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Accelerating Unbiased LLM Evaluation via Synthetic Feedback | Accept (poster) | Summary: This paper introduces Control Variates Evaluation, a novel method for unbiased and cost-efficient evaluation of large language models (LLMs) in head-to-head comparisons. The approach leverages synthetic feedback from LLMs, combined with human annotations, to reduce annotation costs while maintaining evaluation... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your comments below.
> **Weakness 1: Beyond head-to-head comparisons**
Our theory directly applies to many other evaluation tasks, such as single response evaluation, where a human gives scores to a single LLM generation, instead of giving prefer... | Summary: -Paper proposes Control variates evaluation --- the goal being to reduce the cost of LLM evaluations
-It does so using a principled statistical approach that combines human annotations with synthetic feedback (i.e. LLM as a judge).
-Specifically, the synthetic feedback is the control variate to reduce the v... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We address your questions and comments below.
> **Weakness 1 & Comment 2: Only head-to-head evaluation tasks**
Public datasets for other evaluation tasks are limited, and collecting such data may require significant human effort, which is beyond our ... | Summary: The paper proposes Control Variates Evaluation, a method to reduce human annotation costs in evaluating large language models (LLMs) while maintaining unbiased results. By combining synthetic feedback from LLMs, the method achieves variance reduction in win rate estimation. The approach is theoretically ground... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive feedback. We address your comments and questions below.
> **Theoretical Claim: The general application of Control Variates does not require the sampled $x$ to follow a uniform distribution.**
Our method can be applied directly to the setting wher... | null | null | null | null | null | null | null | null |
On the Query Complexity of Verifier-Assisted Language Generation | Accept (poster) | Summary: This paper introduces a mathematical framework for analyzing verifier-assisted language generation and shows that process verifiers can transform intractable generation problems into tractable ones. Through theoretical analysis and experiments on synthetic grammar and code generation tasks, they demonstrate t... | Rebuttal 1:
Rebuttal: Thank you for your review! In the following, we respond to a few points raised in your review.
**1. When constraints are "softer" or probabilistic rather than binary.**
There could be multiple types of constrained generation tasks with non-binary constraints. In the following, we explain our tho... | Summary: The paper presents several theoretical and experimental results on the query complexity of constrained decoding. First, in Section 3, it demonstrates theoretically that there exists tasks for which constrained decoding is difficult without a verifier. Then, in Section 4, it shows that for certain problems, the... | Rebuttal 1:
Rebuttal: Thank you for your review! In the following, we respond to a few points raised in your review.
**1. Scope of our theory, imperfect verifiers**
We see one of our main theoretical contributions as being a new formalism for theoretically reasoning about algorithms for verifier-assisted language gen... | Summary: This work studies the problem of constrained autoregressive language generation from the perspective of computational complexity, showing that for even very simple autoregressive oracles (i.e. autoregressive language models) and very simple constraints, the task of constrained generation can be computationally... | Rebuttal 1:
Rebuttal: Thank you for your review! In the following, we respond to a few points raised in your review.
**1. Additional references.**
Thank you for the recommendations! Methods like FUDGE [1] are indeed related to a slight extension to our framework. In particular, the attribute predictor in FUDGE can be... | null | null | null | null | null | null | null | null |
TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision | Accept (poster) | Summary: The paper introduces TimeStep Master (TSM), a novel approach for fine-tuning diffusion models efficiently. Unlike traditional Low-Rank Adaptation (LoRA), which applies the same tuning across all timesteps, TSM uses TimeStep LoRA experts specialized for different noise levels. It consists of two stages: fosteri... | Rebuttal 1:
Rebuttal: Response to Reviewer eNpx:
We sincerely appreciate your thoughtful and detailed review. Your insightful comments have been invaluable in guiding us to improve our manuscript. Below we provide our point-by-point responses, and we hope our clarifications and planned enhancements address your concer... | Summary: This paper introduces TimeStep Master (TSM), a diffusion fine-tuning framework using an asymmetrical mixture of timestep LoRA experts. Rather than applying a single LoRA module across all timesteps, which limits the adaptability of different noise levels in the diffusion process, TSM introduces a timestep-spec... | Rebuttal 1:
Rebuttal: Response to Reviewer zVmL:
Thank you for your careful reading and analysis of our article, and for providing valuable feedback. Below are our responses to your comments:
**Question1:** Recent studies have provided in-depth analyses of timestep modeling in diffusion training, including approaches... | Summary: This article addresses the issue of limited model performance during the fine-tuning process of diffusion models, which arises from the use of the same LoRA across different time steps. We propose the TimeStep Master method, which employs different LoRAs at varying time step intervals to fine-tune the diffusio... | Rebuttal 1:
Rebuttal: Response to Reviewer whQF:
We sincerely appreciate the time and care you invested in reviewing our manuscript. Your insightful comments and suggestions have been extremely valuable, and we are grateful for the opportunity to clarify these points. Below are our detailed responses:
**Question1:** ... | Summary: This paper introduces TimeStep Master (TSM), a method that employs multiple LoRA experts, each specialized in specific timestep regions. The authors empirically analyze the degradation caused by sharing LoRA parameters across all timesteps, which motivates their proposal of expert LoRAs tailored to distinct ti... | Rebuttal 1:
Rebuttal: Response to Reviewer 4dgm:
Thank you for your thorough review of our paper and your valuable suggestions. Below are our point-by-point responses:
**Question1:** Some closely related works that explore similar approaches but are not cited include DMP and Decouple-Then-Merge.
**Answer1:** We sinc... | null | null | null | null | null | null |
Efficient ANN-SNN Conversion with Error Compensation Learning | Accept (poster) | Summary: This paper focuses on ANN-to-SNN conversion methods and proposes three techniques to mitigate conversion errors. The first technique, a clipping function, is introduced to replace the ReLU activation in ANNs, thereby improving their compatibility for conversion to SNNs. The second technique, the Dual Threshold... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and constructive feedback. Below, we address the concerns raised and clarify the contributions of our work.
## **Response to Weaknesses**
### 1. Fixed Hyperparameter Choices
While our method uses empirically determined hyperparameters (e.g., nega... | Summary: This paper proposes a new ANN-SNN Conversion framework by combining a learnable threshold clipping function, dual-threshold spiking neurons, and an optimal membrane potential initialization strategy.
Claims And Evidence: Yes
Methods And Evaluation Criteria: The design of dual-threshold spiking neuron effecti... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and constructive feedback. Below, we address the concerns raised and clarify the contributions of our work.
### 1. Difference Between Dual-Threshold Neurons and Spiking Models in [1, 2]
Thanks for your feedback. Our design is fundamentally differe... | Summary: This paper proposes an efficient ANN-to-SNN conversion method that significantly reduces conversion errors and inference latency. The key contributions include: A learnable threshold clipping function to mitigate clipping errors. Dual-threshold neurons to dynamically reduce quantization errors. Optimized membr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and constructive feedback. Below, we address the concerns raised and clarify the contributions of our work
## **Response to Weaknesses**
### 1. Computational Overhead of Dual-Threshold Neurons
Thanks for your feedback. We find that the additional... | null | null | null | null | null | null | null | null |
Zero-Shot Offline Imitation Learning via Optimal Transport | Accept (poster) | Summary: This paper proposes ZILOT, a MPC-style inference-time trajectory optimization technique that can learn from a single incomplete state-only expert demonstration with pretraining of a dynamic model from an unlabeled state-action dataset. The paper theoretically proves prior method, which is using a goal recogniz... | Rebuttal 1:
Rebuttal: # Definition of zero-shot
We would like to clarify our definition of “zero-shot”. _We refer to a method as “zero-shot” if it retrieves an optimal policy for unseen objectives provided at test-time, with modest compute overhead_. “Zero-shot” methods may be allowed a compute-heavy pre-training phase... | Summary: - The paper concerns zero-shot offline model-based imitation learning
- A new method is proposed based on Optimal Transport to match the occupancy measure of the learning and expert policies
- Experiments conducted on three tasks (Fetch, HalfCheetah, PointMaze), comparing the proposed method with existing ... | Rebuttal 1:
Rebuttal: Thank you for your assessment and valuable feedback. We first address your main concern, our empirical evaluation, and then your other questions.
# Q5 on further experiments
Our experiments are chosen to be representative of the 3 most common types of MDPs found in robotics: manipulation (Fetch)... | Summary: The problem of greediness in goal conditioned imitation is resolved by matching goal occupancies. The proposed algorithm can learn from a single demonstration with partial observability. It is shown that minimizing wassertein distance between goal occupancies of expert and learner is equivalent to minimizing a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and comments.
Thank you for pointing out that the acronym “GC-RL” was not introduced in the paper. We will introduce it properly given a chance to update the paper.
The subscripts in the definitions of $\mathcal{D}\_\beta$ and $\mathcal{D}\_... | null | null | null | null | null | null | null | null |
MODA: MOdular Duplex Attention for Multimodal Perception, Cognition, and Emotion Understanding | Accept (spotlight poster) | Summary: The paper identifies the attention deficit disorder problem in SOTA MLLMs, characterized by inconsistent cross-modal attention and layer-by-layer decay of attention activation. Then, the authors introduce a linear-based attention mechanism that simultaneously conducts inner-modal refinement and inter-modal int... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer Ack2**
Thank you for your insightful comments and questions. For your reference, we summarized the main results and included the attached file in our response to Reviewer XinM.
**A1. Analysis for linear complexity of duplex attention alignment**
**(1) Complexity Analy... | Summary: This paper proposes a MOdular Duplex Attention (MODA) for multimodal perception, cognition and emotion understanding. The proposed method is evaluated and the paper is well organized.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: yes
Experimental Designs Or Analyses: yes... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer Ack2**
Thank you for your insightful comments and questions. For your reference, we summarized the main results and included the attached file in our response to Reviewer XinM.
**A1&A2. Discussion on DDA**
(1) **Actually, DDA can be interpreted from the perspective of ... | Summary: This paper proposes a novel attention mechanism called Modular Duplex Attention (MODA) to tackle the attention inconsistency problem in Multimodal Large Language Models (MLLMs). MODA showcases outstanding performance in multimodal perception, cognition, and emotion understanding tasks. Specifically, the 34B ve... | Rebuttal 1:
Rebuttal: ## **General Response**
We sincerely appreciate all the Reviewers and the Area Chair for their time and effort in reviewing our paper. Following the valuable suggestions and insights provided in the reviews, we summarize **the additional results and evidence** included in the rebuttal based on th... | Summary: The paper identifies a critical limitation in MLLMs, where inconsistent attention across layers leads to errors in fine-grained emotion understanding ("deficit disorder attention problem"). To address this, the authors propose MOdular Duplex Attention (MODA), which separates attention into self-modal and cross... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer Mf8z**
Thank you for your insightful comments and questions. For your reference, we summarized the main results and included the attached file in our response to Reviewer XinM.
**A1. Claims on DDA**
(1) Actually, DDA is supported by evidence from two aspects: rationale... | null | null | null | null | null | null |
FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence | Accept (spotlight poster) | Summary: This paper introduces FedSSI, a novel regularization algorithm for continual federated learning that addresses the challenges of knowledge forgetting and data heterogeneity without replay. FedSSI can empirically and theoretically reduce computational overhead and outperform state-of-the-art methods．
## update... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable comments. In the following, we give point-by-point responses to each comment.
> **Q1. Concerns about the storage overhead caused by PSM.**
**R1:** Thank you for this constructive suggestion. The PSM will be trained along with the global model on th... | Summary: This paper focuses on the continual federated learning and systematically analyzes the resource consumption of existing works. The authors propose a resource-friendly method based on the SI algorithm, FedSSI, which balances local and global knowledge. Extensive experiments and analytical understanding have bee... | Rebuttal 1:
Rebuttal: Thank you very much for providing us with positive comments. In the following, we give detailed responses to each review.
> **Q1. Concerns about the selection of the hyperparameter $\lambda$**
**R1:** Thanks a lot for raising this concern. In Table 3, $\alpha$ refers to the degree of data hete... | Summary: The paper introduces a continual federated learning method, FedSSI, aimed to mitigate catastrophic forgetting without rehearsal. FedSSI employs the personalized surrogate model to strike a balance between global and local knowledge during the training process. Experimental results show that FedSSI can outperfo... | Rebuttal 1:
Rebuttal: Thank you very much for this professional review. The critical comments have been addressed carefully, and responses have been given one by one.
> **Q1. Minor typos in our manuscript.**
**R1:** Thank you very much for this helpful comment. We are sorry for the wrong spelling in Table 2 and will... | Summary: The paper introduces FedSSI, a regularization-based continual federated learning (CFL) method designed to address catastrophic forgetting and data heterogeneity without requiring data rehearsal or heavy computational overhead. It identifies limitations in applying traditional regularization techniques like Syn... | Rebuttal 1:
Rebuttal: > **Q1&Q4. Concerns about selection of the hyperparameter $\lambda$ and its theoretical bound**
**R1:** Thank you for this valuable comment. We conducted relative experiments in Table 3. In Table 3, $\alpha$ refers to the degree of data heterogeneity, while $\lambda$ is a control coefficient in ... | null | null | null | null | null | null |
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$ | Accept (poster) | Summary: This paper proposes a variable selection method for input variables related to the output. The proposed method is used for data preprocessing in symbolic regression and can improve the accuracy and speed of symbolic regression. Specifically, the authors propose a method called PAN+SR, which combines a key idea... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' thoughtful, constructive, and positive feedback on our work. We are glad that the motivation behind our approach was found to be clearly presented (pCrp, gjaN, pZx6), and that our focus on minimizing false negatives (FNs) in variable selection (VS) for symbolic regress... | Summary: This paper proposes a rank-clustering PAN strategy for screening for relevant features before running symbolic regression (SR) methods on very high dimensional data, where the goal is to minimize the false negative rate (avoiding missing important variables). The idea is to repeatedly run BART and use the rank... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' thoughtful, constructive, and positive feedback on our work. We are glad that the motivation behind our approach was found to be clearly presented (pCrp, gjaN, pZx6), and that our focus on minimizing false negatives (FNs) in variable selection (VS) for symbolic regress... | Summary: The authors propose a feature selection preprocessing step to enhance the performance of symbolic regression algorithms. They introduce the method and evaluate its usage on SRBench across a number of algorithms and datasets.
Claims And Evidence: The authors do a good job overall of making evidence-based claim... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' thoughtful, constructive, and positive feedback on our work. We are glad that the motivation behind our approach was found to be clearly presented (pCrp, gjaN, pZx6), and that our focus on minimizing false negatives (FNs) in variable selection (VS) for symbolic regress... | Summary: The authors are interested in the problem of discovering mathematical equations from raw data. One of the largest bottleneck for SR methods is that it's extremely hard to scale the equation search to > 10 variables. This is because each additional variable considered combinatorially increases the search space ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' thoughtful, constructive, and positive feedback on our work. We are glad that the motivation behind our approach was found to be clearly presented (pCrp, gjaN, pZx6), and that our focus on minimizing false negatives (FNs) in variable selection (VS) for symbolic regress... | null | null | null | null | null | null |
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data | Accept (poster) | Summary: The paper proposed a mechanism to train generative models. The main idea is to regard each data sample as a charge. The training process involves training networks to predict fields (gradients of potentials). The sampling process is done by solving an ordinary differential equation (moving data samples along f... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for reviewing our paper. Below we answer your questions and comments.
**(Q1) Only some visual comparisons with PFGM are provided. However, a comparison with RF/FM is necessary. Only toy datasets like 2D point sets and MNIST are provided.**
It is worth noting that our m... | Summary: The paper introduces Electrostatic Field Matching (EFM), a novel generative modeling framework inspired by the physics of an electrical capacitor. In EFM, source and target data distributions are assigned positive and negative charges on two parallel plates, and a neural network is used to learn the resulting ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for reviewing our paper. Below we answer to your questions and comments.
**(Q1) [...] performance on high-dimensional or complex tasks (real-images) [...] testing on more diverse and
complex datasets [...] quantitative results are lacking..**
It is worth noting that our ... | Summary: The paper proposes Electrostatic Field Matching (EFM), a method for generative modeling and distribution transfer based on electrostatic principles. EFM generalizes the Poisson Flow Generative Model (PFGM) by enabling mapping between arbitrary distributions. It represents source and target distributions as cha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Please find the answers to your questions below.
**(Q1) [...] quantitative evaluations or comparisons [...] alternative approaches from the literature (e.g., SB-based, GAN-based). CIFAR [...]**
Following your request, we include **additional exper... | Summary: In this work, the authors propose Electrostatic Field Matching (EFM), which transforms between two distributions in the same space by placing the two distributions on two parallel plates with opposite charge, training a neural network to predict the electric field in the space between the two plates, and trace... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for reviewing our paper. Below we answer your questions.
**(Q1) [...] Experiments on more complex datasets[..]some quantitative evaluations would also help.**
Following your request, we include **additional experiments** with more complex data such as CIFAR-10 and the r... | null | null | null | null | null | null |
TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness | Accept (poster) | Summary: This paper introduces TCP-Diffusion, a multi-modal diffusion model for tropical cyclone precipitation forecasting. It leverages an Adjacent Residual Prediction (ARP) mechanism to predict rainfall changes, integrates numerical weather prediction data, and employs an Environmentally-Aware 3D U-Net within a diffu... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the contributions of our ARP mechanism. We also understand your concerns regarding the motivation behind using both NWP data and the diffusion model. Below, we provide detailed responses to these questions, and we hope they will help alleviate some of your concerns abou... | Summary: This paper addresses two key challenges in medium-range tropical cyclone forecasting: current methods suffer from cumulative errors and the lack of physical consistency. A multi-modal model is proposed, equipped with ARP mechanism to focus on rainfall change to reduce cumulative errors. The integration of NWP ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of integrating our method with NWP. We also understand your concerns regarding the potential impact of using NWP data on the flexibility of our approach. These insights will serve as valuable guidance for our future research. Below, we provide responses to your ... | Summary: This paper proposes a diffusion model to do precipitation nowcasting relative to a predefined tropical cyclone. The idea is to track the location of a tropical cyclone and to do nowcasting relative to the tracked location. The proposed model also incorporates additional information for the forecasting includin... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work, including the novelty of the task itself, the comprehensiveness of our experiments, and the clarity of the manuscript. We also understand the reviewer’s concerns regarding our use of IFS data. Below are our responses to some of the issues raised, and we hope the... | Summary: The article proposes a multimodal diffusion model that integrates data on rainfall, environment, tropical cyclone attributes, and meteorological predictions to generate precipitation due to tropical cyclones globally. Its results outperform other deep learning methods and numerical weather prediction (NWP) mod... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and for your valuable comments and stylistic recommendations. These suggestions will help us further improve the quality of this manuscript. We will incorporate the corresponding revisions in the camera-ready version. Below are our responses to the issues you rai... | null | null | null | null | null | null |
Towards the Causal Complete Cause of Multi-Modal Representation Learning | Accept (poster) | Summary: The paper explores causal completeness in multi-modal representation learning, addressing issues where existing methods may capture unnecessary or insufficient information. It introduces the Causal Complete Cause (C3) framework, which ensures learned representations are both sufficient (contain all necessary i... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer eaJB's constructive feedback and the time and effort dedicated to the review process. We are also grateful for the recognition of our work and sincerely hope the following responses can eliminate the concerns.
## Response to W1
We appreciate the suggestions and apo... | Summary: This paper addresses the problem of multi-modal representation learning from a causal perspective. It analyzes the insufficiency and redundancy of information across multiple modalities. The authors propose a novel concept termed Causal Complete Cause ($C^3$), supported by identifiability guarantees under weak... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer fkSa's constructive feedback and the time and effort dedicated to the review. We are grateful for the recognition of our work and sincerely hope the following responses can eliminate the concerns.
## Response to W1
We sincerely appreciate the suggestions and h... | Summary: This paper proposes Causal Complete Cause Regularization (C³R) Risk, a metric that quantifies the likelihood that a learned representation is causally complete. A lower C³ Risk indicates that the representation satisfies both causal sufficiency and causal necessity, meaning that spurious correlations have been... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer ZspQ's feedback and the time and effort dedicated to the review. We are also grateful for the recognition of our work and sincerely hope the following responses can eliminate the concerns.
## Response to W1 & Q3
- The true counterfactual effect involves unobserved ... | null | null | null | null | null | null | null | null |
Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation | Accept (poster) | Summary: The authors identify an issue in VLMs / MLLMs of unstable segmentation against variations in textual prompt. They propose a text-to-image diffusion model based test-time optimization technique combined with language guided prompt tuning to solve this issue. Their resulting framework, tagged Segment Anyword, is... | Rebuttal 1:
Rebuttal: __Q1__: "__Exact model to generate segmentations__"
__R1__: We use the official implementation and pretrained checkpoint from ReLA. The final segmentation mask is produced by the pixel decoder whose outputs are weighted by a language-supervised object activation map.
__Q2__: "In plot, is IoU cal... | Summary: This paper introduces Segment Anyword, a training-free framework for open-set language-grounded image segmentation. It leverages token-level cross-attention maps from a frozen diffusion model to generate mask prompts, which are then refined by the Segment Anything Model (SAM) for accurate segmentation. To addr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed feedback. We are glad that the reviewer found our paper to present clear claims with evidence, supported critiques. And our method is computationally lightweight.
__Q1__: "__training-free warrants scrutiny__"
__R1__: The term “training-free” ref... | Summary: This work proposes Segment Anyword, an approach for language-guided open-set segmentation. It uses a diffusion model to create initial correspondence between words in the text prompt and points in the image, refines the point prompts based on linguistic analysis, and prompts SAM to generate the final segmentat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and detailed feedback, which have significantly helped improve the quality of our manuscript. Below, we address the reviewer’s concerns point by point.
__Q1__: "__Definition on "Word-Grounding" and "Novel-Concept"__"
__R1__: We refer "word-gr... | Summary: This paper introduces Segment Anyword, a novel framework for open-set grounded segmentation. The key idea is to invert the mask prompting process by leveraging a pre-trained diffusion-based text-to-image model (e.g., Stable Diffusion) to generate high-quality, grounded segmentation masks. The method achieves c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable questions and insights. We are pleased that they find our proposed method novel, resource-efficient, and highly generalizable.
We have made several amendments and addressed the reviewer's specific queries as detailed below:
__Q1__: "__Inference ... | null | null | null | null | null | null |
GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation | Accept (poster) | Summary: The authors propose GIVE, an innovative framework designed to enhance the performance of large language models in scientific reasoning tasks. The framework consists of three main stages: expert data observation, divergent thinking, and information synthesizing. The large language model constructs a structured ... | Rebuttal 1:
Rebuttal: We understand the reviewer's concerns about the role of expert knowledge in the reasoning process, as well as in the answer-generation process, and we appreciate your suggestion on our literature discussion and experiments. We hope the following clarifications addressed all the questions and we wi... | Summary: The paper introduces Graph Inspired Veracity Extrapolation, a reasoning framework that enhances LLMs by integrating parametric and non-parametric memories for more accurate reasoning with minimal external input. GIVE operates through three key steps to select relevant expert data, engage in query-specific dive... | Rebuttal 1:
Rebuttal: > In-depth analysis of token consumption
We understand the reviewer's concern about the token efficiency and provide a comparison of token consumption between GIVE and ToG on 100 random questions from each dataset. For each question, we calculate the total number of input/output token in the whol... | Summary: This paper proposes a novel reasoning method based on LLMs and an external knowledge graph. The motivation of this paper is to address the reasoning quality under the situations that the knowledge graph is sparse. This is reasonable as CoT has no external knowledge, while RAG and ToG suffer from the scale of t... | Rebuttal 1:
Rebuttal: > Compared to RAG that can retrieve any text information, graph-based retrievers (also for GraphRAT, etc.) naturally require an external knowledge base graph that is of high quality and is typically not yet learned by the LLMs. This might not be a `weakness’ but the common limitation of graph-base... | Summary: This paper introduces GIVE to facilitate the reasoning ability of LLMs in specific domains. GIVE extracts the relevant information of a knowledge graph and bridges it with the query by using LLM’s internal knowledge to justify the veracity of the extrapolated knowledge. GIVE also incorporates counterfactual kn... | Rebuttal 1:
Rebuttal: > From Figure 4 we see that the expert knowledge set plays an important role for GIVE. However, from Table 8, GIVE achieves better performance than ToG and RAG when using n=1 and only the affirmative knowledge set, why can GIVE achieve good performance without using the expert knowledge set?
We a... | null | null | null | null | null | null |
Improving Memory Efficiency for Training KANs via Meta Learning | Accept (poster) | Summary: This paper proposes MetaKAN, which leverages a hypernetwork to generate the B-spline coefficients of KANs. Each edge function is associated with a learnable prompt (usually 1D) so the G+K+1 number of coefficients is compressed down to a single scalar, achieving parameter reduction. They apply MetaKANs to funct... | Rebuttal 1:
Rebuttal: C1: Some algorithmic choices seem arbitrary
A1: We appreciate the reviewer's point regarding the clarity of our algorithmic choices. We acknowledge that the rationale behind certain design choices, such as the specific architecture of the meta-learner (e.g., a two-layer MLP) and the initial dimen... | Summary: In this work, the Authors combine KANs with Hypernetworks and show the improved/comparable accuracy at the lower parameter counts.
Claims And Evidence: The claims are supported by the evidence: the proposed model is designed in a standard way, as far as hypernetworks are considered, and is tested on the datas... | Rebuttal 1:
Rebuttal: C1: Show a task solvable by MetaKAN but not by KAN
A1:We thank the reviewer for this important suggestion. In fact, our experiments in Section C.1 (High dimensional function) and Table 4 (function fitting task) already demonstrate a key scenario where standard KANs fail while MetaKAN succeeds:
F... | Summary: The paper proposes a novel memory optimization method for Kolmogorov-Arnold Networks known as MetaKAN. MetaKAN leverages the use of meta-learners—2-layer neural networks that generate B-spline coefficients on-the-fly—to reduce the parameter count of KANs to a level comparable to that of MLP’s. Across various... | Rebuttal 1:
Rebuttal: C1: Complexity comparison and Training time between KAN and MetaKAN.
A1: We thank the reviewer for the insightful question. Below is a summary of theoretical complexity
1. Complexity Analysis
notations see https://github.com/icmlrebuttal25/Append
| Model | Total FLOPs |
| ------- | -----------... | Summary: This paper concerns a meta-learning approach to training Kolmogorov Arnold Networks (KANs) that enables a reduction in the number of trainable parameters in KANs that is substantially larger than that of standard deep learning models like MLPs. The meta-learning approach is quite standard; it proceeds by assum... | Rebuttal 1:
Rebuttal: C1:Why use the MLP as the meta-learner?
A1: We understand the reviewer's interest in the theoretical motivation and deeper implications of employing an MLP as the hypernetwork, particularly in comparison to works like Low-Rank PINNs where architectural choices may have stronger connections to the... | null | null | null | null | null | null |
Copilot Arena: A Platform for Code LLM Evaluation in the Wild | Accept (poster) | Summary: This paper presents EvalX, a platform for evaluating coding LLMs in real-world environments. Integrated into developers' IDEs, it collects user preferences on code completions. Unlike static benchmarks, EvalX provides real coding tasks and optimizes latency. Findings show model rankings differ from traditional... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. We address your comments below:
*[Weakness 1: The evaluation metric of the model is relatively simple and can only reflect user preferences.]*
- There is an extensive list of existing literature that follows this paradigm (see the first paragraph of the related w... | Summary: This paper discusses EvalX, a system deployed in-the-wild to gather human preferences regarding code. It constructs a leaderboard based on user preferences and identifies notable differences compared to existing static benchmarks and human preference leaderboards. By analyzing EvalX’s diverse and unique data d... | Rebuttal 1:
Rebuttal: Thank you for the helpful suggestions. We address the comments below:
*[Weakness 1: While the deliverables are well-suited for the community, the main methodology appears to offer limited contribution to this ML community. Despite these concerns, I am inclined to accept the paper.]*
We thank the... | Summary: The paper presents EvalX, a platform for evaluating coding capabilities of large language models (LLMs) in real-world settings. Unlike existing evaluations that rely on synthetic benchmarks or chat-based interactions, EvalX integrates directly into developers' VSCode environments to collect authentic user pref... | Rebuttal 1:
Rebuttal: Thank you for the helpful suggestions. We address your comments below:
*\[Claim \+ Evidence 1: The position bias in the interface design is concerning. The preference data may reflect convenience rather than quality assessment, which may lower the data quality.\]*
* Since we randomize the orderi... | Summary: Authors introduce EvalX a platform to compare the effectiveness of different LLMs for the use case of coding assistants. Their deployed platform has already collected over 11000 responses on comparisons between 10 different models. The model ranking presented from these results gives new insights on user prefe... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and appreciation of our work. We aim to address your comments below:
*\[Claim \+ Evidence 1: focus on code completions\]*
* We agree that the paper already provides value to the research community and will be more precise about the scope of our work early on (e.... | null | null | null | null | null | null |
One-dimensional Path Convolution | Accept (poster) | Summary: The authors propose an alternative for 2D convolutional networks that is lighter. They use a topological 1D path across all the pixels based on Hilbert or Z paths. They use more than one such path at each layer to make up for locality. Other than the convolution layer they have several other building blocks su... | Rebuttal 1:
Rebuttal: We are profoundly grateful for your exceptionally insightful and supportive evaluation of our work. Such constructive affirmation not only underscores the significance of this research within the current paradigm but also inspires our dedication to advancing this direction toward practical applica... | Summary: The use of 1D CNN kernel can greatly reduce the model size comparing aginst the traditional 2D. However, the lack of locality information hidders the use of 1D kernel. This paper proposes a new traversal mechanism which can be used for Hilbert and Z-order scans for 1D CNN model. The result shows the proposed 1... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback. We have summarized your concerns and will respond to each accordingly, followed by addressing your questions.
## Related works & experimental settings
> Moreover, convolution can be used in other vision tasks, validating the method on other tas... | Summary: This paper proposes a path convolution (PathConv) architecture based on one-dimensional convolution, aiming to solve the problem that traditional one-dimensional convolution destroys the spatial locality of the image. By introducing the Hilbert/Z-order curve as the image traversal path and combining the path s... | Rebuttal 1:
Rebuttal: We are grateful that the motivation and proposed method of this paper have received your endorsement. We will try to address your concerns in the following sections.
## Performance on high-resolution/non-square images
> The actual performance of high-resolution (such as 256×256) or non-square im... | Summary: This paper presents an enhancement to 1D convolutional networks with space-filling curves (traversal path) for the image classification task.
Space-filling curves are paths that pass through every point within a higher-dimensional discrete space. Recently, 1D convolution has successfully been applied to severa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort for your review and insightful comments. The key concerns raised will be addressed in the following sections.
## Scalability to higher resolutions
> As this problem is NP-hard, there is no evidence that finding such paths for practical resolutions used in Imag... | null | null | null | null | null | null |
Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts | Accept (poster) | Summary: The authors introduce a novel Mixture-of-Expert model for Diffusion Transformers. The main novelty aspect lies in its routing strategy, Expert Race, which allows to tune experts assignment not only by token, but also by batch element (ie, by time-step in the diffusion process). As a consequence, the model gain... | Summary: - This paper proposes an innovative MoE block integrated with a simple routing strategy and multiple regularization techniques within diffusion transformer frameworks, to concurrently optimize performance and computational efficiency. In particular, the authors broaden the routing strategy's exploration space ... | Summary: This paper introduces Race-DiT, a novel approach for applying Mixture of Experts (MoE) to diffusion transformers with a flexible routing strategy called Expert Race. The key innovation is allowing tokens and experts to compete together in a global selection process, enabling dynamic allocation of computational... | null | null | null | null | null | null | null | null | |||
Pareto-frontier Entropy Search with Variational Lower Bound Maximization | Accept (poster) | Summary: This work introduces a multi-objective Bayesian optimization estimation framework that utilizes an information-theoretic acquisition function. It presents an effective approach for conducting variational inference when the continuous Pareto frontier is not fully known. By employing a mixture of distributions w... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions.
1) About $\lambda$: We can prove $\lambda = 0$ cannot be the maximizer, and thus, the optimization problem is well-defined. This can be derived from (8). First, from the definition, we see $\theta_{MAP} \in (0,1)$ (Note that $\hat{p} \in (... | Summary: This paper considers a method for multi-objective Bayesian optimization based on entropy search. Many existing entropy search methods rely on a discrete approximation of the Pareto frontier (which may be continuous in many cases), which typically leads to over-truncation of the predictive distribution. The pap... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments.
1) Population size of NSGA-II: As the reviewer mentioned, if the population size of NSGA-II increases, the approximation error between over truncation and the true truncation decreases. On the other hand, the continuous Pareto-frontier is the $L-1$-dimens... | Summary: This paper introduces Pareto-frontier Entropy Search with Variational Lower Bound Maximization (PFEV), an acquisition function for multi-objective Bayesian optimization (MOBO). It addresses a key limitation in prior information-theoretic MOBO methods, which rely on over-truncation when approximating the predic... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments.
1) About benchmark function: The results on DTLZ is shown [here](https://anonymous.4open.science/r/AuthorResponse-0652/Fig26-DTLZ.pdf). Note that these were performed before the review opening. We see that, in DTLZ 2, 5, 6, and 7, the proposed method show... | Summary: The paper considers an information-theoretic acquisition function -- namely Pareto Frontier Entropy Search -- for multi-objective Bayesian Optimization. The authors propose a novel "variational" approximation to the predictive distribution given the Pareto frontier based on over and under truncation of the Par... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments.
1) HVKG in Appendix F: The linked figure ([link](https://anonymous.4open.science/r/AuthorResponse-0652/Fig20-Decoupled.pdf)) shows a comparison with BoTorch HVKG (called qHypervolumeKnowledgeGradient, which is based on the well-known 'one shot' optimizati... | null | null | null | null | null | null |
Generalization Analysis for Controllable Learning | Accept (poster) | Summary: The paper establishes a statistical generalization theory for controllable learning, which enables machine learning models to dynamically adapt to task requirements during testing. This adaptability is crucial in many real-world applications of decision systems, such as recommender systems and information retr... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to Suggestions.**
The difficulty of theoretical analysis for controllable learning lies in two aspects.
First, the devel... | Summary: This paper investigates the theory bounds of controllable learning methods, highlighting the need for a deeper understanding of controllable learning methods from a generalization perspective. This paper first gives a formal definition of the general function classes of controllable learning, then develops a n... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to the Weakness 1.**
The theoretical results in Section 5 can be used on the multi-target bipartite ranking loss. When the... | Summary: This paper analyzes the generalization of controllable learning methods to understand controllability in trustworthy machine learning better. It establishes a unified and practical framework for the theoretical study of controllable learning methods and proposes a novel vector-contraction inequality that can d... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to the Weakness and Suggestion.**
All proof sketches and detailed proofs of the theoretical results in this paper have bee... | Summary: This work focuses on the generalization analysis of the controllable learning scenario. It establishes a unified theoretical framework and develops a novel vector-contraction inequality for controllable learning based on the Lipschitz continuity of loss functions.
The authors first formalize the function clas... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to Weakness.**
We will add relevant explanations after Eqn. (2). For embedding-based controllable learning methods, the ta... | null | null | null | null | null | null |
RZ-NAS: Enhancing LLM-guided Neural Architecture Search via Reflective Zero-Cost Strategy | Accept (poster) | Summary: This paper studies LLMs in NAS from a novel perspective. It begins by addressing the challenges in existing LLM-in-NAS strategies and explores the potential of combining LLM’s code- and text-level comprehension with zero-cost computation proxies. The proposed approach introduces the reflective module within LL... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive comments! We truly value your kind words about our contributions. We hope our responses address your concerns and enhance your view of our work! If there are any additional comments or questions, you can post them in the next stage.
**1. More theoretic... | Summary: This paper rethinks the role of LLMs in NAS. It doesn't directly apply LLM to NAS architecture optimization but optimize the process of random population generation utilizing the code- and text-level understanding of LLM. The novelty lies in the proposed reflective strategy that achieves architecture generatio... | Rebuttal 1:
Rebuttal: We greatly appreciate the positive feedbacks and your recognition of our contributions! We will respond to your valuable comments and detailed suggestions!
**1. More literature review on current reflection techniques**
In the revised version, we will include a more complete literature review of ... | Summary: This paper proposes Reflective Zero-cost NAS (RZ-NAS), a Large-Language-Model-to-Neural-Architecture-Search (LLM-to-NAS) method that integrates evolutionary search with LLM guidance and zero-cost (zc) proxies. The approach leverages LLM reflection to iteratively refine architecture mutations, improving search ... | Rebuttal 1:
Rebuttal: Thanks for recognizing our contributions and detailed feedback. Hoping our responses address your concerns.
**URL** https://anonymous.4open.science/r/rebuttal-ECC4/icml_rebuttal.png
**1. Reflection mechanism**
Thank you for this comment. Due to the character limit, detailed responses can refer ... | Summary: The paper presents an LLM-guided NAS framework that leverages Zero-Cost proxies for evaluating neural architectures without full training. The approach employs a reflective strategy in which the LLM mutates candidate architectures and, via a reflection module that uses training-free proxy scores (e.g., GraSP, ... | Rebuttal 1:
Rebuttal: Thanks a lot for your insightful comments and detailed suggestions! We hope our responses address your concerns and enhance your view of our work!
**1. Detailed explanation about reflection mechanism**
The reflection mechanism includes two modules: internal system reflection in the system promp... | null | null | null | null | null | null |
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning | Accept (poster) | Summary: This paper tackles Multi-Label Online Continual Learning (MOCL) from a novel perspective. Unlike existing approaches that focus on challenges like class imbalance and missing labels, this paper first analyzes the localization capabilities inherent in pre-trained models and introduces a CUT-out-and-Experience-R... | Rebuttal 1:
Rebuttal: _Respected Reviewer Nhoa,_ we first thank you for your valuable and insightful feedback, and for recognizing the analysis and advantages of our proposed method. Below, we address your concerns in a point-by-point manner.
Q: **Experiments on other incremental settings should be included.**
A: We a... | Summary: The paper tackles Multi-Label Online Continual Learning (MOCL) problem through a novel two-step approach. First, it identifies object-specific regions corresponding to labeled samples within each learning phase. Then, it selectively replays these regions, effectively circumventing the challenging missing label... | Rebuttal 1:
Rebuttal: _Respected Reviewer NiGx,_ we thank you for your valuable and insightful feedback. Below, we address your concerns in a point-by-point manner.
Q: **Additional experiments with varying numbers of classes per stage should be included.**
A: We appreciate this suggestion. We have followed the setti... | Summary: In this work, authors concentrate on Multi-Label Online Continual Learning (MOCL), mainly focusing on three main challenges: catastrophic forgetting, missing labels, and imbalanced class distributions. They introduce CUTER (CUT-out-and-Experience-Replay), a method that identifies and utilizes label-specific re... | Rebuttal 1:
Rebuttal: _Respected Reviewer eZ9b,_ we first thank you for your valuable and insightful feedback, and for recognizing our motivation and theoretical analysis. Below, we address your concerns in a point-by-point manner.
Q: **How the the accuracy and reliability of proposed localization process is ensured?*... | null | null | null | null | null | null | null | null |
The Diffusion Duality | Accept (poster) | Summary: This paper finds that discrete diffusion models are just a transformed version of continuous Gaussian diffusion using an argmax operation. This lets them borrow techniques from continuous diffusion, like curriculum learning for faster training and distillation for super-fast sampling. The result is training th... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their constructive and detailed feedback.
# Concern 1: Transition kernel and the loss landscape
We emphasize that while the $\arg \max$ operator maps Gaussian marginals to discrete marginals, it also preserves the transition dynamics and Markov property. In `anon... | Summary: This work presents a new training scheme for a uniform state discrete diffusion model based on the correspondence between Gaussian diffusion in continuous state and uniform state discrete diffusion. The authors state that while uniform state discrete diffusion and Gaussian diffusion are two separate Markov cha... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their constructive feedback. We address the reviewers comments and questions below.
# Concern 1: USDMs still lag MDMs
As mentioned in the response to Concern 1, USDMs lag MDMs only when measured in terms of perplexity which isn’t necessarily the best metric for co... | Summary: This paper presents the first theoretical connection between discrete and continuous diffusion models, showing that discrete diffusion emerges from an underlying continuous Gaussian diffusion process. Building on this insight, the authors propose a curriculum learning strategy to improve training efficiency. F... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their constructive feedback. We address the reviewers comments and questions below.
# Concern 1 : Generating trajectories using DDIM.
As noted in lines 250–252 (left), the deterministic Gaussian trajectories assume an optimal denoiser: given clean data $\mathbf{x... | Summary: This paper proposed a continuous parametrization of uniform-state discrete diffusion models. The key finding is a noise schedule (Eq. 11) that ensures the equivalence of the marginal distributions of the uniform-state discrete diffusion process and a Gaussian continuous diffusion process transformed by an argm... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their constructive feedback. We address the reviewers comments and questions below.
# Concern 1: Interpreting training loss (Fig 2) vs Tab 2
We will clarify Fig 2 by highlighting the key takeaway: DUO’s training loss exhibits significantly lower variance than bo... | null | null | null | null | null | null |
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling | Accept (poster) | Summary: This paper proposes an automatic data synthesis framework for LLM optimization modeling. The method can control the problem complexity starting with some seed data. Then, the method obtains the natural language description using a backtranslation step. Experiments demonstrate the effectiveness of training vari... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback. We address the specific questions:
**Regarding Claims And Evidence:**
- **Problem Length vs. Complexity:** We fully agree that scenario understanding is crucial for LLMs. However, more complex scenarios naturally require longer, more detailed NL descriptions ... | Summary: This paper proposes OptMATH, a method for data generation in the field of optimization modeling, which primarily combines the "Back Translation" technique from previous work with the "rejection sampling" method. Strictly speaking, it falls under the categories of data augmentation and data annotation within da... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback. We address the specific questions raised:
**1. Regarding Questions 1 & 4: OptMATH-Bench Curation, Distinctiveness, and In-Domain Evaluation**
We clarify OptMATH-Bench's curation and distinction from OptMATH-Train to address concerns about evaluating only in-d... | Summary: The paper proposes a framework named OptMATH for synthesizing high-quality datasets aimed at optimization modeling from natural language descriptions. This framework addresses the scarcity of optimization datasets by generating problem data through mathematical formulations and back-translation into natural la... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate the suggestions for improving the clarity and scope of our work. We address each point below:
**Regarding Weakness 1:** We will revise the **'Our Contributions' section** to more explicitly contrast OptMATH with prior prompting-based methods. Ke... | Summary: The paper presents OptMATH, a scalable bidirectional data synthesis framework designed to address the challenge of data scarcity in optimization modeling. It automatically generates high-quality optimization problem data with controllable complexity, starting from curated seed data with mathematical formulatio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review on the potential areas for improvement. We appreciate the opportunity to address these points:
**Essential References Not Discussed:** See “Essential References Not Discussed” section of response to Reviewer PCa3
**Regarding Weakness 1:** Our framework direct... | null | null | null | null | null | null |
Action Dubber: Timing Audible Actions via Inflectional Flow | Accept (poster) | Summary: Authors design a novel task of audible action temporal localization. A new dataset called $Audible623$ and a $TA^2Net$ architecture are proposed for this task. Experiments shows the advantages of them.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: The correctness of any... | Rebuttal 1:
Rebuttal: **1. Formatting Issues**
We sincerely appreciate your valuable feedback. We will revise the figure citations, adjust the formatting of Figure 2, correct the subscripts in the equations, and improve the overall layout of the paper. Additionally, we will fix the typographical error in line 376 to e... | Summary: The paper proposes a new task named "audible actions" and introduces a new dataset for this task. It introduces an inflectional flow estimation and an auxiliary self-supervised training method to improve the performance. Experiments are conducted on Audible623, UCFRep, and CountixAV datasets.
## update after ... | Rebuttal 1:
Rebuttal: **1. Distinction Between Our Method and TAL Methods, and Applicability to TAL**
We respectfully clarify that our method targets a task that is fundamentally different from traditional TAL. While TAL focuses on localizing the full temporal extent of actions, our method is designed to identify prec... | Summary: This paper introduces a new task called audible action temporal localization, aimed at predicting the frame-level positions of visible audible actions.
And the paper further proposes a dedicated dataset called Audible623, derived from Kinetics and UCF101.
Finally, the paper proposes a baseline method TA$^2$N... | Rebuttal 1:
Rebuttal: **1. Ambiguity on Timing Boundaries of Actions**
Temporal boundary ambiguity is indeed a well-known and widely acknowledged challenge in general action understanding tasks. Precisely defining the start and end of an action, particularly for semantically complex or continuous activities such as "s... | Summary: This paper introduces the task of audible action temporal localization and proposes a novel framework $TA^2Net$ alongside the Audible623 dataset. The method features a inflectional flow estimation technique grounded in the second derivative of the position-time images. Additionally, the authors develop a self-... | Rebuttal 1:
Rebuttal: **1. User Study**
As suggested, we conducted a user study comparing five approaches: TriDet, TransRAC, X3D, Hiera, and our proposed method. We curated a set of eight videos and enlisted 30 participants for the study. Each video was dubbed using sound aligned to the audible action locations detect... | null | null | null | null | null | null |
S4S: Solving for a Fast Diffusion Model Solver | Accept (poster) | Summary: Solving ODEs in diffusion models using traditional ODE solvers is expensive due to the iterative Neural Function Evaluations. If only use a few NFEs, the evolution trajectory is broken because of large step sizes. Targeting this problem, the authors propose S4S, a method that learns optimized, few-NFE solvers ... | Rebuttal 1:
Rebuttal: Thank you so much for the time taken to review our work and your helpful feedback! Please find our responses below:
> However, the focus is largely on improvements in the few-NFE regime for training-free methods, and it remains somewhat unclear how the student solver matches the output of a teach... | Summary: The sampling process of diffusion models heavily depends on numerical solvers. This paper provides a comprehensive overview of existing works, including
(1) Vanilla solver-based fast samplers, such as single-step, multi-step, predictor-corrector methods;
(2) Data-driven solver-based fast samplers, which invol... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, we address your questions and concerns, starting with two of the weaknesses you mentioned.
> Lack of direct experimental comparisons in the main context
[In the linked image](https://imgur.com/a/jkFi6tJ), we provide direct comparisons against the recent ... | Summary: The paper proposes S4S and S4S-Alt, methods to optimize diffusion ODE solvers for fast, high-quality sampling with minimal neural function evaluations (NFEs). S4S learns solver coefficients via a distillation objective, matching the output of a high-NFE "teacher" solver while minimizing global error (not local... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our work, and for recommending acceptance! We hope we answer your outstanding questions below.
> How sensitive is performance to the radius $r$? Does the $r\propto m^{-5/2}$ heuristic hold across varying $m$?
In practice, the heuristic $r \propto m^{-5/2}$ works... | Summary: The paper proposes the S4S method for optimizing diffusion model solvers. The optimization space includes the solver coefficients, time discretization schedule, and time correction terms. The optimization objective is a relaxed version of the global error with LPIPS as the distance metric, which only requires ... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our work and for your recommendation of acceptance. Below, we hope to address the weaknesses you identified and the questions you raised.
> With a larger parameter space, the method is more data-driven and less theory-grounded than traditional solvers. The coeffi... | null | null | null | null | null | null |
A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments | Accept (poster) | Summary: This paper addresses the problem of dynamic forecasting with noisy and irregularly sampled data. A model is proposed that 1) a physics-based SSM is applied to integrate partial physics knowledge and 2) a physics state regularization is used to constrain the latent states with noisy and irregularly sampled data... | Rebuttal 1:
Rebuttal: ## Response to Reviewer gB8T
**Q 4.1**: About clarifying the difference between our method and existing hybrid modeling approaches [1-3] that address incomplete physics knowledge.
**A 4.1**: We have discussed the difference between our model and hybrid modeling approaches as follows. The models i... | Summary: The paper aims to improve long-term forecasting using state space models based on deep learning to a) embed prior knowledge about physical systems and b) handle noisy irregularly sampled data. Specifically, the paper proposes to separate the state matrix into known and unknown / learnable elements. To this end... | Rebuttal 1:
Rebuttal: ## Response to Reviewer xWcK
**Q3.1**: About handling uncertainty in domain knowledge within our model.
**A3.1**: We do not explicitly handle uncertainty in domain knowledge in this work. However, such uncertainty can be modeled within the unknown dynamics. A possible approach is to apply conform... | Summary: This paper proposes a general-purpose framework that integrates partial physics knowledge into state space models. The topic is attractive and key innovation is clear.
Claims And Evidence: 1. Phy-SSM effectively integrates partial physics into SSMs for improved generalization. The dynamics decomposition (Eq. ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ooWi
**Q2.1**: About validating physical integration beyond the ablation drone experiment, especially for nonlinear and multi-variable systems.
**A2.1**: We evaluate our method on three real-world nonlinear, multi-variable systems: COVID-19, drone, and vehicle dynamics as ... | Summary: This paer proposes Phy-SSM, a general-purpose framework that integrates partial physics knowledge into state space models (SSMs) for long-term dynamics forecasting in complex environments. Our motivation is that SSMs can effectively capture long-range dependencies in sequential data and model continuous dynami... | Rebuttal 1:
Rebuttal: ## Response to Reviewer VtD1
**Q1.1**: About understanding the method’s complexity and presentation clarity.
**A1.1**: To help you better understand our work, we outline the problem, motivation, and key contributions below.
This work addresses the problem of long-term dynamical forecasting in c... | null | null | null | null | null | null |
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching | Accept (poster) | Summary: The paper studies the problem of training an RL/planning agent that takes as input (i) a Signal Temporal Logic (STL) specification and (ii) a start state and then generates a plan/trajectory in the environment that starts at the given start state and satisfies the given STL specification. The proposed learning... | Rebuttal 1:
Rebuttal: We thank Reviewer 8aYy for the thoughtful feedback and appreciation of our ablation studies and graph-based encoding, which aim to enhance symbolic understanding in decision-making. Below, we address the comments on novelty and trajectory realizability.
**Significance of STL planning**: (same to ... | Summary: This paper provides a flow-matching based approach to generate plans for a diverse range of Signal Temporal Logic (STL) specifications. Consequently, the proposed method can be optimized to be significantly faster than a diffusion-based approach as considered previously by skipping Ordinary Differential Equati... | Rebuttal 1:
Rebuttal: We appreciate Reviewer GKRz's thorough review and positive evaluations. We are glad to see that our contributions (first STL generative model; diverse setups; fast inference) have been recognized. We conducted new experiments as requested. Below are our responses to the concerns and questions.
**... | Summary: This paper proposes to use learning-based methods to generate planning trajectories for STL specifications. The authors introduce some STL templates and then present a GNN to encode the STLs into feature representations. These features are then fed into flow matching as the conditioning factor for end-to-end l... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful reviews. Below are our responses to the concerns.
**Extend LTL work to STL**: We argue that it is non-trivial to extend the existing LTL2Action [1] to STL, particularly for the key technique “progression” [2] used to update task spec based on assignments. ... | null | null | null | null | null | null | null | null |
WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs | Accept (poster) | Summary: This paper proposes a benchmark called WikiBigEdit for evaluating knowledge editing. The benchmark is constructed from Wikidata edits and includes 500k question-answer pairs. They evaluate a number of existing methods for knowledge editing and find that current techniques have limitations where continual fine-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful feedback. We’re pleased they found the paper well-written, the benchmark well-designed, and the findings insightful. We appreciate the recognition of *WikiBigEdit* as a valuable contribution for evaluating real-world limitations of knowledge ... | Summary: Enabling LLMs to retain up-to-date knowledge is of significant practical importance. To avoid costly full-parameter retraining, recent research has proposed various lifelong knowledge editing methods to inject new knowledge into models at minimal expense. However, these knowledge editing approaches have two no... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive review. We are pleased that the reviewer found the motivation and contributions of our work to be of practical importance — particularly our effort to investigate the limitations of existing knowledge editing techniques under realistic cond... | Summary: This paper proposes a large-scale knowledge editing benchmark *WikiBigEdit* based on the Wikidata, which contains over 500k question-answer pairs. At the same time, this work also constructs a pipeline that can automatically update data to adapt to the changes in real data, while mitigating the overfitting pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed evaluation. We appreciate the recognition of our key contributions: the construction of *WikiBigEdit* as the first large-scale, automatically extensible benchmark for real-world knowledge editing; the automated pipeline enabling continual upd... | null | null | null | null | null | null | null | null |
On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds | Reject | Summary: This paper studies the effect of explicit regularization in adversarial training using sharp high-dimensional asymptotics. Its main qualitative result is that regularizing using the dual of the norm with respect to which the perturbation budget is defined yields significant performance gains.
## Update after ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and insightful review. Regarding all the typos and clarity suggestions, we will fix them in the camera-ready version.
> [...] I think an experiment showing that the main conceptual result (i.e., regularizing with the dual norm improves performance) transfers to a real... | Summary: This work studies how to select the appropriate regularization norm in high-dimensional adversarial training for binary classification. The authors provide an exact asymptotic description of the robust, regularized empirical risk minimizer for various adversarial attacks and regularization norms. They also con... | Rebuttal 1:
Rebuttal: Thank you very much for your critical and thorough review of our work.
> The evaluation methods used are generally appropriate for assessing the proposed method. However, it would also be interesting to test the robustness of the results by examining scenarios where the linear classifier assumpti... | Summary: The authors investigate the impact of regularization geometry on adversarial training in a binary classification problem. The primary objective of the study is to control the robust generalization error under input perturbations constrained by a specified norm. To achieve this, they optimize the robust empiric... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review of our work.
> Could the authors provide a comparison and contrast of their results with those of Tanner et al., 2024, particularly in terms of the technical settings and innovations in their technical analysis? Additionally, what were the key technica... | null | null | null | null | null | null | null | null |
Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions | Accept (poster) | Summary: This paper presents a novel deep constrained topology optimization algorithm. Using the SIMP formalism, the authors propose learning an encoding of a space of fabricable shapes and solve the optimization problem on this space using gradient-based methods.
## update after rebuttal
In light of these comparisons... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review! We are grateful for your detailed comments, which have not only highlighted the strengths of our methodology but also pointed out key areas for improvement, ultimately helping us enhance the clarity and impact of our research.
***Refactoring mech... | Summary: This paper designs a new learning-based approach for feature mapping based topology optimization. The major advantage of the proposed method against previous works is that the generated voids are guaranteed to be directly manufacturable, thus circumventing cumbersome post-processing procedures. Technically, th... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to provide such a detailed and insightful review. Your comment has been extremely valuable in enabling us to carefully analyze various aspects of our method's implementation, including the integration of the Kreisselmeier-Steinhauser (KS) function.
***Using... | Summary: The authors propose a novel neural approximation framework based on a variational autoencoder (VAE) model to approximate the Heaviside function of the Signed Distance Function (SDF), enabling a unified representation of diverse geometric features in a single latent space. This approach integrates machine learn... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and constructive feedback on our work. We value your insights on the VAE encode-decode structure, the Neural Heaviside SDF representation, and the potential challenges related to geometric approximation and boundary precision.
**Novelty of the Proposed Method.** W... | Summary: In this work, the authors work on topology optimization, specifically, they propose a deep learning method to simulate Feature-Mapping Topology Optimization (FMTO) (and not SIMP). They propose two decoders, one for the reconstruction and another to approximate the heaviside function. They show results on varia... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review!! We deeply appreciate the time and effort you invested in evaluating our work.
***Comparison with other methods*** Our method follows the FMTO approach by creating topology with geometric primitives. Unlike traditional free-form optimization tha... | null | null | null | null | null | null |
Kernel Quantile Embeddings and Associated Probability Metrics | Accept (poster) | Summary: This paper introduces kernel quantile embeddings (KQEs) as a novel way to represent probability distributions in reproducing kernel Hilbert spaces (RKHS), extending beyond traditional kernel mean embeddings (KMEs). The authors leverage KQEs to define a new family of probability metrics that require weaker kern... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and for recognizing the desirable properties established for KQEs and the connections drawn between KQDs and Wasserstein distances. We address the points raised below and hope that, in light of these responses, the positive feedback from other re... | Summary: This paper presents an alternative of kernel mean embeddings of distributions through the use of quantiles rather than the mean. More precisely, the embedding of a distribution on a set X is given by the collection of all alpha-quantiles of the pushforwards of the distribution all directions, each given by a u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and for recognizing the soundness and novelty of our contributions, including generalizing KME-derived distances and kernelizing Sliced-Wasserstein distances. We address the key comments below.
## I. Additional Applications:
KQD is a general-purp... | Summary: This paper introduces the concept of Kernel Quantile Embeddings (KQEs) in reproducing kernel Hilbert spaces (RKHS) and investigates how these embeddings can be leveraged to define a new family of probability metrics: Kernel Quantile Discrepancies (KQDs). The authors argue that KQEs are a natural analogue of qu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our work. We appreciate the recognition of our theoretical contributions, the rigor of our proofs, and the relevance of our evaluation criteria. We are also glad the reviewer found our near-linear Monte Carlo estimator promising for large-scale ... | Summary: The paper proposes an alternative to kernel mean embeddings where they embedd the quantile functions instead of the mean, each quantile function being computed in the direction of a unit-norm function in the RKHS. They show that such embedding is injective under milder conditions on the kernel than the known c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive evaluation of our work. We appreciate the recognition of the originality of using quantile embeddings, the clarity of our presentation, and the relevance of our contribution to the ICML community. We have carefully addressed all your... | null | null | null | null | null | null |
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network | Accept (poster) | Summary: This work introduces an algorithm for continuous control with discretized actions. Building upon coarse-to-fine Q learning, this algorithm further models advantages and policies in an autoregressive fashion, breaking the limiting assumption of independence between action dimensions. Update rules are derived fr... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable suggestions. We appreciate your careful reading and constructive
feedback.
Supplementary Material for our response is at
[THIS LINK](https://anonymous.4open.science/r/icml25-Submission9509_2/fig_9ufZ.pdf).
Below we address your specific questions ... | Summary: The paper proposes ARSQ, a value-based reinforcement learning method to improve learning from suboptimal data in continuous control tasks. Previous methods estimate Q-values independently for each action dimension, neglecting their interdependencies, leading to biased action selection with mixed-quality data.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback.
Supplementary Material for our response is at
[THIS LINK](https://anonymous.4open.science/r/icml25-Submission9509_2/fig_L88h.pdf).
Below, we address each point raised:
## 1. Analysis of the Nature of Suboptimality
W... | Summary: This paper studies the problem of reinforcement learning for continuous control with action discretization, specifically focusing on offline RL (D4RL benchmark) and RL from demonstrations (RLBench) as problem settings. A key limitation of prior work that leverages action discretization is the explosion in dime... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and constructive feedback. We appreciate the positive remarks about
the motivation, clarity, originality, and empirical results of our work.
Below, we respond to the reviewer’s concerns point-by-point:
## 1. Additional Baseline Results on D4RL
... | null | null | null | null | null | null | null | null |
Pairwise Maximum Likelihood For Multi-Class Logistic Regression Model With Multiple Rare Classes | Accept (poster) | Summary: This paper addresses the problem of multi-class logistic regression in scenarios with class imbalance: specifically, one class is overwhelmingly dominant and the remaining classes are rare. The authors develop a theoretical framework demonstrating that, under appropriate assumptions and asymptotic conditions, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestions. The concerns have been well addressed as follows.
1. **Motivation and Practical Impact.**
- **Theoretical Motivation.** The focus here is the theoretical investigation of logistic regression with rare classes. Specifically, we find that ... | Summary: This paper focuses on multi-class logistic regression with one major class and multiple rare classes, a problem arising from real applications.
By the suggestions from Theorem 2.1, the standard maximum likelihood estimators as well as the re-parametrized version are asymptotically independent for different rar... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the constructive suggestions. All the concerns have been well received and carefully addressed as follows.
1. **Diverging Number of Classes.**
With a diverging $K$, the expected percentage of rare classes should be even smaller. To ensure a diverging sample size fo... | Summary: This paper studies the parameter estimation problem for Multi-class logistic model with one major class and $K$ rare classes. The main observation is that, under certain decay rate assumptions on the coefficients of rare classes. The joint MLE estimator is asymptotically equal to the pairwise MLE estimator bet... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the constructive suggestions. All the concerns have been well received and carefully addressed as follows.
1. **Relationship between Two Models.** Note that the model (2) is a special case of the model (1), which is a more general model. As you have correctly noted, ... | null | null | null | null | null | null | null | null |
Compressing tree ensembles through Level-wise Optimization and Pruning | Accept (poster) | Summary: This paper proposes a new algorithm for compressing a learned tree ensemble while keeping its generalization performance.
In each depth of a given tree ensemble, the proposed method prunes its redundant subtrees and adjusts the remaining leaf values.
Through the experiments on binary classification datasets,... | Rebuttal 1:
Rebuttal: Many thanks for your insightful comments. Below, we address your main concerns: (a) baselines for comparison and (b) scaling behavior.
Existing baselines:
Thank you for pointing us to this related work. Among the listed papers, we find Nan et al. (2016) less relevant as it solves a different prob... | Summary: The manuscript proposes a novel method (LOP) for compressing tree-based ensembles by pruning leaves and/or entire trees. LOP is based on sparse optimization and has applications to bagging or boosting ensembles. Experimental results show that the approach cuts down model size with minimal impact on performance... | Rebuttal 1:
Rebuttal: Many thanks for your positive comments! We will take your suggestions into account. | Summary: The paper "Compressing tree ensembles through Level-wise Optimization and Pruning" proposes a combination of ensemble pruning, decision tree pruning and leaf-refinement to reduce the memory footprint of forests for reduced resource usage during deployment. To do so, the authors re-formulate the inference of a ... | Rebuttal 1:
Rebuttal: Thank you for your positive comments, and especially that this paper “could become the go-to paper in this area”, and “more [experiments] is always better, but not always necessary”. Thanks also for the suggestions for improvement, which we will take into account.
To answer your questions:
Q1, Q... | Summary: The paper introduces LOP (Level-wise Optimization and Pruning), a method for compressing decision tree ensembles by pruning subtrees level by level while updating leaf values to maintain predictive accuracy. Unlike prior methods that prune entire trees or merge only leaf nodes, LOP can remove subtrees at any l... | Rebuttal 1:
Rebuttal: Many thanks for your comments. We acknowledge that this work has many links with XAI (including knowledge distillation), and also with robustness, verification, inference efficiency, and other topics. However, the focus of this work is on the specific task of ensemble compression; we position this... | null | null | null | null | null | null |
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method | Accept (poster) | Summary: In the work, the authors propose a randomized Riemannian submanifold approach for optimization on Stiefel manifolds. The authors prove that the proposed method converges under certain conditions.
Empirical results are included to support the effectiveness of the proposed method.
Claims And Evidence: The claim... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive evaluation of our work. We are glad that the reviewer found our work to be interesting and original.
**1. Whether the proposed idea requires a compatible Riemannian metric to effectively reduce the computational cost.**
We thank the reviewer for... | Summary: This paper improves the efficiency of the retraction operation in the geometric optimization algorithms over the Stiefel manifold by constraining the optimization into a randomly sampled smaller manifold. Specifically, for an element X in the Stiefel manifold, it learns an orthonormal matrix U that acts on X, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and overall positive assessment on our work. We would like to take the chance to address the comments in detail.
**1. On the convergence of MNIST/CIFAR experiment.**
**R1**: In our MNIST/CIFAR experiments, our aim was to highlight the e... | Summary: The paper presents an efficient Riemannian optimizer for Stiefel manifolds, introducing two parameterization strategies that reduce the computational complexity of optimization steps while ensuring rigorous convergence analysis.
Claims And Evidence: The paper introduces two parameterization strategies—orthogo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments and feedback. We would like to address your comments as follows.
**1. Discussions on relevant references.**
**R1**: We thank the reviewer for highlighting references [1,2]. Reference [1] re-parameterizes variables in Euclidean space v... | Summary: This paper develops a new approach to performing optimization with orthogonality constraints, with an emphasis on keeping computational complexity low. The authors are able to provide convergence bounds in expectation for nonconvex losses. The authors then run a number of experiments on well-known baselines to... | Rebuttal 1:
Rebuttal: We are grateful that the reviewer found our work interesting and well-written. Below are our responses to your comments.
**1. On the novelty of this work compared to related works.**
**R1**: Thank you for the comment. We would like to clarify the novelty of our work. We have carefully compared... | null | null | null | null | null | null |
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills | Accept (poster) | Summary: This work studies the domain randomization (DR) in reinforcement learning and the focus is on the design of task distribution. With the help of normalizing flows, the author proposes an entropy-regularized policy optimization methods for DR. The experiments are conducted in sim simulated and one real-world rob... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and practical recommendations regarding our claims and experimental metrics, which have significantly strengthened our manuscript. Below we address each of your comments and questions.
> … The claim does not hold all the time, for example, in curriculum learning... | Summary: Domain randomization is a known and useful technique to transfer models trained in simulation to the richness of the real-world. There are several known methods in the past. The usual workflow of the algorithm is to learn or estimate the distribution of valid parameters that is solvable by the policy optimizat... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and suggestions on expanding our related work and improving figure clarity, which have enhanced the overall presentation of our paper. Below we address each of your comments and questions.
> The citations can be more comprehensive with more mention of doma... | Summary: This paper proposes a normalizing flow based approach to learn sampling parameters for domain randomization. Instead of doing naive sampling for domain randomization hyper-params, the paper uses a more principled way (which has been proposed before). But different from previous works, this paper proposes using... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful analysis and positive review. Below we address each of your comments and questions.
> For the other mujoco experiments it was not immediately clear how the base sampling distributions were chosen
We attempted to select ranges for these parameters that were large en... | Summary: The paper proposes updating environment parameters for policy training by learning a sampling distribution parameterized as a normalizing flow. Normalizing flow is known to be capable of representing more expressive distributions. The distribution is trained to maximize policy performance, maximize its margina... | Rebuttal 1:
Rebuttal: Thank you for your detailed insights on experimental design and parameter selection, which have greatly helped us clarify our approach. Below we address each of your comments and questions.
> … how are the initial distribution and target distribution decided? How are $\alpha$ and and $\beta$ chos... | null | null | null | null | null | null |
Can Transformers Learn Full Bayesian Inference in Context? | Accept (poster) | Summary: The paper introduces an innovative approach to full Bayesian inference using in-context learning (ICL) with transformers. By leveraging ideas from continuous normalizing flows and flow matching, the authors propose a framework that learns to approximate the posterior distribution P(z|x) directly from synthetic... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our manuscript and for providing detailed feedback.
>More advanced and large datasets like image datasets should be used to verify the effectiveness of the proposed frameworks.
Please note that, to the best of our knowledge, our paper presents the first tho... | Summary: The paper proposes an in-context learning (ICL) approach for performing Bayesian inference over three classes of models: Generalized Linear Models (GLMs), Gaussian Mixture Models, and Factor Analysis (FA). The paper shows that their ICL method can produce similar posterior samples to Hamiltonian Monte Carlo (H... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our manuscript and for providing detailed feedback.
> Default hyperparameters are used for the VI approaches according to the appendix (no hyperparameter optimization)
We would like to kindly point out that we investigate the role of the learning rate, whic... | Summary: The authors leverage transformers architecture to amortize Bayesian posterior estimation based on training data / observations fed in-context to the model. They conduct analysis on generalized linear models, factor analysis and mixture models and highlight that the proposed method discovers the true posterior ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions — we extended the experiments as proposed and now prominently highlight Mittal et al. as particularly relevant related work.
> The key contributions of the work lie in their experimental setup [...].
We fully agree that our core contribution lie... | Summary: The authors present an approach for (approximate) Bayesian inference, based on in-context learning with a combined transformer/flow model. The approach relies on access to the generative model, and generates data to learn the inverse model. The method is reasonable and performs well relative to baselines.
Cla... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our manuscript and for providing detailed feedback.
> The paper is missing an application that motivates a reader, and it is currently not clear that one really exists.
We would like to point out that the question addressed in our paper is “can transformers... | null | null | null | null | null | null |
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation | Accept (poster) | Summary: This paper establishes an upper bound on the convergence rate of the on-policy policy mirror descent (PMD) algorithm in an agnostic learning setting for discounted MDPs. The authors replace the commonly assumed closure condition with a weaker variational gradient dominance (VGD) assumption. Under this conditio... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and for your thoughtful comments.
**“Overall, this paper is well-structured and provides a solid theoretical contribution...”** We were happy to read that the reviewer appreciates the value of our contribution, and in particular that the reviewer recognizes **“Usi... | Summary: The paper addresses the convergence of policy mirror descent method for generalized policy classes ie policy classes with a general parametrization. Most of the prior literature considers either the tabular case where each state can be mapped to any probability vector over the action space, or they consider po... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing thoughtful comments. We were glad to hear you appreciate our work, in particular, that the reviewer recognizes our paper **“addresses an important problem in the literature”**, and **“relaxes an assumption (from closed policy class to VGD) typically ... | Summary: This paper studies the convergence of policy mirror descent under the variational gradient dominance condition.
Claims And Evidence: Under the variational gradient dominance condition, this work obtains the first convergence rate of PMD. I have some major concerns:
1. I am confused about the claim in Section ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and comments.
**1.** We show in the paper (Appendix A.2) that perfect closure in the negative entropy case implies VGD. Following your comment and those of the other reviewers, we have extended this implication to hold for general approximate closure assumptio... | null | null | null | null | null | null | null | null |
Safe-EF: Error Feedback for Non-smooth Constrained Optimization | Accept (poster) | Summary: he paper establishes a convergence lower bound for the non-smooth convex distributed setting, where EF-21 and similar methods operate. Next, it proposes Safe-EF (Algorithm 1), an extension of EF14 (Seide et al., 2014) that incorporates safety constraints and bidirectional compression. Safe-EF is provably effec... | Rebuttal 1:
Rebuttal: 1. We thank the reviewer for this valuable comment and pointing out this reference! Indeed, the workers cloning idea described in that paper can be extended to our result in convex (smooth) setting. We have revisited the convergence analysis of EF21 in our supplementary Theorem C.1. following the ... | Summary: In this paper, the authors investigate a non-smooth optimization setting with bounded gradients in the context of Top-K compression. Communication compression using contractive compressors (e.g., Top-K) is commonly preferred in practice due to its efficiency; however, it can significantly degrade performance i... | Rebuttal 1:
Rebuttal: 1. We appreciate the reviewer’s comment and agree that further elaboration on the lack of prior work in this area would strengthen the manuscript. To the best of our knowledge, constrained optimization with communication compression remains largely unexplored. In fact, besides the two works we cit... | Summary: The paper presents Safe-EF, an error feedback (EF) algorithm designed for non-smooth constrained optimization in distributed settings. It establishes lower complexity bounds for first-order algorithms with contractive compression and introduces Safe-EF, which matches these bounds while ensuring safety constrai... | Rebuttal 1:
Rebuttal: 1. We appreciate the reviewer's high evaluation of our theoretical guarantees and empirical results. For empirical evaluation, we (i) test our method on a well-controlled synthetic environment with different levels of heterogeneity and (ii) provide an extensive ablation study in more challenging c... | null | null | null | null | null | null | null | null |
MaskTwins: Dual-form Complementary Masking for Domain-Adaptive Image Segmentation | Accept (poster) | Summary: This paper tackles image segmentation within the unsupervised domain adaptation (UDA) framework. Rather than employing random masks, the authors develop dual-form complementary masked images to strengthen the generalization capabilities of their approach. They demonstrate that robust, domain-invariant features... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and comments. The main concerns are addressed below.
> **W1:** Complementary Mask-only framework
The proposed complementary masking strategy certainly works on the Complementary Mask-only framework and we highlight that it does not rely on the network structure ... | Summary: In this paper, the authors introduce MaskTwins, a UDA framework that integrates masked reconstruction into the main training pipeline. They argue that existing UDA methods leveraging masked image modeling treat masking merely as a form of input deformation and lack theoretical analysis, resulting in a superfic... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and comments. The main concerns are addressed below.
> **W1:** Application on more challenging segmentation datasets
Thank you for the concerns on the generalizability of the proposed method.
On the one hand, the SYNTHIA dataset we use is of a relatively large... | Summary: This paper introduces a complementary masking strategy for the semantic segmentation UDA task. Building on the existing masked image consistency (MIC) training paradigm, which relies on pseudo-labels from the teacher model, the authors propose a complementary masking loss to further enforce consistent predicti... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and comments. The main concerns are addressed below.
> **W1:** Misunderstanding on Complementary Masking Techniques
Thank you for your comments again. But we respectfully cannot agree with the justifications here, and would like to further clarify as below:
- F... | Summary: This paper explores the connection between Masked Image Modeling (MIM) and consistency regularization in Unsupervised Domain Adaptation (UDA). It reframes masked reconstruction as a sparse signal reconstruction problem and theoretically proves that complementary masks can effectively extract domain-agnostic fe... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and comments. The main concerns are addressed below.
>**W1:** Misunderstanding on Complementary Masking Techniques
First, the ideas related to Masked Image Modeling (MIM) can be novel. For instance, the latest ECCV 2024 paper MICDrop [1] explored the masking of m... | null | null | null | null | null | null |
SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization | Accept (poster) | Summary: The paper presents a variance reduction framework for Riemannian non-convex optimization. The proposed framework covers many existing variance reduction algorithms on manifolds and theoretical analysis matches the best known results in the Euclidean space. Numerical experiments are conducted on PCA, LRMC and R... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. You are primarily concerned with the contributions of this paper compared to previous variance reduction algorithms in Euclidean space, as well as whether the assumptions made in this paper can be satisfied. Additionally, you are interested in the impact of th... | Summary: The paper proposes the combination of SVRG and SARAH for stochastic variance reduced non-convex optimization, and extends it to the Riemannian setting. Combinations of Riemannian stochastic methods with or without variance reduction, existing or proposed in this paper, all can be subsumed into a unified updat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments.
1. We will increase the discussion on the motivation behind the proposed SRVRG algorithm. The motivation for proposing SRVRG is that when $u_k$ is an SVRG-type estimator, parameter $\beta$ can be larger, which allows the variance of $v_k$ to decreas... | Summary: In this paper, the authors provide a unified framework for several Stochastic Recursive Variance Reduced Gradient algorithms. They first propose a new algorithm that integrates recursive momentum with variance reduction techniques, called Stochastic Recursive Variance Reduced Gradient (SRVRG), and extended it ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and valuable comments.
We introduce the SRVRG estimator, which is proposed for the first time in both Euclidean space and Riemannian space. Numerical experiments illustrate the superiority of the R-SRVRG algorithm. Furthermore, we present a unifi... | Summary: This paper proposes a unified stochastic recursive variance reduced gradient framework for Riemannian non-convex optimization.
Claims And Evidence: The algorithm and its analysis are presented with derivations and proofs, but clarification on assumptions and the construction of the variance reduced gradient n... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and useful comments. Please find our responses below.
1. Methods And Evaluation Criteria:
Thank you for your valuable feedback regarding the applicability of our proposed methods. First, please allow me to explain why the RNGD method was not compared previousl... | Summary: This paper proposes a generalization of stochastic recursive variance reduced gradient framework (SERENA) that unifies various gradient-like objects that serve as the first order information for Riemannian optimization. Based on the appropriate formulation of the gradient estimator proposed in this framework, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and valuable comments and suggestions. We will respond to each point below.
1. Methods And Evaluation Criteria:
Thank you for your insightful comments regarding the evaluation criteria and complexity analysis in our paper. As you mentioned, R-SR... | null | null | null | null |
Nesterov Method for Asynchronous Pipeline Parallel Optimization | Accept (poster) | Summary: In this paper, the authors proposed a Nesterov Accelerated Gradient algorithm for asynchronous pipeline parallel optimization. It use Nesterov acceleration to reduce the negative impact of delay in asynchronous iterations. The authors firstly provide the convergence guarantee that the Nesterov acceleration can... | Rebuttal 1:
Rebuttal: We thank the reviewer for encouraging comments and address the specific points below.
## Results on the validation set
We have **already provided the results on the validation set** in Table 1 (perplexity scores) and Fig. 3,9 (validation loss curves). Additionally, as requested by Reviewer tQWW,... | Summary: This paper tells the story of overcoming a major challenge in training huge neural networks with pipeline parallelism. When models are split into stages running on different devices, asynchronous updates keep the pipeline full but introduce the problem of stale gradients, updates based on outdated information.... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and address the specific concerns below.
## Ablations for NAG
We have **already provided the ablation experiments for our modifications in Sec. 5.6**. Specifically, the effect of the momentum coefficient for NAG is reported in Fig. 6 and the effect... | Summary: The gradient staleness caused by existing asynchronous pipeline parallelism mechanisms hinders the practical usage in contrast to synchronous pipeline parallelims. This paper aims to solve tackle the stale gradients when using asynchronous pipeline parallelisms by nesterov accelerated gradients. The paper has ... | Rebuttal 1:
Rebuttal: We thank the reviewer for encouraging comments and address the specific points below.
## Scalability and sensitivity to multiple GPU servers
Our **SWARM experiments were conducted on multiple GPU servers** (24 to be exact) in GCP, confirming that our method works seamlessly in such scenarios (Fi... | null | null | null | null | null | null | null | null |
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update | Accept (poster) | Summary: This paper proposes a one-pass algorithm for stochastic linear bandits with heavy-tailed noise, reducing per-round computational cost from $\mathcal{O}(t \log T)$ to $\mathcal{O}(1)$ using an online mirror descent framework. Unlike existing methods that require storing and processing all past data, the propose... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. Below, we address your main questions regarding the technical contributions, extensions to GLB, experiments, and related works. For other minor issues (e.g., typos), due to limited space, we will directly revise the paper according to your suggestions.
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**Q1.... | Summary: This work introduces a new algorithm, inspired by [1], where solve the computation issues with the algorithms in [1].
Claims And Evidence: see Other Strengths And Weaknesses
Methods And Evaluation Criteria: see Other Strengths And Weaknesses
Theoretical Claims: see Other Strengths And Weaknesses
Experiment... | Rebuttal 1:
Rebuttal: Thanks for your helpful suggestions. Below we will address your main concerns regarding the technical contributions and report additional experiments.
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**Q1.** about the contributions ("Compared to [1], the contribution of this work is incremental", "elaborate on the main theoretical contribu... | Summary: This paper studies the heavy-tailed linear bandits problem. Doing OMD on the Huber loss, the authors yielded an algorithm with near-optimal $\tilde{\mathcal O}(d T^{1/(1+\epsilon)})$ regret which only needs $\mathcal O(1)$ computation per round (instead of doing a huge Huber regression as previous ones in the ... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback and appreciation of our work! We will revise the paper accordingly. Below, we answer your questions.
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**Q1.** More discussions about algorithms without knowledge of $\epsilon$ and $\nu_{t}$ in advance.
**A1.** Thanks for the suggestion. Relaxing this assumpti... | Summary: The paper proposes a method for linear bandits with heavy-tailed noise variable, that efficiently estimates the bandit parameter with a single pass through the data and doesn't require processing the complete historical data.
Claims And Evidence: It is claimed that the method can be adapted to more generalize... | Rebuttal 1:
Rebuttal: Thanks for your suggestions on experiments and literatures. We will revise the paper accordingly. However, there is an important misunderstanding regarding our technical contributions that we need to clarify.
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**Q1.** "contribution is not significant...only contribution is the replacement of ... | Summary: This paper proposes a novel, computationally efficient algorithm for heavy-tailed linear bandits that achieves the best-known regret bound in the literature.
Claims And Evidence: The claims are clear and supported by convincing evidence from the literature.
Methods And Evaluation Criteria: The methods and ev... | Rebuttal 1:
Rebuttal: Thanks for your careful review! In the following, we will address your main concerns and report additional experiments.
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**Q1.** "How will the empirical performance of the algorithm vary with changing $\nu_t$? I suggest the authors provide results for varying $\nu_t$ to support the claim that... | null | null | null | null |
Balanced Learning for Domain Adaptive Semantic Segmentation | Accept (poster) | Summary: The paper proposes Balanced Learning for Domain Adaptation (BLDA) to address class bias in unsupervised domain adaptation (UDA) for semantic segmentation. BLDA analyzes logits distributions to assess prediction bias and introduces an online logits adjustment mechanism to balance the class learning in both sour... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive and constructive feedback. We appreciate your recognition of **motivation**, **theoretical intuition**, and **clear writing**, as well as your kind comments on our visualizations and formula design.
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Regarding your suggestion to include more ... | Summary: This paper presents Balanced Learning for Domain Adaptation (BLDA), an innovative approach to address class imbalance and distribution shifts in Unsupervised Domain Adaptation (UDA) for semantic segmentation. Specifically, it identifies over-predicted and under-predicted classes through the analysis of predict... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback and thoughtful comments. We appreciate the recognition of our **clear writing**, **comprehensive experiments**, and **extensive qualitative analyses**. We address each of your concerns point by point.
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**Q1: Clarification on Eq. (4)... | Summary: The paper proposes Balanced Learning Domain Adaptation for addressing class bias in unsupervised domain adaptative semantic segmentation. The authors identify class imbalance and distribution shifts as major obstacles in UDA and propose techniques to analyze logit distributions to assess prediction bias. The m... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive and constructive feedback. We appreciate your recognition of **our method’s versatile design**, the **thoroughness of our experimental validation**, and the **clarity of our theoretical explanation**. We address each of your concerns point by poin... | Summary: The study conducts an in-depth investigation into class bias within UDA scenarios, demonstrating that this bias stems from simultaneous shifts in both the label and data distributions, which complicates the domain adaptation process. To address this challenge, the authors introduce a novel approach that evalua... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive feedback, as well as for acknowledging our contributions, including the **clear problem formulation**, the **clarity of the writing**, and the **effectiveness of empirical validation**. We address each of your concerns point by point.
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Clustering Items through Bandit Feedback: Finding the Right Feature out of Many | Accept (poster) | Summary: This paper studies a problem of clustering $n$ items into two groups using bandit feedback. This setting considers an $n \times d$ matrix $M$, where each row represents an item's feature vector. The $n$ rows are partitioned into two unknown groups, such that items within the same group share the same feature v... | Rebuttal 1:
Rebuttal: First, we would like to thank you for your time and effort in reviewing our paper. Below, we address the key remarks and questions raised in your review:
- **Extension to $K>2$ clusters**
In the paper, we analyze the case of two clusters, as even in this simpler setting, significant challenges... | Summary: This paper investigates the problem of clustering items via bandit feedback. The items can be partitioned into two unknown groups that share the same feature vector within each cluster. The authors propose a sequential and adaptive setting where the learner can only select one item-feature pair per round. The ... | Rebuttal 1:
Rebuttal: First, we thank you for your time and effort in reviewing our paper. We will correct the typographical errors you identified. Below, we discuss about the different weaknesses that you identified. We will provide intuitions and thorough discussions on these points in the final version of the paper... | Summary: This paper addresses the problem of clustering items based on bandit feedback in a sequential and adaptive setting. Each of $n$ items is characterized by a $d$-dimensional feature vector, and the items are partitioned into two unknown groups where items within the same group share the same feature vector. The ... | Rebuttal 1:
Rebuttal: We first would like to thank you for your time and effort in reviewing it, and for your insightful questions. We now overview the remarks and questions that you formulate in your review:
- **Oversampling from row 1**
First, we would like to emphasize that improving the budget compared to non... | Summary: This paper considers the problem of classifying $ n $ items into two categories based on bandit feedback. Each item is associated with $ d $ features that vary depending on the class it belongs to. In each observation, an item and a feature are selected, and the corresponding feature value is observed with noi... | Rebuttal 1:
Rebuttal: First, we would like to thank you for your time and effort in reviewing our paper. We corrected the small typo in Section 6 that you pointed out. Besides this, we would like to provide more insights regarding the two limitations you mentioned.
- **Discussion on the optimal trade-off $H$ (Eq. (3))... | null | null | null | null | null | null |
BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models | Accept (poster) | Summary: This paper explores the streaming generation of high-quality binaural audio from monaural audio, considering the spatial positions of both the speaker and listener. Specifically, the task is approached as a generative problem, utilizing a flow matching model to generate stochastic binaural details absent in th... | Rebuttal 1:
Rebuttal: We thank the reviewer for your constructive comments. We will address your concerns below and will revise our paper following your suggestions.
**Performance on the public dataset** (Claims And Evidence)
We agree that the proposed method performs comparably to the baseline (BinauralGrad) on some... | Summary: This paper proposes a streaming binaural speech synthesis method using a causal architecture design and flow matching models. It introduces a flow matching model to generate binaural speech from a single-channel input. Additionally, it adopts a causal architecture to predict the next frames based on past infor... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work. We will address your questions in the responses below.
**Related work** (Claims And Evidence)
We thank the reviewer for pointing out these related works. PeriodWave designs a multi-period flow matching model for high-fidelity waveform generation.... | Summary: They sought to address the binaural speech synthesis task by using mono-channel audio to generate binaural speech. To support streaming and produce audio aligned with a given pose, they employed a flow-matching-based generative model with a causal structure. In the process, they introduced streaming STFT/ISTFT... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work. We will address your concerns and include your suggestions in the revision.
**Dataset** (Questions For Authors)
Regarding open-sourcing the dataset, we fully understand the importance of reproducibility and transparency in research. However, due ... | null | null | null | null | null | null | null | null |
C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning | Accept (poster) | Summary: This paper considers offline CMDP problem where one needs to maximize the expected cumulative reward subject to the constraint that the expected total cost is below a certain threshold. The paper then proposed constrained conditioned implicit Q learning for safe offline RL. The main novelty is redefining the r... | Rebuttal 1:
Rebuttal: ### Q1: The main novelty is redefining the reward function that only gets rewards when the policy is safe. The contribution part is not clear.
In SORL, the most important concern is the OOD problem, while existing methods can only mitigate it via policy constraining but cannot avoid it. The first ... | Summary: The paper introduces Constraint-Conditioned Implicit Q-Learning (C2IQL), a novel approach for Safe Offline Reinforcement Learning (SORL) that improves constraint satisfaction while maximizing rewards. The key innovations include a Cost Reconstruction Model (CRM), which estimates non-discounted cumulative costs... | Rebuttal 1:
Rebuttal: ### Q1: An experiment of how errors/inaccuracies in cost estimation impact safety guarantees is needed.
Thank you for your suggestion. To address your concern, we have conducted additional experiments to analyze the impact of cost reconstruction error on policy performance and safety guarantees.
... | Summary: This work focuses on the offline safe RL, where the existing baseline methods often suffer from the OOD issues (as in the general offline RL). To do so, this paper proposes the C2IQL method that employs the cost reconstruction model to derive non-discounted cumulative
costs from discounted values and incorpora... | Rebuttal 1:
Rebuttal: ### Q1: More complex benchmark is needed.
Thank you for your suggestion. To address the reviewer’s suggestion, we have also incorporated additional experiments on the SafetyGymnasium benchmark to diversify the test tasks. Specifically, we have selected the 4 Point and 3 Velocity tasks as additiona... | Summary: Offline RL has gained popularity recently as it can be trained with offline batched data without interacting with a simulation environment. Constrained offline RL further extends the idea by adding threshold penalty on the entire trajectory cost. While offline RL and constrained on-policy RL are relatively wel... | Rebuttal 1:
Rebuttal: ### Q1: My only concern is that the threshold penalty is chosen randomly. Adding experiments on real-world problem settings is appreciated.
Thank you for your suggestion. The threshold penalty is choose based on small (<50%), middle (50%), and large (>50%) constraint of the maximum cost, which is ... | null | null | null | null | null | null |
Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models | Accept (poster) | Summary: This paper presents a new method for posterior sampling from a wide variety of generative models (GANs, Flow, VAE) that can be expressed as a deterministic transformation x ~ f(z) for z sampled from a simpler distribution like Gaussian noise. The key idea is to train an auxiliary diffusion model to produce an ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and constructive feedback, as well as the positive comments on the exposition and evaluations.
To address the questions:
### __Efficiency of training a model for each task__
It is true that the outsourced diffusion sampler must be trained separate... | Summary: This paper addresses the posterior inference problem using diffusion sampling. By comparing their approach with existing MCMC methods and amortized inference methods, the authors demonstrate that their proposed outsourced diffusion sampling method, optimized through the trajectory balance objective, is both ef... | Rebuttal 1:
Rebuttal: We appreciate your good questions and constructive feedback. We have responded to your points below.
### __Algorithm details__
We agree that the addition of an algorithm block would help readers understand how to implement the core training loop, and added this to __Algorithm 1 in page 2 of the ... | Summary: This work targets the problem of generating samples from the posterior $p(x | y) \propto p(x) r(x, y)$, where the prior $p(x)$ is a (pre-trained) generative model and $r(x, y)$ is a reward function. The authors argue that (most) generative models can be formulated as the application of a pushforward $f(z) = x$... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback, as well as pointing out that the paper is very well written.
You raise some important concerns, which have helped us improve the paper and which we hope to address:
### __Posterior evaluation__
We agree that more comprehensive poster... | Summary: ## Summary
* This paper proposed a more general approach towards posterior inference for generative model with Gaussian prior (e.g. GAN, Flow, Diffusion models). More specifically, it proposed to learn a non-Gaussian prior (noise space z).
* The paper is evaluated on various models and various scale from toy s... | Rebuttal 1:
Rebuttal: Thank you for the helpful review. We address the weaknesses and questions raised by you in our response below.
### __Comparison with model-specific approaches__
We thank the reviewer for pointing out the additional model-specific baselines with which to compare out general approach.
- We note t... | null | null | null | null | null | null |
Ehrenfeucht-Haussler Rank and Chain of Thought | Accept (poster) | Summary: The paper studies the expressivity of one-layer Transformers with hard attention (in particular for Boolean functions), where it's shown that the Ehrenfeucht-Haussler rank of a Boolean function is equal to the number of (continuous) chain-of-thought (CoT) tokens that a single-head model has to produce to expre... | Rebuttal 1:
Rebuttal: Thank you a lot for your comments and questions. We respond to all three below.
*Q1: What is the central message of the paper for the ML community?*
The ability of Transformers to perform function composition has garnered increasing attention in recent years, as understanding this capability she... | Summary: The paper shows that the minimal number of CoT steps a single-layer hard-attention Transformer needs to compute a function exactly corresponds to the function's EH rank. In essence, it proves that the EH rank equals the minimum depth of decision trees (over assignment queries) that simulate the Transformer's c... | Rebuttal 1:
Rebuttal: Thank you kindly for all your comments and inspiring questions!
*The claim that “multi-head attention cannot circumvent inherent sequential steps” applies only to t-Comp/k-thOne—not general tasks.*
A following function with H-head rank 1 and 1-head rank H establishes tightness of proposition 5.... | Summary: This paper characterizes the notion of rank for Boolean and non-boolean functions with a one-layer transformer. Specifically, they show that the rank of a function is equivalent to the minimum number of chain-of-though steps required by a single-layer Transformer with
hard attention to compute the function. Th... | Rebuttal 1:
Rebuttal: Thank you for all your valuable comments. Below we respond to some:
1. *The specific tasks considered, t-Comp and k-thOne, are very simple. It would be great to consider some more practical tasks, e.g. the arithmetic tasks and Dynamic Programming in [1]. Or maybe give more examples of functions w... | Summary: The authors study the expressivity of single-layer transformers with hard attention by studying the question: How many chain of thought steps are required to compute particular functions that map strings of length n to some finite output set. The Ehrenfeucht-Haussler rank of a Boolean function measures the com... | Rebuttal 1:
Rebuttal: Thank you for the positive review and for noticing some typos! | null | null | null | null | null | null |
Self-Consistency Preference Optimization | Accept (poster) | Summary: This paper introduces SCPO, a novel training method for large language models (LLMs) that leverages self-consistency to improve performance on complex reasoning tasks without requiring gold labels. SCPO iteratively trains models to prefer consistent answers over inconsistent ones by generating multiple respons... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and comments. We are also glad to see you found our paper to have “intuitive motivation” as well as “extensive experimental results”. Please find our in-depth response to your comments below.
> Additional relevant baselines:
Thanks for pointing these papers... | Summary: This paper considers self-alignment of LLMs, where the data consist of only prompts but not the ground truth. The authors propose to use the self-consistency to choose winning and losing samples, where the responses corresponding to the most/least final answers are considered as the winning/losing samples.
Cl... | Rebuttal 1:
Rebuttal: We thank you for your review and for appreciating the “intuitive and simple” design of our method as well as our evaluation setup.
> The choices are to certain extent reasonable, one concern here is that the self-improvement baseline, LSMI, is not quite up-to-date. Considering self-alignment as ... | Summary: The paper introduces Self-Consistency Preference Optimization (SCPO), a novel approach to self-training large language models (LLMs) for complex reasoning tasks without requiring gold labels/solutions. SCPO extends the concept of self-consistency (typically used only at inference time) to create preference pai... | Rebuttal 1:
Rebuttal: We thank you for your extensive review and comments and are glad to see you appreciate the “creative application of self-consistency”, “impressive results”, and “thoughtful ablations”. Please find our detailed response to your comments below and let us know if you have any follow up questions.
> ... | Summary: The paper introduces Self-Consistency Preference Optimization (ScPO), an unsupervised method for training LLMs to improve reasoning tasks. ScPO leverages the concept of self-consistency—traditionally used at inference—to iteratively optimize models by preferring answers with high consensus over inconsistent on... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and questions. Please find our response below your comments:
> Significance of Results
Our test sets include \>= 1K samples for GSM8K and ZebraLogic, and 5K problems for MATH. In our primary unsupervised paradigm with greedy decoding, ScPO **consistently** o... | null | null | null | null | null | null |
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization | Accept (spotlight poster) | Summary: The author investigates how mechanistic interpretability improves the precision and robustness of knowledge editing and unlearning in LLMs. They distinguish between methods that preserve outputs and those that target high-level mechanisms with predictable states. The findings show that localizing edits to look... | Rebuttal 1:
Rebuttal: Thank you for reading our work and providing feedback. We appreciate the opportunity to clarify our contributions and address your concerns. We believe that the assessment and low score was based on certain misunderstandings of our work: we focus on model editing of factual relations; our editing ... | Summary: The authors investigate the effectiveness of adopting techniques from mechanistic interpretability to improve editing and unlearning in large language models. In particular, the work focuses on analyzing the benefits of unlearning and editing brought by localization techniques based on factual lookup (FLU) ins... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable proposals. In response, we proposed a way to automate our method, which allowed us to test it at scale in terms of the number of facts to be edited. We also added experiments demonstrating that adding attention heads does not improve performance.
Ad... | Summary: The paper studies the mechanistic localizations for knowledge unlearning and editing. There are two main categories of mechanistic localizations in the literature: Output Tracing and Fact Lookup. Through a designed experiment, the paper finds that Fact Lookup localizations make knowledge unlearning / editing m... | Rebuttal 1:
Rebuttal: Thank you for your comments, we will make sure to improve the description of the FLU localization technique for CounterFact in Sec 2.2, and we report new positive experimental results editing significantly more facts. The methodology is briefly summarized here:
An important pre-requisite is “pa... | Summary: This paper focuses on machine unlearning i.e. when the model needs to be prevented from outputing a certain information such as the profession of a person, or any mention of a given sport. Specifically they show that a lot of known methods might be preventing access to specific facts, but not overwriting the f... | Rebuttal 1:
Rebuttal: Thank you for a thorough read of our submission. Below we answer your question regarding the generalization of relearning in the Sports-Athlete-Editing and Full-Sports-Editing tasks, and explain our rationale behind the task choice for relearning attacks.
Our relearning experiments in Section 3.2... | null | null | null | null | null | null |
Unsupervised Transfer Learning via Adversarial Contrastive Training | Reject | Summary: This paper presents a novel unbiased self-supervised approach, Adversarial Contrastive Training (ACT), aimed at mitigating biased sample risk. The proposed ACT method is both simple and effective, utilizing matrix G to enhance self-supervised transformation learning. Notably, the k-NN evaluation demonstrates s... | Rebuttal 1:
Rebuttal: We thank you for your thorough review of our manuscript and for your constructive suggestions. Our point-by-point responses to your comments are given below.
> **C1** Improve the presentation.
Thank you for your constructive suggestion. Please see the response to **C4** of Reviewer f4DL.
> **C2... | Summary: This work studies the theoretical aspects of contrastive learning. Specifically, the authors focus on the regularization framework for model collapse in contrastive learning, where the main issue in current works is the population-level bias and sample-level bias cannot be simultaneously mitigated. To deal wit... | Rebuttal 1:
Rebuttal: We thank you for your thorough review of our manuscript and for your constructive suggestions. Our point-by-point responses to your comments are given below. Thank you for pointing out these typos. We correct them in the revised manuscript.
> **C1** The dimension of ACT.
* We appreciate the sugg... | Summary: This paper focuses on the task of transfer learning and proposes a loss function based on adversarial contrastive learning. More concretely, based on this adversarial conservative learning framework, this paper learns a representation map from source data which can be transferred to the target distribution for... | Rebuttal 1:
Rebuttal: We thank you for your thorough review of our manuscript and for your constructive suggestions.
> **C1** Examples of Assumption 3.8.
We exemplify the source/target distributions as **Gaussian mixtures** of the same componential variances. Then $\epsilon_{1}$ is the maximum distance between the m... | Summary: This paper introduces Adversarial Contrastive Training (ACT), a novel approach to unsupervised transfer learning that addresses bias issues in existing contrastive learning methods. The authors provide both theoretical guarantees and empirical evidence demonstrating the effectiveness of their approach. The the... | Rebuttal 1:
Rebuttal: We thank you for your thorough review of our manuscript and for your constructive suggestions. Our point-by-point responses to your comments are given below.
> **C1** Lack of practical implementation.
We add a detailed PyTorch-type pseudo-code in the appendix of the revised version.
> **C2** Th... | Summary: The paper presents a novel self-supervised learning (SSL) transfer learning technique that is unbiased and with provable guarantees.
The method is part of the class of SSL decorrelation approaches that align the cross-correlation matrix of learned representations with the identity matrix.
The paper first hig... | Rebuttal 1:
Rebuttal: We thank you for your thorough review of our manuscript and for your constructive suggestions. Our point-by-point responses to your comments are given below. Thank you for pointing out these typos. We correct them in the revised manuscript.
> **C1** Additional experiments: (1) comparisons with SO... | Summary: The paper proposes a method for unsupervised transfer learning called Adversarial Contrastive Training (ACT). The key idea is to address the bias present in sample-level estimators of self-supervised contrastive learning by reformulating the regularization term into a minimax (adversarial) framework. In this f... | null | null | |
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP | Accept (poster) | Summary: With wide-spread deployment of CLIP image- and text-encoding for use in downstream tasks, CLIP has been revealed as a useful attack surface for subverting inference in the downstream model. A natural question is how to craft an effective perturbation once by way of ensemble methods which then subverts many mod... | Rebuttal 1:
Rebuttal: We are grateful for your careful review of our paper. Please find our detailed responses to your questions below.
---
**Q1:** Choice of sampling strategies and improvement over random sampling
**A1:**
We would like to clarify and emphasize the comparison of sampling strategies presented in Sect... | Summary: This paper reveals a universal adversarial vulnerability in CLIP models, where a single perturbation achieves super-transferability across datasets, domains, models, and tasks. The authors find that proxy encoder selection, rather than the dataset, is the key factor. They propose X-Transfer, a novel attack tha... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and careful review of our work. Below, we provide detailed responses to address each of your concerns.
---
**Q1:** Using OpenCLIP lacks generalisability and real-world models.
**A1:**
(1) OpenCLIP is an open-source framework, protocol, and API for CLIP and it... | Summary: This paper introduces X-Transfer, a novel adversarial attack method that generates universal adversarial perturbations (UAPs) with "super transferability" across data, domains, models, and tasks for CLIP-based vision-language models. The core innovation is an efficient surrogate scaling strategy that dynamical... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and insightful comments. Please find our responses to your questions below.
**Q1:** Mechanism behind the cross-task transferability on VLM
**A1:** X-Transfer exploits a common weakness in CLIP image encoders, even when they are trained on different da... | Summary: This paper introduces an algorithm to find universal adversarial perturbations for CLIP-like image encoders. The vulnerability works across domains, tasks, and samples. The main algorithm follows standard methods for finding adversarial perturbations: finding a perturbation whose \( L_{\infty} \) norm is small... | Rebuttal 1:
Rebuttal: We sincerely appreciate your review, valuable feedback, and kind recognition of our work. Below are our responses to your questions.
---
**Q1:** The common assumption that an ensemble is necessary.
**A1:** We agree that it is indeed surprising that the vanilla version of X-Transfer without an e... | null | null | null | null | null | null |
Evaluating Neuron Explanations: A Unified Framework with Sanity Checks | Accept (poster) | Summary: This paper introduces a systematic framework designed for evaluating neural explanations. The core of this framework lies in its ability to quantitatively measure how well a given explanation aligns with neuron behavior across different samples.
The framework operates on a few key components. First, for any e... | Rebuttal 1:
Rebuttal: Thank you for the review and positive feedback! Your summary is very well written and highlights a strong understanding of our work. We would like to address your concerns below:
**Weakness 1 - Labeling Cost**
This is a great point. It is true that a significant reason for the use of recall is t... | Summary: This paper proposes NeuronEval for the meta-evaluation of input-based explanation metrics. Given a textual explanation of an input (and resultant activation), a variety of metrics have been proposed to evaluate how faithfully the description describes the neuron (or “any scalar function of network inputs”). Ne... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and positive feedback!
**Re: Additional Related Work:**
Thank you for pointing out these references. While these references focus on a very different type of XAI, in particular local input-importance evaluations and as such cannot be applied to our setting, they... | Summary: The paper proposes NeuronEval, a unified meta-evaluation formalism for assessing neuron explanation evaluation metrics. It reformulates 19 commonly used metrics under a shared mathematical notation. The authors assess the reliability of these metrics using two diagnostic tests—missing labels and extra labels—a... | Rebuttal 1:
Rebuttal: Thanks for the detailed review! We have conducted extensive additional experiments available at: https://drive.google.com/file/d/1OHMxyMW1KVIzxd2Rd_Hx34qIjVecUVmo/view, see in particular Tab G5-G7 as these experiments were conducted to address your questions.
**1.Realistic failure modes**
We arg... | Summary: This paper focuses on evaluating neuron-level explanations in deep learning models, particularly in the context of mechanistic interpretability. While many existing methods generate textual explanations for individual neurons, a critical challenge remains: how to assess the quality and reliability of these exp... | Rebuttal 1:
Rebuttal: Thanks for the review!
Please see https://drive.google.com/file/d/1OHMxyMW1KVIzxd2Rd_Hx34qIjVecUVmo/view for our new experimental results, in particular Tables G1-G4 as those experiments were conducted to address your questions. Below we address your concerns and questions in detail.
> **Weakne... | null | null | null | null | null | null |
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective | Accept (poster) | Summary: This paper investigates reward transfer in the context of online active RLHF. Existing investigations (based on active preference collection using on-policy sampling) have regret bounds proportional to instance-dependent properties, such as the cardinality of the action space. This investigation assumes access... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful suggestions! We address your comments as follows.
## 1. Methods And Evaluation Criteria & Experimental Designs Or Analyses
### 1.1 Comparison with other online algorithms
Regarding online RLHF methods, we first point out that other exi... | Summary: This paper studies the provably benefits of transferring knowledge from imperfect reward models (RMs) in online reinforcement learning from human feedback (RLHF). First, this paper identifies an important property specific to KL-regularized RLHF: the coverability for the optimal policy can be upper bounded by ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive suggestions! We address your specific comments in the following.
## 1. Claims And Evidence
### 1.1 About Theorem 3.2
As correctly pointed out, the sub-optimality gap is $\tilde{O}(T^{-1/2} + Cov(\Pi) T^{-1})$, and that’s why we cl... | Summary: The paper proposes a transfer learning algorithm that utilizes offline and online preference-based policy learning methods for RLHF. They provide a policy selection algorithm in each step where a new policy is selected based on a set of imperfect reward models and is used to further augment the training datase... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. It seems there may be some misunderstandings regarding our setting and several our key claims. To clarify, we start with a general remark, followed by detailed point-to-point responses. We hope our replies improve the clarity of our submission and help the r... | Summary: This paper studies RLHF under the contextual bandit setting with KL
regularization. In usual bandit problems, there is a need to balance exploration
and exploitation. However, they show that there is a "blessing of
regularization" in which the a policy that has low policy value gap will also be
a good explorat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive suggestions! We address your specific comments in the following.
## 1. Claims And Evidence
### 1.1 About Claim in Thm. 3.2
Thank you for the suggestion. We will follow it and clarify our claim in our next revision.
> ...why the s... | null | null | null | null | null | null |
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation | Accept (poster) | Summary: The paper presents DiTAR (Diffusion Transformer Autoregressive Modeling), a novel approach that combines an autoregressive language model (LM) with a diffusion transformer (LocDiT) to improve continuous speech generation. The key idea is a patch-based modeling strategy, where the LM predicts the sequence at a ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive review and insightful comments.
Most of your points are aligned with the contributions we aim to convey in our paper. Next, we address your questions organized according to the review sections.
We have attached audio samples of our method in this link:http... | Summary: This paper proposes DiTAR (Diffusion Transformer Autoregressive Modeling), a patch-based autoregressive framework for zero-shot text-to-speech synthesis that combines language models with diffusion transformers. The method uses a divide-and-conquer strategy where continuous speech tokens are partitioned into p... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We provide detailed responses to your concerns as summarized below:
**Q1. Subjective evaluation**
Audio samples can be found in this link: https://spicyresearch.github.io/ditar/#hard-cases
**Q2. Connection with VALL-E 2**
They are different in many aspe... | Summary: This paper introduces Diffusion Transformer Autoregressive Modeling (DiTAR), a patch-based framework that combines a language model with a diffusion process to generate continuous speech tokens. By employing a “divide-and-conquer” strategy, the language model processes aggregated patch embeddings, and the diff... | Rebuttal 1:
Rebuttal: We appreciate your careful reading of our paper and your insightful comments. Due to the word limit, we reply point by point in a concise manner:
**Questions in "Other Strengths And Weaknesses":**
1. Audio samples are presented here: https://spicyresearch.github.io/ditar/#hard-cases
2. No, $h_i$... | Summary: This paper introduces DiTAR, a speech generation method that integrates a causal language model (LM) with a shallow diffusion module using a bidirectional diffusion transformer (LocDiT). The approach incorporates several key techniques, including patchifying continuous audio tokens, directly feeding historical... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We provide detailed responses to your concerns as summarized below:
Demo page: https://spicyresearch.github.io/ditar/#hard-cases
**Q1. Fluid[1] as an essential reference not discussed**
To clarify, we have indeed been discussing MAR[2], the forerunner of... | null | null | null | null | null | null |
All-atom inverse protein folding through discrete flow matching | Accept (poster) | Summary: All-atom Discrete Flow Matching Inverse Protein Folding (ADFLIP) is a generative model for designing protein sequences conditioned on full atomic structures. Unlike existing inverse folding methods, ADFLIP progressively incorporates predicted side chains during sequence generation. Additionally, ADFLIP employs... | Rebuttal 1:
Rebuttal: **1.What is more impactful, the architecture introduced or the discrete flow matching? A lot of attention to the architecture is provided and it clearly is beneficial but deeper ablations as to what degree does the underlying generative framework and architecture play a role would strengthen the c... | Summary: A method named ADFLIP is proposed for inverse folding in all-atom structural contexts, e.g., containing ligand, nucleotide, and metal ions. The method is based on conditional discrete flow matching and a hierarchical GNN architecture. Additionally, it incorporates amino acid sidechains predicted by an external... | Rebuttal 1:
Rebuttal: **1.Related Work**
Thank you for pointing this out. We mentioned the paper by Yi et al. [1] in the introduction, but agree it should also be included in the related work. We will rewrite the related work section and discuss the discrete diffusion method for inverse protein folding from Yi et al a... | Summary: This paper proposed a new method, namely ADFLIP, a generative model for inverse protein folding that designs sequences based on all-atom structural contexts. It is designed to handle complexes with non-protein components and dynamic structures using ensemble sampling. ADFLIP progressively incorporates side-cha... | Rebuttal 1:
Rebuttal: **How does ADFlip perform on protein only dataset such as CATH**
Thank you for the suggestion. We retrained ADFlip on the CATH 4.2 dataset and evaluated its performance based on sequence recovery rate. The results are summarized below:
| Method | Sequence RR (%) |
|---------------|-------... | Summary: This paper introduces ADFLIP, a model for inverse protein folding designed for complex biomolecular systems. By by incorporating all-atom structural context (including protein backbone, non-protein components like ligands and metal ions, and progressively predicted side chains) and handling dynamic protein com... | Rebuttal 1:
Rebuttal: ## Clarification on "All-Atom" Structure in ADFLIP
Perhaps we had not explained clearly enough what we mean by ‘all-atom’. This interpretation of “all-atom” was introduced previously with RoseTTAFold-all atom[1].
In this context, all-atom refers not only to including the full set of protein atom... | null | null | null | null | null | null |
From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering | Accept (poster) | Summary: This work proposes a spectrum-free and baseline-view-free multi-view clustering method with double-track proximity, DTP-SF-BVF. It aims at improving the clustering stability and alignment flexibility as well as the anchor itself characteristic exploration. Unlike current methods that usually overlook the prox... | Rebuttal 1:
Rebuttal: **Q1:** Final steps for deriving clustering and variable initialization.
**A1:** After getting $\mathbf{C}$, we derive the clustering by sequentially identifying the row numbers where the element 1 is located. We initialize $\mathbf{A}_p$, $\mathbf{T}_p$, $\mathbf{B}_p$ and $\boldsymbol{\alpha}$ ... | Summary: In this paper, the authors concentrate on three key issues in multi-view clustering field: the neglect of anchor-anchor geometric proximity, the reliance on the baseline view for anchor alignment, and the instability caused by spectrum. Firstly, the authors adopt a self-expression subspace skill to explicitly... | Rebuttal 1:
Rebuttal: **Q1:** The introduction about the computational cost of permutation seems a bit concise.
**A1:** More details are provided here. Due to $\mathbf{A}_p\in{d_p\times m}$, $\mathbf{S}_p\in{m\times m}$, $\mathbf{B}_p\in{m\times k}$, $\mathbf{C}\in{k\times n}$ and $\mathbf{X}_p\in{d_p\times n}$, buil... | Summary: This paper develops a multi-view clustering algorithm named DTP-SF-BVF to address the problems: (1) current methods usually focus only on the anchor-sample proximity and fail to take into account the anchor-anchor relationship; (2) they require to select the baseline view; (3) existing spectrum paradigm induce... | Rebuttal 1:
Rebuttal: **Q1:** Can the binary be further refined? How is the performance under orthogonality?
**A1:** Thanks. Orthogonal constraints (OC) could deteriorate semantic topological continuity and limit the model's expression ability. Moreover, they will change the value and distribution of anchors. The foll... | Summary: The paper builds up double-track proximity for multi-view clustering to investigate the manifold structure among samples. In particular, it encodes anchor-anchor relation into anchor-sample similarity using self-expression learning and topology learning concurrently. It relieves the restriction of baseline-vi... | Rebuttal 1:
Rebuttal: **Q1:** The parameter searching may undermine the practical capability.
**A1:** Thanks! $\lambda$ governs the trade-off between error reconstruction and anchor self-expression, while $\beta$ adjusts the cross-view consistency guidance. They collaboratively modulate the model's capacity. Despite ... | null | null | null | null | null | null |
Progressively Label Enhancement for Large Language Model Alignment | Accept (poster) | Summary: The paper proposes PLE (Progressively Label Enhancement) for LLM alignment that could make use of all model generated responses. The proposed algorithm learns the contrast between principle guided response and original response using a ranking loss when the reward difference of the two are larger than a thresh... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and providing valuable feedback. I appreciate your efforts in ensuring the quality of the research. Regarding your concerns, I would like to provide the following explanations:
> If I understand correctly, the paper seems to assume that $\pi^\star... | Summary: The paper introduces a novel framework named PLE addressing inefficiencies in aligning large language models (LLMs) with human preferences. Current methods like RLHF face stability and scalability challenges, while alternative approaches rely heavily on large high-quality datasets and treat data generation and... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and providing valuable feedback. I appreciate your efforts in ensuring the quality of the research. Regarding your concerns, I would like to provide the following explanations:
> The proofs lack intuitive explanations for key parameters, leaving t... | Summary: Authors propose a novel framework that couples data generation and model training, leading to inefficient utilization of generated data. Authors provide a theoretical prove that with the progressively updated threshold strategy, our approach can bound the error rate between the trained model and the optimal mo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and providing valuable feedback. I appreciate your efforts in ensuring the quality of the research.
We would be happy to answer any additional questions or provide any further information you may need. | Summary: he paper introduces Progressively Label Enhancement for LLM Alignment, a framework designed to improve the alignment of Large Language Models (LLMs) with human expectations, addressing ethical and legal concerns. PLE tackles these issues by dynamically adjusting the model's training process based on the qualit... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and providing valuable feedback. I appreciate your efforts in ensuring the quality of the research. Regarding your concerns, I would like to provide the following explanations:
> The proposed approach PLE shares considerable similarity with prior ... | null | null | null | null | null | null |
Multiple-policy Evaluation via Density Estimation | Accept (poster) | Summary: The paper introduces CAESAR, an algorithm for efficiently evaluating multiple policies in finite-horizon MDPs to a desired accuracy and confidence level. CAESAR first obtains coarse estimates of the visitation distributions with low sample complexity, and then refines these estimates by computing importance we... | Rebuttal 1:
Rebuttal: Thank you for your efforts on reviewing our paper. We are confident that we can solve your concerns and questions. And we kindly request you to consider increasing your score if you think we address your concerns.
> The hybrid setting of online and offline is confusing.
Sorry to cause the confus... | Summary: This paper addresses the problem of evaluating the performance of multiple target policies in reinforcement learning (RL) using a sample-efficient algorithm called CAESAR. Existing methods for single-policy evaluation can be inefficient when applied separately to each policy, especially when policies are simil... | Rebuttal 1:
Rebuttal: Thank you for your efforts on reviewing our paper. We really appreciate your positive feedbacks on our work. We agree that framing the task as 'offline' multiple-policy evaluation is kind of wired and may cause some unnecessary confusions. Thanks for pointing it out and we will consider reorganizi... | Summary: This paper tackles the problem of multiple-policy evaluation, by proposing an offline off-policy approach named “CAESAR”, in contrast to the online on-policy approach by Dann et al. 2023.
The proposed approach first performs a coarse estimate of the visitation distributions of the target policies. These esti... | Rebuttal 1:
Rebuttal: Thank you for your efforts on reviewing our paper. We appreciate your comments. And we kindly request you to consider increasing your score if we solve your concerns and questions.
> The intuition behind $\beta$-distance does not seem entirely clear.
The $\beta$-distance is defined as follows $d... | Summary: Propose algorithm for off-policy estimation of a set of $K$ policies. To me, the main idea is to try to improve the linear dependence in $K$ in the naive $K / \varepsilon^2$ sample complexity (where one simply estimates the visitations of all $K$ policies via Monte Carlo) by collecting samples from a covering ... | Rebuttal 1:
Rebuttal: Thanks the reviewer for the detailed comments on our work. We are confident that we can solve your concerns well. And we kindly request you to consider increasing your score based on our following elaboration.
One of your main concerns is about our optimization objective (5):
> The problem (5) is... | null | null | null | null | null | null |
Label Distribution Propagation-based Label Completion for Crowdsourcing | Accept (poster) | Summary: To complete the missing labels, this paper proposes a novel label completion method for crowdsourcing by utilizing label distribution propagation. Both the worker similarity and the label correlation are considered to generate the label distribution for missing labels. Based on the worker similarity, the weigh... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments. Please find our detailed responses to your concerns as follows.
**Author Response to Q1:** We choose WSLC as the primary comparison method because WSLC is the most recent and relevant for our work. Moreover, both WSLC and our work are designed to address multi-clas... | Summary: This paper proposes a novel label distribution propagation-based label completion (LDPLC) algorithm to address the sparsity issue in crowdsourced label matrices. Existing worker similarity-based label completion (WSLC) algorithm only considers the correlation of labels annotated by different workers on individ... | Rebuttal 1:
Rebuttal: **Q1:** The key motivation for proposing LDPLC in this paper lies in the limitation that WSLC does not take into account the correlation of the labels annotated by different workers among similar instances. However, why WSLC has this limitation and what consequences it may lead to are not discusse... | Summary: 1. The paper primarily addresses the shortcomings in WSLC, which traditionally considers only the correlations among labels annotated by different workers for individual instances. The authors propose the LDPLC algorithm, which additionally accounts for correlations among labels annotated by different workers ... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments. Please find our detailed responses to your concerns as follows.
**Author Response to Convergence Efficiency:** As shown in Figure 4, LDPLC converges after just 4 iterations on the “LabelMe” dataset. To address the reviewer's concerns, we further observe the converg... | Summary: This paper proposes a crowdsourcing label completion method to complement subsequent truth-inference/label-integration methods.
The proposed method primarily focuses on improving the existing method WSLC, which “considers solely the correlation of the labels annotated by different workers on per individual in... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments. Please find our detailed responses to your concerns as follows.
**Author Response to Research Motivation and Research Problem:** Label integration (truth inference) is indeed a long-standing research problem. Over the past decades, numerous algorithms have been pro... | null | null | null | null | null | null |
GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning | Accept (poster) | Summary: The paper introduces GRADEO, a model for evaluating text-to-video generation using multi-step reasoning. The authors propose GRADEO-Instruct dataset, which contains 3.3k videos and 16k human annotations, to train GRADEO to mimic human evaluation. The evaluation metrics include multiple dimensions, including qu... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comprehensive comments and constructive advice. We are very excited to see that the reviewer finds our work (1)"provides an **inspireful insight**", (2) "evaluation criteria is **comprehensive and well-structured**", (3)"**comprehensive experiments**", (4)and "paper... | Summary: This paper introduces a benchmark for evaluating T2V models. The authors sample 10 video generation models and employ five distinct annotators to perform CoT labeling on the outputs, providing both reasoning and scores. The resulting dataset is then used to fine-tune Qwen2-VL-7B. Across the seven evaluation di... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and constructive feedback. We are grateful that you recognized the strengths of our work, including (1) "**comprehensive and well-rounded**" method, (2) "provides a **promising** direction", (3) and "highlighting its **broader relevance and utility** in addres... | Summary: This paper introduces GRADEO, a novel approach for evaluating text-to-video (T2V) generation models using human-like multi-step reasoning. The authors identify key limitations of existing evaluation methods, which often lack high-level semantic understanding and reasoning capabilities, making them inadequate f... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and appreciate your valuable comments. We are motivated to see that the reviewer finds our work (1) **the novelty and validity** of introducing CoT, (2) **the comprehensiveness and rationalization** of dimension definitions and experimental setups, (3) CoT reas... | Summary: This paper addresses the challenge of evaluating video generation models by introducing GRADEO, a novel video evaluation model designed to provide explainable scores and assessments through multi-step reasoning. The authors curate GRADEO-Instruct, a multi-dimensional dataset with 3.3k videos and 16k human anno... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and appreciate that they valued the thorough evaluation. We would like to address your question as follows. Sorry for the space constraints, the table is provided via an anonymous link: https://imgur.com/a/VzqdmYY.
**[Q1] Experimental setup.**
**[A1]** We wou... | null | null | null | null | null | null |
Maximum Total Correlation Reinforcement Learning | Accept (poster) | Summary: This paper proposes an algorithm called MTC, which is a SAC-style approach that utilizes total correlation regularization. The basic idea behind MTC is that reducing unnecessary variations in states and actions increases robustness; accordingly, the authors propose an algorithm that maximizes the total correla... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our work and appreciating the performance and robustness of our method, and the persuasive evidence of our main claims.
> benefit of total correlation maximization in non-periodic tasks
Our main hypothesis that simple behavior that does not overfit to slight va... | Summary: This paper proposes a method that learns compressible policies via a lower bound on the total correlation over a trajectory.
This is motivated by aiming to increase robustness by learning more simplistic policies.
In practice, the method trains a recurrent latent state and action predictor and adds the predict... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our submission, and acknowledging the soundness of the main experiments, the good presentation and the empirical results that show more robust and compressible policies.
> the claim of reducing spurious correlation should be investigated more directly or removed
... | Summary: An RL framework is presented that encourages total correlation within trajectories, leading to smoother, more periodic looking, and more compressible trajectories. This leads to robustness to observation noise, action noise, and robustness to changes in dynamics. The proposed algorithm is employed for DeepMind... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our submission, and appreciating our contributions, and thorough experimentations.
> is MTC better because of total correlation, or because of it being non-Markovian?
While our total correlation objective results in a reward function that depends on the past in ... | Summary: The paper proposes an extension to standard reinforcement learning by introducing a regularization objective that maximizes the total correlation across latent state representations and actions in an agent’s trajectory. It derives a variational lower bound on this total correlation, which is incorporated into ... | Rebuttal 1:
Rebuttal: Thank you for providing valuable comments and acknowledging the novelty and empirical performance of our approach.
> Method uses a lower bound for total correlation that is always negative, making it unclear how accurately it reflects the true value.
As stated under limitations, our lower bound... | null | null | null | null | null | null |
Anytime-Constrained Equilibria in Polynomial Time | Accept (poster) | Summary: This paper introduces anytime constraints to the Markov game setting and develops a comprehensive theory of anytime-constrained equilibria (ACE). The authors present three main contributions: (1) a computational characterization of feasible policies, (2) a fixed-parameter tractable algorithm for computing ACE,... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We will make sure to improve the paper using the suggestions mentioned. Please see our general rebuttal in the rebuttal section for Reviewer Gjeu, which addresses your concerns about empirical evaluation and existing methods. For your other concerns, please see below.
... | Summary: This paper introduces the concept of anytime-constrained equilibria in the context of constrained Markov games, where agents must adhere to strict budget constraints at every time step. The authors extend the notion of anytime constraints from single-agent settings to multi-agent settings. The authors provide ... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We will make sure to improve the paper using the suggestions mentioned. Please see our general rebuttal in the rebuttal section for Reviewer Gjeu, which addresses several of your concerns, including practical applicability. For your other concerns, please see below.
*... | Summary: The paper extends anytime constraints to the Markov game setting and the corresponding solution concept of anytime-constrained equilibrium (ACE). The authors provide: (1) a computational characterization of feasible policies, (2) a fixed-parameter tractable algorithm for computing ACE, and (3) a polynomial-tim... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Please see our general rebuttal below, which addresses the relevancy of anytime constraints in the literature and comparisons to standard expectation constraints. We also provide additional commentary below the general rebuttal.
--- *General Rebuttal* ---
We apprecia... | null | null | null | null | null | null | null | null |
Improving Out-of-Distribution Detection with Markov Logic Networks | Accept (poster) | Summary: This paper proposes a novel framework for improving out-of-distribution (OOD) detection using Markov Logic Networks (MLNs), which combine probabilistic reasoning with human-interpretable logical constraints. The approach addresses limitations of traditional OOD detectors, such as reliance on superficial statis... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the comprehensive and constructive feedback. Below, we address the specific points raised by the reviewer.
### Comment 2
> the phrase "maximizes the weighted sum of the discriminative power J" leaves readers unclear about the meaning of "discriminative power."... | Summary: The paper presents a novel approach to OOD detection by integrating Markov Logic Networks (MLNs) with existing OOD detectors. This fusion of probabilistic reasoning with logical constraints over human-understandable concepts distinguishes it from traditional statistical or neural representation-based methods.
... | Rebuttal 1:
Rebuttal: ### Explainability
Quantitatively measuring explainability remains notoriously challenging, thus we rely primarily on qualitative and structural justification. However, we would highly appreciate if the reviewer could suggest some metrics.
By construction, our method's explainability stems direc... | Summary: This work explores enhancing out-of-distribution (OOD) detection using Markov Logic Networks (MLNs), which integrate probabilistic reasoning with logical constraints for improved structure and interpretability. The proposed framework augments existing OOD detectors by incorporating MLNs to define human-underst... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the comprehensive and constructive feedback. Below, we address the specific points raised by the reviewer.
### Contradictory Constraints
> How does the algorithm handle contradictory constraints?
During training, the algorithm can automatically down-weights th... | null | null | null | null | null | null | null | null |
A Certified Unlearning Approach without Access to Source Data | Accept (poster) | Summary: This work focuses on certified unlearning when source data are unavailable due to resource limitation or regulation constraints. Instead, this work uses surrogate datasets to guide unlearning process, where the dataset mimics the original set to a certain extent. The indistinguishability guarantee is based on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Please see our responses below.
**About the datasets:** It is worth noting that prior works in certified unlearning have similarly employed datasets such as CIFAR‑10 and StanfordDogs [R5-R7]. Our primary objective in using these datasets was... | Summary: In this paper, the authors studied an unlearning problem with the assumption that at the time of unlearning, the model provider lost access to the original training dataset, but has access to a surrogate dataset that’s very close to the original training dataset in terms of distribution. They adapted the secon... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
**Justification of the motivation:** In our experiments, most surrogate datasets were synthetically generated to provide a controlled environment for evaluating our theoretical results. However, for real-world applications, surrogate datasets... | Summary: This paper studies certified unlearning in a setting where the original training data is inaccessible. Prior work on certified unlearning guarantees that the unlearned model is statistically indistinguishable from a model retrained without the deleted data, however it requires access to the original training d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
**Experiments with different forget ratios:** We conducted extensive experiments on the StanfordDogs dataset with varying forget ratios to assess how forget set ratio impacts unlearning. The results in the table below show that our method Unl... | Summary: This paper proposes a novel certified unlearning framework that enables the unlearning process without requiring access to the original dataset. The authors use an estimated surrogate dataset Hessian to approximate the second-order unlearning process. The surrogate dataset is generated by estimating the model'... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Please see our responses below.
**Experiments on complex models:** Our main focus in the current work was the theoretical foundations, to explore provable certified unlearning mechanisms in the source-free setting, and provide rigorous theore... | null | null | null | null | null | null |
NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations | Accept (poster) | Summary: The paper considers the problem of learning the termination conditions of the Option framework from task-agnostic demonstrations (demonstrations that are either exploratory or for other tasks). The key idea of the paper is to use state-action novelty to find states where termination will likely be reasonable t... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive suggestions. We address your questions below.
**Q1: Details in skill termination and execution**
Thank you for your feedback. To clarify, the variable-length skill embedding space $z$ is learned **offline** during the skill extraction phase using a deep... | Summary: This paper presents NBDI (Novelty-based Decision Point Identification), a state-action novelty-based decision point identification method that allows an agent to learn terminated skills from task-agnostic demonstrations. The key advancement presented in this work includes the mechanism for determining critica... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Please find our responses to your concerns below.
**Q1: Diversity of the task demonstration set**
Thank you for your insightful suggestion. In our experiments, we found that the initial task-agnostic demonstration set needs to be diverse enough to cover the ... | Summary: This paper introduces NBDI (Novelty-based Decision Point Identification), a novel approach for learning termination conditions to extract skills from task-agnostic demonstrations. The method consists of using state-action novelty to identify critical decision points where skills should terminate, allowing for ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Please find the responses to your questions below.
**Q1: Details in implementation of termination conditions**
Thank you for the suggestion. To improve clarity, we will revise Line 232 (second column) to include a more detailed explanation of the terminati... | Summary: The paper is on the topic of skill learning in reinforcement learning. Its contribution is on the subject of when skills should be terminated. The authors build on the existing work on the literature on skill learning, where the learned skills are executed for a fixed number of time steps. Here the authors add... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and constructive comments. We hope we can address your concerns below.
**Q1: Description of the train/transfer environments**
In Appendix E, we introduced the train/transfer domain similarity metrics for the environments used in our experiments. To demonstr... | null | null | null | null | null | null |
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders | Accept (poster) | Summary: This paper performs a systematic investigation of the various modelling choices for SAEs, particularly the choice of the encoder. The paper shows both theoretically and empirically (on synthetic data) that there exists an "amortization gap" in the sense that SAEs are unable to recover latent features due to th... | Rebuttal 1:
Rebuttal: # Response to Reviewer WhEs
We thank the reviewer for their thoughtful engagement with our paper and their recognition of its theoretical and empirical contributions. We address their specific concerns below.
## Regarding inconsistencies between synthetic and real data experiments
The reviewer ... | Summary: The authors prove that typical SAEs (ReLU, JumpReLU, TopK) cannot recover the optimal encoding (sparse coefficients) compared to sparse coding methods that solve individual examples iteratively.
They empirically demonstrate this using multiple synthetic experiments.
Finally, they apply sparse coding using the ... | Rebuttal 1:
Rebuttal: # Response to Reviewer STGo
We appreciate the reviewer's thoughtful analysis of our work and their recognition of the theoretical contribution. We would like to address several points regarding the practical implications of our findings.
## Regarding the claim about optimality gap in LLM activat... | Summary: The authors study Sparse Autoencoders (SAE), first showing that simple linear-nonlinear encoding leads to an amortisation gap. Next the authors compare different SAE architectures on synthetic settings, showing that better architectures can beat standard SAE in this setting.
Finally they study the interpretabi... | Rebuttal 1:
Rebuttal: # Response to Reviewer JWnB
We thank the reviewer for their time spent evaluating our paper. We believe there are several misunderstandings in the review that we would like to address, as they appear to have led to an incomplete assessment of our work.
## Regarding Theorem 3.1
The theorem is in... | null | null | null | null | null | null | null | null |
Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention | Accept (poster) | Summary: In a spatiotemporal forecasting setting, the authors consider traning prodiction models adapted to the task of selecting the top-K sites for intervention. The authors consider BPR as the desired metric and develop an algorithm for training models using a gradient-based approach. The main difficulty of this app... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments about our work. We try to address key points below.
> RE Question about “the title of Algorithm A.1”
Algorithm A.1 is meant to summarize the decision-aware ML training (DAML) approach described in Sec. 4.3. Current title is “Decision-aware ML... | Summary: This paper studies measures of best possible reach (BPR) to select the best subset of interventions. They analyze different measures based on a probabilistic model, as well as different way to train these probabilistic models from historical data. They also propose new methods for training, including a decisio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful feedback. We are glad to hear the overall story of our manuscript made sense to you.
We offer a few responses to the questions you raised below:
> One suggestion I could make is to expand on the minimization of JBPR when the model is misspecified... | Summary: The paper proposes an approach for learning a potentially misspecified spatialtemporal probabilistic model for decision-making settings. They specifically focus on the decision problem optimizing best possible reach (BPR) which closely corresponds to the ranking top-K items problem. Their approach consists of ... | Rebuttal 1:
Rebuttal: We thank ZTv9 for their thoughtful review, especially in introducing related work and PyEPO. We offer brief replies to key points below. We will revise to address all points raised.
## Essential References
Thanks for several useful references. Please see “Essential References (Common Issue)” in ... | Summary: The paper tackles two main issues related with a metric called Best Possible Reach (BPR): (1) the ranking problem, basically how to rank sites numerically to select the top K for intervention, based on a probabilistic method, to solve this, the paper works on a tighter bound on BPR and utilizes the ratio estim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We are glad that they thought our DAML method “provides a nice way to balance the goals of achieving high BPR for decision-making and maintaining good likelihood for overall forecast accuracy.”
> the approach is evaluated only on …Gaussian mixtu... | null | null | null | null | null | null |
EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning | Accept (poster) | Summary: This work develops a new instruction-tuning dataset called SwiftCode for efficiency-aware fine-tuning of LLMs for code generation. After fine-tuning on SwiftCode, LLMs are able to generate more efficient code on popular code generation benchmarks.
## Update after rebuttal
The rebuttal has addressed my concer... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for his insightful comments and suggestions. We provide detailed responses point by point. We hope our responses can address your concerns.
**W1: LLM-generated code may be inefficient**
Thank you for raising this important point about the efficiency of our candidate... | Summary: This paper introduces SWIFTCODE, a method to enhance code generation in large language models (LLMs) through efficiency-aware fine-tuning. The paper involves leveraging multiple LLMs to generate diverse code solutions for various tasks across different programming languages, then evaluating these solutions by ... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for his insightful comments and suggestions. We provide detailed responses point by point. We hope our responses can address your concerns and lead you to consider increasing your rating of our work.
**C1 & W1 Relation & Limitation.**
Thank you for acknowledging the... | Summary: SwiftCode introduces a novel approach to improving code generation in large language models (LLMs) by focusing on both correctness and efficiency. Traditional methods primarily optimize correctness, often neglecting execution speed and memory usage. SwiftCode addresses this gap by fine-tuning LLMs with a curat... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for his insightful comments and suggestions. We provide detailed responses point by point. We hope our responses can address your concerns and lead you to consider increasing your rating of our work.
**W1 & Q4 Novelty and Comparison with PIE and Mercury**
Our paper'... | Summary: This paper studies the problem of using an LLM to generate higher performance code. The authors propose a pipeline that first constructs a training dataset by sampling the LLM and choosing generations that have higher performance. Then, they finetune the LLM on slow-fast pairs to get it to generate faster code... | Rebuttal 1:
Rebuttal: We would like to thank you for your insightful comments and suggestions. We provide detailed responses point by point below. We hope that our clarifications, additional experiments, and responses can address your concerns and lead you to consider increasing your rating of our work.
**W1 Limited n... | null | null | null | null | null | null |
It's My Data Too: Private ML for Datasets with Multi-User Training Examples | Accept (poster) | Summary: This paper discusses the user-level privacy under the multi-attribution scenario, where each user is associated with multiple examples but also each example can be attributed to multiple users. It starts with defining the differential privacy used in this case as the fixed-graph DP. Then it goes to discuss how... | Rebuttal 1:
Rebuttal: Thanks for your support of the paper, and for the editing suggestions which we plan to incorporate. Here we respond to some of the reviewer’s questions. As space permits, we will plan to include more detailed explanations along these lines in the revision:
* __“X_i cannot be repeated in e_i”__: We... | Summary: The paper studies user-level differential privacy when each training sample can be attributed to multiple users, called multi-attribution model. It proposes a new privacy definition called fixed-graph DP, where users are nodes and examples are hyperedges. And the neighboring database is defined as arbitrary c... | Rebuttal 1:
Rebuttal: Thanks for your support of the paper. We respond to a few points below:
* __"The greedy algorithms are somewhat simple.”__: We agree the algorithms are simple, and we have framed them as baselines to emphasize this. We note that our empirical results, which demonstrate the baseline algorithms are ... | Summary: The paper introduces a novel differential privacy (DP) definition for datasets with multi-user attribution, where each training example is associated with multiple users (e.g. emails attributed to both senders and recipients).
Their proposed adjacency definition, termed "fixed-graph DP," protects the content ... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for their support of the paper. We want to respond to the two gaps raised by the reviewer.
* Gap 1: We agree that the graph structure can be a privacy risk. We do not advocate that _every_ algorithm that achieves fixed-graph-DP is appropriate for ML settings, and do not adv... | Summary: The work describes how to apply two differentially private training methods for data in which more
than one individual can contribute to each data instance (i.e. email sender and receivers, or
author set in a publication). The approach is based on building a subset of the dataset so as to
upper bound the numbe... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for their feedback. We agree that broadly, there is much more work to be done to fully understand the multi-attribution model, and part of our goal with this work is to motivate future research into this setting. Below we respond to individual points we disagree with in the ... | null | null | null | null | null | null |
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations | Accept (poster) | Summary: LMAct is a benchmark designed to evaluate the multimodal in-context imitation learning capabilities of state-of-the-art, closed-source large multimodal foundation models (LMs). LMAct systematically evaluates model performance over extremely long-context inputs, testing how effectively these models utilize a va... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment and insightful feedback. We are pleased that they think that our `paper is particularly relevant to the current trend of reasoning models` and that `it is great that this paper identifies areas where people can evaluate their reasoning models on ... | Summary: The paper presents a benchmark to evaluate the capabilities of today’s frontier models on multimodal decision making task in the very long context regime. The paper investigates the in-context learning abilities of these models. The authors compare a variety of the latest multimodal LM models on tasks like che... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment and constructive feedback. We are pleased that they think our `paper addresses an important problem`, our `paper covers a wide range of tasks`, and our `paper considers a wide variety of frontier models, making for a comprehensive study`.
**Could... | Summary: The authors created a benchmark for empirical evaluation of the multimodal in-context imitation learning capabilities of some state-of-the-art LLMs (Claude 3.5 Sonnet, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemini 2.0 Flash Experimental, GPT-4o, o1-mini, o1-preview, and o1) on several of interactive decision-making... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, constructive feedback, and pointers to additional relevant related work. We are pleased that they think that `the soundness and validity of the experimental design are beyond doubt` and that `the originality of the work arises from creative combinat... | Summary: This paper benchmarks the decision-making ability of several frontier multimodal models in interactive environments through in-context imitation. It investigates whether these models can be effectively prompted with few- or many-shot demonstrations to solve interactive tasks. The overall finding is that most f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and interesting questions. We are pleased that they think that our `claims are clear and supported with convincing evidence`, that our `experiments are well designed and presented`, and that our `results are an important sanity check of the current... | null | null | null | null | null | null |
Canonic Signed Spike Coding for Efficient Spiking Neural Networks | Reject | Summary: The paper aims to improve the conversion of Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) by developing a more efficient spike coding scheme, which has improved encoding capacity and reduced computational overhead.
Claims And Evidence: Based on a careful review, the claims in the paper a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Below, we address some key points of your concerns.
---
First, we would like to emphasize that our core contribution lies in the innovation of the ___encoding method___. Our work is not a continuation of Li et al. (2022) and Wang et al. (2022) because our co... | Summary: The paper proposes the Canonic Signed Spike (CSS) coding scheme, which enhances encoding capacity while maintaining network simplicity. Additionally, the Over-Fire-and-Correct method is introduced to enable efficient computation. The primary contribution lies in minimizing conversion loss when transforming art... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Below, we address some key points of your concerns.
---
### **Reference Implementation in Hardware**
Compared to traditional rate coding with IF neurons, our method introduces three additional components: _1. Membrane potential amplification_, _2. Silent... | Summary: In this work, the authors proposed a new neural coding, which is named canonic signed spike (CCS) coding. For the proposed encoding, they also introduced over-fire-and-correct and threshold optimization methods. The proposed coding method can efficiently transmit various information by transmitting information... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Below, we address some key points of your concerns.
---
### Energy Overhead of Spike Weighting
To achieve nonlinear encoding, we double the membrane potential at each time step. First, we would like to emphasize that __this method enhances encoded informa... | Summary: The paper proposes an implicitly weighted spiking mechanism for direct ANN-to-SNN conversion. The weight of the spikes, $\beta^{T-t}$, is determined by the temporal location $t \in [1,2, \cdots ,T]$ of the spikes, where an earlier spike gets a higher weight than spikes that arrive later, as $\beta > 1$. Furthe... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Below, we address some key points of your concerns.
---
### Comparison with LIF
The neuron dynamics of TSA and LIF can both given by the following equation:
$$u_{i}^{l}[t]=\beta u_{i}^{l}[t-1]+z_{i}^{l}[t]-S_{i}^{l}[t]$$
Apart from the difference in han... | null | null | null | null | null | null |
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics | Accept (poster) | Summary: This paper studies how non-zero initialization improves the perforamnce of LoRA, especially the stabilitiy.
- The authors define 1) the notation of stabilitity, $BAX = \Theta(1)$ for all LoRA layers when the width is infinity, where $X$ is the input. 2) the notation of efficiency, the linear update term is $\... | Rebuttal 1:
Rebuttal: **Hi Reviewer wxon:**
Thank you for your detailed and insightful comments. Below, we provide responses to each point individually. Additional experimental results can be found in **https://anonymous.4open.science/r/nzlora_rebuttal-7D3E**. To save space, we denote zero initialization as ***ZI*** a... | Summary: This paper investigates the impact of non-zero initialization on the fine-tuning dynamics of LoRA. Traditionally, in LoRA, one of the low-rank matrices, A or B, is initialized to zero to ensure fine-tuning starts from the pretrained model. However, this practice lacks theoretical justification. The authors the... | Rebuttal 1:
Rebuttal: **Hi Reviewer rPo6:**
Thank you for your detailed and insightful comments. Below, we provide responses to each point individually. Additional experimental results can be found in **https://anonymous.4open.science/r/nzlora_rebuttal-7D3E**.
***Q1: "typos in lines 62 and 799, and Eq (19)"***
**R1... | Summary: This paper investigates the impact of non-zero initialization in Low-Rank Adaptation (LoRA) fine-tuning, challenging the conventional practice of initializing one of the LoRA matrices (A or B) to zero. Through theoretical analysis and empirical validation, the authors demonstrate that simultaneously initializi... | Rebuttal 1:
Rebuttal: **Hi Reviewer zK3d:**
Thank you for your detailed and insightful comments. Below, we provide responses to each point individually. Additional experimental results can be found in **https://anonymous.4open.science/r/nzlora_rebuttal-7D3E**.
***Q1: "accuracy's dependence on the learning rate in Tab... | Summary: This paper considers scaling of hyperparameters for LoRA finetuning from an infinite width perspective following [1, 2]. The key difference compared to past works is that a non-zero random initialization of both the B and A adapter matrices is considered. The initialization can optionally be subtracted from t... | Rebuttal 1:
Rebuttal: **Hi Reviewer uk4U:**
Thank you for your detailed and insightful comments. Below, we provide responses to each point individually. Additional experimental results can be found in **https://anonymous.4open.science/r/nzlora_rebuttal-7D3E**.
***Q1: "typos"***
**R1:**
Thanks again for catching the... | null | null | null | null | null | null |
Topology-aware Neural Flux Prediction Guided by Physics | Accept (poster) | Summary: This paper proposes a topology-aware prediction framework that adopts explicit difference matrices that model directional gradients and incorporates implicit physical constraints to difference matrices which enhances the consistency with physical laws. Experiments on two real-world data demonstrate the effecti... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. We address them in a Q\&A format as follows.
Q1: How does PhyNFP perform on similar physical systems with different constraints?
A1: The PDEs adopted by PhyNFP (Saint-Venant (S-V) for hydrodynamics and Aw-Rascle (A-R) for traffic flow) are ... | Summary: The paper proposes a PhyNFP framework that aims to improve GNNs for modeling flow dynamics
in directed graphs. The main hypothesis of the paper is that the directional insensitivity
of traditional GNNs and their inability to capture high-frequency components arise, because
GNNs inherently smooth out directi... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. We address them in a Q&A format as follows.
Q1: How does PhyNFP scale to large networks and what are its computational bottlenecks?
| Number of Nodes | Average Runtime per Epoch(s) |
|-------------------------|------------------------------... | Summary: This paper addresses the challenge of preserving high-frequency components in Graph Neural Networks (GNNs) when applied to directed graphs, which is crucial for accurately modeling flow dynamics. Traditional GNNs often fail to distinguish between forward and reverse graph topologies, leading to information los... | Rebuttal 1:
Rebuttal: Thank you, and we address your questions in a Q\&A fashion as follows
Q1: Why the reverse problem will amplify numerical errors? Is it a concern for the forward problem?
A1: Solving inverse problems is ill-posed, as many different upstream conditions can lead to the same downstream fluxes. When ... | Summary: The authors proposed PhyNFP, a topology-aware neural flux prediction framework that integrates GNNs with physical principles to improve flow dynamics modeling in directed graphs. Traditional GNNs struggle with directional sensitivity and high-frequency information loss due to their inherent low-pass filtering ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. We address them in a Q&A format as follows.
Q1: How was the flux prediction task modeled? Is it autoregressive for the past 24 time steps?
A1: The flux prediction task is formulated as a supervised node regression problem rather than an au... | null | null | null | null | null | null |
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment | Accept (poster) | Summary: This paper proposes MPO, an efficient post-processing framework for mixing diverse preference alignment. The authors use batch stochastic mirror descent to find the optimal combination coefficients for output combination.
## update after rebuttal
Most of the concerns are resolved, so the reviewer raises the... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful feedback! Here we provide a detailed response to address all of your concerns below.
> Confusion of Algorithm 1 and Figure 1.
Thank you for the question and apologies for any confusion. As shown in Thm3.4, we have
$$\pi^*(y|x) \propto \prod... | Summary: This paper studies how to align diverse objectives of human preferences. The authors propose a post-processing approach that combines the optimal policies for each objective without requiring retraining from scratch. Moreover, the author also studies the max-min RLHF, and shows that we can find the optimal po... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful feedback! Here we provide a detailed response to address all of your concerns.
> Differences between main theorem and the one in Shi et al., 2024
Thank you for your feedback. Compared to Thm 1 in Shi et al., our approach differs in both obje... | Summary: This work proposes Mixing Preference Optimization (MPO), a post-processing framework for aggregating single objective policies with a mixing of preference alignment. Specifically, the authors combine two multi-objective RLHF approaches, MORLHF and MaxMin-RLHF using a post-processing strategy to combine single-... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful feedback! Here we provide a detailed response to address all of your concerns.
> How can the correctness of Eq. 10 be verified?
Thanks for the question. As we explained after Eq. 10 in our original submission, the result follows directly fro... | Summary: This paper proposes MPO, a framework designed to mix diverse single-objective policies for aligning LLMs with human preferences. Instead of training a costly multi-objective RLHF model from scratch, this paper shows how pre-trained, single-objective policies can be aggregated using a batch stochastic mirror d... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful feedback! Here we provide a detailed response to address all of your concerns below.
> It would be better to add more validations on the GPT-based evaluation.
Thanks for the suggestion. When utilizing GPT-based evaluations, we have experimen... | null | null | null | null | null | null |
Free Process Rewards without Process Labels | Accept (poster) | Summary: The paper introduces a method to train Process Reward Models (PRMs) without requiring expensive step-level annotations. By parameterizing outcome rewards as the log-likelihood ratio between a policy model and a reference model, PRMs can be implicitly derived from Outcome Reward Models (ORMs) trained on respons... | Rebuttal 1:
Rebuttal: > 1. The core concepts and theoretical framework have already been established in [1]. Even with the proposed extensions, the theoretical contribution remains marginal.
**A**: We note that the derivation of our work is different from [1] and provides a more general conclusion. [1] is tailored to ... | Summary: This paper proposes a new way of training process reward models without expensive fine-grained step-level annotations by training an ORM (reward modeled as log-likelihood ratios of the policy and the reference model) and using it as an implicit PRM. The authors show that their training of the implicit PRM is m... | Rebuttal 1:
Rebuttal: > [W1] Limited Datasets
**A:** Though we do not include other tasks in this paper due to our limited capacity and the limited space, after ICML submission deadline, there are recent works showing that Implicit PRM is helpful in best-of-N sampling on agent tasks [1], and adopting Implicit PRM for ... | Summary: This paper shows that the PRM can be obtained implicitly without additional training by parametarization.
Claims And Evidence: From the table, the gain delta from the proposed PRM is not that large, and on Mistra-7B is not helping, Besides, the pass@1 is not clear if it helps, would be great to show a curve h... | Rebuttal 1:
Rebuttal: > From the table, the gain delta from the proposed PRM is not that large, and on Mistra-7B is not helping, Besides, the pass@1 is not clear if it helps, would be great to show a curve here.
**A:** This might be a misinterpretation of Table 1. In fact, our approach achieves very strong performanc... | Summary: This paper introduces a method to create a process reward model (PRM) without the need for expensive step-by-step annotations. The authors propose that an implicit PRM can be derived by training an ORM using only response-level labels, by parameterizing the outcome reward as a log-likelihood ratio between the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. Here are our responses.
> The true test of PRM would be to run beam search or to use it as dense rewards for online RL.
**A:** We choose best-of-N as our setup because it is a standard practice in recent literature and presents as a valuable a... | Summary: Verifiers, such as process reward PRMs and ORMs, evaluate LLMs' partial or full responses, providing feedback and pushing the boundaries of LLMs' ability to solve complex reasoning tasks. . PRMs provide better, fine-grained feedback than ORM based on the nature of their training procedure. However, training PR... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and are glad that you find the method is suitable for the problem and the experiments and analysis are sound. Here are our responses.
> the proof appears to have some issues. Furthermore, the derivation of cross-entropy loss is missing the normaliza... | null | null | null | null |
(How) Do Language Models Track State? | Accept (poster) | Summary: This paper investigates state tracking mechanisms in transformer based language models. In particular, this paper applies interpretability analyses to Pythia and GPT-2 models as they solve the word problems in $S_3$ and $S_5$. The authors find signatures relating to two computational models, PAA and AA, and de... | Rebuttal 1:
Rebuttal: We are glad the reviewer found this paper to be a “really solid piece of work” and “as a whole [...] very well written”! We are happy to clarify your questions below:
## Pretraining vs training distinction, were pre-trained models pre-trained explicitly on state-tracking problems or the entire in... | Summary: This paper investigates how language models track dynamic states through systematic analysis of permutation composition problems . The study reveals that both pretrained and fine-tuned Transformer models learn two distinct state-tracking mechanisms: an Associative Algorithm (AA) and a Parity-Augmented Associat... | Rebuttal 1:
Rebuttal: Thank you so much for your time and feedback. Below, we address some of your general critiques and specific questions.
## How could these mechanisms be applied to practical scenarios?
To emulate a more practical scenario, we train Pythia models on a version of our task with permutations express... | Summary: The paper studies the mechanisms that Transformers learn for performing state tracking - predicting the state after a sequence of operations. Specifically, the model is provided with a sequence of permutations $a_1, \dots, a_t$, and needs to compute the state, which is the composition of the permutations $s_t ... | Rebuttal 1:
Rebuttal: We are thankful for your positive feedback and thoughtful comments. Below, we address some specific questions:
## Are permutations provided as individual tokens (i.e., the size of the vocabulary is the number of permutations)?
Yes, they are individual tokens! We append them as special tokens t... | Summary: It is known that transformers are theoretically able to capture certain formal language tasks of length $n$ with depth $\log(n)$. Empirically, large language models, which are primarily based on transformers, do appear to learn to state track. A full understanding of the mechanism that they learn for state-tra... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review, especially the time you took to examine the full submission, including the Appendix. We’re also grateful for your affirmation of our work’s contribution to the literature. Below, we address some of your specific questions:
## Unclear if signatures are suffici... | null | null | null | null | null | null |
BILBO: BILevel Bayesian Optimization | Accept (poster) | Summary: This paper proposes a bi-level Bayesian optimization algorithm for black-box functions. By optimizing both upper- and lower-level problems simultaneously, it improves the sample efficiency. Theoretically, BILBO achieves a sublinear regret bound for common kernels. It also demonstrates strong empirical performa... | Rebuttal 1:
Rebuttal: Thank you for the detailed review, and for appreciating our novel approach to a significant problem, sample efficiency of our method, sublinear regret bound, and effective experimental results. We would like to clarify the following points.
>One issue is that some of the results shown in the expe... | Summary: This paper introduces BILBO (BILevel Bayesian Optimization), a BO (Bayesian Optimization) algorithm used to address constrained bilevel optimization problems where the constraints, the upper objective and the lower objective are all black boxes and assumed expensive to evaluate. BILBO distinguishes itself from... | Rebuttal 1:
Rebuttal: Thank you for your insightful review, and for recognizing our sublinear cumulative regret and sample efficiency when both upper- and lower-level objectives are expensive blackboxes.
>1. Have you studied the performance of BILBO when the trusted spaces are built on discretized versions of continuou... | Summary: The paper proposes a UCB based method for bilevel Bayesian optimization. The proposed method, called BILBO, selects a next point by the upper bound based surrogate model bilevel optimization. The authors provide a regret analysis based on an approach of the well-known GP-UCB analysis.
Claims And Evidence: Sin... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for appreciating the potential of our problem. We would like to address some questions and concerns raised.
>1. Many lemmas (e.g., Lemma 4.4 and 4.6) should be a probabilistic bound because most of them depend on Corollary 4.2, but is often shown as a determ... | Summary: This paper introduces BILBO, a Bayesian optimization method for bilevel problems with noisy, constrained black-box objectives. It jointly optimizes both levels using trusted sets from Gaussian process confidence bounds. BILBO provides theoretical regret guarantees and outperforms baselines in empirical evaluat... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, and for recognizing the importance and practicality of our bilevel problem, clear and first theoretical guarantees in this setting, and good empirical support. We would like to address the questions raised.
>Could you concisely highlight which specific aspects ... | null | null | null | null | null | null |
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference | Accept (poster) | Summary: This paper presents a verifiable inference framework for LLMs served behind APIs that improves on existing techniques via better space-time complexity whilst maintaining robust security guarantees. In their empirical evaluations, the demonstrate near perfect reliability in terms of verifying proofs under benig... | Rebuttal 1:
Rebuttal: We are thankful for the thorough reading and review of our paper and appreciate the comments you have written. It is assuring that you found the problem to be well motivated and are convinced by the experiments of the reliability of the method in distinguishing permissible and undesirable modifica... | Summary: In this paper, the authors introduce a novel method called TOPLOC that provides cheap verifiable inference for large language models. TOPLOC efficiently encodes intermediate tensor activations into (k−1)-degree polynomial for top-k values. By doing so, it reduces a huge amount of storage for the communication.... | Rebuttal 1:
Rebuttal: Thanks for the review. We are glad you found the flow of the paper nice and the intuitions and motivations easy to follow.
**Comparisons to previous methods and baselines**
We fully agree that having more comparisons with baselines would be helpful. To provide some early additional results, we'v... | Summary: This paper proposes TopLoc, a locality-sensitive hashing-based method for verifying that an output generated by an LLM actually comes from the LLM that the LLM serving provider claims to be using. The author claims that traditional methods for verifying LLM output (e.g., cryptographic approaches or testing mod... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We are glad you found the problem interesting and the proposed method easy to follow.
**Comparisons to previous methods and baselines**
We fully agree that having more comparisons with baselines would be helpful. To provide some early additional... | Summary: This paper presents TOPLOC, a method to achieve verifiable LLM inference. It uses locality-sensitive hashing for intermediate activations to detect potential unauthorized modifications during the computaion. It uses a polynomial encoding scheme of the memory overhead of proof generation by 1000x.
## update af... | Rebuttal 1:
Rebuttal: Thank you for your review and for recognizing the importance of the problem our paper addresses.
**Comparison to zkLLM**
To provide some context on the speed and memory claim, we provide some early additional results. Here we evaluated zkLLM (https://arxiv.org/pdf/2404.16109), SVIP (https://arxi... | null | null | null | null | null | null |
PARQ: Piecewise-Affine Regularized Quantization | Accept (poster) | Summary: The authors propose a convex piecewise regularizer for quantization aware training. They utilize an aggregate proximal stochastic gradient method and prove that it has last-iterate convergence. They denote that their method is equivalent to a previously proposed ProxConnect method, however they derive their me... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the strength of our paper (sound methodology and being well written) and giving us constructive suggestions on having more benchmark evaluations. We agree that additional empirical evaluations, especially on modern language models, will make the paper stronger... | Summary: This paper proposed PARQ, a convex, piecewise-affine regularizer (PAR) for training the weights to cluster to a set of quantization points. Also, a practical implementation called PARQ is introduced. Overall, this paper has sufficient motivation, clear writing, grounded citations, and experiment enough to demo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive feedback to our paper and especially recognizing our main novelty and contributions. Here we mainly address the reviewer’s question on the regularization approach proposed in the following reference, which we call Ref [1] hereafter.
[1] Towards Accu... | Summary: they contribute a new QAT quantizer, specially optimize PAR-regularized loss functions using an aggregate proximal stochastic gradient method (AProx) and prove that it enjoys last-iterate convergence.
Claims And Evidence: convincing
Methods And Evaluation Criteria: make sense
Theoretical Claims: theoretical... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our main contribution on the PAR regularization, the AProx method and proving its last-iterate convergence. We will work on better readability in revising the paper as suggested by the reviewer. Here are answers to the reviewer’s questions.
1. Line 160 the de... | Summary: The paper proposes PARQ, a convex piecewise-affine regularization method for quantization-aware training. It introduces the AProx algorithm that transitions from soft to hard quantization, interprets STE as its asymptotic case, and proves last-iterate convergence.
## update after rebuttal
I confirm that I ha... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our paper’s contributions in advancing QAT by bridging gaps between heuristic approaches and theoretical foundations. We address the reviewers comments and questions as follows:
1. “The baselines in the experiment are too old. Why is PARQ not compared with ne... | Summary: This paper presents a principled QAT method PARQ via convex piecewise-affine regularization (PAR). The authors examine that PAR can induce network weights to approach discrete values. Then, the paper proposes an aggregate proximal stochastic gradient method (AProx) and theoretically demonstrates its last-itera... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our main contributions on convex regularization for inducing quantization and the AProx method with last-iterate convergence. Our response will focus on the experiment results and analysis.
We agree that we can make the discussion on experiments more compreh... | null | null | null | null |
CodeSync: Synchronizing Large Language Models with Dynamic Code Evolution at Scale | Accept (poster) | Summary: This paper introduces CodeSync, a data engine for identifying outdated code patterns and collecting real-time code knowledge updates from Python third-party libraries. Building upon CodeSync, the authors further develop CodeSyncBench, a comprehensive benchmark for assessing LLMs’ ability to stay synchronized w... | Rebuttal 1:
Rebuttal: **Dear Reviewer Jxkj,**
We would like to express our sincere gratitude for your thoughtful and constructive feedback. We have addressed all of the comments and thoroughly presented our most recent experimental findings.
---
**Q1:** The paper provides limited details on how 220 APIs are selected ... | Summary: The paper proposes a new benchmark called CODESYNC to address the issue that in the real world, library functions evolve over time while LLM code generation models are not updated. They source the API function updates from 6 real-world repositories: pandas, numpy, scipy, tensorflow, torch, and flask. They coll... | Rebuttal 1:
Rebuttal: **Dear Reviewer oxwH,**
We sincerely appreciate your thoughtful review and your recognition of our work’s contributions to the community.
---
**Q1:** Why is the HumanEval score for Qwen2.5-Coder-7B-Instruct so low? The results from the Qwen team show that it achieves 88.4% on HumanEval.
**A1:**... | Summary: The paper introduces CodeSyncBench, a benchmark to evaluate LLMs’ abilities to invoke the most recent versions of Python APIs. The benchmark shows that LLMs struggle to invoke APIs in the benchmarks correctly as measured by the chosen metrics. Further, the authors use various alignment techniques such as SFT, ... | Rebuttal 1:
Rebuttal: **Dear Reviewer HLiK,**
We sincerely appreciate your suggestions and assessment of our work! Motivated by your feedback, we are committed to improving our manuscript and providing a more comprehensive benchmark and evaluations!
---
**Q1:** How do the results of the paper change if you use static... | null | null | null | null | null | null | null | null |
Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs | Accept (spotlight poster) | Summary: This paper studies the problem of pooling methods in transformers for non-sequential tasks where only a subset of input tokens (signal) is relevant for downstream decision-making, while the rest (noise) may degrade performance.
The authors formulate a theoretical framework that formalizes pooling as a vector ... | Rebuttal 1:
Rebuttal: We appreciate your time and feedback. Your questions raise some important points that we should have discussed in the original submission.
**1. Computational Complexity**: AvgPool and MaxPool can be implemented with `O(n * d)` algorithms, as both require each of the `d` features of each of the `... | Summary: This paper investigates pooling methods for transformer embeddings in tasks where only a subset of inputs carries signal and the remainder are noise. It shows that standard methods like average and max pooling can collapse in performance as the signal-to-noise ratio fluctuates. The authors propose an attention... | Rebuttal 1:
Rebuttal: We appreciate your comprehensive review. We saw the weak reject rating as a great opportunity to improve the quality of the paper, and hope to address the questions you raised regarding performance outside of our theoretical assumptions.
**1 & 3. Idealistic Assumptions:** As questions 1 & 3 are r... | Summary: This work analyzes the various pooling methods used in deep neural architectures. They establish a connection between pooling and vector quantization and demonstrate adaptive pooling is more robust to signal-to-noise ratio. They provide experimental results with a carefully created synthetic dataset and multi-... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our work! Regarding your questions -
Upon further empirical analysis and review of our proof of Theorem 3.11, we realized that our assumption that the margin M must be greater than zero (i.e. signal and noise must be linearly separable) was not ne... | Summary: The paper studies pooling methods for aggregating transformer embeddings—particularly in settings where only a subset of input vectors (signal) is task-relevant while the remainder (noise) may deteriorate performance. The authors reframe pooling as a vector quantization (or lossy compression) problem and show ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work and provide thorough feedback.
The work from Przewiezlikowski et al. is indeed interesting and highly relevant to our discussion, and we will update our related works to acknowledge it. Their method appears equivalent to AdaPool with a learned embe... | null | null | null | null | null | null |
HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion | Accept (poster) | Summary: This paper proposes a spatial-spectral heterogeneous graph learning network for panchromatic and multispectral images fusion. Specifically, the authors explore the pansharpening-specific relationships in the heterogeneous graph structure. Then, they extract the multiple relationship patterns by the designed ba... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. The point-to-point answer is provided below.
**A1.** We analyze the spatial relationship of a large number of PAN/LR-MS/target HR-MS (GT) image pairs in the Appendix. As can be seen from Fig. 3 in the Appendix, the spatial histogram of each sp... | Summary: For pansharpening, recent modeling frameworks are relatively rigid and have limitations when dealing with irregular ground objects in remote sensing images. To address this issue, this paper proposes a spatial-spectral heterogeneous graph learning network named HetSSNet. It constructs a heterogeneous graph str... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. The point-to-point answer is provided below.
**A1.** The convolution kernel of CNN slides on a regular grid and cannot adapt to the curved, broken, radial and other remote sensing image features. The core of Transformer is global self-attentio... | Summary: In this paper, authors propose a spatial-spectral heterogeneous graph learning network, termed as HetSSNet. Specifically, it segments each band of the pansharpening data into non-overlapping patches. By leveraging graph structures, it captures intrinsic relationships among these patches. Furthermore, it employ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. The point-to-point answer is provided below.
**A1.** The non-Euclidean characteristics of the method are reflected in two aspects: the data structure and relationship modeling. **(a) Spatial-spectral heterogeneous graph construction.** We cons... | Summary: Remote sensing pansharpening involves fusing panchromatic (PAN) images with low-resolution multi-spectral (LR-MS) images to produce high-resolution multi-spectral (HRMS) images. Traditional methods like CNN and Transformer treat images as grids of pixels in Euclidean space, which struggle with irregular ground... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. The point-to-point answer is provided below.
**A1.** The convolution kernel of CNN assumes that local features are spatially invariant, but the morphology of objects (such as rivers and roads) may change nonlinearly with the terrain. For examp... | null | null | null | null | null | null |
Autonomy-of-Experts Models | Accept (poster) | Summary: This work proposes Autonomy of Experts (AoE) to replace the traditional MoE model. Instead of employing a router to select the experts, AoE computes the internal activations of all experts and select the best one to proceed. The authors conduct the pre-training experiments to investigate various properties of ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments! We hope our rebuttal helps address your concerns. If so, we would be grateful if you could consider increasing the overall recommendation.
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# Contribution and Novelty
Thank you for listing [Pham et al.]. Our paper fundamentally differs... | Summary: The authors introduce a new Mixture of Experts (MoE) paradigm called Autonomy-of-Experts (AoE), where experts independently decide whether to process inputs. The foundation of AoE lies in the understanding that an expert can gauge its ability to effectively handle a token by observing its internal activations.... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal helps address your concerns. If so, we would be grateful if you could consider increasing the overall recommendation.
---
# If the baselines should be allowed to pre-train for addit... | Summary: This paper introduces Autonomy-of-Experts (AoE), a novel approach to Mixture-of-Experts (MoE) models that addresses a critical issue in traditional MoE architectures: the separation between routing decisions and expert execution. In traditional MoE, a router decides which experts process which inputs, creating... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal helps address your concerns. If so, we would be grateful if you could consider increasing the overall recommendation.
---
# To discuss training time more explicitly
Here are the tot... | Summary: This paper proposes a new scheme to select expert sublayer in the mixture-of-experts (MoE) language model. Rather than empoying a router layer to choose the expert to process incoming embedding, the method utilizes a factorized subcomponent of feed-forward layer to calculate the importance score ("norm" in the... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal helps address your concerns. If so, we would be grateful if you could consider increasing the overall recommendation.
---
# Comparison with more MoE works
We trained the Hash-Layer... | null | null | null | null | null | null |
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning | Accept (poster) | Summary: This paper explores the relationship between Neural Collapse, Out-of-Distribution detection, and OOD generalization. It provides that strong NC enhances OOD detection but damages generalization, while weaker NC has the opposite effect. In order to balance these objectives, the authors use a method that control... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We have carefully considered your concerns and tried to address them. Below, we have provided detailed responses to each review separately.
# Weaknesses
**W1. Is the proposed method computationally efficient?**
Yes, the proposed method... | Summary: This paper empirically shows a trade-off between Out-of-distribution (OOD) detection and OOD generalization on multi-class classification task in the deep neural network (DNN) with in-distribution (ID) training data: a network can either impose a stronger neural collapse (NC) and improves OOD detection or weak... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive feedback. We have carefully considered your concerns and tried to address them. Below, we have provided detailed responses to each review separately.
# Clarifications
**Non-vision datasets or tasks**
Most prior work on OOD detection and/or ... | Summary: This paper studies the role of neural collapse on the out-of-distribution (OOD) detection and generalization tasks. The authors also propose an entropy regularization technique to control the degree of collapse in intermediate layers and show that stronger collapse in the final layers can aid in OOD detection ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and valuable feedback. We have carefully considered your concerns and tried to address them. Below, we have provided detailed responses to each review separately.
# Clarifications on Tables 2 and 3
- In Table 3, we isolate the effect of entropy regularizati... | Summary: The paper proposes a novel claim that: stronger NC(neural collapse) improves OOD detection but degrades generalization, while weaker NC enhances generalization at the cost of detection. The explanation of above claim is sufficient and the experiment proves the statement.
Claims And Evidence: Yes
Methods And ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and valuable feedback. Please let us know if you have any questions or suggestions. We would be happy to address them. | null | null | null | null | null | null |
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials | Accept (poster) | Summary: This paper proposes a new model based on all-atom Diffusion with transformers, for generating both periodic crystals and non-periodic molecules. Experiments are performed on standard benchmarks for these appications. The authors show how this model helps speedup standard equivariant diffusion models and how i... | Rebuttal 1:
Rebuttal: > small systems - simple setting…include experiments on more challenging tasks
---
**[In our response to Reviewer WqZ3](https://openreview.net/forum?id=89QPmZjIhv¬eId=RrA8d9t9eq), we presented results for GEOM based on your suggestion. ADiT continues to show strong results, generating physica... | Summary: The authors introduce All-atom Diffusion Transformer (ADiT), a framework aimed at unifying latent diffusion approach across different spatial molecular structure modalities. The proposed method focuses specifically on small molecules and crystals. ADiT leverages a combination of joint variational autoencoder a... | Rebuttal 1:
Rebuttal: > relatively small structures - benchmark against GEOM
---
**[In our response to Reviewer WqZ3](https://openreview.net/forum?id=89QPmZjIhv¬eId=RrA8d9t9eq), we presented results for GEOM based on your suggestion. ADiT continues to show strong results, generating physically realistic molecules ... | Summary: The paper introduced a unified framework called All-atom Diffusion Transformer (ADiT) for generating periodic (crystals) and non-periodic (molecules) atomic systems. ADiT employs a two-stage approach: A Variational Autoencoder that maps atomic systems into a shared latent space, and a Diffusion Transformer gen... | Rebuttal 1:
Rebuttal: > trained on relatively small datasets - scalability on larger datasets
---
**[In our response to Reviewer WqZ3](https://openreview.net/forum?id=89QPmZjIhv¬eId=RrA8d9t9eq), we presented results for the larger GEOM dataset of small molecules with up to 180 atoms. ADiT continues to show strong ... | Summary: The authors note that current generative models for atomic systems - such as molecules and crystals - are are fragmented and overly specialized to the specific type of system they model. They propose all atom diffusion transformers (ADiT), which uses a two-step latent diffusion framework in which, first, mixed... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and excellent summary of the work! Thanks for appreciating that the latent diffusion idea can be further applicable to multi-modality data in other scientific domains, too.
> does not necessarily indicate that the model has learned the underlying physics
ADi... | null | null | null | null | null | null |
Flexibility-conditioned protein structure design with flow matching | Accept (poster) | Summary: This paper introduces a novel framework for flexibility-conditioned protein structure design. The authors present BackFlip, an SE(3)-equivariant neural network that predicts per-residue flexibility from protein backbone structures. Using BackFlip, the authors propose GAFL-Flex, a flow matching-based generative... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and helpful review. We are happy the reviewer finds the problem of flexibility-conditioned design important. Due to character constraints, we try to focus on the most important concerns below.
- We note that we retrained GAFL-Flex on the larger PDB dat... | Summary: This paper takes a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as catalysis or molecular recognition. The authors first introduce BackFlip, an equivariant neural network for predicting per-resid... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in reading the paper and for their constructive feedback. We are happy that the reviewer agrees with our claims and evidence and appreciates the methods and evaluation criteria and our experimental design. Below we will discuss the comments line by lin... | Summary: This paper introduces a novel framework for de novo protein design that explicitly incorporates residue-level flexibility—a dynamic property critical for biological function—into the generative process. Current methods prioritize static structural features (e.g., motifs, symmetry), limiting their ability to en... | Rebuttal 1:
Rebuttal: We cordially thank the reviewer for their time invested in reading the paper and for their constructive review. We discuss questions and concerns below line by line.
- We note that we retrained GAFL-Flex on the larger PDB dataset and observe enhanced performance at the original benchmark (answer ... | Summary: This paper proposed a framework for conditional structure generation conditioning on desired flexibility, a key characteristic in catalytic interactions and molecular recognition. They develop BackFlip, a backbone flexibility predicter that can be used for large-scale flexibility annotation, and combine it wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time they invested in reading the paper and their helpful suggestions!
i. **General response**
We retrained GAFL-Flex on the BackFlip-annotated PDB dataset of 22977 monomeric protein structures filtered by the (i) length between 60 and 512 residues and (ii) absence... | null | null | null | null | null | null |
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks | Accept (poster) | Summary: The paper introduces the Deep Double Poisson Network (DDPN), a novel neural network model for count regression that provides accurate input-conditional uncertainty quantification. The main conceptual idea is that DDPN extends deep ensembles to count regression by using the Double Poisson distribution, which al... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback.
## Monotonicity vs tend to infinity
We agree with this remark and propose to change Def. 3.2 to:
> where $\lim_{\hat{\phi} \to \infty} d(\hat{\phi}) = \infty$ and $\lim_{\hat{\phi} \to \infty} a(\hat{\phi}) = 0 $
Fortunately, the proof in Ap... | Summary: The work introduces deep double poisson networks for the count regression problem. The proposed approach can quantify both the aleatoric and epistemic uncertainty with ensemble. Also, double poisson network allows unrestricted variance to model discrete count data, and can show robustness to outiers. Authors c... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments and helpful feedback.
## Comparison to the Natural Posterior Network
We have followed the official repository to download the `bike-sharing` dataset file and pre-process exactly as used in the paper reviewer mentioned. For training, the paper menti... | Summary: In this paper, the authors consider the problem of estimating heteroscedastic uncertainty within the context of counting tasks, where the final outputs should represent positive integer numbers. While many successful solutions have been proposed for heteroscedastic uncertainty in general (real-valued) regressi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments.
## Introducing additional uncertainty approaches, such as ensembling methods (Deep Ensembles, Batch Ensembles, etc.), could be beneficial.
An important aspect of our work is the interplay between DDPN and Deep Ensembles. We demonstrate this con... | Summary: The paper focuses on outputting distributions for non-negative integer predictions (i.e., count data). To do so, the paper has a model output the parameters for a Double Poisson distribution, which admits separate mean and variance parameterizations. Then the paper further utilize ensembles to include epistemi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the recognition of our work. | null | null | null | null | null | null |
OmiAD: One-Step Adaptive Masked Diffusion Model for Multi-class Anomaly Detection via Adversarial Distillation | Accept (poster) | Summary: The paper introduces OmiAD, a one-step adaptive masked diffusion model for multi-class anomaly detection (MUAD).
The authors propose Adaptive Masking Diffusion Model (AMDM) to mitigate "identical shortcut" issues by dynamically adjusting mask ratios based on noise levels and Adversarial Score Distillation (A... | Rebuttal 1:
Rebuttal: We are grateful for the time you spent reviewing our paper in detail. Your insightful comments have been extremely helpful, and we deeply appreciate your input.
**Q1: Comparison with Recent Multi-class UAD Methods**
A1：We have compared our results on the MVTEC and VISA datasets with MambaAD, ViT... | Summary: To address the slow inference speed due to the iterative denoising nature of the diffusion model, this paper proposes a one-step masked diffusion model for multi-class anomaly detection, OmiAD, which uses a multi-step Adaptive Masked Diffusion Model (ADM) with compression using ASD. State-of-the-art performanc... | Rebuttal 1:
Rebuttal: We truly value the time and effort you invested in carefully reading our paper. Your thoughtful and constructive feedback is highly appreciated.
**Q1:Effectiveness of Adaptive Masking for Localized Anomalies**
A1: Thank you for raising this important point. We agree that anomalies heavily depend... | Summary: The paper proposes a new multi-class anomaly detection method named OmiAD based on diffusion models. First, a diffusion model is trained. Different from standard diffusion models, the images are additionally partially masked to enforce the model to learn the global context. The trained diffusion model is then ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you invested in carefully reviewing our paper. Your insightful and constructive comments are greatly valued and have helped us improve the clarity and rigor of our work.
**Q1: Inference stage and Anomaly Score Computation**
A1:In the inference stage, w... | Summary: This paper presents OmiAD, a one-step adaptive masked diffusion model designed for multi-class anomaly detection with enhanced inference efficiency.
## Paper contributions:
- The paper introduces an innovative Adaptive Masking Diffusion Model (AMDM) strategy that dynamically adjusts masking patterns based on ... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough review of our paper. Your valuable feedback and constructive suggestions have provided us with a clearer direction for improvement.
**Q1: Inference stage and Anomaly Score Computation**
A1: In the inference stage, we process both normal and anomalous data. The... | null | null | null | null | null | null |
Disentangled Graph Spectral Domain Adaptation | Accept (poster) | Summary: To break away from the attribute and topology entanglement on Unsupervised Domain Adaptation (UDA), this paper introduces a novel method, DGSDA, directly aligning complicated graph spectral filters. This paper conducts experiments on various types of graph datasets to demonstrate the effectiveness of DGSDA.
C... | Rebuttal 1:
Rebuttal: > Q1. The authors fail to clearly attribute the source of performance improvement in their method. Further empirical results are needed to isolate the specific role of disentanglement.
R1. To address your concerns, we have conducted an additional experiment to clearly identify the source of perfo... | Summary: This study addresses the challenge of unsupervised graph domain adaptation in scenarios involving distribution shifts and missing labels by proposing a novel solution that disentangles the distribution shift. Specifically, the method DGSDA refines the topology alignment into GNN alignment and incorporates spec... | Rebuttal 1:
Rebuttal: > Q1. The title appears to be somewhat ambiguous. The title does not reflect the focus on the **unsupervised** problem in graph domain adaptation, which is a key aspect of the study.
R1. Thank you for pointing this out. The primary focus of this paper is indeed on the unsupervised problem in grap... | Summary: This paper introduces a novel pipeline for unsupervised graph domain adaptation by disentangling attribute and topology alignments by considering that attribute alignment has been widely investigated. Based on the aligned node attribute, the topology alignment is converted to the model alignment by taking into... | Rebuttal 1:
Rebuttal: > Q1. The main weakness is the lack of source code.
R1. The source code has been made available at (https://anonymous.4open.science/r/DGSDA) for verification purposes. We promise to make the code public once this paper is accepted.
---
> Q2. Whether the predicted pseudo-labels on the target dom... | Summary: This paper proposes Disentangled Graph Spectral Domain Adaptation (DGSDA) to alleviate the inaccuracies of pseudo-labels and the limited expressive ability of graph encoders to capture rich topology information. It decomposes the attribute and topology alignments and replaces the topology alignment with the po... | Rebuttal 1:
Rebuttal: > Q1. The symbols, especially the theory part, are too complex to read.
R1. Thanks for your feedback. We will thoroughly review and modify all the symbols to make them easier to read.
---
> Q2. Some explanations should clarify the theoretical differences from [You et al., 2023].
R2. The theo... | null | null | null | null | null | null |
Scaling Laws for Floating–Point Quantization Training | Accept (poster) | Summary: This paper constructs scaling laws for floating point quantized training based on curve fitting to many small to medium scale LLM training experiments.
Claims And Evidence: This paper seems to be mostly based on empirical curve fitting, as most scaling law papers are.
Methods And Evaluation Criteria: This pa... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal could help address your concerns, and we would be grateful if you could consider increasing the overall recommendation of our work.
## Q1: Extension to larger models.
A1: Thanks for the suggestion... | Summary: This paper proposes a scaling law for LLM performance prediction according to model size, dataset size, exponent bit, and mantissa bit while training LLM under FP quantization. Based on previous research, the paper tries to predict LLM performance more precisely. To achieve this objective, the paper proposes t... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal could help address your concerns. If so, we would be grateful if you could consider increasing the overall recommendation of our work.
## Q1: Novelty. Several works have already emphasized the imp... | Summary: The paper proposes a new scaling law tailored specifically for floating-point quantization during training of large language models (LLMs). Authors extensively studied how quantization parameters—exponent bits, mantissa bits, and scaling block sizes—impact LLM performance. Through extensive empirical experimen... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive suggestions and valuable comments! We hope our rebuttal could help address your concerns, and we would be grateful if you could consider increasing the overall recommendation of our work.
## Q1: Experiments capped at relatively modest model scales (ma... | null | null | null | null | null | null | null | null |
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking | Accept (poster) | Summary: This paper introduces the energy loss phenomenon in RLHF, where the L1 norm difference between input and output in the final layer of LLM increases during fine-tuning, leading to reward hacking. To mitigate this, the authors propose EPPO (Energy loss-aware PPO), which penalizes energy loss during RL optimizati... | Rebuttal 1:
Rebuttal: Thank you for your comments. We would like to highlight that our **main contribution** is the **empirical observation of the energy loss phenomenon** and **the corresponding RL regularization design**, as acknowledged by all other reviewers (wKXn, mgdS, and mmxN). **Theoretical analysis is include... | Summary: This paper identifies the Energy Loss Phenomenon in RLHF, where increasing energy loss in the final layer of LLMs signals reward hacking, and provides a theoretical framework showing how this increase lowers response-context relevance, a key factor in reward hacking. To address this issue, The authors propose ... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on the clarity, novelty, theoretical insights, and compelling experimental results of our paper. We will address each of your comments and concerns below and also in our revised manuscript.
---
> **Q1:** Why does the comparison method PPO with length penalty ... | Summary: This paper observes that the energy loss in the last layer of LLMs tends to get larger when using RL to train. Other than this finding, the authors also give theoretical analysis, which shows that under mild conditions, the increased energy loss reduces the upper bound of contextual relevance in LLMs. This wil... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our empirical observation, the effectiveness of our approach for various LLMs, and your acknowledgment of both the theoretical analysis and comprehensive evaluations. We will address each of your comments and concerns below and also in our revised manuscript... | Summary: This work identifies the Energy Loss Phenomenon in RLHF, where increasing energy loss in the final layer of an LLM is linked to reward hacking. To address this issue, the paper proposes EPPO, which penalizes energy loss growth in the RL reward function.
Claims And Evidence: The authors support their claims wi... | Rebuttal 1:
Rebuttal: Thank you for recognizing our research perspective. We will address your comments below.
---
> **Q1:** Does the energy loss phenomenon occur in other layers?
**RQ1:** Thanks for your thoughtful comment. We agree that, in theory, excessive energy loss at any layer could reduce contextual releva... | null | null | null | null | null | null |
Test-Time Graph Neural Dataset Search With Generative Projection | Accept (poster) | Summary: This paper addresses the test-time adaptation challenge in graph neural networks (GNNs).
The main focus is on the generalization about graph data and test-time GNN inference.
The main challenge is that GNN models trained on training graphs may not work well on a new, unseen test graph. The authors propose a ... | Rebuttal 1:
Rebuttal: **[Re-Weakness(1)] Computation and Explanations of Multiple Module Components in PGNDS**
>For the three modular components in the proposed PGNDS—dual conditional diffusion, dynamic search, and ensemble inference—we provide a simple one-sentence explanation for each module to help ease understandin... | Summary: This paper tackles a really interesting and new problem called 'test-time graph neural dataset search.' It enables GNN models handle data they’ve never seen before at test time by creating new graphs similar to the training set. To tackle this problem, the authors propose PGNDS, a method that reconstructs an u... | Rebuttal 1:
Rebuttal: **[Re-Weakness(1)] Computation of Dual Conditional Diffusion and Dynamic Search**
>While PGNDS introduces dual conditional diffusion and dynamic search, we have carefully designed the framework to remain efficient at inference time **without introducing substantial overhead**. As shown in Table 4,... | Summary: This paper introduces test-time graph neural dataset search with generative projection to improve test-time adaptation for Graph Neural Networks (GNNs) facing distribution shifts. The proposed method, PGNDS, uses a generative projection approach to refine test graphs without modifying the trained GNN. PGNDS co... | Rebuttal 1:
Rebuttal: We thank the reviewers for highlighting **the novelty and practicality of our proposed test-time graph neural dataset search (PGNDS)**, as well as the **clear organization and strong empirical results**. Detailed responses regarding the more key concept explaination, the dynamic search process and... | Summary: The authors introduce a new problem, test-time graph neural dataset search, to learn the optimal distribution of unknown test graph datasets. For this purpose, they propose PGNDS, a generative projection driven by a diffusion model. By projecting test graphs back to the training distribution, PGNDS learns test... | Rebuttal 1:
Rebuttal: **[Re-Claims and Evidence (1): Difference between “Test-Time Graph Neural Dataset Search” and “Test-Time Graph Adaptation”]**
>We thank the reviewer for recognizing our contribution in proposing the **novel problem of test-time graph neural dataset search (test-time GNDS)**. In brief, **test-time... | null | null | null | null | null | null |
One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory | Accept (poster) | Summary: The paper proposes FedGMT, a federated learning framework that leverages sharpness-aware minimization (SAM) to enhance generalization, especially in highly skewed non-IID settings. To achieve this, the framework employs an exponential moving average (EMA) of the global model as a proxy for the global loss surf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
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**W1. Clarification of communication and computational cost in Table 1**：We apologize for not making it clear and we will modify the caption of Table 1 and add a discussion section wi... | Summary: This paper proposes a new Federated Learning algorithm, named FedGMT, to effectively cope with data heterogeneity by reducing the sharpness of the global model through a global model trajectory. This paper provides the convergence analyze of FedGMT in the non-convex and smooth cases. Experimental results show ... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions! We answer your questions below.
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**1.Optimization target explanation.**
By referring the Theorem 1 in original SAM(Foret et al.,2020), the objective loss function of FedSAM in each client $m$ can be rewritten as the sum of the vanilla loss and the... | Summary: The article proposes a novel solution to deal with the client drift problem of federated learning. It is based in sharpness aware minimization - addressing the two problems: how to make it efficient? How to guarantee that the global rather than client objectives are targeted?
It proposes a novel algorithm whic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
---
**1.Comment on realistic client drifts.**
Client drift in federated learning refers to the phenomenon where data distributions across devices (clients) change over time or space, lea... | Summary: This paper studies sharpness-aware minimization (SAM) in federated learning (FL) in the presence of data heterogeneity. The major problem for SAM in FL is that the clients cannot get an accurate estimate of the global objective/gradient due to heterogeneous data distribution. Existing literature has proposed u... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
---
**W1.FedGMT vs. FedGKD.** While FedGKD appears to be similar to our method, there are fundamental differences. Practically, FedGKD takes an element-wise average over the latest 5 roun... | null | null | null | null | null | null |
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference | Accept (poster) | Summary: SparseVLM is a training-free framework that optimizes VLMs by reducing computational load through selective pruning of visual tokens based on relevance to text tokens. Using self-attention matrices, it introduces a rank-based adaptive sparsification strategy and a token recycling mechanism to retain essential ... | Rebuttal 1:
Rebuttal: We sincerely thank the **reviewer SmbH** for the effort in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.
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> **1. The discussion examines how effectively SparseVLM performs on highly complex tasks.**
Thank you for your attention to our perf... | Summary: SparseVLM introduces a text-guided visual token sparsification framework for efficient VLM inference without significant performance loss. The key idea is to use the textual input to identify which image regions (visual tokens) are most relevant and prune away the rest.
Claims And Evidence: Based on the motiv... | Rebuttal 1:
Rebuttal: We sincerely thank the **reviewer bpD8** for the effort in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.
---
> **1. The analysis of the generality of SparseVLM to other general multimodal tasks.**
We sincerely appreciate your valuable suggest... | Summary: This paper presents SparseVLM, a text-guided, training-free token optimization mechanism that improves the efficiency of vision-language models (VLMs). SparseVLM selects relevant text tokens to evaluate the significance of visual tokens using self-attention matrices and then progressively prunes irrelevant tok... | Rebuttal 1:
Rebuttal: ---
> **1. References on the computation of the rank of the matrix.**
The rank of a matrix is the maximum number of linearly independent rows, determined by singular value decomposition. We will add a reference to the corresponding supplementary part.
---
> **2. Explanation of the selection vi... | Summary: This paper presents a novel framework for sparsifying visual tokens to enhance the efficiency of Vision-Language Models (VLMs) in a training-free manner. It proposes a strategy to select relevant text tokens as evaluators of visual tokens, followed by pruning redundant visual tokens with a recycling mechanism ... | Rebuttal 1:
Rebuttal: We sincerely thank the **reviewer TyrH** for the effort in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.
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> **1. The further explanation of our claim about "first attempt to explore text-aware guidance" in vision token sparsification.**
Ou... | Summary: The paper introduces SparseVLM, which is a training-free technique to optimize number of visual tokens in a Vision-Language Model. The method consists of two main components, i.e. to identify relevant text tokens to rate visual tokens and method to prune and recycle visual tokens based on its significance. Fur... | Rebuttal 1:
Rebuttal: We sincerely thank the **reviewer PtfS** for the effort in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.
---
> **1. The analysis of the efficiency of input image resizing and vision token pruning in VLM, which supports dynamic image resolution... | null | null | null | null |
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence | Accept (poster) | Summary: This paper explores the refusal behavior of LLMs and introduces a gradient-based algorithm called RDO to identify refusal directions $r$ in the activation space. These directions can shift the model’s behavior towards either refusal or acceptance. The authors design three distinct loss terms to help identify d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort to review our manuscript which helps us to improve.
### Cosine similarity captures linear relationships. RepInd also catches non-linear interactions.
If the reviewer means that the orthogonality of the cone basis vectors from section 5 is not enough,... | Summary: This paper investigates the refusal mechanism of LLMs with a gradient-based representation engineering method. They extract refusal directions with gradient optimization by transferring the two refusal direction requirements (addition and ablation) into two loss functions. Further, they give several novel find... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time for the review, the kind words, and the positive assessment.
### Additional Data
We agree that we should increase the number of datasets we use for our evaluations. We thank the reviewer for the pointer towards SORRY-Bench and decided to evaluate our... | Summary: This work challenges the notion that refusal behavior in language models is mediated by a single direction. Previous research suggested that by ablating the activation strength along a specific direction, LMs could be made to refuse more or less often. The authors introduce a novel approach called Refusal Dire... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort to examine our work. Below, we address the reviewers points.
### Overall performance
We agree with the reviewer that our previous side-effect evaluation should be extended. We now provide results for the benchmarks: Arc Challenge, GSM8K, MMLU, and T... | Summary: This paper investigates the mechanisms behind refusal behaviors in large language models (LLMs) and discovers that refusals are controlled by multiple- dimension in the model’s activation space. The authors introduce RDO to enhance the refusal capability of large language models (LLMs) through training a learn... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent reviewing our manuscript!
### Ablation studies & Safety and Utility Tradeoff
We added a detailed ablation study, investigating the three loss components. We show the results in [Figures 18 & 19](https://figshare.com/s/4ab2ec422f6bd0262b30) for ... | null | null | null | null | null | null |
Confounder-Free Continual Learning via Recursive Feature Normalization | Accept (poster) | Summary: This paper introduces a Recursive metadata normalization (R-MDN) layer, in order to remove confounders, that are extraneous variables that affectboth the input and the target. The paper extends the confounder-removing activity to continual learning community. R-MDN performs statistical regression via the recur... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful feedback—we respond below to the concerns you have raised.
---
> The paper only reports results in medical datasets. It seems that the method should cover various datasets (at least from the title and abstract). The reviewer concerns that the pro... | Summary: This paper studies how to remove confounder in continual learning process, and proposes the Recursive-MDN (R-MDN) layer. The R-MDN adopts statistical regression via the recursive least squares for maintaining an internal state. By removing the confounder factors, the model will be less fitted to the irrelevan... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful feedback—we respond below to the concerns you have raised.
---
> No theoretical analysis is provided to justify the proposed method.
A more extensive exploration of the theoretical framework would surely be valuable. However, we would like to h... | Summary: The paper develops a method to debias intermediate representations in a continual learning setting. The idea is to regress the representation on the biased feature and the label. Then, only the residuals after removing the role biased feature are used as the representations. The experiments show the method imp... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful feedback—we respond below to the concerns you have raised.
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> Without the theory, the experiments should have explored more synthetic examples…
We agree that additional synthetic setups would strengthen the empirical results we observe. We se... | Summary: To remove the influence of confounding variables from intermediate feature representations, the authors introduce the Recursive MDN (R-MDN) layer. They note that such layer can be integrated into any deep learning architecture--including vision transformers--and at any model stage.
R-MDN performs statistical... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful feedback - we respond below to the concerns you have raised.
---
> … marginal improvements or I was not able to see clear improvement separation
In this work, we introduce a method that allows DNNs to learn confounder-free features during conti... | null | null | null | null | null | null |
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias | Accept (poster) | Summary: This paper proposes FARMS, a method for reducing the bias in the estimation of heavytailness (HT) metrics due to the aspect ratio of the weight matrix. FARMS samples submatrices with a fixed aspect ratio and averages the sampled empirical spectral density (ESD). Empirically, using HT metrics estimated by FARMS... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive comments. We have addressed your comments as follows.
### Broader Literature
Thank you for suggesting references [1, 2]. Tensor Programs IV [1] shows that standard parametrizations can collapse to the kernel regimes in the infinite-width limit. It pr... | Summary: The paper studies the problem of eigenspectrum analysis of DNN weight matrices and its relationship with model quality in terms of the training process. The paper proposes Fixed-Aspect-Ratio Matrix Subsampling (FARMS) to address the aspect ratio bias in existing Heavy-Tailed Self-Regularization (HT-SR) eigensp... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive comments. We have addressed your comments as follows.
### Weakness 1 and Question 1
- **Previous work on training quality**
Previous work on HTSR has established that the heavy-tailness of ESDs is strongly correlated with the test accuracy of mode... | Summary: This paper introduces an approach called FARMS (Fixed Aspect Ratio Matrix Subsampling) that addresses a current deficiency of approaches that rely on quantifying the heavy-tail degree of an empirical spectral density (ESD) without accounting for matrix shape. In particular, the authors observe that random Gau... | Rebuttal 1:
Rebuttal: Thank you for your insightful and constructive comments. We have addressed your comments as follows.
### Weakness 2
While we agree that error bars would help show statistical significance, we respectfully point out that several representative works [1-3] on LLM pruning did not provide error bars ... | null | null | null | null | null | null | null | null |
Mechanistic PDE Networks for Discovery of Governing Equations | Accept (poster) | Summary: This work extends mechanistic neural networks to discover PDEs. The approach relies on a numerical solver for PDEs, specialized for linear PDEs; however, the approach may still be applied to nonlinear PDEs by using nonlinear basis functions. Solutions are constructed as simple trees, which are restricted to be... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments.
**Main Contribution and Paper Structure**
We would like to strongly emphasize that we do not claim to replace or improve existing PDE solvers. Our contribution is to enhance the MechNN model (Pervez et al, ICML 24) that works with ODEs to support PDE repres... | Summary: This paper presents a model that learns spatial-temporal PDEs from data samples. The model selects a set of basis functions with spatially and temporally varying coefficients over the problem domain. Next, it implements a multigrid solver to solve the proposed PDE. Finally, the model learns a PDE by backpropag... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
The reviewer’s point regarding use of the term ‘discovery’ is well received. However, the term is now endemic in the literature and we chose to employ the same term.
We would like to appreciate the reviewer’s recognition of the effort that went into buildi... | Summary: The paper presents mechanistic PDE networks, a method for discovering PDEs from spatiotemporal data. The proposed method integrates a differentiable PDE solver into the neural network and uses the neural network predictions to model the PDE instead of the raw data. The method discovers PDEs using the spatiotem... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We attempt to address the concerns raised below.
**Non-autonomous systems**
Yes, in this paper we focus on autonomous systems. However, non-autonomous systems can be similarly handled since the method allows spatiotemporally varying terms. Any known extern... | Summary: The paper introduces a new methodology for learning PDEs on data. The key contribution is an optimization of how partial derivatives are handled in the network. Firstly, the theoretical formulation includes a dual formulation that elides a way to backpropagate through a linear solve effectively. Secondly, they... | Rebuttal 1:
Rebuttal: We appreciate the detailed review.
**Clarification of our contribution.**
We wish to clarify the nature of our contribution. We do not claim to replace or improve existing PDE solvers. Our contribution is to enhance the MechNN model (Pervez et al, ICML 24) that works with ODEs to support PDE rep... | null | null | null | null | null | null |
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