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SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution | Accept (poster) | Summary: The authors proposed a new federated backdoor defense method named SPMC. SPMC employs Shapley value to assess the contribution of each client and then reweights their model updates (server side). Additionally, SPMC utilizes knowledge distillation to calibrate the direction of gradients (client side). The autho... | Rebuttal 1:
Rebuttal: Dear Reviewer 7qEv：
Thank you for your thoughtful review and for raising key concerns regarding our work. We hope the following responses will address your concerns and update the score.
**Question**
**Q1: Lack of substantiation and theoretical claims.** (Claims And Evidence&Theoretical Claims)... | Summary: This paper introduces a technique named SPMC (Self-Purifying Federated Backdoor Defense via Margin Contribution), which aims to detect and mitigate backdoor attacks in federated learning systems by leveraging the concept of Shapley values. SPMC not only focuses on the behavior of individual clients but also em... | Rebuttal 1:
Rebuttal: Dear Reviewer VbBm：
Thank you very much for your valuable suggestions—they have provided us with clear direction for further exploring the details of our method. We hope the following responses will address your concerns.
**Question**
**Q1: Lack of support from other experiments.** (Experimenta... | Summary: Existing defenses rely on assumptions like individual behavior isolation and passive purification, which malicious clients can bypass. This paper proposes SPMC, inspired by the Shapley Value. It measures inter-client margin contributions to identify malicious attackers and self-purifies the parameter distribut... | Rebuttal 1:
Rebuttal: Dear Reviewer 7GF4：
We sincerely thank you for the valuable comments and suggestions. We hope our responses below address your concerns and provide a clearer understanding of our approach and results.
**Question**
**Q1: Theoretical** **description for shapley value.** (Theoretical Claims)
A1: ... | Summary: This paper presents a federated backdoor defense method named SPMC, which applies Shapley values to quantify the contribution differences among clients and implements margin contribution-based aggregation at the server side and gradient alignment technology at the client side. These measures work together to e... | Rebuttal 1:
Rebuttal: Dear Reviewer hJgY：
Thank you for your valuable feedback and for your time reviewing our work. We hope our responses below help clarify the issues and update the score.
**Weaknesses**
**W1: Missing an explanation why the combination of LGAlign and MCAgg leads to better performance.**
A1: For o... | null | null | null | null | null | null |
Improving LLM Safety Alignment with Dual-Objective Optimization | Accept (poster) | Summary: This paper introduces DOOR, a novel safety alignment framework for large language models that addresses vulnerabilities in existing methods like DPO. DOOR combines robust refusal training, which encourages refusal even when partial unsafe content is generated, with targeted unlearning of harmful knowledge. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and thoughtful questions. We address each of your concerns in detail below:
### 1. Justification for Combining Token-Level Refusal Training and Sequence-Level Unlearning
This is a great question. Our design reflects the intuition that refusal and... | Summary: In this paper, the authors propose novel objectives to enhance safety refusal and harmful response unlearning in LLM post-training. The paper first introduces the objective DOOR, which is a linear combination of two objectives that focus on safety refusal enhancement (even when the model starts generating a ha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and constructive suggestions. Below, we address each concern in detail:
> **However, better justification is needed for using part of the benchmark data as training data, and the small number of data used in the fine-tuning.**
We appreciate your... | Summary: This paper proposes Dual-Objective Optimization for Refusal (DOOR), a novel alignment framework that addresses limitations in Direct Preference Optimization (DPO) for LLM safety. The authors identify two key issues with DPO: imbalanced refusal reinforcement and poor out-of-distribution generalization. DOOR com... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed and insightful questions. Below, we clarify key aspects of our methodology, contributions, and evaluation setup:
### 1. Novel Contributions Relative to Prior Work
Thank you for prompting us to make the contributions more explicit. Our work builds ... | Summary: The paper aims to improve Standard Direct Preference Optimization (DPO) against jailbreaking attacks. In particular, it proposes Dual-Objective Optimization for Refusal (DOOR) and its token-weighted variant, W-DOOR to aim for robust refusal training and targeted unlearning. Experiments show substantially impro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback, as well as for recognizing the contributions of our work. Below, we address the specific concerns raised:
> **Change of loss function in LLM also needs to test the performance of other capabilities (e.g., math, coding)... | null | null | null | null | null | null |
Temporal Misalignment in ANN-SNN Conversion and its Mitigation via Probabilistic Spiking Neurons | Accept (poster) | Summary: This paper analyzes the spike temporal dynamics in the ANN-SNN conversion framework and investigates the impact of spike firing timing on the stability of the conversion. It identifies a phenomenon termed temporal misalignment, where random spike rearrangements across SNN layers can lead to performance improve... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in assessing our paper and their constructive comments.
**3.** We consider the setting from Theorem 1: we assume that the accumulated membrane potential in the first phase is $v:=TX$, that the threshold $\theta=1$ and that $0\leq TX\leq T$ (other cases being t... | Summary: The authors in this paper report a seemingly unusual phenomena in the ANN to SNN conversion framework, wherein by random spike arrangement of the output of SNN layers there was a increase in performance. Following this, the authors introduce a probabilistic neuronal model namely TPP, which improves the accurac... | Rebuttal 1:
Rebuttal: **Other Strenths and Weaknesses**
(a) Theoretical justification:
We argue that ``temporal misalignment'' happens primarily due to the fact that in ANN-SNN conversion, SNN models will need a few time steps to accumulate enough potential to start firing.
We start by noting that in ANN-SNN conve... | Summary: The paper investigates the ANN-SNN (Artificial Neural Network to Spiking Neural Network) conversion process, identifying a phenomenon called "temporal misalignment," where random permutations of spike trains across SNN layers improve performance. The authors propose a novel two-phase probabilistic (TPP) spikin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in assessing our paper and constructive comments.
**Essential References Not Discussed:**
Thank you for pointing these out, we will include them in the revised manuscript.
**Questions For Authors:**
**1.** We reported in Tables 8-10 the number of spikes ... | Summary: This paper presents a new framework for ANN-SNN conversion, which is motivated by an interesting phenomenon called “temporal misalignment”. The authors observe that the performance of converted SNN becomes better if they rearrange the temporal order of output spike trains of each layer. Based on such observati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive comments.
**Claims And Evidence: Not Clear**
It is important to ensure not only that the expected number of spikes matches the value of the ANN activation, but also that these spikes are distributed in a uniform manner. In particular, even th... | null | null | null | null | null | null |
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains | Accept (poster) | Summary: To solve the problem of poor evolving domain generalization caused by spurious correlation between data and targets across domains, this paper proposes a time-aware structural causal model with static-dynamic causal representation learning (SYNC). SYNC introduces mutual information to constrain the model to le... | Rebuttal 1:
Rebuttal: > Q1. Modeling the drift factors may be not necessary.
R1. We argue that modeling $Z^d$ is indispensable. Although both $Z^d$ and $Z^{dy}$ are influenced by $L$ and contribute to $Y$, $Z^{dy}$ operates in the feature space while $Z^d$ pertains to the label space which is designed to capture mecha... | Summary: This paper addresses the challenge of generalizing deep models in evolving domains, where data distributions shift dynamically over time. The author claims that exiting Evolving Domain Generalization (EDG) approaches suffer from spurious correlations, which degrade their generalization ability. To mitigate thi... | Rebuttal 1:
Rebuttal: > Q1. Further analysis of spurious correlation caused by time factors and more concrete examples.
R1. Thanks. Herein, we provide a formal characterization of the spurious correlation problem. In the proposed time-aware SCM, the time factor constitutes a latent confounder whose components $G$ and ... | Summary: This paper proposes a framework called Static-DYNamic Causal Representation Learning (SYNC) to deal with distributional drift in dynamic environments for generalization. By designing a time-aware Structural Causal Model (SCM), SYNC models dynamic causal factors and causal mechanism drifts, leveraging a sequent... | Rebuttal 1:
Rebuttal: > Q1. The significance of Proposition 3.3 and its connection to SCM.
R1. Thanks. Here we provide a detailed explanation of Proposition 3.3.
**The Significance for learning causal representation:** The two points of Proposition 3.3 respectively guide the learning of static and dynamic causal repr... | Summary: This paper proposes SYNC, a method for improving temporal generalization in evolving domains by explicitly disentangling static and dynamic causal representations. It introduces a sequential variational autoencoder (VAE) with mutual information minimization constraints to separate static and dynamic causal fac... | Rebuttal 1:
Rebuttal: > Q1. The contribution of causal factors to performance.
R1. Thanks. We perform a further analysis of causal factors. Please refer to R2 of Reviewer RWjv for details.
> Q2. Model assumptions and their impact on performance.
R2. Thanks. Given the challenging of the EDG problem, most existing met... | null | null | null | null | null | null |
Counting in Small Transformers: The Delicate Interplay between Attention and Feed-Forward Layers | Accept (poster) | Summary: This paper shows how a small Transformer can implement robust counting by arranging token embeddings with sufficiently low overlap and then leveraging architectural components (like softmax and BOS tokens) to preserve that separation under mixing. The authors demonstrate that different choices in hyperparamete... | Rebuttal 1:
Rebuttal: Dear reviewer Jx43,
We thank you for reading and evaluating our work, and providing us with feedback. We are glad you generally appreciate the soundness of our work and the approach to change hyperparameters to understand which parts of the architecture are impactful.
You are indeed correct, in ... | Summary: This paper investigates the counting mechanism behind transformer blocks using the histogram counting task as a case study. Two types of counting tasks were studied: relation-based counting leveraging attention for pairwise token comparisons, and inventory-based counting using feed-forward layers to memorize t... | Rebuttal 1:
Rebuttal: Dear reviewer UA3n,
We thank you for taking the time to evaluate our work and give us feedback. We are especially pleased that you find the aspect of how softmax and the dot-product attention influence robustness interesting. We agree that this point deserves further discussion and for a camera-... | Summary: This paper investigates how small transformer architectures implement counting mechanisms in a controlled histogram task. The study identifies two distinct counting strategies: relation-based counting, which leverages local pairwise token comparisons, and inventory-based counting, which relies on memorization ... | Rebuttal 1:
Rebuttal: Dear reviewer fzMo,
We thank you for taking the time and effort to evaluate our work. We are glad you found it clear, systematic and rigorous and that you also appreciate the controlled setting that the histogram task provides for our analysis. While we agree with you that these very same propert... | Summary: This paper explores the delicate interplay between the attention mechanism and the feed-forward layers, and further offers deep insights into how subtle architectural choices can drive algorithmic behavior in Transformer-base models.
As an example, the authors investigate how small transformer models tackle ... | Rebuttal 1:
Rebuttal: Dear reviewer S9FV,
We thank you for taking the time and effort to carefully evaluate our work, including the larger part of the supplementary material. We are glad you found it clear and rigorous as well as providing an inspiration for studying how transformers solve other algorithmic tasks; and... | null | null | null | null | null | null |
From Crowdsourced Data to High-quality Benchmarks: Arena-Hard and Benchbuilder Pipeline | Accept (poster) | Summary: This paper proposes Bench-O-Matic, a pipeline for curating high-quality benchmarks (Eval-O-Matic) from large volumes of crowdsourced queries. The pipeline combines hierarchical clustering with a set of LLM-based filters keyed to “prompt quality” dimensions (e.g., complexity, specificity, domain knowledge). The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedbacks. Below, we address your concerns and propose our revisions.
**W1**: We acknowledge that our pipeline heavily utilizes LLMs for prompt quality scoring and response evaluation. To address this concern and enhance credibility, we validated annotat... | Summary: The paper introduces a new method for automatic generation of robust, high‐quality benchmarks for evaluating LLMs. The approach is designed with key features: it controls for the style and length of generated content, shows strong alignment with human preference, and can be done in a cost- and time-efficient m... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable and insightful feedback. We address your concerns as follows:
> "The choice of chatbot arena as a reference for computing confidence agreement and separability is somewhat limiting. Does this method extend to more challenging tasks, such as Olympia... | Summary: This paper introduces Bench-O-Matic, a pipeline that automatically constructs high-quality, large scale benchmarks to evaluate LLMs from crowdsource datasets such as Chatbot Arena. To measure the quality of this benchmark, the authors proposed new metrics to measure properties that are important in when curati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. Below, we address each concern raised and propose revisions:
> "However, one large concern is the fact that the experiments rely on the LLM-as-a-Judge evaluation framework, which, as noted in the paper, is known to exhi... | Summary: This paper introduces Bench-O-Matic, an automated pipeline for curating high-quality benchmarks from large-scale crowdsourced datasets, and Eval-O-Matic, a benchmark dataset generated using this pipeline. The motivation is that existing benchmarks are either static (leading to saturation and test-set leakage) ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We address your concerns and propose corresponding revisions:
**W1**: We do not treat Chatbot Arena’s user-vote-based rankings as an unquestionable “gold standard.” Instead, we and other works (e.g., Dubois et al., Lin ... | null | null | null | null | null | null |
VerbalTS: Generating Time Series from Texts | Accept (poster) | Summary: The paper introduces VERBALTS, a novel diffusion‐based framework that generates time series from unstructured textual descriptions rather than structured metadata. The authors argue that traditional time series synthesis is limited by the reliance on structured, expert‐annotated conditions and that text provid... | Rebuttal 1:
Rebuttal: Thanks for your recognition and valuable suggestions!
> **Claims**: The paper's method contradicts the claim that can't simply use image diffusion‐based methods. The reviewer also think simpler adaptations might be feasible for text-to-ts (time series) generation.
We apologize for any ambiguity.... | Summary: This paper proposes VerbalTS, a novel framework for generating time series from unstructured textual descriptions. VerbalTS employs multi-focal alignment and generation framework that effectively models their complex relationships. Empirical evaluations demonstrate the benefits of VerbalTS in generation qualit... | Rebuttal 1:
Rebuttal: Thanks for the comments and suggestions!
> **Exp**: Extended Analysis covers the subset of the used datasets.
Thanks! The used datasets represent both synthetic and real-world datasets, showcasing diverse data coverage.
Per your suggestion, we conducted additional experiments on other datasets,... | Summary: This paper studies time series generation, specifically generating time series from text. It proposes a method named verbalTS, which employs a multi-focus alignment and generation framework to effectively model the complex relationship between them.
Claims And Evidence: NA
Methods And Evaluation Criteria: I ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
> **Evaluation**: I think the main issue is that the authors omit several straightforward yet crucial baselines: directly using LLMs for time series generation, as well as employing visualizations for iterative revision. From my experience, the revision-based... | Summary: This paper presents a new task of generating time series data from unstructured text and introduces VERBALTS, a method that combines a multi-view time series noise estimator with a multi-focal text processor. Additionally, it establishes a new benchmark featuring multi-faceted time series datasets enriched wit... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments!
> **W1**: The key aspect of this paper appears to be the alignment between text and time series data. However, the explanation of this process is unclear. It is not evident how different views and varying scales of time series data are aligned with the textua... | null | null | null | null | null | null |
LipsNet++: Unifying Filter and Controller into a Policy Network | Accept (spotlight poster) | Summary: Deep reinforcement learning suffers from the action fluctuation problem. This problem has been studied in many different forms in the past. Initial works constrained the Lipschitz constant of various aspects of the policy and various value functions to achieve desired smoothness. This paper proposes two method... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
We are encouraged by the positive aspects of your assessment, i.e., ***method effectiveness, sound experimental design, and strong benchmarks and figures***. Your recognition of the frequency-domain filtering, Section 3.2, and Fig. 12 is especially appreciat... | Summary: The paper proposes a new policy network, LipsNet++, to mitigate the action fluctuation problem in real-world robotic applications. The proposed network uses a Fourier filter layer to smooth the observation input which is then fed into a MLP network with its local Lipschitz constant regularized via Jacobian reg... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
We appreciate your recognition of our ***well-written and convincing*** results and analyses, ***reasonable*** evaluation criteria, ***nice*** ideas, the proposed methods that ***make sense***, and the ***easy-following*** nature of our paper. The positive a... | Summary: Action fluctuation is a major issue in reinforcement learning. The fluctuation in action can be caused due to measurement noise or steep changes in the policy due to large Jacobians. This paper addresses the measurement noise issue using Fourier filter and steep Jacobian issue using Jacobian regularization. Th... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
We appreciate your recognition of our work as a ***well-motivated*** approach that makes a ***strong contribution*** and as an ***excellent specimen of leveraging theoretical insights to solve a crucial issue in RL***. Your positive assessment greatly encour... | Summary: The paper introduces LipsNet++, a novel policy network designed to mitigate action fluctuation in reinforcement learning (RL). The authors identify two primary causes of action fluctuation: observation noise and policy non-smoothness. To address these, LipsNet++ integrates: A Fourier filter layer, which proces... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
We are encouraged by the positive aspects of your assessment, i.e., ***well-supported*** claims, ***mathematically sound*** derivations, ***valuable*** insight, ***comprehensive*** related works, ***novel*** integration of filtering and control, ***clear*** ... | null | null | null | null | null | null |
Towards Memorization Estimation: Fast, Formal and Free | Accept (poster) | Summary: This paper introduces cumulative sample loss, CSL as a way to measure memorization in neural networks. The key idea is that by tracking the cumulative loss over training, CSL can identify mislabeled and duplicate examples efficiently. The authors argue that CSL is both cheaper than stability-based methods and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, we address their questions below. Tables and Figures are provided at ****[Rebuttal Page clickable link](https://lively-dune-08c51d610.6.azurestaticapps.net/).****
1. Which other architectures and dataset sizes?
**A**: Please see our response to ... | Summary: The paper proposes a computational efficient proxy metric (CSL) to the popular notion of memorization proposed by Zhang and Feldman (2020). The authors support this metric with theoretical analyses and empirical results on standard image classification benchmarks.
Claims And Evidence: The empirical claims tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, we address their questions below. Tables and Figures are provided at ****[Rebuttal Page clickable link](https://lively-dune-08c51d610.6.azurestaticapps.net/).****
1. There is no description of how CSL is used to detect mislabeled samples (Section... | Summary: This paper introduces Cumulative Sample Loss (CSL) as a novel proxy for measuring memorization in deep learning models. The authors formally adopt the memorization definition established by Feldman(2020); Feldman & Zhang (2020) and develop a theoretical framework connecting CSL to both training time and memori... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, we address the questions below. Tables and Figures are provided at ****[Rebuttal Page click here](https://lively-dune-08c51d610.6.azurestaticapps.net/).****
1. A primary concern is the authors' use of memorization scores precomputed by Feldman & ... | null | null | null | null | null | null | null | null |
FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks | Accept (poster) | Summary: The paper proposes FuseUNet, a multi-scale feature fusion method for U-Net-like networks that enhances skip connection mechanisms by reinterpreting feature fusion as solving an initial value problem (IVP). It employs a linear multistep numerical method with neural memory ODEs (nmODEs) and a predictor-corrector... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work and your detailed and constructive comments. Below is a summary of our responses.
---
### **Claims and Evidence**
**1. Multi-scale Feature Interaction**
In the first part of the ablation experiment, we compare the performance of different fu... | Summary: This paper introduces a new multi-scale feature fusion method for skip connections and for the U-Net framework called FuseUNet, which aims to address the problems of lack the capability for multi-scale information interaction. Specifically, it defines the differential relationship between the skip connections ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the constructive and encouraging comments. We appreciate your recognition of our use of Linear Multistep Methods, the clarity of the experiments, and the improvements to U-like networks. Your feedback has helped us refine the paper. Below we provide detailed responses to... | Summary: A new variant of U-Net involving a new way to fuse features across different scales. It achieves half the compute of nnUNet while matching performance evaluation.
Claims And Evidence: Not supported. See below.
Methods And Evaluation Criteria: There are multiple evaluation issues for which I have provided a w... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments and suggestions, which have significantly helped us improve the clarity and depth of the paper. Below, we provide point-by-point responses to each concern.
---
### **Methods and Evaluation Criteria**
**1. Dataset selection and missing benchmarks*... | null | null | null | null | null | null | null | null |
Hybrid Quantum-Classical Multi-Agent Pathfinding | Accept (poster) | Summary: This paper studies the problem of multi-agent pathfinding. The authors proposed a framework that can formulate the problem to a two-level optimization problem and then be able to use a quantum computing method to efficiently solve the problem (QUBO). The authors proved that the algorithm is able to find the op... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and thoughtful observations. Below, we address each of the main concerns and suggestions raised.
### Clarification of Contributions and Novelty:
We appreciate the reviewer’s request for a clearer articulation of our contributions. Our key novelt... | Summary: The paper proposes novel hybrid quantum-classical algorithms leveraging quantum annealers for the problem of multi-agent path finding (MAPF). It proposes two iterative variant algorithms, QUBO-and-Price (QP) and QUBO-and-Cut-and-Prince (QCP) based on the idea of branch-and-cut-and-price (BCP) to find conflict-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. Below, we address each of the raised concerns and clarify several technical points. We will incorporate any remaining clarifications and adjustments into the final version.
### Relevance to ICML and the Machine Learning Community... | Summary: The paper presents a quantum-classical hybrid approach to multi-agent path finding based on solving the restricted master problem via a QUBO translation.
Claims And Evidence: Claims are supported, but the claims are rather weak anyway, involving only certain baseline solvers in a specific setup.
Methods And ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and structured feedback. We address your concerns below and will incorporate unresolved items into the final version.
Any other points not mentioned in this answer will be fixed in the camera ready version.
### On the Strength of Our Claims and Baseline Sele... | Summary: This paper approaches large scale multi-agent path finding as a hybrid quantum computing problem by combining branch-and-cut-and-prize (BCP) with quadratic unconstrained binary optimization (QUBO) formulations for resolving path conflicts. The resulting hybrid algorithms are evaluated on several common large-s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and insightful evaluation of our paper. We are encouraged by the positive assessment regarding our motivation, theoretical contributions, experimental validation, and presentation. We address the reviewer’s specific comments and questions below. Everything no... | null | null | null | null | null | null |
ADIOS: Antibody Development via Opponent Shaping | Accept (poster) | Summary: The paper introduces ADIOS (Antibody Development via Opponent Shaping), a meta-learning framework that designs antibodies capable of both neutralizing current viral strains and influencing viral evolution to favor less dangerous variants. By framing antibody-virus interactions as a two-player zero-sum game, th... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and feedback! We appreciate it a lot. We are glad that you thought that applying opponent shaping to antibody design is "a good contribution that extends beyond traditional ML-based antibody optimisation"!
# Answering Questions
## 1. Evaluating ADIOS o... | Summary: The authors consider a very interesting and important problem of antibody development against viral strains which would control and defend against newer strains evolved from this one. So, the antibody development problem is viewed as a sequential decision making problem which is modeled as a two player zero-su... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address everything possible within the character limit:
## Accuracy + Real-World Utility of Absolut!
To address the real-world utility of our simulator we quote the original Absolut! paper: “Of note, Absolut! is neither suited nor designed to directly predict... | Summary: This paper introduces a long-term strategy using opponent shaping, a concept from game theory and reinforcement learning, to design antibodies that not only bind effectively to the virus but also influence the virus's evolutionary trajectory to make it less dangerous over time.
The algorithm involves three ma... | Rebuttal 1:
Rebuttal: Thank you for your review!
As we understand, you have three primary concerns:
1. Lack of discussion of antibody design methods
2. Unclear how Myopic Antibodies are generated
3. No comparison to other reinforcement learning algorithms
## 1. Discussion of Antibody Design Methods
You are completely ... | Summary: The manuscript presents ADIOS as a method for optimizing antibodies while considering viral escape. In essence, the proposed approach is a simplified version of adversarial training. Experiments were conducted using a binding prediction method (Absolut!), and the results indicate that the proposed method outpe... | Rebuttal 1:
Rebuttal: Thank you for your review. Below we address your concerns and questions:
# Extra Experiments
Following your feedback, we have now conducted extra experiments with 3 additional viruses: flu, MERS and the West Nile virus. In all of these cases, our experiments show that the antibody shapers generate... | null | null | null | null | null | null |
Flow Q-Learning | Accept (poster) | Summary: This paper proposes using flow models to tackle offline RL tasks. To leverage the Q-function for guiding the learning of the flow-based policy while avoiding the computational cost of multiple backpropagations, this paper proposes learning a one-step action generation policy through a flow policy constrained d... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback on this work. We especially appreciate the clarification questions about deterministic policies and policy constraints, as well as several helpful suggestions. We also conducted an additional ablation study on ODE solvers. Please find our... | Summary: The paper introduces Flow Q-learning (FQL), an offline RL method that combines flow-matching policies with Q-learning to address challenges in modeling complex action distributions. FQL uses two components: (1) an expressive flow-matching policy trained via behavioral cloning (BC) to capture multimodal dataset... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback on this work. We especially appreciate your pointing out related work in different domains. Please find our response below.
---
* **Inference details of policies.**
Thanks for asking this clarification question! In FQL, *neither* the B... | Summary: This paper propose Flow Q-learning, and offline reinforcement learning that integrates expressive flow-matching policies for modeling complex action distributions.
## Update after rebuttal:
I have read the rebuttal and the discussions from other reviewers. I am maintaining my score.
Claims And Evidence: The... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback about this work! We would be happy to address any additional questions or concerns you may have, so please feel free to let us know. If there are no further concerns or questions, would you consider raising your rating? | Summary: This paper proposes the offline-RL method Flow-Q-Learning (FQL) which leverages an expressive flow-based generative model for modeling the action distribution while avoiding common issues such as unstable backprop through each time-step or less efficient re-weighting schemes. This is achieved by introducing a... | Rebuttal 1:
Rebuttal: Thank you for the highly detailed review and constructive feedback about this work. We especially appreciate your question about the expressivity of the one-step policy, for which we conducted an additional experiment. Please find our response below.
---
* **Is the one-step policy expressive eno... | null | null | null | null | null | null |
Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning | Accept (poster) | Summary: The authors propose Hyperbolic Backward-Compatible Training (HBCT), which is essentially an objective for backwards-compatible representation learning in hyperbolic space. HBCT balances the objective of the embedding loss (e.g. cross-entropy on image classification) with a hyperbolic entailment loss that encou... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and suggestions. We answer the remaining questions by points as follows.
> Q1. Ablation on hyperparameter sweeping
Due to restricted space, we report the ablation for the clipping threshold. We refer to our response to Reviewer fRer for the abl... | Summary: This paper aims to improve backward compatible representation learning by using hyperbolic embeddings instead of Euclidean embeddings. This paper claims that using hyperbolic embeddings achieves greater compatibility with previous models without compromising the performance of new embedding models.
Methods-wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and suggestions. We answer the remaining questions by points as follows.
> Q1: why the Lorentz uncertainty was defined the way it was.
We define the Lorentz uncertainty using the isometric relation between the Poincaré and Lorentz models. Both ... | Summary: The paper proposes to leverage hyperbolic geometry for backward compatible learning: setting in which a model is updated and its representation should preserve compatibility with representations from the model before the update. The authors propose a loss composed of two terms to (i) constraint the new embed... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and suggestions. We answer the remaining questions by points as follows.
> Q1: Training could be more expensive with hyperbolic geometry
We agree hyperbolic models may be more expensive, but our method only adds a simple exponential map to a Eu... | null | null | null | null | null | null | null | null |
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design | Accept (poster) | Summary: This paper introduces a unified framework, called UniMoMo, for general target-specific binder generation. The target is a protein, and the binders could be peptides, antibodies, or small molecules. UniMoMo aims to train a single generative model to tackle general binder generation problem, while being able to ... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and the positive comments!
> Q1: How did you balance data from different all three domains during training?
Thanks for the question! In the current implementation, we have not extensively explored domain-specific data balancing strategies. For our joint training appr... | Summary: In this paper, the authors propose a new generative model for 3D molecule design conditioned on a protein target.
The proposed model, UniMoMo, unifies generation of different ligand modalities (small molecules, peptides and parts of antibodies) into a single model. This is done by considering each molecule, i... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and the constructive comments!
> Q1: The paper only evaluates on in-silico metrics, which are known to be far from perfect. The proposed method do achieve good results on these metrics (compared to baselines benchmarked).
Thanks for the comments! While we are current... | Summary: This paper addresses the task of generating de novo binder molecules to target proteins. Importantly, the paper introduces a single unified framework and model, *UniMoMo*, that can generate peptide binders, antibody binders, and small molecule binders. To this end, the paper proposes a variational autoencoder ... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and insightful feedback, which is very helpful in improving our paper!
> W & Q2: If my understanding is correct, the method only works if the binding site on the target protein is known. What if we do not know the binding site?
Yes, your understanding is correct. Our ... | null | null | null | null | null | null | null | null |
An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning | Accept (poster) | Summary: This paper introduces EADS, an framework for learning equations efficiently
. EADS is inspired by the efficiency of natural systems in learning and solving complex equations. The authors argue that EADS overcomes the limitations of traditional ML methods, such as high computational cost and complex models. The... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments. We will address your questions below.
**1.The number of parameters**
We will report the number of parameters in the Appendix. Due to the character limit, we present parameters for a dataset below. For the PEMS04 dataset: Graph WaveNet: 250,689; ... | Summary: The paper proposes an Expressive and self-Adaptive Dynamical System (EADS) that can learn a wide range of equations with efficiency. The authors propose an efficient on-device learning method that leverages intrinsic electrical signals to update parameters, making EADS self-adaptive at a reduced cost. The auth... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and constructive feedback. We address each concern in detail.
**1.Relation to Equation Discovery**
We clarify that our work fundamentally differs from equation discovery: rather than discovering equations, our work focuses on efficiently solving equations with... | Summary: The authors propose a method to learn equations efficiently from data. Current methods such as neural networks have high complexity and high operational cost which hinders widespread applications. Recently electronic dynamic systems have shown great promise in solving simple learning problems with great effici... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and constructive suggestions. Below, we address each of your points in detail to further improve the manuscript.
**1.Additional explanations for comparable accuracy experiments**
Thank you for this insightful suggestion. In our evaluations, we compa... | null | null | null | null | null | null | null | null |
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds | Accept (poster) | Summary: The paper titled introduces a novel skeleton-based framework, SMART-PC, designed to enhance the robustness and efficiency of 3D point cloud classification models during test-time training (TTT). This paper leverages skeletal representations to extract robust geometric features that are less sensitive to corrup... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty of our skeleton-based framework and the effectiveness of skeletal representations, as well as our thorough experimental validation.
**Weakness**
**1 and 2**. Our method supports two modes of adaptation: one with backpropagation and one that is ba... | Summary: This paper proposed the method of test-time training for point cloud classification by leveraging skeletal representations. It aims to enhance the model's robustness to different distribution in test time samples. To this end, it introduced skeleton feature extraction branch besides classification branch to en... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty, clarity, and effectiveness of our method in improving robustness and efficiency for point cloud test-time training.
**1. METHODS AND EVALUATION CRITERIA - inconsistent Notation for Mask Predictors**
We'll correct the notations in the final versi... | Summary: This paper proposes a test-time adaptation framework for point cloud recognition, establishing a novel self-supervised fine-tuning paradigm that utilizes Skeletal Representation as a pretext task. By predicting skeletal points and their corresponding radii, the method extracts noise-insensitive geometric featu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and their recognition of our contribution to pretext task design and its connection to prior work in test-time adaptation for point cloud recognition.
**1. Theoretical Claims**
To clarify, **our method does not use labeled source data during the ... | null | null | null | null | null | null | null | null |
Do We Really Need Message Passing in Brain Network Modeling? | Accept (spotlight poster) | Summary: This paper investigates brain network modeling and identifies previous methods' shortcomings, including Graph Neural Network (GNN)-based methods and Graph Transformer (GT)-based methods. Specifically, they often use the Pearson correlation coefficients between pairs of ROIs (Regions of Interest) to construct b... | Rebuttal 1:
Rebuttal: > Q1.Reproducibility is a concern. Although the authors claim significant performance improvements over state-of-the-art models with a relatively simple architecture, they have not provided code access, making independent verification challenging.
R1. According to your suggestion, the source code... | Summary: The paper identifies a limitation in the message-passing framework for brain network analysis and proposes an approach, the Brain Quadratic Network (BQN), to address this issue. BQN demonstrates superior performance compared to standard Graph Neural Networks (GNNs) and graph transformers on widely used brain n... | Rebuttal 1:
Rebuttal: > Q1. Stacking multiple layers of Eq. 10 is theoretically equivalent to a single-layer formulation. The paper lacks theoretical or empirical justification for stacking multi-layer of Eq. 10 leads to performance gains.
R1. There may be a serious misunderstanding. Stacking multiple layers of Eq. 10... | Summary: The paper proposes Brain Quadratic Network (BQN), a novel approach for brain network modeling that replaces traditional message-passing mechanisms with quadratic networks and Hadamard products. It shows that BQN outperforms GNNs and Transformers on fMRI datasets, achieving higher accuracy and efficiency. Theor... | Rebuttal 1:
Rebuttal: > Q1.The parameters *b* and *c* in Eq. 7 are not represented or utilized in BQN(Eq. 8). Should the layers of the model use MLP?
R1. Yes, the matrix $\mathbf{W}$ in Eq. 8 represents a Multi-Layer Perceptron (MLP), and the parameters $b$ and $c$ in Eq. 7 are realized in the model implementation by ... | Summary: This paper investigates the GNN and Transformer, which follows the message passing pipeline, in brain network modeling. It observes that these two methods can’t enhance the performance compared to the vanilla classifier. Following by the analysis of the weakness of them from the brain network construction, it ... | Rebuttal 1:
Rebuttal: > Q1. The model analysis on GNN and transformer provides evidence to question the message passing in brain modeling. The proposed BQN makes sense by theoretical analysis on its clustering property. Its rationality is also guaranteed by the theory progress in the Quadratic Network. I suggest that t... | null | null | null | null | null | null |
AnalogGenie-Lite: Enhancing Scalability and Precision in Circuit Topology Discovery through Lightweight Graph Modeling | Accept (poster) | Summary: This work introduces a decoder-only transformer for analog topology generation by solving three critical challenges. At the graph level, it simplifies current approach by removing redundant nodes and edges. At the sub-graph level, it employs subsequent subgraph minging to identify commonly reused subgraphs in ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments.
> **Q1:** It would be better if there is a discussion for time cost of each method. The two techniques introduced in this work, subgraph mining and solving Chinese Postman problem, seems to introduce much time.
We would like to evaluate the time c... | Summary: AnalogGenie-Lite presents a decoder-only framework designed to discover novel analog circuit topologies by leveraging lightweight graph modeling. Its key contributions lie in three innovations:
- A precise and efficient graph modeling approach that prunes redundant nodes and edges.
- Optimal sequence modeling ... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's valuable and constructive feedback.
> **W1 & Q1:** Ablation: Additional discussion on the sensitivity of performance to hyperparameter choices in the pruning and subgraph mining processes could be beneficial. How sensitive is the overall performance to the threshold used... | Summary: The paper proposes AnalogGenie-Lite, a generative model for discovering analog circuit topologies using lightweight graph modeling. The main contributions of this work are converting device-pin representations from graph to sequence and employing LLMs to design the compressed sequence which implicitly contains... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments. We address your concerns below.
> **Q1:** Why the baseline, AnalogGenie, is the version without fine-tuning? This work also does fine-tuning by reinforcement learning with human feedback. It seems that there is an unfair comparison.
Our evaluation is designe... | Summary: This paper addresses the challenge of sustaining integrated circuit (IC) performance in the post-Moore era by proposing AnalogGenie-Lite, a decoder-only generative model for discovering novel analog circuit topologies. Leveraging lightweight graph modeling, the framework incorporates concise device-pin represe... | Rebuttal 1:
Rebuttal: > **Q1:** Compared to the original AnalogGenie work, the innovation appears incremental. Although the improved scalability is promising, the performance gains also seem relatively modest in scope.
We appreciate the reviewer’s insightful comments and would like to elaborate on our innovations in t... | null | null | null | null | null | null |
DeFoG: Discrete Flow Matching for Graph Generation | Accept (oral) | Summary: This paper introduces **DeFoG**, a novel **graph generative framework** that decouples the **training and sampling** processes to improve efficiency and flexibility. The key innovation is the **discrete flow-matching (DFM) formulation**, which ensures **node permutation equivariance** and allows more expressiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for positively assessing our framework’s novelty, theoretical grounding, and empirical results. Below, we address the raised concerns:
**I - Methods And Evaluation Criteria**:
1) *Hyperparameters sensitivity*: In the paper, we perform extensive hyperparameter sensitivity ana... | Summary: This paper proposed a novel graph generative model via discrete flow matching. This framework provides flexible and efficient training and sampling methods. The paper also provides theoretical guarantee for this disentanglement framework. With rich empirical validation, the proposed DeFoG shows powerful modeli... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Below, we address the raised concerns in detail.
**Essential Reference**:
We thank the reviewer for proposing this interesting reference. We will expand our related work section to include a discussion over methods that support both continuous ... | Summary: The authors adapt discrete flow matching for graph generation, replacing the usual SOTA discrete diffusion framework. This authors also utilize the flexibility of flow matching to further tune the sampling process to make it more efficient and generate higher quality samples in much fewer steps. This is primar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, as well as for recognizing the importance of our algorithmic improvements, the thoroughness of our experimental validation, and the practical value of our contributions to the graph generation community. Aligned with the reviewer's perspective... | Summary: The authors apply discrete flow matching to graph generation.
Claims And Evidence: Claimed contribution 1:
> We introduce DeFoG, a novel flow-based graph generative model that effectively disentangles training and sampling for improved flexibility and efficiency;
I feel this is misleading. DFM already decoup... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We address the raised concerns below:
**Claims And Evidence**.
We agree with the reviewer that the training-sampling disentanglement is a contribution of DFM. In this regard, our contribution lies in making this decoupling effective for graph g... | null | null | null | null | null | null |
Towards Understanding Parametric Generalized Category Discovery on Graphs | Accept (poster) | Summary: This paper first proposes a theoretical analysis about parametric GCD, and then design a new graph contrastive learning method, SWIRL, with the insights from the theoretical analysis. They propose the first GCD loss upper bound theory and identifying some necessary conditions about category relationships for g... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and guidance!
### **Weaknesses**
+ **W1**: You are absolutely right! Thank you for your meticulous attention. We will revise this.
+ **W2**: In subsequent calculations involving **W**, we set $\lambda = 1$, with assuming that the embeddings and labels are imp... | Summary: This paper introduces Generalized Category Discovery on Graphs (GGCD), a novel task addressing open-world learning on graph-structured data where unlabeled nodes may belong to both known and novel classes. The authors defined a surrogate GCD loss to reflect the GCD performance and established a theoretical bou... | Rebuttal 1:
Rebuttal: We are grateful for your constructive suggestions!
### **Experimental Designs or Analyses**
We will include a **complexity analysis** in the revised version. Let:
- $n$: Number of samples
- $K$: Number of prototypes
- $I$: SS-KM iterations
- $d$: Embedding dimension
**SS-KM** (execut... | Summary: The paper focuses Graph Generalized Category Discovery (GGCD), an node-level task aimed at identifying both known and novel categories in unlabeled nodes by leveraging knowledge from labeled old classes. The authors provide the first theoretical analysis for parametric GCD on graphs, quantifying the relationsh... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments that helped improve our paper!
---
### **Claims and Evidence**
**Theorem 3.5** summarizes three key aspects of **GCD**: **Old**, **New**, and **Reject Capability**. This work primarily focuses on the latter two (involving new-class data), so we ... | Summary: This paper studies the generalized category discovery problem in graph node classification context. The authors aim to answer the question “When and how do old classes help (parametric) generalized category discovery on graphs?” in a theoretical way. The answer from the authors is based on the relationships be... | Rebuttal 1:
Rebuttal: Thanks for your constructive suggestions! We hope the response can address your concerns.
+ **W1**: When keeping other hyperparameters consistent, adding **SWIRL** loss alone to **SimGCD** not only achieves a better **HRScore** but also reaches a high performance level as fast as at **epoch=5**. ... | null | null | null | null | null | null |
COExpander: Adaptive Solution Expansion for Combinatorial Optimization | Accept (poster) | Summary: This article proposes an Adaptive Expansion (AE) paradigm for COPs, demonstrating its advantages in a series of experiments on COP problems.
## update after rebuttal
Thanks for your detailed response. All of my concerns are clearly resolved so I have raised my score to 4.
Claims And Evidence: Generally clea... | Rebuttal 1:
Rebuttal: Dear Reviewer F5eW,
We are more than grateful for your time and recognition of our contributions. We sincerely present our point-to-point clarification to your questions below.
> **Q0-Comparing with UDC.**
**A0:** First, we have cited the UDC method and explained our exclusion of further compar... | Summary: The paper proposes a learning-based approach for solving combinatorial
optimization problems that combines global and local paradigms to achieve better
performance. The authors describe their method and evaluate it empirically.
Claims And Evidence: The claim to have reimplemented a range of state-of-the-art s... | Rebuttal 1:
Rebuttal: Dear Reviewer CvKY,
We appreciate your time reviewing and acknowledging the novelty and empirical efforts of our work. There appear to be several misunderstandings, chiefly concerning the claim about the "reimplementation" of baseline solvers and existing neural methods. We offer point-by-point c... | Summary: This paper introduces an adaptive approach for combinatorial optimization, where solution variables are incrementally determined using a dynamically adjusted decision step-size rather than fixed-size decisions. The method integrates global prediction (GP), producing probabilities for all variables at once, and... | Rebuttal 1:
Rebuttal: Dear reviewer zhdB,
We appreciate your meticulous review and constructive advice. Below we respond to the 3 major points mentioned in your comments.
> **Q1: Comparison with Lwd.**
**A1:** **Firstly**, it was our oversight not to compare LwD initially. Indeed, LwD is a highly relevant counterpar... | Summary: This paper introduces COExpander, a method to solve combinatorial optimization problems by diffusion models. Unlike previous neural methods, COExpander is informed by local partial solutions and iteratively improves upon them to obtain a final solution. The authors study 6 graph CO problems and demonstrate goo... | Rebuttal 1:
Rebuttal: Dear Reviewer UKMH,
Thanks for your recognition and valuable questions.
>**Q1: Interpretation of "adaptive"**
**A1:** In our work, "adaptive" means exactly the fact that *the number of ascertained decision variables within one round of determination is not fixed*. Specifically, it is achieved v... | null | null | null | null | null | null |
GenMol: A Drug Discovery Generalist with Discrete Diffusion | Accept (poster) | Summary: The paper focuses on drug discovery and introduces a Generalist Molecular Generative Model (GenMol), a new discrete diffusion model for molecular generation. This approach has the potential to be applied to various drug-related tasks, including de novo generation, linker generation, and hit/lead generation. Th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments that our contribution could aid drug discovery and GenMol is a generalist. We address your concerns below.
---
> One baseline in de novo and fragment-based generation.
We included JT-VAE (Jin et al., 2018) in Table 9, and have provided the results of DiGress... | Summary: GenMol is a generalist molecular generative model that uses a masked discrete diffusion framework with a BERT-based architecture to generate SAFE molecular sequences, enabling efficient, non-autoregressive decoding and better sampling efficiency. It introduces fragment remasking to optimize molecules by select... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments that all our claims are supported by extensive evaluations, that our proposed fragment-based generation approach aligns well with industry needs, and that our paper demonstrates strong methodological innovation. We address your questions below.
---
> Addition... | Summary: The paper introduces a versatile molecular generative model based on discrete diffusion applied to the Sequential Attachment-based Fragment Embedding (SAFE) representations. The method can address various drug discovery tasks uniformly (de novo generation, fragment-constrained generation, hit generation, and l... | Rebuttal 1:
Rebuttal: We sincerely thank you for your comments. We appreciate your positive comments that our paper introduces a versatile molecular generative model that can address various drug discovery tasks using a single backbone model, that all the claims are supported well by the experiments, and that the exper... | null | null | null | null | null | null | null | null |
Mastering Board Games by External and Internal Planning with Language Models | Accept (spotlight poster) | Summary: - This paper focuses on game playing for board games with LLMs
- it compares external search, where model acts as a proposal function for a symbolic search algorithm, with internal search, where the model is trained on search trajectories to perform search itself
- The paper claims three contributions
- Mult... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully review our paper and for the positive feedback!
We have not tried self-consistency since we currently use greedy decoding, which we found to improve the strength of the MAV models compared to sampling. However, sampling multiple outputs from the internal... | Summary: This paper enhances LLM planning capabilities in board games through two approaches: external search (model-guided MCTS without game engines) and internal search (in-context linearized search trees). Using a pre-trained Multi-Action-Value (MAV) model for Chess, Chess960, Connect Four, and Hex, both approaches ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully review our paper and for the positive feedback!
> The internal search performance remains inferior to external search
We evaluated internal search only up to breadth=4, depth=2, resulting in a search budget of 21 nodes, whereas external search was evalu... | Summary: The paper introduces a specialized Multi-Action Value (MAV) 2B model trained exclusively on game data from Chess, Chess960, Connect Four, and Hex. MAV is designed to predict legal moves, track game states, identify the top-k actions, and determine the resulting board state after executing the optimal action. T... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully review our paper and for the positive feedback!
1. Regarding training cost, we used more than an order of magnitude more tokens to train the model on MAV data compared to the tokens used for fine-tuning the MAV model on internal search traces. We will cl... | null | null | null | null | null | null | null | null |
Learning dynamics in linear recurrent neural networks | Accept (oral) | Summary: The authors study the learning dynamics of linear RNNs. This is similar to the method in Saxe et al 2014, but extended to the recurrent setting. The extension leads to an energy function that has a sum of recurrent terms, and thus there is an interplay between recurrent and feedforward modes. The authors show... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. In addition to answering the question about our main derivation and related work, we would like to point the reviewer to the list of new developments we have made since the initial submission, available in our rebuttal to Reviewer CWVm ([direct link... | Summary: The authors study the learning dynamics of linear recurrent neural networks (LRNNs). Using the approach of Saxe et al. (ICLR '14) to study deep linear feed-forward neural networks, the authors develop a similar theory for LRNNs. Under some assumptions on the weight matrices, the LRRN dynamics decouple into a s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the encouraging words. In addition to one clarification about this review below, we would like to point the reviewer to the list of new developments we have made since the initial submission, available in our rebuttal to Reviewer CWVm ([direct li... | Summary: While there has been substantial progress in understanding the learning dynamics of feedforward networks, there is relatively less work on studying it in the context of recurrent networks, especially when considering the task dynamics as well. In this paper, the authors analyze the learning dynamics of a linea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the clear summary of our work. In addition to addressing the limitations identified in this review, we would like to point the reviewer to the list of new developments we have made since the initial submission, available in our rebuttal to Review... | Summary: This paper analyzes the learning dynamics encountered when training a linear recurrent neural network (i.e., a linear time invariant system) using gradient descent. The paper derives a reduced form of the learning dynamics, and connects the stability of the model to the task being trained on.
Claims And Evid... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback on the relevance and potential of the work for the neuroscience and machine learning community. These comments will enable substantial improvement to the manuscript.
In addition to addressing the limitations below, we would like to highlight additional results ... | null | null | null | null | null | null |
XAttention: Block Sparse Attention with Antidiagonal Scoring | Accept (poster) | Summary: This paper introduces a plug-and-play attention sparsity method with minimal additional computational overhead. The proposed approach uses the sum of antidiagonal values in the attention matrix as a proxy to determine block importance, enabling block selection to reduce the computational density of attention m... | Rebuttal 1:
Rebuttal: ### 1. Antidiagonal Selection: Performance and Insights
Antidiagonal selection offers significant advantages over other patterns:
- Retains **all token information** while simultaneously detecting both **vertical and slash patterns** critical in LLM prefill
- Diagonal patterns **miss** slash pat... | Summary: This paper introduces XAttention, a plug-and-play framework that accelerates long-context inference in Transformer models through block sparse attention. The key innovation is using the sum of antidiagonal values in the attention matrix as a proxy for block importance, allowing for identification and pruning o... | Rebuttal 1:
Rebuttal: ### 1. Model Generalizability
We tested XAttention across diverse architectures with consistent results:
**LLMs Accuracy (RULER)**
| Model | Method | Average (4k-128k) | Performance Delta |
| ----------------- | ----------- | ----------------- | ----------------- |
| Mistral Ne... | Summary: - This paper proposes an efficient attention model.
- It finds out that the antidiagonal values in the attention matrix provides a powerful proxy for block importance
- Unlike existing methods that primarily rely on computationally intensive and lossy solutions
like token pooling to identify important blocks,... | Rebuttal 1:
Rebuttal: ### 1. Algorithm Clarification
XAttention precomputes attention within the **antidiagonal pattern** and uses these scores to guide block-sparse attention selection. Our method:
- Requires no fine-tuning
- Achieves **lowest overhead** among prefill acceleration approaches (Figure 5)
- Delivers up... | Summary: This paper introduces XAttention, a novel block-sparse attention mechanism leveraging an "antidiagonal scoring" method to efficiently approximate standard transformer attention. XAttention aims to accelerate inference in Long-Context Transformer Models (LCTMs) by using an antidiagonal scoring strategy to ident... | Rebuttal 1:
Rebuttal: ### 1. Antidiagonal Selection: Performance and Insights
Antidiagonal selection offers significant advantages over other patterns:
- Retains **all token information** while simultaneously detecting both **vertical and slash patterns** critical in LLM prefill
- Diagonal patterns **miss** slash pat... | null | null | null | null | null | null |
GradPS: Resolving Futile Neurons in Parameter Sharing Network for Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper investigates parameter sharing techniques in cooperative multi-agent reinforcement learning (MARL). The authors observe gradient conflicts among multi-agent policies and propose a new partial parameter-sharing scheme. This method exhibits superior performance on benchmarks such as SMAC and PredatorP... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions, your comments are very important to us. We will improve our work based on your suggestions. We address your concerns as follows.
## Methods:
>1. ... visualize the group assignment pattern...
Thank you for your suggestion. It is very valuable,... | Summary: This paper studies the gradient conflict in parameter-sharing networks and proposes a Gradient-based Parameter Sharing (GradPS) method to resolve futile neurons in the PS network. It dynamically creates multiple clones for each futile neuron. For each clone, a group of agents with low gradient-conflict shares ... | Rebuttal 1:
Rebuttal: Thanks for viewing our work as interesting. Thanks for your valuable comments regarding the novelty of the futile neuron phenomenon and neuron gradient conflicts in MARL. We will improve our work based on your suggestions. We address your concerns as follows.
## Experimental:
>1. How do you det... | Summary: This paper addresses the balance between parameter sharing and behavioral diversity in MARL from the perspective of gradient conflict and resolution. First, the concept of gradient conflict in MARL is introduced, and experiments verify its impact and patterns on multi-agent policy training. Subsequently, a met... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments, we address your concerns as follows.
## Claims:
>1. .."neuron-based"..
GradPS is a neuron-based method **from the angle of gradient conflict**. We agree that pruning-based PS can be viewed as neuron-based too.
>2. ..a momentary gradient direction..
We agree ... | Summary: This paper identifies "futile neurons," neurons with conflicting gradient updates, in parameter-shared multi-agent reinforcement learning (MARL). It proposes GradPS, which dynamically clones these neurons, grouping agents with low gradient conflict to promote diversity and efficiency. Experiments on SMAC and P... | Rebuttal 1:
Rebuttal: ## Claims:
> 1. ...convergence plots are missing, ...More explicit reproducibility analysis...
All experiments were repeated with **5 different random seeds** to ensure reproducibility, with means and variances shown in all experimental result figures of this work.
Convergence of GradPS are eval... | null | null | null | null | null | null |
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization | Accept (poster) | Summary: The work proposes an extension to the dynamic algorithm configuration (DAC) framework. Particularly, the work proposes to use graph convolutional neural networks to learn embeddings of Pareto fronts such that multi-objective combinatorial optimization algorithms are dynamically optimized while proposing novel ... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their thoughtful review and helpful suggestions. We have provided our responses to the comments below.
**Statement general DAC**
Our intention was to convey that DRL-based DAC approaches are primarily applied to single-objective (continuous) optimization problems. W... | Summary: This work considers the problem of dynamically configuring the parameters of evolutionary algorithms for solving combinatorial optimization problems. It specifically focuses on multi-objective optimisation problems, in which there are multiple (and often conflicting) objective functions. Unlike previous method... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback.
**Graph Configuration and Rationale**
The graph serves as a representation of the solutions in the current population on multiple objective planes. The values used as node features in the graph consist solely of the objective values from the different solution... | Summary: This paper composed a graph-neural network (GNN) based deep reinforcement learning method to dynamically optimize the configurations of multi-objective combinatory optimization problems. The proposed model takes the normalized muti objectives as input and uses the GNN to learn to iteratively involve the algori... | Rebuttal 1:
Rebuttal: Thank you for these insightful points. Here are our detailed responses.
**Motivation behind the graph structure**
The motivation behind adopting a graph representation is to eliminate the need for manual state space design, a process known to be cumbersome and suboptimal. In this context, we us... | Summary: This paper presents a DRL approach for dynamically configuring evolutionary algorithms in multi-objective combinatorial optimization. The process is modeled as a Markov decision process, where solution convergence is represented as a graph, and a GNN enhances state representation. Experimental results show tha... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's effort and insightful feedback. Here are our detailed responses.
**Insufficient survey of DRL methods for assisting MOEAs**
To the best of our knowledge, our literature review (Section 2, L137) includes most existing works that propose frameworks for controlling mult... | null | null | null | null | null | null |
Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent | Accept (poster) | Summary: The paper considers coordinate descent with updates performed on iid coordinates (RCD) versus coordinates chosen by random permutation (RPCD). The permutation updates are empirically known to outperform the iid baseline, but this has lacked theoretical justification. The main result of the paper proves the R... | Rebuttal 1:
Rebuttal: We appreciate the valuable questions and comments, and we express our gratitude for the positive feedback and recognizing the novelty of our work. We provide detailed responses to the questions below.
### Methods And Evaluation Criteria
> *The evaluations generally make sense for the setting. On... | Summary: Authors reiterate the problem of proving that the coordinate descent method with a random permutation of coordinates (RPCD) is theoretically faster than classical random coordinate descent (RCD). They prove that the asymptotic lower bound on the convergence rate of RCD is worse than the upper bound on the conv... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's valuable questions and rich comments. Below, we have put together our responses to the questions.
### Relation To Broader Scientific Literature
> *As the authors themselves note, their convergence results for RPCD (Equation 10 in the paper) are weaker than thos... | Summary: The paper studies the stochastic coordinate descent method for quadratic optimization and focuses on two schemes: uniform sampling versus random permutation. Under the mild unit-diagonal assumption, the authors show that random-permutation coordinate descent (RPCD) converges faster than random coordinate desce... | Rebuttal 1:
Rebuttal: We deeply appreciate the constructive review and feedback. We thank the reviewer for reading the paper thoroughly in detail, and we hope our response relevantly addresses all points raised in the review.
### Appendix
> *Line 622: $k\to \infty$ should be $T\to \infty$.*
Thanks for finding the ty... | Summary: This paper investigates the convergence rates of random coordinate descent (RCD) and random permutation coordinate descent (RPCD) for minimizing a class of quadratic functions. The key contributions are: (a) a novel lower bound for RCD's contraction rate on general positive definite quadratic functions and a s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive response and meaningful questions. Below, we summarize and respond to your questions one by one.
### Weaknesses
> *The main weakness is that the results only hold on a restricted function class, and it is likely that the technical tool developed in this pap... | null | null | null | null | null | null |
Improving Model Alignment Through Collective Intelligence of Open-Source Models | Accept (poster) | Summary: This paper introduces MoAA to enhance the alignment of LLM by using collective intelligence of multiple open-source LLMs. The authors propose a 2 stage training method: the first stage uses MoA to generate diverse, high-quality synthetic SFT data, and the second stage applies DPO using MoA as a reward model. T... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and thoughtful assessment of our work. We appreciate your recognition of the originality, significance, and methodological clarity of our MoAA framework.
> The paper provides a strong context by … (Jiang et al., 2023).
>
Thank you for suggestin... | Summary: The paper proposes Mixture of Agents Alignment (MoAA) that uses multiple LLMs to (1) generate high-quality responses for SFT training (2) provide high-quality rewards for DPO training. Experiment results show that MoAA performs better than using a single teacher LLM and the benefits are not just due to having ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback and thoughtful evaluation of our work on Mixture of Agents Alignment (MoAA). We appreciate your recognition of our paper's contributions and the validity of our claims and experimental design. We will address each concern below.
> Fo... | Summary: The paper demonstrates the usage of a preexisting technique, Mixture of Agents, as an alignment method. The core contribution is to use a combination of open-source LLMs as a replacement for larger proprietary models, while still obtaining competitive results with the larger proprietary models.
The authors f... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation of our work. We appreciate your recognition of the contributions of our alignment framework. We are pleased that you found our experimental design sound and our analysis of the quality of the MoAA-generated SFT and preference synthetic ... | Summary: This work proposes a novel alignment framework that uses multiple open-source LLMs within an MOA(mixture of agents) architecture to enhance model alignment via synthetic data generation (MoAA-SFT:) and preference optimization (MoAA-DPO).
Key experimental results are presented for aligning Llama-3.1-8B-Instru... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and the supportive overall recommendation. We are pleased that you recognize our novel application of the MOA architecture for alignment through both synthetic data generation (MoAA-SFT) and preference optimization (MoAA-DPO). We appreciate... | null | null | null | null | null | null |
Test-Time Adaptation for Online Vision-Language Navigation with Feedback-based Reinforcement Learning | Accept (poster) | Summary: This paper investigates the problem of Vision-Language Navigation (VLN) adaptation during deployment and introduces FEEDTTA, which uses feedback-based reinforcement learning. The main idea is to give the agent simple binary feedback after each navigation attempt (+1 for success or -1 for failure). To enhance l... | Rebuttal 1:
Rebuttal: Thank you for your **positive evaluation** of our work. We are committed to enhancing the quality of the figures in the final version upon acceptance. If there are any further questions or suggestions during the discussion period, we would greatly appreciate the opportunity to address them and fur... | Summary: FeedTTA is a test-time adaptation (TTA) framework for online vision-language navigation (VLN). It utilizes LLM for external interaction, providing binary feedback to the deployed navigation algorithm and establishing a feedback-based online reinforcement learning mechanism. By leveraging binary episodic feedba... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback. Below, we provide our responses to each comment and hope they contribute to a better evaluation of our work.
### Q1. Computational feasibility of FeedTTA
> A : We first clarify that **FeedTTA does not require high-end server-grade GPUs and can be efficiently d... | Summary: The paper introduces FEEDTTA, a test-time adaptation (TTA) framework for vision-language navigation (VLN) that uses binary episodic feedback to adapt navigation policies in unfamiliar environments. To maintain stability during learning from binary signals, the authors propose Stochastic Gradient Reversion (SGR... | Rebuttal 1:
Rebuttal: Thank you for your **positive evaluation** of our work. We hope our response fully addresses all concerns and demonstrates the strength of our contributions.
### Q1. How does different sequence orderings affect adaptation?
> We agree that online learning is sequence-dependent. However, we show th... | null | null | null | null | null | null | null | null |
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity | Accept (poster) | Summary: The paper presents a significant advancement in SE(3) equivariant neural networks by introducing a scalable and efficient approach for Hamiltonian prediction. Through innovative sparse gating mechanisms and an adaptive training scheduler, SPHNet achieves remarkable computational savings without sacrificing acc... | Rebuttal 1:
Rebuttal: We thank all your valuable comments very much, and would like to discuss more on the 4th question of ‘Other Strengths And Weaknesses’:
We had conducted a series of ablation study to examine the contribution of each module, and the results had been included in the Appendix B.2. As shown in Append... | Summary: In this paper, the author proposes a new efficient equivariant operation based on Tensor Product (TP), named Sparse tensor product gate, to improve the efficiency of equivariant networks for Hamiltonian matrix prediction task. From the experiment, the proposed model achieves SOTA performance on QH9 and PubCHem... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We address each of the comments individually below.
**Weakness：**
Your assumption regarding the effect of the sparse gate is correct. As demonstrated in the ablation study presented in Table 6 of Appendix B.2, both types of sparse gates signific... | Summary: This paper tackles the Hamiltonian prediction task. It proposes to learn a mask to select important pairs in pair-wise interactions in both node interactions and non-diagonal pair construction blocks. Moreover, the paper also use similar techniques to select important paths in the tensor product during pair co... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments and suggestions. Below are our detailed responses.
**Claims And Evidence**
1. Thank you for your question. As you noted, SPHNet’s lightweight design allows it to run 1.73× faster than QHNet on the PubChemQH dataset. However, the primary acceleratio... | Summary: This paper introduces SPHNet, an SE(3) equivariant graph neural network designed to efficiently and scalably predict Density Functional Theory (DFT) Hamiltonian matrices. The core contribution is the incorporation of adaptive sparsity to address the significant computational cost associated with high-order ten... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments and suggestions. Below are our detailed responses.
**Other Strengths And Weaknesses**
Thank you for pointing that out—your notation is the more precise way to express it. $C$ should indeed be $n \times n_0$ in Equation 1, where $n_0$ corresponds to ... | null | null | null | null | null | null |
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models | Accept (poster) | Summary: This paper proposes the IntLoRA quantization method, which can adapt quantized diffusion models with integer-type low-rand parameters to include inference efficiency during training. The proposed IntLoRA enables the pre-trained weights to be quantized during training and the IntLoRA weights can be seamlessly m... | Rebuttal 1:
Rebuttal: > ### Results on More Diffusion Models.
Good comments! As suggested, we evaluate our IntLoRA on the FLUX.1-dev model. Since FLUX is notoriously costly to fine-tune even using LoRA, due to limited computational resources, we only give the results of FP16 vanilla LoRA and our IntLoRA on 15 text-sub... | Summary: The paper introduces IntLoRA, which uses integer-type LoRA weights to fine-tune directly on the quantized models, for both training and inference efficiencies. To achieve this, the authors propose three novel techniques. First, the authors propose the Adaptation-Quantization Separation (AQS), which allows for ... | Rebuttal 1:
Rebuttal: > ### More discussions of Adaptation Quantization Separation (AQS).
The main motivation of the proposed AQS is to **address the effect of zero-initialized weights of LoRA during quantization**. Specifically, the zero-initialized weight leads to infinite results during quantization due to the divi... | Summary: This work proposes a LoRA-based method to fine-tune the quantized weights of diffusion models that consists of Adaptation-Quantization Separation (AQS) for addressing the issue of zero-initialized weights in LoRA tuning for the quantized pre-trained model and Variance Matching Control (VMC) to determine an app... | Rebuttal 1:
Rebuttal: > ### Why Introduce Many LLM Related Works?
Thanks for your comment! Although there is a lot of work on Diffusion quantization, **very little work** explores the adaptation of quantized diffusion, which is also recognized by Reviewer wfKN. Therefore, we introduce related methods in quantized LLM ... | Summary: This paper proposes IntLoRA that allows for seamless weight merging after efficient low-bit parameter-efficient fine-tuning (PEFT). The paper is motivated by the observation that existing low-bit PEFT (e.g., QLoRA) requires an additional round of PTQ due to a mismatch in precision between pre-trained and (low-... | Rebuttal 1:
Rebuttal: > ### The significance of weight merging
Good comments! In fact, the efficiency of INT-type matrix multiplication is extremely efficient in the highly optimized GEMM on modern GPUs, and we demonstrate below **the INT8 matmul is even faster than that of FP32 low-rank matrix multiplication**.
Gi... | null | null | null | null | null | null |
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders | Accept (spotlight poster) | Summary: The paper proposes AXBENCH, a benchmark for evaluating steering and concept detection methods in LLMs using synthetic data.
## Data generation
For data generation, AXBENCH does the following:
* Given a list of natural language descriptions of concepts, they used SAE concept lists for GemmaScope.
* They then ... | Rebuttal 1:
Rebuttal: Thanks for your comments! We address them point-by-point below. **In our responses to other reviewers, we provide additional human evaluations on our LLM judges and updated, much higher quality SAE concepts**.
> **Q1**: Please define $n$ in section 4 notation.
**A1**: Thank you for the suggest... | Summary: This work develops a benchmark for testing different concept detection and model steering methods in LLM representation spaces. They test many different types of methods such as SAEs, finetuning, and prompting on their benchmark, including novel methods and novel applications of existing methods. As noted in t... | Rebuttal 1:
Rebuttal: Thanks for your comments! We address them point-by-point below. **We supply preliminary human evaluations as well as results for a new prompting baseline.**
> **Q1: Performing a proper human evaluation to ground AxBench.**
**A1**: Great suggestion! We conducted a preliminary human evaluation b... | Summary: This paper proposes a new large-scale benchmark for steering and interpretability, with reported results on Gemma2. They find that prompting outperforms all existing interpretability and steering methods, followed by fine-tuning, for steering. They also find that SAEs are not competitive for either task. The a... | Rebuttal 1:
Rebuttal: Thanks for your comments! We address them point-by-point below. **We provide preliminary results by using a set of high-quality SAE labels released recently below.**
> **Q1**: One concern is that the features chosen are the labels the authors found for the evaluated SAEs on Neuronpedia. However, ... | Summary: - Authors introduce AxBench, a benchmark to evaluate LLM steering, ie. capability at following instruction. In that context, authors evaluate multiple representation-based techniques, including sparse auto-encoders and linear probes, to achieve that goal. The findings show that representation techniques are st... | Rebuttal 1:
Rebuttal: Thanks for your comments! We address them below. **We articulate our goal and how AxBench differs from IFEval-like benchmarks, and supply preliminary human evaluations.**
> **Q1**: The Section 2 related work does not mention any concurrent benchmarks to evaluate model instruction following (e.g.,... | null | null | null | null | null | null |
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective | Accept (poster) | Summary: This paper establishes the connections between the formulations of LLM alignment and information retrieval (IR). Inspired by this discovery, it introduces various practices in information retrieval into LLM alignment, including hard negative mining and ranking loss functions. Empirical studies demonstrate the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and address the points below:
- **Baselines with Strong Reward Models.** We include iterative DPO with a strong reward model for fair comparison:
| Model | ApEval 2 (LC WR) | ApEval 2 (WR) | MixEval | MixEval-Hard |
|--|--|--|--|--|
| **Mistral-Ba... | Summary: This paper views LLM alignment as a retriever optimization problem, presenting a systematic framework that connects LLM alignment with information retrieval (IR) methodologies. The paper maps LLM generation and reward models to the retriever-reranker paradigm in IR. Based on three key IR principles—retriever o... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and believe it has significantly strengthened our manuscript. We have carefully addressed each of your comments as detailed below:
- **Use of SimPO Datasets.** We exclude MTBench and Arena-Hard because (1) SimPO shows minimal differences among methods on MTB... | Summary: This paper demonstrates that concepts from information retrieval can be ported over to shed light on numerous aspects of language model alignment tuning, including both RLHF-type functions and the data generation methods that feed those objectives. From a more technical perspective, this paper demonstrates emp... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback, which has strengthened our work. Please find our point-by-point responses below:
- **Fig 2: Single dataset?** Thank you for the suggestion. We plan to incorporate this in the revised manuscript (e.g., using NQ for both 2(a) and 2(b)).... | Summary: The paper established a connection between LLM alignment and IR, particularly the retriever-reranker framework. With such a connection, the paper applied multiple techniques used in IR for LLM alignment, specifically, (1) IR objectives; (2) use of hard negatives (from a reasonably good model); (3) candidate li... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback, which has significantly strengthened our manuscript. We address each comment below.
- **Without hard negatives, how's the performance?** Thank you for the question. As shown in Section 6.2 and Figure 4(a), removing hard negatives (i.e., using only easy/easi... | null | null | null | null | null | null |
WeGeFT: Weight‑Generative Fine‑Tuning for Multi‑Faceted Efficient Adaptation of Large Models | Accept (poster) | Summary: The paper proposes Weight-Aware Fine-Tuning (WAFT) to generate fine-tuning weights directly from the pretrained weights.
Results show promising results on three datasets based on the LLaMA series of models.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: No theoretical clai... | Rebuttal 1:
Rebuttal: Dear reviewer Zn72,
Thank you for your feedback and efforts in reviewing our submission. We address your concerns as follow, which will be carefully incorporated in the revision.
> **Experimental designs, especially for using what foundation models and on what datasets, are not logically clear**... | Summary: The authors:
1. Identify current limitation of existing PEFT methods -- the replicated structures compromise time and memory efficiency.
2. Propose a novel approach that shares PEFT paratemers across layers.
3. Evaluate the proposed method on different testbed, including image classification, reasoning and g... | Rebuttal 1:
Rebuttal: Dear reviewer M1uG,
Thank you for your feedback and efforts in reviewing our submission. We address your concerns as follow, which will be carefully incorporated in the revision.
> **Comparison with FacT**
We acknowledge that Factor Tuning (FacT) shares a similar parameter-sharing concept with ... | Summary: 1. Fine - tuning large pretrained Transformer models has two main focuses: parameter - efficient and representation - efficient fine - tuning. LoRA is a pioneering method, but its variants often sacrifice compute and memory efficiency or performance. ReFT is another approach, yet it has performance limitations... | Rebuttal 1:
Rebuttal: Dear reviewer NsTU,
Thank you for your feedback and efforts in reviewing our submission. We address your concerns as follows, which will be carefully incorporated in the revision.
> **Performance in constrained environments**
We use an A100 GPU to accelerate experiments, but the implementation ... | Summary: This paper introduces Weight-Aware Fine-Tuning (WAFT), a novel parameter-efficient fine-tuning method for large pre-trained Transformer models. WAFT proposes to generate fine-tuning weights directly from the pre-trained weights using a low-rank formulation with shared linear layers across multiple transformer ... | Rebuttal 1:
Rebuttal: Dear reviewer UHSy,
Thank you for your feedback and efforts in reviewing our submission. We address your concerns (C1-C5) as follows, which will be carefully incorporated in the revision.
> **C1: Weight-awareness in LoRA and WAFT**
In general, any PEFT methods have to be (pretrained) weight-awa... | null | null | null | null | null | null |
PDUDT: Provable Decentralized Unlearning under Dynamic Topologies | Accept (poster) | Summary: The paper proposes PDUDT, a Provable Decentralized Unlearning algorithm under Dynamic Topologies. The key contribution is a decentralized method that eliminates the influence of a specific client without additional communication or retraining. The authors provide rigorous theoretical guarantees demonstrating t... | Rebuttal 1:
Rebuttal: We thank the Reviewer 6f9d for the time and valuable feedback! We would try our best to address the comments one by one.
**1. Response to “Experimental Designs Or Analyses”:**
Thank you for your constructive feedback. We agree that providing more details on topology evolution and the impact of no... | Summary: The authors introduce a novel algorithm PDUDT, which is designed to enable efficient and provable unlearning in decentralized learning systems with dynamic network topologies. PDUDT allows clients to remove the influence of a specific client without retraining or additional communication by using historical gr... | Rebuttal 1:
Rebuttal: We thank the Reviewer vxha for the valuable feedback! We would try our best to address the comments one by one.
**Response to “Claims And Evidence (1)” ＆ “Essential References Not Discussed” ＆ “Questions For Authors (1)”:**
We have carefully examined these works and provide a detailed comparison ... | Summary: This work focuses on the provable unlearning in decentralized learning under dynamic topologies. The proposed PDUDT algorithm addresses this by using historical gradient information of clients and their neighbors to eliminate a specific client's influence without extra communication or retraining. The authors ... | Rebuttal 1:
Rebuttal: We thank the Reviewer rNoR for the time and valuable feedback! We would try our best to address the comments one by one.
**1. Response to “Claims And Evidence”＆ “Questions For Authors”:**
Thank you for your insightful feedback. As we discussed in Related Work, approximate unlearning has demonstr... | null | null | null | null | null | null | null | null |
Learning Changes in Graphon Attachment Network Models | Accept (poster) | Summary: This paper introduces the Graphon Attachment Network Models (GAN-M), a new framework for modeling evolving networks by utilizing attachment probabilities inspired by graphon analysis. The authors extend the classical CUSUM method to accommodate graphon-inspired attachment probabilities through the use of subgr... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our manuscript, and for your recognition of our methods and theoretical results. We appreciate your acknowledgment of the novelty of our approach, as well as the effectiveness and practical applicability of our algorithm. Below, we will address the q... | Summary: The authors propose an extension of graphons to model growing networks, where nodes are added over time and each new node forms connections to existing nodes. To this end, the authors define a so-called Graphon Attachment model, where starting from an initial graph new nodes iteratively generate undirected edg... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful feedback. We are grateful for your recognition of the interest and soundness of our work. In the following, we will address the questions you raised.
1.**Regarding the comparison with ex... | Summary: This work proposes a methodology for learning structural changes in these networks over time. Our
approach uses graph counts—frequencies of substructures such as triangles and stars—to capture
shifts in network topology, called GAN-M.
## update after rebuttal
The authors provided two useful examples in their... | Rebuttal 1:
Rebuttal: Thank you for taking the time and effort to review our manuscript and for providing positive feedback. We appreciate your recognition of the interesting nature of our work. Below, we will address the questions you raised.
1.**Regarding the final objective of detecting the changes.**
**Reply:** T... | Summary: The authors introduce the Graphon Attachment Network Model (GAN-M), a new theoretical framework for evolving graphs that integrates graphon theory with network growth dynamics. In GAN-M, nodes arrive sequentially and form edges with existing nodes according to a time-dependent graphon function $h_{T,t}(x,y)$Th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your review of our manuscript and your positive evaluation of our work. We are grateful for your recognition of the theoretical contributions of our paper, including your comments and affirmations regarding our theorem conditions, proofs, and underlying ideas. Additionally,... | null | null | null | null | null | null |
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection | Accept (spotlight poster) | Summary: This paper makes progress on the problem of hyperparameter selection with statistical guarantees. The work provides a new, adaptive algorithm for hyperparameter selection that presents an extension of the Learn-Then-Test (LTT) framework that was previously introduced. The LTT framework uses p-values of hypothe... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. Below we address your question point by point:
**Claims And Evidence**
- **Missing Theorem**: We will add the following theorem on the statistical validity of aLTT “*Given a hyperparameter set $\\Lambda$, a reliability level $\\alpha \\in [0,1]$, and an er... | Summary: This paper extends the LTT strategy to accommodate adaptive MHT procedures. The resulting procedure is more data-efficient and can be used in situations where the loss function as a function of lambda is not a-priori known, but must be evaluated (with some non-negligible associated cost) on a per-lambda basis.... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your positive comments! Below, we address each comment point by point:
- *"Why no main theorem/proof?..."*. - Thank you for the suggestion! In the revised version, we will add the following theorem on the statistical validity of aLTT “Gi... | Summary: This paper essentially proposes the following method, as summarized in Section 2.3: Sequential and Adaptive Hyperparameter Selection. Given candidate hyperparameters $\Lambda = \lambda_1, \dots, \lambda_N$, for rounds $t = 1, \dots, T$, where $T$ is some stopping time:
- Choose $\mathcal{I}^t \subset \Lambda$... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. Below we address your question:
**Questions For Authors:**
Yes, adaptive LTT (aLTT) substantially outperforms LTT, even when LTT uses fixed-time valid p-values. The table below reports final TPR for aLTT and LTT using p-values from Waudby-Smith & Ramdas (2... | Summary: The authors propose Adaptive Learn-Then-Test (aLTT), a method designed for calibration of AI applications over discrete sets of hyperparameters. The problem is formulated as a multiple hypothesis testing (MHT) task. Hypotheses are tested using e-processes, enabling adaptive and sequential selection of hyperpar... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper! Below is our response to your question and your comment regarding the absence of formal proofs:
- The intuition behind aLTT’s inability to discover all hyperparameters is that, in our experiments, we use finite datasets, which may sometimes be i... | Summary: The submission proposes an adaptive algorithm for bandit multiple hypothesis testing with e-processes and anytime-valid false discovery rate control. The algorithm is dubbed "adaptive learn then test" (aLTT) for its connections to the "learn then test" (LTT) framework, which finds a subset of hyper-parameters ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. Below we address your question point by point:
**Theorem and Stopped e-process**: In the revised version, we will formally state the aLTT guarantee in a theorem and provide a proof in the Appendix, which follows from the validity of the e-processes. “*Given... | null | null | null | null |
Preference Optimization for Combinatorial Optimization Problems | Accept (poster) | Summary: The paper studies the problem of solving combinatorial optimization problems with reinforcement learning, focusing on two key issues: diminishing reward signals that slow down learning and inefficient exploration in large solution spaces. To address this, it introduces preference optimization, a method that re... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful comments, which help us improve our presentation clarity. Below, we address your concerns regarding terminology and methodology.
---
### **1. Regarding the Claims of Optimality**
We apologize for the confusion in Contribution 1 regarding *optimal solutio... | Summary: This paper proposes a way to modify reinforcement learning (RL) so that it can deal with diminishing reward signals. The key idea is to turn the reward signals into pairwise preferences from which an underlying reward function can be learned. Another contribution of the paper is to use local search to generate... | Rebuttal 1:
Rebuttal: We sincerely thank you for your affirmation on our work, and are grateful for the insightful review and constructive comments. We provide point-to-point responses as follows.
---
### **1. Math implications behind the proposed PO algorithm**
We appreciate this insightful question on the rationale ... | Summary: This paper attempts to improve the training paradigm of end-to-end deep learning solvers for combinatorial optimization problems. It introduces Preference Optimization (PO) to alleviate the issue of diminishing advantage signals in the later stages of reinforcement learning (RL) training, thereby preventing mo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough review and valuable insights. Your constructive feedback has significantly improved our manuscript. We present the detailed responses as follows and hope that could address the concerns.
---
### **1. Broadening comparison with SOTA solvers on large-scale p... | Summary: This paper introduces Preference Optimization (PO), a novel method for solving CO problems like TSP and CVRP.
Key contributions:
1. The authors transform quantitative reward signals into qualitative preference signals, addressing two major challenges in reinforcement learning for COPs:
- Diminishing reward ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and constructive comments. Your insightful suggestions are invaluable for improving our work. Below, we address your concerns in detail.
---
### **1. Including comparison with Poppy and COMPASS**
Thank you for providing the related references, i.... | null | null | null | null | null | null |
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions | Accept (poster) | Summary: This paper introduces the QuEst method, which combines a few observed data with a large quantity of imputed data to derive enhanced estimates and reliable confidence intervals for quantile-based distribution metrics (QBDM). The method demonstrates its value in real-world applications, such as LLM Auto-Evaluati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful assessment of our work and the thoughtful suggestions. Below, we respond to particular concerns.
[Q1: Comparisons with more methods, such as those in Section 3.2]
With respect to baselines, prior PPI variants are incompatible with the measures considered i... | Summary: Authors propose a novel framework—QuEst—that enhances quantile based distributional measure estimation by combining scarce high quality observations with abundant model-predicted data. It produces an asymptotically unbiased estimator with reduced variance and can be further optimized using spline functions. Em... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful remarks. Please see below for responses to individual concerns.
[Q1: Imputed data quality]
First, we would like to clarify that our remark in the limitations section concerned the problem that **if we want to have significant gain in variance reduction*... | Summary: The authors propose QuEst to estimate a quantile-based distributional measure (see Defintion 1) in a setting where there is a small set of high-quality data and a large set of low-quality data. For the high-quality data, it assumes that their low-quality estimates are also known and follow the same low-quality... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and consideration in reviewing our work. Here we respond to specific concerns raised in the review. We hope that our answers can resolve the misunderstanding and help to increase our score.
[Q1: Novelty compared to existing work.]
At the first glance, the su... | Summary: The authors introduce QuEst, a framework for estimating quantile-based distributional measures (e.g., VaR and CVaR in financial mathematics), which combines a smaller set of real data with a much larger set of output data from machine learning predictions, in a similar fashion as the prediction-powered inferen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and for recognizing the contribution of QuEst. Below we address your concerns and questions.
[Q1: Relation to existing approaches]
This is correct. Existing PPI methods deal with estimators written in the form of sums of i.i.d. random variables.... | null | null | null | null | null | null |
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery | Accept (poster) | Summary: This paper introduces Neural Interpretable PDEs (NIPS), a novel neural operator architecture that enhances both predictive accuracy and computational efficiency in modeling complex physical systems. NIPS builds upon Nonlocal Attention Operators (NAO) by employing a linear attention mechanism combined with a le... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Our response:
**Multiple runs with different random seeds**: We repeat the experiments three times with randomly selected seeds in the first case of experiment 1, using NIPS and the major baseline models including NAO and AFNO. The results are re... | Summary: This article introduces an operator learning method that computes the solution to a PDE with a data-dependent kernel called NIPS. The kernel comes from a linear attention mechanism performs in Fourier space, which improves the efficiency of the method. The method can also be used for the inverse problem to dis... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and questions. Our response:
**Larger discretization**: We demonstrate the scalability of NIPS using larger discretizations (e.g., $121\times121$ with 14,641 tokens in total), and compare its per-epoch runtime and peak GPU memory usage with the b... | Summary: The paper introduces Neural Interpretable PDEs (NIPS), an attention-based neural operator architecture for solving forward and inverse PDE problems. NIPS utilizes a learnable kernel network to optimize efficiency in Fourier transform interactions by mitigating the computation and storage of the pairwise intera... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments . Our response:
**Formalized interpretability metric**: We thank the reviewer's valuable suggestion. A quantitative evaluation of the interpretability can be provided by the recovered kernel. Taking the Darcy flow example for instance, the discove... | null | null | null | null | null | null | null | null |
Conformal Prediction for Hierarchical Data | Reject | Summary: This paper studies the problem of conformal prediction for hierarchically-structured multivariate regression datasets. Hierarchical coherence of the different outputs is encoded via a projection matrix. A novel split conformal prediction algorithm is introduced with two objectives in mind: marginal coverage fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments; as listed in the strengths, our main objective was indeed to develop a theory (of conformal prediction for hierarchical data). This endeavour turns out to be much different from the theory of conformal prediction for multivariate data as we detail i... | Summary: This paper addresses an important question in distribution-free inference--constructing prediction intervals for the multivariate response. Motivated by forecast reconciliation, this paper proposes to utilize the hierarchical information among multivariate responses to enhance the accuracy of prediction sets.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading, and also for considering the connections built between conformal prediction and forecast reconciliation as one of the strengths of this submission. We go over the two issues pointed out.
**Issue 1 - Projection vs. de-correlation and answer t... | Summary: The authors extend the conformal prediction framework to hierarchical data, defined as multivariate data where some variates are linear combinations of covariates. The authors establish tighter efficiency bounds on the size of the conformal intervals, and also establish new bounds on a component-wise coverage ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading and the positive comments w.r.t. experiments.
We actually conducted the ones with synthetic data on larger hierarchies (and obtained similar results) but found it difficult to report the results. Figure 2 is already quite complex with the simple 5-2-... | Summary: The work proposes application of conformal prediction to hierarchical data. The idea is a combination of two approaches, i.e. split conformal prediction and forecasting reconciliation. The proposed method not only provides global coverage but also component-wise coverage with the computed prediction regions ef... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading and the positive comments --- especially the ones relative to the well-conducted and neat experiments.
On the issues raised:
**1. I don't see a coherent discussion about related works such as [1]**
We will discuss [1] in the revised version... | null | null | null | null | null | null |
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data | Accept (poster) | Summary: This paper investigates memorization behavior in diffusion models for tabular data generation. The authors examine how factors like dataset size, feature dimensionality, and model architecture influence memorization. They propose a data augmentation method called TabCutMix and an enhanced version, TabCutMix-Pl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. All responses and corresponding revisions have been incorporated into the updated manuscript, available at: https://anonymous.4open.science/r/TabCutMix-3F7B/TabCutMix.pdf.
[**Q1**] The paper's theoretical claims about memorization in diffusion ... | Summary: This paper introduces TabCutMix and TabCutMixPlus, two augmentation methods designed to reduce memorization in tabular diffusion models. The authors demonstrate that state-of-the-art diffusion models tend to memorize tabular datasets and show that their proposed augmentations mitigate this issue. They further ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. All responses and corresponding revisions have been incorporated into the updated manuscript, available at: https://anonymous.4open.science/r/TabCutMix-3F7B/TabCutMix.pdf.
[**Q1**] The results in Table 1 show that, across different datasets, th... | Summary: The paper explores the issue of data memorization in tabular diffusion models, highlighting its potential privacy risks and negative impact on generalization. To explain why memorization arises in a tabular diffusion setting, the authors present a theoretical analysis that connects denoising score matching to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. All responses and corresponding revisions have been incorporated into the updated manuscript, available at: https://anonymous.4open.science/r/TabCutMix-3F7B/TabCutMix.pdf.
[**Q1**] I think studying memorization in tabular data might be benefici... | Summary: The paper examines memorization in diffusion models for tabular data, providing theoretical insights that demonstrate the optimal score function under the empirical distribution and show that generated data can replicate training samples. To mitigate memorization, the study introduces TabCutMix and TabCutMixPl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. All responses and corresponding revisions have been incorporated into the updated manuscript, available at: https://anonymous.4open.science/r/TabCutMix-3F7B/TabCutMix.pdf.
[**Q1**] I would suggest including (or at least referencing) important m... | null | null | null | null | null | null |
A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver | Accept (poster) | Summary: In the context of neural solvers for vehicle routing problems (VRP), the paper introduces a methodology to learn embeddings in non-Euclidean space, where the embedding space is divided into several subspaces with adaptively learned curvatures.
Claims And Evidence: The authors claim that their proposition can ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer gmJz's insightful questions and constructive feedbacks.
#### **[Acronyms]** Thank you for the suggestion. We will ensure all acronyms are properly defined upon first use and provide a reference table in the final version. For example, "VRPs" stands for Vehicle Routing Pr... | Summary: This article considers modeling the VRP tasks in multi-task NCO in Riemannian space instead of the original Euclidean one. This article modifies the embedding layer based on this idea, and the experiment demonstrated the effectiveness of the proposed method.
## update after rebuttal
I think the topic of this... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer qtXz for detailed and valuable suggestions and comments.
#### **[Dimension of $X$]** Since graph is discrete, Ollivier-Ricci metric is used for measuring curvature based on edges weights, which doesn't require dimensions of inputs. Thi... | Summary: This paper presents a pre-training framework for multi-task vehicle routing solver. The main difference between this framework with existing literatures is the integration of the geometric structures. Specifically, this framework utilizes the curvature of the routes and encodes the geometric features in mixed-... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer dd5d for the valuable comments and suggestions. We provide the point-to-point responses below to address the concerns.
#### **[Figures to show curvatures of inputs]** Figure 1 visualizes the curvature information on 6 training tasks. We use 1,000 VRP instances (size... | null | null | null | null | null | null | null | null |
polybasic Speculative Decoding Through a Theoretical Perspective | Accept (poster) | Summary: The paper introduces the polybasic speculative decoding framework, aimed at accelerating the inference of large language models (LLMs) through multi-model collaboration. Its core contributions include: 1) Establishing a theoretical framework, deriving the optimal inference time formula for multi-model systems ... | Rebuttal 1:
Rebuttal: Thank you for these insightful questions that help clarify the practical implications of our theoretical framework.
**Regarding Theorem 3.2's sensitivity:** We recommend measuring the relevant parameter values under identical experimental conditions. While measurement errors may occur, our theore... | Summary: This paper explores using a chain of draft models rather than a single draft model during speculative decoding, such that the first draft model generates tokens autoregressively, and each subsequent draft model verifies the tokens generated. When an intermediate draft model rejects a token the first draft mode... | Rebuttal 1:
Rebuttal: **1. On the Connection Between Theory and Experiments**
We appreciate your concern about the gap between our theoretical foundation and experiments. To address this, we've conducted specific experiments that directly validate our theoretical framework:
|$T_i $|$L_{i-new}$|$T_{new}$|$L_{new} $|$T... | Summary: The submission describes an new theoretical framework to improve speed of LLM decoding in order to reduce latency.
## update after rebuttal
I think this rebuttal was very useful and reaffirmed me in my slightly improved score of 3. I still share concerns with Reviewer KeQh in the areas of coherence and clari... | Rebuttal 1:
Rebuttal: **1. Response on Accuracy Evaluation**
We appreciate your highlighting the importance of accuracy evaluation. This is indeed a critical point that deserves attention.
Our method inherently preserves output distribution through the verification mechanism in Algorithm 1. The VERIFY procedure ensur... | Summary: This paper proposes a novel polybasic speculative decoding framework. Specifically, the authors prove a fundamental theorem that characterizes the optimal inference time for multi-model speculative decoding systems. Through the theoretical investigation of multi-model token generation, the authors propose a th... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful feedback and the positive evaluation of our work.
**1. Experiments on Larger Models (13B, 70B)**
Following your suggestion, we have conducted additional experiments with Vicuna-13B and LLaMA-2-chat-70B. ($c$: speed ratio, $μ$: average acceptance length)... | null | null | null | null | null | null |
SHE: Streaming-media Hashing Retrieval | Accept (poster) | Summary: Existing CMH methods often implicitly assume that all modalities are prepared before processing. However, in practice applications (such as multi-modal medical diagnosis), it is very challenging to collect paired multi-modal data simultaneously. Specifically, they are collected chronologically, forming streami... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Our responses are listed below.
**R-Weakness-1**: To enhance readability and understanding of our proposed SHE, we provide a detailed explanation of some key losses, which can be found at **`R-Weakness-3’** in [Reviewer TSiu](https://openreview.net/forum?id=JqLK... | Summary: This paper proposes a novel CMH paradigm, specifically designed for cross-modal retrieval in streaming-media scenarios. The proposed SHE framework comprises three key modules: the Knowledge Library Mining (KLM) module, the Knowledge Library Transfer (KLT) module, and the Discriminative Hash Learning (DHL) modu... | Rebuttal 1:
Rebuttal: Thanks for your constructive review. Our responses are listed below.
**R-Weakness-1**: To evaluate the quality of knowledge mined from different modalities, we design a score, which can be found at **`R-Questions’** in [Reviewer cJMB](https://openreview.net/forum?id=JqLKV0L5hM¬eId=i9vSJNoRoW).... | Summary: This work addresses a less studied but practically valuable problem in cross-modal retrieval, i.e., streaming-media hashing retrieval. The paper points out the key challenge is to preserving cross-modal interactions. Through knowledge library mining on existing modalities and knowledge transferring only on the... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. Our responses are listed below.
**R-Weaknesses**: To make the experiment more rigorous, a detailed description of the training procedure for the compared methods is provided. Specifically, there are two cases in the training. 1) For the Wikipedia and NUS-WIDE... | Summary: While most previous research in CHM have assumed that all modalities are prepared before processing, the authors propose a novel CHM paradigm named Streaming-media Hashing rEtrieval (SHE) that enables parallel training of each modality for streaming-media data, where the data is collected chronologically.
The... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Our responses are listed below.
**R-Weakness-1**: We have revised this issue, providing the relevant explanation when KLR first appears.
**R-Weakness-2**: In the introduction section, we list CMH methods (Such as UCCH, DHaPH, DCH-SCR[1], SCH, and CICH[2]) to il... | null | null | null | null | null | null |
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization | Accept (poster) | Summary: This paper introduces a mixed-precision weight-only quantization method for large language models (LLMs). The authors propose a greedy algorithm to allocate bitwidths across weight channels, followed by K-means clustering based on the assigned bitwidths. To enhance performance, the authors further incorporate ... | Rebuttal 1:
Rebuttal: 1. Clarification on Objective Rankings:
The ranking of objectives (S-full > L-full > S-diag > L-diag) is elaborated in Section 4.1, where we demonstrate that L-full outperforms S-diag. This is because S-diag simplifies computation via a diagonal assumption, introducing a certain degree of bias com... | Summary: The paper introduces SKIM (Scaled K-means clustering wIth Mixed precision), a novel post-training quantization method for Large Language Models (LLMs) that supports any-bit quantization. The key contributions include a greedy algorithm for optimal bit allocation across weight channels, addressing the significa... | Rebuttal 1:
Rebuttal: 1. More Information about the Greedy Algorithm:
Through empirical validation, we have demonstrated the effectiveness and efficiency of the greedy algorithm:
- Time Efficiency: The cpp/jit implementation of the dynamic programming algorithm remains slow. For a single block in LLaMA-7B, it requir... | Summary: The work describes a post-training quantization technique that allows for non-integer size quantization of model parameters.
## update after rebuttal
The rebuttal process helped to address my concerns and I thank the authors for their supporting answer(s). As a result I increased my score to 3, in hope the d... | Rebuttal 1:
Rebuttal: 1. Comparison with ShiftAddLLM:
Thank you for the suggestion. We have now included a comparison with ShiftAddLLM. Overall, our method is competitive compared to ShiftAddLLM. In fact, ShiftAddLLM uses a smaller group size (128) rather than channel-wise quantization when quantizing LLaMA, so its pre... | null | null | null | null | null | null | null | null |
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization | Accept (poster) | Summary: The paper proposes a novel approach called Direct Noise Optimization (DNO) for aligning diffusion models with continuous reward functions at inference time. DNO optimizes the injected noise during the sampling process to maximize the reward function, without requiring any fine-tuning of the model parameters. T... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our work. Here, please allow us to provide specific responses to your major comments.
### 1. Comparing to prior works
Please allow us to emphasize and reiterate our main contributions here, which are distinct from all prior works on noise optimization.
* **A ... | Summary: This paper conducts a comprehensive investigation in optimizing the noise in the sampling process of diffusion models for alignment. The main contributions are: 1) giving a rigorous definition of noise optimization and extending it to SDE sampling, 2) explaining and quantifying the root of OOD issues in noise ... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our work. Here, please allow us to provide specific responses to your major comments.
### 1. "What is the level of M1 and M2 value for diffusion models with hacked reward alignments?"
In this work, we did provide a more direct illustration of this point. In ... | Summary: This paper investigates the alignment problem of diffusion models during inference and proposes a tuning-free, prompt-agnostic method named Direct Noise Optimization (DNO). The authors theoretically investigate the properties of DNO and propose variants of DNO, aiming to solve problems of out-of-distribution r... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our work. Here, please allow us to provide specific responses to your major comments.
### 1. On the smoothness assumption.
Conceptually, it is quite straightforward to argue that the reward function is smooth with respect to pixel changes. Because small chang... | Summary: This paper introduces a novel approach for optimizing diffusion input noise based on a specified reward function. The key advancements over prior work include:
- A new regularization technique that ensures the optimized noise remains within the distribution of the diffusion model.
- A method for handling non-d... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our work. Here, please allow us to provide specific responses to your major comments.
Regarding your comment:
"Optimizing noise to increase darkness with the prompt 'white [animal]'—does not fully reflect real-world use cases. Are there more practical scenario... | null | null | null | null | null | null |
A General Framework for Inference-time Scaling and Steering of Diffusion Models | Accept (poster) | Summary: This study proposes FK steering, a scalable inference method for both image and text diffusion models. FK steering consists of a proposal generator, a potential function, and an intermediate reward. In each denoising interval, the diffusion model generates multiple proposals, and the potential function uses on... | Rebuttal 1:
Rebuttal: Thank you for highlighting the strength of our results. We also deeply appreciate your comments about the high-quality execution of our paper, the rigor/strength of our evaluations, and the clarity of our exposition.
_Can FK steering make a Stable Diffusion 3 medium (2B) [A] model outperform the... | Summary: This paper proposes Feynman-Kac steering for diffusion models using pretrained reward functions like ImageReward.
**Update after rebuttal**
My main concern was that earlier works proposing FK for diffusion were not clearly acknowledged and discussed. During the rebuttal phase the authors proposed specific re... | Rebuttal 1:
Rebuttal: Thank you for highlighting the quality of our writing and experiments, and the practical applications of our approach.
_Concerns regarding novelty claims in this paper._
Various fields such as statistics, posterior inference [Naesseth et al 2019], have used Feynman-Kac interacting particle syste... | Summary: This paper proposes a general framework for inference-time scaling and steering of diffusion models using Feynman-Kac (FK) particle resampling. It claims to unify various existing steering methods by casting them into a single FK-IPS (Feynman-Kac Interacting Particle System) framework. The framework is validat... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper, and for noting the clarity of our writing. We discuss your concerns below.
_The main claim—unifying inference-time steering methods into a single FK-based framework— might lack sufficient novelty_
- We show that Feynman-Kac interacting particle systems... | Summary: The paper presents Feynman-Kac (FK) steering, a general particle-based framework for inference-time steering of diffusion models to generate outputs aligned with user-defined reward functions without requiring additional training or fine-tuning. FK steering generates multiple parallel sample trajectories from ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, and for highlighting that FK steering is a practical, effective approach for efficient, controllable generation with diffusion models. We also appreciate your assessment of our paper's clarity and strong theoretical grounding.
We address each of your concer... | null | null | null | null | null | null |
Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach | Accept (poster) | Summary: This paper proposes and evaluates a semi-supervised approach to training Bayesian Neural Network (BNN) optimization proxies for predicting solutions to constrained optimization problems. Specifically, the paper proposes augmenting training on labeled data (from solving optimization problem instances) with trai... | Rebuttal 1:
Rebuttal: **Methods and evaluation criteria**
We agree with the reviewer that BNN training can be sensitive to various random choices in the training. We have run a new batch of experiments for the 118 bus problem with increased dataset size. The variance in the results from different learning experiments ... | Summary: The paper introduces a Bayesian Neural Network (BNN) framework as a surrogate model for constrained optimization, leveraging its intrinsic uncertainty quantification for robust predictions. It employs a semi-supervised training strategy that alternates between supervised learning on limited labeled data and un... | Rebuttal 1:
Rebuttal: **Claims And Evidence: The 2×MPV Heuristic and Its Broader Validation**
We appreciate this insightful comment. We believe that the 2MPV heuristic is sufficient to capture total variance in the error, as substantiated by the studies shown in Fig 4. This heuristic will require similar empirical va... | Summary: The paper proposes a new Bayesian Neural Network (BNN) for solving non-linear constrained optimization problems that can be computationally expensive. The Bayesian Neural Network serves as a proxy for solving the original problem and is computationally more efficient. The downside of similar existing approache... | Rebuttal 1:
Rebuttal: **Weaknesses**
**1. Scalability**
We agree that exploring scalability is important. To address this concern, we have added additional experimental results showing that while our method excels in low-data regimes, it also remains competitive as the amount of available data increases. These experi... | null | null | null | null | null | null | null | null |
Sort Before You Prune: Improved Worst-Case Guarantees of the DiskANN Family of Graphs | Accept (poster) | Summary: This paper aims to strengthen the theoretical analysis of graph-based ANN algorithms. In DiskANN indexing, candidate vertices are considered in ascending order from their distance to the out-vertex $v$, a detail not leveraged in prior theoretical analysis works. By combining this sorted-distance implementation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind words and suggestions. As the reviewer noted, extending the existing analysis to the practically motivated beam search regime was a key motivation for this work. Additionally, we adopted a distance-based approximation scheme, leveraging our novel factor-revealin... | Summary: This paper introduces a novel strategy for constructing graph structures to enhance Approximate Nearest Neighbor Search (ANNS) on high-dimensional vectors. This approach ensures a superior approximation ratio for L2 distance metrics compared to existing methods.
## **Update After Rebuttal**
The rebuttal has c... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed feedback and would like to clarify the key contributions of our work. Rather than proposing a new algorithm that outperforms DiskANN, our primary contribution is theoretical, providing a deeper understanding of the DiskANN algorithm in two key ways.
First, we ... | Summary: This paper conducts a theoretical analysis of graph-based approximate nearest neighbor search algorithms. In particular, the authors propose a new theoretical framework called the $\alpha$-reachable graph and, using this framework, provide the first worst-case complexity analysis of beam search in situations w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and suggestions. We will address all typos and wording mistakes in the final version. We want to emphasize one point: in addition to giving the first analysis for beam search, which is widely used in practice, we also improve the current state of... | Summary: This paper addresses the problem of Approximate Nearest Neighbor Search (ANNS), which is crucial for various applications dealing with large datasets in high-dimensional spaces. Graph-based data structures, particularly those in the DiskANN family, have shown strong empirical performance. However, theoretical ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words and thoughtful suggestions. As suggested by the reviewer, we have included a link to plots on two large 100M-scale datasets from big-ann-benchmark, confirming the importance of sorting irrespective of dataset size. We will include all of these plots in th... | null | null | null | null | null | null |
Compositional Scene Understanding through Inverse Generative Modeling | Accept (poster) | Summary: This work demonstrates how generative vision models can be composed to enable robust compositional scene understanding. The proposed solution enables different scene understanding tasks involving discrete or continuous factors, such as localization and multi-attribute classification. The method can also incorp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and comments! Please see our response below about your concerns.
**1. For the experiments on CelebA, I am wondering why the authors opt to use all male faces as the OOD set, which effectively removes the female/male attribute from classification. Th... | Summary: The paper introduces an innovative inverse generative modeling framework designed to perform compositional scene understanding. Its primary innovation is to interpret visual scenes as compositions of smaller generative models, enabling effective generalization to complex or unseen scenes. Specifically, the aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and comments! Please see our response below about your concerns.
**1.How scalable is the compositional inverse generative modeling approach when dealing with a very large number of visual concepts or categories? Would an alternative optimization str... | Summary: This paper casts scene understanding as an inverse generative modeling problem, where attributes of the scene are extracted by seeking parameters of the generative model that best fit a given image. To facilitate generalizability, the paper proposes to model visual scenes compositionally - by model sub compone... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments! Please see our response below about your concerns.
**1. The tested datasets contain simple concepts (limited number of objects in CLEVR, limited set of attributes in face), so although performance evaluation positively supports the... | Summary: This paper proposed an inverse generative modeling approach to understand attributes of a single image. This approach trains conditional generative models based on different conditions to construct a multi-conditional generative model through the composition of EBMs. The compositional modeling can generalize t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments! Please see our response below about your concerns.
**1. One concern is the fairness of comparison with unsupervised Slot Attention. I recommend to compare with more advanced DINOSAUR.**
Thank you for the insightful suggestion! We ... | Summary: This paper presents a computational framework for mining the structural properties of natural scene images by recasting the problem as an inverse generative modeling task. Specifically, the authors propose a generic inverse generative modeling paradigm that integrates compositionality into a diffusion-based ge... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments! Please see our response below about your concerns.
**1. CelebA images contain only a limited number of attributes (typically 2–3). How does the proposed method perform on tasks involving a greater number of attributes?**
Thank you... | null | null | null | null |
Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning | Accept (poster) | Summary: This work addresses an important problem: reconstructing cell lineage trees, which provide insights of how diverse cell types arise from a single progenitor. More to the point, the authors set an additional challenge, which is to use transcriptomic data to construct cell linage trees, which sets them apart fro... | Rebuttal 1:
Rebuttal: ## Concern 1: Joint optimization claim
### Response
We thank the reviewer for recognizing the importance of the cell lineage reconstruction problem and for raising this valuable point about our optimization formulation. We agree that Section 4.1 could be clearer in distinguishing between joint and... | Summary: This paper introduces **CellTreeQM**, a novel deep learning-based method for reconstructing cell lineage trees using single-cell RNA sequencing data.
The method leverages a **Transformer architecture** combined with **metric learning** to optimize the geometry of an embedding space, enabling accurate reconstr... | Rebuttal 1:
Rebuttal: ## Concern 1: Lack of comparisons to SOTA methods
> There are many state-of-the-art (SOTA) cell lineage methods that have not been compared.
### Response
Thank you for raising the concern regarding comparisons to state-of-the-art (SOTA) cell lineage methods. Below, we clarify why existing methods ... | Summary: The authors propose an algorithm CellTreeQM to reconstruct lineage relationships from phenotype data (unlike genotype data, which has been the main focus in the area of lineage reconstruction). The main new idea in the paper is the use of loss function based on four point condition which ensures that embedding... | Rebuttal 1:
Rebuttal: ## Concern 1: No theoretical results
> they do not appear to claim any significant new theoretical results (no new theorems/proofs).
### Response
While we do not present new theorems or proofs, our contribution lies in empirically demonstrating that the four-point condition serves as a strong pri... | Summary: This paper poses the reconstruction of lineage trees from phenotypic data as a metric learning problem, and devises a contrastive loss function to learn a metric given partial information about the topology of the lineage tree. The authors test this algorithm on ground truth data from C. elegans, as well as si... | Rebuttal 1:
Rebuttal: ## Concern 1: Overstated claim about unsupervised learning
> Although acknowledged as preliminary, unsupervised performance is mentioned in the abstract.
### Response
We appreciate the comment concerning the limited investigation of the unsupervised setting. We demonstrate the unsupervised settin... | null | null | null | null | null | null |
MedRAX: Medical Reasoning Agent for Chest X-ray | Accept (poster) | Summary: This paper introduces an AI agent multimodal, multi-task chest X-ray (CXR) interpretation and analysis called MedRAX. This method leverages an LLM-controlled reasoning and acting (ReAct) loop, dynamically reasoning through multi-step queries and selecting pretrained, task-specific tools to complete each step. ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful comments. We have worked hard to answer their concerns and think the suggestions have helped improve the clarity of our work.
**Key Methodoloy.** The full details of Algorithm 1 are provided in response to `Reviewer KTkS`. Additionally, an an... | Summary: The paper introduces MedRAX, an AI-driven agent for the interpretation of chest X-rays (CXRs). MedRAX integrates various specialized state-of-the-art chest X-ray analysis tools and multimodal large language models into a single unified framework. Unlike existing solutions that often operate independently, MedR... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful feedback.
**ChestAgentBench Representativeness.**
We appreciate the reviewer's question on benchmark representativeness. We designed ChestAgentBench to be both representative and capable of assessing advanced reasoning, addressing limitations... | Summary: This paper proposes MedRAX, a modular AI agent that integrates specialized chest X-ray (CXR) analysis tools with large language models to perform complex, multi-step medical reasoning. It introduces ChestAgentBench, a large benchmark of 2,500 expert-curated CXR reasoning tasks, to evaluate its performance. Exp... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their thorough and thoughtful comments.
**Statistical Significance.** We appreciate the reviewer's point about statistical measures. All experiments were run deterministically (LLMs with temperature 0 and deterministic tools), thus eliminating variability in mod... | Summary: The key contributions of this work are:
- MedRAX: An AI framework integrating multiple chest X-ray (CXR) analysis tools without extra training, dynamically orchestrating components for complex medical queries.
- ChestAgentBench: An evaluation framework featuring 2,500 queries across seven categories, built fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback and appreciate their recognition of the importance of this problem in the healthcare domain.
**Algorithm 1 Core Methodology.** Algorithm 1 outlines the iterative reasoning process of the MedRAX agent in the ReAct loop, as follows:
1. **`Observ... | null | null | null | null | null | null |
Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective | Accept (poster) | Summary: This paper introduces Variational Bayes Continual Generalized Category Discovery (VB-CGCD), a Bayesian framework designed to address key challenges in Continual Generalized Category Discovery (C-GCD), including label bias, pseudo-label errors, and the learning-forgetting tradeoff. VB-CGCD comprises four key co... | Rebuttal 1:
Rebuttal: ## Claims And Evidence:
>1.
The constrained optimization equation formally represents the objective of "learning new classes without forgetting old ones." We observed that maximizing overall accuracy inevitably leads to a decline in the classification performance of previous classes, as illustra... | Summary: This paper addresses Continual Generalized Category Discovery (C-GCD), or say, iGCD, a task where a model must incrementally learn new classes from unlabeled data streams while preserving knowledge of previously learned classes, a challenge exacerbated by mixed-class data streams and catastrophic forgetting. C... | Rebuttal 1:
Rebuttal: ## Weakness:
>1.
We acknowledge that DINO-ViT-B16 was pre-trained on the ImageNet dataset, though its training was based on self-supervised learning using unlabeled data, and it was not trained on CIFAR or CUB. Since all our baselines including HAPPY (SOTA) utilize DINO for feature extraction, ... | Summary: This paper studies the task of Continual Generalized Category Discovery (CGCD). It analyzes C-GCD’s forgetting dynamics through a Bayesian lens, revealing that covariance misalignment between old and new classes drives performance degradation. To solve these issues, this paper proposes Variational Bayes C-GCD ... | Rebuttal 1:
Rebuttal: >1.
We appreciate the reviewer’s valuable feedback. We have regarded HAPPY as an important SOTA but inadvertently missed its discussion in the related work section. We will include the discussion of HAPPY in the revised version, with a particular emphasis on its technique of using Gaussian distr... | Summary: This manuscript investigates the problem of Continual Generalized Category Discovery, addressing the challenges of mixed new and old categories and high uncertainty in unlabeled data under a continual learning setting. The authors propose a new variational Bayesian framework that utilizes offline fine-tuning a... | Rebuttal 1:
Rebuttal: >1.
We conducted an ablation study on the benefit of introducing Mahalanobis distance, and the results obtained by replacing the Mahalanobis with the Euclidean distance are as follows:
Datasets|Methods|All\(S0\)|All\(S5\)|Mf|Md
--|---------|---|---|----|---
C100|w/oMahalanobis|90\.52|76\.06|10... | null | null | null | null | null | null |
Adaptive Estimation and Learning under Temporal Distribution Shift | Accept (poster) | Summary: This paper focuses on estimation and learning time series data in the presence of temporal distribution shifts. The authors propose a wavelet soft-thresholding estimator that optimally estimates the ground truth sequence under unknown shifts and provide theoretical error bounds for their method. Their approach... | Rebuttal 1:
Rebuttal: **Experiments on real dataset**
Please see the response to Reviewer c9dT under the same comment title.
**How to do optimal wavelet selection.**
This is a well-known model selection problem in wavelet denoising, not a drawback unique to our work. We acknowledge this in Section 7. Practitioners t... | Summary: This paper investigates the problem of learning under temporal distribution shift, where the task is to estimate the ground truth related to the last observation under minimal stationarity assumptions. They prove new bounds on existing versions of a soft-thresholding algorithm for the problem, and translate th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Please see the responses below.
**Experiments on real dataset**
As an application of our proposed methods, we conduct a model selection experiment using real-world data.
We evaluate our method on data from the Dubai Land Department (https://www.dubaipuls... | Summary: The paper studies the problem of temporal distribution shift, by formulating the problem as parameter estimation in an univariate non-stationary sequence with sub-gaussian noise. The authors propose a wavelet denoising approach for this estimation problem, and theoretically upper bound its error rate. The auth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Please see the responses below.
**Why DB8 can outperform Haar wavelets in the synthetic experiments**
The main reason why higher order wavelets can outperform Haar is because, $k+1$-th order wavelets provide an ideal basis for sparsely compressing the in... | Summary: Given noisy observations of independent but non-identical random variables, the authors consider the problem of estimating the most recent ground truth.
A key insight of the paper is that although the ground truth sequence may be non-stationary in the time domain, its wavelet transform reveals a sparse struct... | Rebuttal 1:
Rebuttal: We thank the reviewer for correctly recognizing and appreciating our contributions.
**Rates in Theorem 11**
The displayed rates are indeed the optimal rates for squared and absolute losses.
**Paragraph below corollary 8**
We will fix the typos and rewrite it for better clarity. | Summary: In this paper, the authors study the problem of estimation and learning under temporal distribution shift. In simple words (of the author), they consider an estimation task where n independently drawn observations are given by y_n, \hdots, y_1 and E[y_i] = \theta_i. The goal is to construct an estimator for... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Please see the responses inline.
**Experiments on real dataset**
Please see the response to Reviewer c9dT under the same comment title.
**Experiments are based on ablation**
Ablation on noise level was conducted primarily to test the validity of the al... | null | null | null | null |
Accelerating Spectral Clustering under Fairness Constraints | Accept (poster) | Summary: - This paper proposes a computationally efficient algorithm for the fair spectral clustering problem.
- The key of the proposed algorithm is the use of DC (Difference of Convex functions), which leads to the ADMM framework.
- The authors claim that the proposed method is empirically faster than two existing al... | Rebuttal 1:
Rebuttal: > Q1. FacebookNet dataset
To address the reviewer's concern, we ran additional experiments on FacebookNet.
**Table ztMM-1:** Clustering results on FacebookNet
|#Clusters|Method|Time (s)|Balance|SC Cost|
|---|---|---|---|---|
|2|o-FSC|0.798|1.00|0.126|
|2|s-FSC|0.193|1.00|0.126|
|2|Ours|0.055|1.0... | Summary: This work addresses the issue of fairness in spectral clustering by proposing a new efficient method for fair spectral clustering (Fair SC). The authors introduce a novel algorithm that casts the Fair SC problem within the difference of convex functions (DC) framework and employs an alternating direction metho... | Rebuttal 1:
Rebuttal: > Q1. Comparative analysis with recent works [FEN24, ZHA24]
We first provide discussions, followed by experiments.
- Our work designs a much *faster* method for the *existing* Fair SC problem defined in [3]. [ZHA24] focuses on improving balance and is orthogonal to our algorithmic contribution: i... | Summary: This paper studies the problem of fair spectral clustering. The authors propose an ADMM-like algorithm for optimization with theoretical guarantee, and the experimental results show effectiveness in improving fairness.
Claims And Evidence: Overall the claims are justified by evidences. However, the performanc... | Rebuttal 1:
Rebuttal: > Q1. Our contribution within the broader literature
We note that our primary contribution is to design a significantly faster method for the *existing* well-established Fair SC problem as defined in [1], rather than improving the balance of Fair SC.
We respectfully disagree that the performance... | Summary: This work considers fair spectral clustering, demonstrating a reformulation of the fairness constraints which allows for a considerable improvement in the runtime of existing fair algorithms. Specifically, by reformulating the trace maximization problem often used for FSC into one of multiple subproblems in th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and address the remaining concerns below.
> Q1. Expand on the convergence analysis.
Our convergence analysis is based on the theory for ADMM applied to nonconvex problems from [1], which analyzes the convergence of ADMM for structured nonconvex pro... | null | null | null | null | null | null |
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching | Accept (poster) | Summary: This paper attempts to address two limitations in VAR work: (1) the complex and rigid scale design and (2) the dependency between the generator and the tokenizer. To address these issues, the paper makes two simplifications: (1) each scale is simply double the previous one; (2) the coarse scale tokens are obta... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the concerns below.
> Essential References
Thank you for the suggestions. FlowAR focuses on ***next-scale*** prediction through ***autoregressive modeling***, whereas the referenced works explore diffusion-based approaches with mult... | Summary: In this work, the authors propose FlowAR which generates images sequentially at different scales. FlowAR first generates conditional vector of different resolution using AR model and then relies on flow matching model to generate clean image of corresponding resolution. Unlike VAR, FlowAR is more flexible whic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the concerns below.
> Q1: speed comparison with VAR
While FlowAR requires additional denoising steps due to the use of flow matching, it is only slightly slower than VAR, and FlowAR-L still achieves a significant 10× speedup over ot... | Summary: This paper proposes a multi-scale approach for image generation by combining autoregressive modeling with flow matching at each scale. Instead of using a VQVAE-based tokenization as in the VAR approach, the method uses continuous latents from a VAE which are downsampled to get tokens at different scales. An a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the concerns below.
> Essential References
We thank the reviewer for the suggestion. These works represent early efforts in applying autoregressive modeling to image generation in pixel space. We will cite and discuss them in the re... | Summary: The paper explores image generation using a next-scale prediction objective, in which image latents (from a VAE) are progressively super-resolved from 1x1 scale to 2x2, 4x4, and so on. To that end, the authors present a method in which an autoregressive model predicts a conditioning for a per-scale flow-matchi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
> C1: importance of AR
Unlike Matryoshka Diffusion Models (MDM), which use a nestedUNet to jointly denoise all scales, FlowAR conditions on previously generated scales. Below, we include a baseline, “diffuse all noisy scales” (without AR), whi... | null | null | null | null | null | null |
Disparate Conditional Prediction in Multiclass Classifiers | Accept (poster) | Summary: This paper introduced the measure of Disparate Conditional Prediction (DCP) to multi-class classifiers based on equalized odds. They provided optimization methods for scenarios with and without the access to confusion matrices and justified the algorithm efficacy by using decision trees and NN models on three ... | Rebuttal 1:
Rebuttal: Thank you for your helpful review and comments. Please find below our response to your comments and questions.
**Comment**: The results comparisons are among different strategies or with the true DCP. It would be better to simply declare this is the best choice if there are not any feasible base... | Summary: The authors proposed a multi label extension of the framework developed by Sabato & Yom-Tov (thats only binary) that allows to compute bounds on fairness metrics only using population level quantities.
Dcp is determined by the matrix, and the proportions and a function of conditional prediction rates and suc... | Rebuttal 1:
Rebuttal: Thank you for your helpful review and comments. Please find below our response to your comments and questions.
**Comment**: The derivation of the multiclass DCP should be a theorem.
**Reply**: Thank you for the suggestion. We will add a formal theorem statement for the derivation of DCP.
**Co... | Summary: This paper provides methods to bound the unfairness of multiclass classifiers. In particular, they extend the Disparate Conditional Prediction (DCP) metric from prior work to the multiclass setting. The DCP measure for a classifier quantifies the fraction of the population for whom the classifier's prediction ... | Rebuttal 1:
Rebuttal: Thank you for your helpful review and comments. Please find below our response to your comments and questions.
**Question 1**: What are the limitations of the other metrics in multiclass settings?
**Reply**: Standard unfairness measures (see the Related Work section) have several disadvantages ... | Summary: This work extends disparate conditional prediction, which measures the deviation from equalized odds, to multiclass classification problems. As the confusion matrices are not available, the authors derived the lower bound and the upper bound of the DCP of a multiclass classifier. The upper bounds are obtained ... | Rebuttal 1:
Rebuttal: Thank you for your helpful review and comments. Please find below our responses to your comments and questions.
**Comment**: There is no proof for the theoretical claims.
**Reply**: All theoretical claims are proved, either in the body of the paper or in the appendix. If there are any theoretic... | null | null | null | null | null | null |
Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation | Accept (poster) | Summary: This paper proposed a graph dataset distillation method via Graph Neural Tangent Kenrels (GNTK) for efficient graph continual learning.
The main idea is using the Bernoulli sampling method to approximate the graph Laplacian which is required for computing the gradients.
By carefully setting the probability of... | Rebuttal 1:
Rebuttal: ## Q1: Why use Bernoulli distribution than largest r eigenvalues?
We adopt Bernoulli sampling because it **effectively balances high-frequency and low-frequency components, preserving both local and global graph structures**. In contrast, **directly selecting the largest $r$ eigenvalues mainly ca... | Summary: This work introduces a novel LIGHTGNTK that contains a low-rank GNTK approximation via Bernoulli sampling and a unified subgraph anchoring strategy for efficient and effective dataset distillation in multi-level tasks.
Claims And Evidence: The main claim of the paper is that the low-rank approximation of the ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments, suggestions, and every effort spent on reviewing our work. Here we attempt to address all your remaining concerns. In the following, we quote your comments and then give our detailed response point-by-point.
---
## W1: Ablation studies on key components ar... | Summary: The paper introduces LIGHTGNTK, a novel framework for efficient Graph Continual Learning (GCL) via dataset distillation. It enables GNNs to adapt to diverse downstream tasks without extensive fine-tuning, overcoming high computational costs that hinder Large Graph Models (LGMs). Specifically, LIGHTGNTK utiliz... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments, suggestions, and the time spent reviewing our work. Below, we address each of your concerns in detail.
---
## W1: Confusion in using symbols $\theta$ and $\Theta$ throughout the text
We sincerely apologize for the inconsistency in the notation of $\theta$ ... | Summary: This paper introduces a novel dataset distillation method called LIGHTGNTK for graph continual learning, which benefits the efficient and effective fine-tuning of large graph models. Specifically, the proposed LIGHTGNTK utilizes the low-rank approximation of the Laplacian matrix in Graph Neural Tangent Kernel ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments, suggestions, and the time spent reviewing our work. Below, we address each of your concerns in detail.
---
## W1: Concepts of GNTK have been previously discussed
Thank you for your valuable comment. While the concepts of GNTK have indeed been explored in K... | null | null | null | null | null | null |
Learning Event Completeness for Weakly Supervised Video Anomaly Detection | Accept (poster) | Summary: The main challenge in video anomaly detection is the lack of dense frame-level annotations, leading to incomplete localization in existing WS-VAD methods. To tackle this, authors introduced Learning Event Completeness for WS-VAD, featuring a dual structure that captures both category-aware and category-agnost... | Rebuttal 1:
Rebuttal: Reviewer zcbm
__Q1: The formulation of the local transformer and global GCN needs clarification. What are the main contributions of these modules? how does it affect the proposed approach?.__
We appreciate the reviewer's insightful suggestions. The CLIP image encoder is primarily used to extract... | Summary: This paper is dedicated to improving anomaly detection performance by enhancing the completeness of predicted events. A dual-branch structure is introduced to capture both category-aware and category-agnostic semantics between vision and language. A prototype learning mechanism based on a memory bank is propos... | Rebuttal 1:
Rebuttal: Reviewer MBS1
__Q1: The dual structure has already been proposed in VadCLIP, so it would be preferable not to present it as a primary contribution.__
Thanks for your suggestions. We will update it on the camera-ready version.
__Q2: The ablation experiments use the setting where only one module ... | Summary: This paper proposes a new WSVAD framework, LEC-VAD, that utilizes visual and language modalities for category-agnostic and category-aware anomaly detection. The authors employ a Gaussian mixture method to guide the model in predicting more complete anomaly boundaries. Additionally, a memory bank-based prototyp... | Rebuttal 1:
Rebuttal: __Q1: The fine-grained detection assumes closed-set categories. However, the reliance on category labels in VAD may limit the generalizability, since most abnormal categories are unknown/unpredictable.__
Thanks for your suggestion. We agree that an over-reliance on category labels could potential... | Summary: This paper explores weakly supervised video anomaly detection, introducing a dual-structure framework that captures both category-aware and category-agnostic semantics through vision-language integration. To enhance anomaly scoring, the authors propose a learnable Gaussian mixture mask that produces smoother s... | Rebuttal 1:
Rebuttal: Reviewer QCZn
__Q1: The impact of the local transformer layer and the GCN module in enhancing video features.__
Thanks for your advice. The CLIP image encoder is primarily used to extract image features. However, it exhibits limited proficiency in modeling temporal dependencies inherent in video... | null | null | null | null | null | null |
Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation | Accept (poster) | Summary: Universal Domain Adaptation (UniDA) tackles unsupervised domain adaptation where the target domain may contain classes that differ arbitrarily from the source, except for a common subset. A common approach, partial domain matching (PDM), aligns only the shared classes but often fails when many source classes a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and are glad to hear that our research problem resonates with real-world scenarios and that the observed dimensional collapse is found to be intriguing. The reviewer’s main concerns lie in the novelty of using SSL and the lack of experiments on p... | Summary: This paper addresses the universal domain adaptation problem, which is useful in real-world applications. They identify the failure of partial domain matching by dimensional collapse and propose to jointly leverage the alignment and uniformity techniques to avoid dimensional collapse. Experiments on four datas... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. We address the raised concerns below.
**Novelty and contributions**
While SSL has been explored in various DA contexts, we respectfully argue that our contribution lies beyond using SSL for UniDA.
Prior SSL for UniDA works typically rely on pretext tasks... | Summary: The paper investigates the cause of the performance degradation of partial domain matching (PDM) in (extreme) universal domain adaptation (UniDA). Specifically, the paper presents the failure mode of PDM resulting from dimensional collapse (DC) in target representations, in extreme UniDA where the source-priva... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive feedback. The primary concern raised is whether the utilization of unlabeled target data during training is still consistent with the Extreme UniDA setting. We address this concern below:
**Concern about the utilization of target data, whic... | Summary: This work focuses on the problem of extreme Universal Domain Adaptation (UniDA). Firstly, UniDA considers a domain adaptation problem where a model has to be trained with a labeled source domain and an unlabeled target domain, such that the label sets of source and target domains are disjoint (i.e. some classe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and are glad our analysis strongly motivates the proposed approach. Below, we address the raised concerns.
**Missing references**
We appreciate the reviewer’s feedback on our discussion of SSL for DA. While prior work primarily employs SSL to ... | null | null | null | null | null | null |
CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features | Accept (poster) | Summary: The article proposes using compact spatiotemporal features for RGB-X tracking. Unlike the commonly used two-stream frameworks, It employs a one-stream structure to reduce computational overhead. Experiments on multiple downstream tasks demonstrate that this paper achieves the SOTA performance.
## update afte... | Rebuttal 1:
Rebuttal: **Dear Reviewer f47k,**
Thanks for your time and effort in reviewing our work. Your recognition of our novel method, comprehensive experiments, SOTA performance, and writing quality greatly encourages us. We hope the following responses can address your concerns.
___
### **Q1: Comparison with rec... | Summary: The paper introduces CSTrack, an RGB‐X tracker that leverages a compact spatiotemporal feature representation to improve tracking performance while reducing computational complexity. Unlike existing methods that typically employ dual-branch architectures to process RGB and X modalities separately, CSTrack inte... | Rebuttal 1:
Rebuttal: **Dear Reviewer W7oh,**
We sincerely appreciate your thorough review of our work and are grateful for your recognition of our sound motivation, effective method, and extensive experimental analysis. In response to your concerns, we have provided detailed explanations below:
___
### **Q1: Noise In... | Summary: This paper introduces CSTrack, a novel RGB-X tracker designed to enhance tracking performance by leveraging compact spatiotemporal features. Traditional RGB-X trackers typically process RGB and auxiliary modality (X) inputs separately using dual-branch architectures, which increases computational complexity an... | Rebuttal 1:
Rebuttal: **Dear Reviewer waVm,**
Thank you for your time and effort in reviewing our work. We appreciate your recognition of our novel, innovative, and efficient method, along with the comprehensive, well-structured, and convincing experiments, as well as easy-to-follow writing and the extensive supplemen... | null | null | null | null | null | null | null | null |
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications | Accept (poster) | Summary: The paper proposes DUNIA (Dense Unsupervised Nature Interpretation Algorithm), a method that generates pixel-level embeddings by aligning forest vertical structure information (obtained from space-born full waveform LiDAR) with satellite imagery, by using contrastive learning. The pixel-level nature is what ma... | Rebuttal 1:
Rebuttal: First, we wish to thank reviewer **zT7y** for their complete & thorough review of our submission.
1.Unsupported claims
> 1\. Concern (C): The struggle to directly estimate the full vertical structure
Response (A): Reconstructing the full vertical structure (W) requires modeling a complex di... | Summary: The paper presents DUNIA (Dense Unsupervised Nature Interpretation Algorithm), an approach for learning pixel-sized embeddings for Earth observation applications through cross-modal alignment between satellite imagery and LiDAR data. The main contributions include a framework that learns dense pixel-level embe... | Rebuttal 1:
Rebuttal: We thank reviewer **hPcT** for the thorough review, comments, and suggestions.
1. Claims and Evidence
> 1\. Concern (C): The paper's claim that it "often outperforms specialized supervised models" is partially supported by the evidence presented
Answer (A): We agree and have included perfor... | Summary: The paper introduces DUNIA, a new approach to learn pixel-level embeddings of Earth observation images. DUNIA is trained contrastively, aligning satellite images with full-waveform LIDAR data to enable understanding of both “vertical” and “horizontal” structures. Experiments measure the effectiveness of the em... | Rebuttal 1:
Rebuttal: We would like to thank reviewer **WDGb** for the thorough review and the helpful comments.
1. Methods And Evaluation Criteria
> 1\. Concern (C): There may be at least one baseline that should be compared against for proper contextualization in the literature.
Answer (A): Thank you for this c... | Summary: - The paper introduces DUNIA, a novel framework that generates pixel-level embeddings through cross-modal alignment between Sentinel-1 & 2 imagery and LiDAR waveform data.
- The model incorporates several components, including a multi-modal pre-training model, two autoencoders, dual decoders with neighborhood ... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for their feedback and their appreciation of our work. The following are our responses to your comments.
1. Questions For Authors
> 1\. Concern (C): The Methods section could be improved by adding a brief subsection that explains the overall flow of the proposed p... | null | null | null | null | null | null |
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing | Accept (poster) | Summary: The paper introduces BalancEdit, a method designed to address the challenge of balancing generality and locality in multi-modal model editing. Existing methods often fail to dynamically adjust the influence scope of edits, leading to over-correction or under-correction. The authors introduce the generality-loc... | Rebuttal 1:
Rebuttal: Thanks a lot for the valuable feedback! We would like to clarify some points below.
>Why our locality sample is harder?
A: Thanks for the question. Our locality sample is harder due to the **sematical similarity** to the editing knowledge. For example:
|Aspect|MMEDIT|Our (OKEDIT)|
|-|-|-|
|**Edi... | Summary: Large-scale multimodal models suffer from knowledge decay as facts change, and traditional fine-tuning is often impractical due to their size. Instead, direct knowledge editing is preferred, but current methods neglect differences in fact influence, creating a trade-off between generality and locality. To addr... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback to improve our paper. We have addressed your hesitations below.
> Please highlight the contributions compared to existing works.
A: Thank the reviewer for the valuable suggestions. We agree that clearer differentiation is necessary. Below, we ... | Summary: Existing multi-modal model editing methods struggle to dynamically adjust the influence scope of an edit, balancing generality and locality.
To address the issue, this paper proposes a novel model editing method, i.e., BalancEdit, with process as follows
* For each image-text pair to be edited, the averaged e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback, we are glad for the opportunity to clarify some points.
>Why is our method parameter-efficient?
A: Thanks for the valuable comment! Knowledge editing is a challenging task, and we acknowledge that memory grows linearly with the number of edits du... | Summary: The paper introduces a method for efficiently updating multi-modal LLMs. Existing model editing struggles with balancing generality and locality. BalancEdit addresses this by using a codebook mechanism that stores discrete edits, dynamically adjusting their influence scope with positive and negative samples. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Here we're glad for the chance to clarify some points.
>Quality of the generality and locality samples?
A: We appreciate the reviewer’s thoughtful concern. To ensure the semantic quality of the OKEDIT dataset and the meaningful separation between g... | null | null | null | null | null | null |
A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization | Accept (poster) | Summary: The paper gives a parameter-free zeroth-order algorithm for convex and Lipschitz continuous stochastic functions $f(x) = E[F(x,\xi)]$ over a convex and compact (section 3/4) or convex and closed (section 5) set. It achieves optimal convergence rates (up to a logarithmic factor) without knowledge of the Lipschi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We highlight our technical contribution and new insights as follows:
1. To the best of our knowledge, the proposed POEM is the first parameter-free algorithm for zeroth-order stochastic optimization, which is also mentioned by all other reviewers.
2. Our met... | Summary: This paper introduces a novel parameter-free zeroth-order optimization algorithm named POED for stochastic convex optimization problems. The key idea is to eliminate the need for parameter tuning including learning rate and smoothing parameter. Inspired by difference of gradients (DoG), the proposed method lev... | Rebuttal 1:
Rebuttal: **Q1** The empirical evaluation is limited to a few datasets and could be extended to cover more diverse and large-scale scenarios.
**A1** Thank you for your suggestion.
We have addressed your comment by including the experiments on the higher-dimensional datasets "qsar" ($d=1,024$, $n=1,687$) f... | Summary: This paper proposes a parameter-free stochastic zeroth-order method that achieves near-optimal rate in the convex setting with a bounded domain. The authors also consider the unbounded domain case and prove that it is impossible to construct an ideal parameter-free algorithm in this setting.
Claims And Eviden... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and appreciation of our work.
**Q1** Figure 3 has not been mentioned in the main text. Add a description to it.
**A1** We sincerely thank you for your careful review.
We will modify the text in lines 436-438 of the left column to include a clear reference to... | Summary: This paper addresses the stochastic optimization problem
$$
\min_{x \in \mathcal{X}} \mathbb{E}_{\xi}[F(x, \xi)],
$$
where each function $F(\cdot, \xi)$ is convex and $L$-Lipschitz, and
$\mathcal{X}$ is a simple convex set in $\mathbb{R}^d$. The authors propose a
parameter-free zeroth-order optimization method... | Rebuttal 1:
Rebuttal: **Q1** The main novelty—adapting at each iteration rather than fixing it in advance—introduces only minor modifications to the DoG analysis. While this is a useful refinement, it does not introduce a fundamentally new idea.
**A1** Thank you for your thoughtful review. We highlight our novelty as ... | null | null | null | null | null | null |
Spherical-Nested Diffusion Model for Panoramic Image Outpainting | Accept (poster) | Summary: This paper presents a new diffusion-based panoramic image outpainting control net model that incorporates 1) spherical noise as structural prior into the equirectangular projected image for better handling the ERP distortion and 2) spherical deformable convolution layers to handle the varying CNN reception fi... | Rebuttal 1:
Rebuttal: Many thanks for your positive opinion and valuable comments!
- **Tab.1 - 4 are in https://anonymous.4open.science/api/repo/tabl/file/T.pdf?v=5327f2ef**
- **Fig.1 - 9 are in https://anonymous.4open.science/api/repo/figu/file/F.pdf?v=2d1be253**
> ### **1 Supplementary Material: Further Analysis on... | Summary: This work proposes SpND, a novel pipeline for panoramic image outpainting based on diffusion model. The authors tackle the limitation of previous works in which the spherical nature of panoramic image is injected using soft regularization techniques, often failing to fully enforce the spherical constraint. To ... | Rebuttal 1:
Rebuttal: Many thanks for the positive comments and insightful suggestions.
- **Tab.1 - 4 are in https://anonymous.4open.science/api/repo/tabl/file/T.pdf?v=5327f2ef**
- **Fig.1 - 9 are in https://anonymous.4open.science/api/repo/figu/file/F.pdf?v=2d1be253**
> ### **1 Claims and Evidence 4: Elaboration on ... | Summary: This paper proposes a spherical reformulation of diffusion models for panoramic image outpainting. It focuses on the fact that the processing unit and the spatial stochastic patterns in plain diffusion models do not align well with panoramic image outpainting. To handle this problem, the paper proposes redesig... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer very much for the positive opinion and insightful suggestions.
- **Tab.1 - 4 are in https://anonymous.4open.science/api/repo/tabl/file/T.pdf?v=5327f2ef**
- **Fig.1 - 9 are in https://anonymous.4open.science/api/repo/figu/file/F.pdf?v=2d1be253**
> ### **1 Weaknesses:... | Summary: This work proposes to impose the sphere nature in the design of the diffusion model, such that the panoramic format is intrinsically ensured during the learning procedure, named the spherical-nested diffusion (SpND) model. In particular, the authors design to employ spherical noise in the diffusion process to ... | Rebuttal 1:
Rebuttal: Many thanks for the valuable comments!
- **Tab.1 - 4 are in https://anonymous.4open.science/api/repo/tabl/file/T.pdf?v=5327f2ef**
- **Fig.1 - 9 are in https://anonymous.4open.science/api/repo/figu/file/F.pdf?v=2d1be253**
> ### **1 Weakness 1: Novelty of Our SDC Layer**
To the best of our knowle... | null | null | null | null | null | null |
Fast Estimation of Partial Dependence Functions using Trees | Accept (poster) | Summary: This paper proposes an efficient tree-based algorithm called FastPD for estimating partial dependence (PD) functions and extracting functional decompositions. The authors highlight how it can unify PD plots, Shapley values, and higher-order interaction effects under one consistent framework. By carefully cachi... | Rebuttal 1:
Rebuttal: Thank you for your comments.
In our response to the first reviewer we have conducted further experiments of our method on real data (see **1. Real-world application** in our response to R1). In our response to the second reviewer, we advice using shallow trees to reduce complexity for large-scale... | Summary: This paper proposes FastPD, an efficient, and consistent algorithm for estimating Partial Dependence (PD) functions in tree-based models. FastPD addresses computational inefficiencies present in existing methods by decomposing the estimation process into a two-step procedure: a tree augmentation step leveragin... | Rebuttal 1:
Rebuttal: Thank you for your comments, we address them below.
1. **Real-world applications:**
We have addressed this in our reply to the first reviewer. Please see **1. Real-world application** in our response to R1.
2. **High-complexity models:**
The computational complexity of both our algorit... | Summary: This paper introduces FastPD, a novel tree-based algorithm for estimating partial dependence (PD) functions, which are central for interpreting machine learning models via SHAP values. The paper identifies a critical limitation in the commonly used TreeSHAP-path method: while TreeSHAP-path is theoretically exa... | Rebuttal 1:
Rebuttal: Thank you for your comments, we address them below.
1. **Real-world application:**
- **Benchmark:**
For an additional comparison between `FastPD`, `FastPD-100`, `FastPD-50`, and the path-dependent method, we have now added a benchmark considering 33 regression and 29 classification dat... | null | null | null | null | null | null | null | null |
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design | Accept (poster) | Summary: This paper proposes Step-DAD, a semi-amortized approach to Bayesian experimental design (BED) that extends the existing Deep Adaptive Design (DAD) framework. While DAD pre-trains a fixed policy network before experimentation, Step-DAD allows for test-time adaptation of this policy during deployment, periodical... | Rebuttal 1:
Rebuttal: Thank you kindly for your helpful review.
>It would be nice to see a comparison with RL-based BED approaches such as the work by Blau et al. (2022). Given that the semi-amortized framework is architecture-agnostic, it would be valuable to see how Step-DAD principles could be applied to RL-BED me... | Summary: They propose a semi-amortized approach to Bayesian experimental design, in which a policy is learned offline (as in the standard fully amoritzed approach) and then is adapted online. This increases computational cost but results in more adaptive designs, which are more robust to model misspecification. The pap... | Rebuttal 1:
Rebuttal: Thank you kindly for your helpful review.
> For the eval metric, have you considered using synthetic data where the ground truth theta* is available, and then assessing distance between E[theta|hT) and theta* where hT is from a particular design policy?
Yes! Assessing the distance between the po... | Summary: The paper deals with Bayesian adaptive experimental design for identifying model parameters. Fully adaptive strategies are costly and myopic. Recent work has proposed amortized experimental design in which a neural net maps from observed data directly to the experimental design policy. That work, however, is n... | Rebuttal 1:
Rebuttal: Thank you kindly for your helpful review.
>I expected to see a plot showing the trade-off between achieved EIG and fine-tuning wall-time
Thank you for the great suggestion. We have now implemented this analysis, and the chart illustrating this trade-off can be found here: https://tinyurl.com/EI... | Summary: This paper introduces Step-DAD, a hybrid between traditional and fully amortized policy-based approaches to Bayesian Experimental Design (DAD) that retrains its policy on-line as it gathers additional observations. The authors argue that this allows to retain the benefits of policy-based BED while overcoming i... | Rebuttal 1:
Rebuttal: Thank you kindly for your helpful review.
> The Step-DAD setup is reminiscent of Model Predictive Control (MPC)... This approach is sufficiently related that this connection warrants discussion in the paper.
Thank you for drawing this perceptive connection and we agree there are nice analogues ... | null | null | null | null | null | null |
Trustworthy Machine Learning through Data-Specific Indistinguishability | Accept (poster) | Summary: This paper proposes a concept of (gaussian) data-specific indistinguishability (DSI), which relaxes Input-Independent Indistinguishability (or differential privacy in many sense) by enforcing constraints only for a set of pre-defined input pairs instead of globally.
Similar to what we already have for differe... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and valuable suggestions.
**1. Differential Trustworthiness (DT) vs. Differential Privacy (DP)**
We fully agree with your insightful comment: “Data-Specific Indistinguishability (DSI) and Differential Privacy (DP) (or Input-Independent Indistinguishability... | Summary: The paper aims at combining various privacy preserving mechanism for machine learning into a unified framework, for instance reducing memorization, providing copyright protection, differential privacy and so on. The main mathematical technique is to use Data Specific Indistinguishability which aims at providin... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful suggestions.
**1. Relationship Between Privacy and Differential Trustworthiness (DT)**
We fully agree with your insightful comment that (differential) privacy is recognized as a stronger guarantee, which, as a **sufficient** condition, can produ... | Summary: This paper proposes a unified framework for Differential Trustworthiness (DT), which models trust-related concerns in machine learning algorithms, such as memorization, data poisoning, and copyright issues. The framework aims to regulate the divergence between the outputs of a model trained on a target dataset... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and insightful questions.
**1. Comparison to other Noise-based Solutions**
We appreciate your references to prior works that apply noise for trustworthy machine learning. Existing noise-based solutions typically face two key challenges: **a) High utility o... | Summary: The idea of DSI is equivalent to per-instance privacy (see "Y.-X. Wang. Per-instance differential privacy. Journal of Privacy and
Confidentiality," or the equivalent individual privacy of "V. Feldman and T. Zrnic. Individual privacy accounting via a renyi filter. Advances in Neural Information Processing Sys... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions!
**1. Comparison with (Per-Sample/Individual) Differential Privacy (DP)**
We believe there are at least three fundamental differences between Differential Trustworthiness (DT) and Differential Privacy (DP).
1-1) First, on **motivation**, thoug... | null | null | null | null | null | null |
Variational Learning of Fractional Posteriors | Accept (poster) | Summary: The authors introduce a novel variational inference method based on fractional posteriors, parameterized by a single scalar
γ∈(0,1). This approach generalizes Bayesian variational inference by tempering the likelihood term, leading to fractional posteriors that provide improved calibration and robustness. It e... | Rebuttal 1:
Rebuttal: > some claims … insufficiently supported by the empirical
Lines 12 (right) and 87 (right) are general remarks on robustness of fractional posteriors, citing others. Statement on line 44 (right) follows these, but may be misunderstood. We will change to “an alternative to”.
Alignment with prior i... | Summary: The paper introduces a novel one-parameter variational objective that generalizes the standard evidence lower bound (ELBO) by enabling the estimation of fractional posteriors. By leveraging Hölder’s inequality, the authors derive a new bound L₍γ₎ which recovers the conventional ELBO in the limit as γ → 1. The ... | Rebuttal 1:
Rebuttal: ### Relation To Broader Scientific Literature
Thank you. We will try our best to incorporate these into the paper.
### Other Strengths And Weaknesses
> The method involves nested integrations and Monte Carlo estimates (especially in the hierarchical and semi-implicit formulations), which may lea... | Summary: The paper proposes a variational objective targetting fractional posteriors based on the Holder inequality instead of the more typical Jensen's inequality approach. Furthermore, for hierarchical models, minor variations of the variational objective are considered. The utility of the approach is demonstrated th... | Rebuttal 1:
Rebuttal: We suspect misunderstandings. We ask for further clarifications below.
### Claims & Evidence
> contributions ... confounded
Our key contribution is a lower bound that _also_ approximates fractional posteriors. Having both at once is new in ML and statistics. Moreover, ELBO is a special case. So... | Summary: The classical variational inference (VI) often underestimates the uncertainty, motivating the research of generalized Bayesian inference. Nevertheless, the theoretical connections between generalized Bayesian inference bounds and the marginal likelihoods are only established approximately/asymptotically, hinde... | Rebuttal 1:
Rebuttal: ### Methods And Evaluation Criteria
> For the methodology, only the derivation of $\mathcal{L}^{bh}$ is not clear to me. As in A.3, there are two possible objectives, but the main text uses the one with both $\boldsymbol{u}$ and $\boldsymbol{u}$’ in the integrand. I do not see why this one is ch... | null | null | null | null | null | null |
Revisiting Cooperative Off-Policy Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper studies the extrapolation error in off-policy multi-agent reinforcement learning that is caused by the curse of multi-agent. To mitigate the errors, the paper proposed to focus on target estimation error (TEE). To further address the TEE, 3 different approaches, annealed multi-step bootstrapping, av... | Rebuttal 1:
Rebuttal: We are thankful for your time and effort in reviewing our paper, which has greatly helped us improve the quality of our paper. We were glad to hear that you found our proposed methods sensible and that our empirical results demonstrated promising improvements over baseline approaches.
Below, we ad... | Summary: The work studies the problem of overestimation and target estimation errors in off-policy multi-agent reinforcement learning (MARL). The work first outlines how action-value estimation in MARL often suffers from estimation errors as a consequence of the exponential growth of the joint action space. To substant... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, especially for your detailed feedback on the evaluation of TEE and the impact of our proposed techniques. We were glad to hear that you found our work well-structured, clearly motivated, and systematic empirical analysis. Below, we address the key concerns... | Summary: This paper identifies a problem of erroneous Q-target estimation, primarily caused by extrapolation errors, which worsens as the number of agents increases. The authors follow the previous work on single-agent error decomposition and apply it to multi-agent Q-learning, decomposing the error into Target Approxi... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, which has greatly helped us improve the quality of our paper. We were glad to hear that you found our claims convincing and that our proposed methods make sense for addressing the problem. Below, we address the key concerns you raised.
> 1. The authors fr... | null | null | null | null | null | null | null | null |
Visual Autoregressive Modeling for Image Super-Resolution | Accept (poster) | Summary: This paper proposes VARSR, a visual autoregressive framework for image super-resolution (ISR), addressing the trade-off between fidelity and realism. By leveraging next-scale prediction, prefix tokens for LR conditioning, scale-aligned rotary positional encodings (SA-RoPE), and a diffusion refiner for quantiza... | Rebuttal 1:
Rebuttal: **A1. Implementation details.**
Thank you for your suggestions. We will elaborate further on these details in our supplementary materials, for example, the MLP architecture of the diffusion refiner, which consists of linear and activation layers. Additionally, our code will be open-sourced to he... | Summary: This work presents a novel generative model for image super-resolution, leveraging visual autoregressive modeling. The generative process is conditioned on the low-resolution input, treated as a prefix token. To enhance performance, the authors introduce scale-aligned positional encoding and a diffusion-based ... | Rebuttal 1:
Rebuttal: **A1. Effectiveness of CFG.**
CFG leads to a certain reduction in fidelity metrics but significantly improves the perceptual quality of the image. Existing fidelity metrics have limitations in accurately measuring human perceptual quality, especially when the original HR image quality is low. In ... | Summary: This paper proposed a image super resolution framework that is built on top of the VAR framework -- next-scale prediction. They modify the original VAR architecture to digest tokens from low res image inputs. And then, they leverage the original VAR architecture to upsample until the highest granularity. Given... | Rebuttal 1:
Rebuttal: **A1. Comparison with more SOTA.**
BSRGAN and Real-ESRGAN are still commonly used SOTA GAN-based models due to their excellent performance. Other ISR works (e.g., SeeSR, PASD) also chose them as GAN-based baselines for comparison.
GigaGAN does not provide open-source models or code for testing ... | Summary: This paper presents VARSR, a Visual Autoregressive Model for Image Super-Resolution (ISR). VARSR leverages autoregressive modeling with next-scale prediction, prefix tokens for integrating low-resolution conditions, Scale-aligned Rotary Positional Encoding (SA-RoPE) for preserving spatial structure, and a Diff... | Rebuttal 1:
Rebuttal: **A1. Training with different datasets.**
Thanks for your comments. In fact, baseline methods are trained on different datasets (e.g., SeeSR uses LSDIR and PASD uses DIV2K/FFHQ, with a tenfold difference in data quantity). There are also differences in the pretrained models, which are important... | null | null | null | null | null | null |
When, Where and Why to Average Weights? | Accept (poster) | Summary: The paper studies weight averaging, a "trick" rooted from the old Polyak averaging that never disappoints practitioners from fitting logistic regression to training modern LLMs. This paper provides a relatively large-scale study on AlgoPerf, a benchmark suite for optimizer, aiming at understanding: 1. Whether ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. We appreciate your recognition of the value of our study! We address specific points below.
> The results for other claims are less thoroughly evaluated, e.g. the combination with Shampoo is only evaluated on one dataset. Comparision of WA with LR schedules (... | Summary: This submission presents the experimental findings and evaluation of two weight-averaging (WA) techniques, LAWA and EMA, on the AlgoPerf optimization benchmark, on seven tasks. They find strong training speed improvements (15% reduction in GPU-Hours), consistent with varying hyperparameters (learning rate and ... | Rebuttal 1:
Rebuttal: Thank you for the thorough review! We appreciate the detailed feedback.
> I think only studying two WA methods is a bit limited.
We agree that broader benchmarking would be valuable and hope this works encourages further study at scale. We focus on LAWA and EMA due to their success and adoption. ... | Summary: The authors benchmark weight averaging techniques using AlgoPerf, and find that without learning rate annealing it strongly accelerates training and generalization. It composes positively with learning rate annealing with small gains but cannot fully replace annealing.
Claims And Evidence: * Weight Averaging... | Rebuttal 1:
Rebuttal: Thank you for your review and for your insightful comments. We appreciate your recognition of the value of our study! We respond below to specific comments.
> It would be useful to discuss existing application of weight averaging a little bit, especially in relation to robust finetuning [1] and O... | Summary: This paper systematically explores two existing weight averaging techniques, EMA and LAWA, across the seven diverse tasks in the 2024 Algoperf competition. The authors find that weight averaging can either increase the final performance for a given compute budget or speed up training to specific performance th... | Rebuttal 1:
Rebuttal: Thank you for your comments! We respond to specific comments below, but please let us know if you have any additional questions.
> I do not agree that the paper shows how to optimally combine the two. What the paper does show is that combing standard weight averaging with standard learning rate s... | null | null | null | null | null | null |
Test-Time Canonicalization by Foundation Models for Robust Perception | Accept (poster) | Summary: The paper introduces FoCAL, a zero-shot framework for achieving approximate invariance to complex transformations at scale without requiring additional training. FoCAL operates in two steps: (1) generating multiple transformed variations of an input and (2) ranking them using energy functions derived from CLIP... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad you found value in our idea and we hope FOCAL can be a key step towards a zero-shot approach towards a wide range of visual invariance. We address your concerns and questions below:
- **Clarification question**: By DA, are you referring to data au... | Summary: The paper proposes to construct an energy measure from outputs of pretrained foundation models. This energy can be minimized over transformations of the input image to obtain a canonicalization of the image. The minimization can be done through bayesian optimization. The experimental results are good.
Claims ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We are glad you found our experimental results to be good and we hope FOCAL can be a key step towards a zero-shot approach towards a wide range of visual invariance. We respond to your concerns and questions below:
- **“If the downstream model is trained to... | Summary: The paper introduces FOCAL, a zero-shot framework designed to achieve invariant perception at test-time using pre-trained foundation models like CLIP and Stable Diffusion. FOCAL generates transformed versions of input images and selects a canonical version by minimizing an energy function derived from these mo... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We are glad you recognize our empirical strengths in canonicalizing images on a range of transformations, and we hope FOCAL can be a key step towards a zero-shot approach. We respond to your concerns and questions below:
- **“The paper lacks an analytical e... | Summary: This paper introduces Foundation-model guided Canonicalization (FOCAL), a novel zero-shot framework designed to enhance the invariance of vision models to various transformations by test-time optimization. The method generates candidate transformations (e.g., 3D novel views) and selects a canonical version by ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad you found our use of foundation models for optimizing the energy function clever and we hope FOCAL can be a key step towards a zero-shot approach towards a wide range of visual invariance. We address your concerns and questions below:
* **“‘The pr... | null | null | null | null | null | null |
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training | Accept (poster) | Summary: This paper introduces a conformal training algorithm called Direct Prediction sEt Minimization (DPSM). Existing training method suffers from a learning bound depending on the batch size. DPSM is formulated as a bilevel optimization that minimizes the prediction set size (upper level) conditioned on the learned... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review. We provide an **external PDF** [(click this link to access)](https://anonymousicml.tiiny.site) to present the empirical results needed to answer some of your questions.
**Q1: No theoretical results on the convergence of bilevel optimization are obt... | Summary: This paper introduces a novel training strategy for deep classifiers, leveraging insights from conformal prediction. Specifically, it proposes a new training loss function that is formulated as the sum of two components: a conventional loss function—aiming to enhance predictive accuracy—and a (regularized) con... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review. We provide an **external PDF** [(click this link to access)](https://anonymousicml.tiiny.site) to present the empirical results
**Q1: Evaluation: No conditional coverage**
A1: Please refer to A2 for Reviewer aEdV and **Fig 4 in external PDF** for ... | Summary: This paper introduces the Direct Prediction Set Minimization (DPSM) algorithm, a novel bilevel optimization approach that minimizes prediction set sizes in conformal classifier training with a learning bound of $O(1/\sqrt{n})$, surpassing prior methods limited by $\Omega(1/s)$. Experimental results on benchmar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review. We provide an **external PDF** [(click this link to access)](https://anonymousicml.tiiny.site/) to present the empirical results needed to answer some of your questions.
**Q1: Assessing the score function with fixed hyper-parameter settings provide... | Summary: This paper proposed a new method, called DPSM, for minimizing the size of the prediction set for conformal prediction (CP) via bi-level optimization. The main idea is to reformulate the quantile estimation in CP into an optimization problem, such that it can be treated as a lower-level optimization, while the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review. We provide an **external PDF** [(click this link to access)](https://anonymousicml.tiiny.site) to present the empirical results needed to answer some of your questions.
**Q1: Learning bound not empirically tested? Questionable if achievable due to ... | null | null | null | null | null | null |
Bi-perspective Splitting Defense: Achieving Clean-Seed-Free Backdoor Security | Accept (poster) | Summary: This paper addresses poisoning backdoor attacks. Specifically, the authors aim to challenge the assumption of accessing clean data in current backdoor defense literature, with the main idea of utilizing both easier-to-obtain target labels and clean, hard samples. They propose a Bi-perspective Splitting Defense... | Rebuttal 1:
Rebuttal: Thank you for your comment.
# R1 Assumption&Novelty
The so-called implicit assumption that `implicitly assumes ... backdoor data` was not a weakness of our paper, and it does not influence the novelty when taking [1] into account. See below for details.
## R1.1 Clarification
As the reviewer ma... | Summary: This paper proposes a backdoor attack defense method, Bi-perspective Splitting Defense (BSD), which does not rely on additional clean subsets. BSD utilizes semantic characteristics through open set recognition-based splitting (OOS) and loss statistics characteristics through altruistic model-based data splitti... | Rebuttal 1:
Rebuttal: Thank you for your comments.
**Extended Figure/Tables in anonymous link: https://postimg.cc/68rDsgN3.**
# R1 Understanding BSD's Robustness Against Clean-Label Attacks
First, in response to `Given that clean-label...single benchmark dataset?`, we provided the experiment results of BSD on GTSRB... | Summary: This paper introduces Bi-perspective Splitting Defense (BSD), a novel in-training backdoor defense framework designed to train robust models from poisoned datasets without requiring clean data. By integrating semantic and loss-based perspectives, BSD addresses critical limitations in existing defenses, partic... | Rebuttal 1:
Rebuttal: **Thank you for your constructive feedback and for recommending our paper for acceptance. We sincerely appreciate your thorough review and valuable insights. In response to your remaining concern, we present more baselines.**
# Additional results
As you suggested, which aligns with other reviewer... | Summary: This paper introduces a clean-data-free method, named BSD, to defend against backdoor attacks in deep neural networks. BSD employs two complementary perspectives: Open Set Recognition-based Splitting (OSS) which uses semantic information, and Altruistic Model-based Loss Splitting (ALS) which leverages loss sta... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We appreciate your recognition of our contributions.
We address your remaining concerns in the following sections.
# Potential Failure of Target Label Estimation
We would like to kindly emphasize that while target label estimation is an ... | null | null | null | null | null | null |
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines | Accept (poster) | Summary: The paper studies so called "joint-equivariant networks", which are neural networks that are simultaneously equivariant with respect to a group action on the input data as well as the parameter space. This can be viewed as a generalization of previously studied group-equivariant neural network, in the sense th... | Rebuttal 1:
Rebuttal: > How are your depth-n G-convolutional layers related to the group convolutional layers by Cohen et al?
Thank you for your question regarding concrete examples. We fully acknowledge the pioneering contributions of Cohen and colleagues in the development of group-equivariant neural networks.
The ... | Summary: The main result of this paper is Theorem 3.10, which provides a closed form formula for a ridgelet transform of learning machines with joint-group-equivariant maps. Previous works have been deriving the closed form formula for the ridgelet transforms of depth-2 networks. This paper has generalized these result... | Rebuttal 1:
Rebuttal: > The authors mention several times that this result unifies the universal approximation theorem. But it is not clear how Theorem 3.10 can imply the universal approximation theorem, say the one established by (Pinkus, 1999).
There is no unique definition of "universality." In the context of machi... | Summary: This paper introduces a framework for proving constructive universal approximation theorems for general classes of neural networks. By invoking ideas from representation theory, the paper generalizes the ridgelet transform to a larger class of models which satisfy "joint G equivariance." As a consequence, the ... | Rebuttal 1:
Rebuttal: > This paper only handles the infinite width (i.e continuous) setting, and does not provide any quantitative results on how much width is needed for a certain architecture to approximate a target function, akin to the Barron norm in (Barron, 1993).
In our main result, the inverse operator for the... | Summary: The goal of the paper is to subscribe a general framework for proving universality type theorems for a generalized class of models, that the authors call joint-group-equivariant machines. Joint-group-equivariant machines are models consisting of a sequence of joint-group-equivariant features maps, maps from th... | Rebuttal 1:
Rebuttal: > On times the paper uses abstract mathematical language to give alternative descriptions to constructions which may obfuscate the reading (for example some category-theoretical parallels, whose validity I cannot testify). However, I find them redundant/complementary to the actual message of the p... | null | null | null | null | null | null |
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing | Accept (poster) | Summary: This work proposes a modified midpoint method, which is a well-known second order numerical solver. One drawback of midpoint method is that it needs an additional model evaluation for each sampling step, which makes the overall solver slow. This work instead proposes to piggyback on the previous model predicti... | Rebuttal 1:
Rebuttal: We sincerely appreciate the authors for carefully verifying our theoretical claims and constructive comments. Responses to your concerns as follow:
Q1: Include the original midpoint method's results in Table 4 and its inference time in Table 5.
- Although both ours and Midpoint are 2nd-order solv... | Summary: This paper proposes a low-cost alternative method for a second-order ODE solver aimed at Rectified Flows. Compared to the common second-order ODE solver, which requires $2T$ NFEs, this method only needs $T+1$ NFEs while maintaining the sampling quality of the second-order ODE solver. The core idea is to replac... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s detailed feedback and constructive suggestions for improving our paper. Below are our responses to your concerns:
Q1: As shown in Table 2, the effect of the 20-step method should be compared again to observe how much better its solver performance is compared... | Summary: The authors proposed a second-order ODE solver to speed up inversion and reconstruction of flow-based model. It reused saved midpoint velocity so the computation efficiency remains the same as first-order ODE. It is proven to be faster in speed and higher in quality, and also benefit image editing by a large m... | Rebuttal 1:
Rebuttal: We sincerely appreciate the authors for carefully verifying our theoretical claims, especially given the heavy review process. We will do our best to address your concerns.
Q1: It is a fully training-free method which does not involve 'learning' methods which are better aligned with ICML.
- We re... | Summary: This paper introduces FireFlow, a fast inversion and editing method for Rectified Flow (ReFlow) models, designed to enable accurate image reconstruction and semantic editing with minimal computational overhead, enabling fast, high-fidelity image editing while fully leveraging the model's inherent linear motio... | Rebuttal 1:
Rebuttal: Q1: Results on PIE-Bench are reported with fixed seed? Variance across multiple runs is not reported.
- Thank you for your valuable feedback. To clarify, T2I generation starts from random noise determined by seed, while inversion-based editing starts from a fixed image as the initial point in ODE ... | null | null | null | null | null | null |
Symmetry-Aware GFlowNets | Accept (poster) | Summary: This paper aims to rectify equivalent actions in the graph generation process of GFlowNet. In GFlowNet’s graph generation, actions that result in identical graphs are considered distinct actions, leading to incorrect learning of their probabilities. The paper presents a theoretical framework for these actions ... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback and for recognizing our work as a significant contribution to the community!
---
## **Time Comparison with the Positional Embedding (PE)**
**Since PE must be computed at every transition, its computational cost scales poorly with the trajectory horizon.** To e... | Summary: The authors claim that when applying GFlowNets to the graph generation problem, the presence of equivalent actions—where different actions produce the same graph structure—introduces additional bias.
Unlike previous works that require computationally expensive calculations at each iteration, the authors propos... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback and your recognition of our work's potential impact in this area!
---
## **Impact of Incorrect Automorphism Counting**
While we are uncertain about the conditions under which the *bliss* algorithm might fail, we believe it produces exact outputs g... | Summary: The authors propose a formal characterization of the equivalent action problem, first outlined by Ma et al. (2014), along with a simple fix by re-scaling the reward to account for automorphisms.
Claims And Evidence: To my best judgment, the work is theoretically sound, and the authors provide compelling evide... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback, and recognizing our work as important.
---
## **Comparison with Ma et al. (2024)**
### **Implementation of Positional Encoding (PE) method**
**To better position our work, we implemented the PE method to compare with Ma et al. (2024)** and made our best e... | Summary: This paper addresses the equivalent action problem in GFlowNets for graph generation, providing a theoretical foundation and solution to a bias previously identified in Ma et al. (2024). While the paper offers valuable theoretical insights and formal proofs, the core solution (reward scaling based on automorph... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments and valuable feedback. In particular, we sincerely hope that our response helps evaluation recognizing our contributions, especially in providing a rigorous theoretical foundation and conducting extensive experiments on the problem.
---
## **Clarif... | null | null | null | null | null | null |
Enhancing Target-unspecific Tasks through a Features Matrix | Accept (poster) | Summary: Partial parameter optimization methods face challenges in handling target-unspecific tasks due to overfitting, which causes the model to lose its general knowledge essential for these tasks. To address this issue, this paper proposes a regularization technique using a Feature Matrix (FM). Through extensive eva... | Rebuttal 1:
Rebuttal: # **Thanks for review**:
Thank you very much for the reviewer's appreciation of our work. Thank you to the reviewer for your valuable time for our paper, we express our **heartfelt** gratitude.
## Response：
Our work is based on pre-trained CLIP (ICML2021), and learnable and non learnable prompt... | Summary: The paper proposes to mitigate the overfitting observed in parametric optimization methods when optimizing on the target domain. To address this issue, the authors introduce a feature matrix-based approach. This method leverages features extracted from multiple handcrafted prompts, combined with features from ... | Rebuttal 1:
Rebuttal: # **Thanks for review**:
We express gratitude to the reviewers: 1) for providing rich and **comprehensive** comments of our work, 2) for investing a significant amount of time and effort in reviewing our work, and 3) for providing practical guidances. We will add acknowledgements in the final ve... | Summary: ## Summary
This paper addresses out-of-distribution generalization problem in prompt fine-tuning "CLIP" kind of model. The challenge is common: fine-tuning on a specific task boosts the performance in this task, but hurt the performance on other general tasks.
This work addresses this problem by adding a r... | Rebuttal 1:
Rebuttal: # **Thanks for review**:
Thank you to the reviewer for taking time to take detailed research and valuable reference on our work. We would like to express our **gratitude**. We will also **carefully** revise according to the opinions of the reviewers.
In addition, thank you to the reviewer for pr... | Summary: This paper proposes a Features Matrix regularization method to improve model performance on target-unspecific tasks. FM preserves general knowledge and reduces overfitting by extracting and leveraging semantic information from diverse inputs. The approach, compatible with existing frameworks, enhances generali... | Rebuttal 1:
Rebuttal: # **Thanks for review**:
We thank the reviewer for the valuable time and consideration of our manuscript. The comments provided by the reviewers are very useful for our work, we express our heartfelt **gratitude**.
## Response:
>**Q1**: The detailed explanation of the illustrations can be i... | null | null | null | null | null | null |
Diff-MoE: Diffusion Transformer with Time-Aware and Space-Adaptive Experts | Accept (poster) | Summary: The paper introduces Diff-MoE, a framework integrating DiT with MoE to enhance scalability and performance in generative modeling. The proposed modules in Diff-MoE are specially designed for diffusion model, including the spatial-temporal adaptive experts and global feature recalibration. Extensive experiments... | Rebuttal 1:
Rebuttal: Q1: Typo on Tab.5.
We apologize for the typo in Table 5. The correct FID and IS scores for "+GLU" should be 38.20 and 41.43, respectively. The ability of GLU to enhance model capacity compared to a simple MLP has been discussed in works such as Llama [1] and StarNet [2]. We will fix this typo in ... | Summary: Diff-MoE introduces a novel integration of temporal and spatial adaptation in MoE for diffusion models. The module proposed in Diff-MoE takes into account the characteristics of the diffusion model. The experimental results are also impressive, proving the effectiveness and scalability of Diff-MoE.
Claims And... | Rebuttal 1:
Rebuttal: Thanks for the thorough reviews. Below we try to solve issues one-by-one.
Q1: Typo on Tab.5.
We apologize for the typo in Table 5. The correct FID and IS scores for "+GLU" should be 38.20 and 41.43, respectively. The ability of GLU to enhance model capacity compared to a simple MLP has been dis... | Summary: This paper introduces Diff-MoE, whihc is a novel framework combining Diffusion Transformers with Mixture-of-Experts to enhance scalability and flexibility in generative modeling. It achieves better FID scores across different model sizes compared to standard DiT models.
Claims And Evidence: Yes, most of the c... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments and the recognition of novelty.
Q1: Limited evaluation at extreme scales due to computational constraints.
We acknowledge the limitation in fully characterizing Diff-MoE's scaling laws and commit to conducting large-scale evaluations once additional computati... | Summary: This paper proposes Diff-MoE, a method that captures both timestep and spatial contexts for expert routing. The approach consists of 1) Expert-Specific Timestep Conditioning – Unlike previous spatial MoE approaches, this enables each expert to adapt its operations based on the timestep, improving adaptability ... | Rebuttal 1:
Rebuttal: Thanks for the thorough reviews. Below we try to solve issues one-by-one.
Q1: Code release.
We sincerely appreciate the insightful feedback and recognition of this work. As astutely noted, training stability remains a critical challenge in MoE architectures, where conventional load-balancing ... | null | null | null | null | null | null |
Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation | Accept (poster) | Summary: This paper introduces GRATIN, a novel graph data augmentation method using Gaussian Mixture Models (GMMs) to enhance the generalization of Graph Neural Networks (GNNs) for graph classification. It provides a theoretical framework analyzing the impact of augmentation on GNN generalization via Rademacher complex... | Rebuttal 1:
Rebuttal: Response to Reviewer wgAh
==============
We thank Reviewer wgAh for the feedback. In what follows, we address the raised questions and weaknesses point-by-point.
***[Weakness 1] Section 3 Clarity*** We appreciate the feedback and will do our best to improve the organization and clarity of Sectio... | Summary: The authors propose a novel graph data augmentation method, GRAFIN, which leverages Gaussian Mixture Models (GMMs) to learn the distribution of hidden representations generated by a trained Graph Neural Network (GNN). The method then augments the training data based on this learned distribution. Furthermore, t... | Rebuttal 1:
Rebuttal: We thank Reviewer eSPE for their review. In what follows, we address the raised questions point-by-point.
**[R1] Penultimate Inequality in the Proof of Thm 3.1**
Thank you, indeed, the inequality can be replaced with an equality. We will update the proof in the camera-ready (CR) version.
**[R2, ... | Summary: This paper introduces GRATIN, a novel graph data augmentation approach leveraging Gaussian Mixture Models (GMMs) to enhance the generalization and robustness of Graph Neural Networks (GNNs). The authors argue that GNNs often face challenges in generalizing to out-of-distribution (OOD) data, especially with lim... | Rebuttal 1:
Rebuttal: Response to Reviewer 4GM3
======
We thank Reviewer 4GM3 very much for their careful review. In what follows, we answer point-by-point.
**[S1 and Q1] Robustness Exp.** While our primary contribution lies in improving generalization via data augmentation, we also address robustness as a secondary ... | null | null | null | null | null | null | null | null |
The Relationship Between No-Regret Learning and Online Conformal Prediction | Accept (poster) | Summary: The paper investigates the relationship between coverage and various definitions of regret in online learning. The results of this investigation are as follows:
- Sublinear regret with respect to the pinball loss implies coverage guarantees if the data sequence is i.i.d. and a smoothness condition holds on the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We address your main concerns here:
1. **Benefits / Relevance of our work:** In regards to the guarantees of our paper, we wish to clarify a misconception the reviewer may have. Our goal in this paper is to elucidate the relationship between regret and cove... | Summary: - This paper studies the relationship between no-regret learning and online conformal prediction
- They show that no-external regret guarantees imply non-trivial coverage guarantees if data is selected stochastically. Moreover, this is *not* true if the data stream is adversarially generated.
- Moving beyond ... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We will fix the typos you’ve pointed out, and address your other specific concerns / questions below:
1. **$\Pi_T$ as a random variable:** We apologize for any confusion here - In Theorem 3.2, $\Pi_T$ is a realization of the transcript where $(x_t, y_t)$ are drawn fr... | Summary: This paper explores the connection between online conformal prediction, which aims to construct prediction sets that cover the true labels with a specified probability, and online learning algorithms minimizing the pinball loss, which aim to achieve a certain regret guarantee. The authors show that standard ex... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed comments! First we would like to note a small change to the statement of Theorem 3.5. It should read: $|Cov(\Pi_T, G_\tau) - q| \leq \frac{\rho}{2} + \frac{\rho}{rn} + \sqrt{\frac{2\gamma}{T_{G, \tau}\alpha r} + \frac{\rho}{\alpha}\left(\frac{1}{r} + \fra... | Summary: This paper explores how to make better predictions with reliable uncertainty estimates in challenging, real-world settings—like when data changes over time or comes from many different groups. It focuses on *conformal prediction*, a method that builds a set of likely outcomes for each input, with a guarantee t... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback! We will try to address your concerns and comments.
First, as a clarification: there are two kinds of relationships we develop in this paper - (1) the relationship between the *properties* of no-regret and of conformal coverage guarantees, agnostic to the algo... | null | null | null | null | null | null |
EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control | Accept (poster) | Summary: The authors propose EvoControl, a hierarchical, bi-level policy network for motor control. EvoControl separates low-level high-frequency control and high-level, low-frequency control, by training two separate policies which output actions at different frequencies. The high-level policy is trained with RL, faci... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. Below, we address each concern:
> do the environments considered for the evaluation have the characteristics requested by the constructive proof of prop. 2.1?
Thank you; **Prop. 2.1** formally motivates that certain C... | Summary: The paper learns high-frequency control with a two-level structure. A high-level policy working at a lower frequency is trained with PPO. A low-level high-frequency policy is obtained with evolutionary algorithms. The two-level design works better than directly training a single high-frequency control policy w... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. Below, we address each concern:
> not clear whether it directly suits control applications. Specifically, what is the inference speed of the low-level policy? Can it be deployed at its desired frequency in the real wo... | Summary: The manuscript presents a novel bi-level optimization reinforcement learning method for high frequency control. The method combines a high-level low frequency policy with a low-level high frequency controller. Both controllers are learnable. The authors motivate the bi-level learning/optimization scheme. Overa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. Below, we address each concern:
> authors do not really motivate why such MDPs exist, and why they are important
### (A) Why Such MDPs Exist
Our Proposition 2.1 is meant to show that there are environments/continuous-time MDPs... | Summary: The paper introduces a bi-level policy and training method for high-frequency and continuous-time control. The bi-level policy consists of a high-level policy that operates at a low frequency and issues a latent action. The low-level policy decides the final action based on the environment state and the latent... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback, particularly their appreciation that all the claims are well supported and the paper does a tremendous job supporting the findings, with the idea being novel and interesting. Below, we address each concern:
> fixing the total ti... | null | null | null | null | null | null |
Active feature acquisition via explainability-driven ranking | Accept (poster) | Summary: **Edit:** Thank you to the authors for their response. Since repeated experiments have been run, an RL baseline, and reproducibility details have been provided, I have raised my score from 2 (weak reject) to 3 (weak accept). There are remaining points which I have detailed in the Rebuttal Comment, if they are... | Rebuttal 1:
Rebuttal: Theoretical concern and Q5: We acknowledge that our original description of the oracle definition may have been misleading and we clarify the distinction here. Our oracle is not a purely greedy policy. Rather, it first identifies the optimal subset of features that minimizes prediction loss under ... | Summary: This paper tackles the problem of active feature acquisition, a setting where all features might not be available at inference and the model needs to make accurate predictions while minimizing the number of features acquired. The authors propose a framework that dynamically selects instance-specific features b... | Rebuttal 1:
Rebuttal: Methods and evaluation criteria: The reviewer raises a thoughtful point about the role of the second-stage training. The first stage provides clean supervision through ground-truth feature importance rankings generated by explanation methods. However, during inference, the policy must operate on p... | Summary: The authors propose an active feature acquisition (AFA) framework that selects features based on their importance to each individual case. The method leverages local explanation techniques to generate instance-specific feature importance rankings. The authors reframe the AFA problem as a feature prediction tas... | Rebuttal 1:
Rebuttal: Response to W1: We agree that the performance of our AFA framework is influenced by the accuracy of local explanation methods. However, our experiments show that rankings generated by widely used explanation techniques (e.g., SHAP, LIME) consistently match or exceed the performance of rankings der... | Summary: This paper introduces a novel approach to active feature acquisition (AFA) by reframing the problem as a feature prediction task guided by explainability-driven rankings. Specifically, the authors leverage local explanation methods (e.g., SHAP, LIME, TreeSHAP) to generate instance-wise feature importance ranki... | Rebuttal 1:
Rebuttal: Response to W1 & Q1: We appreciate the reviewer’s comments on the potential variability of post-hoc explanation methods and their impact on the robustness of our learned acquisition policy. To address these concerns, we conducted several robustness evaluations. As shown in Table 3, our method perf... | null | null | null | null | null | null |
Layer-wise Quantization for Quantized Optimistic Dual Averaging | Accept (poster) | Summary: *I am not familiar with this line of research, so my confidence in the following review is limited. While I will provide feedback based on my understanding, please keep in mind that my assessment may not be entirely precise.*
- The paper provides theoretical guarantees for layer-wise quantization and demonstr... | Rebuttal 1:
Rebuttal: Thank you very much for your time and your comments on our work. We will address your concern as follows:
Q1: To my understanding, the QODA algorithm appears to be agnostic to the choice between layer-wise and global quantization. The authors present results for both approaches under the QODA-bas... | Summary: Authors develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training on variational inequalities (VIs) method. Authors first apply layer-wise minimizing quantization variance upon the quantization progress. Then aut... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments.
1: Regarding (1), we believe our significant speedup with 16 GPUs are sufficent for a **theory** paper on distributed VIs (DVIs). We list all the related theory DVIs works (in top ML conferences) and no. of GPUs used. [2] is the only one with 16 GPUs like us, ... | Summary: This paper introduces a layer-wise quantization framework that adapts to heterogeneities over the course of training (DNNs). Instead of applying a uniform quantization strategy across all layers, the proposed approach optimizes quantization sequences per layer with tight variance and code length. Building on t... | Rebuttal 1:
Rebuttal: Thank you very much for your time and your comments on our work. We will address comments in the following QnA format:
Q1: The paper does not analyze accuracy trade-offs-does layer-wise quantization affect generalization or model stability?
A1: We hope to clarify that in Figure 1, we show that o... | Summary: Based on theoretical analysis, this paper presents a layer-wise quantization framework that adapts to the unique properties of different neural network layers. This framework leads to the development of the Quantized Optimistic Dual Averaging (QODA) algorithm, which uses adaptive learning rates to achieve comp... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for reviewing and giving us feedbacks. We will address your comments in a QnA temmplate.
Q1: The motivation in terms of layer-wise quantization (which is directly related to Mishchenko et al., 2024 or [4]) in training could be further clarified, as layer-wise ... | null | null | null | null | null | null |
Enhancing Decision-Making of Large Language Models via Actor-Critic | Accept (poster) | Summary: This paper proposes a gradient-free Actor-Critic framework (LAC) to enhance the decision-making capabilities of LLMs. LAC integrates a value-based critic that offers quantitative feedback to guide policy improvement and employs a gradient-free optimization approach to update actor. Experimental results demonst... | Rebuttal 1:
Rebuttal: > `Q1: Lack of intuitive explanations and in-depth analysis.`
**A:** We appreciate the reviewer's emphasis on intuitive explanations and analyses. In Fig.10 and 11 (Appx.A.7), we have provided detailed illustrative analyses using representative examples from ALFWorld and BabyAI-Text, which demons... | Summary: The paper proposes LAC, a framework for improving decision-making capabilities of LLMs by integrating base LLM with action evaluations derived from token logits and trajectory rollouts. The authors conduct experiments across ALFWorld, BabyAI-Text, and WebShop benchmarks and show the superiority of LAC over bas... | Rebuttal 1:
Rebuttal: Thanks for your comments and valuable suggestions. Here we provide detailed explanations to address your questions.
> `Q1: Lack of discussion on failure cases. It would be nice to have a small section on the failure cases of using Q_llm (are there any specific kinds of tasks/trajectories where Q_... | Summary: To enable planning with a next-token generation autoregressive model, this paper proposes to evaluate each action by a critic.
Rather than directly self-judging actions, the critic ranks them based on the output logits, associated with the likelihood of predicting actions being good or bad. The policy is then ... | Rebuttal 1:
Rebuttal: Thanks for your comments and valuable suggestions. Here we provide detailed explanations and experimental results to address your questions.
> `Q1: Questions regarding gradient-free policy updates: (1) How many actions are evaluated per state? (2) Is the policy updated after each action, or in ba... | null | null | null | null | null | null | null | null |
Are Large Brainwave Foundation Models Capable Yet ? Insights from Fine-Tuning | Accept (poster) | Summary: The authors performed the analysis of the current Brainwave Foundation Models. The results indicate that current LBMs show limited improvement over traditional deep-learning models. The authors further introduced LoRA for the fine-tuning of LBMs. LoRA fine-tuning technique can sufficiently reduce the training ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful review and valuable feedback. As mentioned in our response to Reviewer QMtY, we have decided also to extend our analysis results to capture one more popular BCI paradigm, namely Motor. Therefore, we added a new movement benchmark based on th... | Summary: In this paper, the authors compare state‐of‐the‐art Large Brainwave Foundation Models (LBMs) with traditional deep learning baselines on multiple EEG‐based tasks and find only marginal accuracy gains despite a massive increase in parameters. They then apply Low‐Rank Adaptation (LoRA) to substantially reduce tr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful review and valuable feedback. As mentioned in our response to Reviewer QMtY, we have decided also to extend our analysis results to capture one more popular BCI paradigm, namely Motor. Therefore, we added a new movement benchmark based on th... | Summary: The paper evaluates the performance of two Large Brainwave Foundation Models, LaBraM and NeuroGPT, by fine-tuning them on multiple EEG-based benchmark tasks. The authors compare these LBMs to well-known deep learning baselines (e.g. EEGNet, EEGInception) and investigate parameter-efficient fine-tuning via Low-... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful review and valuable feedback. As mentioned in our response to Reviewer QMtY, we have decided also to extend our analysis results to capture one more popular BCI paradigm, namely Motor. Therefore, we added a new movement benchmark based on th... | Summary: An interesting perspective-style paper comparing state of the art EEG-focused ML models in traditional versus foundation application. The paper is well written and presents a solid comparison of two methods resulting in a statement state-of-the-art LBMs achieve only marginal improvements (0.5%) over tradition... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful review and valuable feedback. As the reviewer highlights we have meticulously selected various benchmark BCI paradigms datasets, reflecting the current state-of-the-art within the EEG community. We decided also to extend these results to cap... | null | null | null | null | null | null |
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse | Accept (poster) | Summary: The paper investigates the conditions under which CoT prompting, a widely used technique to improve the performance of LLMs/LMMs, can actually reduce model performance.
The authors draw inspiration from cognitive psychology, focusing on six tasks where verbal thinking (deliberation) has been shown to impair ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review.
**Essential References:**
“Include a more thorough discussion of prior work on the limitations of CoT prompting (e.g., Kambhampati et al., 2024).”
**Response:**
This paper is already cited in our related work (L124-126): “In related settings ... | Summary: This paper aims to explore the settings where CoT reduces performance from the perspective of cognitive psychology. It focuses on six tasks where extensive thinking affects human performance. For three of these tasks, the authors find that current models experience a performance drop when allowed more reasonin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging review!
**Weakness 1:**
“It is difficult to determine the generalizability of the findings, especially since each task is specifically designed for its respective category.”
**Response:**
**A. Generalizability within categories**
The studies we cho... | Summary: This work delves (no, not written by an LLM) into the conditions under which CoT works. Often in ML research, only positive results are presented, and the many things that don't work never see the light of day. The contribution of this work is firstly to make explicit that CoT doesn't always works, and more i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. We are also grateful that the reviewer appreciates our approach of providing our full results rather than cherry-picking cases.
**Claims and Evidence 1:**
“While the paper purports to "understand and predict" when CoT has a negative effect, the ... | Summary: As Chain-of-thought (CoT) prompting becomes a widely used practice, this paper aims to answer the limitations of the approach. Authors propose a "heuristic" for determining limitations of CoT by drawing a comparison between CoT prompting and humans engaging in verbal thought. Inspired by psychological literatu... | Rebuttal 1:
Rebuttal: **Methods And Evaluation Criteria 2:**
“I'd like to see more overall evaluations on whether the given heuristic is providing trustworthy predictions of CoT impairment.”
We agree with the reviewer that more depth is better. We add **4 additional experiments**:
1. Artificial grammar learning, vary... | null | null | null | null | null | null |
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks | Accept (poster) | Summary: This paper tackles the recurrent approach to the large action model, where current robotic policies are de facto implemented as transformers. The paper systematically analyzes xLSTM under various conditions and across different environments, demonstrating the superior performance of xLSTM compared to transform... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We appreciate your positive assessment and are very glad that you believe that our work may mark a potential turning point for real-world agent design.
**Real-World Experiments:** We agree that experiments in real-world robotics settings would be valuable... | Summary: This paper investigates various architectures, including Transformers, Mamba, and xLSTM, for reinforcement learning. Building on the Decision Transformer framework, it systematically compares these architectures across 432 tasks spanning six datasets. The empirical results highlight xLSTM’s advantages in both ... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback and positive assessment of our work.
**Imitation Learning:** We agree with the reviewer that studying modern recurrent architectures in settings other than the DT setting is important. In this work, our goal is to better understand whether modern recurrent bac... | Summary: The authors propose changing the Decision Transformer (DT) backbone from a transformer to the recently-proposed xLSTM. They perform large-scale experimentation and compare to other DT backbones.
Claims And Evidence: The authors contribute:
- A Large Recurrent Action Model (LRAM) using an xLSTM with favorable ... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We are glad that you consider our experiments well-founded and the paper well written.
**Embedding space:**
* Regarding your question on the embedding space, we want to clarify that Figure 5 is constructed using the aggregated hidden states (averaged across t... | Summary: The authors introduce a Large Recurrent Action Model (LRAM), which replaces traditional Transformer architectures with xLSTMs to address real-time robotic applications. They demonstrate that the proposed model achieves significant speed improvements without compromising performance. Experimental validation acr... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback on our work and your positive assessment. We are glad that you consider it a clear demonstration, robust experimental validation and valuable dataset pipeline. We address your open points in the following.
**Policy formulation:** We agree with the reviewer tha... | null | null | null | null | null | null |
On Fine-Grained Distinct Element Estimation | Accept (poster) | Summary: This paper considers the distinct elements problem in a distributed setting. There are $\alpha$ servers and a universe of $n$ elements, represented as a frequency vector $S$ over all elements in the universe. Each server has a subset $S_i$ of the items. The goal is for the server to send messages to a coordina... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and constructive criticism.
> Mridul Nandi, N. V. Vinodchandran, Arijit Ghosh, Kuldeep S. Meel, Soumit Pal, Sourav Chakraborty: Improved Streaming Algorithm for the Klee's Measure Problem and Generalizations. APPROX/RANDOM 2024
> It also uses subsampling ... | Summary: The submission provides a more detailed theoretical analysis of the Distributed Distinct Element Estimation problem and shows that under certain assumptions on the distribution of the data points across servers (in particular concerning the number of collisions), previous lower bounds can be overcome. The main... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment and thoughtful questions.
> The problem itself also seems well-studied, although I am wondering whether it is still relevant in contemporary ML applications. If the authors are aware of more recent applications of the problem in ML, they are wel... | Summary: The paper studies distinct element estimation in a distributed setting, where $\alpha$ servers each hold a subset of elements from $[n]$. The goal is to compute the total number of distinct elements approximately while minimizing communication cost.
For a $(1 + \epsilon)$-approximation, prior works establis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and the pertinent question regarding Algorithm 1.
> Is Algorithm 1 necessary? As stated in Line 58 on Page 2, a one-pass streaming algorithm for distinct element estimation can be transformed into the distributed setting, yielding a protocol with ... | Summary: In this paper the problem of distinct element estimation is studied. More precisely, we are given an universe [n] and $\alpha$ servers and each of the servers receives a subset of the universe. Now, the goal is to compute a $(1+\epsilon)$-approximation of the number of distinct elements using minimal communica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and insightful questions.
> For the experiments...more data sets can be generated
We agree that increasing the number of datasets could provide additional empirical validation. Given the structure of the data, we anticipate that the overall patte... | null | null | null | null | null | null |
Causal Abstraction Learning based on the Semantic Embedding Principle | Accept (poster) | Summary: The authors use a category theoretical formalization SCM and causal abstraction to derive and optimize similarity measures over the measurable spaces of observed distribution of the corresponding low- and high-level representations of data.
Towards this existing notions of $\alpha$-abstractions and constructi... | Rebuttal 1:
Rebuttal: Thank you to the Reviewer for their effort, valuable comments, and appreciation of our work. We address below all the Reviewer’s concerns, also providing an additional theoretical contribution in [Q2]. We are happy to further discuss any additional concerns.
_Claims_
See [W1-W2].
_Experimental ... | Summary: This paper introduces a framework for learning causal abstractions (CA) when structural causal models (SCMs) are unknown, interventional data is unavailable, and observational data is misaligned. The authors proposed the Semantic Embedding Principle (SEP), which helps to reconstruct the relationship between th... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort, valuable comments, and appreciation of our work. We address below the Reviewer’s concerns in a concise manner due to text limit. We are happy to further discuss any additional concerns.
__Weaknesses__
__[W1]__
The Reviewer is right, in real-world applicat... | Summary: This paper addresses the challenge of learning causal abstractions (CAs) between structural causal models (SCMs) at different resolutions, a critical task for bridging causal evidence across scales (e.g., molecular vs. organism-level processes). The authors propose the Semantic Embedding Principle (SEP), which... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort and valuable comments. We address below the Reviewer’s major concerns on the relationship between CA learning and CRL, and we are happy to further discuss to clarify any additional points.
__Claims And Evidence__
Causal abstraction is intuitively introduced... | Summary: This paper formalises the problem of causal abstraction in category theory language, then it introduces the Semantic Embedding Principle (SEP). Intuitively, SEP states that if we go from a high level model to a low level and then abstracting back, one should get the initial high level model back, the way I und... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort, valuable comments, and appreciation of our work. We address below the Reviewer’s concerns. We are happy to further discuss to dispel any additional concerns.
__Claims And Evidence__
__[Our claims]__ Our claims are highlighted in the “Contributions” paragra... | null | null | null | null | null | null |
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding | Accept (poster) | Summary: This paper introduces MedXpertQA, a challenging benchmark comprising 4,460 clinical medical questions. Comparing to previous simple Question-Answering (QA) datasets, MedXpertQA conducts difficulty and diversity filtering to ensure the data quality.
Additionally, the authors evaluate frontier LLMs on the propo... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and hope to further address your concerns.
# Response 1 - Experimental Designs Or Analyses
### **1.1 Evaluation on Different TTS Methods**
Thank you for the valuable suggestion. We will include these comparisons in the next versions of the paper. At the sa... | Summary: Authors present a new expert-level knowledge and reasoning benchmark for real-world clinical scenarios. It seems to be the largest Multi-modal dataset in this category (with human annotations). It also is the second largest in the text only category. There seems to be a new barrier in this particular task with... | Rebuttal 1:
Rebuttal: **Thank you very much for your recognition of our work and your valuable suggestions!**
# Response 1 - Other Strengths And Weaknesses
**You make a great point. Please refer to Response 1.2 (Specialist Model Results) to Reviewer vM2L. Thank you for your understanding!**
# Response 2 - Other Comme... | Summary: The paper introduces MedXpertQA, a novel and challenging benchmark for evaluating expert-level medical knowledge and reasoning. MedXpertQA consists of 4,460 questions covering 17 medical specialties and 11 body systems, divided into text-based (Text) and multimodal (MM) subsets. The authors employed a rigorous... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments!
# Response 1 - Claims And Evidence
### **1.1 Leakage Prevention**
First, we note that data leakage prevention is an extra precaution we took on top of our main contribution, a challenging, clinically relevant benchmark. Models' subpar performance on MedXpertQA ... | Summary: In this work, the authors contribute a new synthetic dataset for the evaluation of medical reasoning of large language models (LLM), and the newest models in this class, also called large reasoning models (LRM). The creation of the dataset follows several steps that are well described, to ensure the benchmarki... | Rebuttal 1:
Rebuttal: We hope that our clarifications fully address your concerns!
# Response 1 - Claims And Evidence
### **1.1 Target Audience**
- We agree with the value of expert insight. We worked closely with medical practitioners when designing and reviewing MedXpertQA.
- Audience from an AI background is irrepla... | null | null | null | null | null | null |
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion Models | Accept (poster) | Summary: The paper presents an adversarial attack on image-to-image diffusion models to generate NSFW content. The authors train a Variational Autoencoder (VAE) to encode NSFW content into clean images and introduce an adaptive attack method to circumvent existing NSFW defense mechanisms. Through experiments on two ima... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments. Below, we thoroughly address each point with additional experiments and clarifications.
**W1:** Experiments mostly conducted in a white-box setting; limited effectiveness on SDv3.0.
**A1:** We have conducted additional black-box experiments to evaluate tran... | Summary: - This paper proposes AdvI2I, a novel framework that induces diffusion models to generate NSFW content using adversarial images.
- It circumvents existing defense mechanisms, such as Safe Latent Diffusion (SLD), without modifying text prompts, underscoring the urgent need for stronger security measures to prev... | Rebuttal 1:
Rebuttal: Thank you for insightful feedback. We have conducted additional analyses and experiments to address your concerns.
**W1:** The robustness of the method against adversarial defense strategies, such as DiffPure.
**A1:** We have now evaluated the robustness of AdvI2I against DiffPure as suggested. ... | Summary: This paper proposes AdvI2I, a framework for adversarial image attacks on image-to-image (I2I) diffusion models to induce NSFW content generation without modifying text prompts. By training a generator to inject perturbations aligned with NSFW concept vectors (extracted via contrastive text pairs), AdvI2I bypa... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and suggestions.
**W1:** Concerns regarding sample transferability.
**A1:** Our training and test samples were randomly split, meaning images are not entirely overlapping. We have also verified sample transferability of AdvI2I on unseen data (new images an... | null | null | null | null | null | null | null | null |
How to set AdamW's weight decay as you scale model and dataset size | Accept (poster) | Summary: This paper proposes a simple framework for understanding weight decay in AdamW through its similarity to an exponential-moving-average of weight updates. The hypothesize that the timescale of the EMA (analogous to the half life) is a useful quantifier of the training dynamics. In particular, the authors show t... | Rebuttal 1:
Rebuttal: Thanks for your extraordinarily extensive and thoughtful review! We agree with all your points and have extensively updated our working manuscript in response.
## Claims And Evidence
**Approximate EMA:** Fixed.
We do NOT **assume that the initial weights are zero in Eq 9**! We assume that the ... | Summary: This paper studies the AdamW optimizer. the authors provide empirical studies on the hyperparameters of the AdamW under different settings. Specifically, the paper first reformulate the AdamW itself is an EMA. then provide experiments on resnet, vit and LLM training, showing some empirical weight decay paramet... | Rebuttal 1:
Rebuttal: Thanks for your careful review.
# Value of the EMA perspective
We fully agree that the reformulation of AdamW to EMA is almost trivial.
Our key insight was noticing that this almost trivial connection provides novel, powerful insights into hyperparameter transfer for weight decay, that we then v... | Summary: The authors study the scaling behavior of the optimal AdamW weight decay hyperparameter with respect to model and dataset sizes. They provide a theoretical insight by framing AdamW's learned weights as an exponential moving average (EMA) of recent updates, identifying the EMA timescale as the key underlying hy... | Rebuttal 1:
Rebuttal: Thanks for your extremely positive review!
Great catch with Chen et al. (Theorem B.6) and Liu et al. (Theorem A.1)! We have added a discussion of these papers to our working draft and adjusted our contributions section. In short, we aren't surprised that someone has used an EMA-like result/form a... | null | null | null | null | null | null | null | null |
Discrete Neural Algorithmic Reasoning | Accept (poster) | Summary: This paper addresses the problem of neural algorithmic reasoning, where the objective is to train a neural network to mimic each step of a given classical algorithm. The authors propose a novel architecture for this task. They partition the input graph instance into discrete and continuous components and proce... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive review and positive feedback! We address the questions below.
> The performance on other algorithmic tasks in the benchmark dataset should be presented
Let us highlight the main part of our contribution: we consider the proposed model in its current for... | Summary: Neural reasoners are robust to the noisy data but struggling with out-of-distribution data. Classic symbolic algorithms have complementary features – they are crisp to noisy inputs, but applicable for any out-of-distribution data.
Authors propose a novel approach that guides neural reasoners to maintain the e... | Rebuttal 1:
Rebuttal: Thank you for the detailed review! We addressed the questions and concerns below.
> I am not very clear about the detailed process that described from Section 3.3 and 3.4. It would be nice that authors can give a concrete example in the supplementary material.
For example, consider the Dijkstra ... | Summary: This paper introduces a novel approach to neural algorithmic reasoning by forcing neural networks to maintain execution trajectories as combinations of finite predefined states.
Claims And Evidence: I think the claims made in this submission are strongly supported by the experimentation. The author demonstrat... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback! We address the raised concerns below.
> The approach sacrifices some expressivity for perfect generalization, which may limit its applicability to certain problems
Let us highlight the main part of our contribution: we consider the proposed model in its cu... | Summary: The authors define a learning paradigm where they force a neural reasoner to stay exactly on an execution trajectory as provided by the algorithm they aim to imitate, thus achieve perfect generalization. The architecture allows for verification.
They highlight three crucial architectural choices:
feature discr... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper! We address your questions and concerns below.
> The method mostly needs supervision from the algorithms they aim to mimic to work well
Let us note that for the current state of the field, learning with hints is an important and unsolved problem. E.g., the ... | null | null | null | null | null | null |
It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design | Reject | Summary: This work aims to generate synthesizable molecules that meet multi-parameter optimization (MPO) objectives while simultaneously adhering to a predefined set of building blocks. They design reward functions based on chemistry principles and introduce the TANGO reward function to generate synthesizable molecules... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We respectively suggest that our work has been misinterpreted and we want to clarify our framework.
## **1. Clarify retrosynthesis uses single-step model + search algorithm**
We will clarify this in the updated manuscript.
## **2. Starting-... | Summary: Through this paper, the authors propose TANimoto Group Overlap (TANGO), a reward function for constrained synthesizable molecule generation based on reinforcement learning. The proposed TANGO augments molecular generative models to directly optimize for constrained synthesizability while simultaneously optimiz... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their feedback and for the opportunity to clarify our work.
`FP` = fingerprint
`NN` = nearest-neighbor
## **1. Why is constrained synthesizability important?**
We kindly refer to our response to **Reviewer 5JW4**.
## **2. Why existing synthesizable design works c... | Summary: The paper "It Takes Two to Tango" introduces a new approach to generative molecular design that explicitly optimizes for synthesizability under real-world constraints. The key problem is existing molecular generative models often optimize for molecular properties (such as drug-likeness or docking scores) but f... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their positive assessment of our work. The point about true synthesizability is very important to us, as experimental validation is always the end goal of generative design.
As we are unable to update the manuscript version at this time, we wanted to provide more di... | Summary: This paper focuses on the challenge of directly optimizing for constrained synthesizability in generative molecular design. Controlling the synthesizability of generated molecules is crucial for closed-loop discovery and robotic synthesis automation. Existing methods have limitations, and there is a lack of mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and an opportunity to clarify our problem setting.
## **Generative approach for constrained synthesizability. Why is this useful?**
We answer all questions from the reviewer in this single response since they are related.
**What is the problem of constra... | null | null | null | null | null | null |
3D Question Answering via only 2D Vision-Language Models | Accept (poster) | Summary: This paper proposes to address the task of 3D question-answering (3D-QA). Proposed method takes as input a set of posed RGB images and operates by only using 2D large vision language models (in this case LLAVA-OV). The method does not operate on any 3D input, and instead uses the set of available images to ans... | Rebuttal 1:
Rebuttal: ### **1. "... finetuning LLaVA-OV ..."**
Due to space limitation, please kindly refer to the response of PgGq `2`
---
### **2. "... analysis of the types of questions" & "a discussion on the limitations ..."**
We conduct a detailed analysis of question types on the SQA dataset, with per-type perf... | Summary: In this work the authors proposed to solve 3D question answering with 2D VLMs only. Specifically, 3D scenes are first rendered into 2D images, which are then used to prompt 2D VLMs (e.g., LLaVA-OneVision). Moreover the authors found that the key to good performance is how to select views that are most relevant... | Rebuttal 1:
Rebuttal: ### **1. "... the unfair comparison ..."**
We agree that LLaVA-OV is a strong 2D LVLM. However, we do not consider the comparison unfair, as our core motivation and contribution lie in exploring how to leverage powerful 2D LVLMs in a zero-shot manner for 3D-QA.
- Compared to 3D-based methods, cur... | Summary: The paper aims to only use 2D vision-language models to address 3D question answering task. The authors propose a new framework cdViews, which select critical and diverse views and then perform 3D question answering using the 2D vision-language model. The proposed framework is evaluated on two widely used benc... | Rebuttal 1:
Rebuttal: ### **1. “... ensure the quality for the training of viewSelector.”**
We agree that using LVLMs alone for annotation may lead to unreliable views.
To address this, viewAnnotator is designed to capture informative views beyond simply image matching. Specifically, we incorporate a step-by-step sys... | Summary: This paper introduces cdViews, a zero-shot method for 3D question answering that avoids fine-tuning large vision-language models (LVLMs). Initially, viewSelector is employed to automatically select the most relevant views based on the input question. Then, viewNMS enhances diversity by eliminating redundant vi... | Rebuttal 1:
Rebuttal: ### **1. “The claim of zero-shot is questionable”**
Sorry for the confusion caused. For our best method LLAVA-OV + $F_{cdViews}$, the term `zero-shot` could be more precisely scoped: we will revise Lines 153–156 to state it as `zero-shot 2D LVLM inference` (rather than fully zero-shot).
We would ... | null | null | null | null | null | null |
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