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LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization | Accept (poster) | Summary: This paper introduces LangTime, which is a language guided unified model for time series forecasting. It integrates LLM with RL based fine-tuning using PPO for time series analysis. Among the designed components, the Temporal Comprehension Prompts (TCPs) align time series data with LLM by embedding data-specif... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and insightful comments. In our response, our model was jointly pre-trained on the ETTh1 and Weather datasets, and experiments involving the TimePPO stage were fine-tuned on individual datasets. We presented the average of the results, and detailed ex... | Summary: This paper presents LangTime, a novel language-guided model for time series forecasting that addresses key challenges in leveraging large language models for this task. Specifically, the authors construct Temporal Comprehension Prompts to help LLMs understand domain-specific time series data, along with a new ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and insightful comments. In our response, models were jointly pre-trained on the ETTh1 and Weather, and experiments involving the TimePPO stage were fine-tuned on individual datasets. We presented the average of the results, and full results are avail... | Summary: This paper introduces LangTime, a unified framework that leverages large language models (LLMs) for time series forecasting across multiple domains and modalities. The authors identify key challenges when applying LLMs to temporal data—Cross-domain generalization, cross-modality alignment and error accumulatio... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and insightful comments. In our response, our model was jointly pre-trained on the ETTh1 and Weather datasets, and experiments involving the TimePPO stage were fine-tuned on individual datasets. We presented the average of the results, and detailed ex... | Summary: The paper introduces LangTime, an approach that builds on top of existing large language models (LLMs) to effectively perform time series forecasting. The paper identifies 3 crucial problems with adapting LLMs for forecasting tasks - cross-domain generalization, cross-modality alignment, and error accumulation... | Rebuttal 1:
Rebuttal: As for Eq.(8), it should actually be: $-||y-\hat{y}||^2_2$ (refer to `actor_loss_fn` in `ppo_trainer.py`). We will modify the description of hyperparameters and Eq.(6) and Eq.(8) in the next version of the paper. Full results are available https://anonymous.4open.science/r/full-E4EE/README.md.
> C... | null | null | null | null | null | null |
A Closer Look at Multimodal Representation Collapse | Accept (spotlight poster) | Summary: In this paper, the authors contribute with a theoretical understanding of the phenomena of modality collapse in multimodal representation learning model. In particular, the authors show that modality collapse occurs when the predictive features of a given modality become entangled with noise features of anothe... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty and thoroughness of our work, as well as pointing us to important adjoining multimodal learning literature that observe modality collapse. Below, we aim to address their concerns, which we will also incorporate in the final version of the manuscrip... | Summary: The authors propose a new explanation for the difficult problem of modality collapse. Their argument is that the low-rank bias of neural networks lead them to learn low-rank polysemantic neurons rather than high-rank monosemantic neurons. This is a problem, since as the proportion of cross-modal polysemantic f... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting important gaps in our initial submission. Below, we aim to address them, which will be included in the final version.
**CM-AE:** We apologize for the confusion caused here and we thank the reviewer for pointing out what was an error in our reporting. While we eval... | Summary: The manuscript introduces an Explicit Basis Reallocation (EBR) approach to mitigate multimodal collapse. It first explains that multimodal collapse is driven by polysemantic neurons—which increase with the number of modalities—leading these neurons to converge into a low-rank polysemantic subspace, ultimately ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to thoroughly understand our paper and providing important comments, which we believe has helped in significantly solidifying our findings. Below, we provide our response, which we will also incorporate in the final version of our manuscript.
**Multicolli... | Summary: The paper investigates modality collapse in multimodal learning, where models rely only on a subset of modalities. It shows that this collapse occurs due to entanglement of noisy features from one modality with predictive features from another, leading to suboptimal solutions. The authors propose Explicit Basi... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the theoretical and empirical contributions of our work towards understanding modality collapse and providing valuable feedback. Below, we address their concerns, which we will incorporate in our final version.
**Identifiability:** Indeed there are practical ... | null | null | null | null | null | null |
DiffusionVLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression | Accept (poster) | Summary: This paper proposes a novel VLA framework that integrates NTP with a diffusion process. The writing is logical, and the experimental evaluation is thorough. While the core idea is intriguing and somewhat similar to $\pi_0 $[1], the latter was released in October, close to the ICML submission deadline in Januar... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We have addressed each point below. Please see the following responses for details.
## 1. The inference speed of DiVLA(Same as R3#1)
This issue was partially addressed in our response to R13rJ#1. Bellow, we fristly clarify why DiVLA-7B ha... | Summary: DiVLA is a VLA model that connects a VLM with a diffusion model to enable both reasoning and action generation in robotics. It builds upon a pre-trained Vision-Language Model (VLM) for text-based reasoning while incorporating a diffusion model to learn robotic actions through a noise-denoising process. DiVLA i... | Rebuttal 1:
Rebuttal: Thanks for your careful review and valuable comments. We address each question below.
## 1. Why DiVLA-7B has similar number of parameters but is 8 times faster than OpenVLA-7B?
Thank you for pointing out this question.
1) Diffusion-based VLA operates significantly faster than autoregressive VLA... | Summary: The authors propose combining the reasoning capabilities of LLMs with the robot action generalization capabilities of diffusion models, creating DiVLA. DiVLA extracts and interleaves tokens from visual input and text using SigLIP, concatenates them, and processes them in a VLM. The VLM generates action tokens ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback.
## 1. Simulated evaluation
Real-world evaluation is more challenging than simulation. While our work emphasizes complex real-world tasks like long-horizon bin-picking and bimanual table bussing, we also evaluate DiVLA on two standard simula... | Summary: DiffusionVLA unifies autoregressive reasoning with diffusion-based action policies to build robust vision–language–action models for robotic control. By injecting self-generated reasoning directly into the policy head, the framework improves interpretability and decision-making. Extensive experiments on tasks ... | Rebuttal 1:
Rebuttal: Thanks for your careful review and valuable comments. We address each question below.
## 1. Training efficiency for DiVLA compared to baselines
Thank you for your valuable feedback and insightful advice. Since DP and Octo perform significantly worse across most tasks, our comparison focuses on c... | null | null | null | null | null | null |
Towards Lifelong Model Editing via Simulating Ideal Editor | Accept (poster) | Summary: This paper introduces Simulating Ideal Editor (SimIE), a framework that extends standard parameter-modifying methods to lifelong scenarios. SimIE computes the ideal parameter shift as the minimum-norm solution of a linear system using the Moore-Penrose inverse, and allows recursive updates by truncating its li... | Rebuttal 1:
Rebuttal: We are deeply grateful to Reviewer 1PGC for the careful and insightful comments on our manuscript. Our detailed responses to your questions are outlined below.
## Q1: evaluating the method's performance without relying on teacher forcing
Following your suggestion, we conduct additional experiment... | Summary: This paper introduces "Simulating Ideal Editor" (SimIE), a general framework that enables standard model editing methods to perform effectively in lifelong editing scenarios. The authors formulate the ideal parameter shift as the minimum-norm solution to a linear system constructed using the Moore-Penrose inve... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer hGQy for your insightful and constructive comments. We have carefully considered your feedback and responded to each of your points below.
## Q1: larger models perform with the proposed SimIE
We conduct additional evaluations on larger LLMs, specifically Llama-3 (8B) a... | Summary: Standard model editing techniques suffer significant performance degradation in sequential editing setting due to model drift and catastrophic forgetting.
To tackle the issue, this paper proposes a general framework, i.e., SimIE, to restore the performance of **any** standard model editing techniques in lifelo... | Rebuttal 1:
Rebuttal: We extend our heartfelt thanks to Reviewer 7yH3 for your thorough and thoughtful review of our manuscript. Following are our responses to each individual comment.
## Q1: use more recent datasets or metrics
We conduct experiments on a new benchmark, QAEdit, which is tailored for real-world QA task... | Summary: The paper studies the problem of *lifelong* model editing, wherein a model has to *sequentially* incorporate new knowledge without retraining and without altering its behavior on unrelated tasks. Specifically, authors tackle the known issue where consecutive edits cause the model to forget previous edits or co... | Rebuttal 1:
Rebuttal: We are deeply grateful to Reviewer nKJ9 for the detailed and constructive feedback on our manuscript. Below, we address your questions point-by-point.
## Q1: evaluate on newer and, importantly, more accurate LLMs
We conduct additional evaluations on Llama-3 (8B) and Qwen2.5 (7B). We select FT, RO... | null | null | null | null | null | null |
The Hidden Joules: Evaluating the Energy Consumption of Vision Backbones for Progress Towards More Efficient Model Inference | Accept (poster) | Summary: This paper introduces an energy efficiency scoring system and develop the corresponding interactive web application for users to compare models based on accuracy and energy consumption.
The experimental results show that the proposed scoring system is kind of accurate when estimating the energy consumption fr... | Rebuttal 1:
Rebuttal: - Reference: Carbon Emissions and Large Neural Network Training
We appreciate your suggestion to cite this important work. We actually referenced the updated version, "The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink," which significantly inspired our research.
- More ... | Summary: This paper evaluates the energy consumption of ImageNet classification models, focusing on their efficiency across different architectures, datasets, and optimization techniques. The study aims to provide a more accurate assessment of energy consumption in deep learning models and examines accuracy gains relat... | Rebuttal 1:
Rebuttal: - How is this work different?
In addition to evaluating a substantially larger number of models (50–100x more), our work differs from prior studies in several ways:
1-We identified key methodological flaws from previous studies: Henderson et al. (2020) using batch size = 1, and Desislavov et al.... | Summary: This paper conducts a comprehensive analysis of energy efficiency of image classification models, around 1200 ImageNet classifiers. The authors found that there is a steep diminishing return in accuracy gains compared to increase in energy use. They further identified key factors contributing to energy consump... | Rebuttal 1:
Rebuttal: - Batched inference setting
Thank you for raising the important issue of batched inference. Our measurement methodology explicitly focuses on maximizing GPU utilization to ensure fair comparisons across models by testing a variety of different batch sizes and selecting the most energy-efficient i... | null | null | null | null | null | null | null | null |
Attributes Shape the Embedding Space of Face Recognition Models | Accept (poster) | Summary: The paper studies the organization of embeddings in face recognition task with respect to facial attributes. The study is mainly focussed on ArcFace and FaceNet models, while the study uses results on LFW and CelebA.
## Update after rebuttal
In my initial review, most of the local issues raised were w.r.t cla... | Rebuttal 1:
Rebuttal: Here $E$=embedding space; $A$=attribute/s; $MS$=macroscale; $ms$=microscale
1.*"Figures too small, unreadable"*
Thank you for the comment.
Anonymous repo shows improved Figs and Tables: https://shorturl.at/12ZFg
2.*"Paper uses ArcFace and FaceNet while uses results on LFW and CelebA. Meaning ... | Summary: This paper provides comprehensive analysis of the embedding space of face recognition models. Through a geometric structure perspective, this work analyses the macroscale and microscale structures of the embedding space, and quantifies how human interpretable attributes influence the structures. Experimental r... | Rebuttal 1:
Rebuttal: 1. *"The calculation of 'invariance measure' relies on strictly controlable face images, so that it can only be calculated and analysed on GAN-generated images currently, which may limit its' real-world applications."*
Our work requires the ability to act meaningfully on the input space, which ca... | Summary: This paper investigates the geometric structure of the embedding space in Face Recognition (FR) models, focusing on how human-interpretable facial and image attributes influence the learned representations. FR models, which use deep learning and contrastive losses, aim to map images of the same identity closer... | Rebuttal 1:
Rebuttal: 1. *"The invariance energy measure is primarily evaluated on the CelebA dataset, which, while popular, may not encompass the full range of variability seen in real-world face recognition tasks."*
We want to clarify that we use CelebA for the macroscale experiment to compute the KS statistic on a ... | Summary: The paper describes a multi-scale geometric structure in embedding space created by Face Recognition (FR) models' feature embedding. The paper proposes a geometric-based approach to understand the influence of facial and image attributes to FR models. A physics-inspired alignment metric is also introduce.
The... | Rebuttal 1:
Rebuttal: 1. *"There should be a section to conclude on the influence of attributes to both macroscale and microscale with specific examples and further analysis as suggested in the weaknesses section... Lacking of some further analysis on some attributes overlapping between macroscale and microscale analys... | null | null | null | null | null | null |
Learning Monotonic Probabilities with a Generative Cost Model | Accept (poster) | Summary: The paper tackles the problem of enforcing monotonicity in predictions as a function of some variables if the true underlying function also abides by the monotonicity argument. Given the prediction target $y$ that is supposed to be monotonic with respect to the variable $r$, the paper relies on intuitive obser... | Rebuttal 1:
Rebuttal: Thank you for the reviews and valuable suggestions. Here are our responses to the concerns raised:
# Answer 1: Clarification on Figure 6
The scatter plot in the background represents the training instances $(x_i, y_i)$, with $p(y|x)$ defined in the southeast corner of page 7. Consider $y_r|x$ as... | Summary: The paper studies the problem of monotonic regression where the target variable should maintain a monotonous relationship with part of the input variables. It first establishes some analytical properties for the probability model underlying the monotonous regression and then it proposes a Bayesian network mode... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and suggestions. We appreciate the opportunity to address the concerns raised in the review.
# Answer 1: Discriminative vs. Generative Models
We respectfully disagree with the assertion that generative models are inherently more challenging than discriminativ... | Summary: The paper introduces a new generative framework to model monotonic probabilities by reformulating the traditional problem into learning a latent cost variable. Instead of directly designing a monotonic function, the authors propose that for a binary outcome, the probability is given by the event that a latent ... | Rebuttal 1:
Rebuttal: Thank you for the reviews and valuable suggestions. Here are our answers to the concerns raised:
# Answer 1: Bounded Revenue Variable
This issue can arise in practice, for instance, when our model is trained with $r \\in (-10, 10)$, but is used for inference with values $r \\gg 10$. Such a discr... | null | null | null | null | null | null | null | null |
R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning | Accept (poster) | Summary: This paper tackles the problem of high quality multi agent reinforcement learning. Specifically, allowing individual agents to learn unique policies in order to better collaborate to achieve goals. The authors introduce a new approach to training, R3DM, which utilizes contrast learning to encourage individual ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on the paper and for raising interesting and relevant questions. We aim to highlight this clearly in the camera-ready version of the paper.
1) Impact and Details on Contrastive Learning
Contrastive learning plays a crucial role in R3DM by derivi... | Summary: The paper proposes a method for improving the ability of agents to learn to effectively coordinate. The method is based on an existing idea that clusters learning agents into roles based on their observation history using contrastive learning but extends this idea by encouraging diversity among the roles to ai... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time in providing detailed feedback on the paper to better highlight the key contributions and novelties of the paper. We detail our responses to the questions below.
1) Clarification of the contributions
Our primary contribution lies in introducing a no... | Summary: The authors propose R3DM, a new role-based MARL framework that enhances coordination by learning roles that shape agents' future behavior through maximizing mutual information and using intrinsic rewards derived from dynamics models.
Claims And Evidence: The core claims regarding R3DM's ability to learn effec... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and thoughtful feedback, which has greatly contributed to improving the scientific rigour of this paper. We detail our responses to the questions below and add the required ablation studies.
1) Intrinsic Reward:
We clarify the intuition behind ... | null | null | null | null | null | null | null | null |
Enhancing Performance of Explainable AI Models with Constrained Concept Refinement | Accept (poster) | Summary: This paper proposes to tackle the potential gap between performance and understandability of concept embedding models, but allowing the concept embeddings to be refined in a constrained manner, thereby allowing for more flexibility, but still understandability. The authors have proposed a theoretical framework... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort dedicated to reading our manuscript and for providing valuable and constructive feedback. In response to the concerns raised regarding the clarity and coherence of the paper’s central ideas, we propose to make major revisions to the manuscrip... | Summary: In this paper, the author has proposed an improvement for the original structure of the concept bottleneck models and CLIP-IP-OMP models for improving the interpretability and trustworthiness of the model without sacrificing the model's accuracy. The key idea of this method is trying to optimize the concept em... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed feedback and positive assessment. We hope that our clarifications and additional experiments could further elevate their recommendation of our work in the final review.
**Claims And Evidence**
We would like to offer two clarifications that may h... | Summary: This paper addresses the challenge of balancing accuracy and interpretability in machine learning models, particularly for interpretable-by-design methods that often sacrifice accuracy for transparency. The authors identify that deviations in concept representations, a crucial component of interpretable models... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for their thorough evaluation and for the positive feedback on our work. In response to their suggestions, we have proposed additional experiments and provided further clarifications in the revised manuscript. We hope that our responses effectively address... | Summary: The paper proposes CCR to improve both prediction accuracy and model interpretability.
Claims And Evidence: It does not have the impact statements and ends abruptly. Seems like a work in progress?
Methods And Evaluation Criteria: - metrics for interpretability like avg explanation length and avg concept embe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort invested in evaluating our manuscript. Should the reviewer find our clarifications and revisions satisfactory, we would be grateful if they would consider raising their score in the final assessment.
**Claims And Evidence**
We apologize for... | null | null | null | null | null | null |
Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations | Accept (poster) | Summary: The paper introduces a framework for discovering effective degrees of freedom (DOF) in molecular simulations by identifying approximate symmetries of the energy function. Instead of relying on simulation trajectories or training datasets, the authors formulate an optimization problem—called a “symmetry loss”—a... | Rebuttal 1:
Rebuttal: We thank you for your review and we hope we can address some of your concerns below :
### Quantitative Metrics and Baselines ###
We understand your concerns regarding the rigor of the evaluation criteria. Please refer to the response for Reviewer 9HNJ to view a table of quantitative results that ... | Summary: This paper proposes a method to identify effective degrees of freedom (DOF) of dynamics by connecting them with approximate symmetries of the energy function. This is done through identifying a state $x$ with a group element (here restricted to the general linear group) whose action on $x_0$ (a reference state... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review. We address some points below:
### Quantitative Metrics & Baselines ###
Please refer to our response to Reviewer 9HNJ for additional results. Although there are existing methods for CV discovery, our method considers the slightly orthogonal task o... | Summary: This paper introduces a data-free scheme for discovering the effective degrees of freedom in a molecular simulation, which is based upon the exploration of energy landscape symmetry. The effectiveness of this framework is theoretically illustrated and experimentally validated.
Claims And Evidence: Yes. The th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and we try to address some of the concerns below:
### More Quantitative Results ###
As pointed out by the reviewer, some of the results presented in the paper might look a bit qualitative. Here, we provide some more quantitative statements about the resu... | Summary: This work proposes an approach for discovering degrees of freedom in MD simulations, relying primarily on the Hessian matrix rather than large simulation data. The method was applied to two prototypical peptide systems, showing that it is capable of efficient exploration of the configurations space along disco... | Rebuttal 1:
Rebuttal: ### Literature Review for Hessian-based methods ###
We thank the reviewer for pointing us toward this literature. We will include a detailed overview of the work in the literature and a comparison with the current paper. For geometric structure optimization, most of the methods in the literature b... | null | null | null | null | null | null |
Transforming Visual Classifiers for Zero-Shot Text-Based Interpretability | Reject | Summary: The authors introduce a method for increasing the interpretability of image classifiers by combining the image classifier with a text tokenizer and their own trainable FFN. Essentially, the image feature vector obtained from passing the image to the vision classifier is converted into the text embedding space ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper, and for the thoughtful review and strong accept decision. We are delighted that you found our manuscript interesting and appreciated the importance and unique aspects of our work, such as the innovative, novel, and clever solutio... | Summary: The paper aims to make arbitrary image classifiers interpretable by textual explanations. The paper points out as a challenge that existing methods rely on CLIP, which may limit applications. To this end, the paper maps image features of arbitrary visual classifiers to text features of off-the-shelf language m... | Rebuttal 1:
Rebuttal: We thank the reviewer for his time in reviewing our paper. We clarify your concerns below.
> Existing methods (e.g., Text-to-Concept [a]) have the same or lower number of parameters.
As mentioned in L70-L84 in the related work, while the parameters may be the same or less, Text-to-Concept is no... | Summary: The paper introduces a method to provide text explanations for vision models. Given textual representations of the image classes, a text encoder, and a visual classifier to interpret, the authors method associate image samples to textual classes by essentially training an MLP to calibrate the vision latent spa... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper, and for the valuable feedback. We are glad that you liked our paper and thank you for all the positive points you reported for our work. Below, we address your concerns.
> How does the faithfulness vary?
As a function of different te... | Summary: The authors propose a method to convert a pre-trained visual classifier into a text-based classifier that supports interpretability through natural language. Specifically, they train an MLP layer to map visual features (extracted by an existing visual classifier) into a text-embedding space, where the cosine s... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper and for the valuable feedback. We thank you also for acknowledging the strengths of our paper. In what follows, we address your concerns and remarks.
> [C1] In the examples, the “concepts” often appear more like class labels rather tha... | null | null | null | null | null | null |
Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection | Accept (poster) | Summary: The paper addresses AI-generated image detection challenges, showing that detectors rely on spurious real-image artifacts. It introduces Stay-Positive, which retrains the last layer to focus only on fake features by enforcing non-negative weights.
Key contributions:
1. Identifies spurious correlations (e.g.... | Rebuttal 1:
Rebuttal: **Further test the hypothesis from Case Study 2 by applying additional post-processing techniques and evaluating more generative models. This will strengthen the analysis derived from distribution plots, as the observed differences could also be attributed to a generalization gap**
**Test on othe... | Summary: This paper presents a method for improving fake image detection. It makes the observation that a fake image detector may learn spurious features associated with real images, such as post processing artifacts or image quality, which may lead to suboptimal detection performance. It thus proposes to constrain the... | Rebuttal 1:
Rebuttal: **In Figure 7, it appears that the Redcaps images have much higher fakeness probabilities under the Corvi with the proposed method model than under the regular Corvi model. Could the authors provide more discussion about this phenomenon?**
We thank the reviewer for raising this point. In all our ... | Summary: This paper introduces "Stay-Positive," an algorithm designed to improve AI-generated image detection by focusing solely on generative artifacts while disregarding features associated with real images. The authors argue that spurious correlations, such as compression artifacts that detectors mistakenly associat... | Rebuttal 1:
Rebuttal: **The paper could benefit from a more detailed analysis of potential limitations or failure cases of the proposed approach**\
In this work, we have analyzed two limitations in detail, both related to the network's potential to learn spurious fake features. In Section 6, Fig 7, we show that our imp... | Summary: This paper proposed an algorithm designed to constrain the focus of detectors to generative artifacts while disregarding those associated with real data. This method will help the model reduce susceptibility to spurious correlations and enhance robustness.
## update after rebuttal
The authors have addressed ... | Rebuttal 1:
Rebuttal: **The authors' point that the distribution of real samples is harmful to detectors may need further discussion and verification. Prior works show the benefits of “positive samples”**
\
We would like to make some clarifications. We are unsure of the exact meaning of 'positive samples,' but we assum... | null | null | null | null | null | null |
Info-Coevolution: An Efficient Framework for Data Model Coevolution | Accept (poster) | Summary: The paper addresses the challenge of high annotation costs and inefficiency in training models on growing datasets by proposing a framework for online selective annotation that co-evolves data and models. The method combines information gain estimation (model uncertainty and dataset locality via nearest-neighb... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer i2qw for the suggestions as well as the potential improvements. We make responses as follows.
**Q1: The paper only compares with one AL method, DQ (coreset-based), in Fig. 6. More AL methods could be compared, from the early entropy-based methods to recent AL metho... | Summary: This paper points out the current issue of active learning. Traditional active learning methods select informative data points for annotation but suffer from high computational costs, frequent model retraining, and bias in uncertainty-based selection. Considering these, they propose a novel framework called In... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer 9pQu for the suggestions as well as the potential improvements. We make responses as follows.
**Q1: Adding references and baselines**
**A1**:Thanks for the suggestions. We attach more baselines here, and will add the corresponding references.
ImageNet-1k with ViT... | Summary: The paper introduces Info-Coevolution, a framework for selective data collection which aims to improve data annotation efficiency. It proposes strategies to estimate information gain by leveraging Bayesian principles, and also uses ANN structures to help achieve efficient data selection with minimal computatio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer aJ9B for the recognition and appreciation of our work, and for the valuable questions as well as comments. For the comments and questions, here are our responses:
**Q1: The Lipschitz constant may be difficult to compute rigorously and large in practice.**
**A1**:T... | Summary: The paper presents **Info-Coevolution**, a framework aimed at enhancing the co-evolution of data and models through **online selective annotation**. The primary goal is to minimize annotation costs while preserving model performance by utilizing **Bayesian Prediction Fusion** and **data locality analysis** to ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer X845 for pointing out the missing references as well as the potential improvements. We make responses as follows.
**Q1: Dataset Distillation references not discussed.**
**A1**: Thanks for the advice. In general, dataset distillation and active learning have a main... | null | null | null | null | null | null |
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts | Reject | Summary: The authors introduce a novel Mixture of Experts (MoE) architecture for Dynamical Systems Reconstruction (DSR): Mixture of Expert Reconstructors (MixER). MixER employs a top-1 MoE strategy with a custom routing mechanism that enables unsupervised clustering and meta-learning across different DS. The manuscript... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thorough and insightful evaluation. Your recognition of MixER as a novel approach is encouraging. We are grateful for the acknowledgment that MixER addresses an important and previously unaddressed problem in DSR and that our experiments are well-executed acr... | Summary: Authors consider the following system reconstruction formulation: given D-dimensional time series, or measurements, of length T, the goal is to reconstruct the process generating this data. The series are grouped into E distinct environments, which are grouped into F families. It is assumed that the measuremen... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our work and for providing thorough comments. We find your summary of our work especially pleasing to read. Thank you for noting the combination of the ideas of hierarchical meta-learning and the mixture-of-experts technique that our method leverages. We are hap... | Summary: This paper addresses the challenge of dynamical system reconstruction (DSR) in data-scarce environments, where traditional meta-learning approaches struggle to generalize across loosely related system hierarchies. To overcome these limitations, the authors propose MixER (Mixture of Expert Reconstructors), a sp... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for reading and commenting on our work. We are happy you praised the novelty of our gating mechanism from which MoE research stands to benefit. Your appreciation of how well our paper was written and how our exposition made it easy to understand is encouraging. The ... | Summary: The authors propose MixER, an Mixture-of-Experts routing mechanism for learning diverse dynamical systems while preserving adaptability within individual environments. It incorporates an environment-specific context vector for better gating decisions, and integrates k-means clustering for hierarchical learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read and provide comments on our paper. We are happy you found the approach well-motivated and interesting while acknowledging how we redefined a research topic of learning across diverse dynamical systems into meta-learning. We are pleased you found ou... | null | null | null | null | null | null |
The Four Color Theorem for Cell Instance Segmentation | Accept (poster) | Summary: The paper presents a novel cell instance segmentation method inspired by the four-color theorem, which simplifies the segmentation task by transforming it into a four-class semantic segmentation problem. The key contributions include:
Four-Color Encoding Scheme: Cells are treated as "countries" and tissues as... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough evaluation of our manuscript and the many constructive comments provided. We are also profoundly grateful for your recognition of our proposed method's innovation, theoretical contributions, and experimental design. According to your metioned severa... | Summary: This paper introduces a new approach to cell instance segmentation based on the Four-Color Theorem from graph theory. The authors propose reformulating instance segmentation as a constrained semantic segmentation problem using only four classes. This method simplifies the task by ensuring that adjacent cells r... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and thorough evaluation of our work and your recognition of its methodological and theoretical contributions. To address your concerns regarding real-world applicability, baseline completeness, and so on, **we summarize these issues into five point... | Summary: This paper proposes an asymptotic training architecture for cell instance segmentation based on the four-color theorem. Unlike traditional multi-class, multi-channel segmentation methods, the proposed approach follows a step-by-step process: first distinguishing foreground from background, then classifying ins... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and the time dedicated to reviewing our manuscript. To better convey our motivations and address your concerns, **we have organized our response into five key areas:** (1) theoretical contributions, (2) experimental analysis, (3) comparison with re... | Summary: This paper proposes an innovative cell instance segmentation method based on the four-color theorem, aiming to simplify the instance differentiation process. By conceptualizing cells as "countries" and tissues as "oceans," the authors introduce a four-color encoding scheme to ensure adjacent cell instances rec... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback, recognition of the novelty of our proposed FCIS based on the four-color theorem, and positive feedback regarding our experimental design and theoretical contributions. According to your mentioned several issues in the *Weaknesses*, *Comments*, and ... | null | null | null | null | null | null |
MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners | Accept (poster) | Summary: MuseControlLite is an efficient adapter-based, controllable text-to-music based on Stable Audio Open, by adopting decoupled cross-attention (IP-adapter). The key finding is that for time-varying control signals, integrating suitable positional encoding (i.e. RoPE) to the adapter itself is crucial to achieve go... | Rebuttal 1:
Rebuttal: We sincerely appreciate your supportive feedback and hope that our responses below address and alleviate your major concerns.
#### On Melody Representation:
Thanks to your valuable comment, we have identified an oversight that causes the perceived inferiority of our model’s output on the origin... | Summary: Within the domain of raw audio music generation, motivated by the need for (1) lighter alternatives for fine-tuning, together with the need for (2) better control accuracy (i.e. for the user), the authors propose MuseControlLite, a system for time-varying condition control for music generation. MuseControlLite... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful and valuable feedback, which has inspired us to make several significant updates into our work, as detailed below. We hope the reviewer will agree that these revisions greatly enhance the scientific quality of the paper.
#### On Melody Representa... | Summary: The paper introduces MuseControlLite, a lightweight fine-tuning mechanism for text-to-music generation that extends previous control work.
Its main contributions include a new adapter design using decoupled cross-attention with positional embeddings for time-varying musical attributes.
The model claims to ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your supportive feedback and hope that our responses below address and alleviate your major concerns.
#### On the Need of More Empirical Evidence:
As elaborated in our response to Reviewer c3ce, we conducted a user study to bolster the empirical rigor of our paper. Initia... | Summary: The paper introduces MuseControlLite, a parameter-efficient methodology for aligning a pre-trained, DiT-based text-to-music model, to both symbolic and audio controls. The authors demonstrate the capability of MuseControlLite to extend the controllability of a pre-trained StableAudio-Open model, from text prom... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful and valuable feedback. In response to your comments, we have made several plans to update to our submission, as outlined below.
#### On Claims & Evidence:
*Firstly*, you are right that JASCO has effectively integrated symbolic and audio controls.... | null | null | null | null | null | null |
Online Linear Classification with Massart Noise | Accept (poster) | Summary: The paper studies online linear classification under massart noise where noisy label might be flipped with a rate of $\eta$. In the case where the dataset is separable with a margin of $\gamma$. The paper used a scaled leakyRelu function as the surrogate loss and guarantees a mistake bound of $\eta T + O(T^{3/... | Rebuttal 1:
Rebuttal: We thank Reviewer qMz2 for the positive feedback on the paper's writing and clarity of intuition. We will fix the typos pointed out by the reviewer.
**Clarification on $\bar{R}(T)$ Derivation:** We apologize for the confusion regarding the derivation involving $ \bar{R}(T)$ around Line 310. The b... | Summary: They present a computationally efficient alg that achieves mistake bound \eta T + o(T) where \eta is the probability of flipping the ground truth label in the Massart Noise model. Their algorithm is based on performing online gradient descent on a seq of reweighted Leaky-relU loss functions.
Next, they consid... | Rebuttal 1:
Rebuttal: We thank Reviewer WzcY for the positive feedback on the paper's novelty. We respond to the reviewer’s questions below.
**$\bar{R}$ in Lemma 2.2**: Thank you for pointing this out. $\bar{R}$ refers to the regret bound used in the online gradient descent analysis.
Vector $u$ in Lines 245-260: The ... | Summary: This paper considers an online learning setting where context-label pairs are generated with Massart noise.
More specifically, while in standard online classification, both the context and label are generated adversarially, the authors consider a setting where the context is generated adversarially, but the la... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We will fix the typos and address the reviewer’s suggestions to improve clarity. | Summary: The paper studies online linear classification with massart noise. The paper designs computationally efficient algorithms for online linear classification with Massart noise. The paper also extends this model to k-arm contextual badntit setting.
## update after rebuttal
After rebuttal, I maintain my score.
C... | Rebuttal 1:
Rebuttal: We thank Reviewer WUUa for the positive feedback on the clarity and soundness of our theoretical results. We will fix the typos pointed out. In response to the reviewer’s question about the use of the Leaky ReLU:
The standard LeakyReLU loss penalizes points that are further away from the decision... | null | null | null | null | null | null |
Computing Voting Rules with Improvement Feedback | Accept (poster) | Summary: This paper investigates the feasibility of computing voting rules using improvement feedback, a type of iterative preference refinement distinct from pairwise comparisons. It characterizes the positional scoring rules that can be learned under improvement feedback, demonstrating that while plurality can be det... | Rebuttal 1:
Rebuttal: We thank the reviewer.
>Authors consider multiple answer setting, and in that case Condorset winner cannot be determined. However, getting multiple preference answers is costly and difficult and can be decomposed as getting pairwise comparisons n times. Why should we solve the problem which happe... | Summary: This paper discusses the potential for any learning algorithm to identify a social choice winner f when sampling preferences from a distribution D. It specifically talks about t-improvement feedback, where D is hidden but can be sampled such that -- given a candidate $a$ in position $i$, one of the t candidate... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback.
>The paper is confusing about what information algorithms A has access to, and specifically how they conduct t-improvement feedback.
As noted in your summary and in lines 60–62, under the $t$-improvement feedback model, whe... | Summary: The paper studies the concept of "improvement feedback" within computational social choice. Improvement feedback is a response given to a query of a specific voter asking a question along the lines of "which candidate do you prefer over x?" The main question of the paper is whether queries in the improvement f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback.
> Slightly more explanation would be useful. Specifically, an explanation of the approximation ratio -- I think I know how this is defined here but I can imagine a few semi-reasonable definitions.
For a fixed number of agents ... | null | null | null | null | null | null | null | null |
Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion | Accept (poster) | Summary: This paper proposes to optimize model parameters and forward process interpolation coefficients with respect to a simulation-based adversarial loss. The training process consists of two stages. In the first stage, the flow model is trained w.r.t. randomly sampled multi-dimensional interpolation schemes via flo... | Rebuttal 1:
Rebuttal: URL for Additional Figures: https://imgur.com/a/3UiYDVF
$\textbf{[A1] $H_\theta$ estimates the vector field and $\gamma_\phi$ performs inference-time planning}$
We clarify the misunderstanding here. As shown in Additional Figure 2, the roles of $\theta$ and $\phi$ differ clearly. $H_{\theta}$ es... | Summary: This paper introduces Multidimensional Adaptive Coefficients (MAC) for flow and diffusion models, allowing coefficients to vary across dimensions and adapt to different starting points. The two-stage approach combines pre-training with multidimensional coefficients and adversarial refinement. Experiments acros... | Rebuttal 1:
Rebuttal: URL for Additional Figures: https://imgur.com/a/3UiYDVF
$\textbf{[A1] MAC’s core value lies in inference-time planning, orthogonal to vector field tuning methods like CTM and CD+GAN}$
CTM and CD+GAN optimize trajectories by adjusting the vector field parameter $\theta$. In contrast, our method (... | Summary: This paper introduces a new way to handle the interpolation between noise and data in diffusion and flow-based generative models. Unlike standard approaches that use an interpolation with a uniform scale across the entire image (like Rectified Flows, DDPM, and IDDPM), the authors propose extending this interpo... | null | null | null | null | null | null | null | null | |
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves | Accept (poster) | Summary: The paper proposes k-DTW (k-Dynamic Time Warping) as a dissimilarity measure between polygonal curves. The motivation is that many diverse datasets can be thought of as curves and measuring appropriately the distance between them is a fundamental problem. Usually researchers has studied Frechet distances and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. | Summary: The paper proposes a new distance for between curves, the k-DTW distance, and make several types of compelling arguments for its use. These include better near-metric properties than DTW, and better learning complexity than Frechet distance - k-DTW generalizes both. The paper provides an efficient approximat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**Question 1:** Edit Distance with Real Penalties, as in: Chen and Ng, VLDB 2004.
**Answer 1:** We will include some references, discussions and baseline experiments for your suggestion along with another suggestion of reviewer "MvAs". Pl... | Summary: This paper proposes a new distance measure called $k$-DTW, which is positioned as a interpolation between the classical DTW distance and the Fréchet distance. The technical novelty is to consider only the top $k$ matched distances in the alignment path, rather than summing all distances (as in DTW) or taking t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**Question 1:** Formalize robustness.
**Answer 1:** The concept of robustness for curve distance measures could be formalized as follows: given two curves whose matched vertices are at constant distance, say $1$, if we move one vertex away to incr... | null | null | null | null | null | null | null | null |
Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data | Accept (poster) | Summary: This paper introduces the Mixed data Permutation-based Rank Test (MPRT) for testing the rank of cross-covariance matrices in the presence of discretized variables. The authors address a critical gap in existing rank tests, which assume continuous measurements and fail when variables are discretized. MPRT lever... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** While Appendix C.2 briefly discusses non-Gaussian cases, robustness to severe non-Gaussianity or nonlinear relationships is unclear.
*... | Summary: The authors consider the task of finding the rank of cross covariance matrices, when some variables have been discretised. The authors consider a permutation based rank test that is able to handle both discrete and continuous variables. They show that their method is able to perform well with both continuous a... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** About data generation for experiments?
**A1:**
We assume that data is generated following a linear structural causal model $\mathsf{V}\... | Summary: This paper introduces the Mixed Data Permutation-Based Rank Test (MPRT), an approach designed to address the challenge of discretization in rank tests. The proposed MPRT estimates the asymptotic null distribution by leveraging data permutation, which is grounded in the exchangeability condition of the linear p... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which have greatly helped us refine the quality of our paper. We first provide responses to your concerns and then give an updated sketch of proof for Thm 4.
**Q1:** The convergence of $\hat{\Sigma}\_\mathbf{X}$ by pseudo-maximum likelihood.
**A1:** Yes, $... | null | null | null | null | null | null | null | null |
Finite-Time Analysis of Discrete-Time Stochastic Interpolants | Accept (poster) | Summary: This work provides a theoretical analysis of time-discretized stochastic interpolant models. Specifically, they address the problem of convergence with respect to the number of steps in the time discretization, characterizing the error in the modeled distribution with respect to the discretization scheme, the ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! We are grateful for your constructive suggestions, which have significantly guided our improvements. Please find our responses to your comments below.
### Other Comments Or Suggestions:
> Right Column Line 170: “initial distribution mism... | Summary: This paper present discrete-time analysis of the stochastic interpolant framework or as known as flow models, by theorical results, this paper design a schedules for convergence acceleration.
## update after rebuttal
My view has not changed, so I maintain my original score.
Claims And Evidence: I think the ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! We are grateful for your constructive suggestions, which have significantly guided our improvements. Please find our responses to your comments below.
### Methods And Evaluation Criteria
> I believe the authors should explain the novelty ... | Summary: This paper proposes a finite time analysis of discretization of stochastic interpolants. The paper presents assumptions on the initial and final distributions, score estimators, then provides a complexity bound in KL divergence.
Claims And Evidence: The paper claims to approach the problem of analysis discret... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! We are grateful for your constructive suggestions, which have significantly guided our improvements. Please find our responses to your comments below.
### Experimental Designs Or Analyses
A: Thanks for the suggestion. We tested the sampl... | Summary: This paper derives a convergence bound for the stochastic interpolants framework. The discretized SDE analyzed in this work is more general than the SDE analyzed in existing diffusion model convergence bounds. Like in the diffusion setting, the derived bound suggest an that an exponentially decaying discretiza... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! We are grateful for your constructive suggestions, which have significantly guided our improvements. Please find our responses to your comments below.
### Claims And Evidence / Weakness
> The differences introduced by the slightly more g... | null | null | null | null | null | null |
Sub-Sequential Physics-Informed Learning with State Space Model | Accept (poster) | Summary: This paper addresses the two fundamental challenges in training PINN, continuous-discrete mismatch and simplicity bias. The proposed PINNMamba employs 1) State Space Model (SSM) to effectively capture continuous information in discrete temporal sequences and 2) sub-sequence contrastive alignment not to make th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from reviewer PWyp and the time spent on reviewing this paper. We address the questions and clarify the issues accordingly as described below.
>**[C&E:]** I have concerns on the novelty .... with a toy example in Figure 1 does not sound a challeng... | Summary: The authors find the reason of failure modes of PINNs is the dismatch between continous nature of PDE and the discrete nature of sampled observations, and the simplicity bias of PINNs fails fixing this gap. To address this, they propose to use Mamba's sequence modeling ability and enhance it with alignment.
#... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from reviewer Ce7f and the time spent on reviewing this paper. We address the questions and clarify the issues accordingly as described below.
>**[W1]**: Claims to resolve the continuous-discrete mismatch but still relies on a discretized version ... | Summary: This paper proposes PINNMamba, enabling PINNs with state-space model's continuous-discrete capability to address the limitations (simplicity bias and continuous-discrete mismatch) of existing PINNs.
Claims And Evidence: 1. Mainstream PINNs predominantly use MLPs and suffer from the inability to accurately pro... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from reviewer yZBS and the time spent on reviewing this paper. We address the questions and clarify the issues accordingly as described below.
>**[W1]**: The proposed approach is computationally expensive, as shown in Table 5 of the supplementary ... | Summary: The paper introduces PINNMamba, a framework that integrates state space models (SSMs) into physics-informed neural networks (PINNs) to address failure modes in solving partial differential equations (PDEs). It identifies two key issues: the continuous-discrete mismatch, which disrupts initial condition propaga... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from reviewer PwV2 and the time spent on reviewing this paper. We address the questions and clarify the issues accordingly as described below.
>**[W1]**: The authors placed related works in the appendix, which I find unusual and not ideal. Integra... | null | null | null | null | null | null |
UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent | Accept (poster) | Summary: This paper presents a new training paradigm for Vision-Language-Action (VLA) models by training with both multi-modal understanding and future prediction objectives. Multi-modal Understanding is in the form of question-answering given a paired image-text, and the future visual prediction aims to predict the ta... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion.
---
**Q1: I'm curious about the quantitative performance of multi-modal and visual prediction after the pretraining stage, on top of the action performance currently prese... | Summary: The paper presents UP-VLA, a unified Vision-Language-Action (VLA) model designed for embodied agents. The model aims to enhance both high-level semantic comprehension and low-level spatial understanding by integrating multi-modal understanding and future prediction objectives while current VLMs focus on high-l... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: About model efficiency**
ANS:
We would like to clarify that UP-VLA operates at **almost the same control frequency as previous VLA me... | Summary: This paper introduces UP-VLA, a vision-language-action model that can understand, generate predicted future images, and plan actions in the embodied environment. It devises a novel VLA training paradigm that unifies policy learning with visual prediction and multi-modal understanding. The results show that the... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: About stronger baselines.**
ANS: Thank you for your suggestions! We conduct more experiment on Pi0, OpenVLA, and Octo model. Since th... | Summary: This paper presents UP-VLA, a Unified Vision-and-Language Alignment (VLA) model trained with dual objectives: multimodal understanding and future prediction. Building on the foundation of the show-o framework, UP-VLA significantly improves both in-domain performance and generalization to unseen scenarios acros... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper! Based on your review, we added a detailed discussion and additional experiments.
---
**Q1: predicting videos does not necessarily lead to a comprehensive understanding of physical dynamics**
ANS: Whether video models can tru... | null | null | null | null | null | null |
Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures | Accept (poster) | Summary: Main Contributions:
- Reaction Graph (RG) Representation: The authors introduce Reaction Graph (RG), a novel unified graph representation for chemical reactions that integrates both reactants and products into a cohesive framework. This representation incorporates 3D molecular structures, which are crucial for... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our method is **novel**, **crucial**, and **effective**.
We also appreciate your valuable suggestions for improving this work.
## Ⅰ. Impact of Reac and 3D Info in RG
### 1. Impact on Reaction Types
**Tab 1: Impact of Reac and 3D info on 12 reaction types.**
Re... | Summary: The paper introduces a new representation learning framework for chemical reactions. Specifically, the authors propose a graph neural network architecture that takes into account a) explicit inter-reactant/product interactions, and b) the three-dimensional structures of reactants and products. The framework is... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our method "**outperforms SOTA methods**" and that the "**results clearly support the claim**".
We also appreciate your insightful suggestions for improving this paper.
## Ⅰ. Separate vs. Unified Node
RG separates atoms in reactant and product into two nodes, whi... | Summary: This paper proposes a new graph representation for reaction related tasks, named Reaction Graph (RG). Compared to traditional molecule graph representation, RG introduces a new edge type, ie, reaction edge, which indicates the edges that have been changed during the reaction process. The experimental results s... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that this paper proposes a "**new graph representation**" and is "**effective in various reaction tasks**."
We also appreciate your suggestions.
## Ⅰ. Clarification on Reaction Separation and Information Loss in RXN Hypergraph
**Seperate Reactions**: In the RXN Hyper... | Summary: This paper introduces the Reaction Graph to model the chemical reaction as a graph and capture the molecular transformations during reaction. The reaction edge connects nodes representing the same atom in both reactants and products based on atomic mapping relationships.
Main Results:
1. A condition predicti... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our method "**shows superiority on various tasks**."
We also appreciate your suggestions.
## Ⅰ. Comparison with Recent Studies
The performance of our method **outperforms** recent methods ReaKE [2] and UniRXN [3] (see **Tab 1**).
**Tab 1: Comparison with recent b... | null | null | null | null | null | null |
Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models | Accept (poster) | Summary: Authors start with a clear objective in mind: rather than relying on strategies to fix the number token behaviour, it would be better to really treat numbers as special class and impose a new loss which allows the model to be strongly penalized if the prediction is far off. So authors introduced a new loss, ca... | Rebuttal 1:
Rebuttal: We appreciate your constructive and positive review of our work!
(1) **Larger models**: We completely agree that evaluating NTL on larger models beyond 3B would provide further evidence regarding its scalability. We aim to do this in future work, however, at present, our computational resources a... | Summary: The paper introduces a Number Token Loss (NTL), a regression-like loss function designed to improve the numerical reasoning capabilities of Language Models (LMs). The core contribution is twofold:
NTL-MSE: A loss function that computes the Mean Squared Error (MSE) between the numerical value of the label and ... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback. Below are detailed responses & new analyses:
1. **Real-world task from physics:**
To demonstrate applicability to scientific data, we evaluate NTL on estimating molecular solubility — as studied by the Regression Transformer (RT). Each molecule is a SMILES st... | Summary: This study proposes Number Token Loss (NTL), a new regression-like loss to better handle numbers in texts. With the NTL loss, the prediction of a number token is determined by the weighted average of numbers with their softmax probability from logits, and the NTL loss measures MSE between the weighted average ... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! We appreciate your recognition of NTL’s effectiveness and practicality, considering its compatibility with standard pipelines. To address your questions:
**(1) Non-euclidean topology:**
Thanks a lot for raising this interesting point regarding transferability b... | Summary: This paper introduces Number Token Loss (NTL), a loss function designed to improve numerical reasoning in language models (LMs). The core idea is that standard cross-entropy (CE) loss treats numbers as categorical variables, disregarding numerical proximity. NTL aims to address this by incorporating numerical ... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback. We are delighted that you agree that NTL shows consistent improvements. To clarify your questions, we ran additional experiments with decoder-only models on “simpler arithmetic tasks” (multiplication):
(1) **Decoder-only**: NTL is simply a loss function and c... | null | null | null | null | null | null |
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models | Accept (spotlight poster) | Summary: The paper introduces a novel framework for enhancing the logical consistency of large language models (LLMs). The authors propose a universal evaluation framework based on three key properties: transitivity, commutativity, and negation invariance. They also introduce REPAIR, a data refinement and augmentation ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and acknowledgment. Below, we provide our clarifications and explanations.
---
- Potential Limitations of the proposed methods:
In the appendix, we analyze the limitations of different rank estimation methods. Another potential limita... | Summary: This paper investigates whether large language models (LLMs) make pairwise preference judgments without logical contradictions, focusing on three consistency properties: transitivity (A > B > C implies A > C), commutativity (choices remain the same regardless of order/phrasing), and negation invariance (consis... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback. Below, we provide our clarifications and explanations.
---
- Overstatment on "universal framework":
We agree that our work focuses on the logical preference consistency of pairwise preference-ranking scenarios. However, we would like... | Summary: This paper introduces a universal framework for evaluating logical preference consistency in LLMs, focusing on three properties: transitivity, commutativity, and negation invariance. The authors propose quantitative metrics for these properties, conduct comprehensive empirical analyses across state-of-the-art ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and acknowledgment. Below, we provide our clarifications and explanations.
- Extending the Experiment and Method to Less Structured Datasets
We focus on analyzing logical preference consistency, and by '**universal** framework' we mean... | Summary: The main topics of this paper are (1) introducing consistency metrics for several logic/preference orders of LLMs, (2) evaluation of a large number of models and datasets, and (3) impact on downstream applications.
Specifically, the metrics measure transitivity, commutativity, and negation invariance in LLMs b... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and acknowledgment. We are grateful for the time and effort dedicated to reviewing our work.
Best,
Authors | null | null | null | null | null | null |
Score as Action: Fine Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning | Accept (poster) | Summary: The paper proposes a continuous-time reinforcement learning framework to fine-tune diffusion models by treating score functions as actions in a stochastic control problem. The authors derive a policy gradient theorem for continuous-time RL and connects KL regularization to a tractable running reward. The autho... | Rebuttal 1:
Rebuttal: We sincerely thank you for providing thoughtful suggestions to improve our paper. Please find our responses to your questions below:
***Q1: intuitive explanations for the value function design in Equation (20)***
***A1***: The design of our value function in Equation (20) is motivated to captur... | Summary: The paper proposes a continuous-time RL approach for fine-tuning diffusion generative models. It develops a policy optimization framework tailored for continuous-time RL, and empirically validate on the Stable Diffusion v1.5 model.
Claims And Evidence: 1. Most importantly, it is still not clear to me why cont... | Rebuttal 1:
Rebuttal: We sincerely thank you for providing detailed and thoughtful feedbacks to improve our paper. Please find our responses to your questions below:
***Q1: Clarification on the motivation in Figure 1 and Continuous-RL framework***
***A1***: We are sorry if our wording has caused confusion, but we d... | Summary: The paper reformulates fine-tuning diffusion generative models as a continuous-time reinforcement learning problem by treating the score function as a control action in the backward SDE. This continuous-time framework enables direct computation of policy gradients and leads to novel continuous-time analogs of ... | Rebuttal 1:
Rebuttal: We sincerely thank you for providing thoughtful suggestions to improve our paper. Please find our responses to your questions below:
***Q1: add pseudocode, figure of method***
***A1***: Please find the pseucode of our algorithm [here](https://ibb.co/KjhQ4s3m). We will update this part to the re... | Summary: This paper proposes a novel continuous-time reinforcement learning (RL) framework for fine-tuning diffusion models, reframing the learned “score” function as the policy’s action. Unlike discrete-time RL methods for text-to-image (T2I) or other diffusion settings, the paper leverages the underlying continuous-t... | Rebuttal 1:
Rebuttal: We sincerely thank you for providing thoughtful suggestions to improve our paper. Please find our responses to your questions below:
***Q: Comparisons to other important baselines, such as RAFT and DRaFT***
***A***: We conduct additional experiments comparing our method, DDPO, DRaFT [1] and Ali... | null | null | null | null | null | null |
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC | Accept (poster) | Summary: Similar to Rainbow DQN, this paper integrates several improvements from existing RL literature to improve the performance of reinforcement learning agents. The authors show that their ensemble method BTR achieves state-of-the-art performance on Atari tasks using 200M frames on a desktop PC. The authors also sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their concise review.
**Life Information** - We do not use life information in any main paper results, as we specify in Section 4.1. Furthermore, we provide a detailed comparison of BTR with and without life information in Appendix I. If your low score was due to doubts... | Summary: This paper proposes Beyond the Rainbow (BTR), an algorithmic successor to Rainbow DQN that improves asymptotic performance, data-efficiency, and wall-time efficiency through a series of algorithmic and architectural modifications informed by existing but recent literature. Key changes include using an Impala b... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review, and for your appreciation of the evaluation on unique environments.
**Comparison against baselines** - We already provided a discussion on SoTa algorithms for Atari 200M in Table 3, and two of the listed algorithms (BBF and EfficientZero V2) ar... | Summary: Rainbow now for few years has been a SoTA DQN based RL method. In this paper, authors redo the basic idea behind the Rainbow and collect a new, since Rainbow, a set of tips and tricks and include them into Rainbow, thus obtaining BTR, Beyond-the-Rainbow. They mostly evaluate performance of the completed system... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and their appreciation of how our work will be useful for both researchers and practitioners.
**Steps vs Walltime** – We feel the reviewer has somewhat misunderstood the main point of this paper. As stated in the title, abstract, and contributions, the purpo... | Summary: The paper presents BTR, which integrates several well-established techniques to achieve strong performance on Atari and Procgen with limited computational resources. Through detailed ablation studies, the authors demonstrate how each component contributes to their method, showcasing the trade-offs between comp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very thorough and constructive review, which clearly took a great deal of time and effort. We appreciate that the reviewer clearly understands the value of producing an accessible and high-performance algorithm.
**Complexity of Wii Games** - We would like to argue ... | null | null | null | null | null | null |
MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning | Accept (poster) | Summary: This paper introduces MODULI (Multi-Objective Diffusion planner with sliding guidance), a novel algorithm for offline multi-objective reinforcement learning (MORL). MODULI employs a conditional diffusion model for representing the agent’s policy. Besides MODULI, the paper also introduces two techniques for nor... | Rebuttal 1:
Rebuttal: ## Q1: Comparison with the performance of MORvS
MODULI consistently outperforms the strong MORvS baseline, leading in 20/24 metrics across 12 Complete datasets(Tab. 4) and surpassing the baseline in 59/72 metrics on Shattered & Narrow datasets(Tab. 5 & 6).
## Q2: Why use 3 seeds?
We collect 501... | Summary: This work proposes MODULI(Multi Objective DiffUsion planner with sLIding guidance), which employs a preference-conditioned diffusion model as a planner to generate trajectories that align with various preferences and derive action for decision making. MODULI also introduces two techniques 1) new return normali... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and useful feedback, please see the following for our response.
### **Q1: Comparison of Parameters for Different Baseline Algorithms**
Thank you for your suggestion! We would like to clarify that in our experiments, we standardized the parameter size of a... | Summary: The paper studies the problem of offline multi-objective RL with preferences over the objectives. The key contribution of the paper is to introduce a new preference-conditioned diffusion model to generate trajectories aligned with specific preferences and derive actions accordingly. The generation process is e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and useful feedback, please see the following for our response.
### Q1: Novelty of MODULI
We would like to clarify that MODULI is not merely an application of diffusion models in offline RL. The novelty of MODULI lies in the following three aspects:
- **... | null | null | null | null | null | null | null | null |
BSO: Binary Spiking Online Optimization Algorithm | Accept (poster) | Summary: The paper introduces the Binary Spiking Online Optimization (BSO) algorithm, an approach to training BSNNs that reduces training memory overhead while maintaining computational efficiency. The basic BSO eliminates latent weight storage and uses momentum-based gradient accumulation to generate weight-flipping s... | Rebuttal 1:
Rebuttal: **Response to Q1:"The paper lacks analysis of how the proposed methods would perform with more complex network architectures beyond standard VGG and ResNet models, such as transformer-based architectures or recurrent networks."**
We agree that evaluating BSO and T-BSO on more advanced architectur... | Summary: This paper introduces the Binary Spiking Online Optimization (BSO) algorithm, designed to reduce memory overhead in training Binary Spiking Neural Networks (BSNNs) by eliminating latent weight storage and making memory requirements time-independent. It also presents T-BSO, a temporal-aware variant that adjusts... | Rebuttal 1:
Rebuttal: **Response to W1:"T-BSO introduces more computational overhead due to additional gradient computations."**
As noted in our Response to Reviewer Q2xs (Response to W1), we have discussed that T-BSO incurs additional computational overhead due to the necessity for extra gradient computations. We kin... | Summary: The paper introduces Binary Spiking Online Optimization (BSO), a novel training algorithm for Binary Spiking Neural Networks (BSNNs) that significantly reduces memory overhead during training. The key innovations are two-fold:
(1) making memory requirements independent of time steps, and
(2) eliminating latent... | Rebuttal 1:
Rebuttal: **Response to W1. "The T-BSO may introduce some additional computational complexity compared to the base BSO method."**
Thank you for your observation. It is true that T-BSO incorporates a lightweight second-order temporal gradient mechanism on top of the basic BSO method, potentially increasing ... | Summary: The paper proposes Binary Spiking Online Optimization (BSO) and its temporal variant T-BSO for training Binary Spiking Neural Networks (BSNNs) with reduced memory overhead. The work is well-motivated, technically sound, and demonstrates significant improvements in training efficiency and performance across sta... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation and valuable feedback. Our responses are provided below.
**Response to Claim 2 "statistical significance testing (e.g., standard deviations) is missing."**
We acknowledge that including standard deviations in Table 2 and Figure 4 wo... | null | null | null | null | null | null |
Loss Functions and Operators Generated by f-Divergences | Accept (poster) | Summary: The paper proposes using Fenchel-Young losses derived from f-divergences to perform image classification and language model pretraining, fine-tuning, and distillation. The authors present an efficient bisection method for solving for the f-softmax function involved in optimizing the proposed loss. The pape... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and their interest in this work.
> **m1) Why is the relation between (5) and (6) an upper bound? Is it tight e.g. for f of Legendre type?**
There was a typo in these equations, the lower or equal sign should be replaced by a greater than or ... | Summary: This paper proposes a generalization of entropy-based loss functions (such as logistic loss and softmax) by incorporating f-divergences. Specifically, the generalization is formulated using Fenchel-Young duality, where the standard Shannon entropy regularization is replaced with f-entropies. The authors demons... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
> **clarify under what conditions non-uniform reference measures might be useful.**
Thank you for your suggestion. Non-uniform reference measures may be relevant in classification tasks with known imbalanced classes. We directly tried the r... | Summary: The authors propose a framework including operators (f-softmax, f-softargmax, f-softplus and f-sigmoid) and loss functions that generated by f-divergences for multi-class classification. Mathematical derivations and efficient computation algorithm are provided. The practical performance are demonstrated on Ima... | Rebuttal 1:
Rebuttal: Thank you for their constructive feedback. We hope we have addressed your comments.
> **it would be better to include performance variance**
Thank you for this suggestion. We repeated our experiments with multiple random seeds and reported the standard deviation across independent runs. Specific... | Summary: The paper investigates a general framework for generating loss functions using f-divergences, extending the well-known logistic loss (cross-entropy). It introduces a new set of operators, namely f-softmax and f-softargmax, and develops a novel bisection algorithm for computing them. The experimental results fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> **The experimental study is rather limited**
We disagree that our experimental study is limited. We evaluate the proposed losses on tasks of different data modalities, including both vision (ImageNet) and text generation tasks, which cover different t... | null | null | null | null | null | null |
FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates | Accept (poster) | Summary: The paper considers for federated learning in the heterogeneous regime, with compression and partial participation. A new algorithm called FedSMU is proposed.
## update after rebuttal
I think the paper deserves to be accepted and I am confident that the authors will make the recommended changes to make the pa... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments. In the following, we have provided our detailed responses to these comments.
> Experiments on CIFAR-10
We thank the reviewer for this observation. Our experimental results indicate that FedSMU achieves a notably better performance on more com... | Summary: This paper proposes FedSMU, a federated learning algorithm that improves communication efficiency and generalization. It symbolizes model updates (using sign-based compression) to reduce communication overhead and mitigate data heterogeneity. Inspired by the Lion optimizer, FedSMU splits local updates and glob... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments.
> Impact of component of FedSMU
As described in Appendix F.9, we had implemented two variants, Fed-LocalLion and Fed-GlobalLion, to evaluate the isolated impact of Lion on the client and server sides. To further assess the necessity of key com... | Summary: This paper proposes a new federated learning algorithm, FedSMU, designed to reduce communication costs and mitigate data heterogeneity. The key idea is to transmit only the sign of local updates for each parameter. Both theoretical analysis and experimental results are provided.
Claims And Evidence: Yes.
Met... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments. In the following, we have provided our detailed responses to these comments.
> Comment 1: The motivation and core design of the proposed algorithm are clearly articulated. However, it is unclear what trade-offs are made to achieve communicatio... | Summary: In this paper, the authors propose the FedSMU algorithm to address both communication cost and data heterogeneity challenges in federated learning, through sign-based model compression.
## update after rebuttal
I continue to favor acceptance and will leave my rating unchanged.
Claims And Evidence: 1. The mo... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewer for the comments.
> Motivation & Contribution
FL involves intertwined challenges where improving one may worsens another. For example, communication-efficient methods may reduce generalization, methods addressing heterogeneity often increase communication, and thos... | null | null | null | null | null | null |
Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings | Accept (oral) | Summary: This paper presents a new problem of predicting whether the model's performance on a runtime dataset (called user dataset) has decreased compared to the test dataset. To tackle this problem, they introduce a method called suitability filter, which consists in two main steps: 1. several sample-level performance... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent assessing our paper and for their critical and detailed feedback. We particularly appreciate their positive evaluation of our work’s novelty, the framework’s modularity and our experimental evaluation. We provide our answers to the reviewer’s raised conce... | Summary: This paper proposes a new paradigm for detecting whether the performance on unlabeled data processed during deployment, falls by a certain margin below the performance on a held-out dataset sampled from the training distribution but used for evaluation. This is a novel framework for detecting performance deter... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent assessing our paper, and appreciate their positive evaluation of our work’s relevance, novelty, and soundness. | Summary: The authors developed a framework to evaluate how well a trained model will perform when deployment for real-world inference.
The framework combines ideas from distribution shift detection, selective inference, and interestingly dataset inference.
A specific instantiation of the framework is presented and eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent assessing our paper and for their critical and detailed feedback. We particularly appreciate their positive evaluation of our work’s practical applicability, theoretical foundation and experimental design. We provide our answers to the reviewer’s raised c... | null | null | null | null | null | null | null | null |
Risk Quadrangle and Robust Optimization Based on Extended $\varphi$-Divergence | Reject | Summary: This work integrates distributionally robust optimization (DRO) into the Fundamental Risk Quadrangle (FRQ) framework by using $\varphi$-divergence measures. By extending the $\varphi$-divergence measures, they are able to cover a wide range of applications in ML and finance. They derive the primal and dual rep... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and effort in evaluating our work. We understand that not all reviewers will be deeply familiar with both FRQ and DRO, and we sincerely value all feedback.
**Experimental designs or analyses**
- We will make the fonts and figures bigger.
**Addressing weaknesses... | Summary: This paper discussed the extended phi-divergence risk measure
Claims And Evidence: It is hard for me to understand the authors' claims, perhaps due to the imprecise writing of this manuscript. The risk measure and study of phi-divergence have been extensively done in literature, such as [1]. The nuermcial stu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the taking the time to review our manuscript. Below are our point-to-point response.
- **Summary:** The key concept of our paper is the extended $\varphi$-divergence quadrangle. The extended $\varphi$-divergence risk measure is merely one of the four elements in the qua... | Summary: There exists the notion of a Fundamental Risk Quadrangle which discusses the relationships between 4 metrics of uncertainty. Specifically, Risk, Error, Deviation and Regret.
In this current paper, the authors focus on the notion of the FRQ in the context of risk measures (along with others) as derived by the ... | Rebuttal 1:
Rebuttal: We would love to thank the reviewer for checking the claims the proofs, and raising insightful questions. Below are our response.
**Practical applications.** We kindly refer the reviewer to the list on utility gained from the relations in our reply to Reviewer gFjF.
**Extension.** Yes, it can ... | Summary: The authors introduce a new divergence measure that allows for potentially negative weights. They then derive Fundamental Risk Quadrangles based on this divergence measure. It is shown that many known learning tasks fall within this FRQ framework with the proposed extended divergence measure.
## Update after ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for highlighting areas where additional clarification and context are needed, which greatly helps us improve the presentation of our paper. Below, we address the concerns regarding background introduction, the utility of our derived relations, and specific technical... | null | null | null | null | null | null |
Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization | Accept (poster) | Summary: The paper provides an interesting Bayesian optimization based method for selecting merging weights between latest checkpoints saved during LLM pre-training. This method showed performance speed ups in empirical results and theoretical guarantees with reasonable assumptions. The initial pilot experiments were h... | Rebuttal 1:
Rebuttal: > **Q1: Novel contributions compared to Previous Works.**
>
**(A) Our Focus: A Search Perspective for Pairwise Merging**
- **Key Objective.** We propose to **linearly merge** two consecutive checkpoints
$ \widetilde{\Theta} _t = \lambda_t\, \Theta _t \;+\; (1 - \lambda _t)\, \Theta _{t-1},
\qu... | Summary: The paper introduces a novel approach to enhancing the pretraining of large language models by leveraging intermediate checkpoint merging. The key idea is to exploit the information stored in intermediate checkpoints along the pretraining trajectory without incurring additional resource costs. The authors prop... | Rebuttal 1:
Rebuttal: > **Q1: Bounds on the Quadratic Approximation Error.**
>
**Our Response:**
- We appreciate the request for an expanded derivation. Under the assumption that the Hessian $H(\cdot)$ is Lipschitz continuous with constant $L_H$, consider the third-order Taylor expansion remainder term when approxi... | Summary: **Overview**
The paper introduces a novel checkpoint merging strategy aimed at enhancing the efficiency of large language model (LLM) pretraining. The central idea is to exploit intermediate checkpoints by forming linear combinations in the parameter space while optimizing the merging weight via Bayesian opt... | Rebuttal 1:
Rebuttal: > **Q1: Quadratic Approximation Validity (Equation (15)).**
>
**Our Response:**
- Our derivation starts from the assumption that, for small perturbations, the performance function $f(\Theta)$ can be locally approximated by a quadratic form:
$f(\Theta) \approx f(\Theta_t) + \nabla f(\Theta_t)... | Summary: The paper demonstrates that averaging adjacent checkpoints leads to better downstream performance compared to using individual checkpoints. To determine the optimal weighting, the paper proposes using Gaussian process-based Bayesian optimization.
The proposed approach outperforms existing merging strategies on... | Rebuttal 1:
Rebuttal: > **Q1: Clarification Regarding Figure 3**
>
**Our Response:**
We appreciate this observation. **Figure 3** is not a result of the Bayesian Optimization procedure. Instead, it provides an **empirical exploration** of how the merged model’s performance changes when we *uniformly sample* $\lambda... | null | null | null | null | null | null |
BoA: Attention-aware Post-training Quantization without Backpropagation | Accept (poster) | Summary: The paper proposes a novel Hessian-based backprop-free method for quantizing the attention weights of transformer models. The proposed method utilizes the computation pattern between different model weights within the attention modules to compute the Hessian with respect to quantization residual. Compared to p... | Rebuttal 1:
Rebuttal: **1. Hessians for models with RoPE**
- When RoPE is applied, the proposed objective $|| K \Delta Q ^{T} || _{F}^{2} = || K \Delta W _{Q} X || _{F}^{2}$ used to develop the attention-aware Hessian for the query projection $W _{Q}$ (see line 196) is converted to $|| \tilde{K} \Delta \tilde{Q} ^{T} ... | Summary: This paper proposes a backpropagation-free PTQ algorithm, named BOA, to quantize LLMs. Authors consider inter-layer dependencies to optimize the weights being quantized, and in this case they use attention-aware Hessian matrices. As the computational cost of hessian matrices is too expansive, BOA simplifies th... | Rebuttal 1:
Rebuttal: **1. Processing time comparison with GPTQ**
- We appreciate the reviewer's constructive suggestion. The main goal of Table 5 is to compare the processing times of PTQ algorithms _**considering inter-layer dependencies**_. Since GPTQ assumes layer-wise independence, we did not include it in Table ... | Summary: This paper proposes a post-training quantization method based on the construction of attention-aware Hessian matrices to capture inter-layer interactions. The method generalizes GPTQ and also requires no back-propagation. Extensive experiments demonstrate the effectiveness of the proposed approach.
Claims And... | Rebuttal 1:
Rebuttal: **1. Processing time comparison**
- The processing times of block-wise reconstruction techniques (e.g., OmniQuant and AffineQuant) have been summarized in Table 5. As evident, the proposed BoA completes quantization much faster than block-wise reconstruction techniques (e.g., for 30B, about 2.5 t... | Summary: This paper introduces a novel backpropagation-free PTQ method called BoA, which improves the conventional Hessian-based compensation strategy by comsidering layer-wise dependencies. Extensive experiments show its superiorty on both weight-only and weight-activation quantization.
Claims And Evidence: Yes, the ... | Rebuttal 1:
Rebuttal: **1. Memory comparison with GPTQ**
- In Table I, we summarized memory costs of GPTQ and BoA. BoA indeed needs larger memory since BoA additionally uses outputs of other layers to consider inter-layer dependencies, which leads to better performance.
- When the memory resource is limited, BoA can ... | null | null | null | null | null | null |
ConText: Driving In-context Learning for Text Removal and Segmentation | Accept (poster) | Summary: This paper introduces ConText, a visual in-context learning framework tailored for OCR tasks such as text removal and text segmentation.
It employs task-chaining, context-aware aggregation, and a self-prompting strategy to leverage multi-task logic and enhance in-context reasoning. Extensive experiments demons... | Rebuttal 1:
Rebuttal: ### W1 (Limited Contribution & Novelty)
>...incremental improvement in visual in-context learning...lack significant technical novelty...
We respectfully disagree with this and argue that our work, **rather than being merely incremental, offers substantial technical novelty and valuable contribu... | Summary: The authors present a visual in-context learning (V-ICL) approach, ConText, for fine-grained text recognition tasks (segmentation and removal). In order to accomplish this they focus on three novelties:
1. Task-chaining: The two tasks are chained together instead of being done independently to leverage inter-... | Rebuttal 1:
Rebuttal: ### W1 (Designed Modules Efficiency)
>...extra cost of self-prompting and context aggregation...
Below we report the additional costs of these modules, and find that
1. Self-prompting (SP) incurs an additional training burden of **+0.4 minutes/epoch**. However, this cost is deemed acceptable due... | Summary: This paper explores the application of visual in-context learning to fine-grained text recognition tasks, including text segmentation and removal. Rather than employing single-task solutions, the authors propose a task-chain prompt framework that connects multiple tasks. Through extensive experimentation, they... | Rebuttal 1:
Rebuttal: ### W1 (Beyond two tasks)
>...is it possible to generalize to other tasks?...how would the framework perform with three or more chained tasks...
We'd like to argue that **our method can intuitively be integrated with other tasks (even image-level tasks) alongside chaining extension**. Except for ... | Summary: This paper proposes ConText, an adaptation of the visual in-context learning (V-ICL) paradigm specifically tailored for optical character recognition tasks, focusing on text removal and segmentation. To address the single-step reasoning bottleneck in existing V-ICL methods, ConText introduces a task-chaining c... | Rebuttal 1:
Rebuttal: ### W1 (Additional Task Exploration)
>While the task-chaining compositor is shown to be effective...This may be due to dataset limitations, but extending the approach to additional tasks, such as watermark removal or text registration, would be valuable for further exploration.
Thanks for this va... | null | null | null | null | null | null |
TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data | Accept (poster) | Summary: This paper introduces a model-free approach for synthetic data generation based on direct data perturbation. The method operates by sequentially shuffling feature values while conditioning on binned representations of other features. The authors compare their approach to generative models, evaluating its effec... | Rebuttal 1:
Rebuttal: Thanks for your very thoughtful comments/suggestions. We address below your main concerns, and we would be happy to follow up on the remaining ones (or any additional questions) during the discussion period. Please, let us know.
>Authors should compare to approaches based on adding noise data … M... | Summary: The paper proposes a novel method for synthetic data generation. This new method is based on two basic actions: generating new values for each of the features by sampling through from its "interpolated marginal", then shuffling the original data following the algorithm SJPPD and then matching the ranking of th... | Rebuttal 1:
Rebuttal: Thanks for your comments and thoughtful suggestions. While we could only address your main concerns here, we would be happy to follow up on the remaining ones (or any additional questions) during the discussion period. Please, let us know.
>One of the worries I have is what happens when the numbe... | Summary: The paper introduces TabSDS, a non-parametric and model-free method for generating synthetic tabular data. Unlike deep generative models (DGMs), which are computationally expensive and require extensive hyperparameter tuning, TabSDS leverages rank-based transformations and data shuffling to approximate the joi... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments and questions.
>How does TabSDS compare to differentially private synthetic data methods?
We now included comparisons with two additional DP based methods available in Synthcity: ADSGAN [1] and PATEGAN [2]. (We also evaluated the DPGAN [3] but didn’t include i... | Summary: The paper introduces TabSDS, a non-parametric, lightweight framework for generating synthetic tabular data. The approach is based on the Sequential Joint Probability Preserving Data Shuffling (SJPPDS) algorithm, a perturbation method that relies on restricted feature permutations to preserve the joint distribu... | Rebuttal 1:
Rebuttal: Thanks for the very thoughtful comments and questions.
>Q1. In Tables 2 and 3, does the method requires for the same category to have different ranking?
Yes, it requires different ranks for each category. The problem is that if you assign the same rank value for each category the restricted per... | null | null | null | null | null | null |
Point Cloud Dataset Distillation | Accept (poster) | Summary: This paper studies the dataset distillation for point cloud. This paper claims this is the first study on dataset distillation for point cloud, aiming at the two challenges: diverse orientations and resolutions in 3D space. To overcome these issues, this paper 1) proposes a plug-and-play point cloud rotator to... | Rebuttal 1:
Rebuttal: We are grateful for your constructive advice and the opportunity to address your concerns.
### ``Other Strengths And Weaknesses``
> **Q1: The task model is also trained on the original dataset. How does DD significantly reduce the computational cost?**
A1: We would like to clarify that t... | Summary: In this paper, the authors presented DD3D, a novel framework for 3D point cloud distillation that aligns the rotation-invariant data distribution between real and synthetic data by transforming point clouds into a canonical orientation. Once trained, DD3D is capable of synthesizing point clouds at arbitrary re... | Rebuttal 1:
Rebuttal: Thanks for the detailed and helpful comments. We reply to the comments in detail. Hope to address your concerns.
### ``Methods And Evaluation Criteria``
> **Q1: Potential impact of incorporating more recent rotation-invariant or rotation-equivariant models.**
A1: Thanks for the great adv... | Summary: The paper introduces DD3D, a dataset distillation method tailored for 3D point clouds, addressing challenges of orientation diversity and varying resolutions. It proposes a rotation-invariant feature matching approach, a point cloud rotator for canonical alignment, and a point-wise generator that efficiently p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions.
### ``Other Strengths And Weaknesses``
> **Q1: Comparison between dataset distillation, knowledge distillation, and semi-supervised learning.**
A1: Dataset distillation (DD) and knowledge distillation (KD) are two orthogonal directio... | Summary: The paper addresses the problem of distilling large 3D point cloud datasets into a much smaller set while preserving model performance. The paper identifies two key challenges unique to point clouds: random orientation and variable resolution. To tackle these issues, the authors propose a novel framework calle... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback and insightful questions.
### ``Other Strengths And Weaknesses``
> **Q1: Comparing DD3D to a scenario where a rotation-invariant model is used could help delineate the benefits of the proposed approach. Any discussion in this direction is w... | Summary: This paper proposes DD3D, the first dataset distillation method designed specifically for 3D point cloud data. DD3D addresses two critical challenges in point cloud distillation: orientation misalignment and varying resolutions. The authors first establish theoretically that an ideal dataset distillation shoul... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and the recognition of our contributions.
### ``Experimental Designs Or Analyses``
> **Q1: The approach demonstrates relatively limited performance on fine-grained tasks.**
A1: We propose two strategies to further improve the performance of DD3D.
1. Imp... | null | null | null | null |
ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans | Accept (poster) | Summary: This paper introduces ESPFormer, a novel Transformer architecture integrating a fast, doubly-stochastic attention mechanism based on Expected Sliced Transport Plans (ESP). By projecting high-dimensional queries/keys into 1D slices using axis-aligned directions (Θ = I), ESPFormer efficiently computes optimal tr... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback. Below are our responses.
**1. Axis-aligned slices**
We chose axis-aligned slices because the keys and queries are learned parameters. Thus, any potential optimization of slice orientations is implicitly captured by the query and key matrices, $W_Q$ and $W... | Summary: This paper is about achieving doubly stochastic attention in Transformers through ESP (expected sliced transport plans). The softmax is known to be a notorious bottleneck for the expressivity and gradient flow in Transformers, thus is clearly a relevant research area. Moreover, prior art like the Sinkformer re... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. Below, we provide our responses.
**1. The Birkhoff Polytope.**
We thank the reviewer for raising this insightful question.
In the context of the Birkhoff polytope $B_N$, we will consider the transport plans between $\mu=\sum_{i=1}^N\delta_{x_i}$ and $\nu=... | Summary: The paper introduces ESPFormer, a attention mechanism that enforces doubly-stochastic constraints in attention matrices without requiring iterative Sinkhorn normalization. Instead, it leverages Expected Sliced Transport Plans (ESP) to achieve a fully parallelizable and computationally efficient solution. The a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. Below are our responses.
**1. Reliance on Soft sorting:**
Soft sorting introduces a temperature hyperparameter that requires tuning. To address this, we use *temperature annealing*, an exponential decay schedule, to smoothly transition from ... | null | null | null | null | null | null | null | null |
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment | Accept (poster) | Summary: The submission is based on learning a single concept space that is shared between SAEs trained on multiple vision models. The aim is to learn a universal set of concepts which can be used to translate between different models and highlight differences in how models represent visual information.
The experimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough analysis of our work, and thank them for finding our paper is nicely written with well-motivated analyses. We aim to answer the questions raised below. For plots and figures, please refer to: https://sparkling-queijadas-998747.netlify.app/
### **Implement... | Summary: The authors introduce USAEs, a framework that jointly learns a universal concept space for the internal activations of multiple vision models. By optimizing a shared objective, they show that USAEs semantially coherent universal concepts at different levels across vision models. Their results showcase the stro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful analysis of our work, and appreciate the positive compliments regarding the presentation quality, novelty, and extensive experimental results. We answer the questions raised below in text form. For plots and figures, please refer to our temporary anonymou... | Summary: This work introduces Universal Sparse Autoencoders (USAEs), a method to discover concepts shared across different deep learning models. The authors focus on the study of USAEs for the last-layer representations of three popular vision models, showing that the methodology enables the construction of an interpre... | Rebuttal 1:
Rebuttal: We thank reviewer 51dn for their thorough review of our work. We appreciate that they found the paper original and well-structured, and that they found the proposed Coordinated Activation Maximization application a promising tool for concept visualization. For plots and figures, please refer to ou... | Summary: The paper proposes a recipe to jointly train Sparse AutoEncoders (SAE) across different vision models in a shared (universal) space. The novel idea is to force the SAE to extract features (and, therefore, concepts) that are as shared as possible across models. This shared space enables cross-model and new with... | Rebuttal 1:
Rebuttal: We thank 11mk for their thorough analysis of our work. We appreciate that the reviewer found our work to be clearly written and well-motivated. We too believe cross-model interpretability is a truly exciting topic! We answer the questions raised below. For plots and figures, please refer to our te... | null | null | null | null | null | null |
Towards Robust Influence Functions with Flat Validation Minima | Accept (poster) | Summary: The article "Towards Robust Influence Functions with Flat Validation Minima" addresses the challenge of influence estimation in deep neural networks, particularly in the presence of noisy training data. The authors identify a fundamental limitation of existing influence function (IF) methods: their susceptibil... | Rebuttal 1:
Rebuttal: We appreciate the useful comments of the reviewer. We will update our current draft to avoid any confusion.
> [Q1 / Other Weakness 1] How to calculate the $\tilde{\theta} _ {z_{tr}}$
We would like to clarify that it is unnecessary to compute $\tilde{\theta} _ {z_{tr}}$for each training sample. S... | Summary: In this paper, the authors propose a method for estimating influence functions (IFs) for deep neural networks, addressing limitations of existing approaches that struggle with noisy training data and rely on first-order IF approximations without considering the sharpness of validation risk. They demonstrate th... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable suggestion. After pondering over your questions, we compose the following responses and will include all the following elaborations in the revised manuscript.
> [Q1.1] Validation set size
We appreciate the reviewer for highlighting this important f... | Summary: The influence function measures the influence of training samples on the validation loss. While this is typically done using minimas of the training loss, the authors argue for using flat validation minima. They then show experimntally and arguen theoretically that the standard estimators for influence do not ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the theoretical and experimental support for our claims.
> [Broader Sci. Literature.1] Absence of Related Literature section
We kindly note that, due to space constraints, the discussion of related work on influence functions was not included in the ma... | Summary: This paper reexamines influence functions (IF) in deep learning and argues that their standard formulations fail when models are trained on noisy data—primarily because of sharp validation risk landscapes. The authors theoretically link the estimation error of influence functions to the sharpness of the valida... | Rebuttal 1:
Rebuttal: We acknowledge the reviewer’s concern regarding the use of **diagonal approximation of the empirical Fisher matrix for estimating the inverse Hessian**. We are happy to provide a more thorough discussion of this aspect.
### 1. Diagonal approximation for inverse Hessian is simply a practical choi... | null | null | null | null | null | null |
OmniArch: Building Foundation Model for Scientific Computing | Accept (poster) | Summary: The paper introduces *OmniArch*, a foundation model for scientific computing designed to solve multi-scale and multi-physics Partial Differential Equations (PDEs) using a unified architecture. It employs a Fourier Encoder-Decoder to transform spatial-temporal PDE data into the frequency domain and a Transforme... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewer VHRd's constructive feedback. Below, we address each concern with clarifications and additional evidence:
**Q1: [Temporal Mask & Multi-Physics Consistency] How does the temporal mask address inconsistencies in multi-physics, and where is the ablation study?**
**A... | Summary: This paper proposes OmniArch, a foundation model for numerical simulations. They pretrain on 1D/2D/3D data and comapre to other models of the literature. It relies on spatial Fourier encoders/decoders and a causal temporal attention. It relies on PDE-Aligner for fine-tuning.
Claims And Evidence: I don't think... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer urwm for the thoughtful feedback.
**Q1: MPP/Poseidon already did united pretraining.**
**A1**: We **disagree** with highly respect. There is **factual error**, MPP/Poseidon are designed *only* for 2D (no 1D/3D experiments in their papers). DPOT uses a convoluti... | Summary: This paper introduces OmniArch, a foundation model designed for solving multi-scale and multi-physics PDEs. Inspired by foundation models in NLP, OmniArch aims to generalize across different PDEs using a Fourier-based encoder-decoder, a Transformer backbone, and a physics-informed fine-tuning method. The Fouri... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer u5RW for the thoughtful feedback and valuable support of our work. Below, we address each of their questions in detail:
**Q1**: How does OmniArch compare to traditional solvers (e.g., FEM, spectral methods) in accuracy/interpretability?**
**A1**: We thank u5Rw fo... | Summary: OmniArch is a foundation model for solving partial differential equations (PDEs) across 1D, 2D, and 3D domains. It addresses three key challenges: multi-scale modeling (handling different grid dimensions and resolutions), multi-physics capability (processing multiple physical quantities simultaneously), and ph... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 6qGA for the constructive feedback and recognition of our work. We deeply appreciate Reviewer 6qGA's support and hope this work can contribute to the research community. Below, we address the reviewers' questions and provide additional clarifications:
**Q1: The zero... | null | null | null | null | null | null |
Towards Black-Box Membership Inference Attack for Diffusion Models | Accept (poster) | Summary: This paper introduces a black-box membership inference attack method targeting diffusion models. Unlike previous MIA approaches that require access to the U-net or other internal components of diffusion models, their method only utilizes the variation API to determine whether a given image was part of the mode... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comments and suggestions. Below, we address the primary concern that has been raised.
>Q1: The proposed REDIFFUSE algorithm relies on the existence of a variation API. While the paper claims robustness across different diffusion steps, the experimental resu... | Summary: The paper proposes using the average of img2img outputs and comparing it with the original input image, with the difference serving as a metric for black-box MIA. This approach eliminates the need for predicted noise at intermediate time steps, making it applicable to a broader range of scenarios while achievi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive suggestions. In the following, we address the main concern raised.
>Q1: The author assumes for a well-trained diffusion model with full rank Jacobian. I am curious if there is a deeper motivation behind this assumption. One possible intuiti... | Summary: This paper investigates black-box membership inference attacks against diffusion models where attacker has no access to the internal model. The target of attacker is to determine whether or not an artwork was used to train a diffusion model. In this paper, authors firstly identify the limitation of applying ex... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for the insightful comments and suggestions. Please find the details below.
>Q1: Is the intuition of the proposed method general to other datasets? Or just observable to specific datasets utilized in these datasets??
**A1:** Our method has been evaluated... | Summary: The paper introduces a novel black-box membership inference attack method for diffusion models. The authors show their method can reliably detect whether an image was part of the training set or not. They do this by repeatedly applying the variation API and averaging the outputs. They have extensive experiment... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive feedback. We address the questions in detail below:
>Q1: When comparing to other existing methods, this paper do not show a comparison in runtime/cost. I'd suggest to add these details for their audience to have a better understanding of the trade o... | null | null | null | null | null | null |
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents | Accept (spotlight poster) | Summary: This paper considers the marginal version of the value at risk problem. This problem is called risk-averse decision policy optimization.
- The authors derive optimal policy for risk-averse decision makers given prediction sets, which takes a max-min form.
- They establish that prediction sets are a suffici... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and thoughtful questions, which will help us significantly improve the clarity and contribution of our manuscript.
**Question 1:**
Our contributions are twofold, as outlined below: (1) we introduce a novel question within the conformal prediction ... | Summary: This paper studies the decision-theoretic foundations of conformal prediction sets. It shows that prediction sets characterize the optimal strategy of risk-averse decision-making agents, and then connects it to a specific form of conformal prediction sets. Besides population-level characterization, based on th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful and detailed evaluation, their supportive stance toward our work, and their insightful and constructive questions, which allow us to clarify and deepen our results significantly.
**Questions 1, 3, and 4:**
Thank you for these great questions! As t... | Summary: This paper aims to address three questions at the intersection of conformal prediction (CP) and decision making: (1) Understanding what type of uncertainty quantification is best for risk-averse decision makers, (2) How risk-averse decision makers should use prediction sets (ie, what policy they should use, gi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and insightful feedback. We are grateful for the positive recognition of our core contributions—especially the novel linkage between prediction sets and risk-averse decision-making. In what follows, we detail the concrete actions we have taken to ... | Summary: The paper studies the decision-theoretic properties of conformal prediction sets. For risk-averse agents who want probabilistic certificates on certain actions for the utility to be greater than some value with high-probability, the paper suggests that this goal can be accomplished via conformal prediction set... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments, constructive feedback, and positive evaluations of our paper.
**On the motivation for minimax optimality:**
This is an important point, and we appreciate the opportunity to clarify our choice. As you correctly pointed out, minimax opt... | null | null | null | null | null | null |
Improving Out-of-Distribution Detection via Dynamic Covariance Calibration | Accept (poster) | Summary: To reduce redundant information in the covariance matrix in real-time OOD detection, this work proposes adjusting prior geometry based on the input, enhancing sensitivity to OOD samples while preserving essential information for ID classification.
Claims And Evidence: Most claims are supported by clear and co... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive recognition of our work, particularly regarding the novelty and reasonableness of the idea, strong performance, and clarity of supporting evidence. Below, we provide detailed, point-by-point responses addressing each comment.
### Other Strengths And Weaknesses... | Summary: This paper addresses the problem of Out-of-Distribution (OOD) detection, which is critical for ensuring the reliability of AI systems.
The authors observe that while existing subspace-based methods use information geometry to detect OOD data, they fail to address the distortion in geometry caused by ill-distri... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our work's **novelty**, **improved performance** demonstrated through comprehensive experiments, clear **presentation**, and its **significance** for AI system reliability. Below, we address each comment in detail.
**C1**: gradient-based methods
**C1-A... | Summary: This paper proposes a dynamic covariance calibration approach for OOD detection, addressing the sensitivity of distance-based detectors to outliers in the ID data. While existing methods mitigate this issue by projecting features onto principal dimensions, they risk losing valuable ID information. Instead, the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and valuable suggestions. We provide detailed responses to each comment in the following.
### Claims And Evidence
**C1 Detailed ablation study**:
**C1-Ans**: We include AUROC results in the ablation study table. To show full combinatorial result... | null | null | null | null | null | null | null | null |
Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment | Accept (poster) | Summary: This paper proposes an approach to modeling preferences by learning an embedding for each response given a prompt. The preference score between two responses is then computed using these embeddings. The key motivation behind this embedding-based approach is that mapping responses into a multi-dimensional laten... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback on our manuscript. We appreciate the time you took to evaluate our work and provide constructive comments. We believe addressing your points will significantly strengthen the manuscript.
1. Intransitivity Usefulness:
> Q1: The paper wou... | Summary: Since the prevalent Bradely-Terry formulation and pair preference model have limitations respectively in reward modeling, the authors propose a novel formulation GPM with better expressiveness to model complex preference distributions in real world. They further extend GPM to preference learning to build GPO, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review. We appreciate your constructive feedback and the opportunity to clarify aspects of our work. We address your points below:
Below, we try our best to address the points you raised:
1. GPM Architecture, Training, & Efficiency:
> Q1: The authors di... | Summary: This paper introduces preference embedding, a novel approach to model human preferences for aligning foundation models that overcomes the limitations of traditional reward models like the Bradley-Terry model, especially in capturing intricate preferences. The authors propose the General Preference embedding Mo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. We especially appreciate the feedback regarding Table 3 and acknowledge that the acronyms used were not sufficiently explained in the current draft.
1. Clarification of Table 3:
> Q1: First of all, it's full of acrony... | Summary: This paper proposes *General Preference Embedding Model* (GPM) to improve LLM alignment on human preference.
The motivation is mainly on addressing limitations of the classical BT reward models such as challenges when facing intransitivity.
The authors deal with it by embedding the responses into latent spac... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time, insightful feedback, and constructive comments on our paper. We appreciate the recognition of GPM's novelty, theoretical grounding, and universal framework potential.
We would like to address the specific points raised:
> Q1: Generalisability and further re... | null | null | null | null | null | null |
Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models | Accept (poster) | Summary: The author's overall approach is simple and clear: CoT is not always necessary → Token Signature is correlated with CoT gain → The token probability distribution can be used to determine whether CoT is needed. Through experiments, the study validates that Aggregated SC can effectively indicate whether a task b... | Rebuttal 1:
Rebuttal: ## Response to reviewer xvNF：
Dear reviewer:
Thank you for reviewing our article and raising questions and suggestions. We now respond to the corresponding questions as follows.
### Q1: They only tested closed-source transfer on GPT-4o, so it’d be nice to see results on more proprietary models... | Summary: This paper presents token signature, i.e., the spearman correlation between token probability distributions and token indices, that can better help decide if a CoT is needed for a specific task or not. The authors further introduced dynamic CoT, that can do online selection of whether to use CoT or direct answ... | Rebuttal 1:
Rebuttal: ## Response to reviewer YmMf：
Dear reviewer:
Thank you for reviewing our article and raising questions and suggestions. We now respond to the corresponding questions as follows.
### Q1: For the spearman correlation, the authors presented a few examples, is there any intuitive explanation on wh... | Summary: The paper makes this observation that in certain tasks (where CoT) is known to help, the probability of the token predicted generally increases as more and more tokens are generated. They propose to exploit this observation to predict whether CoT would help on a task or not.
Claims And Evidence: They give so... | Rebuttal 1:
Rebuttal: ## Response to reviewer revN：
Dear reviewer:
Thank you for reviewing our article and raising questions and suggestions. We now respond to the corresponding questions as follows.
### Q1: Explanation of the proposed method.
**Response:** Thanks for your question. We will explain it from the f... | Summary: This paper examines the inconsistency of Chain-of-Thought (CoT) reasoning across different tasks and introduces Token Signature, a novel approach for predicting CoT effectiveness based on token probability distributions. The authors develop two key evaluation metrics, Instance Spearman Correlation (Instance SC... | Rebuttal 1:
Rebuttal: ## Response to reviewer qdHF：
Dear reviewer:
Thank you for reviewing our article and raising questions and suggestions. We now respond to the corresponding questions as follows.
### Q1: Several critical technical designs.
1) Restricting the indicator score computation to only the first 50 tok... | null | null | null | null | null | null |
Efficient Core-set Selection for Deep Learning Through Squared Loss Minimization | Accept (poster) | Summary: This paper proposes a two-phase core-set method for selecting a small but representative subset of training data. The first phase selects samples with the highest contributions, while the second phase employs a lightweight proxy model to evaluate the differences between the remaining samples and already select... | Rebuttal 1:
Rebuttal: Your insights have greatly helped us identify key areas for improvement. We address the main concerns below:
### **Response #1: Insufficient Novelty in Contributions**
Rather than introducing a new theoretical guarantee framework, our contribution focuses on the design of a simple, efficient, and ... | Summary: The authors:
*propose a new core-set selection approach that seeks to balance losses between chosen and unchosen samples by minimizing the overall sum of squared loss.
* Introduce the Maximum Reduction as Maximum Contribution (MRMC) criterion, which pinpoints those data points that most substantially reduce ... | Rebuttal 1:
Rebuttal: Thank the reviewer for the feedback and positive comments on our idea and writing. Below we address the main concerns:
### **Response #1: Theoretical Guarantees in Prior Work**
We realize that our original phrasing may have led to confusion. Our intention was to emphasize the practical gap between... | Summary: This paper presents a novel coreset selection method, which minimizes the squared loss to balance contributions between coreset and non-coreset samples. The method follows a two step process: first select samples with the highest MRMC value, then train a proxy model to select samples to increase diversity. Ext... | Rebuttal 1:
Rebuttal: Your suggestions are very helpful for improving the quality of this work. Below we address each of your concerns:
### **Response #1: Missing Related Work**
Thank you for pointing this out. In the revised manuscript, we will add a discussion on “Refined Coreset Selection” (ICML 2024), which formula... | Summary: The authors propose a new objective to select subsamples for training deep learning models. They call it MRMC (Maximum Reduction as Maximum Contribution) which essentially says that a data point that leads to more reduction in the squared error loss during the first few epochs of training relative to other poi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and fair assessment. We appreciate the opportunity to address your concerns.
### **Response #1: Lack of Theoretical Guarantees**
We acknowledge that our method does not provide formal theoretical guarantees. This work aims to bridge the gap betwe... | null | null | null | null | null | null |
Training a Generally Curious Agent | Accept (oral) | Summary: This paper proposed a fine-tuning approach named PAPRIKA to improve LLM Agent’s decision-making capabilities, along with a curriculum learning algorithm that improves PAPRIKA’s sampling efficiency.
The experimental results show that the proposed PAPRIKA approach improves the success rate among different tasks ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and positive review.
> Q1: In Algorithm 1, the pseudocode said that there is an input $B$ …
We thank the reviewer for catching this typo. $B$ in the pseudocode has the same purpose as $C$, in the sense that we collect $C$ trajectories in parallel for each... | Summary: This paper presents Paprika, a finetuning method that enables models to perform in-context rl for unknown environments. Different aspects of Paprika have been studied under different settings but not for this particular setting e.g multi-turn, not interacting with a human and more general environments. A train... | Rebuttal 1:
Rebuttal: We thank the reviewer for the support and thoughtful review. We want to address their feebacks below.
> There is not really an ablation (on the different parts of the method)...
We have conducted additional experiments on different parts of Paprika, we list them below:
1. **Ablation on training... | Summary: This paper introduces PAPRIKA, a SFT + RL approach aimed at enhancing the general decision-making and exploration capabilities of LLMs through diverse synthetic interaction data from various domains. One contribution is that it introduces 10 interesting tasks requiring interacting with the environment and reas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, and hope to address their concerns below:
> One thing probably need to analyze more is…
We note that all our evaluations are conducted on held-out tasks within each group. These tasks require strategic exploration, as they are partially obse... | Summary: The paper proposes PAPRIKA, a method designed to enable large language models (LLMs) to acquire generalizable sequential decision-making capabilities via fine-tuning on synthetic interaction data. The gem of PAPRIKA lies at the use of a scalable online curriculum learning method (Sec. 3.4), where the performan... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and suggestions. They will greatly improve our paper!
> But I suggest the authors to derive...
Indeed, mastermind is the hardest task, as demonstrated by the untrained model only achieving ~4% pass@4 success rate on it, the lowest among all 10 t... | null | null | null | null | null | null |
A Sub-Problem Quantum Alternating Operator Ansatz for Correlation Clustering | Accept (poster) | Summary: This paper proposed the QAOA for correlation clustering, by introducing a Sub Problem QAOA. This is motivated by the nucleus sampling and sub problem is dependent to solve correlation clustering. Although QAOA for correlation clustering has been studied in the literature, Weggemans et al. (2022) is restricted ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback.
There seems to be consensus that our experimental and theoretical claims are correct and that the proposed approach surpasses previous QAOA methods for correlation clustering. Here, we gladly address remaining questions and concerns of Review... | Summary: The paper constructs a new variant of QAOA (rather) specifically for the correlation clustering problem and shows improved performance over QAOA.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: At a quick look, the proof seems fine. The proven property also is simple enou... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. There seems to be consensus that our experimental and theoretical claims are correct and that the proposed approach surpasses previous QAOA methods for correlation clustering. Here, we gladly address remaining questions and concerns of Review... | Summary: The paper presents a new quantum optimization approach called the Sub-Problem Quantum Alternating Operator Ansatz (SQAOA) aimed at solving correlation clustering problems. The approach modifies the Quantum Alternating Operator Ansatz (QAOA) by introducing two key modifications: 1) it uses nucleus sampling to c... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback.
There seems to be consensus that our experimental and theoretical claims are correct and that the proposed approach surpasses previous QAOA methods for correlation clustering. Here, we gladly address remaining questions and concerns of Review... | Summary: The paper explores the power of the Quantum Alternating Operator Ansatz algorithm. In particular, the focus is on the optimization problem called correlation clustering and provides a way to solve it by dividing the Quantum Alternating Operator Ansatz algorithm in subproblems. They show that by iterating to in... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback.
There seems to be consensus that our experimental and theoretical claims are correct and that the proposed approach surpasses previous QAOA methods for correlation clustering. Here, we gladly address remaining questions and concerns of Review... | null | null | null | null | null | null |
Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold | Accept (poster) | Summary: The paper introduces EF-Landing, a novel distributed optimization algorithm for stochastic optimization on the Stiefel manifold. EF-Landing is retraction-free and communication-efficient, incorporating gradient compression and error feedback mechanisms. The authors establish sharp convergence guarantees and de... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and constructive comments. Below, we reiterate our novel contributions which the reviewer had mentioned, provide other theoretical innovations for reference, and show the plan of expanding follow-up experiments.
**1. Reiteration of Con... | Summary: This paper introduces EF-Landing, a retraction-free and communication-efficient algorithm for distributed stochastic optimization on the Stiefel manifold.
Claims And Evidence: The paper's main claims regarding retraction-free optimization, communication efficiency, and error feedback improving convergence are... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and constructive comments. Below, we address each point in detail. All newly added experiments can be found in the **Additional Result Sheet (ARS)** https://anonymous.4open.science/r/EF-Landing-B6E4
**1. Distributed learning**
Decent... | Summary: Paper provides an error feedback based algorithm to solved distributed optimization problem on Stiefel manifold (set of orthonormal matrices). This algorithm generalizes recently proposed retraction-free Landing method to a low-comm complexity distributed algorithm. Authors also provide theoretical convergence... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and constructive comments. Below, we address each point in detail. All newly added experiments can be found in the **Additional Result Sheet (ARS)** https://anonymous.4open.science/r/EF-Landing-B6E4
**1. Landing v.s. Penalty**
The Lan... | null | null | null | null | null | null | null | null |
RSMerge: Bridging Head and Tail Classes via Subsampled Model Merging | Reject | Summary: This paper proposes a method called RSMerge for the long-tailed classification task which merges finetuned CLIP models on independent balanced subsets of data, then retrains the classifier on the entire dataset. A new metric, the head-to-tail ratio $\eta$ is proposed, and limitations of existing methods are id... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and positive comments on our novel findings, rigorous methodology, and thorough empirical analyses. We address the questions as follows:
---
> Q1. I would encourage the authors to include a README explaining how to install/run the code.
We want t... | Summary: This paper explores class imbalance recognition, identifies head-to-tail class ratio as an under-explored problem in this setup, and proposes an approach that successfully integrates the main challenges in imbalanced class learning. The proposed approach leverages observations from the literature to navigate t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and for recognizing the value of our work on imbalanced training. Our responses to their questions, which we found fruitful and led to several new experiments and analyses, are below:
---
> Q1. It would be valuable to see if the weight averaging... | Summary: The paper proposes a method for long-tailed recognition by using CLIP. The proposed method trains multiple models from different distributions and then merges them together. While training each model, the exponential moving average is applied to maintain the original generalizability of pre-trained CLIP.
Clai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and address their concerns in detail below:
---
> Q1. Please cite proper reference for the statement that “LoRA enhances tail-class performance by maintaining weight close to the pre-trained initialization, yet it sacrifices head-class accuracy in Section ... | Summary: This paper conducts a comprehensive analysis of head-to-tail class ratios under different levels of class imbalance, investigating their effects on model performance. Building on these findings, this paper proposes a two-stage approach to address the stability-plasticity dilemma through decoupled learning and ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading, positive feedback on our method's accessibility and effectiveness, and constructive questions that spurred additional experiments. Below, we address their questions in detail:
---
> Q1. Lack of a comprehensive description of the proposed method in the... | null | null | null | null | null | null |
Rethinking Benign Overfitting in Two-Layer Neural Networks | Accept (poster) | Summary: This paper analyzes the training dynamics of a two-layer CNN which can lead to benign overfitting. By analyzing a new data model with feature-specific noise, they claim that neural networks can learn "implicit features" that improve the accuracy when training on long-tailed data.
Claims And Evidence: The theo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing our contributions and for the constructive feedbacks.
Below are our point-by-point responses:
**Clarification on "Data Noise" Definition**:
We agree with your point and will revise the term 'noise' to 'data-specific information' and clarify it... | Summary: This paper establishes an enhanced feature-noise model by considering class-dependent heterogeneous noise across classes. Under this model, the paper demonstrates how memorization of long-tailed data boosts model performance. Additionally, it provides a phase transition of test error between benign and harmful... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing our contributions and for the insightful comments.
Below, we address your concerns in detail:
**Clarification of Long-Tailed Data Definition**:
Our definition is not recursive but instead relies on a fixed value of $T$, with no new data being i... | Summary: This paper explores overfitting in neural networks, challenging the common belief that it harms generalization. Specifically, the authors argue that certain forms of overfitting can be benign and demonstrate—both mathematically and empirically—that benign overfitting can enhance performance on long-tailed data... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing our contributions and for the insightful comments. Below, we address your concerns in detail:
**Clarification on the Choice of Datasets**: We would clarify the reason for experiments on MNIST.
- While MNIST may be considered simpler in compari... | Summary: This work investigates the phenomenon of benign overfitting in two-layer neural networks by rdiscussing a feature-noise model. The authors introduce a class-dependent heterogeneous noise model to attempt to explain why neural nets can leverage long-tailed data distributions for generalization. The paper claim... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing our contributions and for the insightful comments. We will answer your questions in the following.
**Discussion on Datasets**:
Thanks for your comment. Please see the reply to Reviewer pfPE due to the space limit.
**Clarification of Noise Memo... | null | null | null | null | null | null |
Multimodal Medical Code Tokenizer | Accept (poster) | Summary: MEDTOK is a tokenizer designed specifically for medical codes, improving upon traditional approaches (that treat each code as isolated textual units) by considering the textual description of each medical code and its ontological hierarchy and relationships across different medical coding standards. It employs... | Rebuttal 1:
Rebuttal: ### **W1:**
We apologize for the misunderstanding. In the final version, we will clarify that our graph construction approach is relatively simple and straightforward. Specifically, for each medical code, we define the code’s graph as a subgraph of a knowledge graph centered on the node representi... | Summary: Medical codes in electronic health records (EHRs) contain rich textual descriptions and structured relationships. However, existing tokenization methods treat them as isolated textual tokens, failing to capture their ontological and relational context. The authors propose MEDTOK, a multimodal medical code toke... | Rebuttal 1:
Rebuttal: ### **Theoretical Claims:**
Thank you for your comment. Equations 8-9 contribute to the overall optimization process by representing the optimal values of graph embeddings (Eq. 8\) and text embeddings (Eq. 9), which are designed to model modality-specific and modality-shared information. To approx... | Summary: This paper introduces MEDTOK, a multimodal medical code tokenizer that combines text descriptions and relational information from medical ontologies to improve the processing of medical codes in electronic health records (EHRs). MEDTOK encodes both text and graph information into a unified token space, enhanci... | Rebuttal 1:
Rebuttal: ### **Claims**:
**Please refer to the results in ‘Reviewer EVKN: E2’.** The obtained results demonstrate that both shared and specific information optimization enhance performance, with the full optimization achieving the best results across all datasets.
### **E1**:
To address your concerns, we ... | Summary: In this paper, the authors present MEDTOK, a multimodal medical code tokenizer that integrates textual descriptions of medical codes with graph-based relational contextual information. The proposal employs separate encoders to process each modality, and the resulting representations are mapped into a shared sp... | Rebuttal 1:
Rebuttal: ### **Claims:**
We added the following discussion to related work: *"Rather than treating medical codes in isolation, some methods incorporate additional knowledge to enhance their representation using structures like knowledge graphs (Choi et al., 2017; Burger et al., 2023\) or ontology trees (Sh... | null | null | null | null | null | null |
Noise Conditional Variational Score Distillation | Accept (poster) | Summary: This paper proposes a novel method for distilling diffusion models into a generative image denoiser at any noise level. The proposed method is based on a theoretical result showing that the unconditional score function implicitly characterizes the score function of denoising posterior distributions at all nois... | Rebuttal 1:
Rebuttal: *W1: ... additional datasets ...*
Please refer to W7.
*W2: ... visual comparison ...*
Please refer to W8.
*W3: For example ...*
Please refer to W9.
*W4: In L369 ... & W5: The authors say in L369 ...*
We respectfully disagree. The tradeoff between PSNR and LPIPS has been extensively demonst... | Summary: This paper introduces Noise Conditional Variational Score Distillation, which distills a pre-trained diffusion model into a generative denoiser. The generative denoiser enables fast one-step generation while preserving the ability for iterative refinement. Experiments on image generation tasks and various inve... | Rebuttal 1:
Rebuttal: *W1: After reviewing this paper ...*
Conceptually, the key distinction between diffusion models with iterative refinement and our approach lies in how clean data $x\_0$ is predicted from its noisy counterpart $y\_{\sigma} \sim \mathcal{N}(x\_0, \sigma^2 I)$. Diffusion models primarily focus on le... | Summary: This paper proposes a new distillation scheme to learn a few-step posterior sampler of a diffusion process. Unlike existing methods that achieve rich posterior sampling using exhaustive function evaluations (e.g., diffusion) or invertible neural networks (e.g., normalizing flows), the proposed method achieves... | Rebuttal 1:
Rebuttal: *W1：Choice of adaptive ...*
We thank the reviewer for pointing out the ambiguity in our claim. A function $f(\cdot)$ is called L-gradient Lipschitz if it satisfies:
$$
\lVert \nabla f(x\_1) - \nabla f(x\_2) \rVert\_2 \leq L \lVert x\_1 - x\_2 \rVert\_2, \quad \forall x\_1, x\_2.
$$
Provided that... | null | null | null | null | null | null | null | null |
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling | Accept (poster) | Summary: This paper addresses the limitations of existing latent generative models, which often lack equivariance to semantic-preserving transformations like scaling and rotation. To overcome this challenge, the authors propose EQ-VAE, a regularization technique that enforces equivariance in the latent space, simplifyi... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and efforts in reviewing our manuscript. Below, we provide our responses to each of your comments:
---
**W1. Minor improvements in Table 2 compared to REPA.**
We respectfully emphasize that in Table 2 EQ-VAE demonstrates substantial improvements across all... | Summary: The paper introduces EQ-VAE, a novel variant of autoencoders designed to enhance the performance of latent generative models. The authors first identify that commonly used autoencoders in modern generative models are not equivariant to spatial transformations of the input, such as scaling and rotation. They ar... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and efforts in reviewing our manuscript. Below, we provide our responses to each of your comments:
---
**W1. Results with CFG for converged models.**
We appreciate the reviewer’s concern regarding potential performance degradation when integrating EQ-VAE i... | Summary: The paper proposes EQ-VAE, a framework that introduces equivariance regularization into the training of autoencoders. By incorporating 2D transformations such as rotation and scaling, the method improves the structure and representation ability of the latent space. As a result, it accelerates the training of g... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and efforts in reviewing our manuscript. Below, we provide our responses to each of your comments:
---
**Theoretical validation**
While we focus on empirical evidence in this work, we believe that mathematically formalizing the underlying mechanisms behin... | Summary: This paper observes that existing autoencoders lack equivariance to semantic-preserving transformations like scaling and rotation, resulting in complex latent spaces that hinder generative performance. Based on this observation, the authors propose to regularize the latent by enforcing equivariance in the late... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and efforts in reviewing our manuscript. Below, we provide our responses to each of your comments:
---
**W1. EQ-VAE for T2I generation.**
We appreciate the reviewer’s concern regarding the applicability of our method to text-to-image (T2I) generation. To a... | null | null | null | null | null | null |
Robust Online Conformal Prediction under Uniform Label Noise | Reject | Summary: The authors consider online conformal prediction with uniform label noise, where the goal is to solve a sequential classification problem by providing prediction sets at each round, such that over time, the true label will lie in the prediction set with probability approximately $1-\alpha$ where $\alpha$ is a ... | Rebuttal 1:
Rebuttal: > 1. Justification on achieving precise $1-\alpha$ coverage
Thank you for the insightful comment. Achieving precise $1-\alpha$ coverage is one of the common desiderata in conformal prediction [1,2,3,4], since over-coverage results in excessively large prediction sets, reducing their practical uti... | Summary: The paper "Robust Online Conformal Prediction under Uniform Label Noise" addresses the challenge of online conformal prediction (OCP) in the presence of uniform label noise. Conformal prediction is a widely used technique for uncertainty quantification, guaranteeing a predefined coverage probability for predic... | Rebuttal 1:
Rebuttal: While AC suggests that the review is flagged as generated, we still provide detailed responses below:
> 1. No ablation study on the proposed loss
See response #2 to reviewer rF6c.
> 2. Restriction to uniform label noise
See response #3 to reviewer rF6c.
> 3. Variance analysis for the gradie... | Summary: This paper aims to develop an online conformal prediction method that can handle the case where the labels of data are noisy, which ensure the robustness of online conformal prediction. The novelty of method is mainly focus on adjusting the previous pinball loss to a robust pinball loss, which is a weighted su... | Rebuttal 1:
Rebuttal: > 1. Lack of comparative experiments with other online conformal prediction algorithms
Thank you for your insightful suggestion. We conduct new experiments by integrating our robust pinball loss into a new baseline - SAOCP[1]. In particular, we employ LAC score to generate prediction sets with er... | Summary: This paper studies the robustness of online conformal prediction (OCP) under uniform label noise with a known noise rate. The authors demonstrate that label noise introduces a persistent gap between the actual and desired coverage rates, affecting the reliability of prediction sets. To address this, the paper ... | Rebuttal 1:
Rebuttal: > 1. Comparisons with existing noise-robust methods
Thank you for raising this concern. We’d like to clarify that this work focuses on label noise in online conformal prediction, a domain where methods originally designed for classification and calibration cannot be easily adapted. In particular,... | null | null | null | null | null | null |
Self-Consuming Generative Models with Adversarially Curated Data | Accept (poster) | Summary: This paper investigates the effects of adversarially curated data on generative models trained iteratively on synthetic data—referred to as "self-consuming loops." The authors theoretically and experimentally analyze how generative models behave under conditions of noisy and maliciously curated data. They prop... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and encouraging feedback, especially for recognizing the novelty and effectiveness of our empirical validation. We address the reviewer’s concerns below:
> “The gradient-based attack methods are computationally expensive, potentially limiting their practical d... | Summary: This paper investigates a novel adversarial model where the data curation process for generating training data for iterative models is adversarially manipulated. The authors show theoretically that the effectiveness of such adversarial manipulations have is tied to the covariance between the unmanipulated and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and positive assessment of our work. We appreciate the recognition of our theoretical analysis, experimental design, and the contribution of introducing a new adversarial model in the context of iterative retraining. We address the reviewer's concern... | Summary: This work proposes a method for adversarial attack defense when training generative models. Experimental results on synthetic and real datasets show the effectiveness.
Claims And Evidence: All claims have support in the paper.
Methods And Evaluation Criteria: The method makes sense for current generative mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and for recognizing our work as "a new method in generative models' adversarial attack field." We now address the reviewer's concerns regarding the experiment:
> On dataset diversity and model generality
Our experimental setup follows prior wor... | Summary: The paper studies the problem of iterative retraining of generative models on their own synthetic data, in the specific setting where synthetic data have been adversarially curated, e.g., a concurrent platform gives random or adversarial feedback when to vote for their favorite image on MidJourney. In this set... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and encouraging feedback, especially their positive comments on the theoretical analysis, experimental design, and for describing our paper as “well written and very clear”. We provide clarifications to address the main concerns:
> "Do we know how frequent... | null | null | null | null | null | null |
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification | Accept (poster) | Summary: This paper explores the use of large language models (LLMs) as feature enhancers for graph neural networks (GNNs) in graph representation learning, addressing the fundamental properties of this approach using the interchange intervention method from causality theory. To facilitate analysis, the authors constru... | Rebuttal 1:
Rebuttal: Thank you very much for your analysis and suggestions for revising our paper, and your support for our paper. We greatly appreciate your feedback. Below, we will address each of the raised concerns.
**Methods And Evaluation Criteria**:
In the appendix, we provide a detailed description of the c... | Summary: This paper presents a valuable analysis of the LLM-enhancer-plus-GNN framework, exploring its underlying mechanisms and identifying potential areas for improvement. The use of the CCSG dataset and the interchange intervention method provides a novel approach to understanding the relationship between LLMs and G... | Rebuttal 1:
Rebuttal: Thank you very much for your analysis and suggestions for revising our paper, and your support for our paper. We greatly appreciate your feedback. Below, we will address each of the raised concerns.
**Methods And Evaluation Criteria:**
1. Thank you for your suggestions. We have incorporated data... | Summary: This paper proposes a new analysis tool for LLM encoders for GNNs, based on the causal theory. The proposed method is evaluated in one synthetic dataset generated by the authors.
Claims And Evidence: N.A.
Methods And Evaluation Criteria: No. The proposed method is only evaluated in one synthetic dataset, whi... | Rebuttal 1:
Rebuttal: Thank you very much for your analysis and suggestions for revising our paper. We greatly appreciate your feedback. Below, we will address each of the raised concerns.
**Methods And Evaluation Criteria:**
Regarding the AT module, we conducted experimental analysis using several public datasets. T... | null | null | null | null | null | null | null | null |
Teaching Language Models to Critique via Reinforcement Learning | Accept (poster) | Summary: This paper introduces CTRL (Critic Training via Reinforcement Learning), a framework designed to train critic models for iterative refinement in code generation tasks. The authors propose a two-stage pipeline: supervised fine-tuning (SFT) using execution-guided critique synthesis and reinforcement learning (RL... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We appreciate your recognition of our paper’s strengths, especially the `novel combination` of supervised fine-tuning and reinforcement learning for training critics and the `robust experimental design` supporting our claims. Below, we address your specific ... | Summary: This paper presents the CTRL framework - a two-stage training approach that separates the critique function of a language model from its generative capabilities. The authors first synthesize high-quality critiques using execution feedback (running code trying to go through unit tests), which are then used in a... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and thoughtful review! We appreciate your recognition of our work's `solid mathematical and theoretical foundation` and `adequate experimental validation.` We would like to address your concerns in detail:
**Theoretical analysis**:
Our work is primarily empirical... | Summary: The paper presents a method to teach an LLM to critique the response of another LLM, specifically in the domain of coding contest problems. The problem is formalized as maximizing the probability of the latter LLM to succeed at providing a correct response after seeing a critique produced by the former, which ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback! We appreciate your recognition that our main claim `is well supported by the experiments` with `a number of interesting analyses` and that the paper `very clearly motivates its approach by citing relevant related work.` We address your prim... | Summary: Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it
is fundamentally limited by the ability to provide accurate judgments and actionable suggestions. In this work, the authors study LLM critics for code generation and p... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and well-thought-out review! We appreciate your recognition of our work's novelty, effectiveness, experimental design, clear motivation, and solid supplementary material. Your positive feedback is truly encouraging.
Below, we address your concerns about computatio... | null | null | null | null | null | null |
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition | Accept (poster) | Summary: The authors propose a novel spectral graph network, WaveGC, inspired by SWGT. WaveGC filters input features with matrix-valued kernels in the spectral domain and utilizes transformer architectures for wavelet transforms. The wavelet functions are learnable and are parameterized by Chebyshev polynomials with le... | Rebuttal 1:
Rebuttal: We thank the referee for taking the time to review our paper. Please, see below our answer to the raised comments/questions.
> Q1: The quality of Figure 1 is still not good enough.
We have improved the image resolution of Figure 1 at https://drive.google.com/file/d/1rfuYedo4FkeLg1zClq45ZOHU6rH-p... | Summary: This paper proposes a wavelet-based graph convolution method, WaveGC, which integrates spectral bases and matrix-valued kernels. The authors use the odd and even terms of Chebyshev polynomials to learn graph wavelets that satisfy the necessary conditions. Experimental results demonstrate that the proposed meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions. The link for the table is available at https://drive.google.com/file/d/146c3rsB_eJLJmk3I21aWg9fzGPpCvd7S.
> Q1: Compare with SOTA methods based on Fourier ... | Summary: In this work, the authors introduce WaveGC, an innovative wavelet-based graph convolution approach featuring multi-resolution bases and a dense matrix kernel. They construct the necessary wavelet bases by leveraging Chebyshev polynomials of the first kind. For kernel implementation, they draw inspiration from ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our work. Please, see below our answer to the raised comments/questions.
> Q1: Eq. (1) is inappropriate.
Eq. (1) describes requirements for general wavelets as introduced in Mallat’s book in 1999, not only graph wavelets, where g(λ = 0) = 0 is important for an... | Summary: This paper introduces WaveGC, a wavelet-based graph convolution network that integrates multi-resolution spectral bases with a matrix-valued filter kernel. It proposes graph wavelets by decomposing Chebyshev polynomials into odd and even terms and combining them with learnable coefficients, ensuring strict adm... | Rebuttal 1:
Rebuttal: We appreciate the review provided. Please, find below our clarifications on the points raised, and the link to the figures & tables: https://drive.google.com/file/d/1DALF7e1t6O4SSMUkvfi2euMufIpNJARv.
> Q1: Analysis of learned scales and experimental impacts.
1) Learned Scales and Receptive Field... | null | null | null | null | null | null |
FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making | Accept (poster) | Summary: FOUNDER proposes a method that leverages the generalization capability of world model dynamics alongside the prior knowledge embedded in foundation models to improve embodied decision making, and demonstrate its effectiveness through extensive experiments across diverse domains.
Claims And Evidence: The claim... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and valuable feedback on our paper. We are delighted to receive recognition of our method's effectiveness, robustness, and scalability through extensive experiments, and for acknowledging that our claims are supported by convincing evid... | Summary: This paper introduces FOUNDER, a framework for grounding foundation models (FMs) in world models (WMs) to enable generalizable representation learning, multi-modal prompting, and dense reward prediction. FOUNDER shares conceptual similarities with GenRL but incorporates temporal information to enhance reward p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable feedback on our paper. We are delighted to receive recognition of our method's sound experimental design and solid experimental results. Please find below our detailed responses to the reviewer’s comments.
**The influence of temporal distance**: ... | Summary: This work proposes FOUNDER, a method that leverages Visual-Language Models (VLMs) to get representations from visual observations and train RL agents on World Models imagination, starting from an offline dataset of trajectories. It proposes a method of aligning the embeddings from the VLM with the latent state... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and valuable feedback. Below are our responses to each of their concerns:
**Universally applicable claim**: Our claim of task-agnosticism refers to FOUNDER’s architecture—world model, mapping function, and reward generator—enabling dep... | Summary: The paper proposed FOUNDER, a novel framework that integrates Foundation Models (FMs) with World Models (WMs) to enable reward-free, open-ended decision-making in embodied environments. The central idea is to ground FM representations into the WM state space, allowing GCRL through imagination. Instead of relyi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and valuable feedback on our paper. We are delighted to receive recognition of our method's solid performance and strong task generalization demonstrated by extensive and detailed experiments. Please find below our detailed responses to... | null | null | null | null | null | null |
Mean-Shift Distillation for Diffusion Mode Seeking | Reject | Summary: The paper systematically explores behavior of the popular SDS algorithm for sampling of diffusion models and concludes that it does not well cover the true modes of the underlying distribution. Similar observations are made for another recent alternative - SDI. Inspired by the Gaussian paths defining training ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and for recommending acceptance. We are glad they recognize our contributions. Below, we answer questions raised in the review:
> While the results of SDS are well represented in the paper, SDI is not as widely shown. E.g. it is not displayed in ta... | Summary: This paper presents mean-shift distillation, a diffusion distillation technique that provides a provably good proxy for the gradient of the diffusion output distribution.
Claims And Evidence: The claims made in the submission are supported by clear evidence. However, the evidence is not convincing enough. For... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We hope the following will resolve any confusion regarding our contribution and evaluations:
- We would like to emphasize that the goal of our work is to improve mode seeking and achieve better distillation for diffusion models, not to improve metrics for ... | Summary: This paper proposes a new diffusion distillation technique called mean-shift distillation (MSD), intended to solve the problem of mode-seeking when leveraging pre-trained diffusion models for tasks like text-to-2D or text-to-3D optimization. Existing approaches such as score distillation sampling (SDS) are kno... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive review. We are glad they recognize our contributions. Below, we answer questions raised in the review:
> Comparisons with other guidance techniques and variance-reduction methods.
**Guidance**. In low-dimensional settings (eg, our toy expe... | Summary: This paper introduces mean-shift distillation, which improves the convergence behavior of the SDS objective. Experiments on synthetic and real datasets demonstrate the effectiveness of the proposed method.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: N/A.
Experimental... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognising our contributions. We acknowledge the feedback regarding our notations.
## 1 Writing clarification.
As we cannot update the submission, we provide them below. We believe these are minor corrections and will incorporate them in our revision.
|Notation|Clarifica... | null | null | null | null | null | null |
Gradient Boosting Reinforcement Learning | Accept (poster) | Summary: This paper presents **Gradient Boosting Reinforcement Learning (GBRL)**, an attempt to integrate **Gradient Boosting Trees (GBT)** into reinforcement learning (RL). GBTs are known for their performance in structured data tasks, but RL has long relied on deep neural networks. The authors propose modifications t... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. Below we address each of your concerns.
1. **Lack of theoretical proof of convergence & handling non-stationarity**: While comprehensive theoretical analysis is beyond the current scope, GBRL builds upon established foundations from both... | Summary: This paper presents a GPU accelerated method to use gradient-boosting trees for use in RL. The contribution is in the fact that existing methods for boosting-trees are tailored to offline learning, whereas, the authors' method is fully incremental. They then show empirically many benefits of using tree based f... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We address your main points below and will incorporate the suggested revisions in the final version.
**Regarding line 71-72 and performance on structured tasks**:
We agree with this point. We will revise the contribution statement (l... | Summary: The paper proposes GBRL, a framework that adapts gradient boosting trees (GBTs) to reinforcement learning (RL) tasks. Recognizing that neural networks (NNs) often struggle with structured and categorical features, the authors leverage the natural strengths of GBTs—namely, their ability to handle heterogeneous ... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our paper, recognizing the comprehensive experiments and the value of our approach for structured data in RL. We address each of your concerns below and will revise the paper accordingly.
**Regarding the "unbounded growth of the ensemble as the policy i... | Summary: The authors introduce a reinforcement learning method based on gradient-boosted trees (Gradient Boosting Reinforcement Learning, GBRL).
The method consists of gradually training an additive ensemble of decision trees to output both a policy and Q-value estimates at the leaves. The loss function has two terms... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Below we address your points, which we will also clarify in the revised paper.
**Stable Baselines3 and RL Zoo3**: Our GBRL method was implemented within the Stable Baselines3 framework, a widely-used reinforcement learning library providing standard implem... | null | null | null | null | null | null |
The Generalized Skew Spectrum of Graphs | Accept (poster) | Summary: The paper generalizes the Skew Spectrum to obtain permutation invariant and isomorphism-invariant to graph embeddings. In detail, the authors propose multi-orbit spectra that can handle attributed graphs, multi-layer graphs and hypergraphs. Then $k$-Correlation Spectra is introduced to theoretically characteri... | Rebuttal 1:
Rebuttal: **References:** Thank you for the references, we will include them in our manuscript.
**Introduction:** More introductory material would help reach a broader audience, but the page limits presents a substantial challenge. Upon acceptance, we will consider both leveraging the extra page to revise ... | Summary: This paper proposes a family of permutation-invariant graph embeddings, which generalizes the Skew Spectrum of graphs introduced by Kondor & Borgwardt (2008). Grounded in group theory and harmonic analysis, the method introduces a new class of isomorphism-invariant graph invariants that can embed more complex... | Rebuttal 1:
Rebuttal: **Computational cost:** Benchmarking the k-Spectra (kSP) and their Doubly-Reduced (DRkSP) version would be extremely interesting. However, a fair comparison requires a substantial amount of work, which has to be covered in future research. Fair benchmarking requires using the same programming lang... | Summary: In this paper, authors extend the Skew Spectrum-based graph representation method to handle rich graph structures and achieve preferable computational efficiency. The idea is interesting.
Claims And Evidence: A lot of theoretical analysis are made. The expression of the paper is easy to follow.
Methods And E... | Rebuttal 1:
Rebuttal: **Message-Passing Neural Networks:** We stress that our main claim is not to improve message-passing neural networks (MPNN), but rather the Skew Spectrum of graphs. However, we think these methods can improve MPNNs and report evidence in the answers. Most MPNNs architectures aren’t more powerful W... | null | null | null | null | null | null | null | null |
RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization | Accept (poster) | Summary: This paper proposed RWKVQuant, a method for PTQ of RWKV. They propose a hybrid method combining both scalar and vector quantization, and a decision rule for assigning different layers to these two methods. For appropriate values of hyperparameters (such as $\tau_c$ and $\tau_f$), they demonstrate strong perfor... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable time and efforts in reviewing our manuscript. We have addressed each comment and made the necessary revisions to improve the quality and clarity of our manuscript.
>Concerned about the setting of hyperparameters.
1. For hyperparameters $\tau_c$ and $\tau... | Summary: This paper aims at reducing the memory usage and inference latency of RWKV through post-training quantization (PTQ) techniques. However, authors find that non-linear operators and larger amount of uniformly distributed weights hinder the effectiveness of previous PTQ methods. Therefore, authors propose RWKVQ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable time and efforts in reviewing our manuscript. We have addressed each comment and made the necessary revisions to improve the quality and clarity of our manuscript.
>Why authors choose to use hybrid quantization instead of only use VQ since the outliers in ... | Summary: RWKV is a modern RNN architecture that faces deployment challenges on resource-constrained devices. RWKVQuant, a post-training quantization (PTQ) framework, is proposed to address the limitations of applying existing quantization methods to RWKV models. RWKVQuant uses a coarse-to-fine proxy to adaptively selec... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable time and efforts in reviewing our manuscript. We have addressed each comment and made the necessary revisions to improve the quality and clarity of our manuscript.
> The paper would benefit from a broader comparative analysis with other advanced quantizati... | Summary: This paper introduces RWKVQuant, a post-training quantization framework for the RWKV model family. The main contributions are: 1) revealing the limitations of existing Scalar Quantization (SQ) and Vector Quantization (VQ) methods on RWKV; 2) proposing a coarse-to-fine proxy strategy to guide the hybrid use of ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable time and efforts in reviewing our manuscript. We have addressed each comment and made the necessary revisions to improve the quality and clarity of our manuscript.
>Only empirical setting of $\tau_c$ and $\tau_f$ mentioned in experimental section.
Sorry fo... | null | null | null | null | null | null |
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity | Accept (poster) | Summary: This paper explores compression techniques for linear RNNs, including unstructured sparsity and fixed-point quantization, and evaluates their acceleration on neuromorphic hardware. The study investigates the trade-offs between latency, energy, and accuracy compared to dense RNNs by introducing these compressio... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for their effort and feedback. We would like to address their questions, and we’d appreciate any raise in score if our arguments convince them. We will be happy to hear and address any further feedback.
### Generalization to different architectures: preliminary re... | Summary: This paper explores the efficiency-performance trade-offs of sparse linear RNNs through a scaling study. The models achieve SOTA results in real-time audio denoising. By quantizing and deploying them on the Intel Loihi 2 neuromorphic chip, the work significantly reduces latency and energy consumption compared ... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for their effort and feedback. We would like to address their questions, and we’d appreciate any raise in score if our arguments convince them. We will be happy to hear and address any further feedback.
### Clarification on Figure 6
We regret the suboptimal presen... | Summary: This paper explores unstructured sparsity in linear recurrent neural networks (RNNs) to improve efficiency in edge AI applications, particularly when deployed on neuromorphic hardware (Intel Loihi 2). The authors examine various model compression techniques and conduct a scaling study to determine the Pareto f... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for their effort and feedback. We would like to address their questions, and we’d appreciate any raise in score if our arguments convince them. We will be happy to hear and address any further feedback.
### Generalization to different accelerators
Please refer to ... | Summary: The paper presents a method to accelerate the computations of linear Recurrent Neural Networks (RNNs) using unstructured sparsity for edge computing applications. This work is motivated by a case study showing that highly sparse linear RNNs achieve superior efficiency-performance trade-offs compared to dense b... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for their effort and feedback, and we would like to address their questions. We will be happy to hear and address any further feedback.
### Generalization to different accelerators
Reviewers VTiJ and jRFp raised the need for a discussion about the generalizability... | null | null | null | null | null | null |
Steer LLM Latents for Hallucination Detection | Accept (poster) | Summary: The paper proposes a method named Truthfulness Separator Vector (TSV) to detect the hallucinations in LLMs. The TSV is a lightweight vector that reshapes the LLM's latent space during inference without altering model parameters. A two-stage training framework is used to explore TSV in a similar manner to a sem... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful and valuable comments. Below, we provide detailed responses to each of your questions and comments.
> A1. Optimal separability of the representation
Thank you for your insightful question! We acknowledge that there can indeed be failure cases where the repre... | Summary: This paper introduces the Truthfulness Separator Vector (TSV), a lightweight approach for hallucination detection in LLMs that reshapes the model's latent space during inference without modifying its parameters. The method employs a single trainable vector added to an intermediate layer, trained through a two-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and insightful questions. Below, we provide detailed responses to each of your points. All the added experiments are performed with LLaMA-3.1-8b.
>A1. Longer generations
Great point. In this work, we focus on short-form QA (phrase and sentence-level... | Summary: The paper introduces the Truthfulness Separator Vector (TSV), a lightweight and flexible steering vector designed to reshape the latent space of Large Language Models (LLMs) during inference to enhance the separation between truthful and hallucinated outputs without modifying model parameters. TSV is trained u... | Rebuttal 1:
Rebuttal: > A1. Steering vector for LLMs
Thank you for your insights! While it is true that the steering vectors have been explored in LLMs, our key contribution lies in how we specifically design them for hallucination detection, as recognized by you and Reviewers iT9L and tv5M. Sections 4.2 and 4.3 illus... | null | null | null | null | null | null | null | null |
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation | Accept (poster) | Summary: The paper proposes DP federated learning algorithms that allows partial participation based on the DP $\mu^2$ SGD algorithms from (Reshef & Levy, 2024). The objective is to minimize the average of the population risks across all clients. The authors consider both cases of trusted and untrusted servers and achi... | Rebuttal 1:
Rebuttal: Thank you for your positive review and for your comments.
We address all of your concerns and kindly ask you to update your score accordingly.
---
**Q: Adding experiments**
**A:** We have now added experiments that demonstrate the applicability of our approach and corroborate our theoretical g... | Summary: The paper addresses the challenge of applying DP in FL when only a subset of clients are active per iteration.
It mainly improves on a previous paper called 'Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems', in which all settings are same but the clients are... | Rebuttal 1:
Rebuttal: Thank you for the review.
We address all of your concerns and kindly ask you to increase your score accordingly.
---
**Q: Novelty over the full-participation paper [Reshef and Levy, 2024]**
**A:** We understand that our approach seems simple in hindsight. However,
let us highlight that com... | Summary: This paper proposes a differentially private algorithm for federated learning when the untrusted server and with partial participation. The approach is an extension of the previous work of the paper "Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems", Roie Reshe... | Rebuttal 1:
Rebuttal: Thank you for the review.
We address all of your concerns and kindly ask you to increase your score accordingly.
Regarding your comment:
---
**Q: Novelty over full-participation paper [Reshef and Levy, 2024]**
**A:** We understand that our approach seems simple in hindsight. However,
let ... | Summary: The paper addresses the challenge of ensuring Differential Privacy (DP) in Federated Learning (FL) under partial participation, where only a subset of devices engage in each training round. Existing approaches struggle to extend DP guarantees from full-participation settings to practical FL scenarios with inco... | Rebuttal 1:
Rebuttal: Thank you for your positive review and for your comments.
We address all of your comments and kindly ask you to update your score accordingly.
Regarding your comments:
---
**Q: Comparison to [Gao et al. 2024]**
**A:** The work of [Gao et al. 2024] indeed provides guarantees for DP-FL learnin... | null | null | null | null | null | null |
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment | Accept (poster) | Summary: - The paper introduces the MM-RLHF dataset containing 120k preference-pairs aimed at improving MLLMs at image/video understanding and safety. They employ a human-assisted pipeline to ensure the high quality of the dataset annotations with available MLLMs generating initial responses.
- The authors also introdu... | Rebuttal 1:
Rebuttal: **Concern 1: MM-RLHF-Reward Model Training Details**
We sincerely apologize for any lack of clarity regarding our reward model training process. Here are the key details:
1. Base Model: We initialized our reward model from LLaVA-OV-7B, following common practice in both LLM and MLLM research wher... | Summary: This paper introduces MM-RLHF, a multimodal alignment pipeline combining a large preference dataset, a critique-based reward model, and MM-DPO, an enhanced DPO algorithm with dynamic reward scaling. The proposed approach is evaluated on 10 tasks across 20+ benchmarks, showing consistent gains in conversational... | Rebuttal 1:
Rebuttal: **Concern 1: Critical Baseline Missing: There is no direct comparison between MM-DPO and standard DPO, making it impossible to quantify the benefit of dynamic reward scaling itself.**
Actually, we compare DPO with MM-DPO in Figures 1 and 11, where MM-RLHF refers to training with the MM-RLHF datas... | Summary: This paper introduces MM-RLHF, an approach for aligning multimodal large language models (MLLMs) with human preferences with thousands of human annotated preference pairs and ratings. It's proved that conducting training on MM-RLHF dataset and the future DPO on the preference pairs can make the model safer.
I... | Rebuttal 1:
Rebuttal: **Concern 1: The paper acknowledges this limitation but doesn't provide strong solutions.**
It's important to note that not all models show minimal improvements on these benchmarks. For example, InternVL2 performs better on RealWorld tasks (from 43.1 to 44.9), which may relate to its image segmen... | Summary: This work introduces MM-RLHF, a new dataset for fine-tuning multimodal large language models (MLLMs) with human preference. The data samples are collected from diverse sources and carefully annotated by expert human annotators. Based on this new dataset, a reward model training framework is developed, which ge... | Rebuttal 1:
Rebuttal: Thank you for your time and for acknowledging our work. We will address each of your concerns below:
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**Concern 1: This work does not seem to explicitly incorporate a mechanism that prevents test data leakage/contamination**
Thank you for raising this important issue. In our work, we have i... | null | null | null | null | null | null |
Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization | Accept (poster) | Summary: This paper extends the idea of natural gradient descent from back-propagation based training (first-order) to zero-order based training of neural networks. Specific care is taken to enable efficient approximations to the Fisher-Information-Matrix for deep neural networks by using block-wise FIM. Several experi... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and evaluating that the contribution is sufficient and the paper is very well written.
> If the authors include a discussion on the memory-costs of their algorithm and provide experimental results on (a subsection of) the ZO-Bench datasets with LLM fine-tuning, i... | Summary: This paper proposed a novel sampling strategy for zeroth-order optimizaiton for training neural networks. Specifically, the authors propose natural perturbations that incorporate not only the parameter space discrepancy but also a function space discrepancy. To make the approach practical for networks with lar... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and evaluating the idea of designing the sampling distribution as innovative.
> Can the authors elaborate on how the method might be adapted or scaled for neural networks with orders of magnitude more parameters (e.g., in the millions), especially regarding the c... | Summary: This paper introduces the concept of natural perturbations for black-box training of neural networks using zeroth-order (ZO) optimization. The authors propose a novel sampling strategy for parameter perturbations that maximizes entropy while regularizing the distribution to prevent drastic changes in the neura... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and evaluating that the proposed method makes sense, experimental improvements are consistent, and the theoretical analysis provides a solid foundation.
> How does the proposed natural perturbations method scale to larger neural network architectures, particularl... | Summary: The paper proposed an sampling strategy for ZO optimization, where the perturbation is sampled from a multivariate Gaussian distribution with a covariance matrix. The covariance matrix is designed by not only minimize the expected PSD but also FSD. Adopting the concept of natural gradient, this perturbation is... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and evaluating that the concept of natural perturbation is well established.
> It would be interesting to see if the proposed method outperforms the baseline with the same computation budget, or the proposed method gives an interesting trade-off.
The computation... | null | null | null | null | null | null |
Can DBNNs Robust to Environmental Noise for Resource-constrained Scenarios? | Accept (poster) | Summary: This paper investigates the robustness of DBNNs under environmental noise in resource-constrained scenarios. The authors identify that the vulnerability of DBNNs stems from binary weights and scaling factors and propose an L1,∞-norm constraint to improve robustness. The proposed method introduces an auxiliary ... | Rebuttal 1:
Rebuttal: Q1: How does the proposed method generalize to non-vision tasks such as NLP?
A1: Thank you for your insightful question. From a theoretical perspective, extending to NLP tasks such as text classification or machine reading, it is necessary to first construct BERT models that binarize weights and ... | Summary: This paper addresses the robustness of deep binary neural networks (DBNNs) under environmental noise perturbations in resource-constrained scenarios. The authors propose an $L_{1,\infty}$-norm constraint on binary weights and scaling factors to derive a tighter robustness upper bound compared to existing metho... | Rebuttal 1:
Rebuttal: Q1.The reviewer wants to know how robust of proposed algorithm can be against adversarial attacks with latest BNNs (e.g., CycleBNN, Fontana et al., 2024).
A1. Thanks for the reviewer's nice suggestion. We opted for a standard training and testing phase to introduce PGD attacks, thereby evaluating... | Summary: The paper investigates whether Deep Binary Neural Networks (DBNNs) can be robust to environmental noise, particularly in resource-constrained scenarios such as bio-electrical signal classification and medical imaging. The authors identify that DBNNs' robustness vulnerabilities stem from binary weights and scal... | Rebuttal 1:
Rebuttal: Many thanks to the reviewers for their positive comments and constructive comments.
Q1. However, its significance is somewhat limited by the lack of real-world deployment or validation on actual edge devices.
A1. We apologize for the fact that the inference experiments on actual edge devices. H... | Summary: In this work, the authors investigate the robustness of deep binary neural networks (DBNNs) under environmental noise perturbations in resource-constrained scenarios. Specificity, the authors propose an $L_{1,\infty}$-norm constraint on objective function for binary weights to derive a tighter robustness upper... | Rebuttal 1:
Rebuttal: Q1: Authors should give priority to the visual results of medical image classification in the main text.
A1: We extend our gratitude to the reviewer for providing this constructive suggestion. We fully concur with the suggestion to include visual results in the main text to effectively demonstrat... | null | null | null | null | null | null |
Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks | Accept (spotlight poster) | Summary: This paper studied the decentralized optimization problem where the mixing matrix is row-stochastic. This paper first derived the lower bound. Then, this paper analyzed PULL-DIAG-GT, showing that PULL-DIAG-GT requires an additional assumption, Assumption 4, to converge to the stationary point. Finally, this pa... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and constructive comments. Below, we address each point in detail.
**Q1:** Could you please describe how the authors tuned hyperparameters, e.g., learning rate, in Sec. 6? This information is very important for reproducing the experime... | Summary: A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed for doubly-stochastic and column-stochastic mixing matrices,
the row-stochastic scenario remains unexplored.
This paper bridges thi... | Rebuttal 1:
Rebuttal: **Reviewer's Comment:**
Weakness: The techniques used in this paper are mainly from ''Towards better understanding the influence of directed networks on decentralized stochastic optimization''. (Liang et al. (2023))
**Authors' Response:**
We sincerely thank the reviewer for their insightful fe... | Summary: This paper presents a theoretical analysis of decentralized optimization with row-stochastic mixing matrices. It is the first to establish a lower bound for convergence. Gradient tracking-based algorithms are shown to achieve linear speedup in convergence under an additional assumption. To overcome this limita... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and constructive comments. Below, we address each point in detail. **All newly added experiments can be found in the Rebuttal Experiment Sheet (RES)** https://anonymous.4open.science/r/ICML-2025-Rebuttal-Experiment-Sheet-B6C0/
**Experi... | null | null | null | null | null | null | null | null |
Probably Approximately Global Robustness Certification | Accept (poster) | Summary: The authors propose an algorithm that extends local robustness certification techniques to the entire input space with probabilistic guarantees. To achieve this, they introduce a novel approach for quantitatively characterizing a Deep Neural Network's (DNN's) robustness across the input space. Specifically, th... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough and insightful review. We address your comments in the following.
**Dependence on the dataset**. We lift local robustness statements about a finite dataset to a (probabilistic) global robustness guarantee over the data distribution, i.e., to a robustness gu... | Summary: The authors propose a probabilistic framework to evaluate global robustness in neural networks. Global robustness for a neural network is defined such that a neural network is globally robust if it is robust at all confident predictions. The approach relies on \epsilon-nets and is evaluated on MNIST and CIFAR-... | Rebuttal 1:
Rebuttal: Thank you very much for your review and comments. We address them below.
**Size of the certified NNs.** Techniques like CROWN and randomized smoothing do indeed scale up to large networks for *local robustness* certification.
However, our work is concerned with using local robustness checks to p... | Summary: This paper introduces a novel method for certifying the global robustness of neural networks (NNs). The method employs a sampling procedure to create an $\epsilon$-net, which is used along with a local robustness verifier to provide probabilistic guarantees on the robustness of the model, depending on its pred... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for the interesting and relevant references.
**Quality of distribution approximation.** In the paper, "sufficiently well for our purpose" is intended to reflect that such an approximation is able to provide guarantees that generalize to unseen data. To avoi... | Summary: This method tackles the problem of constructing a method for estimating global robustness of a NN (or other function) that has a formal guarantee. The authors take the approach of probabilistically relaxing the definition of robustness and producing a probabilistic certificate of global robustness. The size of... | Rebuttal 1:
Rebuttal: Thank you for your comments. As you suggested we added additional experiments to demonstrate the potential of our approach.
In our reply, we also address the questions about our experimental evaluation from other reviewers.
We first point out that
**the goal of our experiments is to show that... | null | null | null | null | null | null |
Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations | Accept (poster) | Summary: The paper introduces Hyperbolic GNN, a novel method that models a graph as a system of differential equations by leveraging the good properties of hyperbolic differential equations. It incorporates the topological structure characteristics of the graph into the message passing by modeling the differential equa... | Rebuttal 1:
Rebuttal: **[Cons 1/Q1]**: *Computational costs under different modules.*
**[Answer]**: The efficiency of the proposed method primarily depends on the choice of the spectral GNN filter. A more efficient filter leads to higher efficiency. Additionally, hyperparameters ($i.e.$, termination time $T$ and time ... | Summary: This paper introduces "Hyperbolic GNNs" (Hyperbolic Graph Neural Networks), a type of spectral graph neural network where the message passing is implemented through hyperbolic partial differential equations. The network can thus be viewed as a kind of dynamical system. The authors provide several experiments d... | Rebuttal 1:
Rebuttal: **[Cons]**: *The question about the concept of hyperbolic PDE.*
**[Answer]**: We appreciate your theoretical endorsement for our approach. As you pointed out, the hyperbolic PDE in this paper differs fundamentally from the hyperbolic geometry mentioned in the literature [1,2].
The focus of hyper... | Summary: This paper proposes a Hyperbolic Graph Neural Network (GNN) framework based on a system of hyperbolic partial differential equations (PDEs), establishing a novel message-passing paradigm that derives topology-aware node representations by solving these equations. Supported by a solid theoretical foundation and... | Rebuttal 1:
Rebuttal: **[Cons 1]**: *Slightly lower performance on some datasets.*
**[Answer]**: In this paper, we aim to propose a general framework for GNNs, with the advantage of being applicable to spectral GNNs. The hyperbolic PDE-based paradigm allows the model to learn complex graph filters, generating better r... | Summary: This paper proposes to formulate message passing in spectral GNNs as a system of hyperbolic PDEs by extending the concepts of
gradient and divergence on manifolds to graphs. Based on this formulation, node features are shown to propagate messages along specific directions of eigenvectors and therefore better c... | Rebuttal 1:
Rebuttal: **[Cons 1/2]**: *Somewhat limited experimental evaluation, and some marginal improvements on a few datasets.*
**[Answer]**: The proposed method is essentially a general framework that enhances the capability of spectral GNNs, as shown in Tables 4 and 5. The results on both homophilic and heteroph... | null | null | null | null | null | null |
Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion | Accept (poster) | Summary: The authors present Score-Fusion, a volumetric translation model that learns 3D representations by assembling perpendicularly trained 2D diffusion models in score function space. It can reduce the 3D training computational cost and data demand, and the results is comparable in different downstream tasks. Howev... | Rebuttal 1:
Rebuttal: Dear reviewer XUPB,
Thank you for your detailed and constructive feedback. Here is our response to the raised questions and concerns:
## Weakness (1-2): Generalizability beyond brain MRI:
Thank you for your insightful review. We highly agree that results on datasets beyond brain MRI can signifi... | Summary: In this work, the authors focus on using diffusion models for 3d volume-to-volume translation tasks such as super resolution and modality translation. Since 3d volumes (characteristic of medical data) are too big to computationally run a diffusion model, the authors first train two diffusion models in perpendi... | Rebuttal 1:
Rebuttal: Dear Reviewer vsot,
Thank you for your insightful review of our work. Here is our response:
## Experimental Question 1, LDM for diffusion framework:
The reason why we are not using LDM is related to our task setting.
__Intuitively,__ the main aim of Super-resolution is to recover/generate the ... | Summary: The authors study medical volume-to-volume translation, presenting Score Fusion, a 3D volumetric translation model. The model is based on a fine-tuning process, which starts from an average of 2D models. The method is tested on multiple tasks on two medical datasets, being compared with a number of approaches.... | Rebuttal 1:
Rebuttal: Dear reviewer AnYS,
We want to thank you for your feedback noting that "the topic is interesting and method is timely." and providing constructive suggestions for our paper.
## Experimental Question 1, multi-scale SR:
Thanks for raising this point. We agree that multi-scale super-resolution is... | Summary: The paper proposes to improve 3D representation learning of medical image volumes in diffusion models. Unlike earlier methods that ensemble 2D models by averaging their weights, the proposed method does “fusion in score function space”. This is shown to improve. They show improved performance in downstream tas... | Rebuttal 1:
Rebuttal: Dear reviewer ELBW,
We sincerely thank you for taking the time to review our paper and for your insightful and positive assessment, noting that "the paper is very well written and easy to follow with good synthesis of literature" and "the contribution is well motivated." Here is our response to... | null | null | null | null | null | null |
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction | Accept (poster) | Summary: The authors propose a novel Vision-Language-Action robotic policy called OTTER. Motivated by the computational burden that fine-tuning VLMs for robot policy use entails, as well as by observations that fine-tuning VLMS for action generalization risks weakening the pre-trained vision-language alignment, the aut... | Rebuttal 1:
Rebuttal: *W1: OTTER .. performs on par with them on the synthetic Libero benchmark...*
The Libero benchmark consists of simple tasks with fixed scenes, distractors, and minimal object variation. As a result, all methods can achieve comparable performance by fitting well to the benchmark’s constraints. Sin... | Summary: This paper aims to address a critical issue wherein the simultaneous fine-tuning of visual and language encoders during VLA training significantly exacerbates the risk of overfitting and impairs their original perceptual capabilities. To overcome this, the authors propose an explicit alignment strategy, which ... | Rebuttal 1:
Rebuttal: *The authors, however, did not compare their approach with more recent baseline methods like Pi-0.*
We thank the reviewer for pointing this out. We have added experiments comparing OTTER with Pi-0 as below.
| Method | Pouring | Drawer | Poking | Pick and Place | Mean±Std. Err. |
|-----------|---... | Summary: OTTER is a Vision-Language-Action (VLA) model that enhances robotic task execution by leveraging the semantic alignment capabilities of pre-trained Vision-Language Models (VLMs) without fine-tuning. By extracting text-aware visual features aligned with task instructions, OTTER preserves the rich visual-languag... | Rebuttal 1:
Rebuttal: Q1: "Effectiveness of text-aware visual feature extraction are not well explored."
We agree that explicitly isolating the impact of our proposed text-aware visual feature extraction is important. The ablation study (DFP-OTTER baseline in Tables 1 and 3 and Appendix D, Table 9) was designed to ill... | Summary: The OTTER keeps pre-trained VLMs fixed to preserve the rich semantic understanding acquired during pretraining, enabling strong zero-shot generalization to novel objects and environments, as demonstrated in both simulation and real-world experiments. This text-aware visual feature is extracted using pre-traine... | Rebuttal 1:
Rebuttal: **Claims and Evidence**
Q1: Clarification on "unseen scenarios"
Thank you for raising this point. In our paper, "unseen scenarios" explicitly refer to tasks involving entirely novel objects or novel combinations of objects and spatial configurations not encountered during training. Crucially, o... | null | null | null | null | null | null |
Graph Adaptive Autoregressive Moving Average Models | Accept (spotlight poster) | Summary: In this paper, the authors propose addressing the computational complexity issues in traditional graph transformers and the over-squashing problem in GNNs by converting the input graph into a sequential representation and incorporating an autoregressive moving average model with an attention selection mechanis... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the thorough and constructive feedback. We have carefully addressed all concerns and believe the revisions have strengthened our paper. We hope our responses are satisfactory and that you will consider updating your score.
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**Regarding R1-R6:** The Reviewer is correc... | Summary: This paper introduces a Graph Adaptive method GRAMA based on a learnable ARMA framework to address limitations in existing Graph State Space Models. GRAMA preserves permutation equivariance while enabling efficient long-range information propagation via a selective attention mechanism. Theoretical connections ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for acknowledging the **“strong theoretical foundation”** with **“detailed proofs”**, with an **“exceptionally thorough”** experimental section. We would also like to express our gratitude for the thoughtful comments and feedback, to which we have done our best efforts to res... | Summary: This paper introduces a novel GNN architecture based on a state-space model, proposing a new method to transform graph data into a sequence. Unlike previous approaches with similar goals, this work presents a principled approach to sequence generation, ensuring a provably permutation-invariant framework. The p... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the positive feedback and assessment of our paper. We are also grateful for the actionable feedback to which we respond below. We have made our best efforts to accommodate each of your comments, and we hope you find our responses satisfactory.
---
**Regarding ... | Summary: This paper introduces GRAMA, which utilizes ARMA (autoregressive moving average) to design graph state space models. The paper claims this way of design can preserve permutation equivariance and enable long-range message passing with good empirical accuracy across multiple datasets.
Claims And Evidence: - The... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the thoughtful comments and the actionable feedback. We have taken significant measures to accommodate each of your comments. We hope that you will find our responses satisfactory, and that you will consider raising your score. We are also happy to read that the Reviewer ... | null | null | null | null | null | null |
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data | Reject | Summary: This paper proposes the deep positive unlabeled anomaly detection framework, to address the contaminated training samples problem for semi-supervised anomaly detection. Several anomaly detection datasets, including MNIST, CIFAR10, CIFAR100, etc., are utilized to evaluate the effectiveness of the proposed metho... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which we shall address below.
> Weakness 1: The setting and motivation do not appear to be novel in the context of anomaly detection. For example, the noisy-AD (SoftPatch) setting in industrial anomaly detection has been previously explored. Therefore, the authors sho... | Summary: This paper presents a deep positive-unlabeled anomaly detection framework designed to address the issue of contaminated unlabeled data in anomaly detection. The framework integrates PU learning with deep anomaly detection models such as autoencoders and deep support vector data descriptions. It enables the app... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which we shall address below.
> Experimental Designs Or Analyses: However, the paper lacks sufficient details in the experimental settings, such as data preprocessing steps, specific parameters of the model architecture, and hyperparameter settings during training. Th... | Summary: The paper presents a novel semi-supervised anomaly detection method to improve the anomaly detection performance in handling contaminated unlabeled data. It integrates PU learning with deep anomaly detection models such as AE and DeepSVDD. The proposed approach outperforms existing anomaly detection methods.
... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which we shall address below.
> Question 1: The compared methods are relatively outdated. Both the semi-supervised and unsupervised approaches used for comparison are from earlier works. Many new models may outperform those based on AE and DeepSVDD, so it is necessary... | Summary: This paper tackles the task of semi-supervised anomaly detection when unlabeled training data is contaminated with anomalies. Specifically, this paper leverages positive-unlabeled learning to estimate anomaly scores for normal and anomaly data for anomaly detection. The quantitative results demonstrate the sup... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which we shall address below.
> Weakness 1: ... conducting experiments on real datasets ...
We provide a qualitative evaluation using a toy dataset in Figure 1,
and quantitative evaluations using real datasets such as SVHN, CIFAR10/100, and Path/OCT/Tissue-MNIST in S... | null | null | null | null | null | null |
Super Deep Contrastive Information Bottleneck for Multi-modal Clustering | Accept (poster) | Summary: To fully explore the complex latent information and interdependencies among multi-modal data, this paper propose a super deep contrastive information bottleneck for multi-modal clustering method. It incorporates the rich information from the hidden layers of the encoder into the clustering process to comprehen... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**Q1: Hidden layer information integration mechanism: Is it a shared encoder or an independent encoder? How to fuse the hidden la... | Summary: This paper proposes a Super Deep Contrastive Information Bottleneck (SDCIB) for multi-modal clustering, designed to fully exploit the latent information in multi-modal data. SDCIB integrates the rich information from the hidden layers of the encoder into the clustering process, optimizing both feature distribu... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions.
**Q1: The paper discusses the use of deeper hidden layers to explore relationships, but the specific advantages of this approach are not explicitly outlined.**
***Response：*** Thank you for the insightful comment. The use of hi... | Summary: This paper proposed an information bottleneck based method named SDCIB for addressing the multi-modal clustering problem, which aims to mine the complex correlations and interdependencies among modalities. It mainly contains two aspects, first, it incorporates the different hidden layers into loss functions to... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**Q1: The full name of some abbreviations is missing, such as KL, which may influence the readability and the understanding on ... | Summary: In multi-modal clustering, effectively capturing the complex relationships between modalities remains a challenge. For solving this, this paper propose a new super deep contrastive information bottleneck method to maximize the utilization of latent information in multi-modal data. It firsts introduces hidden l... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**Q1: It is good to see that the authors give some recent IB works on MMC problem, it is suggested to give a deeper analysis on t... | null | null | null | null | null | null |
Inverse Bridge Matching Distillation | Accept (poster) | Summary: This work incorporates the technique of score distillation in diffusion models to diffusion bridges for accelerated generation. The empirical performance demonstrates that the proposed approach is superior compared to existing baselines under multiple image-to-image tasks.
Claims And Evidence: Most of the cla... | Rebuttal 1:
Rebuttal: Dear Reviewer kKRF, thank you for your comments.
**(1) Relation to SiD [1] and FGM [2].**
Following your suggestion, we will extend the discussion of the related work in the main text. Below, we present the preliminary version of the extension:
Unlike SiD [1] and FGM [2], we focus on diffusion-... | Summary: The paper introduces IBMD, an inverse bridge matching distillation method for inverse problems. The key idea is to consider bridge matching distillation as an inverse problem and convert the constrained problem into an unconstrained one using the reparameterization trick. Based on the teacher models DDBM and $... | Rebuttal 1:
Rebuttal: Dear Reviewer bxNd, thank you for your comments. Here are the answers to your questions and comments.
**(1) It would be better to clarify the reasoning behind the statement in Section 3.2, line 240: 'The key difference in the reformulated problem is that it admits clear gradients of the generato... | Summary: This paper proposes a new distillation scheme for diffusion bridge models. The main idea is to parameterize the entire formulation based on stochastic generator G. The student must follow input-output pairs produced by G, which constrains the path of diffusion bridge, which must coincide with the teacher path.... | Rebuttal 1:
Rebuttal: Dear Reviewer AN8r, Thank you for your comments. Here are the answers to your questions and comments.
**(1) In my understanding, the second term with \phi here acts as an "expander," which means, G can become trivial (or can collapse) without this term ... In other words, the proposed method tra... | null | null | null | null | null | null | null | null |
Preference learning made easy: Everything should be understood through win rate | Accept (poster) | Summary: The paper first introduce the concepts of preference consistency and prevalence consistency, and then proved that the only form of loss function that satisfies both preference and prevalence consistency is a type of win rate, proposing the h-win rate. The paper then argues the benefits of h-win rate, and analy... | Rebuttal 1:
Rebuttal: Thank you for your review. We answer your questions (paraphrased for brevity) below:
1. [datasets and model of Figure 2?]
- The reviewer is correct that the model is Pythia 2.8B. The dataset is Open Assistant.
2. [Table 1 shows different h win rate methods, but did not show a clear winner, and t... | Summary: The paper examines what constitutes a "grounded" evaluation of a policy or language model's alignment with (human) preferences. Assuming the evaluation function is both preference-consistent and prevalence-consistent, meaning it is linear in the distribution of contexts, the distribution of alternatives, and t... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review! Questions (paraphrased) and responses below:
1. [Undesirable that optimal solution depends on anchor, not true for reward maximization]
- This dependence is a limitation of the information contained in the distribution of pairwise comparisons itself. As mention... | Summary: This paper introduces two consistency measures to study preference models. The paper proves that the only evaluation criteria that respects both is the win-rate. This finding is generalized into h-WinRate -- win rate under a monotonically non-decreasing transformation $h$. This measure is used as an optimizati... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We answer your questions (paraphrased for brevity) below:
1. [Theory in the paper is similar to IPO. Proposition 4.1 can be proven using equation 7 in IPO with $\tau$ going to zero. More empirical analysis can distinguish the paper.]
- We agree that one can u... | Summary: The paper argues that win rate should be the primary evaluation metric in preference learning, as it is the only measure that respects both preferences and prevalences in pairwise comparison data. The authors introduce a win rate-centric framework and classify preference learning methods into WRO and non-WRO a... | Rebuttal 1:
Rebuttal: Thanks for the review. Responding to your concerns below:
1. [Overly idealized assumptions (in-distribution assumption, unreliable preference models) diminish practical significance of conclusion]
- Our framework is not focused on idealized assumptions per se but rather what can be learned fro... | null | null | null | null | null | null |
UnHiPPO: Uncertainty-aware Initialization for State Space Models | Accept (poster) | Summary: This paper studies the HiPPO framework with noisy data. While the original HiPPO(-LegS) framework is based on projecting a function onto Legendre polynomials, it assumes that the input function is noise-free. The paper proposes an alternative way of formulating the HiPPO framework, called UnHiPPO, which is bas... | Rebuttal 1:
Rebuttal: Thank you for your review and careful reading.
**Phrasing**
We will clarify Section 4 based on your feedback. The phrase "observations do not exist in HiPPO" refers to the fact that the data does not take the role of an observation in HiPPO, but rather of a control signal. We will update the sec... | Summary: The paper extends HiPPO by incorporating uncertainty-awareness, enhancing the robustness of state space models (SSMs) to noise.
- The study is limited to SC10; testing on additional datasets and tasks would improve generalizability, a limitation acknowledged in the paper.
- The choice of regularization meth... | Rebuttal 1:
Rebuttal: Thank you for your review. Please see Figure 9 in Appendix B for a visualization of the effect of different discretizations. It demonstrates the instability of some methods in both HiPPO and UnHiPPO and in particular the remarkable stability of the closed-form solution. | Summary: The paper proposes to extend high-order polynomial projection operators (HiPPO) that are used to initialise the dynamics of recent state space models. HiPPO theory is agnostic to measurement noise. The paper extends HiPPO operators to capture uncertainty arising from measurement noise. Specifically, the pape... | Rebuttal 1:
Rebuttal: Thank you for your review.
**How was the noise added? How were the signals normalized?**
We follow LSSL implementation and the code from repository of the authors. After the audio files are loaded, signals are divided by 32k to be normalized (see [here)](https://github.com/state-spaces/s4/blob/e... | Summary: This paper investigates state space models (SSMs) through the lens of linear stochastic control theory and proposes a novel initialization method to enhance robustness against input noise. The authors first reformulate the linear recurrence in SSMs as a homogeneous linear dynamical system with noise, replacing... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
**Time-dependence of matrices**
It is correct that SSMs learn $A$ and $B$ and discretize them on the fly. However, at least for LSSL, the time step at which the learned matrices are discretized is fixed per feature to make the model adapt to multiple timescale... | null | null | null | null | null | null |
Scalable Equilibrium Sampling with Sequential Boltzmann Generators | Accept (poster) | Summary: This paper proposes Sequential Boltzmann Generators, consisting of two conceptual ingredients: first, that invertible normalizing flows operating on Cartesian coordinates can scale to molecules as large as hexapeptides by leveraging non-equivariant transformers and the recent TarFlow framework; second, that an... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer S3Vr
We would like to thank the reviewer for their time, feedback, and positive appraisal of our work.
We are heartened to hear that the reviewer feels that the “contribution made by this paper should significantly change the course of future work in this area.” We also thank... | Summary: This paper improves data-driven learning-based Boltzmann Generators (BG) with Sequential Monte Carlo (SMC), based specifically on a non-equilibrium transport method (NETS) recently proposed by (Albergo & Vanden-Eijnden (2024)). Unlike NETS whose source energy is based on a pre-defined prior, here the source en... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer HyU2
We thank the reviewer for their time and effort. We are glad that the reviewer found our empirical results to “demonstrate the effectiveness” of our method SBG. We next clarify the main points raised in the review and note that additional results are included in this link... | Summary: The manuscript presents the Sequential Boltzmann Generators (SBG), a novel extension to the existing Boltzmann generator framework for scalable sampling of molecular states in thermodynamic equilibrium. The framework removes the SE(3)-equivariance and encodes equivariance softly via data augmentations, achievi... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer 9RzA
We thank the reviewer for their thoughtful comments and feedback. We value that the reviewer found that most of our claims were supported by “clear and convincing evidence” and that our mathematical claims were “theoretically sound and mathematically consistent”. We are a... | Summary: This paper introduces Sequential Boltzmann Generators (SBG), an extension to the Boltzmann generator framework. By replacing conventional importance sampling with a non-equilibrium annealing process , the authors aim to transport proposal samples toward the target Boltzmann distribution. The authors also propo... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer VNPc
We thank the reviewer for their time, feedback, and nuanced comments. We are glad that the reviewer found the non-equivariant NF an “interesting alternative” which is “necessary as we scale up to even larger systems and datasets”. We also appreciate that the reviewer reco... | null | null | null | null | null | null |
Sleeping Reinforcement Learning | Accept (poster) | Summary: This paper considers a tabular episodic reinforcement learning setting where the set of available actions is not fixed but varying over episodes, states and time steps. The paper studies two different ways the available actions are revealed to the learner: per-episode (available actions are revealed at the beg... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work, and for having appreciated the significativeness of our work. We also thank the Reviewer for the comments on the results which we will use to expand the discussion on the results exploiting the additional page. Below, our answer to the R... | Summary: The paper introduces Sleeping Reinforcement Learning (SleRL), a new reinforcement learning paradigm where the set of available actions varies over time due to external constraints or stochastic processes. Two settings are considered: per-episode disclosure where available actions for all states are revealed a... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work. Below, our answer to the Reviewer's questions and concerns.
> Is it possible to add some numerical results to verify your results?
To numerically validate the algorithm and show the impact of action availability on performance, we cons... | Summary: This paper studies a new paradigm called Sleeping Reinforcement Learning, where the available action set varies during the interaction with the environment. The authors study several settings, including the per-episode disclosure, in which the available action sets are revealed at the beginning of each episode... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper and for having appreciated the motivation, clarity and novelty of our work. Below, our answer to the Reviewer's comments.
> It may be better to also provide a lower bound for the setting of independent per-stage disclosure.
> It is pos... | null | null | null | null | null | null | null | null |
Fast Video Generation with Sliding Tile Attention | Accept (poster) | Summary: this paper addresses the problem of slow speeds in video generation. the paper proposes a method called sliding tile attention that is designed to address the challenge. the proposed method learns to do sliding and attending over local spatial and temporal region, allowing the reduction of redundancy in comp... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer BHCc for their supportive feedback and genuine interest in our work.
> *Will the code be released to ensure reproducibility*
We appreciate your emphasis on reproducibility. We confirm that all the code, configurations, and scripts required to reproduce our experiment... | Summary: This paper introduces sliding tile attention (STA) to address prohibitive compute cost in attention calculation. The authors observed that attention scores in pretrained video diffusion models predominantly concentrate within localized 3D windows. The proposed STA can eliminate redundancy from full attention b... | Rebuttal 1:
Rebuttal: We thank the reviewer nmHo for their constructive feedback. Below, we address specific comments one by one.
> *More training details about STA w. Training are missing.*
We have provided the primary details of STA training, including datasets, prompts, learning rate, and hardware specifications, ... | Summary: This paper introduces sliding tile attention (STA) that operates tile-by-tile with a novel hardware-aware sliding window design, preserving expressiveness while being hardware-efficient. STA achieves 1.36–3.53× end-to-end speedup with no or minimum quality loss.
## update after rebuttal"
Thanks for the author... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer C6tW for their insightful suggestions and valuable questions. Below we address your comments and strengthen our paper.
> *Are the results generalizable to other models than Huanyuan-Video?*
As suggested, we further validated STA's generalizability beyond Huanyuan-V... | Summary: This paper introduces Sliding Tile Attention (STA), a novel attention mechanism designed to accelerate video generation using Diffusion Transformers (DiTs). The key idea is to leverage the observation that attention scores in pretrained video diffusion models are predominantly concentrated within localized 3D ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 3sP9's insightful question regarding the constraints on input resolutions and aspect ratios for Sliding Tile Attention (STA). Below, we clarify and expand upon these points:
> *I think STA should have restrictions on input resolution, length, and aspect ratio. The authors s... | null | null | null | null | null | null |
Trusted Multi-View Classification with Expert Knowledge Constraints | Accept (spotlight poster) | Summary: This paper proposes Trusted Multi-View Classification with Expert Knowledge Constraints (TMCEK). There are core contributions: (1) Integrating expert knowledge into multi-view learning to enhance both interpretability and uncertainty estimation, and (2) proposing a novel distribution-aware subjective opinion f... | Rebuttal 1:
Rebuttal: Thank you for your constructive and encouraging comments. Below are our responses.
**Q: The paper presents the method primarily for sleep staging, but there is limited discussion on how this method might generalize to other domains.**
A: Thanks for your professoional question. While our method i... | Summary: This paper introduces an innovative trusted multi-view classification approach designed to tackle the significant shortcomings of existing methods, namely, opacity at the feature level and imprecise confidence assessments at the decision level. Its primary contribution resides in advancing current trusted mult... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions. Below are our responses.
**Q: In regards to the lower classification performance observed for the N1 stage (Figure 4), have the authors investigated methods to mitigate class imbalance or improve classification accuracy for this specific stage?**
A: R... | Summary: This paper proposed a novel trusted multi-view classification method, called TMCEK. Compared with the existing trusted multi-view classification methods, TMCEK embeds the Gabor function into the first convolutional layer as its kernel to enhance feature-level interpretability. Moreover, it introduces a distrib... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for dedicating their time and effort to review our work. Below are our responses.
**Q: The use of Gabor kernels for feature extraction at the first convolutional layer is a key feature of the model. Could you provide further details on how the Gabor kerne... | Summary: This study proposes an expert knowledge-guided trusted multi-view classification framework that achieves dual advancements in interpretability and uncertainty quantification. Specifically, the proposed method introduces expert knowledge as a tool of the feature-level interpretability and defines distribution-a... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback and thoughtful questions. Below are our responses.
**Q: Could you clarify why certain Gabor kernel outputs are considered redundant during the training process? Are there any strategies for improving kernel optimization?**
A: Some Gabor kernels become redunda... | null | null | null | null | null | null |
SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space | Accept (poster) | Summary: This paper introduces the Spherical Diffusion Policy (SDP), a novel method for robotic manipulation that enforces continuous SE(3) equivariance with spherical Fourier representations. The authors propose a spherical modification of the FiLM layer commonly used in diffusion-based policy networks, prove the inva... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We respond below:
> "However, it's not 100% clear how these experiments were performed from the description: "We train all the baselines on progressively tilted environments with 100 demonstrations." Does tilt=30 degrees imply that demonstratio... | Summary: This paper propose one new method called “Spherical Diffusion Policy (SDP)” for robot manipulation. The paper focus on 3D generalization, using SO(3) and T(3) equivariance (i.e., full SE(3) group) to handle random tilts and object placements. It design a special spherical encoder with spherical FiLM layer and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We respond below:
> "Could authors tune some parameters of this baseline to see, like the longer prediction horizon? Besides, it is good to report the parameters for all baselines."
We use the DP3 results reported in the CoRL paper Equivariant... | Summary: This paper works on improving the equivariance of diffusion policy for manipulation tasks. To this end, the authors align the input point clouds and robot state into a canonical coordinate frame to achieve translational equivariance, and then project the encoded observations onto spherical harmonic basis to ac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We respond below ([link to figures](https://limewire.com/d/DAndu#qMh5UOCXI6)):
>"... the point clouds and robot arm states can be further aligned within gripper's local coordinate frame by applying a known rotation.
If the authors target to ach... | Summary: This paper presents a novel SE(3)-equivariant diffusion policy, named Spherical Diffusion Policy (SDP), aimed at improving generalization for robotic manipulation tasks across varying 3D transformations. The key motivation stems from the assumption that embedding states, actions, and denoising processes in sph... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We respond below:
> "Some SE(3) Diffusion Models should be cited as the related work, such as: ..."
Thank you for highlighting these relevant works. We will include citations to these papers in the revised related work section.
>"Although dem... | null | null | null | null | null | null |
Learning Curves of Stochastic Gradient Descent in Kernel Regression | Accept (poster) | Summary: This paper analyzes the excess risk and the minimax lower bound of single-pass stochastic gradient descent (SGD) in kernel regression under the combinations of the following settings:
1) the model is well-specified or misspecified ( the source condition constant $s<1$ or $s\geq 1$ );
2) the number of data is... | Rebuttal 1:
Rebuttal: We would like to extend our sincere appreciation to you for the thorough review and valuable feedback. We are grateful that you not only accurately summarized our contributions but also expressed strong appreciation. We would like to address your suggestion and the question you raised regarding po... | Summary: This paper studies the generalization performance of kernel regression trained by online / single-pass SGD and compares it with offline methods such as ridge regression. Specifically, the analysis is conducted under a standard source condition assumption on the target (including the misspecified case) and focu... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and recognition of our contributions. Below, we address your concerns and questions in detail.
***
# Author's Response to Essential References:
>*1. "Related References Bordelon et al. (2020) and Cui et al. (2021)":*
Thank you for sharing these important ... | Summary: This paper proves 4 results about the excess risks of least squares RKHS regression optimised by stochastic gradient descent. The settings of the four results are divided along two axes: firstly, the high-dimensional regime, in which the input dimension grows with the sample size, versus the fixed dimension re... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to carefully review our paper and for providing detailed and highly valuable feedback. Below, we respond to your questions and concerns one by one.
***
# Response to Questions:
>*1. 164R: $N(d,k)$.*
The confusion stems from a typo in Line 142L, where we mis... | Summary: This paper examines the problem of using SGD to train a kernel regressor. In particular, the paper considers the dot product kernel with input data that is uniformly distributed on the sphere. Assuming that the targets $y = f_*(x) + \varpepsilon$ for $f_*$ with certain smoothness properties, the paper shows th... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful review and positive feedback on our work. Your comments have provided concrete guidance for improving the paper and have been a great source of motivation for us. Below, we address your questions and concerns in detail.
***
# Author's Response to Essential ... | null | null | null | null | null | null |
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces | Accept (spotlight poster) | Summary: In the proposed manuscript, the authors present a novel approach for Bayesian optimization within the latent space of a VAE applied to structured domains like molecular design. They aim to address multiple drawbacks of conventional methods that often result in suboptimal performance when the latent space does ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful assessment of our work. We appreciate your recognition of both the descriptive and empirical strengths of our approach. It is encouraging to hear that you find our decoupled strategy promising, and that our competitive results align well with other state-of-the-art me... | Summary: This paper looks for valid molecules with a high probability of improving a desired property of the molecule. The novelty of this method is that they decouple the two probabilities: the probability of a molecule existing is estimated by VAE which is initially trained/pre-trained, then the probability of a mole... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and positive feedback regarding our experimental design and validations. We greatly appreciate your recognition of our method's strong performance. We have carefully addressed all of your suggestions and implemented the requested revisions, detailed b... | Summary: This manuscript proposes an alternative approach to latent space Bayesian optimization in high-dimensional/structured spaces. Contrary to previous methods that aim to efficiently couple a generative model (typically VAE) and GP surrogate, the proposed method decouples the generative model and GP surrogate. Th... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and valuable suggestions. We sincerely appreciate your recognition of the novelty, clarity, rigorous empirical evaluation, comprehensive background coverage, and overall manuscript quality of our work.
We have addressed all your suggestions thoroughly... | Summary: This paper proposes a novel approach to Bayesian Optimization (BO) over structured spaces such as molecular design. Instead of fitting a Gaussian Process (GP) surrogate model within the latent space of a Variational Autoencoder (VAE), which often leads to poor predictive performance, the authors introduce COWB... | Rebuttal 1:
Rebuttal: Thank you for your strongly positive review and for highlighting the novelty, clarity, and thorough experimental design of our work. We have carefully considered your two main comments, both of which guided additional discussion and improvements in the final version of the paper. These insights ha... | null | null | null | null | null | null |
ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning | Accept (poster) | Summary: Multi-task reinforcement Learning (MTRL) algorithms face challenges when tackling tasks with varying complexities and reward distribution. In this work, the authors propose a method for tackling the varying reward magnitude across tasks by adaptively scaling the reward of each task using a history-based reward... | Rebuttal 1:
Rebuttal: We thank Reviewer Cp7C for their thoughtful feedback and valuable suggestions, which have significantly improved our paper. We have carefully addressed each comment, strengthened our experimental results, and clarified our key contributions accordingly. Below, we respond in detail to each point ra... | Summary: The paper introduces Adaptive Reward Scaling (ARS), a novel framework designed to tackle the difficulties caused by varying reward distributions in multi-task reinforcement learning.
ARS employs a history-based reward scaling strategy that dynamically adjusts reward magnitudes to ensure balanced training fo... | Rebuttal 1:
Rebuttal: We thank Reviewer mXD6 for their thoughtful feedback and valuable suggestions, which have significantly improved our paper. We have carefully addressed each comment, strengthened our experimental results, and clarified our key contributions accordingly. Below, we respond in detail to each point ra... | Summary: This paper introduces Adaptive Reward Scaling, a novel framework for multi-task reinforcement learning that dynamically adjusts reward magnitudes using a history-based scaling strategy and integrates a periodic network reset mechanism to mitigate overfitting and biases toward simpler tasks. The empirical resul... | Rebuttal 1:
Rebuttal: We thank Reviewer SzVus for their thoughtful feedback and valuable suggestions, which have significantly improved our paper. We have carefully addressed each comment, strengthened our experimental results, and clarified our key contributions accordingly. Below, we respond in detail to each point r... | null | null | null | null | null | null | null | null |
Learning to Route LLMs with Confidence Tokens | Accept (poster) | Summary: The paper introduces Self-REF, a lightweight fine-tuning framework designed to teach large language models (LLMs) to express confidence in their answers through confidence tokens. These learned tokens, indicate whether the model is confident or uncertain about its prediction, improving reliability and performa... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and reference. We've incorporated them into our related work and made the suggested expository improvements in our revised paper.
**[Q-1] More analysis on a systematic investigation of failure cases to identify patterns in when and why self-REF misclassifies c... | Summary: Self-REF is a lightweight method for training a language model to show when its answers are correct or incorrect by using “confidence tokens.” The approach starts with a base model that generates predictions and labels each instance as “confident” or “unconfident” based on the answer’s correctness, creating an... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback. We respond to each point below, and will update the discussion accordingly:
**[Q-1] The assumption that <CN> tokens represent correctness and <UN> represent incorrectness lacks a clear theoretical grounding. The paper implicitly assumes confidence directly cor... | Summary: Authors proposed a lightweight training strategy to teach LLMs to express confidence in whether their answers are correct in a reliable manner. Using this, the authors build a router algorithm that reduces latency and improves overall QA performance.
Claims And Evidence: The claims are well stated and support... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and ideas. We have updated our discussion accordingly.
**[Q-1] Your framework is only able to predict the confidence token at the end of the answer? Is that correct? If so, I see the drawback in terms of routing capabilities. Since the model need to generate th... | Summary: The paper proposes Self-REF, a training strategy that adopts LoRA to fine-tune an LM on a dataset augmented with confidence tokens, based on prediction correctness. Self-REF enhances downstream applications like model routing and answer rejection by leveraging the learned confidence token scores.
Claims And E... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and reference. We've incorporated them into our related work and made the suggested expository improvements in our revised paper.
**[Q-1] Self-REF requires a dedicated training/validation set and a fine-tuning stage for different downstream tasks/datasets, whic... | null | null | null | null | null | null |
Understanding the difficulties of posterior predictive estimation | Accept (poster) | Summary: The authors study the problem of Monte Carlo estimation of the density of the posterior predictive distribution for (approximate) Bayesian inference. They show that simple Monte Carlo estimation can have a low signal to noise ratio (SNR) if the training data and test data are substantially different, the dimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments. We will address the typos, equation formatting, and other minor suggestions as is and offer comments to other concerns below.
>... Bayesian CLT approximations ... provide additional detail ...
While there are several versions of Bernstein-von M... | Summary: This paper addresses the issue of unreliable posterior predictive density (PPD) estimates when using a simple Monte Carlo (MC) approach, highlighting the previously under-recognized issue of low signal-to-noise ratio (SNR). The authors theoretically analyze and empirically demonstrate that the SNR for posterio... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and encouraging review. We appreciate your recognition of our theoretical analysis and the empirical validation of our proposed LIS method, as well as your insights into potential computational challenges. Your constructive feedback is truly invaluable. | Summary: This paper provides a theoretical framework explaining the severe signal-to-noise ratio (SNR) degradation observed in naive posterior predictive distribution (PPD) estimators.
It rigorously demonstrate that, even with exact inference, SNR diminishes with increasing: (1) training-test data mismatch, (2) latent... | Rebuttal 1:
Rebuttal: Thank you so much for your thoughtful and constructive review. We appreciate your recognition of the theoretical contributions and the thoroughness of our analysis. Your feedback is invaluable and greatly encouraging.
For the camera-ready version, we will plan to use the extra space to add more ... | Summary: This paper provides a theoretical investigation of Monte Carlo estimation of posterior predictive distributions. The signal-to-noise ratio (SNR) of Monte Carlo estimation is shown to decrease under three conditions: increasing mismatch between training and test, increasing dimensionality of latent space, and i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We will address the minor concerns and offer some specific comments below.
>Why is SNR a good metric for analyzing the quality of Monte Carlo estimation of the PPD?
We study SNR because it is equivalent to relative variance and bakes in the idea of how ... | null | null | null | null | null | null |
FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation | Accept (poster) | Summary: This paper introduces FairICP, a novel fairness-aware learning method designed to promote equalized odds in machine learning models when dealing with complex and multi-dimensional sensitive attributes. The method combines adversarial learning with an innovative Inverse Conditional Permutation (ICP) strategy to... | Rebuttal 1:
Rebuttal: We appreciate the feedbacks by the reviewer, here are our responses:
1. "Could you discuss more on the challenges and potential inaccuracies associated with density estimation, especially in high-dimensional spaces or with complex data types."
**Reply.** Thank you for this question. We m... | Summary: - This study introduces a permutation-based learning algorithm for developing fair predictive models.
- The fairness notion considered in this work is equalized odds.
- The proposed permutation mechanism generates pseudo-sensitive attributes with aligning the distributions of $(\hat{Y}, A, Y)$ and $(\hat{Y}, \... | Rebuttal 1:
Rebuttal: We appreciate the feedback by the reviewer. Here are our responses:
1. Methods: more baselines.
**Reply.** We thank the reviewer for this valuable suggestion. We add two more baselines in experiments: FDL [4] and Kearns et al. [5]. [Link for experiments added](https://docs.google.com/d... | Summary: The paper introduces FairICP, a method for enforcing equalized odds fairness in machine learning models that handle multiple sensitive attributes. The key idea is to improve how synthetic versions of sensitive attributes are generated in fairness-aware learning. Instead of relying on traditional resampling or ... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and constructive feedback on our paper, here are our responses:
1. Explanation on KPC.
**Reply.** Thank you for this constructive feedback. KPC [1] is a recently proposed non-parametric measure for conditional independence without constraints on the shape o... | Summary: The authors tackle the problem of enforcing equal odds during training, where the protected attribute is multi-dimensional. They propose FairICP, which constructs a synthetic sensitive attribute via a procedure which involves sampling from a learned $q(Y \mid A)$. Then, a discriminator network is added to dist... | Rebuttal 1:
Rebuttal: We appreciate the feedback from the reviewer. Here are our responses:
1. Novelty of the paper over FDL.
**Reply.** Thank you for sharing your concern. We agree that our core insight is not complicated, yet it has not been previously explored and is effective.
1) While we adopted a... | null | null | null | null | null | null |
Fast Exact Unlearning for In-Context Learning Data for LLMs | Accept (poster) | Summary: This paper proposes ERASE, an in-context learning method combined with quantized k-means clustering for exact unlearning in LLMs. ERASE combines in-context learning with quantized k-means clustering, aiming to achieve dataset- and model-independent unlearning costs while maintaining competitive performance. A ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and time. We respond to specific concerns below.
> The paper claims dataset-independent unlearning costs based on quantized k-means, but fails to provide adequate justification for why this specific combination would be uniquely effective for LLMs. While q... | Summary: The paper proposes an exact unlearning algorithm, ERASE, for in-context learning. The core unlearning idea revolves around performing exact unlearning in AutoCOT, which clusters in-context examples using their sentence representations and uses the samples close to the cluster centroids as the ICL examples. ERA... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We discuss specific points below.
> Some of the underlying assumptions related to this work need to be mentioned. For example, for exact unlearning in this setup, the underlying assumption is that the ICL examples were not used during any stage o... | Summary: This paper studies unlearning for in-context learning task adaptation, which is claimed to be understudied. Unlearning is mostly studied in setting where parameters are updates are required such as SISA. The overall method is a follow up on ACoT but in a new setting which is unlearning (ACoT was in learning pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and time.
> Some of the claims in the contribution are difficult to verify for instance. 1) The first claim in line 073-074 is vague and I am not sure how to verify it. 2) The claim 3 and 4 are also not novel and vague.I request the authors to re-write the... | Summary: this paper proposes a novel exact un-learning approach for in-context learning. Given a training sample, the goal of exact unlearning is to obtain as quickly as possible an algorithm that one would have obtained without training on that data sample. The authors study this problem in the context of in-context l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback! Below we discuss questions raised in the review. Other suggestions will be implemented in our revised draft.
>the main weakness to me is the title and positioning itself. It is the first time that I see ICL defined as a fine-tuning method...
We ... | null | null | null | null | null | null |
GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression | Accept (spotlight poster) | Summary: In this work, the authors developed a new estimator for the generalized linear low-rank trace regression problem. The estimator improves existing works by considering instance-dependent information. Additionally, the estimator is nearly minimax optimal locally around the global optimizer. The authors also disc... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing detailed and insightful reviews, and we are especially encouraged by your overall positive evaluation of our paper. Let us respond to each point that you have raised.
---
**Discussions regarding Burer-Monteiro Factorization (BMF) Approach**
Thank you... | Summary: The authors study the problem of generalized linear low-rank trace regression. They build on the previously established algorithm LowPopArt, which applies to the linear setting. Their main result (Theorem 3.1) provides the tightest known upper bound for recovery in the operator norm, incorporating instance-spe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing detailed and insightful reviews, and we are especially encouraged by your overall positive evaluation of our paper. Let us respond to each point that you have raised.
**W1. First stage, which essentially serves to linearize the problem effectively. Al... | Summary: The paper introduces GL-LowPopArt, a new estimator for generalized linear low-rank trace regression. It combines nuclear norm regularization with matrix Catoni estimation, achieving tighter error bounds than previous methods. The authors propose a novel experimental design objective, GL$(\pi)$, and establish a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing detailed and insightful reviews, and we are especially encouraged by your overall positive evaluation of our paper. Let us respond to each point that you have raised.
**W1. Lack of empirical evaluation**
Thank you for your suggestion. We provide prel... | null | null | null | null | null | null | null | null |
Griffin: Towards a Graph-Centric Relational Database Foundation Model | Accept (poster) | Summary: This paper proposes Griffin, a unified model for Relational DataBase(RDB). Griffin has a classification prediction head and a regression prediction head to handle the classification and regression tasks, correspondingly. The classification head can handle arbitrary classes by comparing the inner product betwee... | Rebuttal 1:
Rebuttal: We thank Reviewer 95m6 for acknowledging the contributions of our work and for the helpful suggestions and questions. We address each concern below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://... | Summary: This paper proposes Griffin, a foundation model specifically designed for Relational Databases (RDBs), which leverages graph neural networks (GNNs) to unify the processing of diverse RDB tasks. Experiments demonstrate that Griffin exhibits superior or comparable performance across multiple benchmarks, especial... | Rebuttal 1:
Rebuttal: We thank Reviewer XrAk for the thoughtful and constructive feedback. We address your concerns in detail below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebu... | Summary: The paper introduces Griffin, the first graph-centric foundation model designed specifically for relational databases. Griffin combines advanced architectural innovations such as unified encoders for categorical and numerical features, cross-attention modules for selective information aggregation, and enhanced... | Rebuttal 1:
Rebuttal: We thank Reviewer yHkP for acknowledging the contributions of our work and for the constructive questions and suggestions. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/... | Summary: The proposes Griffin, a pretrained model for relational databases. Griffin uses concepts of unified representation of inputs and tasks, cross-attention mechanisms, and graph neural networks (or MPNNs), tasks of cell completion and supervised learning for pretraining. The proposed framework was tested on severa... | Rebuttal 1:
Rebuttal: We thank Reviewer w5kU for the careful reading and detailed feedback. We address the concerns and questions below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-... | Summary: This paper introduces Griffin, which is claimed to be a novel foundation model specifically designed for RDBs. Griffin aims to unify differet tasks from single table to multi table RDBs. To do that, Griffin is pretrained by sampling the sub graphs from RDBs, and use a unified encoder/decoder to generate unifie... | Rebuttal 1:
Rebuttal: We thank reviewer zrC4 for the detailed and constructive feedback. Below, we address each of the main concerns. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Reb... | null | null | null | null |
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks | Accept (poster) | Summary: Achieving collaborative fairness in federated learning involves contribution assessment and reward allocation mechanisms. This paper proposes a new reward mechanism that leverages slimmable neural networks (a client with lower contribution would get a neural network of smaller width and lower accuracy). Aequa ... | Rebuttal 1:
Rebuttal: > Experimental Designs
**E1:** We appreciate the reviewer's effort in scrutinizing the experimental design. However, we would like to clarify an **important point**: there is no alternative definition of collaborative fairness in our work or in the literature - the goal is consistent across meth... | Summary: The paper introduces a framework (Aequa) for fair model rewards in collaborative learning (CL) by leveraging slimmable neural networks. The core idea is to proportionally allocate model capacity to participants based on their contributions, rather than distributing identical models to all. The method ensures t... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper, for highlighting its strengths, and for their valuable feedback.
> W1. Continuous model performance assumption
We acknowledge that the assumption of continuous model performance across the interval $[\ell, u]$ may not always hold p... | Summary: The manuscript studies the question of assigning rewards to participants with different models whose performance faithfully reflects their heterogeneous contribution, and extends/repurposes the concept of the slimmable network for fairness in federated learning, so as to make sure that model rewards are propor... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work, for highlighting its advantages and for their valuable positive feedback.
> The writing quality of Section 5.2 should be improved.
We appreciate the reviewer highlighting the clarity issues in Section 5.2. We sincerely apologize for ... | Summary: This paper introduces Aequa, a framework to ensure collaborative fairness in federated learning using slimmable neural networks. It trains a single global model whose sub-networks of varying widths serve as rewards aligned with participant contributions. Experiments on six benchmark datasets show Aequa achieve... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable positive feedback.
> Reliance on TEEs for security
We acknowledge the reviewer's concern regarding reliance on TEEs. This limitation is already discussed in the paper, in Section 7. While TEE is a necessity for Aequa, Aequa can also operate in... | null | null | null | null | null | null |
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