSpeed inference serving framework (MII). In the autoregressive process, for relatively short prefill lengths, our method may be slower than... | Summary: This work presents a robust approach to deploying gated linear attention (GLA) in practical production environments, addressing key limitations through two primary innovations.
First, the authors address GLA’s strong recency bias, which impairs long-context performance. To mitigate this, they propose a dual-s... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive comments! Please see below.
**A1. Comparison to Other Baselines**
Thanks for the great suggestions. We compare our 1.5B-scale model with the Phi-Mamba-1.5B [1] and 7B-scale model to distilled Mamba [2]. As demonstrated in table A and B, our method co... | Summary: This paper introduces an online distillation framework that dynamically converts Transformer layers to dual state linear attention (DSLA) during inference to improve efficiency for long-context LLM serving. DSLA uses two specialized hidden states to better preserve both historical context and recent informatio... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive comments! Please see below.
**A1. Intuition behind dual state and model capacity**
This work stems from our observation (Fig 1) that a single-state linear attention struggles to capture the full range of contextual information handled by self-attention... | Summary: This paper introduces Dual-State Linear Attention (DSLA), a novel attention mechanism designed to mitigate the short-range bias of traditional linear attention methods while maintaining the efficiency benefits necessary for long-context LLM serving. The key idea is to maintain two specialized hidden states, on... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive comments! Please see below.
**A1. Comparison with RetNet and Mamba**
We added more baselines including RetNet [1] and Mamba [2], and measured zero-shot performance on challenging tasks including PiQA, ARC-challenge, Hellaswag (HS), MMLU, Winogrande. O... | null | null | null | null | null | null |
Rethinking Time Encoding via Learnable Transformation Functions | Accept (poster) | Summary: This paper proposes a learnable time representation framework—referred to as LeTE—that aims to improve upon prior time encoding methods which rely on fixed or narrow inductive biases (e.g., purely sinusoidal functions). The authors introduce two learnable approaches for modeling time: one based on Fourier seri... | Rebuttal 1:
Rebuttal: **Rescaling**
Thank you for the insightful comment. We agree that empirically demonstrating rescaling invariance is important.
To directly support this, we conducted an additional tiny experiment by applying the Combined LeTE to Wikipedia/TGN, using two different time input scales: t=t/60 (inter... | Summary: The paper proposes a time encoding method that can work as a plug-and-play functionality to capture diverse patterns in the real world. The method is motivated by the observation that the existing time encoding approaches struggle to capture non-periodic and mixed patterns. To capture such complex patterns, th... | Rebuttal 1:
Rebuttal: **Sensitivity to the hyperparameter**
Actually, the performance gain can be influenced by the hyperparameter $p$. As we analyze in the experiments on dynamic graphs (please refer to Appendix G.2, “Comparative Analysis of Different Variants of LeTE,” and Table 9 for details), the performance remai... | Summary: This paper proposes LeTE (Learnable Transformation-based Generalized Time Encoding), a flexible and learnable time encoding framework that generalizes existing methods (e.g., Time2Vec, Functional Time Encoding). By parameterizing nonlinear transformations via Fourier series and B-spline functions, LeTE provide... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. As Reviewer 1gph also raised similar concerns, and due to space limitations, **we have addressed some of these points in our response to Reviewer 1gph. Could you kindly review the first part of our reply there? Below is the continuation of our ... | Summary: This paper proposes Learnable Transformation-based Generalized Time Encoding, a new approach to encoding time in machine learning tasks. LeTE generalizes popular functional time encoding strategies and makes the non-linear transformation fully learnable. The authors use techniques from Fourier expansions and s... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions. We are happy to further discuss the interpretability of our method real data scenarios. We would also like to clarify that demonstrating interpretability for very high-dim encodings is challenging. We choose to use a 4-dim Combined LeTE to pres... | null | null | null | null | null | null |
ASCENSION: Autoencoder-Based Latent Space Class Expansion for Time Series Data Augmentation | Reject | Summary: A summary of the paper: This paper presents ASCENSION, a VAE-based data augmentation framework designed to address distribution discrepancies in Class Expansion. It uses latent space to improve the applicability of data augmentation and evaluates ASCENSION’s impact on classification performance across various ... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we clarify the novelty and motivation of our work and provide new results supporting our key hypotheses
## Summary
"**C4.1** *Claimed novelty lies in latent space and use of VAE for DA in time-series, but these are not fundamentally new **C4.2** Motivation of the ... | Summary: This paper introduces a VAE-based generative data augmentation approach for time-series data called ASCENSION. This work aims at progressively expanding inter-class boundary during the generation, enabling the exploration of underrepresented or unseen latent distribution in the training data. The major technic... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed and constructive comments. We address each of the raised concerns below. We clarify the novelty and rationale of ASCENSION compared to the referenced works below. We also provide ablation study results.
## Claims And Evidence & Relation To Broader Scientific ... | Summary: This paper introduces ASCENSION, a VAE-based data augmentation (DA) technique tailored for time series classification (TSC). The core idea centers on a controllable and progressive latent space class expansion mechanism, leveraging the structured latent space of VAEs. ASCENSION aims to overcome the limitations... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive critique. We provide the requested clarifications below and provide comparison results with the suggested baselines. We will include all this in our revised manuscript.
## Claims And Evidence & Methods And Evaluation Criteria
"**C2.1** *Seve... | Summary: This work introduced a data augmentation method for time series data called ASCENSION. The method is based on the classic VAE-based training and sampling process, but the authors incorporated a clustering loss into training for better classification performance. The proposed method iteratively augments the dat... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our work and offer such constructive feedback. We appreciate the recognition of the systematic nature of our experiments, as well as the suggestions to discuss the application of our method to multivariate data and expanding our ablation analyses.
... | null | null | null | null | null | null |
GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models | Accept (poster) | Summary: The paper proposes GANQ, a GPU-Adaptive Non-Uniform Quantization framework leveraging lookup table (LUT)-based mixed-precision GEMM for efficient deployment of Large Language Models (LLMs). GANQ introduces a post-training quantization optimization algorithm to effectively solve LUT-based quantization objective... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Below, we address your concerns one by one.
**Response to Weakness 1**
We acknowledge the value of broader evaluation and have conducted additional experiments.
Below are WikiText PPL results for **LLaMA-3.2** models, using the same settings as in the paper.
||1B|3B|1B... | Summary: This paper proposes a look-up-table based non-uniform quantization algorithm for LLMs. The algorithm is based on mixed-integer quadratic programming, with a mathematical proof that the authors deduce. The results show that the proposed method has better accuracy than state of the art methods on 4-bits and 3-bi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and strong support. We appreciate your recognition of our contributions and provide detailed responses below.
**Comment 1:** *The context length of perplexity.*
Thanks for pointing this out. We use a sequence length of 2048 for all models and methods, follo... | Summary: The paper present GANQ, a GPU-Adaptive Non-Uniform Quantization technique specifically tailored for efficient inference of Large Language Models (LLMs). GANQ introduces a principled optimization model based on Mixed-Integer Quadratic Programming (MIQP) to achieve layer-wise quantization using a Lookup Table (L... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and recognition of GANQ's contributions. Below, we address the concerns raised.
## Concern 1: Reliability of Cholesky Decomposition and Sensitivity to Preconditioning of $XX^\top$
**Response to Weakness 1**
Yes, our method relies on Cholesky decomposition t... | Summary: The paper proposes GANQ, a post-training non-uniform quantization method optimized for hardware-efficient mpGEMM for LLMs. GANQ is LUT-based weight-only quantization capable of handling outliers. The experimental results demonstrate that the proposed GANQ outperforms baselines and achieves up to 2.57 times spe... | Rebuttal 1:
Rebuttal: Thanks for the review and helpful suggestions. We appreciate your positive feedback and address your concerns below.
**Comment:** *$m,n,p$ in line 147 and 148 are not introduced.*
**Response:** $m,n,p$ denote the dimensions in a linear layer. In LLMs, $m$ is the output dimension, $n$ the input ... | Summary: This paper proposes a GPU-adaptive non-uniform quantization framework for LLMs. By formulating quantization as a mixed-integer optimization problem, the authors aim to achieve efficient low-bit inference on hardware that lacks native mixed-precision matrix multiplication support. Their approach relies on looku... | Rebuttal 1:
Rebuttal: Thank you for the thorough and insightful feedback. We would like to address your concerns point by point below.
## Clarification of References ([1, 2]) and Response to Weakness 2
Thank you for highlighting the relevance of references "Fast Matrix Multiplications for Lookup Table-Quantized LLMs [... | null | null | null | null |
Matryoshka Quantization | Accept (poster) | Summary: The paper presents a method for multi-scale quantization of large language models across multiple precisions (int8, int4, int2). By utilizing the nested structure of integer data types, the proposed technique allows different precision levels to be nested within one another. The resulting quantized model can t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback. Below are our responses to the comments/questions:
**Comparison with SOTA int2 methods:** We would like to clarify that the main goal of the work is to propose an adaptive quantization method that generates a single model that can do well at any ... | Summary: This paper introduces Matryoshka Quantization, a novel multi-scale quantization technique that enables single-model multi-bitwidth operation. The low-precision models extracted by MatQuant can have significant improvement compared with standard quantization.
## update after rebuttal
The authors address most o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review. Below are our responses to the comments/questions:
**Training memory/compute requirements:** For both QAT and OmniQuant, MatQuant can be up to 30% cheaper than the training three separate baselines (one for 2-bits, one for 4-bits, and one for 8-b... | Summary: The paper introduces Matryoshka Quantization (MatQuant), a novel multi-scale quantization technique designed to jointly learn various bitness representations in one single training, and improve low-bit precision quantization for LLMs.
The method is evaluated on Gemma-2 and Mistral models, and the results demo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful review. Below are our responses to the comments/questions:
**Training memory/compute requirements:** For both QAT and OmniQuant, MatQuant can be up to 30% cheaper than the training three separate baselines (one for 2-bits, one for 4-bits, and one for 8-b... | null | null | null | null | null | null | null | null |
Learning Representations of Instruments for Partial Identification of Treatment Effects | Accept (poster) | Summary: The paper presents a method for learning bounds on CATE (conditional average treatment effect) in the event of observed covariates X, unobserved confounding U, binary treatment A, scalar outcome Y, and an instrument Z which may be high dimnensional.
The proposed method extends:
Schweisthal, J., Frauen, D., ... | Rebuttal 1:
Rebuttal: Thank you a lot for your positive feedback and your comments! We are happy to see that our claims, methods, theory, and experiments are well received and do not raise any additional concerns.
Here, we would kindly like to elaborate on why our paper is **not** an incremental contribution of the w... | Summary: This paper proposes a new method for partial identification of the conditional treatment effect (CATE) when working with continuous or high-dimensional instruments. By mapping complex instruments into a learned discrete representation, the authors apply Manski-style bounds while mitigating the instability that... | Rebuttal 1:
Rebuttal: Thank you for your positive and very actionable review! We took your comments at heart and improved our paper as follows.
# Response to Claims and Evidence
- **Stability of our method compared to adversarial approaches**: This is a very interesting point! As _widely investigated_ in the literatu... | Summary: The authors tackle the problem of partial identification of treatment effect with high dimensional instrument variables that have a complex relationship with the treatment. They do this by learning a discrete representation of the instrument variable and deriving a learning objective that does not require retr... | Rebuttal 1:
Rebuttal: Thank you a lot for your positive review and your helpful feedback! We took your comments at heart and improved our paper as follows.
# Response to Claims and Evidence
**Wording of contribution**: Thank you for this remark! We use the term “effective” to reference the good performance of our me... | Summary: The paper proposes a method for partial identification, that is, bounding, of treatment effects in the instrumental variable (IV) setting. Specifically, the paper studies the scenario where the instruments are continuous and potentially high-dimensional. It proposes an approach for partial identification throu... | Rebuttal 1:
Rebuttal: Thank you for your review and the opportunity to clarify multiple points of our paper! We took your comments at heart and improved our paper as follows.
# Response to concerns around claims and evidence, theoretical claims, and relation to broader scientific literature
- **Novelty of our paper ... | Summary: To partially identify and estimate the bounds of the conditional average treatment effect (CATE) with potentially high-dimensional instruments, the authors propose a two-step approach. This method first learns discrete representations of the complex instrumental variables $Z$, then derives closed-form bounds b... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and the overall positive evaluation of our manuscript! We will take all your comments to heart and improve our manuscript accordingly. Below, we provide answers to all your questions
# Response to Methods and Evaluation Criteria
- **Benchmark datasets**: We a... | null | null | null | null |
Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration | Accept (poster) | Summary: In this paper, the authors propose SUPE, which combines the idea of pretraining offline skills and online exploration via hierarchical policy. SUPE first extracts low-level skills using a variational autoencoder, then pseudo-labels unlabeled trajectories with optimistic rewards and high-level action labels. Us... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and insightful comments. We especially appreciate the comments on the motivation. For your concern on the novelty of our method, we believe there might be a misunderstanding of our method – we believe that what we were describing is actually one of our baselines, “... | Summary: The paper introduces hierarchical RL to train high-level policy to utilize the unlabel offline trajectories, and train reward estiamation, high-level action (skill) and extra offline data in online learning to guide policy learning. It claims to achieve efficient exploration performance in sparse reward tasks ... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and insightful comments. We especially appreciate the additional references you point out and the clarifying question on pseudo-labeling. For your question on how policy operates conditioned on different latent skills, we provide an additional detailed analysis that... | Summary: The paper presents a new algorithm on combining offline data and online RL where one can use the offline data to learn skills, and online perform exploration in the state space and skill space jointly, and relabel offline data with the exploration bonus. The paper performs comparisons with a few baselines on s... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and insightful comments. We especially appreciate the additional references you point out and the clarifying question on UCB. For your concern on the dependence on quality of the offline data, we had ablation studies in our appendix that demonstrate the robustness o... | Summary: The paper studies how to leverage skills pre-trained by unsupervised RL on unlabeled data to improve exploration while solving downstream tasks online. The proposed method (SUPE) first pre-trains skills via a trajectory VAE, then labels offline data with optimistic rewards, and finally trains a policy mapping ... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and insightful comments. We especially appreciate your clarifying question on the setting of our work. Regarding your concern on not comparing to “diversity-encouraging” objectives, we conducted additional experiments with METRA and showed that METRA finds a reward ... | null | null | null | null | null | null |
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need | Accept (poster) | Summary: This work investigates the vulnerability of data-free knowledge distillation (DFKD) when the teacher model is untrusted, particularly in non-transferable learning (NTL) scenarios where knowledge transfer is blocked. It finds that NTL teachers mislead DFKD by shifting the generator’s focus from useful in-distri... | Rebuttal 1:
Rebuttal: Dear Reviewer gtda,
Thanks for your valuable comments! We address the weaknesses below. Please kindly let us know if you have further concerns.
>**Q1: Why do fragile samples align with ID data, and how do we distinguish ID and OOD data.**
We are sorry for the confusion. This assumption of "fra... | Summary: This paper investigates what would happen if one tried to apply data-free knowledge distillation (DFKD) on non-transferable learning (NTL) teachers. The paper proposes the OOD trap effect that the generator shifts to generate OOD samples, causing the student model only learns misleading OOD knowledge. To solve... | Rebuttal 1:
Rebuttal: Dear Reviewer saQK,
Thanks for your valuable reviews! We address your concerns as follows. Please let us know if anything remains unclear.
>**Q1: Why generator has to output OOD-like samples**
This is because DFKDs commonly use BN loss [B1] as regularization for training generator $G$. Specifica... | Summary: This paper identifies the OOD trap effect from NTL teachers to DFKD, i.e., misleading knowledge from OOD data may mislead students' learning process. The authors propose a plug-and-play ATEsc method to ensure that students can benefit from the NTL teacher model. This article can be considered as filling a gap ... | Rebuttal 1:
Rebuttal: Dear Reviewer iM7M,
Thanks for your positive opinions. We address your concerns as follows. If anything remains unclear, please do not hesitate to contact us.
>**Q1: Discuss on distribution shift in DFKD**
Thanks for this valuable suggestion! [1-4] focus on different types of distribution shifts... | null | null | null | null | null | null | null | null |
On Bitrates of Very Sparse Superposition Codes | Reject | Summary: This paper tackles the problem of reliability of already trained (or, hardcoded) sparse auto encoders for feature disentanglement. It does from the information theory perspective. The main result shows that 1-step methods such as SAEs are suboptimal, and that iterative methods are much more efficient. I’m very... | Rebuttal 1:
Rebuttal: Thanks for taking the time to write your review. We took care to make this work reasonably accessible to readers without background in compressive sensing. (See our response to Reviewer VkuK.) We're happy to see that, despite your unfamiliarity with the literature, our claims and evidence were con... | Summary: This paper investigates the efficiency of different methods for decoding "superposition codes" - specifically focusing on the sparse reconstruction problem that appears in sparse autoencoders used for neural network interpretability. The authors focus on a simplified model where the goal is to recover a sparse... | Rebuttal 1:
Rebuttal: Thanks for taking the time to write your review.
Sparse autoencoders are indeed "efficient" in the sense they are computationally inexpensive, at least relative to other dictionary learning methods. In this work, we explain that they are inefficient in an _information-theoretic_ sense. For exampl... | Summary: This paper investigates the efficiency of different decoding methods for superposition codes, which are used in LLM interpretability via sparse autoencoders (SAEs). The authors compare "one-step estimates" (used by sparse autoencoders) with iterative decoding methods. There is lots of theory and some experime... | Rebuttal 1:
Rebuttal: Thanks for raising the question of how our findings relate to real applications of SAEs in interpretability. In fact, we hope this work will help interpretability researchers become more aware of ideas from compressive sensing. However, after reading your review, we realize it may be useful to cla... | Summary: This paper investigates the efficiency of one-step estimates used in sparse autoencoders for recovering latent representations from neural network activations. The key contribution is an analysis of the bitrate required for reliable decoding. The study demonstrates that one-step estimates require significantly... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for bringing up the connection with compressive sensing (CS). Our decision to not reference many tools from this world—like restricted nullspace properties, proximal gradient methods, etc.—may be surprising, but it was made deliberately.
During this work... | null | null | null | null | null | null |
Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning | Accept (poster) | Summary: This paper introduces GLIDER, an approach for fine-tuning LLMs to act as agents in interactive environments. GLIDER relies on a hierarchical architecture in which the LLM is used to both propose a plan (high-level policy) and execute it (low-level policy). Using Behavioral Cloning and offline RL, the authors d... | Rebuttal 1:
Rebuttal: We deeply appreciate your constructive comments for improving our paper. We would be incredibly grateful for your continued support in reviewing our response and offering further encouragement.
***
`Q1. How is the temporal abstraction parameter c determined, given that subgoals are dynamically gen... | Summary: The paper proposes GLIDER, an extension of hierarchical RL to LLM agents where two separate LLM control the high and low level policy. The proposed training framework relies on a combination of supervised fine tuning and implicit Q learning. The authors test on two domains, ScienceWorld and AlfWorld, obtaining... | Rebuttal 1:
Rebuttal: ## Methods
**`Q1. The optimal policy guarantee and the correctness of Eq. (7), given that actions are at the token level while rewards are formulated at the transition level.`**
A1. First, our policy derivation of inherits Advantage-Weighted Regression (AWR) [1-2], a popular algorithm using conv... | Summary: This paper integrates hierarchical reinforcement learning with LLMs, leveraging a high-level planner to decompose tasks and a low-level executor to perform actions. Experimental results on ScienceWorld and ALFWorld demonstrate significant performance gains over existing baselines, with strong adaptability to u... | Rebuttal 1:
Rebuttal: ***
**`Q1. The authors could introduce GPT-based prompting baselines (Reflextion or ReAct) to compare whether small-scale LLMs with hierarchical training can match or even outperform larger models.`**
A1. Following your advice, we compare GLIDER using small-scale LLMs (LIama-3-8B) to ReAct using... | Summary: The work focuses on long-term decision-making problems with LLM's. They propose GLIDER using concepts of hierarchical reinforcement learning, namely decomposing a complicated task into pieces of small tasks. The low-level controller can be goal agnostic and finish its low level task efficiently as directed by ... | Rebuttal 1:
Rebuttal: ***
**`Q1. About online fine-tuning, generalization, empirical evidence on fast online adaptation.`**
A1. Online fine-tuning refers to adapt a pretrained policy to unseen tasks using several fine-tuning steps, i.e., few-shot generalization to new tasks. We train a policy using offline datasets ... | Summary: This paper introduces GLIDER, a hierarchical reinforcement learning framework designed to enhance the decision-making capabilities of large language models (LLMs) in long-horizon tasks. The authors propose a two-layer structure where a high-level policy decomposes complex tasks into sub-goals, which a low-leve... | Rebuttal 1:
Rebuttal: We deeply appreciate your positive and constructive comments for improving our paper. We would be incredibly grateful for your continued support in reviewing our response and offering further encouragement.
***
`Q1. The training pipeline is a bit complicated (three stages), which might be tough ... | Summary: This paper addresses the challenges of using Large Language Models (LLMs) for long-horizon decision-making tasks, specifically their difficulties with exploration and long-term credit assignment, especially in sparse-reward settings. To mitigate these challenges, the authors propose a framework called GLIDER (... | Rebuttal 1:
Rebuttal: We deeply appreciate your constructive comments for improving our paper. We would be incredibly grateful for your continued support in reviewing our response and offering further encouragement.
***
`Q1. What are the advantages of GLIDER compared to direct LLM planning approaches?`
A1. We have c... | null | null |
Random Policy Evaluation Uncovers Policies of Generative Flow Networks | Accept (poster) | Summary: This paper studies generative flow networks (GFlowNet) where one aims to learn a policy which generates states with probability proportional to the rewards of these states, contrast with the goal of reward maximization in typical reinforcement learning. This work tries to build the connection between value fun... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the time and effort in the feedback. We would like to clarify the novelty and significance of our contributions as follows:
>Q1: Concerns regarding insights: Theorem 4.1 didn't bring anything new about the forward policy.
While the reward transformation is a key compone... | Summary: The authors present a relationship between Generative Flow networks and RL via policy evaluation. Specifically, the authors claim that the value function obtained from evaluating a uniform policy is closely related to the flow function in GFlowNets.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Y... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback and positive assessment of our work, and for noting that our work builds a simple, practical, and novel algorithm that bridges RL and GFlowNets. We carefully address your concerns as follows:
>Q1: Questions about Max Entropy RL baselines
Thanks for yo... | Summary: The paper presents a connection between GFlowNets and non max-ent RL. It leverages insights in the special case with a uniform policy to establish the connection, which leads to the development of the RPE algorithm. Empirical results suggest that RPE achieves competitive performance with existing GFlowNet trai... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and for a positive assessment of our work! We carefully address your concerns as follows:
>Q1: Concerns about path invariance scaling factor $g$
Thanks for your question. We would like to emphasize that this condition is satisfied in a broad range of domains of ... | Summary: This paper explores a connection between GFlowNets and policy evaluation in the specific setting of undiscounted Reinforcement Learning with only terminal rewards. The central idea revolves around the flow constraint in GFlowNets, which shows that the flow out of a state must equal the total in-flow from its s... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions and feedback! We carefully address your concerns as follows:
>Q1: $\tau$-dependence regarding transformed rewards $R(x)g(\tau,x)$
Thanks for your question! This concern is addressed by the assumption in Theorem 4.2: "For any trajectories $\tau_1$ and $\tau_2$ ... | null | null | null | null | null | null |
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy | Accept (poster) | Summary: This paper proposes DDAD (Distributional-Discrepancy-based Adversarial Defense), a novel two-pronged adversarial defense method that leverages statistical adversarial data detection (SADD) to improve robustness against adversarial attacks. The paper's contributions include:
1. Demonstrates that minimizing dis... | Rebuttal 1:
Rebuttal: ## 1. Batch-wise Processing
In our humble opinion, the practicality of a method should be evaluated in the context of specific scenarios and application requirements, which means there is no absolute 'practical' or 'impractical' method. The key message we want to deliver here is: **batch-wise eval... | Summary: This work first validates the effectiveness of the SADD-based approach through theoretical analysis and mathematical proofs. To address the limitations of traditional SADD-based methods in utilizing AEs, the authors innovatively propose the DDAD method. By introducing an additional denoiser training module, th... | Rebuttal 1:
Rebuttal: ## 1. Rendering Errors of LaTeX
Thank you for pointing out this unexpected rendering issue caused by LaTex! The entire sentence in line 112 is: *'...the ground-truth labelling functions for the clean and adversarial domains are equal in our problem setting.'* This statement is supported based on ... | Summary: This paper proposes a two stages adversarial defence method based on distribution discrepancy between clean samples and adversarial samples. Firstly, the authors train a model called MMD-OPT by maximise the MMD between distribution of clean data and adversarial data. Then, the MMD-OPT can act as a guidance to ... | Rebuttal 1:
Rebuttal: ## 1. Evidence of 'Minimizing distributional discrepancy can reduce the expected loss on AEs.'
In our paper, we derive a theoretical bound to support our claim, i.e., $R(h, f_\mathcal{A}, \mathcal{D_A}) \leq R(h, f_\mathcal{C}, \mathcal{D_C}) + d_1(\mathcal{D_C}, \mathcal{D_A})$. In previous liter... | Summary: The paper introduces Distributional-Discrepancy-based Adversarial Defense (DDAD), a two-pronged approach that leverages Maximum Mean Discrepancy (MMD) to detect adversarial examples (AEs) and a denoiser to restore them. The paper provides a theoretical justification linking distributional discrepancy minimizat... | Rebuttal 1:
Rebuttal: ## 1. Comparison to MagNet
We acknowledge that MagNet [1] is a very good work. The main reasons we did not include MagNet as a baseline are: (1) MagNet is outdated since it was published 8 years ago, and (2) MagNet cannot defend against adaptive attacks, placing it significantly behind current SOT... | null | null | null | null | null | null |
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks | Accept (poster) | Summary: This paper focuses on the backdoor threat of LLMs from the perspective of in-context learning (ICL). By theoretically analyzing this threat as a two-fold learning mechanism, this paper further proposes an effective defense method against such attacks.
## update after rebuttal
Thanks for your rebuttal. My con... | Rebuttal 1:
Rebuttal: **Q1:** Are $\mathbf{y}_i$ and $\mathbf{y}_j$ different or same？
**A1:** Thank you for pointing out our error. In Eq.2, using the clean label $\mathbf{y}_j$ for the poisoned input is incorrect; this should be replaced with the attack target $y_t$. We will address this issue in the revision. For ... | Summary: This paper addresses the vulnerability of in-context learning (ICL) in large language models (LLMs) to backdoor attacks, where adversaries manipulate model behavior by poisoning ICL demonstrations. The authors propose the dual-learning hypothesis, positing that LLMs simultaneously learn task-relevant and back... | Rebuttal 1:
Rebuttal: **Q1:** However, the assumption of the known poisoned demonstrations is too strong for the defender. In section 5.2, the proposed defense required the selection of clean and poisoned demonstration. If we know the poisoned demonstration in ICT and can control the numbers of clean/poisoned examples,... | Summary: The author first uses theoretical analysis to model the ICL backdoor attack success bound. Based on the formulation in the theory, the author claims that more clean demonstrations with larger similarity to the trigger and higher confidence can diminish the attack success rate.
Based on this observation, the p... | Rebuttal 1:
Rebuttal: Due to the space limitation, Table R1 and Table R2 are provided in https://anonymous.4open.science/r/ICML-Rebuttal-745C/.
**Q1:** Comparison with [1].
**A1:** We have included the method proposed in [1]—random clean sample insertion, similarity-based retrieval, and self-reasoning—as baselines in... | null | null | null | null | null | null | null | null |
Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization | Accept (poster) | Summary: This paper aims to solve Generalized Category Discovery (GCD). This task seeks to discover both known and novel categories from unlabeled data, leveraging another labeled dataset with only known categories. Different from previous works that mainly aim to boost model performance on novel categories, this work... | Rebuttal 1:
Rebuttal: **Q1:** Experiments about the accuracy of the pseudo labels from the additional branch should be conducted.
**A1:** Thank you for this valuable suggestion. We have conducted a comprehensive analysis of pseudo-label accuracy across all datasets, comparing our auxiliary branch (AUX), main branch (C... | Summary: This manuscript addresses the Generalized Category Discovery (GCD) task, which aims to classify unlabeled data containing both existing (base) and novel (unknown) classes. Existing parametric methods often compromise the discriminability of known classes in order to identify novel classes. To remedy this short... | Rebuttal 1:
Rebuttal: **Q1.** It lacks a comparison with the latest state-of-the-art method, FlipClass [1].
**A1.** Thank you for highlighting this important work. We observed that FlipClass employs asymmetric augmentation and mixup techniques for performance enhancement from their supplementary materials. For a fair ... | Summary: The paper proposes a novel approach for Generalized Category Discovery by introducing a Reciprocal Learning Framework and Class-wise Distribution Regularization. RLF enhances base class discrimination through an auxiliary branch that distills reliable base-class predictions to the main branch, while CDR mitiga... | Rebuttal 1:
Rebuttal: **Q1.** There is no analysis of pseudo-label accuracy.
**A1.** Please refer to our response to Reviewer UDum-Q1.
**Q2.** Fig. 6 shows the performance variation of parameters α and β but lacks justification for the chosen values.
**A2.** Parameters α and β control the strength of distillation an... | Summary: This paper studies the task of Generalized Category Discovery (GCD). It builds upon parametric-based GCD methods, and proposes a Reciprocal Learning Framework (RLF) that introduces an auxiliary branch devoted to base classification. Within the framework, the main branch filters the pseudo-base samples to the a... | Rebuttal 1:
Rebuttal: **Q1.** Could the authors explain how the auxiliary helps the main branch intuitively, as well as the effect of Class-wise Distribution Regularization?
**A1.** Thanks for your valuable feedback.
The auxiliary branch supports the main branch in two key ways:
- **Improve Base Performance:** The ... | null | null | null | null | null | null |
Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations | Accept (poster) | Summary: This paper introduces a practical Inductive Gradient Adjustment (IGA) method to address spectral bias in Implicit Neural Representations (INR) by using inductive generalization of the eNTK-based gradient transformation matrix. The effectiveness of IGA is evaluated across a wide range of INR tasks, and a theore... | Rebuttal 1:
Rebuttal: # Discussion on training time
***
Thanks for your overall recognition of our method, experimental design, and analysis. Following your suggestion, we provide a further discussion about training time of our method as a supplement to Table 6 in our Appendix. Please refer to our response **“Time and ... | Summary: The goal of the paper is to mitigate spectral bias in implicit neural representations by changing the training dynamics. The paper considers the well-known connection between the neural tangent kernel (NTK) and the linear training dynamics, which reproduces spectral bias through the eigenvalues of the NTK matr... | Rebuttal 1:
Rebuttal: # Answer for 3D shape and radiance field results
***
Thanks for recognizing our experiments and evaluation approach. We further analyze IGA by comparing it with Fourier Reparameterization (FR) and Batch Normalization (BN), two key training dynamics adjustment methods for mitigating INR spectral bi... | Summary: The paper presents a Neural Tangent Kernel-based approach to improving the spectral bias of implicit neural representations, which have been shown to be biased towards low frequencies. The paper summarizes the NTK theory and proposes a way to estimate the K matrix using a subset of the training samples, making... | Rebuttal 1:
Rebuttal: # Time and memory analysis
***
Thanks for suggesting a more detailed time and memory requirements analysis. We conducted a measurement of the training time and memory of IGA across all experiments. For example, in the **1D simple function approximation**, the vanilla SIREN takes 28ms/iter (ms/iter... | null | null | null | null | null | null | null | null |
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design | Accept (poster) | Summary: Mixture-of-expert LLMs are getting important to reduce the traning cost without sacrificing the final model quality. With more MoE LLMs released, it's important to optimize the serving performance for them. Quantization is an important technique to optimize the LLMs serving by use low-precision data types to s... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. We would like to provide further clarification on several aspects of our work that you have mentioned:
1. Novelty of the Approach and Similarities with Prior Work
- **Linear-Block-Level Quantization**: Our approach is driven by empirical observations rather... | Summary: This paper presents a framework for exploring the design space of mixed-precision quantization in mixture-of-experts (MoE) models. It considers the variation in quantization sensitivity of linear blocks within models, allocating different bitwidths based on sensitivity. Additionally, it takes into account the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s engagement. | Summary: This paper presents MxMoE, a mixed-precision quantization framework for MoE models. The development of MxMoE is driven by three key factors:
1. Parameter Sensitivity, linear blocks exhibit significant variability in their sensitivity to quantization.
2. Expert Activation Frequencies, activation frequencies of... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work. Your insightful questions and suggestions have been instrumental in enhancing the quality of our manuscript. Below, we address your concerns in detail:
1. Rationale for the W5A5 Configuration in Table 1
- **Why does the "strange" W5A5 setting... | Summary: The paper introduces MxMoE, a mixed-precision quantization framework tailored for Mixture-of-Experts (MoE) models, aiming to address deployment challenges posed by their large memory footprint and computational demands. Key insights include:
1. Heterogeneous quantization sensitivity: Linear blocks within MoE e... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback. Below, we address the two key concerns raised:
1. Exploration of Trade-off Parameter $r$ for Different Models and Use Cases
Our ablation study (Figure 6) explores the impact of the hyper-parameter $r$ on model accuracy and hardwar... | null | null | null | null | null | null |
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness | Accept (poster) | Summary: This paper establishes convergence of random reshuffling methods under a l-smoothness condition, where l is a function rather than a constant.
Claims And Evidence: New results that improve our understanding of random reshuffling approaches are potentially interesting, as random reshuffling is heavily used in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contribution and careful attention to details. We will make modifications as suggested by the reviewer. | Summary: This paper investigates shuffling-type gradient methods without assuming Lipschitz smoothness, which is often not satisfied in practice for many machine learning models. The authors propose stepsize strategies that enables convergence guarantees under a more general smoothness condition called "$\ell$-smoothne... | Rebuttal 1:
Rebuttal: Thanks for the careful reading and constructive feedback. Below we respond to each point in detail:
**Assumption 4.3:** Since component gradients behave as gradient estimations of the full gradient, this assumption can be viewed as a generalization of the more common assumption $\mathbb{E}[\|\na... | Summary: - Most of the existing literature on shuffling SGD establishes the convergence rate under traditional gradient lipschitz continuity, which is a condition that does not hold in neural networks. To address this problem, the paper studies the convergence rate of shuffling SGD under the generalized smoothness assu... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and helpful comments. Please find our responses below:
**W1:** We appreciate your perspective but respectfully disagree that the paper is merely combining existing results. As we emphasize in Section 4.2, the main technical challenge arises specifically from ... | Summary: This paper studies convergence upper bounds of shuffling-based SGD on finite-sum minimization problems, focusing on random reshuffling SGD as well as SGD with arbitrary permutation (i.e., theorems that hold for any choice of permutations). The key contribution of this paper is the extension of standard (Lipsch... | Rebuttal 1:
Rebuttal: Thanks for the careful reading and constructive feedback. Below we respond to each point in detail:
**Q1:** Achieving a logarithmic dependency on $1/\delta$ remains an open challenge under $\ell$-smoothness, and establishing such a result would constitute a significant advancement. As noted in Re... | null | null | null | null | null | null |
Compact Matrix Quantum Group Equivariant Neural Networks | Accept (poster) | Summary: The authors consider the problem of extending group equivariant neural networks to compact quantum groups. Compact quantum groups are generalization of commutative groups. In commutative groups, the complex-valued functions on the group form a commutative C*-algebra. In contrast, the functions on Compact qu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critique of our work. We are glad that the reviewer has recognised our main theoretical contribution (namely, constructing a neural network for easy compact matrix quantum groups and characterising its weight matrices), and that they felt that we explained non-commu... | Summary: The authors propose a new type of equivariant neural networks for handling symmetries described by compact matrix quantum groups and obtain new weight matrices for these groups. The new methods are motivated by the study of non-commutative geometry's symmetries (i.e., quantum symmetries) appearing in quantum g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are pleased that they recognise that our work “introduces a new class of equivariant neural networks for compact quantum groups with applications in physics, as explained in the paper.” We are also glad that they found the claims to be supported with ap... | Summary: - This work addresses the limitation that group equivariant neural networks cannot learn from the data with a non-commutative geometry.
- Specifically, it derives the existence of compact matrix quantum group equivariant neural network, a new type of equivariant neural network that encodes symmetries describ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and encouraging critique of our work. We are delighted that they recognise the “original and significant” contributions that are contained in our paper, and that they found the paper to have a “top presentation quality”. We felt that the reviewer r... | Summary: The authors present the definition of a class of equivariant neural network which are equivariant with respect to a more general definition of group, namely of a quantum matrix group. The strategy relies as describing the non-commutative geometry by using non-commutative C* algebras. The authors show that they... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are pleased that they found the paper to be “very well written despite its technical nature” and that they did not encounter any issues with our theoretical contributions. We are also pleased that the reviewer is excited about the potential applications... | null | null | null | null | null | null |
Bridging Layout and RTL: Knowledge Distillation based Timing Prediction | Accept (spotlight poster) | Summary: This paper presents a cross-stage knowledge distillation framework for timing prediction. Under this framework, the layout aware teacher model distills layout charactersistics to an RTL-level student models. Experimental results demonstrate significant improvement compared with other prediction models.
Claims... | Rebuttal 1:
Rebuttal: **Dear Reviewer wjHP,**
We sincerely appreciate the reviewer's positive remarks and valuable suggestions regarding our work, particularly concerning the sources of our benchmark data, the open-source plan, the fairness of comparisons with MasterRTL/RTL-Timer, and the discussion on comparison with... | Summary: The paper proposes RTLDistil, a framework aimed at bridging the gap between early-stage RTL timing prediction and accurate layout-level timing analysis. The method leverages a dual-model setup: a high-capacity teacher model operating on a layout netlist graph and a lightweight student model working on an RTL-l... | Rebuttal 1:
Rebuttal: **Dear Reviewer 55gZ,**
Thanks for your insightful comments. Your feedback greatly helped us refine our work. Below, we address your major concerns.
## 1. Efficiency vs. STA and Left-Shift
The entire flow from RTL to Layout to STA completion usually takes hours to days under rough quantitative es... | Summary: The paper describes a multi-level distillation framework to train a tool that can predict final timing characteristics of a synthesis flow from RTL-level description. The paper provides interesting detailed analysis of their results and argues that their results are much more accurate than prior efforts, many... | Rebuttal 1:
Rebuttal: **Dear Reviewer DZ14,**
Thank you for your constructive comments and recognition of our work's core contributions. We greatly appreciate your thoughtful feedback, which has helped us further refine and clarify several key aspects of our methodology. Below, we provide a structured response to your... | null | null | null | null | null | null | null | null |
On the Duality between Gradient Transformations and Adapters | Accept (poster) | Summary: The authors derive an equivalence between LoRA / adapter training methods, which optimise a low rank addition to weight matrices to reduce optimiser memory usage, and methods that project gradients themselves down to low rank (such as Galore), also to reduce optimiser memory usage. Their result generalises mor... | Rebuttal 1:
Rebuttal: Thank you for your review! We address your questions below:
### Key points
> Is it possible to prove a similar equivalence where you use the moore-penrose pseudo-inverse to project back up?
Great question! It is possible that an equivalence exists if one is not using a LoRA adapter. For example... | Summary: This paper studied the connection between weight-efficient optimization and gradient-efficient optimization of transformers, and found that optimizing a model in a compressed gradient space is equivalent to optimizing just an additive low-rank adapter. Through theoretical analysis and empirical study, the auth... | Rebuttal 1:
Rebuttal: Thank you for your response! We will break down your question into parts:
> Could you further explain your findings from experiments in Sec 4.1?
The key goal of Sec 4.1 is to understand the interplay between the choice of gradient transformation S and overall performance. One important aspect of... | Summary: This paper explores a memory-efficient approach to neural network optimization by mapping gradients into a lower-dimensional space, reducing the memory overhead of both gradient accumulation and optimizer states. After performing updates in this reduced space, parameters are returned to the original space via ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Our main objective in this paper was to explore pretraining, and thus we selected a pretraining dataset that matches the distribution of pretraining data (SlimPajama, which is based on RedPajama, that tried to match the pretraining data used by Llama, one of the most su... | Summary: This paper investigates the duality between linear gradient transformations and adapter-based reparameterizations in memory-efficient LLM training. In essence, it shows that applying linear transformations to gradients (as in GaLore) is equivalent to reparameterizing the model via adapters (like one-sided LoRA... | Rebuttal 1:
Rebuttal: Thank you for your review! We identify three key criticisms pointed out in your review, and address them below
### 1: Conceptual insights in the duality derivation are simple
Indeed, while the essence of our derivation was observed previously in the literature, our derivation is more general; it... | null | null | null | null | null | null |
Causality Inspired Federated Learning for OOD Generalization | Accept (poster) | Summary: I am not familiar with the field of this submission. I suggest the AC find another reviewer and disregard my comments. Apologies for the inconvenience.
Claims And Evidence: I am not familiar with the field of this submission. I suggest the AC find another reviewer and disregard my comments. Apologies for the ... | Rebuttal 1:
Rebuttal: Thank you for your rigorous and responsible review. | Summary: The authors identify a limitation in federated learning, arguing that existing methods primarily capture invariant causal features across clients/environments while failing to account for variant causal features. To address this, they propose a method designed to capture both invariant and variant causal featu... | Rebuttal 1:
Rebuttal: In response to the reviewer's concern that “some detected causal features may, in fact, be non-causal,” we clarify that the features described above are indeed causal. The misunderstanding appears to stem from our use of the misleading term “**fake causal features**” to denote causal features that... | Summary: This paper addresses the challenge of OOD generalization in FL by rethinking how causal features are extracted across clients. The authors argue that instead of limiting the global feature extractor to invariant features (like the traditional FL methods), it should capture the union of all causal features pres... | Rebuttal 1:
Rebuttal: Thanks for your suggestions, and we will include the citation you pointed out in the final version.
**Q1: Experimental Designs**
We conduct additional experiments in response to the suggestions about experimental designs.
**Add Updated Model Architectures**: Following the reviewer's suggestio... | Summary: The paper proposes FedUni, a framework for federated learning. The framework extracts causal features from different clients in training and flexibly selects the features applicable for the target client. It differs from the existing methods in terms of not using a fixed sharing feature pool. The paper provide... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions, and we will enlarge the text font in Fig 5, 7 in the final version.
**Q1: Computational Cost**
We provided the run time per communication round and a theoretical proof for the convergence rate. Due to space limitations, we provide a brief proof here, and th... | null | null | null | null | null | null |
On the Convergence of Continuous Single-timescale Actor-critic | Accept (poster) | Summary: The paper presents a finite time convergence analysis of the single timescale, single loop actor-critic algorithm with Markovian sampling in the discounted reward continuous state action space setting. The main contributions include - (a) extending to continuous state action space using an operator based analy... | Rebuttal 1:
Rebuttal: **(Claim 1: on new operator-based analysis.)**
Operator-based analysis is employed not only to handle the lemmas in Appendix B but also to accommodate the continuous distribution throughout the proof. For instance, it is used in establishing Propositions 3.1 and 3.2.
Prior to our work, it remain... | Summary: The paper analyzes the single-timescale actor-critic with TD(0) updates for the critic and REINFORCE with a baseline for the actor, with linear function approximation for continuous state-action spaces. The samples are taken from a single (Markovian) trajectory, while a generative model that enables an indepen... | Rebuttal 1:
Rebuttal: **(Q1: challenges in dealing with continuous action space.)**
Previous results derived for finite action space assume Lipschitz constants that scale with the number of actions ($|\mathcal{A}|$), which, however, becomes meaningless in the case of the uncountable continuous space. We observe that t... | Summary: This paper addresses the theoretical understanding of single-timescale actor-critic (AC) algorithms in continuous state-action spaces, a widely used reinforcement learning (RL) approach for continuous control tasks such as robotics. While actor-critic methods have demonstrated empirical success, existing theor... | Rebuttal 1:
Rebuttal: **(On removing linear function assumption)**
Thank you for your suggestion regarding the removal of the linear function approximation assumption for the value function. We are aware of recent works (e.g., Tian et al., 2023; Ke et al., 2024) that employ deep neural networks for value function appr... | Summary: This paper considers the problem of analyzing actor-critic algorithms for the discounted, continuous spaces setting when the actor and critic updates occur on the same timescale. The key idea in the analysis is to sample from two distinct processes: a "discounted process" corresponding to the discounted state ... | Rebuttal 1:
Rebuttal: **(Weakness \& Q4: on single-simple)**
1. The terminology ''single-sample" follows the seminal work [Olshevsky \& Gharesifard, 2023], where they directly assume sampling from visitation distribution and stationary distribution for updating actor and critic, respectively. It refers to the fact tha... | null | null | null | null | null | null |
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization | Accept (poster) | Summary: Making LLMs behave in a safe fashion, a major research concern, often comes with unwanted side effects such as overrefusal of prompts that may seem unsafe. This paper makes two contributions. 1. They show an improvement when using finetuning data overgenerated from a more advanced teacher LLM. It also presents... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and comments.
We will answer the points raised individually.
__1. (Other Comments Or Suggestions) I would change the 'toxic question example' in the appendix figure 7 to something a little more explicitly toxic.__
We thank the reviewer for this suggestion... | Summary: This paper addresses the challenges of balancing safety and usefulness in large language models. It explores the effects of using more advanced teacher models to generate completions for instruction finetuning. Their main contributions include:
- They show that using more advanced teacher models (e.g. GPT-4o) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and comments.
We will answer the points raised individually.
__1. (Claims And Evidence) The slight decrease in safety after POROver could be discussed more explicitly.__
We thank the reviewer for this suggestion. We believe the slight decrease in safety m... | null | null | null | null | null | null | null | null | null | null |
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning | Accept (poster) | Summary: This paper proposes a diffusion model optimization method based on multi-diffusion-step regularization, which is different from previous behavior-regularized policy methods.
Claims And Evidence: Most of the claims in this paper are well supported by theory and experiments, but some parts remain difficult to u... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback, which has greatly improved our work. We hope the following clarifications can further address your concerns and enhance your evaluation of our paper.
**Q1: About the proof in Theorem C.1**
First, we would like to restate the question to address a... | Summary: This paper use diffusion policis for offline RL and the main idea is to take the rollout quality of the diffusion process as extra regularization. In other words, for behavior cloning, the paper proposes to measure the similarity between the demonstration action and the learnt action by comparing their distrib... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback. Below, we provide further clarification and results to address your concern and we hope these materials can enhance your evaluation of our paper. **Due to the space constraint, we will post our discussion of the connection between BDPO and broader literat... | Summary: The paper introduces Behavior-Regularized Diffusion Policy Optimization (BDPO), a framework for offline RL that integrates diffusion-based policies with behavior regularization. The key innovation is formulating KL regularization along the whole diffusion steps instead of on the final result, enabling more eff... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for his/her constructive feedback. Below is further clarification for the reviewer's concern.
**Q1: Using EDP for policy optimization?**
Our policy improvement objective is to maximize the expected Q-values while also minimizing the KL divergence:
$$\max\_{p^... | null | null | null | null | null | null | null | null |
Is Noise Conditioning Necessary for Denoising Generative Models? | Accept (poster) | Summary: This paper investigates the necessity of noise conditioning in diffusion models. It provides a theoretical analysis of the effects of removing noise conditioning and presents error bounds. The analysis shows that, under mild conditions, the errors resulting from the removal of noise conditioning are relatively... | Rebuttal 1:
Rebuttal: Thanks a lot for the insightful feedback and the supportive comments to our work!
**1.Definition & Benefits of removing noise conditioning**
**Reviewer FoCJ:** `Without the noise conditioning, how are the models trained? … is it that we still train models with Eq. 2, but only replace `$NN_{\thet... | Summary: This paper investigates whether diffusion models, which are typically noise-conditioning networks, can be converted into noise-unconditional networks. It finds that many models are not significantly affected by the removal of noise conditioning, and in the case of Rectified Flow (RF) models, performance even i... | Rebuttal 1:
Rebuttal: Thank for the constructive feedback and supportive comments. Here, we address the concerns regarding classifier-free guidance (CFG), experimental random variance, model-wise analysis, and explanation on performance of 1-RF.
**1. Latent Diffusion (DiT) & Classifier-free Guidance**
Reviewer 7y3v c... | Summary: The paper tries to debunk a common belief among diffusion model practitioners if a time-condition of the model is necessary for a diffusion model. The authors take both the theoretical and experimental approach to address this issue. The paper mainly focus on the theoretical reasoning rather than practice, whi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful feedback and recognition of our theoretical contributions. Below, we address the concerns regarding experimental scope and practical applicability to large-scale diffusion models.
**1. Large-scale experiment**
To address reviewer gvd2’s concerns on experime... | Summary: This paper analyzes noise conditional diffusion models (DMs) and develops theory supporting the viability of noise unconditional DMs. Empirical evidence supports the author's theoretical claims and demonstrates that noise unconditional DMs are capable of performance similar to noise conditional DMs. This chall... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review and positive feedback regarding our work’s novelty and impact!
We agree that incorporating the suggested reference *Noise2Noise: Learning image restoration without clean data* will provide valuable context for the derivation in section 4.1. We will revise the manu... | null | null | null | null | null | null |
Learning Efficient Robotic Garment Manipulation with Standardization | Accept (poster) | Summary: The authors present APS-Net, a unified framework for garment manipulation that integrates both unfolding and standardization. APS-Net employs a dual-arm, multi-primitive policy to unfold crumpled garments and ensure standardization, which facilitates downstream tasks like folding. Experimental results show tha... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful feedback, insightful questions, and recognition of our work’s strengths. Below we address the comments point-by-point:
## Weaknesses
### 1. Framework Architecture Complexity
We understand the concern about APS-Net's complexity. However, it is specifica... | Summary: In this paper, the authors present an RL framework for grament manipulation, which consists of two stages: standardization and folding. For the standardization stage, a two-primitive polcy of fling and pick-and-place is trained using a factorized reward function, which includes garment converage, keypoint dist... | Rebuttal 1:
Rebuttal: We truly appreciate the time and effort you invested in reviewing our paper. Thank you for recognizing the effectiveness of our two-stage RL framework for garment manipulation, particularly the learning-based primitive selection and factorized reward design. Below, we respond to your comments poin... | Summary: This paper introduce a novel robotic garment manipulation system with standardization, which has better performance than the previous framework in this challenging robotics task.
Claims And Evidence: 1. The standarization for the garment manipulation task can enhance performance.
2. However, the standarizati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful feedback and for recognizing the novelty of our standardized robotic garment manipulation system. Below, we address the key concerns raised, particularly regarding generalizability, the simplicity of the folding strategy, and evaluation metrics... | Summary: This paper introduces APS-Net, a novel framework for robotic garment manipulation that seeks to both unfold garments and align them into standardized orientations—essentially “standardizing” them as part of the unfolding process. Unlike many existing solutions that focus on either single-arm quasi-static appro... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have dedicated to evaluating our work, and we are grateful for your recognition of our key contribution—the integration of dynamic flinging with precision pick&place for garment standardization, and the factorized reward function. Below, we provide a... | null | null | null | null | null | null |
Effective and Efficient Masked Image Generation Models | Accept (poster) | Summary: This paper introduces eMIGM, a unified framework that integrates Masked Image Generation Models and Masked Diffusion Models into a single mathematical formulation. The authors categorize the possible design choices into training and sampling processes to optimize performance and efficiency. By leveraging a tim... | Rebuttal 1:
Rebuttal: We thank reviewer 5pkV for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.
> **Minor Limit** Generalizability of improvements beyond tested configurations unclear
Empirical analysis is a well-established approach in... | Summary: The paper proposes a unified framework that integrates masked image generation models (e.g., MaskGIT) and masked diffusion models. The authors systematically explore the design space of training and sampling strategies to improve both performance and efficiency. The proposed model, eMIGM, achieves state-of-the... | Rebuttal 1:
Rebuttal: We thank reviewer DbgT for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.
> **Claims 1:** Define "comparable" more rigorously
To quantify our comparison: eMIGM achieves an FID of 1.57 on ImageNet 256x256 generatio... | Summary: This paper provides a comprehensive study of masked diffusion models for visual generation, covering training, sampling, and architectural designs with extensive experiments. In other words, this paper investigates how to make a good MDM with regard to training and sampling settings through empirical evidences... | Rebuttal 1:
Rebuttal: > **Experimental Design1:** The claim of "CFG with Mask" and "fake class CFG"
Thank you for your question. In standard Classifier-Free Guidance (CFG) used in diffusion models and MAR, training involves occasionally replacing the class label with a dedicated 'fake class' token, which is distinct ... | Summary: The paper presents eMIGM, a novel model for effective and efficient masked image generation. It unifies masked image generation models and masked diffusion models within a single framework, exploring the design space of training and sampling to identify key factors impacting performance and efficiency. The mod... | Rebuttal 1:
Rebuttal: > **Claims:** Some claims could be further strengthened by additional analyses.
We appreciate your feedback regarding our efficiency claims.
Our analysis of training efficiency examined the relationship between training FLOPs and FID scores. As shown in Fig.4(b), larger eMIGM models achieve be... | Summary: This work explores masked diffusion and image modeling through a unified framework, systematically analyzing several key design choices in this domain. Within this framework, the authors ablate masking schedules, loss weighting, and sampling strategies, leading to improvements over existing standards in each a... | Rebuttal 1:
Rebuttal: We thank reviewer 7cuF for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.
> **Claim1:** using a weight schedule for guidance
Compared to existing work, our approach is motivated by MDMs' unique irreversible token ... | null | null | null | null |
Efficient Heterogeneity-Aware Federated Active Data Selection | Accept (poster) | Summary: This paper considers active linear regression in the federated learning setting. It adapts the leverage score sampling to the federated learning setting for active learning. To make the method work in federated learning, it requires two components, data selection which estimates the leverage scores, and model ... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our manuscript. Below we respectfully and explicitly address your main concerns point-by-point.
> Q1-*Claims And Evidence*: Concerns about privacy preservation and lack of a precise theoretical characterization of what kind of "privacy" is preserved
Thank you... | Summary: This paper proposes FALE algorithm to select informative data points for non-i.i.d. federated regression task. The query strategy performs global data selection using leverage score sampling, where a FedSVD technique is employed to obtain the leverage scores of all data points in federated learning. Furthermor... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review of our paper. Below we respectfully address each of your comments point-by-point.
> Q1-*Other Strengths And Weaknesses*: Concerns regarding potential data leakage and increased communication overhead
Regarding data leakage concern, please see our re... | Summary: This paper presents FALE (Federated Active data selection by LEverage score sampling), a novel Federated Active Learning (FAL) method for regression tasks with non - i.i.d. client data. FALE leverages FedSVD to gather global data information without exposing individual client data. For global model learning, i... | Rebuttal 1:
Rebuttal: Thank you very much for your time in reviewing our paper. Below we respectfully address your concerns point-by-point.
> Q1-*Theoretical Claims*: The relationship and effectiveness of Thrm. 4.1 for FALE
We have improved and rewritten Thrm 4.1 to explicitly clarify its direct connection and effect... | Summary: This paper investigates the data selection problem in Federated Active Learning (FAL) and introduces FALE, a score-based sampling method. FALE leverages FedSVD to extract cross-client query information, enabling a leverage score-based sampling strategy for data selection and re-weighting. Theoretical analysis ... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive suggestions. Below, we address each of your comments in detail.
> Q1- *Claims And Evidence*: Independent communication complexity and privacy analysis of FALE
As Reviewer 2DKU's suggestion, we have included a dedicated analysis of FALE's communication and ... | null | null | null | null | null | null |
UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning | Accept (poster) | Summary: This paper proposes UDora, an iterative method based on GCG. The approach first collects responses z from the target victim model, then introduces a score function to modify the original response z into an attack-desired response z* (the response that achieves the attacker's goal). It then utilizes z* with the... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the clarity of our writing and the relevance of our approach to prompt injection and jailbreak attacks. We appreciate your solid suggestions, which motivate us to further refine our work, and we are happy to address the concerns raised!
> **Q1: Unrealistic th... | Summary: The paper presents UDora, a unified red teaming framework designed to attack LLM agents by dynamically leveraging their reasoning processes. The core idea involves inserting adversarial perturbations into the agent's reasoning traces to steer it toward malicious actions. The framework operates in three steps: ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing UDora’s innovative approach in utilizing reasoning steps and for highlighting the robustness of our empirical results across diverse benchmarks, particularly in the real-world AutoGen email agent attack scenario. We deeply appreciate your thorough an... | Summary: This paper introduces UDora, a novel red teaming framework designed to systematically attack LLM agents by leveraging their own reasoning processes. Unlike traditional adversarial attacks that rely on static prompt injections or optimized adversarial suffixes, UDora dynamically identifies and perturbs reasonin... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewer for their thorough and insightful feedback. Your contribution of time and expertise has significantly enriched the development of our research!
> **Q1: Black-box setting**
Thank you for the insightful question! In a pure black-box environment—where only the... | Summary: This paper introduces UDora, a unified framework for testing security vulnerabilities in LLM agents. It focuses on two scenarios: malicious environments and malicious instructions. UDora works by analyzing an agent's reasoning process, identifying optimal positions to insert misleading information, and optimiz... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for the thoughtful and detailed feedback. The expertise and time invested in this work have been instrumental in enhancing its quality!
> **Q1: UDora assumes the attacker can access either the entire model or its token probability distribution during reasoning, which i... | null | null | null | null | null | null |
SPEX: Scaling Feature Interaction Explanations for LLMs | Accept (poster) | Summary: This paper introduces SPEX, a model-agnostic interaction attribution algorithm designed to scale feature interaction explanations to large input spaces, e.g., LLMs. The key contribution of SPEX is leveraging a sparse Fourier transform with channel decoding to efficiently identify and reconstruct important feat... | Rebuttal 1:
Rebuttal: **This rebuttal contains (anonymized) links to figures. We also built a web app to help explore SPEX: https://anon858023.github.io/spex-webapp/**
Thank you for the review. We hope that with the proposed additions based on your comments, your concerns are addressed and we can convince you that thi... | Summary: This paper proposes a new algorithm to efficiently compute the sparse Fourier transform and identify salient interactions by leveraging the underlying sparse structure of interactions. The proposed methods outperform previous attribution methods and interaction indices in faithfulness measure while costing muc... | Rebuttal 1:
Rebuttal: **This rebuttal contains (anonymized) links to figures. Web app to explore SPEX: https://anon858023.github.io/spex-webapp/**
Thank you for the thorough and helpful review. We hope our proposed additions and clarifications convince you that this paper is not a borderline case, but rather an impact... | Summary: The paper introduces **SPEX**, a scalable method for explaining LLM predictions by recovering feature interactions using **structured feature masking (BCH codes) and sparse Fourier recovery**. SPEX efficiently identifies the most important feature interactions without evaluating all \( 2^n \) subsets, making i... | Rebuttal 1:
Rebuttal: **This rebuttal contains (anonymized) links to figures. We also built a web app to help explore SPEX: https://anon858023.github.io/spex-webapp/**
Thank you for your constructive and positive review! We appreciate your insightful comments and have addressed them with additional experiments and cla... | null | null | null | null | null | null | null | null |
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens | Accept (poster) | Summary: This paper proposes learning an approximate Bayesian model for offline RL and use planning guided by an offline RL learned policy prior for action selection. Standard practices of ensemble architecture and variance penalty are used for planning. Experiment shows improved performance over offline RL only policy... | Rebuttal 1:
Rebuttal: We thank Reviewer BPa5 for dedicating time to review our paper and for the feedback. We are glad you found the experiments demonstrate improved performance and the theoretical motivations sensible, and we appreciate the opportunity to clarify aspects of our problem setting and methodology that see... | Summary: The authors introduce RefPlan, a doubly Bayesian method for offline model-based RL.
RefPlan combines two existing methods (1) the probabilistic control-as-inference formulation of MB planning (using a policy prior) with (2) the variational representation of epistemic uncertainty.
At inference time, RefPlan ma... | Rebuttal 1:
Rebuttal: Thank you for the detailed, constructive review. We appreciate the opportunity to address your feedback, particularly on statistical significance and budget comparisons.
1. Statistical significance: While runs use 3 seeds, Figure 5 uses RLiable [1] for robust aggregate analysis (Appx B.1). Figure ... | Summary: This paper combines ideas from _adaptive_ and _online planning_ Offline RL to achieve a conceptually nice framework and reasonable performance improvements. They are able to improve upon (a) epistemically adaptive methods with no online computation, and (b) online computation methods with no explicit epistemic... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer CFgz for taking the time to thoroughly analyze our paper and provide constructive and insightful feedback.
We address the main points raised below:
1. Epistemic POMDP vs. BAMDP: Thank you for this important point. We agree there's no fundamental conceptual difference an... | null | null | null | null | null | null | null | null |
Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes | Accept (oral) | Summary: This paper proposes a statistical model for estimating the time-varying delay of communication between multiple brain regions. This is achieved through low-dimensional latent variable modeling and incorporating multi-output Gaussian Process models. In addition to the modeling contribution, the paper exploits a... | Rebuttal 1:
Rebuttal: Dear Reviewer sX24,
Thank you for the suggestion! We hope these improvements clarified your concerns, and that they can be taken into account when deciding the final score.
The additional results are: (https://anonymous.4open.science/r/rebuttal-figures-for-ICML-2025-42E6/rebuttal_figures.pdf), i... | Summary: This paper extends GP-based methods for modeling multi-region neural recordings to the case where temporal delays in communication can dynamically shift. This is achieved by a novel combination of GPs with state-space models. The authors evalute their work on both synthetic and real multi-region neural recordi... | Rebuttal 1:
Rebuttal: Dear Reviewer MsHr,
Thank you for the encouraging feedback and suggestion! We have made several clarifications according to your questions and comments. We hope these sufficiently clarified your concerns, and that they can be taken into account when deciding the final review score.
The additiona... | Summary: Modeling neural activity across networks of populations of neurons across multiple brain regions is critical to understand neural computation and how information is processed. While the recent recoding technological advances have made it possible to acquire the data, modeling tools are limited in their ability... | Rebuttal 1:
Rebuttal: Dear Reviewer 9Ksf,
Thank you for the constructive comments! We have made several improvement according to your questions and comments. We hope these sufficiently clarified your concerns, and that they can be taken into account when deciding the final review score.
The additional results are pro... | Summary: This submission describes an approach for inferring latent factors underlying shared neural responses across brain areas. Notably, the approach enables inferring a continuous and time-varying delay factor that captures the temporal delay between two brain areas. The submission formulates this as both a Gaussia... | Rebuttal 1:
Rebuttal: Dear Reviewer Wfpo,
Thank you for the encouraging feedback and practical suggestions! We have made several clarifications according to your questions and comments. Hopefully these will resolve most of your concerns, and that they can be taken into account when deciding the final review score.
> ... | null | null | null | null | null | null |
Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion | Accept (poster) | Summary: The authors proposa a preconditioned Reimannian gradient descent algorithm for low-rank tensor completion.
The provide analysis of the computational cost and convergence guarantees
Claims And Evidence: The claims are mostly clear and have theorem support.
Question:
- Is $G_{t,i}$ the optimal choice, are th... | Rebuttal 1:
Rebuttal: > **Q1:** The paper focuses on Tucker decomposition. Can PRGD be extended to Tensor Train (TT) or Tensor Ring decompositions?
**A1:** Thank you for raising this important question. **Please refer to our response to Q2 of Reviewer eeW6.**
> **Q2:** Can the method be generalized to arbitrary high... | Summary: This paper introduces the Preconditioned Riemannian Gradient Descent algorithm for low-multilinear-rank tensor completion, leveraging the manifold structure to achieve faster convergence than standard Riemannian Gradient Descent while maintaining the same per-iteration complexity.
Claims And Evidence: The pro... | Rebuttal 1:
Rebuttal: > **Q1:** The proposed method appears somewhat unconventional. What is the motivation for introducing (4) and (5)?
**A1:** Thank you for your question. The derivation of PRGD involves **two essential steps:** (1) endowing the preconditioned metric to the tangent space of the iterate on the manifo... | Summary: In this paper, the author introduces a Preconditioned Riemannian Gradient Descent (PRGD) algorithm for low tensor completion based on Tucker decomposition model. A data-driven Riemannian metric is proposed to accelerate convergence. Theoretical analysis is given to guarantee the recovery performance. Experimen... | Rebuttal 1:
Rebuttal: > **Q1:** Could the proposed method be applied to higher-order data (4th-order tensor or higher?)
**A1:** Thank you for raising this important question. Indeed, our PRGD algorithm can be extended to the higher-order tensor case and handle higher-order data.
From the algorithmic perspective, for... | Summary: This paper introduces the Preconditioned Riemannian Gradient Descent (PRGD) algorithm for low-multilinear-rank tensor completion. By designing a data-driven Riemannian metric and an efficient diagonal preconditioner derived from gradient statistics, PRGD achieves 10× faster convergence than standard Riemannian... | Rebuttal 1:
Rebuttal: > **Q1:** In the experimental section, the author presents too few...should be provided.
**A1:** Thank you for your constructive feedback. We have conducted video inpainting on the videos from your recommended source (see section 5.3 and page 25). In response to your suggestion, we expanded the e... | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.