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LSCD: Lomb--Scargle Conditioned Diffusion for Time series Imputation | Accept (poster) | Summary: The paper proposes LSCD for irregular time series imputation, which uses Lomb-Scargle periodograms to compute additional frequency domain loss in the diffusion model. LSCD uses Lomb-Scargle instead of FFT to transform irregular time series into the frequency domain and achieves more accurate imputation results... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment of our paper. Below, we provide detailed responses aimed at clarifying and addressing each concern.
### Claims and Evidence
Thank you for pointing out the discrepancy: in line 22, "frequency domain" should be "time domain". We have updat... | Summary: The paper proposes a novel method designed for performing time series imputation when the input data either has missing data of is not measured at equal intervals. The use of discrete Fourier transform in this case often leads to serious artifacts in the power density spectrum. In contrast, the power density... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive assessment and kind words about our work. We appreciate that you find the method’s contribution to be novel and substantial, and that our empirical evaluations appear fair and rigorous
---
Below we include a general analysis of the computational... | Summary: This paper introduces a novel diffusion-based time series imputation method. Specifically designed for irregularly sampled data, the proposed method leverages the Lomb-Scargle periodogram to enhance imputation performance.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: N... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work and for their useful comments. Below are our detailed responses, we hope they address any remaining concerns.
### Relation To Broader Scientific Literature (Novelty)
We appreciate the presented references and recognize that th... | Summary: This paper introduces Lomb–Scargle Conditioned Diffusion (LSCD), an approach for irregularly sampled time series imputation. Unlike traditional frequency-domain methods that rely on the Fast Fourier Transform (FFT), which assumes uniform sampling and requires interpolation, LSCD leverages the Lomb–Scargle peri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and positive assessment of the manuscript. We appreciate the recognition of the relevance of the work, as well as the modeling and evaluation choices. Below are our detailed responses, we hope they address any remaining concerns.
### Weakness 1 (Rel... | null | null | null | null | null | null |
Learning Parametric Distributions from Samples and Preferences | Accept (spotlight poster) | Summary: This paper studies the conditions under which preference feedback improves parameter estimation. The authors show that preference-based estimator can achieve a better assympototic variance than sample-only estimators. When incorporated with hard constraints with deterministic preference, the authors prove an e... | Rebuttal 1:
Rebuttal: We thank Reviewer yquN for the time spent and the positive feedback. We address the reviewer’s questions below.
**1. Reward models**
Except for Theorem 4.3, all the derivations in Section 4 hold for general (hence reward-based) preference models provided Assumptions 4.2,4.4,4.5 and 4.7 hold. Char... | Summary: This paper studies when adding preference feedback can boost the parameter estimation in the cases of Gaussian and Laplace distributions. The results are mainly theoretical, containing three parts: (1) For M-estimators, adding an additional ``preference'' term related to the logarithm of probability helps redu... | Rebuttal 1:
Rebuttal: We thank Reviewer Gth4 for the time spent and the encouraging feedback. We address the reviewer’s questions below.
**Iterative human preference alignment**
We investigate the case where pairs of observations and their preferences are tied together, which includes the log-likelihood ratio as prefe... | Summary: The paper provides a set of estimators and conditions to improve the estimation error in learning the parameters of continuous parametric distributions when additional preference feedback is available. More concretely, the question is the following: For a continuous parametric distribution $p\_\theta$ with i.i... | Rebuttal 1:
Rebuttal: We thank Reviewer vgrP for the time spent and the detailed comments. Due to the limited space, we only address some of the reviewer’s concerns.
**Restrictive assumptions**
While our research question is inspired by iterative human preference alignment (see the answer to Reviewer Gth4), we do not ... | null | null | null | null | null | null | null | null |
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems | Accept (spotlight poster) | Summary: The paper explores automated failure attribution in LLM multi-agent systems. It introduces and formulates a new research of identifying the agent and specific step responsible for task failures within agentic systems. The research introduces the Who&When dataset, which contains failure logs from 127 LLM multi-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and insightful feedback! We address each of your questions in our responses below.
**[Re Comment 1 on Claims & Comment 4 on Experiments: Need to specify the scope. ]**
We will incorporate all your suggestions and make the following clarifications and r... | Summary: This paper introduces automated failure attribution for LLM-powered multi-agent systems, addressing the problem of identifying which agent causes a task failure and at which step the decisive error occurs. The authors formally define this research area, propose Who&When, a dataset with annotated failure logs f... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments! Please find our response to your comments below.
**[Re Weakness 1: Why not make a comparison/do some analysis with Agent-as-Judge in your experimental section?]**
We appreciate this suggestion; however, the research objective of our fa... | Summary: The paper introduces a new research problem of automated fault attribution in multi-agent systems. The task includes identifying both the agent and the corresponding step that lead to task failure. To study this task, a new benchmark dataset called Who&When is created by manually labeling 127 failure logs. Thr... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and insightful feedback! Due to text limit, additional experiments are shown in anonymous link: **https://shorturl.at/JSeJd**.
**[1. Discussion needed for verifiers & Can [3] be leveraged?]**
Thank you for the suggestions! We acknowledge the necessity ... | null | null | null | null | null | null | null | null |
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery | Accept (poster) | Summary: This paper establishes theoretical guarantees for the preconditioned subgradient algorithm in the context of overparameterized low-rank matrix recovery (LRMR), with a particular focus on the non-smooth case. It is rigorously proven that the proposed preconditioned subgradient method achieves linear convergence... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their effort to review our paper and for the constructive feedback! We address all reviewers' comments/concerns below.
> Summary: The paper is well-written, with clear and coherent exposition, and presents compelling results that contribute meaningfully to ... | Summary: In "Guarantees of a Preconditioned Subgradient Algorithm for Overaparametrized Asymmetric Low-rank Matrix Recovery" the authors provide a novel method to solve robust asymmetric and overparametrized matrix sensing problems without the rate scaling with the condition number of the solution matrix. The authors p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper and the valuable feedback! Next, we provide point-by-point responses to all reviewer’s comments and concerns.
> Other Strengths and Weaknesses
>> No bounds for RIP constants (and consequently restricted smoothness and sh... | Summary: The paper presents a preconditioned subgradient method for robust low-rank matrix sensing using a Burer-Monteiro factorization, focusing on the case where the rank $r$ of the ground truth signal is not known. The preconditioner used is a straightforward modification of the preconditioner proposed by [Xu et al.... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time in thoroughly evaluating our paper and for providing insightful comments and feedback. Below, we provide point-by-point responses addressing each of the reviewer’s comments and concerns.
> Claims And Evidence
We are glad that the reviewer has found our main clai... | Summary: This paper studies the problem of recovering a low-rank matrix from noisy linear measurements of the matrix (i.e. inner products with the vectorization of the matrix). This paper studies the problem at a particular level of generality:
- We have adversarial noisy measurements of an ill-conditioned asymmetric m... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to carefully review our paper and for providing such valuable comments and constructive feedback. Below, we present our detailed, point-by-point responses to each of the reviewer’s comments and concerns.
> Claims & Evidence
Thank you for your positive wo... | null | null | null | null | null | null |
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction | Accept (poster) | Summary: Protriever is an end-to-end differentiable framework for augmenting performance on downstream applications (e.g., fitness prediction) of language models using vector-based retrieval. The model consists of 2 trainable components, the retriever and the reader, and one static vector index. The retriever learns t... | Rebuttal 1:
Rebuttal: **C1: EMDR optimum**
The EMDR loss function is defined as:
$$ \mathcal{L}_{EMDR} = -\log \left[ \sum_k p^{LM} ( \mathbf{q} \vert \mathbf{d}_k ) p^{RETR} ( \mathbf{d}_k \vert \mathbf{q}) \right] $$
(We are experiencing issues with longer equations in OpenReview's Markdown, where subscripts ... | Summary: The paper proposes Protriever, an end-to-end differentiable framework for protein sequence modeling. This approach unifies two steps: protein homology retrieval and downstream modeling tasks. This is done by using a vector similarity search to retrieve homologous protein sequences. The authors train Protriever... | Rebuttal 1:
Rebuttal: **C1: Speed-up: inference analysis**
Instead of reporting per query times from the MMseqs-GPU paper from Kallenborn et al. we move to rigorously benchmark our method, MMseqs2 in CPU and GPU modes.
We randomly sample UniRef50 sequences to create 5 sets of sizes 1, 10, 100, and 1000 each. For each ... | Summary: This paper presents a method for jointly training a homology retrieval model and a conditional language model for fitness prediction. The retrieval model efficiently identifies homologous sequences for a given query protein using efficient vector search.
Claims And Evidence: The paper clearly articulates its ... | Rebuttal 1:
Rebuttal: **C1: PoET / SOTA results**
Our suggested differentiable retriever approach can readily be used to augment SOTA sequence-based architectures on the ProteinGym benchmark such as PoET. The framework requires minimal changes, though the code has to be modified to allow each query in the batch to pro... | null | null | null | null | null | null | null | null |
Retrieval Augmented Time Series Forecasting | Accept (poster) | Summary: The author introduces retrieval-augmented methods into the time series forecasting problem, proposing Retrieval-Augmented Forecasting of Time-series (RAFT). During the forecasting process, the method retrieves the most similar historical windows from the training set to predict future data. The RAFT method has... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Please see our point-by-point response.
> Claims and Evidence & W1. Rationale and ablation study of multiple periods in retrieval
Thank you for raising this important point. First, we would like to clarify that our model performs retrieval across multiple ... | Summary: This paper proposes the RAFT framework, which leverages retrieval-augmented generation (RAG) to retrieve similar time series patterns and integrate them to enhance future predictions. The effectiveness of the RAFT framework has been evaluated on well-adopted time series benchmarks, with comparisons against sta... | Rebuttal 1:
Rebuttal: Thank you very much for acknowledging the novelty and wide applicability of our proposed retrieval method. We plan to further revise our manuscript by including additional experiments and clarifications in the revised manuscript. | Summary: This paper presents RAFT (Retrieval-Augmented Forecasting of Time Series), a method for enhancing time series forecasting models by retrieving relevant “patches” from the training dataset that match the current input pattern. These retrieved patches—subsequent future values corresponding to historically simila... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Please see our point-by-point response.
> W1, Q1. Scalability to extremely long series.
Thank you for highlighting this important point. We fully agree that searching patches in extremely long time-series can be computationally intensive. In light of this... | null | null | null | null | null | null | null | null |
Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment | Accept (poster) | Summary: This paper aims to enhance the reward model in RLHF. Drawing inspiration from active learning, the authors propose Fisher information-based selection strategies to construct an ideal comparison dataset.
The experiments show the effectiveness of the proposed method.
## update after rebuttal
The authors' respon... | Rebuttal 1:
Rebuttal: **We thank our reviewer for their time and effort devoted to improving our paper. We have carefully considered each point of feedback and will provide our point-by-point responses below.**
---
### **P1. Main Contributions**
We thank the reviewers for raising the question and reminding us to furth... | Summary: The paper proposes active learning methods for reward modeling. These active learning methods work as follows:
1. For a large set of prompts, use LMs to generate responses for comparison.
2. Form these generated prompt-response pairs into tuples for either in-prompt comparisons (prompt, response 1, response 2... | Rebuttal 1:
Rebuttal: **We thank the reviewer for investing their time in reviewing our work, and providing insightful suggestions for improving our paper. We have carefully considered each point of feedback and provided our point-by-point responses below.**
---
### **P1. Response to the main concern: cost of oversamp... | Summary: The paper presents an evaluation of different methods to determine which samples in a dataset of (prompt, response one, response two) triplets should receive a preference label and be used to train a LLM reward model using the Bradley-Terry model. The authors propose to use a modification of D-optimality on th... | Rebuttal 1:
Rebuttal: **We thank our reviewer for their encouraging feedback. To respond to the point raised by this reviewer, below, please find our answers to each of the questions.**
---
### **Q1. Translating performance of reward model not to alignment**
- In our experiments, we evaluated the effectiveness of di... | Summary: This paper investigates strategies to leverage adaptive preference labeling for reward modeling in LLM alignment. The authors propose an Active Reward Modeling ARM framework that uses Fisher information to score and select informative preference comparisons to improve annotation efficiency; then, they benchmar... | Rebuttal 1:
Rebuttal: **We thank our reviewer for their careful and detailed review of our work. We appreciate that several valuable points of feedback were included, and we believe that the updated version of our work will be strengthened by reflecting on these points. We address concerns and questions below.**
---
#... | null | null | null | null | null | null |
Prediction-Aware Learning in Multi-Agent Systems | Accept (poster) | Summary: The paper considers learning in time-varying multi-player games using prediction-aware algorithms. It begins by observing that prior results quickly become vacuous when there is a large variation between the games, even though the underlying sequence can be entirely predictable. In light of this, the paper pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very relevant remarks. We answer below to the points they raise.
> “What is ARIMA in Line 130? I might have missed the definition.”
ARIMA refers to Auto Regressive Integrated Moving Average process, a popular process in time series analysis (see, e.g., [3]). We w... | Summary: This paper considers time-varying games where better guarantees (wrt (swap) regret, equilibrium concepts and social welfare) can be achieved when the agents can predict/estimate the time-varying utilities. For a J-player game with utility $c^{j}(w,Z)$ (where $Z$ captures the time-variance), the players use a ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive feedback and the highly relevant reference they have suggested. We will incorporate it into our literature review in the revised version. | Summary: This paper discusses the problem of no-regret learning in general time-varying games with predictions. The authors argue that current regrets, defined as a function of variations in the payoff matrix and variations in the Nash equilibria, become vacuous even in simple examples like the one provided in Example ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We reply to the points they raise below.
> “The significance of the results is questionable. Once the problem is well-defined in terms of costs, etc., the derivations are straightforward and follow standard techniques in the literature.”
We respectfully... | Summary: This paper introduces a prediction-aware learning framework for time-varying games, where agents can forecast future payoffs and adapt their strategies accordingly. The authors propose the POWMU algorithm, a contextual extension of the optimistic Multiplicative Weight Update algorithm, and provide theoretical ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We respond below to the weaknesses they indicate, and reply to their questions.
> “The paper assumes bounded prediction errors, which may not always be realistic.”
We emphasize that our results hold in full generality, without requiring a bounde... | null | null | null | null | null | null |
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams | Accept (poster) | Summary: The paper presents a new approach to solving the frequency estimation problem on a data stream using small space. The idea is to use a neural network to create a sketch that is tuned to the distribution of the input stream. Experiments show that the average frequency error of the approach is lower than competi... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We have addressed your concerns as below and welcome any further discussion should you have additional questions.
## Comparison to insertion-only streams algorithm like Misra-Gries
### Reply to
>...counting-based sketches are often superior for insertion-only stream... | Summary: In this paper, authors have proposed the Lego Sketch, a novel neural sketch designed for data streams. LegoSketch utilize hash embeddings, scalable memory (spread the total space budget across multiple memory blocks, and avoid retraining), memory scanning, and ensemble decoding. During the training phase, the ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback! We have addressed your concerns below and are happy to discuss further if needed.
## Claims
### Reply to
> ...I agree that use sketches to reconstruct global statistic... There are such studies.....
Thank you. While these methods rel... | Summary: This paper proposes a method for estimating the frequency of items in a data stream by means of sketching of embedding vectors of the items. It claims scalable memory use by means of multiple "bricks". It compares with hand-crafted and neural sketch methods on a number of datasets.
Claims And Evidence: The pr... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We have provided responses below and would be happy to engage in further discussion if needed.
Neural sketch represents a promising new direction in the sketch field long dominated by handcrafted designs. Existing neural sketches such as Meta Sketch demonstrate feasib... | Summary: This paper introduces the Lego Sketch, a neural network-augmented sketch for frequency estimation. The sketch consists of several learnable components: a variant of the hash embedding layer, a "memory scanning" module that estimates global characteristics of the stream, and an "ensemble decoding" module that r... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Below, we address your concerns in detail.
## Other Comments Or Suggestions:
### Reply to
> The legibility of the figures in Sec. 5 should be improved.
Thank you for your suggestion. We will improve the readability of the figures accordingly.
## Questi... | null | null | null | null | null | null |
The Minimal Search Space for Conditional Causal Bandits | Reject | Summary: his paper explores the problem of minimizing the search space in CB with single-node interventions, covering both do-interventions and conditional interventions. It presents an algorithm for efficiently identifying the minimal globally interventionally superior set, with experiments demonstrating the empirical... | Rebuttal 1:
Rebuttal: Thank you for your review and questions. We are happy to read that you appreciated the intuition given for our theoretical results.
Your suggestions for definition clarifications will be taken into account in the camera-ready version, if the paper is accepted.
Further, thank you for pointing out ... | Summary: # Summary
- This paper is refreshingly nice paper to read, and the authors have taken care to make the paper easily readable. For instance, I am comparing with general papers I read in this area.
- In that sense, it is similar to papers by Lattimore which rate high for clarity.
- For example, instead of s... | Rebuttal 1:
Rebuttal: Thank you for your review and questions. We are glad that you enjoyed reading our paper and that you appreciate our efforts to make the message clear.
The only paper we were able that seemingly matches your suggestion to add "references to recent works in minimal intervention sets over MECs" was ... | Summary: This paper studies the conditional causal bandit problem, where interventions depend on observed variables rather than being fixed. It provides a graphical characterization of the minimal set of nodes that guarantees the presence of the optimal conditional intervention. An efficient algorithm with O(|V| + |E|)... | Rebuttal 1:
Rebuttal: Thank you for your review and questions. We are glad to read that you found our arguments clear and convincing.
About the weaknesses that you mentioned, we agree that the cases with latent confounding and K-node interventions would be an interesting research direction.
From our perspective, each... | null | null | null | null | null | null | null | null |
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM | Accept (poster) | Summary: Leveraging the multi-image comprehension and instruction-following capabilities of the multimodal large language model (MLLM), this paper studies the personalization of diffusion models. It utilizes MLLM to capture the visual elements based on the provided reference images and instruction. The proposed framewo... | Rebuttal 1:
Rebuttal: Dear Reviewer gzGG,
Thanks for your advice. We will address your concerns below.
**Q1. Missing references and discussions.**
Thank you for raising this point. We have discussed the differences between these MLLM-based frameworks and EasyRef in the Q1 of rebuttal for Reviewer nUGh. We will ci... | Summary: This paper proposes EasyRef, a plug-and-play method for diffusion models to generate consistent images from multiple references under instruction controls. It uses a multimodal large language model (MLLM) to capture consistent visual elements and introduces an efficient aggregation strategy and progressive tra... | Rebuttal 1:
Rebuttal: Dear Reviewer nUGh,
Thank you for appreciating our approach. We will address your concerns below.
**Q1. The novelty of this paper.**
While we acknowledge that our project does not introduce groundbreaking new architectures, we wish to emphasize that it offers several important conclusions an... | Summary: This paper proposes to use a vision language model (VLM) to encode subjects in reference images, and convert them to soft tokens to personalize diffusion models. It can take objects, animals and human faces as subjects.
UPDATE after author response:
Per my request, the authors provided extra evaluation data ... | Rebuttal 1:
Rebuttal: Dear Reviewer 35bv,
Thanks for your comments. We will address your concerns below.
**Q1. What are the "Inconsistent Result" shown in figure 2? There's no spatial information encoded in subject embeddings.**
1. The essence of the "Inconsistent Result" lies in the insufficient understanding of... | Summary: In the area of image personalization, tuning-free methods fail to capture consistent visual elements across multiple references, and tuning based methods require finetuning for new groups. In response, to learn a effective and efficient subject representation across a group of references, this paper proposes E... | Rebuttal 1:
Rebuttal: Dear Reviewer sCdX,
Thanks for appreciating our work and your advice. We will address your concerns below.
**Q1. Can EasyRef capture intricate details?**
We provide visualizations on the DreamBench benchmark (https://github.com/anonymous-projectuser/image), demonstrating our method's ability... | null | null | null | null | null | null |
Risk and cross validation in ridge regression with correlated samples | Accept (poster) | Summary: This is a theoretical paper that studies cross-validation in ridge regularized linear regression. The authors study an understudied regime in the literature on cross-validation: when the samples are *not* i.i.d. In this case, they show that the traditional generalized cross-validation (GCV) estimator does not ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and forthright assessment of our paper. We appreciate your concerns regarding the clarity of our manuscript, and will revise it to make its impact clearer.
- Following your suggestion, we will re-organize the main text and Appendices into Theorem-Proof st... | Summary: The paper proposes CorrGCV, a modified version of the more well known generalized cross validation estimator (GCV) to estimate out-of-sample risk from in-sample data.
Claims And Evidence: The theoretical derivations are sound.
Methods And Evaluation Criteria: The theoretical derivations are sound, the experi... | Rebuttal 1:
Rebuttal: We are glad that the referee found our theoretical results "sound'', and our experiments "convincing.'' We appreciate the referee's concerns regarding demonstration of real-world applications, but given that our paper is theoretical in nature we are surprised by the strongly negative assessment. I... | Summary: This paper investigates the problem of high-dimensional ridge regression with correlated data, which is a common feature in time series. Using methods from RMT and free probability, the authors derive sharp asymptotics for the in- and out-of-sample risk, showing that the standard cross-validation estimator fai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our manuscript, and are gratified by their strongly positive assessment. We will update the paper to address all three of their comments:
- We will change the color used for the CorrGCV to to distinguish it from the NaiveGCV\_1.
- We will adop... | Summary: This paper employs novel techniques from random matrix theory and free probability to analyze the asymptotic properties of the generalized cross-validation (GCV) as an empirical risk estimator for high-dimensional ridge regression, particularly in settings with cross-sectional and temporal correlations in both... | Rebuttal 1:
Rebuttal: Thank you for your careful assessment of our manuscript. First, with regards to your questions and concerns:
1. You are correct in stating that with the introduction of a weighting $M$ the definition of the effective vector of measured responses changes. Our observation here is simply that under... | null | null | null | null | null | null |
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales | Accept (poster) | Summary: The paper proposes an extension of conformal test martingales (CTMs) by the use of weighted conformal p-values rather than standard conformal p-values. The authors argue that such weighted p-values permit testing for more general hypotheses beyond simple (online) exchangeability, and propose a hypothesis that ... | Rebuttal 1:
Rebuttal: Thank you very much for your time, interest, & detailed feedback! We refer to the this anonymous link for supplemental figs: https://sites.google.com/view/authorresponse/home
**Claims and Evidence:**
- *SOTA statement:* We have removed the quoted statement, & revised it to refer specifically to ... | Summary: This paper proposes WATCH, a novel method that is able to check machine learning models after deployment to identify if their input data changes unexpectedly. WATCH uses a new approach, Weighted Conformal Test Martingales (WCTMs), to detect these changes. WATCH can ignore small data changes that do not affect ... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback! Please find our responses to your questions/comments roughly in order below. We refer to the following anonymous link for supplemental figures and algorithms: https://sites.google.com/view/authorresponse/home
**Claims and Evidence:**
- *Novelty of weighted-c... | Summary: This works proposes a weighted generalization of conformal test martingales (WCTMs), for online change point detection, which can continuously adapt to benign shifts without raising unnecessary alarms and quickly detect harmful shifts.
Claims And Evidence: partially, see "Other Comments Or Suggestions" below,... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback! We refer to the following anonymous link for supplemental figures and algorithms: https://sites.google.com/view/authorresponse/home
(1) **Clarifying writing, especially novelty of contributions relative to Vovk et al:** Regarding your comment on how “writing ... | null | null | null | null | null | null | null | null |
EBMaC: Empirical Bayes and Matrix Constraints for Label Shift | Reject | Summary: This paper introduces EBMaC (Empirical Bayes and Matrix Constraints), a new method for estimating importance weights in label shift problems. EBMaC uses hierarchical models via empirical Bayes to accommodate data dispersion beyond what multinomial models allow, and employs linear programming techniques to comp... | Rebuttal 1:
Rebuttal: ___\#1.___ _The paper appears ... EBMaC approach._
**Response**
We actually spent much effort to write the paper, we apologize that you found it poorly written. Our contributions are that we firstly fomulated the problem under the EB framework, while the current methods are largely frequentist. S... | Summary: This paper aims to estimate and infer confidence intervals for the importance weights under the assumption of label shift. In prior work, this was typically done by first estimating the confusion matrix of a given classifier on the source domain and its predicted label prevalence, with the estimation performed... | Rebuttal 1:
Rebuttal: ___\#1.___ _What ... prevalence?_
**Response** Our Bayesian modeling consists of hierarchical modeling with Dirichlet prior and hyperparmeter estimation with empirical Bayes.
First, posing a prior increases model flexibility to accommodate more data distributions, for example, overcoming the limi... | Summary: The paper presents a method to address the problem of label shifts and presents a method for estimation of importance weights and the corresponding confidence sets. It constructs confidence regions of the confusion matrix and the predicted label distributions using the empirical Bayes method. Tighter confidenc... | Rebuttal 1:
Rebuttal: ___\#1.___ _The paper lacks enough ... to motivate the problem well._
**Response** In the revision, we have provided
more motivation of the problem and why it is important to
estimate the importance weights and the associated confidence
sets. We added examples and discussed more existing
... | Summary: The paper addresses the label shift problem. Label shift occurs when label distributions differ between source and target domains. The authors propose EBMaC, a method combining empirical Bayes and matrix constraints. Traditional methods assume multinomial distributions. EBMaC uses hierarchical models for great... | Rebuttal 1:
Rebuttal: ___\#1.___
_The paper misses some key references. In fact, I consider the
article's handling of related work the weakest part of the article._
**Response** Thank you for pointing this out. Originally we only included references in label
shift which is the topic of this paper. We agree with you... | null | null | null | null | null | null |
Improving Consistency Models with Generator-Augmented Flows | Accept (spotlight poster) | Summary: The paper studies some theoretical aspects of the Consistency models (in particular, the consistency training regime). As the practical contribution, the authors propose to augment consistency training with so-called Generator-Augmented flows. The idea is to substitute the basic independent/mini-batch OT coupl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and constructive review. We address the concerns below.
---
> **Theoretical Claims / 1.[C]** Lines 791-793: as I understand, $\dot{\tilde{x}}_t=\dot{\sigma}_t z$?
$\dot{\tilde{x}}_t = \frac{d(\hat{x}_t)}{dt} + \dot{\sigma}_t z \neq \dot{\sigma}_t z$ wher... | Summary: # Update
My criticism is minor, and I think the authors have adequately addressed it. As I have already given a positive review, I decided not to change my evaluation.
# Old Summary
Consistency model is a type of generative model that sends points along the sampling trajectory of a diffusion model to the la... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed and positive assessment. We address the raised questions/weaknesses below.
---
> The paper claims that [$\tilde{\mathcal{R}}_t$] is **"a proxy term for $\mathcal{R}(\theta)$"**, [...] but does not provide any mathematical justification for ... | Summary: The paper analyzes and discusses a discrepancy between consistency distillation and consistency training in consistency models, and proposes a novel consistency training procedure to ameliorate the problem by leveraging the solution of the probability flow ODE learned by the model during training.
## Update a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough and constructive review. We address the raised questions/weaknesses below.
---
> **1.** How would you explain that using **minibatch optimal transport on LSUN slightly outperforms** the generator coupling? *[emphasis ours]*
One of our contr... | Summary: This paper examines consistency models, a technique for achieving single-step (or few-step) sampling in diffusion-based generative modeling and proposes to modify the data-noise coupling used during training so as to reduce the discrepancy between consistency training and consistency distillation. Specifically... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed and positive assessment. We address the raised questions/weaknesses below.
---
> **1.** The experiments cover moderate-scale. **Could the authors add an experiment on $256\times256$**? *[emphasis ours]*
We cannot conduct experiments on larg... | null | null | null | null | null | null |
Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts | Accept (poster) | Summary: The execution of mixture-of-experts model contains two all-to-all communication steps on the critical path of computation. The authors of this paper propose to use the activations before the attention layer of the current block as the input for the experts in the current block, which breaks the sequential depe... | Rebuttal 1:
Rebuttal: > 1. It's better to include more modern designs like the ones used in deepseek v2 (more smaller experts).
Given our constraints on available hardware, we cannot conduct experiments on large-scale MoE models like DeepSeek-V2 (236B). Nonetheless, we conduct experiments on the OLMoE model (7B), a re... | Summary: The paper proposes an new method for expert parallelism, which is a paradigm for distributed training and inference of large scale MoE models by dividing experts across multiple devices. The authors address the bottleneck of all-to-all communication between experts and present a new strategy that can overlap c... | Rebuttal 1:
Rebuttal: > 1. I think the analysis can be stronger if all of the the results are consolidated across different model sizes.
We have summarized and supplemented evaluations of GPT models across various sizes to better demonstrate the impact of model size as a standalone variable. The following table prese... | Summary: This paper presents ScMoE to enhance the computational efficiency of Mixture-of-Experts (MoE) models. By incorporating a shortcut connection that integrates information from the preceding layer with the current layer's computations, ScMoE introduces a concurrent processing mechanism which allows for overlappin... | Rebuttal 1:
Rebuttal: > 1. The claim that model quality is always maintained or improved is only shown on small models. Sometimes the accuracy improvements are very small, and the results might not work for larger models (like OLMoE) or different tasks. The experiments are limited to only small models, which may limit ... | Summary: This paper proposes Shortcut-connected MoE (ScMoE) to reduce the All-to-All communication bottleneck in expert parallelism of MoE model training. Traditional MoE models suffer from high All-to-All communication costs due to dependencies between computation and communication. ScMoE solves this by using a shortc... | Rebuttal 1:
Rebuttal: > 1. Concerns regarding the model structure may be outdated, particularly in relation to the compatibility of ScMoE with the mainstream MoE architecture, where MoE layers are arranged sequentially rather than interspersed with MLP layers.
We would like to clarify that we have already conducted ex... | Summary: MoE model is an effective way to scale up model parameters while preserving the inference latency. When training MoE models, due to the extremely large parameters, expert parallelism is widely used to distribute the computational workload. However, this also introduces expensive all-to-all communication cost. ... | Rebuttal 1:
Rebuttal: > 1. I think the accuracy on benchmark datasets are a bit low to provide meaningful comparisons. It'd be better to train bigger models or longer. It would be great if the idea can be validated at larger model scale and longer training runs. Currently the accuracy on benchmarks are a bit low.
Give... | null | null | null | null |
KGMark: A Diffusion Watermark for Knowledge Graphs | Accept (poster) | Summary: Briefly summarize the paper (including the main findings, main results, main algorithmic/conceptual ideas, etc. that the paper claims to contribute). This summary should not be used to critique the paper. A well-written summary should not be disputed by the authors of the paper or other readers.
This paper in... | Rebuttal 1:
Rebuttal: Dear Reviewer voAk:
Thank you for your detailed and constructive feedback.
We note that your concerns mainly focus on four aspects:
- Scalability and generalizability of KGMark
- Comparative analysis
- Embedding strategy and robustness–transparency trade-off
- Presentation and writing quality
... | Summary: This manuscript presents a novel watermarking method for knowledge graph embeddings to ensure the traceability and auditability of knowledge graphs, claiming to embed invisible signatures into diffusion-based latent representations using the Fourier transform. It addresses key challenges by incorporating multi... | Rebuttal 1:
Rebuttal: Dear Reviewer 1f2k:
We sincerely appreciate your thoughtful comments, which primarily concern the following three aspects:
- Design choices behind KGMARK
- Algorithmic ration for the redundant embedding strategy
- Consideration of computational efficiency
-----
## KGMark's Designing
KGMARK ... | Summary: This paper proposes a watermarking method for knowledge graphs (KGs) using diffusion models. The authors claim their method embeds watermarks into KGs via diffusion-based encoding, ensuring traceability, integrity, and copyright protection. The method primarily relies on diffusion encoding, subgraph preservati... | Rebuttal 1:
Rebuttal: Dear Reviewer dihZ:
We sincerely thank you for your constructive and thoughtful suggestions.
We understand that your comments mainly concern the following four aspects:
- The robustness of KGMARK under stronger adversarial attacks
- Concerns about KGMARK’s performance across datasets
- Explanat... | Summary: The paper presents KGMark, a watermarking framework designed for Knowledge Graphs (KGs), which are widely used in applications like semantic search, question answering, and recommendation systems. The primary goal of KGMark is to embed robust, detectable, and transparent watermarks into dynamic KGs to protect ... | Rebuttal 1:
Rebuttal: Dear Reviewer zE4M:
We appreciate your insightful comments, which mainly focus on:
- Empirical validation under stronger adversarial attacks
- Clarification of the limitations of KGMARK
- Discussion of KGMARK’s role in privacy protection for sensitive data
-----
## Adversarial Attacks
We ha... | null | null | null | null | null | null |
The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction | Accept (poster) | Summary: The authors focus on manifold learning and the effect of noise on the Laplacian operator in that context.
The paper is mainly a theretical study with some experiments to back up the claims.
The scope of the experiments is rather limited as they must fulfill strong assumptions (manifold data, uniform noise).
C... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive reviews! Geometric Data Analysis (GDA) is a small area, and your attention to it is appreciated.
Here we respond to the main points raised by all reviewers.
Is the result suprising and new in its particular area?
Our sharp threshold result is actuall... | Summary: This work brings of the interesting Sasaki metric into manifold learning and Laplacian Enginmap, and prosed theoretical analysis results to support low frequency eigen recovery under noise constrains defined in abstract.
Strengths: the high-level idea of introducing Sasaki metric, with both "tangent" and "no... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive reviews! Geometric Data Analysis (GDA) is a small area, and your attention to it is appreciated.
Here we respond to the main points raised by all reviewers.
Is the result suprising and new in its particular area?
Our sharp threshold result is actuall... | Summary: the authors provide a theoretical analysis that shows that Laplacian eigenfunctions capture the geometry of the underlying
manifold, without needing the noise amplitude or dimension to vary with the same size. The main technique leverages the so called Sasaki metric in Riemannian geometry. They conduct experi... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive reviews! Geometric Data Analysis (GDA) is a small area, and your attention to it is appreciated.
Here we respond to the main points raised by all reviewers.
Is the result suprising and new in its particular area?
Our sharp threshold result is actuall... | null | null | null | null | null | null | null | null |
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models | Accept (poster) | Summary: This paper introduces GSM++, a hybrid graph sequence model that combines Mamba (RNN) and Transformer architectures for graph learning. It leverages Hierarchical Affinity Clustering (HAC) for efficient graph tokenization and Hierarchical Positional Encoding (HPE) to enhance structural representations. Experimen... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review. We are also glad that the reviewer has found our work novel and effective.
> *Missing study*
Thank you for bringing this relevant paper to our attention. We will make sure to properly discuss this paper in the final version of our submis... | Summary: The paper introduces a general Graph Sequence Model (GSM) framework aimed at systematically studying graph-based learning methods utilizing sequence models. It identifies core limitations in existing approaches, notably their inability to simultaneously capture local structures and long-range dependencies effi... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review.
> *Broad applicability*
Please see the response in our message to `Reviewer psEP`.
> *The performance gain attributed to hybridization*
Please note that we follow the original benchmarks. We use 100K for MNIST and CIFAR, while using 500... | Summary: This paper transforms graph data into sequential data through tokenization, global encoding, and local encoding, and applies GSM for graph learning. The paper also analyzes the strengths and weaknesses of different sequence models in handling various tasks. Furthermore, they enhance the model by proposing GSM+... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review.
> *The ablation studies fail to sufficiently isolate and quantify the individual impacts of key contributions (HAC and MoT).*
We kindly want to bring to your consideration that the ablation for both HAC and MoT are already reported in th... | null | null | null | null | null | null | null | null |
Trajectory Inference with Smooth Schrödinger Bridges | Accept (poster) | Summary: The authors proposed the Schrodinger Bridge (SB) with smooth priors guided by the Gaussian process to reduce the exponential cost in K for solving multi-marginal SB problems. The problem is first discretized (suffers from exponential cost) and then lifted to high dimensions and solved by belief propagation met... | Rebuttal 1:
Rebuttal: We thank the reviewer for these comments.
**Comment:**
Can we use the probability flow ODE to avoid Brownian motion?
**Response:**
While probability flow ODEs are indeed powerful tools, they are not ideal for our specific goals for several reasons:
1. Inferring Individual Trajectories:
-... | Summary: **Disclaimer: Despite the forthcoming criticisms, I find this paper intriguing and recommend its acceptance.**
------------------------------
This paper introduces a class of smooth Gaussian processes as priors for Schrödinger bridges and designs efficient algorithms for their computation. A key insight is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for these insightful comments.
**Comment:** Which processes are absolutely continuous with respect to the GAP? Is the minimizer of the SB problem with GAP prior smooth?
**Response:** When $R$ is a smooth prior, the solution to (1) is also smooth, in the same sense. To be mo... | Summary: The authors proposed a novel method to learn smooth trajectories in an SB problem, extending the usual SB method to allow non-Markovian reference by lifting it to phase space, that also effectively extending momentum SB. The method is accompanied with an approximated belief propagation algorithm.
Claims And E... | Rebuttal 1:
Rebuttal: # Response to N7xY
We thank the reviewer for these insightful comments.
**Comment:**
What is the assumptions on the marginals in Lemma 2.1 (and 3.1)? In fact I would like the authors to double check if there is any missing assumptions for the marginals across the paper.
**Response:**
Thank you ... | Summary: The paper proposes solving multi-marginal Schrödinger bridges w.r.t. a reference process based on autoregressive Gaussian process. Interpretation to phase space is constructed, which leads to a tractable algorithm based on probabilistic graphical models and belief propagation.
Claims And Evidence: - In the en... | Rebuttal 1:
Rebuttal: # General remarks
We thank the reviewers for their helpful comments and questions. We are gratified that the reviewers found our method "interesting" (XtBd), "novel," "very useful" (N7xY), and theoretically "sound" (okGt).
We would like to address an important issue that arose in several reviews... | null | null | null | null | null | null |
A Variational Information Theoretic Approach to Out-of-Distribution Detection | Accept (poster) | Summary: The paper introduces a variational and information bottleneck-informed framework for performing feature shaping for out-of-distribution detection. Namely, the proposed objective maximizes the KL distribution between the distribution of ID features $Z$ and OOD features $\tilde{Z}$ subject to an information bot... | Rebuttal 1:
Rebuttal: *Question 1: It's not clear to me how the proposed theory predicts the generalized piecewise linear feature shaping function proposed later in the work, as mentioned in the abstract.*
**Response 1:** See Response 2 to Reviewer p7gJ.
*Question 2: Where does the claim "Note this forms a Markov Cha... | Summary: ### Background
- This paper works on the OOD detection task.
- Previous methods use different feature shaping functions to reshape features from the penultimate layer of a pre-trained network. They achieve SoTA performance on OOD detection benchmarks, but lack in theoretical evidence and may not generalize to... | Rebuttal 1:
Rebuttal: *Question 1: The proposed method is a piecewise linear shaping function, which contains 7 hyperparameters. According to the supplementary materials, the values of these hyperparameters are varying with different models on different benchmarks. For example,
is 0.73 when using ResNet-50 but 1.76 wh... | Summary: The paper introduces a variational information-theoretic framework for OOD detection. It models OOD features as random variables by optimizing a loss function that balances KL divergence for feature separability and Information Bottleneck (IB) regularization for compactness.
Claims And Evidence: Yes.
Methods... | Rebuttal 1:
Rebuttal: *Question 1: The method assumes some prior knowledge of the OOD distribution (e.g., Gaussian, Laplacian). How does it perform when the OOD distribution is completely unknown or highly complex?*
**Response 1:** Our experiments show the performance of our piecewise linear family that encompasses G... | Summary: The paper presents a novel theoretical framework for constructing out-of-distribution (OOD) detection features in neural networks using a variational information-theoretic approach. The key contribution is a novel loss functional that consists of a KL divergence term that maximizes the separation between in-di... | Rebuttal 1:
Rebuttal: Key Question 1
**Response 1**: Thanks for the ref, we’ll cite. We agree with the use of likelihood ratio (LR) as a score over the likelihood. However, our work focuses on feature shaping not scoring (L60-61, 2nd column). Our work separates the distributions of the resulting OOD feature shape d... | null | null | null | null | null | null |
Ergodic Generative Flows | Accept (poster) | Summary: The paper studies the problem of generative modelling and sampling (a bit confusingly referred to in the paper as IL and RL). The authors focus on the framework on GFlowNets in continuous spaces and learning directly from samples. The paper presents an alternate theoretical framework of Ergodic Generative Flow... | Rebuttal 1:
Rebuttal: Dear HCTd,
We thank you for your detailed review. Before going into detail, allow us to emphasize that the present submission is an "Exercise in Style": how far can we go with "pure" GFN without a separate energy model? We understand it may be restrictive, but this is the game we decided to ... | Summary: This paper introduces Ergodic Generative Flows (EGFs), a novel framework that extends Generative Flow Networks (GFNs) to address key challenges in training generative models for both reinforcement learning (RL) and imitation learning (IL). The authors identify four main challenges with existing GFNs: intractab... | Rebuttal 1:
Rebuttal: Dear mQ7G,
We thank you for your particularly detailed review.
To begin with, we will add clear references to proofs in the appendix.
3. Theoretical Claims:
- Your interpretation of Definition 3.3 is correct. We may move the paragraph after Theorem 3.4 to after Definition 3.3 and expand it to ... | Summary: This work introduces Ergodic Generative Flows (EGFs) to tackle several issues not satisfactorily resolved in generative flow networks by using ergodicity to create simple generative flows with globally defined transformations and tractable flow-matching loss. Furthermore, a new KL-weakFM loss is proposed for I... | Rebuttal 1:
Rebuttal: Dear 6zNQ,
We thank you for your detailed review.
We acknowledge the need for higher dimensional experiments, it is part of an ongoing project to scaling up EGFs as well as training conditioned EGFs.
Regarding your questions:
1. Unfortunately, they do not directly. A straightforward strat... | Summary: This paper proposes a new family of generative flows called Ergodic Generative Flows (EGFs) which are capable for both RL and IL tasks in continuous settings. The generative flows are built upon finitely many globally defined transformations, with probable universality over continuous spaces like tori and sphe... | Rebuttal 1:
Rebuttal: Dear moVi,
We thank you for your detailed review and valuable feedback. Please find our responses to your comments below:
1. Methods and Evaluation Criteria
Stable Loss:
We appreciate your observation regarding the difference between our stable loss and the one proposed by Brunswic et al. (2024... | null | null | null | null | null | null |
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion | Accept (poster) | Summary: This paper explores the use of noisy images to mitigate memorization issues in diffusion models, examining the tradeoff between fidelity and memorization. Under the assumption of normality in the data distribution, the authors analyze the problem of information leakage. They also investigate the memorization i... | Rebuttal 1:
Rebuttal: From the questions raised, it appears that there are some major misunderstandings. In what follows, we do our best to clarify them, and we urge the reviewer to reread some part of our work and reassess their evaluation. In our end, we will do our best to improve the presentation based on the Revie... | Summary: This paper addresses the issue of memorization in diffusion models, proposing a framework to reduce memorization while maintaining high-quality image generation. The authors introduce a simple method that utilizes noisy images to learn the initial portion of the generation trajectory, followed by high-quality ... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful reading, for their feedback and suggestions!
**Lemma 4.2 being about a single point**: We preferred to present the statement for 1 point since it already presents the advantage of ambient diffusion compared to vanilla DDPM: Given a generation of m points from eac... | Summary: The paper investigates the links between good performance of generative models (i.e., low FID) and memorization of the training set. in particular, the paper investigates the Pareto front. of performance vs memorization. Based on a recent "ambient score matching loss" [1], the paper introduces a new diffusion ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable reviews and suggestions!
**On no of duplicates in figure 2**: By the no of duplicates, we mean the percentage of generated samples whose similarity to their nearest neighbor in the training set is greater than 0.9. The nearest neighbor is found using the c... | Summary: In this paper, the authors propose a simple but effective method for the diffusion model to generate creative images rather than memorizing the training data. Their method is motivated by previous work of (Feldman, 2020) about the generalization in classification problems, where they showed that the model tend... | Rebuttal 1:
Rebuttal: We thank the reviewer for their Review and suggestions.
**On the blurry output of the diffusion models**: Figure 3 contains a noisy image $x_t$ and the learned model’s approximation of $E[x_0 | x_t]$. Intuitively, this expectation represents an average over all possible clean images $x_0$ that co... | null | null | null | null | null | null |
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety | Accept (spotlight poster) | Summary: This paper is subsequent work following (He et al, 2024) .The paper proposes a better benign fine-tuning attack based on the influence function techniques.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes. Fine-tuning attack on benign data is making perfect sense because benign data are hard to... | Rebuttal 1:
Rebuttal: > Q1.Virus and some other missing relevant work
Thanks for the suggestions. We have added all the work to our manuscript.
> Q2.1 Additional experiment on GSM8k
> Q2.2 Utility downgrade raises concern on fine-tuning
Thanks for the great question.
**Fine-tuning on GSM8K** We fine-tune LLaMA-2-7B-... | Summary: This submission investigates a vulnerability in the fine-tuning stage of large language models (LLMs), demonstrating that benign datasets can be exploited to compromise safety alignment. The authors examine this problem via the lens of outlier identification, using Self-Inf-N to discover and remove outlier sam... | Rebuttal 1:
Rebuttal: > Q1.The difference between our paper and [1]
[1] raises an important observation: even fine-tuning on benign samples can lead to a certain degree of safety degradation. However, despite this initial investigation, two key questions remain unexplored:
- The increase in harmfulness is quite limit... | Summary: This paper puts forth the idea that fine-tuning can compromise the safety of large language models. In particular, they leverage existing work from the field of outlier detection to exploit these data points in benign datasets and then demonstrate that fine-tuning on these examples (which are still, by constru... | Rebuttal 1:
Rebuttal: > Q1.Why using API-based detection tools?
Thank you for the insightful question. **We totally agree that the suggested evaluation model can further enhance the robustness of our analysis, hence we have already included it in our updated results.**
Method|LlamaGuard|GraniteGuard|WildGuard
-|-|-|-... | Summary: This paper investigates a vulnerability in the fine-tuning stage of LLMs, where even benign datasets can lead to a significant increase in the harmfulness of LLM outputs. The authors propose a novel attack method, Self-Inf-N, which identifies and selects outlier samples from benign datasets to fine-tune LLMs, ... | Rebuttal 1:
Rebuttal: > Q1. The paper discusses practical scenarios like continuous learning and data poisoning, but the experiments are somewhat limited in scope.
Thank you for the insightful question. The continuous learning and data poisoning settings represent our preliminary exploration into how the proposed meth... | null | null | null | null | null | null |
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection | Accept (oral) | Summary: In this paper, the authors explain the issue of lacking generalization in deepfake detection from the perspective of SVD. Specifically, they argue that due to the limited nature of fake features, models trained on deepfake datasets tend to produce low-rank matrices, which leads to a failure in capturing key co... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer SVkD for the constructive comments, insightful questions, and useful suggestions.** We address the reviewer's concerns below.
> **Q1.** The authors should provide additional visualizations for different testing datasets. For instance, face deepfakes might be less ... | Summary: This paper proposes Effort, a novel SVD-based adapter tuning method, for generalizable AI-generated image detection. The key idea is constructing two orthogonal subspaces, where the principal components preserve the pre-trained knowledge from the vision foundation models while the residual components are utili... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer j2Rb for the constructive comments, insightful questions, and useful suggestions.** We greatly appreciate and are encouraged by the reviewer's recognition of our insightful and in-depth analysis, methodological novelty, and comprehensive experiments with high generali... | Summary: This paper investigates the failure of generalization in AI-generated image detection, identifying an asymmetry phenomenon where detectors overfit to limited fake patterns, resulting in a low-rank and constrained feature space. To mitigate this, the authors leverage the vision foundation models and propose a n... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer 4w2k for the constructive comments, insightful questions, and useful suggestions.** We greatly appreciate and are encouraged by the reviewer's recognition of our motivation with sufficient and reasonable evidence, methodological novelty, and interesting analysis metho... | Summary: The paper proposes a novel approach for detecting AI-generated images (AIGI), particularly deepfake and synthetic images. It highlights that existing detectors suffer from poor generalization ability when encountering unseen forgery methods, primarily due to overfitting to forgery patterns in the training set,... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer cn1X for the constructive comments, insightful questions, and useful suggestions.** We greatly appreciate and are encouraged by the reviewer's recognition of our motivation, thorough experimental and theoretical analysis, methodological novelty, and superior experime... | null | null | null | null | null | null |
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory | Accept (poster) | Summary: This paper addresses the challenge of estimating epistemic uncertainty on graphs that do not follow the homophily assumption, where neighboring nodes often belong to different classes. The authors provide an information-theoretic analysis of Message Passing Neural Networks (MPNNs) and derive an analog to the d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We want to distinguish our contributions from related work.
## Performance improvement through Heterophily
Our theory shows that, from an information perspective, the intermediate layers (i.e. aggregation) in GNNs can provide additional information... | Summary: This work addresses the lack of epistemic uncertainty measures on heterophilic graphs by studying the uncertainty of GNNs through information theory. The main contribution is the development of a post-hoc estimate, Joint Latent Density Estimation (JLDE) as a measure of the density of latent embeddings which is... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and suggestions. We want to address their points with the following revisions of our paper:
- **Synthetic Experiments with varying Homophily**: That is a great idea! We investigate how JLDE compares to the state-of-the-art homophilic estimator GEBM ... | Summary: This paper proposes an uncertainty estimation method for heterophilic graphs, primarily through the utilization of multi-layer embeddings. The authors conduct comprehensive analyses, such as examining how information propagates through message passing in neural networks, to validate their claims. They introduc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and questions and address them with the following revisions to our paper in addition to the additional experiments shown [here](https://figshare.com/s/05c97f1c4314003ce379?file=53331224).
1. **Efficiency**: The computational cost of KNN-based JLDE i... | Summary: The paper explores uncertainty quantification (specifically epistemic uncertainty) in graphs without homophily -- notably previous works in this direction assumed homophily as another source of information about the ground truth probability revealing information about similarity in the conditional label distri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their in-depth feedback and questions. We are happy they find our paper to provide novel insights into an interesting and untapped research area. We provide additional material [here](https://figshare.com/s/05c97f1c4314003ce379?file=53331224).
## Connection between Theor... | null | null | null | null | null | null |
Adversaries Can Misuse Combinations of Safe Models | Accept (poster) | Summary: This paper introduces a new threat model for misuse where an adversary combines weaker (less-safe, but also less-capable) open-source generative models with stronger (more-safe and more capable) closed-weight generative models to perform unsafe tasks. The adversary accomplishes this by decomposing tasks into s... | Rebuttal 1:
Rebuttal: Thanks for your review! We’re glad you found the threat-model “novel and realistic” and the task we evaluate on “complex and realistic”. We respond to your questions and comments below.
---
_Most of the results are shown on tasks where there is a clear and simple task decomposition. The task de... | Summary: The paper examines how adversaries can misuse multiple AI models in combination, even when each individual model is designed to be "safe" and refuses to generate harmful content. The authors demonstrate that by decomposing a malicious task into benign subtasks, an adversary can leverage a capable frontier mode... | Rebuttal 1:
Rebuttal: Thanks for your review! We respond to your comments below and hope if our comments help assuage your concerns, you’ll consider increasing your score.
---
_The main concern is with Section 5: Automated Decomposition—despite the claim of automation, the process is not genuinely automated. The aut... | Summary: This manuscript suggests that "safe" models with higher capabilities may be used by adversaries to help low-capability models perform "unsafe" tasks, thus yielding an overall "unsafe" model system, while existing works usually evaluate the safety of models on a per-model-basis.
Claims And Evidence: The findin... | Rebuttal 1:
Rebuttal: Thank you for your review of our work! We’re glad you found our findings “interesting”, and respond to your comments below
---
_The findings in this manuscript are interesting. Yet, the major drawback is that the concepts used in the claims are not defined, e.g., the "safety" of models. The high... | Summary: - the paper explores the idea of completing a (malicious) task using a collection of otherwise "safe" models
- the key idea is to break down the task into a set of subtasks, such that each task alone is benign (or deemed benign enough) for the safe models, and then assemble the subtask solutions back into the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our work! We’re glad you found that it “explores a useful direction, and the key ideas are worth spreading”, and that it is “well-written and easy to follow”. We respond to your comments below, and hope that if our responses improve your impression of the pa... | null | null | null | null | null | null |
ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis | Accept (poster) | Summary: The paper introduces ProofAug, a novel approach for enhancing neural theorem proving (NTP) by integrating large language models (LLMs) with traditional automation tools. Unlike prior approaches such as the Draft, Sketch, and Prove (DSP) framework, which generate rough proof sketches and rely on automation tool... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below is our response to your concerns:
**Q1**: The authors discuss limitations of the DSP framework in the introduction and as motivation for their approach. However, aside from a couple of examples, there doesn't seem to be any quantitative e... | Summary: Recursive theorem decomposition and rebuilding to obtain SOTA scores on miniF2F-test
## update after rebuttal
Satisfied with responses to questions : Rating remains "4: Accept"
Claims And Evidence: * Claim: ProofAug enjoys superior sample efficiency.
+ Table 2 shows that ProofAug outperforms the DSP baseli... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from the reviewer, and especially thank you for recognizing and expressing interest in the experimental details in our code. Below is our response to your concerns:
**Q1**: Experimental results of the ERP module make it seem like it produces only a marginal gai... | Summary: This paper introduces ProofAug, a novel theorem-proving method that enhances the sample efficiency of proof synthesis by integrating automation tools at multiple granularity levels. Unlike prior approaches that use automation tools either selectively or at a single level, ProofAug applies fine-grained structur... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, especially the concise and accurate comments on our ProofAug method. Below is our response to your concerns:
**Q1**: (On the major experimental claims: The improvement seems minimal, and correction of data might cause unfairness)
**A1**: Thank... | Summary: The paper proposes ProofAug, a method for achieving efficient neural theorem proving by combining LLMs with automated theorem proving (ATP). The paper conducts extensive experiments comparing ProofAug with baseline methods, categorizing them by different proof styles.
Claims And Evidence: Yes. The paper addre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear and constructive feedback. As to your questions, we address them as follows:
**Q1**: Regarding lines 261-263 in Section 3.2: Instead of replacing the outer block with sorry, have the authors considered generating more fine-grained drafts from the exact inner th... | null | null | null | null | null | null |
ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset | Accept (poster) | Summary: This paper tackles the new problem of multimodal question answering over multivariate time series data (Time-Series QA).
The authors introduce EngineMT-QA, a multitask dataset tailored to evaluate models' abilities in “understanding”, “perception”, “reasoning”, and “decision-making” tasks using real-world ae... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and detailed feedback. We address each of your concerns below.
# **1. On Methodological Motivation and Case Study**
We appreciate the reviewer’s feedback. While **Transformer-based architectures** are commonly used in vision-language and speech... | Summary: The paper presents ITFormer, a novel framework that bridges time series and natural language for multi-modal temporal-textual question answering. Specifically, ITFormer uses time token position encoding (TPE) to encode the time series, then uses learnable instruct tokens (LIT) to facilitate the alignment betwe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and detailed feedback. We address each of your concerns below with clarifications and new experimental results.
# 1. **Generalization and Benchmark Scope**
Thank you for your insightful feedback on the importance of generalization in our approac... | Summary: This paper introduces ITFormer, a novel framework that bridges time-series signals and natural language for multi-modal question answering (QA). To support this task, the authors release EngineMT-QA, the first large-scale, multi-task dataset designed to capture complex interactions between temporal data and te... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and detailed feedback.
# 1. **Generalization and Benchmark Scope**:
Due to space limitations, the relevant results have been included in the rebuttal to Reviewer zRtW. Kindly refer to it for further details.
# 2. **Dataset Contribution and D... | Summary: The paper addresses the problem of multimodal time series modelling. The main motivation is to enrich time series with natural language to enrich the time series with textual information. For this reason, a benchmark for answering time series questions is proposed, focusing on real-world aircraft engine operat... | Rebuttal 1:
Rebuttal: # 1. **Generalization and Benchmark Scope:**
Thank you for your valuable comments. We conducted additional experiments to evaluate generalization beyond the aero-engine domain:
1. **ITFormer generalizes across domains**, achieving top performance on the domain-agnostic TimeSeriesExam benchmark.
2... | null | null | null | null | null | null |
Learning Invariant Causal Mechanism from Vision-Language Models | Accept (poster) | Summary: This work aims to leverage Invariant Causal Mechanisms in causality to improve prediction under distribution shifts. However, a detailed summary is challenging for me due to several fundamental issues, including an unclear problem formulation, misconceptions of key concepts, and unrealistic theoretical assumpt... | Rebuttal 1:
Rebuttal: ## Re: Claims And Evidence & Q1
**Regarding the definition of domain shift and open-class.**
1. In our context, domain shift primarily refers to covariate shift, where the $p(x)$ differs between the training and testing phases while $p(y|x)$ remains unchanged. This scenario is widely adopted in... | Summary: The paper analyzes the OOD generalization of CLIP via the lens of causal/invariant predictor learning, where the goal is to make predictions via the invariant (causal) features for the downstream task. Motivated by the failure cases of naive funetuning of CLIP, the authors propose CLIP-ICM as a principled appr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful evaluation and positive feedback. We appreciate the acknowledgment that our approach offers a **solid** theoretical foundation and demonstrates **clear** empirical benefits for OOD generalization. We also value the reviewer’s recognition that our **claims... | Summary: This work is motivated from the OOD generalization issue in CLIP, it addresses this problem via learning an invariant causal mechanism and proposes CLIP-ICM framework, which includes collecting interventional data, estimating a linear projection matrix, and predicting in the invariant subspace. The proposed CL... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and positive feedback. We are pleased that the reviewer recognizes our work as **well-supported**, highlighting our **clear** pipeline and **sound** theoretical analysis. Below, we provide detailed responses addressing the specific concerns raised ... | Summary: This paper introduces CLIP-ICM, a framework that improves CLIP’s OOD robustness by leveraging a causal perspective to separate invariant and variant factors. By learning a linear mapping to the invariant subspace using interventional data, CLIP-ICM enhances performance across multiple OOD datasets.
Claims And... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and valuable suggestions. We sincerely appreciate the reviewer for their positive feedback, especially for finding our claims **well-supported**, recognizing the **clarity** and **comprehensiveness** of our paper's presentation, affirming that ou... | null | null | null | null | null | null |
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms | Accept (poster) | Summary: In their paper "Diss-l-ECT: dissecting graph data with local Euler characteristic trasnform", the authors suggest a local version of Euler characteristic transform (ECT) that, given a graph with node features, assigns to each node an additional feature vector containing Euler characteristics of local subgraphs... | Rebuttal 1:
Rebuttal: Dear Reviewer,
we thank you for the constructive and thoughtful feedback. We’re especially grateful for recognizing the clarity of our exposition, the soundness of our method, and the convincing experimental results. Below, we respond to all concerns.
> Question regarding illustration and intuit... | Summary: The paper introduces a local Euler Characteristic Transform (l-ECT), a local topology measure. l-ECT is an application of ECT for analysis of a neighborhood. Then, author apply it to enhance expressivity and interpretability of graph representations (mostly graph's nodes classification).
Authors identify cruci... | Rebuttal 1:
Rebuttal: Dear Reviewer,
we sincerely thank you for the thoughtful and detailed feedback! **We are glad that you found our contributions novel and our empirical and theoretical results sound.** We especially appreciate your engagement with the proofs and your recognition of l-ECT’s usefulness for node clas... | Summary: The paper introduces the Local Euler Characteristic Transform (l-ECT), a novel approach for graph representation learning that extends the Euler Characteristic Transform (ECT) to local neighborhoods. The key innovation is capturing local structural information in graphs without relying on conventional message-... | Rebuttal 1:
Rebuttal: Dear Reviewer,
we sincerely thank the reviewer for the careful and thoughtful evaluation of our work. We appreciate your recognition of our contributions, particularly the novelty of integrating topological data analysis into graph representation learning, and your acknowledgment of the soundness... | Summary: The paper introduces a novel method called the Local Euler Characteristic Transform (ECT), designed to enhance graph representation learning by preserving critical local structures while maintaining global interpretability. ECT provides a lossless representation of local neighborhoods around graph nodes. This ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
we sincerely thank you for the thoughtful and constructive feedback.
We are pleased that you found our paper to be well-written and our proposed method—the Local Euler Characteristic Transform (l-ECT)—to be novel and well-motivated. Below, we address your main concerns point-by-po... | null | null | null | null | null | null |
A Parametric Contextual Online Learning Theory of Brokerage | Accept (poster) | Summary: This paper studies brokerage as a contextual online learning problem. Under the assumption that traders' valuations depend linearly on a context available to a broker, the authors design an algorithm achieving a regret bounded by sqrt T. They also derive a corresponding lower bound. The paper then considers th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Essential References** After reviewing the submission in light of the reviewer remarks, we agree that the mentioned statements could be weakened a bit. We are happy to make the requested changes in the revised version.
**Q1** Great question!... | Summary: This paper considers the brokerage problem between traders for contextual online bilateral trade where in each round, two traders arrive and a context is revealed, then the broker reveals a price, then broker only observes whether the trade with the given price occurred and the identity of buyer and seller. Un... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading the paper and for their kind words! Thanks also for spotting the typos ($\xi_{t} \rightsquigarrow \xi_{t,\theta}$ and $\zeta_{t} \rightsquigarrow \zeta_{t,\theta}$) on Line 407. We will correct them in the revised version.
It is our understanding that ... | Summary: This paper addresses the problem of sequentially determining transaction prices between two parties based on contextual information. Transactions occur, and rewards are obtained, only when the proposed price falls between the private valuations of the two parties. It is assumed that the expected values of thes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review of our work and the comments on our submission.
We are pleased to read that the reviewer evaluates positively the soundness of our setting, the correctness of our results, and our discussion of the relevant related literature.
It is our understanding that ... | Summary: The paper introduces the contextual version of the online brokerage problem. The broker observes a (possibly adversarially generated) context and sets a trading price. The buyer and seller whose private valuations are a perturbed linear function of the context agree for the trade if the price is between the lo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
**Additional references** We thank the reviewer for bringing the two additional references to our attention. We will add them to the revised version. Regarding one-sided problems (like auctions or dynamic pricing), no techniques we are aware of... | null | null | null | null | null | null |
Learning Classifiers That Induce Markets | Accept (poster) | Summary: This paper considers a standard binary strategic classification problem with a twist: the costs of manipulating features are endogenized, i.e., determined by a market. For example, college applicants could improve their SAT scores by paying for an SAT prep course, but the cost of the course is determined by ma... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and insightful questions.
> Simplifying assumptions such as linear classifiers and linear costs
Our choice to focus on a simple setup stems from several considerations. Indeed, one consideration is tractability (of both pricing and learning problems). Anothe... | Summary: This paper extends strategic classification to a setting where users seeking positive predictions can purchase features from sellers, leading to the formation of a competitive market. The authors analyze how users respond to prices, how market prices adjust based on demand, and how classifiers influence these ... | Rebuttal 1:
Rebuttal: Response:
Thank you for your positive review! We were happy to hear that you found our paper exciting and novel. If you have any further questions we would be glad to discuss.
> Would've preferred to validate on multiple datasets
We are happy to report that **we have extended our experimental s... | Summary: The paper studies strategic classification in settings where the cost function for modifying inputs depends on the chosen classifier, via the market this classifier induces. In particular, the chosen classifier determines which features are more "important" for positive decisions and therefore affects the dema... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We were glad to hear you see our paper as making a clear conceptual contribution – this was indeed our primary aim and focus.
Your review mentions that you believe our results can be strengthened, in particular by considering (i) alternative proxy losses an... | Summary: The authors propose a market-based perspective in strategic classification and challenge the key assumption that cost functions do not depend on the classifier and are fixed. The paper builds on the premise that classifiers, when used in the real world, incur demand for their features, especially when they lea... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and important comments. Overall, it seems that your concerns are: (i) clarity of exposition and captions, (ii) discussion of the empirical findings in Sec. 6, (iii) the use of a single dataset, and (iv) contribution.
For (i) and (ii), **we believe these are eas... | null | null | null | null | null | null |
A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression | Accept (poster) | Summary: The paper addresses the problem of applying conformal prediction to multiple continuous output prediction systems. The challenge is obtaining a small average region with a reasonable computation complexity. The paper presents a unified view of the current methods and proposes two novel approaches.
Claims And ... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestions for improvement.
**Missing relation to family-wise error rate (FWER) control methods:**
Thank you for raising this point. We do briefly mention multiplicity control approaches in Appendix A (citing Timans et al., 2024 [59]). FWER methods (like Bonferro... | Summary: This paper provides an overview of multi-output conformal regression methods, putting them in a unified setting, and propose two new approaches based on scores CDF, that generalizes some previous methods, as well as a latent-based approach, that generalizes other families of approaches.
## update after rebutt... | Rebuttal 1:
Rebuttal: Thank you for the thorough, positive review and your support for acceptance. We appreciate you finding the paper enjoyable and the methods sound. We will correct the reference issues you kindly pointed out in the final version. | Summary: This paper performs a unified comparative study of existing conformal methods with different multivariate base models for constructing multivariate prediction regions. It generalizes two classes of conformity scores from the univariate to the multivariate case. Moreover, it conducts large-scale experiments com... | Rebuttal 1:
Rebuttal: Thank you for your feedback and recognizing the value of our work.
**Proposed scores seem not to have finite-sample conditional guarantees:**
You are correct. Our focus is on developing flexible conformal methods applicable to complex, modern generative models (density, sample, or latent-based) ... | Summary: The paper considers conformal prediction for high-dimensional regression. While one can extend uni-dimensional regression algorithms to multi-dimensional ones, other algorithms that explicitly work in $\geq 1$ dimensions also exist.
This paper introduces two conformity scores, C-PCP and L-CP, exploring their ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
**The paper mentions that CP²-PCP is similar to C-PCP. What is the reason for not including it?**
CP²-PCP was proposed concurrently with our submission period and published very recently (ICLR 2025). Hence, we were unable to include it in our empirical compa... | Summary: This paper reviews latest developments of conformal prediction methods in multi-output regression tasks.
Claims And Evidence: Yes. The paper did a comprehensive overview and detailed analysis of various methods, categorized them into different variants, and compared the results both numerically and visually.
... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and constructive feedback. We address your questions below.
**Comparison of CP under different conditional density estimation (CDE) variants:**
We agree that evaluating CP methods across different CDEs is important for robustness. We performed extensive ... | null | null | null | null |
Beyond Cropped Regions: New Benchmark and Corresponding Baseline for Chinese Scene Text Retrieval in Diverse Layouts | Accept (poster) | Summary: The paper addresses Chinese scene text retrieval challenges, focusing on the complex layouts of Chinese text in real-world scenes. Current approaches that adapt English text retrieval methods to Chinese contexts show limited performance. The authors introduce DL-CSVTR, a benchmark for evaluating Chinese text r... | Rebuttal 1:
Rebuttal: Thank you for your comment, and we would like to clarify these questions according to subjects.
**Question about DL-CSVTR datasets**
1. Claims And Evidence's Para 2
We ensured that the process involved three annotators, with one main annotator overseeing the data quality and consistency. The... | Summary: This paper focuses on Chinese scene text retrieval, which aims to extend previous English scene text retrieval to Chinese. The authors establish a Diversified Layout benchmark for Chinese Street View Text Retrieval (DL-CSVTR) to assess retrieval performance across different text layouts. They also propose Chin... | Rebuttal 1:
Rebuttal: **Question 1**
Tab.1, why is CSTR-CLIP much faster than all other methods except CLIP? This should be explained in detail.
**Response 1**
Thank you for the valuable comment. The faster performance of CSTR-CLIP compared to other methods can be attributed to the simplified nature of our approach.... | Summary: This paper addresses the limitations of existing Chinese scene text retrieval methods, which inherit the solution for English scene text retrieval and fail to achieve satisfactory performance in Chinese scene text retrieval. Therefore, the authors first introduce DL-CSVTR, a new benchmark featuring vertical, c... | Rebuttal 1:
Rebuttal: **Question 1**
In Table 2, we found that CSTR-CLIP has significantly improved performance in various challenge scenarios. However, the basic horizontal cases are not reported together. Can the author provide the corresponding performance results?
**Response 1**
We appreciate the reviewer’s comm... | Summary: In this paper, the authors aim to solve the problem of Chinese scene text retrieval in complex and diverse layouts. They first establish the DL-CSVTR benchmark including vertical, cross-line and partial alignments. In addition, the authors propose CSTR-CLIP method which integrates global visual information wit... | Rebuttal 1:
Rebuttal: **Question 1**
In Figure 2, we can conclude that horizontal text occupies 92.62%. It is not clear how the oriented text is classified – is it classified as horizontal or vertical?
**Response 1**
We appreciate the reviewer’s observation. To clarify, we classified the visual representation of que... | null | null | null | null | null | null |
Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration | Accept (poster) | Summary: The paper targets misalignment between rationales and answers in Large Vision-Language Models (LVLMs), particularly in VQA tasks. It introduces Self-Rationale Calibration (SRC), a framework that iteratively aligns rationales with answers using a combination of rationale fine-tuning, pairwise candidate scoring,... | Rebuttal 1:
Rebuttal: Dear reviewer, due to **space limits** of initial rebuttal, we are unable to elaborate on details or minor points, but we would be glad to clarify any further concerns in the next-round reply.
---
> **The novelty and contribution of SRC.**
We sincerely appreciate the reviewer’s positive feedbac... | Summary: This paper proposes Self-Ratationale Calibration, a novel framework to align the rationales and answers and LVLMs. SRC shows consistent improvement on both LLaVA-1.5 and LLaVA-Next on several benchmarks.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: NaN
Experimental Desi... | Rebuttal 1:
Rebuttal: Dear reviewer, due to **space limits** of initial rebuttal, we are unable to elaborate on details or minor points, but we would be glad to clarify any further concerns in the next-round reply.
---
> **The discrepancies in reported benchmark results.**
We sincerely appreciate your careful examin... | Summary: The paper introduces Self-Rationale Calibration, a framework designed to enhance the alignment between rationales and answers in VLMS. The motivation stems from the observation that LVLMs can generate correct answers but often fail to provide factually grounded rationales, leading to inconsistent reasoning. Ge... | Rebuttal 1:
Rebuttal: Dear reviewer, due to **space limits** of initial rebuttal, we are unable to elaborate on details or minor points, but we would be glad to clarify any further concerns in the next-round reply.
---
> **Training efficiency of SRC.**
We sincerely appreciate the reviewer's feedback considering the ... | Summary: This paper attempts to address the misalignment between the final answers and the perceptual reasoning, i.e., rationales, from LVLMs' outputs. With a prior fine-tuning for the model to generate rationales, the authors propose a pairwise scoring strategy considering model confidence and LLM-driven assessment, i... | Rebuttal 1:
Rebuttal: Dear reviewer, due to **space limits** of initial rebuttal, we are unable to elaborate on details or minor points, but we would be glad to clarify any further concerns in the next-round reply.
---
> **The usability of the confidence scores in Confidence-weighted Winning Score.**
While we acknow... | null | null | null | null | null | null |
LRA-QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers | Accept (poster) | Summary: This paper presents LRA-QViT, a novel framework integrating low-rank approximation (LRA) and quantization to improve the efficiency and robustness of Vision Transformers (ViTs), particularly for deployment in resource-constrained environments such as edge and mobile devices. The authors introduce Reparameteriz... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and provide the following responses.
# A1) Difference from QAT
>- Our proposed WADS includes an optimization process distinct from existing PTQ methods, yet it remains fundamentally different from QAT.
>- As shown in the right part of Figure 1, WA... | Summary: This paper introduces a novel framework that integrates reparameterizable branch-based Low-Rank Approximation (RB-LRA) with Knowledge Distillation (KD) to reduce the number of parameters and inference computational complexity. Additionally, the authors propose an LRA-aware post-training quantization method to ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. Refreshing the page (F5) helps generate equations properly!
# A1) Computational Complexity Analysis
>- We analyze the computational complexity (i.e., FLOPs) and inference speed improvements.
>- Furthermore, Table 3 demonstrates the actual acce... | Summary: This paper proposes RB-LRA, a low-rank approximation scheme integrated with quantization to reduce the number of parameters in vision transformers (ViTs) and mitigate inference delay. To minimize approximation errors introduced by singular value decomposition (SVD), RB-LRA employs block-level knowledge distill... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and provide the following responses.
# A1) Additional Computation Analysis
>- RB-LRA and WADS require one-time fine-tuning (FT) and calibration only during pre-deployment, without inference overhead.
>- As shown in Table D, we measured the FT time... | Summary: The authors propose RB-LRA method which introduces a reparameterizable residual branch to compensate for information loss due to LRA. Weight reconstruction (WR) initializes the residual branch with weights discarded during decomposition, mitigating accuracy loss. To further improve accuracy, the method incorpo... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. Refreshing the page (F5) helps generate equations properly!
# A1) Evaluation of Large-Scale Models
>- As suggested by the reviewer, we evaluate our framework on ViT-L for large-scale models.
>- Experimental results show that applying the RB-LR... | null | null | null | null | null | null |
Learning Imperfect Information Extensive-form Games with Last-iterate Convergence under Bandit Feedback | Accept (poster) | Summary: The authors propose an efficient algorithm (namely, with closed form solution update) which attains a last-iterate convergence rate of order $K^{-1/8}$ when run in self-play and when only bandit feedback is available. They also provide a lower-bound on the convergence which does not match the rate attained by ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. Our response to each question is provided below.
**Q1. Specifically, the techniques seem to me ... when proper distance generating function are employed).**
Thank you for this comment. Indeed, our analysis scheme is inspired by [1], as we have men... | Summary: The paper proposes an algorithm for two-player zero-sum extensive-form games (2p0g) with bandit feedback. Under the self-play setting, the last-iterate of a profile computed by the proposed algorithm converges to the Nash equilibrium in a rate of $k^{-1/8}$ (or $k^{-1/6}$ in expectation). The main innovation i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided below.
**Q1. Additional References.**
Thank you for referring to this! We compare our work with some notable works studying achieving last-iterate convergence in games with noisy feedback [... | Summary: The paper studies two-player zero-sum POMGs, proposes an negentropy-regularization-based algorithm, and establishes the last-iterate convergence. Though the rate seems quite loose, it compares favorably to the rate in a very relevant work Cai et al. [2023] with bandit feedback and in terms of last-iterate guar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided below.
**Q1. "The paper mentions ... not include them in the main paper."**
Thanks for pointing this out. We will be sure to explicitly include more descriptions of the experiments in the m... | null | null | null | null | null | null | null | null |
UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model | Accept (poster) | Summary: This work introduces a method for uncertainty quantification of SAM, based on Bayesian entropy formulation. A lightweight post-hoc UQ method is trained based on the formulation. Results on multiple public benchmarks demonstrate the effectiveness of the proposed method.
## update after rebuttal
I am glad to ke... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and positive assessment.
To further support the reviewer’s inclination toward acceptance, we would like to highlight the linked code repository in the main paper, which demonstrates the ease of use of our proposed framework. We remain open to any additional ... | Summary: This paper introduces an interesting method to measure the uncertainty of SAM in image segmentation tasks. To achieve this, this paper proposes USAM, an efficient post-hoc method that can quantify the uncertainty of SAM and help users determine whether the model results are reliable, which tunes a lightweight ... | Rebuttal 1:
Rebuttal: We acknowledge the in-depth review that recognizes our strengths and provides valuable directions for addressing flaws, particularly regarding related methods. Below, we address questions and concerns. Issues already covered in other responses are only referenced.
---
## Claims
Thank you for p... | Summary: This paper makes a series of efforts to enable uncertainty quantification for SAM. To achieve this, the authors first adopt Monte Carlo sampling to estimate the predictive, epistemic model, aleatoric prompt, and aleatoric task uncertainty. Then, to release the computation burden of the sampling process during ... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for highlighting our strenghts and asking questions that are valuable for eliminating weaknesses and enhancing our paper in terms of readability and evaluation.
---
Before addressing the questions, we need to clarify an issue that affects this review as well as rev... | Summary: The paper introduces UncertainSAM (USAM), a method for uncertainty quantification (UQ) in the Segment Anything Model (SAM). By decomposing uncertainty into epistemic (model), aleatoric (prompt/task), and task ambiguity components, USAM employs a Bayesian entropy framework and lightweight MLPs to estimate uncer... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the detailed feedback and comprehensible remarks that will help enhance the paper. Our responses to the proposed improvements are as follows:
---
## Weakness 1
We only partially agree with the statement that we do not compare to SAM-specific UQ. For example, our Prom... | null | null | null | null | null | null |
Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences | Accept (poster) | Summary: This paper proposes SmPO-Diffusion, a novel method for aligning text-to-image diffusion models with varied human preferences. The authors introduce two core contributions: (1) a smoothed preference modeling approach, replacing the binary preference distribution with a smooth distribution derived by reward mode... | Rebuttal 1:
Rebuttal: Thank you for highly recognizing the value of our study and helpful feedback!
---
**Q1:** *Why do reward models outperform large human preference datasets despite their simplicity?*
**A1:** This is a great question! We believe reward models have the following advantages:
1、We note that PickSc... | Summary: This paper proposes a smoothed extension to DPO, where the preference data is smoothed to incorporate non-binary preference labels. The authors first created smoothed preference labels for image pairs using the likelihood estimation of a reward model. Then it uses noise-inversion to provide a better posterior ... | Rebuttal 1:
Rebuttal: We're truly grateful for your enthusiastic reception of our manuscript and your insightful feedback!
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**Q1：** *Table 7 caption can be improved.*
**A1：** We sincerely appreciate your constructive feedback! We have implemented the following improvements:
+ **Caption Revision**: We have revise... | Summary: The paper introduces SmPO-Diffusion, an approach for aligning text-to-image diffusion models with AI preferences by refining the Direct Preference Optimization framework. Instead of using a binary preference, the authors propose a smoothed preference distribution based on a reward model.
Claims And Evidence: ... | Rebuttal 1:
Rebuttal: Thank you for your feedback and we'll do our utmost to resolve your concerns.
---
**Q1:** *For evaluation, Image Reward, and Aesthetic score could also be considered.*
**A1:** We have incorporated both metrics: **Image Reward** and **Aesthetic Score** are reported in **Table 2,8 and 9** (Qua... | Summary: This paper proposes a post-training method for diffusion models, named SmPO, which is modified from diffusion-dpo. SmPO recognize the variability of human preferences by replacing binary preferences with smoothed preference distributions, thereby mitigating label bias. In addition, Renoise Inversion method is ... | Rebuttal 1:
Rebuttal: We are honored by your favorable evaluation and have carefully considered your suggestions!
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**Q1**: *The rationale for designing the weight-to-sensitivity ratio as given in Equ (12) is unclear.*
**A1**: Thank you for your feedback! According to Equ (8), $\tilde{p}(x_{0}^{w}|c) = \frac{p(x_... | null | null | null | null | null | null |
On Explaining Equivariant Graph Networks via Improved Relevance Propagation | Accept (poster) | Summary: The paper introduces a novel method called EquiGX aimed at enhancing the explainability of equivariant graph neural networks (GNNs) specifically designed for 3D geometric graphs via the deep Taylor decomposition framework. In the initial version, the authors used incorrect notations (e.g., the relevance score ... | Rebuttal 1:
Rebuttal: We thank Reviewer 6J4F for comments on the paper. We have provided pointwise responses below.
>**According to the formula in this paper, the contribution of the atom may change with the reference frame in which the molecule is observed.**
We believe there is a misunderstanding. The node explanat... | Summary: Explaining equivariant GNNs for 3D geometric graphs is challenging due to their complex architectures and the difficulty of handling positional data. Existing explainability (XAI) methods mainly focus on 2D graphs and struggle to adapt to equivariant GNNs. To address this, this paper introduces the EquiGX, a n... | Rebuttal 1:
Rebuttal: We are very glad Reviewer ffwb had a positive initial impression and appreciate your constructive comments. We provide pointwise responses below.
>**Figure 1 is confusing.**
We apologize for the confusion. In Figure 1, the ground truth is shown in the upper-left corner. Nodes forming the cube mo... | Summary: The paper proposes a new method, EquiGX, to explain equivariant GNNs. The method is based on Deep Taylor Decomposition and extends it to perform layer-wise relevance propagation for spherical equivariant GNNs. Specifically, the authors propose new rules to attribute tensor product operations. The experiments s... | Rebuttal 1:
Rebuttal: We are very glad Reviewer KEDe had a positive initial impression and appreciate your constructive comments. We provide pointwise responses below.
>**The method is limited to TP-based models.**
We admit that our method is focusing on spherical equivariant GNNs (TP-based models) and leaving genera... | Summary: The paper introduces EquiGX, an explanation method for (3D) equivariant GNNs. Existing graph explanation methods mainly focus on 2D GNNs and struggle to explain 3D GNNs. EquiGX extends Deep Taylor decomposition to derive layer-wise relevance propagation (LRP) rules for equivariant GNNs. The method is evaluated... | Rebuttal 1:
Rebuttal: We are very glad Reviewer V1gi had a positive initial impression and appreciate your constructive comments. We provide pointwise responses below.
> **This paper is an extension of the LRP framework that has been widely applied for vision tasks. Although tensor-product networks are different from... | null | null | null | null | null | null |
Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning | Accept (poster) | Summary: Previous works in the area of reinceforcement learning (RL)/foundation models have shown that pre-trained language models (PLMs) can enhance the performance of offline RL. This paper studies an important question: what kind of knowledge from PLMs has been transferred to RL to achieve such good results? They st... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. We appreciate your positive comments about the novelty and presentation of our work. Please kindly find the response to your concerns below.
**W1. For figure 1 and figure 4, DT is tested on only one task.**
We have tested DT and GPT-DT on more ta... | Summary: The paper identifies Markov heads in Pretrained Language Models (PLMs), attention heads with extreme focus on the most recent token. These heads transfer to Decision Transformers (DTs) in offline Reinforcement Learning (RL), improving short-term (Markovian) tasks but harming long-term planning. The paper intro... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and constructive comments. We appreciate your acknowledgement regarding the originality, significance, clarity and presentation of our work. Please find our response to suggestions and questions.
**S1&Q3. Test other PLMs to confirm generality.**
We have t... | Summary: Incorporating Pretrained Language Models (PLMs) into Decision Transformers (DTs) has shown promise in the area of offline reinforcement learning (RL). However, it is unclear why the representations obtained from NLP tasks would be beneficial for RL tasks. The authors aim to address this question by analyzing t... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Please kindly find the response to your concerns below.
**Q1. Would a MoE (mixture of experts) perform better in these long-term environments?**
It’s interesting to investigate whether MoE perform well in long-term environments, however, MoE is not considered ... | Summary: This paper investigates why pre-trained language models (PLMs) boost Decision Transformer performance in offline RL setting. The authors identify crucial "Markov heads" within PLMs that strongly focus attention on the most recent input state. While beneficial for short-term tasks like MuJoCo, theoretical analy... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Please kindly find the response to your concerns below.
**W1. The improvement of GPT-DTMA is within the confidence interval.**
The experiment are repeated three times to ensure significance. We would also like to emphasize that, one important objective for our... | null | null | null | null | null | null |
PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity | Accept (poster) | Summary: The paper introduces an algorithm for Incremental Data Selection (IDS) that selects training examples from a continuous data stream by combining prediction error and kernel similarity. The problem is important for the machine learning community. IDS addresses the challenge of efficient data utilization in deep... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for their detailed assessment and helpful suggestions.
**Weakness-1**
We acknowledge that our theoretical contribution builds incrementally on existing frameworks rather than proposing an entirely new theoretical foundation. Our primary contributions are twofold. ... | Summary: This work introduces Incremental Data Selection (IDS) and proposes PEAKS, a method that selects training samples based on prediction error and kernel similarity. PEAKS efficiently builds training datasets while improving model performance. Experiments show it outperforms existing methods, significantly reducin... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's supportive and positive evaluation of our work.
**The Neural Tangent Kernel seems to be applicable only to infinitely wide networks. Are there alternative methods for other networks?**
The reviewer raises an important clarification point regarding the Neura... | Summary: This paper poses Incremental Data Selection (IDS) problem where examples arrive continuously during training. Then it proposes a prediction error-based method PEAKS to address the problem, showing that a sample’s impact is influenced by both its position in feature space and its prediction error. Experimental ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback.
**Weakness-1**
We thank the reviewer for pointing out that the draft lacks discussion of why PEAKS is effective. In the revised manuscript, we will explicitly clarify this. We believe PEAKS' main advantage is its ability to discriminate hard exa... | Summary: This paper focuses on data selection of DNNs that data arrives as a continuous stream and must be selected without access to the full data source. Based on this, the incremental data selection (IDS) problem is formulated as a three-stage process which including initialization with random samples, (streaming) d... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and constructive comments.
**Lack of analysis about the empirical time complexity**
We thank the reviewer for highlighting this concern. We will clarify in the main text that all baselines are subject to similar time complexity constraints. All meth... | null | null | null | null | null | null |
Cross-regularization: Adaptive Model Complexity through Validation Gradients | Accept (poster) | Summary: A method for alternating optimization of regular parameters $\theta$ and regularization hyperparameters $\rho$ is presented: the training data is split into training and regularization sets, and $\theta$ and $\rho$ are alternately fit using each split respectively. The method is proven to converge for convex m... | Rebuttal 1:
Rebuttal: We appreciate your insightful review and constructive technical feedback. Your points have significantly improved the paper's theoretical foundations and connections to existing literature.
### Parameter Independence and Bounded Coupling
You correctly identified that our theory requires some deg... | Summary: This paper designs an approach to tune model regularization parameters automatically. Instead of relying on cross-validation, which requires training multiple models, the proposed method adapts regularization parameters dynamically by using validation gradients during training. This approach alternates between... | Rebuttal 1:
Rebuttal: We appreciate your review and address your concerns below:
### 1. How We Apply Gradient Descent on Regularization Parameters
Our method works by making regularization parameters explicit and directly optimizable:
**L2 Regularization Example:**
1. We rewrite weights as `w = ρθ` where `||θ||₂ = 1... | Summary: This paper proposes a cross-regularization method that eliminates manual hyperparameter search by directly optimizing weight norms. The approach orthogonally decomposes weight parameters into two complementary components, transforming the optimization problem into two subproblems solved through an alternating ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful assessment and constructive feedback. Below, we address the main concerns raised:
### Recent Literature
We agree that our literature review requires updating. In the camera-ready version, we will incorporate recent advances in adaptive regularization and... | null | null | null | null | null | null | null | null |
Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization | Accept (poster) | Summary: This paper introduces a new framework, the $d$-rectangular linear robust regularized Markov decision process ($d$-RRMDP), for offline RL and develops a family of algorithms called robust regularized pessimistic value iteration (R2PVI) to learn robust policies. Upper bounds on the sub-optimality gap and the inf... | Rebuttal 1:
Rebuttal: We thank the reviewers for positive feedback on our work. We hope our response fully addresses your questions
---
**Q**: A more comprehensive discussion on how choosing λ affects robustness while maintaining efficiency compared to prior methods would strengthen the argument. (more experiment)
*... | Summary: The authors proposed a framework to solve the d-rectangular linear RRMDP. They extend the previous work under the distributional robust MDP framework by unifying three ways that define the potential MDPs consistent with the offline datasets and provide the theoretical analysis on the proposed method. Some brie... | Rebuttal 1:
Rebuttal: We thank the reviewers for positive feedback on our work. We hope our response fully addresses your questions
---
**Q**: Difference with [Pessimistic q-learning for offline reinforcement learning: Towards optimal sample complexity]
**A**: We claim the difference between our work and [Pessimisti... | Summary: This paper studies ways to learn a good policy in offline RL with Linear MDPs such that the policy is robust to changing the model within some f-divergence neighborhood. More precisely, the authors consider linear MDPs and suppose they have access to an offline data set of trajectories. Unlike standard offli... | Rebuttal 1:
Rebuttal: We thank the reviewers for positive feedback on our work. We hope our response fully addresses your questions
---
**Q**: “While I understand it is important for the analysis, it seems to me that requiring robustness to changes on $\mu$ is a fairly weak notion and more applicable notions of robust... | null | null | null | null | null | null | null | null |
Stabilizing Sample Similarity in Representation via Mitigating Random Consistency | Accept (poster) | Summary: This paper addresses the challenge of measuring and improving representation quality in deep learning models. The authors propose a novel loss function called Pure Square Euclidean Distance (PSED) that measures the discriminative ability of representations by computing the Euclidean distance between a similari... | Rebuttal 1:
Rebuttal: Dear Reviewer WSCE,
We are very grateful for your valuable comments and questions. The responses are as follows:
**Response to Weaknesses 1:**
For the image dataset, we solely employ VGG and MoCo v3 for feature extraction. The specific methodol... | Summary: The manuscript introduced a new sample similarity measure. The main difference with the existing sample similarity measure is that the new measure mitigates random consistency. The measure forces class-level discrimination. Several theoretical results regarding the measure have been introduced (quality of stoh... | Rebuttal 1:
Rebuttal: Dear Reviewer GtCV,
We are very grateful for your valuable comments and questions. The responses are as follows:
**Response to Methods And Evaluation Criteria:**
For the image dataset, we firstly employed feature extractors such as MoCo v3 or V... | Summary: This paper proposes a loss function for image classification. It follows the idea of promoting better representation learning and proposes an improvement on mitigating the random consistency of existing methods. Properties such as unbiasedness and generalization bounds are theoretically investigated. Empirical... | Rebuttal 1:
Rebuttal: Dear Reviewer 72cG,
We are very grateful for your valuable comments and questions. The responses are as follows:
**Response to Claims And Evidence:** The proposed loss function serves as a universal quality measure for similarity matrices, which are foundational elements across various learning ... | null | null | null | null | null | null | null | null |
Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks | Accept (oral) | Summary: Assuming that any model is trained for a number of iterations that are compute-optimal given its size, this paper shows, both empirically and theoretically, that the training curves of models of different widths are identical up to an affine transformation.
Deviations due to randomness in the training procedur... | Rebuttal 1:
Rebuttal: Thank you for your feedback and supportive review. We have attached some additional figures [here](https://drive.google.com/file/d/1ZkobNTqh90nnUcunKqUT2Dyx3T4zAb5O/view), and address your specific questions below.
**On the limited range of widths in CIFAR-5M experiments.**
We acknowledge the li... | Summary: This paper introduces the concept of "supercollapse," where the loss curves of networks trained under compute-optimal conditions collapse to a universal curve after affine rescaling. The authors demonstrate this phenomenon across various architectures (transformers and mlps, with different dim sizes) and learn... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review.
Your point about the limited diversity of the datasets is well taken. To address this point, we conducted additional experiments on two non-image domains: the [Lichess chess games dataset](https://huggingface.co/datasets/Lichess/chess960-chess-games) dataset ... | Summary: The paper intvestigates the phenomenon of "supercollapse," where loss curves from compute-optimally trained neural networks collapse to a single universal curve, after an affine rescaling. This universality is observed across different model sizes and learning rate schedules, and it is characterized by deviati... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our draft and supportive review. We will update the paper to ensure that all notations are clearly defined in the main text and fix the typos you identified. Specifically regarding the definition of $k_i$, we first sample a scalar $s_i$ from the power-law dist... | Summary: - When neural networks are trained under compute optimality, their loss curves across model widths collapses to a single universal curve under a simple affine rescaling
- The authors call this "supercollapse" because deviations between curves are smaller than fluctuations from multiple training runs (where the... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback! We provide several additional results and clarifications here and will include them in the updated paper. Corresponding figures are in [the linked PDF](https://drive.google.com/file/d/1ZkobNTqh90nnUcunKqUT2Dyx3T4zAb5O/view).
**Further evidence for the gene... | null | null | null | null | null | null |
Identifying and Understanding Cross-Class Features in Adversarial Training | Accept (poster) | Summary: Adversarial Training (AT) is a widely adopted technique for enhancing the robustness of deep learning models against adversarial examples. However, a critical challenge associated with AT is robust overfitting. As training progresses, the robust accuracy on the training set continues to improve, yet the robust... | Rebuttal 1:
Rebuttal: Dear Reviewer RcTN,
Thank you for your valuable feedback. We address your concerns below.
---
**Q1**: A concern I have is the paper’s discussion on its significance for future research. While previous work may not have fully understood how AT utilizes class features, we still have an intuitive ... | Summary: This paper explores a unique characteristic of adversarial training from the perspective of class-wise feature attribution. Specifically, it highlights that data often contain **cross-class features**, such as the feature of wheels shared by the automobile and truck classes in the CIFAR-10 dataset.
The autho... | Rebuttal 1:
Rebuttal: Dear Reviewer v6k6,
Thank you for your valuable feedback. We address your concerns below.
---
**Q1**: **A more solid explanation for Figure 8 is needed**
**A1**: Thank you for your thoughtful comment. First, we would like to clarify that Figure 8 aims to show that our theory for adversarial tr... | Summary: While successful at defending models against adversarial examples, the dynamics of adversarial training (AT) are poorly understood. This paper attempts to explain two properties of AT: robust overfitting, and the utility of soft labels over one-hot labels. These properties are studied through the lens of cross... | Rebuttal 1:
Rebuttal: Dear Reviewer oCW6,
Thank you for your valuable feedback. We address your concerns below.
---
**Q1**: Implementation details
**A1**: Thank you for your careful reading. We are committed to publishing our code upon publication. For implementation details regarding model training, we utilize th... | Summary: This paper proposed a novel perspective to understand adversarial training. By splitting features into cross-class features and class-specific features and investigating model learning behaviors on cross-class features, this paper demonstrated the importance of cross-class features in improving model robustnes... | Rebuttal 1:
Rebuttal: Dear Reviewer HgFV,
Thank you for your valuable feedback. We address your concerns below.
---
**Q1**: It would be better if authors could discuss, based on the findings in this work, any possible ways to develop advanced adversarial training methods.
**A1**: Thank you for the thoughtful commen... | null | null | null | null | null | null |
Ad-Hoc Human-AI Coordination Challenge | Accept (spotlight poster) | Summary: The paper introduces the AH2AC2 to evaluate human-AI teamwork in Hanabi, a cooperative card game. Key contributions include:
- AH2AC2 Framework: A standardized benchmark using human proxy agents (trained via behavioral cloning + RL) as evaluation partners, hosted via a controlled API.
- Open-Source Dataset
- H... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for the detailed and constructive feedback. We appreciate the opportunity to address your points and clarify aspects of our work. Please find our answers and improvements below.
## Addressing Reviewer Questions
### Q1. Human Validation
We agree that testing human proxi... | Summary: This paper trains a human proxy model from human gameplay records on the Hanabi game and proposes that the proxy model can be used as a cheaper evaluation for algorithms developed for human-AI coordination. They also open-sourced a smaller human dataset on Hanabi.
Claims And Evidence: Yes.
Methods And Evalua... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your positive feedback and detailed review of our paper. We especially appreciate you taking the time to read the appendices. We hope the following answers clarify the points you raised.
### Question 1
We thank the reviewer for raising this point, and we agree with ... | Summary: This paper proposes a new ad-hoc human AI co-ordination challenge using the game of Hanabi. The authors have trained a human proxy agent and have created a controlled benchmark environment for researchers to test their new ad-hoc coordination algorithms.
Claims And Evidence: This is a benchmark paper. The mai... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough review and constructive feedback. We have carefully considered your questions and provided our responses below.
**Q1. Benchmark Fairness**
We thank the reviewer for raising this point and agree that it is theoretically possible for researchers to attem... | Summary: The authors present a new test-bench for Human-AI collaborative RL using Hanabi.
Claims And Evidence: The main claim is the creation of a benchmark test which is present but with only one team's submissions. Thus the claims that the benchmark will impact the community or improve RL in general aren't supported... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your feedback. We appreciate the opportunity to address your concerns and clarify the contributions of our work.
## Adaptivity of Human-Proxy Agents
You raised an important concern about whether using human proxies, trained using fixed parameters, sufficiently captu... | null | null | null | null | null | null |
A New Rejection Sampling Approach to $k\text{-}\mathtt{means}$++ with Improved Tradeoffs | Reject | Summary: This paper presents a new fast adaptation of k-means++ algorithm.
Claims And Evidence: The algorithm and results are clearly stated and match the claims in the paper.
The only claim that is not supported is the advantages of the approach in this work compared to the baselines. Authors provide comparison wit... | Rebuttal 1:
Rebuttal: Dear reviewer yGTT,
Thank you for your thoughtful and constructive review. We address the concerns below:
> (1) The running time presented in this paper for Cohen-Addad seems to be incorrect ...
The statement in Cor 5.5 did not explicitly state the dependence on $k$ for the algorithm, so we... | Summary: The paper proposed a new seeding algorithm for kmeans clustering, accelerating the kmeans++ by leveraging rejection sampling techniques. The key idea is to select new centroids in a way that maintains probabilistic separation from existing ones (alike the kmeans++). Instead of explicitly computing the D2 distr... | Rebuttal 1:
Rebuttal: Dear Reviewer f83S
Thank you for your thoughtful and constructive review. We address the concerns below:
> (1) Limited impact on ...
There are many reasons to speed up the $D^2$-sampling-based seeding itself, and this has already been well addressed in the literature, for example (paragraph ... | Summary: The paper introduces rejection sampling, an alternative sampling method of k-means++ initialization. By making approximations, the method is able to make faster initial k clusters than its similar counterpart that uses MCMC based sampling on tasks where the number of datapoints is substantially higher than the... | Rebuttal 1:
Rebuttal: Dear reviewer X1mD,
We thank you for your thoughtful and constructive review and suggestions. We address the concerns below:
> (1) The plot comparison ... should also be done with the MCMC method.
As far as the convergence properties are concerned, the MCMC method converges to kmeans++ as wel... | Summary: The paper gives a faster version for the $k$-means++ algorithm while maintaining the approximation guarantee offered by the original $k$-means++. They try to approximate the $D^2$ sampling used in $k$-means++. The authors first preprocess the data to center it and make a data structure that allows sampling fro... | Rebuttal 1:
Rebuttal: Dear Reviewer 1rwo,
> (1) Please address the comparison with the paper ...
We thank the reviewer for pointing out [1] which we had missed out. Most importantly, we point out the difference in the distributions used by us and [1]. [1] uses the distribution $q(x) = 1/2|X| + \Delta(x,\mu)/2\Delta(X... | null | null | null | null | null | null |
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models | Accept (poster) | Summary: This paper proposed the GCR framework, where a trie-based index leverages structured knowledge from KGs to address knowledge gaps and hallucinations in LLM reasoning. GCR employs a lightweight KG-specialized LLM for graph-constrained reasoning and a general LLM for inductive reasoning. Experimental results on ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive and constructive review of our paper. Your feedback is invaluable in helping us refine and clarify our work. Below, we address your comments and questions in detail.
### Efficiency of KG-Trie Construction and Practical time costs of the KG-Trie construction
... | Summary: The paper introduces **Graph-Constrained Reasoning (GCR)**, a novel framework to address hallucination and knowledge gaps in large language models (LLMs) when reasoning over knowledge graphs (KGs). GCR bridges structured KG knowledge with unstructured LLM reasoning by constructing a **KG-Trie**, a trie-based i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive and insightful review of our paper. Below, we address your comments and concerns point by point.
### R1. Scalability of KG-Trie construction for billion-edge KGs.
Scalability is indeed a vital concern, especially for billion-edge KGs. In our current implemen... | Summary: This paper introduces a new approach called Graph-Constrained Reasoning (GCR), which integrates the structured reasoning capabilities of a KG-specialized LLM with the general reasoning abilities of a general-purpose LLM. GCR uses KG-Trie to encode potential KG reasoning paths, to constrain the KG-specialized L... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our submission. Below, we address your specific concerns:
### R1. Handling Filtering Conditions in Queries
GCR can solve this type of query by combining the power of both KG and powerful general LLM. GCR first constrains the reasoning pro... | null | null | null | null | null | null | null | null |
Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback | Accept (poster) | Summary: This paper considers the problem of learning Adversarial (Tabular) MDPs with aggregated bandit feedback, which means only the total loss (instead of per-round losses) incurred in an episode is revealed. Using PO w.r.t. newly-proposed U-functions on each state,
* with known transitions, an $\tilde{\mathcal O}(H... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. Below is our response to your comments and questions.
> *Aside from the innovation of U-function and corresponding performance difference lemma, are there other technical innovations that might be independent interest?*
Major contributions of our work:
T... | Summary: This paper studies regret minimization in tabular MDPs with adversarial losses, fixed transition kernel, and aggregate/trajectory/full- bandit feedback, meaning that at the end of each episode, the algorithm only receives the total loss among all the $H$ visited state-action pairs (i.e., the entire trajectory)... | Rebuttal 1:
Rebuttal: Thank you for the positive review and the great questions. We would be happy to discuss any of the points below in the final version that the reviewer believes will improve the paper.
**Q1:** Indeed, our bounds are specific to the PO framework. The only 'occupancy measure + FTRL/OMD' algorithm fo... | Summary: This paper studies online episodic MDPs with adversarial costs and aggregate bandit feedback. Under aggregate bandit feedback, the agent only observes the entire episode loss, making it less informative than the full information setting (the agent observes the full cost function), and the bandit/semi-bandit fe... | Rebuttal 1:
Rebuttal: Thank you for the positive and constructive review. Below is our response to your comments and questions.
> *despite the paper’s theoretical focus, it would be interesting to include some experimental demonstrations of the U-functions in practice.*
Our work is theoretically focused. Previous the... | Summary: The paper studies finite-horizon MDPs with adversarial losses under the aggregate bandit feedback model. In the known-dynamics case, the paper achieves the first optimal regret bound, while in the case of unknown dynamics it significantly improves the previous best known result.
Claims And Evidence: Yes, the ... | Rebuttal 1:
Rebuttal: Thank you for the positive and constructive review. Below is our response to your comments and questions.
> *I think that the bonus b(s) should not have π in the denominator (see, for example, the analysis in line 684)*
Note that in line 684 the denominator has $\mu_h^k (s,a)$ which is by defini... | null | null | null | null | null | null |
Graph Generative Pre-trained Transformer | Accept (poster) | Summary: This paper introduces the graph generative pre-trained transformer (G2PT) for graph generation using auto-regressive transformers. The method introduces a sequence-based graph representation approach, which fits well to transformer architectures originally developed for NLP. The paper explores fine-tuning meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestion! We address the concerns below.
---
**Q1**. _I would suggest adding a more detailed introduction/explanation for Table 2. From the title and text, it's not straightforward to know what tasks are performed there._
**A1**. Thanks for pointing... | Summary: This paper proposes Graph Generative Pre-trained Transformer (G2PT) as a novel approach to molecular graph generation models. While conventional graph generation models are primarily adjacency matrix-based, this method treats node and edge lists as token sequences and employs an autoregressive Transformer (Tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We believe the suggestion are very constructive in improving the quality of our draft, below we address the raised concern.
---
**Q1**. _On the other hand, the way of representing graphs as token sequences is very straightforward and cannot be s... | Summary: Authors introduce a new way to represent graph as sequence of tokens, that contains both node definitions and edge definitions. They use this representation and standard transformer architecture trained on next-token prediction task to generate new graphs. The method is competetive to SOTA diffusion and non-au... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive review, below we address the raised question/comment.
---
**Q1**. _Discussion with GEEL (https://arxiv.org/pdf/2312.02230) and comparison_
**A1**. We first provide a discussion with GEEL then provide the experimental result of G2PT and GEEL on 6 graph d... | null | null | null | null | null | null | null | null |
Revisiting Unbiased Implicit Variational Inference | Accept (poster) | Summary: In this work they propose importance sampling estimation of the score function needed for minimising the KL divergence between q_z and p_z in SIVI. To do this they use a CNF proposal.
They compare their methods (one which uses the importance sampling estimator and one which doesn't) against a kernel stein dis... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback. We have made the following revisions in response to your comments:
1. **Computational Cost Comparison**: Following your suggestion to investigate computational costs, we have now performed comparisons based on the Conditioned Diffusion Process t... | Summary: This paper proposes a new method to reduce the bias of semi-implicit VI (SIVI). The key idea is to estimate the problematic term $\nabla_z \log q(z) = \nabla_z \log E_{\epsilon} [q(z|\epsilon) ]$ using importance sampling, where the proposal distribution is a normalizing flow. The normalizing flow is learned ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and valuable suggestions. We have made several improvements to address your concerns:
1. **Improved Figures and Tables**: We have revised Figures 2, 3, and 4 for better clarity and ensured they are color-blind-friendly. Specifically, we have revised the [fig... | Summary: Estimating the gradient of the KL divergence between SIVI models and (unnormalized) densities is the core difficulty for training SIVI models. Many efforts have been made to partially solve this problem using, e.g., MCMC, kernel methods, Monte Carlo sampling, etc.
This paper presents a new method for training ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful feedback. We greatly appreciate your suggestions, and we have addressed the following points:
1. **High-dimensional Applicability**: We argue that the definition of "high-dimensional" depends on the specific goal. Finding an adequate approximation... | Summary: This paper revisits Unbiased Implicit Variational Inference (UIVI), which has been largely dismissed due to its computational cost and imprecision from the inner MCMC loop. The authors propose replacing MCMC with importance sampling. By minimizing the expected forward Kullback–Leibler divergence, they ensure a... | Rebuttal 1:
Rebuttal: 1. **Experiment 1 Comparison**: We agree that no comparison is needed for Experiment 1, as it primarily serves as a sanity check rather than a competitive benchmark.
2. **Correlation in Experiment 2**: We computed all possible correlations separately for the true $\beta$ and the estimated... | null | null | null | null | null | null |
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits | Accept (poster) | Summary: The authors of this paper tackle the important problem of LLM quantization, enabling fine-tuning below 2 bits per parameter with minimal performance loss. This is achieved through the proposed LowRA framework, which addresses three key challenges in quantized LoRA fine-tuning: coarse-grained precision assignme... | Rebuttal 1:
Rebuttal: We thank reviewer BPFc for their insightful feedback! Below, we address the comments from the **Weaknesses** section in detail. We will incorporate your valuable feedback into the revised manuscript.
---
## Weakness 1: Clarity on LowRA’s Components
> **Reviewer Concern**: The paper would benef... | Summary: This paper introduces LowRA, a novel framework that enables LoRA fine-tuning below 2 bits per parameter while maintaining model performance. The work addresses three key limitations of existing quantized LoRA methods through innovative techniques: fine-grained precision assignment, adaptive quantization mappin... | Rebuttal 1:
Rebuttal: We thank reviewer oGG6 for their thorough review. We are glad that they find our work valuable overall. Below, we address the key concerns. We appreciate your feedback and will integrate it—along with additional experimental findings—into the revised manuscript.
---
## Training Overhead
We benc... | Summary: This paper introduces **LowRA**, a novel framework for LoRA-based fine-tuning of LLMs in ultra-low bit (sub-2-bit) settings. LowRA is the first to enable LoRA fine-tuning at or below 2 bits with only minor accuracy/perplexity losses and achieves considerable memory savings (30–50%).
The authors observe that c... | Rebuttal 1:
Rebuttal: We thank reviewer 7mTX for their thorough and insightful review. We are glad that 7mTX finds our empirical gains convincing and our paper easy to read and follow. We address all feedback below and will incorporate it, along with new experimental findings, into the revised paper.
---
## Q1: Infer... | null | null | null | null | null | null | null | null |
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling | Accept (poster) | Summary: The paper considers exploration in large action spaces where simple baselines like epsilon-greedy are impractical. The authors motivate that prior SoTA on this problem uses approximate nearest neighbor search to inform a truncated version of Boltzmann exploration, which does not have a clean theoretical charac... | Rebuttal 1:
Rebuttal: **On the interpretation of the algorithm**
The interpretation of our algorithm as a way of accounting for the uncertainty in the query embedding is extremely interesting and will be further investigated in subsequent work. Indeed, the operation of sampling $\tilde{V}$ from a directional distribut... | Summary: Summary: The authors propose to improve exploration for very large actions spaces (e.g., millions of samples). This work attempts to overcome the main limitation of Boltzmann exploration for high dimensional actions spaces which requires calculating cosine similarities between the reference sample and all othe... | Rebuttal 1:
Rebuttal: **On the possible distribution of embeddings**
Regarding your concern, vMF sampling is applicable to a wide range of embedding distributions and is not restricted to the uniform distribution on the sphere, which we assumed in part of the theoretical analysis in Section 4. vMF sampling operates by... | Summary: This paper addresses the challenge of exploration in reinforcement learning (RL) when the action space is extremely large, as in real-world recommendation systems like music streaming platforms. Traditional exploration strategies such as Boltzmann exploration and epsilon-greedy become inefficient or intractabl... | Rebuttal 1:
Rebuttal: **On the uniform distribution**
The reviewer highlights an important point. We agree that the assumption of a uniform distribution over the hypersphere is strong and may not hold in practical settings. However, we would like to emphasize that the vMF-exp method itself remains fully applicable reg... | Summary: This paper proposes a method called vMF-exp: a method for exploration in tasks with large action spaces. One such task is recommender systems, where there are millions of categories to choose from. The paper discusses 3 important properties in order to have good exploration: 1. scalability to sample actions fr... | Rebuttal 1:
Rebuttal: **Point 2**
We begin with Point 2, which we understand to be the reviewer’s primary concern.
This point appears to stem from a misunderstanding, as our paper does, in fact, include extensive experiments on a publicly available real-world dataset.
Specifically, Section 5 and Appendix H present ... | null | null | null | null | null | null |
Variance as a Catalyst: Efficient and Transferable Semantic Erasure Adversarial Attack for Customized Diffusion Models | Accept (poster) | Summary: The paper proposes a novel adversarial attack method, leveraging variance manipulation to efficiently and consistently erase identity semantics from images generated by diffusion models, such as Stable Diffusion. The authors introduce two main approaches, Laplace-based and Lagrange Entropy, to address limitati... | Rebuttal 1:
Rebuttal: ### **Q1: Gradient Explosion and Unstable Oscillations**
Thank you for raising this important point. We acknowledge that rapidly increasing latent variance can lead to gradient explosion and numerical instability. As discussed in **Appendix D.1 and Fig. 4**, the gradient of the LE loss, $\tfrac{... | Summary: This paper protects images from malicious editing by attacking diffusion models. The authors design two loss functions, LA and LE, to attack the image variance after VAE encoding, demonstrating stronger attack effectiveness compared to other methods.
## update after rebuttal
Authors' rebuttal have solved my c... | Rebuttal 1:
Rebuttal: **Q1: Experiments of Mean Attack**
**Table 1: Effectiveness of Attacking Mean vs. Variance**
| Method| ISM ↓ | FDFR ↑ | Brisque ↑| LPIPS ↑|
| -------------------- | ----- | ------ | ----------- | --------- |
| LA_Mean_30step| 0.276 | 0.598 | 29.801| 0.855|
| LA_Mean_50step| 0.234 | 0.703 | 31.... | Summary: This paper proposes two novel loss functions, i.e., Laplace Loss (LA) and Lagrange Entropy Loss (LE), which used for adversarial attacks aimed at disrupting Latent Diffusion Models (LDMs). The key insight is identifying the variance of the VAE latent code as critical for effectively erasing identity semantics ... | Rebuttal 1:
Rebuttal: ### **Q1: Attack and Defense Scenario**
Thank you for raising this point. Our method is designed for a practical adversarial setting involving a victim (User A) and an attacker (User B):
- **Defense Phase**: User A wishes to share photos online but wants to prevent misuse by personalization tec... | Summary: The paper introduces LA and LE loss functions to enhance semantic erasure in customized diffusion models, addressing privacy concerns by completely removing identity-related features. It identifies variance in VAE latent codes as key to image distortion and uses optimized variance expansion for effective erasu... | Rebuttal 1:
Rebuttal: **Q1: Advantages of Variance-based Attack and Better Transferability**
**1.Model Architecture**
Earlier diffusion models (e.g., SD1.5, SD2.1) use U-Net backbones, enabling effective attacks based on U-Net gradients or cross-attention. However, newer models like SD3.5 and FLUX.1 adopt Transforme... | null | null | null | null | null | null |
EKM: An Exact, Polynomial-Time Divide-and-Conquer Algorithm for the K-Medoids Problem | Reject | Summary: The submission provides a novel algorithm for the K-medoids problem.
Claims And Evidence: The technical claims are supported by evidence, with the exception of the fact that the problem definition is not presented clearly.
Methods And Evaluation Criteria: The methods and evaluation criteria are correct, but ... | Rebuttal 1:
Rebuttal: > 1. Strengths And Weaknesses, "The closest thing to a ... then the algorithm cannot work"
This is a very insightful suggestion, our algorithm will be true for any non-negative objective function that can be calculated in the form of the definition after Equation (1), line 103, this ensures the f... | Summary: The k-medoid problem is to find a subset C of k points from a set X of n points in R^d such that the sum of distances of every point in X to its closest point in C is minimised. The exhaustive search algorithm for this problem goes over all possible size-k subsets of X. This has a running time of $O(|X|^{k+1})... | Rebuttal 1:
Rebuttal: > 1. Theoretical Claims, "I checked the algorithm's high-level idea. There are no specific theorems given in the paper."
We do not think the lack of existence of theorems implies that our theoretical claim is invalid. We did this simply for the reason of readability. The correctness of our recurs... | Summary: The paper presents EKM, a divide-and-conquer algorithm designed to solve the k-medoids problem exactly. Their algorithm guarantees globally optimal solutions in the worst-case $O(N^{k+1})$ time complexity.
They compare EKM against approximate algorithms and a state-of-the-art branch-and-bound (BnB) algorithm... | Rebuttal 1:
Rebuttal: > 1. Claims And Evidence, "However, it is not clear .."
Thank you for raising this question. Our generator’s efficiency stems from two key advantages:
First, as shown in Section 2.3 (lines 169–185, right panel), we organize combinations of the same size into a single list, stored in contiguous m... | Summary: The paper presents a recursive enumeration algorithm (EKM) for solving the K-medoids problem exactly. The proposed method guarantees global optimality and runs in worst-case O(NK+1) time. Its main contribution is a formal derivation of the algorithm using algebraic programming techniques, including a shortcut ... | Rebuttal 1:
Rebuttal: > 1. Theoretical Claims
We disagree with the reviewer’s claim that Ren's algorithm is polynomial. Our experiments in Sec 3.2 show that runtime for the UK, BM, and Seeds datasets—despite similar sizes—varies widely. Subsampling the UK dataset further reveals exponential runtime growth.
While the ... | null | null | null | null | null | null |
Generative Intervention Models for Causal Perturbation Modeling | Accept (poster) | Summary: This paper presents Generative Intervention Models (GIMs), a causal modeling framework designed to predict the effects of perturbations in complex systems with unknown underlying mechanisms. GIMs establish a mapping between perturbation features and a distribution over atomic interventions within a jointly inf... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and feedback. We appreciate that you recognize the strong empirical results of the work and the potential impact in scientific domains such as biology. You raised several important points that we address in detail below. Please let us know if you have any rema... | Summary: This paper studies the problem of predicting the unseen perturbation effect in a causal model. The authors propose the Generative Intervention Model (GIM) framework to learn the relationship between perturbation and distribution shift in a causal model. It is claimed that the GIM can predict perturbation featu... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and feedback. We are glad to hear that you find our work to be supported by clear and convincing evidence, as well as our experiments to be well designed. You raised several important points that we address in detail below.
> Jointly estimating both the causal ... | Summary: The authors considered the problem of predicting the impact of interventions with applications in gene perturbation prediction. In particular, in some applications, when an intervention is performed, it is unknown which variables are intervened on. However, some features of the intervention might be known. The... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We are glad to hear that you find our problem setting relevant, the experiments comprehensive, and the interpretability of our approach valuable. You raised several important points that we address below. Please let us know if you have any remaining concerns o... | Summary: This paper studies the problem of causal perturbation modeling to recover the causal structure and intervention targets given perturbation features from several interventional environments. The use-case in this paper is the gene perturbations in the biology domain. The authors propose generative intervention m... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and feedback. We are glad to hear that you find our approach addresses an important problem in causal generative modeling with an interesting use-case with great potential. You raised several important points that we address in detail below.
> overall objecti... | null | null | null | null | null | null |
Learning Safety Constraints for Large Language Models | Accept (spotlight poster) | Summary: The study proposes a geometric approach called SaP (Safety Polytope) for large language models (LLMs) to mitigate safety risks. SaP learns and enforces linear safety constraints directly in the model's representation space, identifying safe and unsafe regions. Experiments show it reduces adversarial attack suc... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and accurate summary of our paper. We appreciate your recognition that our "claims are supported by the evidence" and that "SaP indeed addresses the problem of learning safety constraints automatically for LLMs."
**Regarding Scalability**
We appreciate your q... | Summary: The authors propose to map unsafe model responses to save model regions in representation space without adjusting the weights of the respective model. Specifically, they represent safety constraints via polytopes and filter responses by assessing the similarity of latent features to the learned polytope. The m... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful review. We particularly appreciate your recognition of our paper's key strengths:
- The "novel method to steer model activations from harmful regions to safe regions"
- Our "several ablation studies to investigate the individual components of [our] mechan... | Summary: The paper introduces SaP, a post-hoc safety mechanism that defines a convex polytope in an LLM’s feature space. Using a Concept Encoder to disentangle safety-related features, it learns linear constraints that steer unsafe outputs into a safe region without retraining the model. Experiments show that SaP drama... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful analysis and valuable feedback.
We would like to first clarify that SaP is not primarily a jailbreak defense paper. Our key contribution is reformulating safety as a geometric constraint learning problem in representation space, which provides ... | Summary: The paper presents a novel approach to increase safety and the adversarial robustness of LLM. Instead of fine-tuning the parameter of the model for safety alignment, the introduced approach SaP (Safety Polytope) is applied during inference by enforcing linear safety constraints using Convex Polytope Machines i... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful analysis and constructive feedback. We appreciate your recognition of our paper's key contributions:
1. The novel geometric approach to LLM safety through representation space constraints.
2. The effectiveness of SaP in defending against adver... | null | null | null | null | null | null |
Faster Global Minimum Cut with Predictions | Accept (poster) | Summary: This paper investigates how predictions can be used to improve the running time of classic algorithms for the global minimum cut problem. Given a weighted graph $G$, the classic algorithm of Karger repeatedly selects edges randomly, proportionally to their weights, and contracts them until two vertices remain,... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and suggestions. We would like to highlight the following points:
+ **Knowing parameters:** Our theoretical guarantees indeed assume knowledge of $\rho, \eta$. However, the same analysis applies when only upper bounds on these parameters are available: simply rep... | Summary: Algorithms for boosting minimum cut algorithms with predictions are studied. The authors propose two methods: the boosted Karger’s algorithm and the boosted Karger-Stein method. These methods rely on predictions from a machine learning model to guarantee multiplicative improvements in runtime (Theorems 1.1 and... | Rebuttal 1:
Rebuttal: Thank you for the thorough review. We see our work as a first step in speeding up a fundamental combinatorial optimization problem, and we certainly agree that extensively assessing and demonstrating the empirical performance improvements of prediction-augmented algorithms for such problems is bot... | Summary: This paper presents an adaptation of two randomized algorithms for
mincut to take into account predictions about whether specific edges
appear in a minimum cut. For the simpler of the algorithms, the change
consists in simply making a randomized choice of edge by weighing the
edges by the prediction. The paper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review, and for appreciating our transparent writing style. | Summary: The paper studies global minimum cut with predictions. The problem is given a weighted graph $G$, find a partition of the vertices $S, V \setminus S$ that minimizes the total weight of edges crossing the cut. Without predictions, there are two main baselines: first a naive version of Karger's minimum cut algor... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We would like to highlight the following points:
+ **Theorem 1.2:** We agree with the reviewer’s observation regarding Theorem 1.2, but this is already taken care of in the theorem statement. Note that the bound in Theorem 1.2 is an ($\eta$-weighted) geometric ... | null | null | null | null | null | null |
Improving Flow Matching by Aligning Flow Divergence | Accept (poster) | Summary: The paper makes a very keen insight that the "TRUE" goal of flow matching is to
approximate the probabilty time series $(t \mapsto p_t)$ with the approximate probability time series
$(t \mapsto \hat{p}_t)$. In doing so, they begin from the difference
$\hat{p}_t - p_t$ and that, in order to make this smal... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We have revised the paper according to all reviewers’ feedback. In what follows, we provide point-by-point responses to your comments.
----
**Q1. It will be great if some knowledge can be shared regarding the relation between the choic... | Summary: The paper proposes a very simple KL loss combined with CFM loss to improve the training of flow-based models. The optimizing results are very general across different tasks with a basic improvement.
Claims And Evidence: NA
Methods And Evaluation Criteria: method
Theoretical Claims: NA
Experimental Designs ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We have revised the paper according to all reviewers’ feedback. In what follows, we provide point-by-point responses to your comments.
----
**Q1. Lack of implementation details which may indicate unfair comparisons.**
**Response:** In... | Summary: The paper proposes a modification to the flow matching / stochastic interpolant loss so as to better control the total variation distance between the model and the target at the final time of sampling, motivated by the fact that the standard loss is not sufficient to control the KL divergence (based on some as... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We have revised the paper according to all reviewers’ feedback. In what follows, we provide point-by-point responses to your comments.
---
**Q1. At the end of page 6, … it is not clear that the equation at the bottom of page 6 is an ex... | Summary: The paper seeks to use PDEs to construct a theoretical bound on flow matching, and improve upon it using said insight by adding a divergence mismatch to the loss term which improves upon the probability path. They construct experiments on simple generative examples, along with DNA sequence generation and video... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We have revised the paper according to all reviewers’ feedback. In what follows, we provide point-by-point responses to your comments.
----
**Q1. I would like to see the performance of their method on some more diverse video datasets o... | null | null | null | null | null | null |
Unifying 2D and 3D Vision-Language Understanding | Accept (poster) | Summary: The paper proposes UniVLG, a model that can be trained on both 2D and 3D vision-language data for both 2D and 3D tasks. Specifically, the model relies on pre-trained 2D image features and lifts 2D data to 3D to take advantage of large-scale 2D datasets. It also defines a mask decoding head which outperforms bo... | Rebuttal 1:
Rebuttal: Thank you for your review.
> (1) Qualitative results are included in the appendix now (figure 3 and 4). They should be included in the main paper.
Thank you for the feedback. We agree that these should be included in the main paper and we will incorporate them using the additional page given f... | Summary: This paper proposes a unified architecture for 2D and 3D vision language understanding. The method is based on Jain et al. 2024 where the additional innovations are in sharing all parameters between 2D and 3D instead of a subset, and extending the application to referential grounding, The paper uses a. number ... | Rebuttal 1:
Rebuttal: Thank you for your review.
> “special finding that instead of **freezing** the visual features, updating them is very crucial for 3D....”
We want to clarify a potential misunderstanding: One of our significant findings is not in unfreezing the visual features but rather on allowing them to att... | Summary: This paper presents UniVLG, a unified vision-language model designed to bridge the gap between 2D and 3D vision-language understanding in embodied AI systems. Given the scarcity of well-annotated 3D datasets, UniVLG explores the transfer of vision-language knowledge from well-curated 2D data to enhance 3D reas... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> “The citations in this paper are primarily limited to point clouds and NeRF (e.g., Panoptic-Lifting). However, relevant works on 3D Gaussian Splatting (3DGS) have been omitted, such as GOI [1] which leverages 2D RES models to achieve 3D RES.”
Thank you for this sug... | Summary: This paper presents a novel model called UniVLG for 3D vision-language tasks, including 3D visual grounding and 3D question-answering. By leveraging 2D visual grounding datasets, the model gains additional benefits, and the authors provide several empirical findings on improving performance—such as updating vi... | Rebuttal 1:
Rebuttal: Thank you for your review. We try to address your concerns below:
> “The method section focuses mainly on 3D-based visual grounding but provides limited details on the 2D-based visual grounding, making that aspect unclear.”
The method is identical between 2D and 3D visual grounding. The model ta... | null | null | null | null | null | null |
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection | Accept (poster) | Summary: This paper introduces **P2P (From Pixels to Perception)**, an instance-wise feature selection method aimed at improving interpretability by selecting **grouped semantic regions** instead of individual pixels. While interpretability is a key challenge, the approach **lacks novelty** and does not sufficiently di... | Rebuttal 1:
Rebuttal: Dear reviewer, we thank you for your comments and feedback. Please find below our answers to the open points.
> How does P2P compare to InfoMask?
We thank the reviewer for pointing out this baseline. InfoMask [1] is a nice information bottleneck-inspired approach that learns a masking on pixel l... | Summary: The paper proposes a method that learns a masking function that is able to semantically separating important information from background noise. As a part of this, the authors introduce a dynamic threshold based on classification probabilities to determine the level of sparsity for the instance. The authors eva... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and questions! Below is our point-by-point response.
> Ablating the choice of super pixel algorithm, since its just assumed as a fixed component without investigating the effect of it
We agree that the superpixels are a central part of our method, thus, war... | Summary: This paper presents P2P (Pixels to Perception), an inherently interpretable image-classification model that performs instance-wise feature selection using grouped feature sparsification at the superpixel level rather than individual pixels. The authors argue that sparsifying at the pixel level can lead to non-... | Rebuttal 1:
Rebuttal: Dear reviewer, Thank you for your thorough review and positive feedback on our work! As there are no questions, we will keep our response brief.
P2P is computationally efficient, as the computational overhead of the logit-normal covariance modeling is negligible compared to the rest of the archit... | Summary: The goal of this method is to improve the interpretability of machine learning models. This work proposes a new approach to inherent interpretability by sparsifying the input images for model predictions. To achieve this, the method masks semantically defined pixel regions instead of individual pixels and empl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and for their positive feedback. As there are no open questions, we keep our rebuttal brief.
We thank the reviewer for their comment on Figure 2 and will improve the clarity of the Figure in the camera-ready version of the paper.
Please let us know if there ... | null | null | null | null | null | null |
O-MAPL: Offline Multi-agent Preference Learning | Accept (poster) | Summary: This paper studies the problem of cooperative multi-agent reinforcement learning from preference data. The authors formulate the problem as cooperative Markov game with a global reward function where the goal of the agents is to learn optimal policies given offline pairs of trajectories with corresponding pref... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for carefully reading our paper, offering a positive evaluation, and providing valuable and insightful comments. To address your questions, we have conducted additional experiments, which are detailed in the **PDF** available via this anonymous link: https://1d... | Summary: This paper introduces O-MAPL, an end-to-end preference-based reinforcement learning framework for cooperative multi-agent systems, addressing the challenge of inferring reward functions from demonstrations in complex MARL settings. Prior methods often separate reward learning and policy optimization, leading t... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's detailed feedback and constructive questions. To address your questions, we have conducted additional experiments, which are detailed in the **PDF** available via this anonymous link: https://1drv.ms/b/s!AgChHLa7t5Bza5QJpMfi7YJX6PI
----
Below, we respond poin... | Summary: This paper proposes a multi-agent offline reinforcement learning algorithm named O-MAPL, addressing the problem of directly training multi-agent policies using human preference data under offline data conditions.
The authors highlight limitations of traditional two-stage methods (first learning a reward model,... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive evaluation and insightful feedback. Below, we provide detailed responses to your comments. To address your questions, we have conducted additional experiments, which are detailed in the **PDF** available via this anonymous link: https://1drv.ms/b/s!... | Summary: The paper introduces O-MAPL, a novel framework for multi-agent reinforcement learning that leverages human preference data to train cooperative agents without explicit reward modeling. Traditional MARL methods often require separate stages for reward learning and policy optimization, leading to instability and... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper and providing valuable feedback. Below, we address your concern in detail.
> I have one concern about the correctness of the derivation of the implicit-reward method. In section 4.2, the authors proposed to design a training objective function ... | null | null | null | null | null | null |
Improved Online Confidence Bounds for Multinomial Logistic Bandits | Accept (poster) | Summary: This paper enhances the regret bound for multinomial logistic bandits. The multinomial logistic bandit problem is a contextual bandit setting where each item is associated with a context vector, the player can select up to K items simultaneously, and the reward is determined probabilistically by a logistic mod... | Rebuttal 1:
Rebuttal: Thank you for your positive review! We are very pleased that the reviewer appreciates the significance of our technical contributions. In particular, we’re delighted that you found our proposed $\ell_\infty$-norm self-concordant property interesting. We hope this new property will inspire further ... | Summary: This work extends the MLE-based $\text{poly}(B)$-free online confidence sequences for generalized linear models, where $B$ is the norm-bound of the parameter set; to the setting of multinomial bandits to give a statistically SOTA algorithm (in terms of the dependence on $B$ and), which is also variance-depende... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our paper and for your valuable feedback! Below is our response to your question:
> *The online confidence sequence is claimed to be the "tightest", so could the authors provide some lower bounds on the width confidence radius itself, or at leas... | Summary: This paper studies the multinomial logistic bandits problem, in which the learner submits an assortment of at most $K$ arms and then receives binary feedback following the MNL model. The main contribution of the paper is the proposal of an improved method for this problem, yielding an online confidence set of ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and provide valuable feedback.
We would like to clarify that our main contribution is the development of the **first $B,K$-free, variance-dependent optimal** regret bound for MNL bandits, which cannot be achieved by existing algorithms such as tho... | Summary: The main contribution of this paper is proposing an improved online confidence bound for multinomial logistic models. Moreover, the authors applied their results to MNL bandits to achieve an enhanced result. Further, they also showed numerical experiments.
Claims And Evidence: I verified the correctness of so... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive support and recognition of the value of our work! We truly hope that this research helps to illuminate a new direction for MNL models and bandit algorithms. Please feel free to reach out if you have any further questions. | null | null | null | null | null | null |
Learning-Augmented Hierarchical Clustering | Accept (poster) | Summary: Hierarchical clustering, wherein vertices (representing items in a dataset) are grouped into clusters of increasing refinement following a tree structure, is a well-motivated procedure of interest to practitioners and gives rise to interesting theoretical problems. In particular, several of the most prominent ... | Rebuttal 1:
Rebuttal: Thank you for your careful review, positive evaluation, and helpful questions. Our responses and clarifications are as follows.
> Relation To Broader Scientific Literature
Thanks for pointing out the additional papers related to our work. We will add them and some discussions about the connectio... | Summary: The paper studies learning-augmented algorithms for hierarchical clustering (HC). In this problem, a set of data points is given along with a similarity measure, which induces a weighted undirected graph $G=(V,E,w)$. The goal is to partition the points/vertices into a binary tree that captures the hierarchical... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions and positive evaluation. Our responses to the questions are as follows.
> The error probability of the splitting oracle cannot be too large
We agree with the reviewer that this is a limitation of our algorithm, and due to the combinatorial nature of many s... | Summary: This paper explores hierarchical clustering in a learning-augmented framework. Unlike other clustering methods such as $k$-means or $k$-medians, hierarchical clustering constructs a clustering tree $\mathcal{T}$ to represent similarity across all item pairs, and does not have a predetermined target number of c... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful questions. Our responses and clarification are as below.
> The paper deviates from the standard setting of learning-augmented algorithms, which assumes no guarantees on the quality of predictions.
Although there is a body of literature that assum... | Summary: Brief Summary of the Paper:
The paper introduces and studies learning-augmented algorithms for hierarchical clustering (HC) where the type of advice given comes in the form of a splitting oracle. This continues a long line of research on algorithms with ML predictions (or augmented with ML advice) and extends... | Rebuttal 1:
Rebuttal: Thank you for your careful review and positive evaluation. Our responses are as follows.
> Additional References
We agree with the reviewer that discussing the related papers and in particular the role of adversarial noise will help demonstrate the challenges with constructing a near-optimal tre... | null | null | null | null | null | null |
Pfeife: Automatic Pipeline Parallelism for PyTorch | Accept (poster) | Summary: This paper proposes Pfeife, an automated tool that integrates with PyTorch to transparently partition and pipeline large machine learning models across multiple GPUs. It leverages PyTorch's JIT tracing to construct a data-flow graph of the model and then optimizes the pipeline schedule. Experimental evaluation... | Rebuttal 1:
Rebuttal: We would to thank the reviewers' time and feedback.
Reviewer aSA1
- Unfortunately, we only have access to the 2 machines we used in the experiments.
We don't have budget for more.
- Regarding correctness, first we note that we run an order of magnitude more
models than most academic papers. I... | Summary: The paper introduces Pfeife, a new tool that integrates with PyTorch to provide automatic pipelining of machine learning models.
Pfeife aims to address the memory limitations of GPUs when training large models by parallelizing the execution of these models across multiple devices.
It leverages PyTorch's tracin... | Summary: The paper introduces Pfeife, a system integrating with PyTorch's `torch.compile` to automate pipeline parallelism without user intervention. Pfeife partitions models at an operation-level granularity across multiple GPUs, employing a cost model combined with beam search to optimize pipeline scheduling. Key cla... | Summary: This paper presents Pfeife, a tool that automatically performs pipeline parallelization of PyTorch models. Compared to prior methods, the main innovation is that the pipelining is performed in a manner that is completely transparent to the developer, requiring no manual annotations. Specifically, Pfeife is imp... | null | null | null | null | null | null | |||
CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation | Accept (poster) | Summary: The paper introduces CombiMOTS - a Pareto Monte Carlo Tree Search based approach that is designed to efficiently handle complex multi-objective optimization problems. In particular, the authors tackle the dual-targeting molecule generation problem and evaluate the framework using three protein pairs. For this,... | Rebuttal 1:
Rebuttal: Dear reviewer **KAF5**, thank you for your constructive suggestions. We address your concerns below and through https://anonymous.4open.science/r/CombiMOTS-0FEB.
Concerns
-
### 4. Synthesizability Metrics and Retrosynthetic Evaluation
We assess CombiMOTS's synthesizability against baselines to ... | Summary: The paper proposes CombiMOTS, a combinatorial multi-objective tree search framework for dual-target molecule generation. It integrates Pareto Monte Carlo Tree Search (PMCTS) with fragment-based synthesis-aware generation. Key contributions include: (1) A reduced synthesizable fragment space via target-aware bu... | Rebuttal 1:
Rebuttal: Dear reviewer **pmFQ**, thanks for providing valuable feedback to our work! The requested theoretical analysis on PUCB significantly clarifies convergence speed and solution optimality. We address your concerns and questions below and through https://anonymous.4open.science/r/CombiMOTS-0FEB.
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... | Summary: The paper introduces CombiMOTS, a Pareto Monte Carlo Tree Search (PMCTS) framework for generating dual-target molecules, which are molecules that can interact with two target proteins simultaneously. The authors argue that existing methods often simplify the dual-target optimization problem by linearly combini... | Rebuttal 1:
Rebuttal: Dear reviewer **h6f3**, thank you for your constructive review which improved our perspective! Particularly, your comments provided more clarity regarding the Pareto superiority against baselines. We address your concerns below and through https://anonymous.4open.science/r/CombiMOTS-0FEB.
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Con... | Summary: This paper introduces CombiMOTS, a novel method for dual-target molecule generation. It addresses the limitations of existing approaches, which often simplify the multi-objective nature of the problem into a linear combination of objectives and may not consider synthesizability. CombiMOTS leverages Pareto Mo... | Rebuttal 1:
Rebuttal: Dear reviewer **6Myy**, we appreciate your insights which helped improve our work! Particularly, ablation studies allowed us to strengthen our claims. We address your concerns below and through https://anonymous.4open.science/r/CombiMOTS-0FEB.
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Concern
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> A) "SA score doesn't guarantee perfec... | null | null | null | null | null | null |
Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data | Accept (spotlight poster) | Summary: The paper addresses Q-value extrapolation errors in offline reinforcement learning (RL). It identifies linear extrapolation beyond the data range as a key issue and proposes two methods to mitigate it: (1) reward scaling with layer normalization (RS-LN) and (2) penalizing infeasible actions (PA). These compone... | Rebuttal 1:
Rebuttal: We sincerely appreciate the careful review of our work, the clarification of various components of PARS, and your thoughtful suggestions.
### [R1] Ablation study separating the effects of RS-LN and PA
Beyond the ablation in Figure 9, we isolated PA in a separate experiment. As shown in **Fig B of... | Summary: In this paper authors address Q-value overestimation problem in offline RL when in the presence of infeasible actions. Authors propose to use two diffferent strategies together, to scale reward and penalize infeasible actions. Layer normalization, as proposed in previous research, is also found to be important... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and your constructive suggestions that allowed us to strengthen areas we may have initially missed.
### [R1] Empirical evidence on Q-value overestimation reduction
Thanks for the suggestion. Measuring extrapolation error in high-dimensional state-act... | Summary: The paper introduces a method for applying an OOD penalty using reward scaling and LN. When combined with a modified TD error loss—incorporating a PA penalty—the proposed approach enhances TD3-BC’s performance, achieving strong results on benchmarks, particularly in maze tasks.
I quite like the paper, as it ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive feedback on our work and the opportunity to clarify any remaining uncertainties.
### [R1] Theoretical justification of PARS
Thank you for the suggestions. As theoretical analysis of deep neural networks with nonlinearities is highly challenging, we empirically ... | Summary: The manuscript introduces penalizing infeasible actions and reward scaling (PARS), a method for discouraging value overestimation caused by extrapolation in offline reinforcement learning (RL). The proposed method uses reward scaling and layer normalization, which are shown to work together to increase the fea... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive evaluation of our contribution and suggestions regarding various prior studies and potential directions for extending PARS.
### [R1] Comparison of tuning search budget for hyperparameters with baselines
As noted in our manuscript, we tuned around 8 hyperparam... | null | null | null | null | null | null |
Grokking Beyond the Euclidean Norm of Model Parameters | Accept (poster) | Summary: The paper studies grokking i.e., delayed generalization analytically in sparse recovery and matrix factorization settings. The main finding i in the paper provides settings that question the accepted wisdom in grokking - a small low $L_{2}$ regularization (weight decay) is necessary for grokking and generaliz... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and thoughtful feedback. We sincerely appreciate the effort put into evaluating our work and offering constructive suggestions. Below, we respond to each of the points raised.
**Theoretical Claims**
We've rewritten the proofs of the two main theorem... | Summary: This paper studies grokking phenomenons in the setting where the model has a certain special property $P$, and reveals that by using GD with a small but non-zero regularization of $P$ it is possible to observe grokking. In addition, it shows that modifying model depth or performing data selection can also ampl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and the time spent reviewing our work. Our responses to the comments are provided below.
**Weaknesses**
1) We have rewritten both sections to emphasize the fundamental difference between the two main theorems (2.1 about sparse recovery and 3.1 a... | Summary: The paper mainly focuses on the theoretical approach to debunk the necessity of L2 norm for exhibiting grokking phenomenon. As an alternative, the authors suggest that sparsity of the solution space can be alternative condition to have grokking phenomenon in deeper layer (proven practically). The paper include... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and insightful comments. Please find our responses to the points below.
**Claims And Evidence**
* We worked in a setting where the notion of sparsity (resp. low rank) is well defined, $\mathbb{R}^n$ (resp. $\mathbb{R}^{n_1 \times n_2}$). This is the number of no... | Summary: The authors study delayed generalization (grokking) in sparse/low rank recovery tasks. The authors focus on the transition from an overfitting solution to a generalizing solution, both in linear sparse recovery and in low rank matrix factorization. For the linear cases, the authors derive the scaling of the gr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. We appreciate the effort taken to evaluate our work and provide constructive feedback. Below, we address each of the points raised.
**Weaknesses**
We understand that the paper is poorly organized. That is why, after submission, we com... | null | null | null | null | null | null |
When Maximum Entropy Misleads Policy Optimization | Accept (poster) | Summary: The paper examines failure cases of Maximum Entropy (MaxEnt) RL, showing how entropy maximization can mislead policy optimization. It introduces the Entropy Bifurcation Extension, a theoretical construct proving that MaxEnt RL can drive policies toward suboptimal actions (Theorem 5.5, Proposition 5.6). Empiric... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and questions. Indeed, we focused on understanding the principles behind potential limitations of MaxEnt (SAC being its best performing and most widely-used version), complementing most existing results on the benefits of entropy regularization. Although we phr... | Summary: The paper examines the trade-off in MaxEnt RL: while the entropy term fosters exploration and robustness, it can mislead policy optimization in tasks requiring precise, low-entropy actions, often causing MaxEnt methods like SAC to converge to suboptimal policies.
The authors formalize this phenomenon with a t... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and questions. We agree with the reviewer that comparisons with wider range of baselines will enhance the empirical analysis, and we will expand the paper accordingly. Our current formulation of SAC-AdaEnt is mainly intended for empirically testing the theoreti... | Summary: The authors analyze the trade-off between exploration/robustness and exploitation in Maximum Entropy Reinforcement Learning though a variety of control tasks. The paper demonstrate that in performance-critical control tasks requiring precise, low-entropy actions, Maximum Entropy approaches can become misguided... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and questions. We agree that more discussion and analysis can be done for SAC-AdaEnt, and we provide some more details below. Indeed, we formulated SAC-MaxEnt less as an algorithm that is intended to replace SAC, but more as an ablation mechanism that tests the... | Summary: This paper posits that the entropy maximization objective in SAC can lead to failure in tasks that require precise, low-entropy policies, essentially "misleading" policy optimization.
Claims And Evidence: No. There are several problematic claims.
1. The central claim about the misleading effect relies on $\a... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions. We will reorganize the writing to make the core claims clear from the beginning of the paper. The toy example seems to have obscured the core results, which we further explain here.
**Q: Large $\alpha$ value?**
> - In fact, the entire argument of the ... | null | null | null | null | null | null |
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion | Accept (poster) | Summary: The authors propose a framework based on the Masked Discrete Language Model (MDLM) (PepTune) to generate and optimize therapeutic peptides. They claim that the main contributions of their model are: 1) using MDLM to generate peptide SMILES representations, 2) introducing NELBO and reverse-posterior, 3) using M... | Rebuttal 1:
Rebuttal: We are grateful for your insightful review.
**Essential References Not Discussed:**
We appreciate the reviewer’s suggestion to consider Wang et al. (2024). Although the title may suggest a close relation to our work, the scope, modeling assumptions, and core goals differ significantly. MMCD gen... | Summary: This paper presents PepTune, a Masked Discrete Language Model (MDLM)-based generative framework for generating new peptides. Unlike existing approaches that struggle with multi-objective optimization or rely on continuous approximations, PepTune operates natively in a discrete sequence space while optimizing f... | Rebuttal 1:
Rebuttal: Thank you for your detailed suggestions.
**Claims and Evidence:**
- We conduct an ablation study as suggested. From each model, we sampled 100 sequences of token length 100 and checked validity with our SMILES2PEPTIDE decoder. We show that both the bond-dependent masking and invalid loss improve... | Summary: The paper proposes a new model for novo generation and optimization of peptide. The model operates on SMILES and performs multi-objective-guided discrete diffusion; it can handle non-canonical amino acids and cyclic modifications. PepTune employs a bond-dependent masking schedule. To guide the diffusion, PepTu... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer’s thoughtful review.
**Essential References Not Discussed:**
We request that the reviewer refer to Appendix C, which provides an extensive analysis of prior discrete diffusion and classifier-based and classifier-free guidance methods. We also discuss at the ... | Summary: The paper proposes PepTune, a discrete diffusion model that enables multi-objective guidance for generating and optimizing therapeutic peptide SMILES. PepTune adapts a bond-dependent masking schedule and global sequence invalid loss to improve discrete diffusion performance on peptide, and uses an MCTS-based s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
First, we would like to respectfully disagree with the assertion that the guidance strategy is inadequately evaluated. Given that the paper is on **Application-Driven ML Track**, we chose case studies that provide robust evaluation to prove eff... | null | null | null | null | null | null |
Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning | Accept (poster) | Summary: Traditional methods predict labels directly from data inputs, but recent tasks often involve more complex labels. To address this, the authors propose Predictive Consistency Learning (PCL), inspired by consistency training in generative models. Unlike traditional approaches, PCL incorporates noise-perturbed la... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, nice suggestions, and for acknowledging our work. Below we respond to your specific comments.
> **Q1: The proposed multi-step prediction approach brings up concerns regarding its efficiency. How much longer does the training process take compared to standard supe... | Summary: This paper aims to leverage more rich label information to enhance supervised learning. To achieve this goal, it proposes a new learning method called PCL (predictive consistency learning). Different from those conventional supervised learning approaches, PCL learns from both the labels as well as the noise-pe... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable comments and nice suggestions. However, we believe there may exist some misunderstandings regarding the methodology. We regret any confusion caused by our presentation and would like to clarify the core methodology of PCL.
What PCL does is to leverage the conc... | Summary: This paper introduces a new prediction paradigm for more complex label spaces than the ones that are traditionally assumed in supervised learning. The paper draws inspiration from the "generative consistency learning" learning paradigm to produce the predictive consistency learning (PCL) paradigm, which the au... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, nice suggestions, and for acknowledging our work. Below we respond to your specific comments.
> **Q1: How this work relates to the field of structured prediction.**
Thanks for your insightful suggestion. Indeed, our work also handles the challenge of structured ... | Summary: The paper introduces Predictive Consistency Learning (PCL), a paradigm for tasks with complex labels. The paper starts with arguing that traditional supervised learning struggles with high-dimensional or structured labels due to the difficulty of mapping compressed input features directly to rich label spaces.... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, nice suggestions, and for acknowledging our work. Below we respond to your major comments.
> **Q1: Qualitative comparison to methods that project the output space to a simpler latent space.**
Introducing an encoder-decoder structure with a $Y\to Y_E$ encoder an... | null | null | null | null | null | null |
SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion | Accept (poster) | Summary: This paper introduces the SBGD model to address the scalability and size generalization challenges of Graph Diffusion Generative Models (GDGMs). Traditional GDGMs struggle with high memory requirements and poor generalization to graph sizes not seen during training. SBGD mitigates these issues by refining grap... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the insightful comments and helpful suggestions. These greatly help us improve upon the current paper, and we appreciate the opportunity for addressing your questions and concerns here.
## Clarification of Graph Size
We intended the term "graph size" to specifically ... | Summary: The manuscript introduces SBGD, a graph diffusion generative model based on a block representation inductive bias and aiming for lower memory complexity in the large-graph limit. SBGD first partitions the graph's nodes into $k$ non-overlapping groups using some pre-determined non-learned algorithm (here METIS)... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your knowledgeable comments and insightful feedback. We greatly appreciate the time and effort you’ve invested in evaluating our work. Below, we address your concerns and questions. Please note that the responses may not follow the exact order of your origin... | Summary: This paper proposes a block graph representation on top of the diffusion model framework for graph generation. It claims to resolve the scalability and size generalization problems of existing models and comes with experiments on various benchmark datasets. The paper is generally well-written and easy to follo... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your detailed comments and thoughtful questions! They significantly help us improve the clarity and rigor of our manuscript. We appreciate the opportunity to address your concerns and questions below:
## Details on Graph Sizes in Datasets
We apologize for ... | null | null | null | null | null | null | null | null |
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization | Accept (poster) | Summary: The paper proposes to use (a specific variant of) preference-based RL losses instead of regular reward-based policy gradients for combinatorial optimization problems.
The primary finding is that this Preference Optimization for Combinatorial Optimization (POCO) loss improves sample complexity and asymptotic p... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
>**Q1:** I would've thought that for combinatorial optimization which indeed has precise rewards, using preferences (which is imprecise) can be worse.
We agree that precise rewards are also crucial to combinatorial optimization. **Meanwhile, we would like to... | Summary: This paper first introduce the concept of Preference Optimization to the area of NCO. Generally, as the expected advantage of PO, POCO demonstrates instance-efficiency comparing to RL and SLL. (As shown in Figure 2 and Figure 3)
## update after rebuttal:
This is a novel and inspiring paper, so I keep my posi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
>**Q1:** I recommend to also includes SL-based POMO as baseline.
We have added results from the SL-based method [1] to the table E. Since all methods use the same model, differences stem from training algorithms.
**POCO achieves comparable or better perfor... | Summary: The introduction of POCO, a training paradigm for NCO to enhance sample efficiency. This is accomplished by (1) design a preference pair construction method for improving exploration and exploitation, (2) gradual building of the loss function. This is evaluated on three problems Job shop scheduling, TSP, and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and incisive questions.
>**Q1:** Concorde is not used for TSP.
We have included Concorde's results from POMO [1] and added them to the table C for comparison. Concorde, LKH3, and Gurobi are traditional iterative search-based algorithms with ide... | Summary: This paper proposes POCO (Preference Optimization for Combinatorial Optimization), a method that integrates preference learning into reinforcement learning (RL) to address combinatorial optimization problems. Additionally, the authors introduce Hybrid Rollout, Uniform Filtering, and Best-anchored Pairing as me... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
>**Q1:** it remains unclear precisely how sample efficiency is enhanced.
We have analyzed POCO's higher sample efficiency in Section 5.4. We further precisely demonstrate significant improvements in sample efficiency by supplementing quantitative results. No... | null | null | null | null | null | null |
Value-Based Deep RL Scales Predictably | Accept (poster) | Summary: This paper studies the scaling law in value-based RL methods. In particular, it provides a thorough analysis on how different components, such as batch size and number of gradient steps, affect the performance and computation budget.
***
## update after rebuttal
I have read the rebuttal as well as other resp... | Rebuttal 1:
Rebuttal: Thank you for the feedback and a positive review of the paper. We are glad that you find our evidence clear and convincing. We answer your questions below. Please let us know if you find your concerns addressed, and if so we would be grateful if you would be willing to raise your score. We are hap... | Summary: The main claim of the paper is that it is possible to predict optimal hyperparameters, data quantity and compute allocation for high budget from low-budget experiments. This is broken down into the following sub-claims:
1. The amount of data needed for a given performance is predictable as a function of the ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and feedback! We have added several new results & clarifications to address the concerns. Specifically, for the main points:
- We have performed the requested additional analysis to improve clarity here: https://sites.google.com/view/value-based-rl-scales/
- We el... | Summary: The authors demonstrate that value-based deep RL scales predictably, showing a Pareto frontier controlled by the updates-to-data (UTD) ratio. This paper shows how the optimal hyperparameters can be predicted from low-cost experiments, enabling an extrapolation to higher data or compute experiments. Validation ... | Rebuttal 1:
Rebuttal: Thank you for the review and the positive feedback. Please let us know if the response below addresses your concerns, and if there are any concerns remaining.
> Although the evaluation criteria is fitting for the problem at hand, I would have liked to see some experiments on harder, pixel-based ... | Summary: This paper investigates the scalability and predictability of value-based RL using TD learning. It establishes predictable, hypothetic equations between three key hyperparameters (batch size, learning rate, and UTD ratio) and shows that data and compute requirements for a given performance lie on a Pareto fron... | Rebuttal 1:
Rebuttal: Thank you for valuable feedback regarding our work. In accordance with your suggestions, we have made a number of changes to our manuscript. These include expanded discussion of limitations and experimental design. We discuss these in detail below:
> (other parameters such as model size, weight d... | null | null | null | null | null | null |
Learn from Downstream and Be Yourself in Multimodal Large Language Models Fine-Tuning | Accept (poster) | Summary: This paper addresses the issue of catastrophic forgetting in the fine-tuning of multimodal large language models (MLLMs). The proposed method, Specialization via Importance Discrepancy Evaluation for Refinement (SPIDER), introduces an innovative approach for measuring parameter importance by utilizing the magn... | Rebuttal 1:
Rebuttal: Dear Reviewer mGiB:
Thank you for your valuable comments and constructive feedback. Below, we carefully address each of your concerns point-by-point, providing detailed explanations and additional evidence to clarify our approach.
**Q1: Memory Complexity** (Other Strengths And Weaknesses)
A1: F... | Summary: In this work, it focuses on the Multimodal Large Language Models (MLLMs) fine-tuning field and reveals the catastrophic forgetting on the pre-training knowledge. Authors assess parameter importance for both generalization and specialization, focusing on identifying downstream-important elements and performing ... | Rebuttal 1:
Rebuttal: Dear Reviewer CY1z:
Thank you for your insightful review and valuable feedback. Below, we address your key concerns in detail, aiming to clarify and demonstrate the effectiveness of our proposed approach.
**Q1: Discussion on Incremental Learning** (Essential References Not Discussed & Questions... | Summary: This paper introduces a novel and well-structured strategy to address the persistent challenge of catastrophic forgetting that arises during the fine-tuning of Multimodal Large Language Models (MLLMs). This method leverages a meticulous analysis of parameter importance discrepancies to guide the optimization p... | Rebuttal 1:
Rebuttal: Dear Reviewer mmMS:
Thank you very much for your affirmation of our work, as well as the insightful concerns and questions you have raised. We have carefully considered each comment and provided responses.
**Q1: Value Range Boundary and Normalization Operation Rationale** （Theoretical Claims）
A1... | Summary: This manuscript addresses the catastrophic forgetting issue in fine-tuning MLLMs. It proposes SPIDER, which measures parameter importance based on pre-trained weight magnitudes and fine - tuning gradients. SPIDER uses Importance Discrepancy Measurement (IDM) to rank parameter importance and Importance Selectio... | Rebuttal 1:
Rebuttal: Dear Reviewer mG7E:
We sincerely appreciate your time and effort in reviewing our paper. Through the detailed responses outlined below, we seek to fully address your concerns and provide transparency into our proposed approach.
**Q1: Essential Reference Discussion and Comparison** (Essential Ref... | null | null | null | null | null | null |
LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D | Accept (spotlight poster) | Summary: This paper proposes a method called Locate3D, which addresses the task of referring expression-based localization in 3D. The method consists of three key stages: (1) pre-processing stage in which an input RGB-D stream is processed using 2D foundation models SAM, CLIP and DINOv2 in order to obtain 2D features ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thorough reading of our manuscript and valuable comments. Below we address each point raised in detail
**Clarification regarding our LX3D**
We noted the reviewer mentions our dataset being automatically generated, we'd like to clarify that our dataset is human... | Summary: This paper focuses on object localization via referring expressions in 3D real-world scenes to understand the 3D physical world, which is a valuable task in 3D perception and understanding. It introduces an end-to-end 3D transformer network to get point cloud, 3D features, and text query as input, and output t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. Their comments have helped us identify areas where we can better explain and justify our technical contributions. We address each comment in detail below.
**Deriving better 3D features from 2D features; 3D-JEPA**
The 3D nature of our approach co... | Summary: The paper proposes Locate 3D, a model for 3D grounding which achieves SOTA performance and strong out-of-domain generalization.
Specifically, a novel self-supervised learning method, 3D-JEPA, is proposed, generating contextualized features for the scene. Also, a new dataset LX3D is proposed to test the robustn... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback. Below we address each point in detail
**Clarification on mesh PC vs sensor PC**
By "Mesh PC" we refer to pointclouds sampled from carefully reconstructed 3D meshes that undergo extensive post-processing. Most prior works use this format as it’s ... | Summary: This paper introduces Locate 3D, a model for localizing objects in 3D scenes from referring expressions, achieving state-of-the-art performance on standard referential grounding benchmarks. The key innovation is 3D-JEPA, a self-supervised learning (SSL) algorithm to learn contextualized scene representations. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and constructive feedback. Below we address each point in detail.
**Clarification about AR application**
Indeed, we do not demonstrate deployment on an AR device. Our intended claim is that Locate 3D's input format is directly compatible with data ... | null | null | null | null | null | null |
AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses | Accept (oral) | Summary: This paper introduced a benchmark for evaluating LLMs' ability to autonomously bypass adversarial defenses. Unlike existing benchmarks, which predominantly comprise small-scale proxy tasks, this framework provides a more rigorous assessment of LLMs' capacity to replicate tasks typically conducted by security p... | Rebuttal 1:
Rebuttal: We are glad the reviewer finds our claims substantiated, the completeness of our paper commendable and our method novel. We also thank the reviewer for providing relevant references. We will provide more details on attack and defense protocols for autonomously testing automated attacks in the came... | Summary: This paper introduces AutoAdvExBench, a benchmark for evaluating large language models' ability to autonomously exploit defenses against adversarial examples. Unlike proxy security benchmarks, AutoAdvExBench directly measures LLMs' success on tasks regularly performed by machine learning security researchers. ... | Rebuttal 1:
Rebuttal: We are happy that the reviewer believes that our benchmark addresses an important gap and recognizes the significant effort and contribution.
**Would success on this benchmark translate on novel research results?** Papers that included breaks of several adversarial example defenses were published... | Summary: The paper proposes a new benchmark to evaluate LLMs and in particular their reasoning capabilities. The tasks purposed in the paper is an end-to-end real world task that consists in generating the code of a new attack based on and existing defence for image classification. In practice the LLM based agent has a... | Rebuttal 1:
Rebuttal: We are glad that the reviewer appreciates the real-world oriented approach of our benchmark. We address the questions as follows:
**Evaluation metric**. Can the reviewer please clarify what they mean more precisely? Does the reviewer believe that the steps that we ask the models to do (i.e., impl... | null | null | null | null | null | null | null | null |
Whitened CLIP as a Likelihood Surrogate of Images and Captions | Accept (poster) | Summary: ## update after rebuttal
I will keep my score as is (3)
This paper proposes Whitened CLIP (W-CLIP) that transforms the CLIP latent space, providing direct access through log-likelyhood function. W is computer only once, based on data, a-priories.
Claims And Evidence: Yes. The whitening transform in section 3... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and comments. Below, we address key concerns regarding the applicability of our approach to zero-shot settings, and address the reviewers questions.
### **Methods and Evaluation Criteria**
To address the reviewer’s comment regarding zero-shot... | Summary: The authors propose the use of a whitening transform in the CLIP space, which offers an efficient solution in closed-form via the SVD. Using “WCLIP” they explore a wide number of practical downstream tasks one can tackle using the now-quantifiable likelihood (OOD image detection, caption complexity, quantifyin... | Rebuttal 1:
Rebuttal: We value the reviewer’s critical insights and suggestions. Below, we provide detailed clarifications and supporting evidence addressing the concerns raised.
### **Experimental Designs or Analyses**
#### **Text Complexity**
One example in Fig. 5 does show higher likelihood for a shorter sentence... | Summary: This paper studies the CLIP features to approximate the likelihood of images and captions. The presented method, Whitened CLIP (W-CLIP), uses an invertible linear operation to convert the CLIP features into a zero-mean, unit standard deviation space. This normalized embedding space can be used in many cases, i... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed and thoughtful feedback. Below, we address each point raised.
### **Methods and Evaluation Criteria**
#### **First Point, first part**
> *"While we have qualitative examples in Fig 2 and Fig 8 to show that W-CLIP can detect artifacts in synthetic images, we... | null | null | null | null | null | null | null | null |
Identifying Metric Structures of Deep Latent Variable Models | Accept (poster) | Summary: This paper addresses the problem of learning identifiable representations from a novel perspective, focusing on the distances between representations rather than their coordinates. The authors begin by discussing the challenge of identifiability in latent variable models, emphasizing that maximum likelihood es... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful feedback, as well as their support for acceptance.
>However, I find that the experiments do not strongly validate the hypothesis that geodesic distances are truly identifiable. The main result, particularly Figure 7
We emphasize that the main result of the pa... | Summary: In this paper, the authors address the challenge of statistical identifiability in deep latent variable models, which are used to extract condensed representations of data. Traditional methods attempt to improve identifiability by imposing constraints like labeled data or limiting model expressivity. Instead, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable feedback and support for acceptance.
>I believe there can be more benchmarks included in this paper. Other commonly used image datasets, such as Fashion-MNIST, SVHN, Cifar-10, should also be computationally cheap to run.
While the focus of our work... | Summary: This paper studies the geometry of latent spaces of latent variable models like VAEs, normalizing flows, diffusion models etc. Primarily, the authors highlight that many seemingly simple factors like the latent coordinates or their pairwise euclidean distances etc. of latent variable models are provably not id... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for valuable feedback and favoring acceptance.
>There is scope to be much more comprehensive in the experiments - for e.g. I would be very glad to see a table similar to Figure 7 for the transcriptomic data example from Figure 1 and that would solidify the main me... | null | null | null | null | null | null | null | null |
Physics-Informed Generative Modeling of Wireless Channels | Accept (poster) | Summary: This paper introduces a physics-informed generative model for wireless channel modeling, addressing challenges in data efficiency, generalizability, and physical interpretability. Unlike traditional stochastic models or recent GAN-based methods, the authors propose a Sparse Bayesian Generative Modeling (SBGM) ... | Rebuttal 1:
Rebuttal: We thank reviewer BGie for the reviewer's comments. In the following, we address the reviewer's questions and concerns.
**To Other Strengths And Weaknesses (2. paragraph):** We agree with the reviewer that further ablations studies regarding various hyperparameters would improve our work's quali... | Summary: The paper presents a new way to model wireless channels by combining basic physical principles with modern generative modeling techniques. Instead of using complex black-box methods like GANs—which require lots of clean data—the authors introduce more structured approaches based on simplified versions of VAEs ... | Rebuttal 1:
Rebuttal: We want to thank reviewer dNNa for the thorough review and the reviewer's assessment. In the following, we address the reviewer's additional questions.
**To Experimental Designs Or Analyses (1. paragraph) & Weaknesses & Questions For Authors (1):** We agree with the reviewer that experimental res... | Summary: This paper introduces a physics-informed generative modeling approach for wireless channels. By using sparse Bayesian generative modeling and knowledge about the conditional channel moments, this paper addresses limitations in existing methods such as the need for high-quality data, lack of generalizability, a... | Rebuttal 1:
Rebuttal: We thank reviewer 9VKv for the review and the reviewer’s assessment. In the following, we address the concerns and questions raised.
**To Relation To Broader Scientific Literature & Other Strengths and Weaknesses (bullet point 1):** We agree that our proposed model can be utilized in the context ... | Summary: The paper proposes a physics‐informed generative modeling framework for wireless channels that integrates physical channel knowledge with sparse Bayesian generative modeling (SBGM). The method consists of several key components:
Channel Representation via Compressibility:
The authors begin by exploiting the f... | Rebuttal 1:
Rebuttal: We thank reviewer 9aFT for the thorough summary and review. In the following, we address the concerns and questions raised.
**To Methods and Evaluation Criteria (2. paragraph) & Relation to Broader Scientific Literature & Questions for Authors (1. paragraph):** We agree with the reviewer that Sec... | null | null | null | null | null | null |
CoDy: Counterfactual Explainers for Dynamic Graphs | Accept (poster) | Summary: This paper introduces CoDy (Counterfactual Explainer for Dynamic Graphs), a method for generating counterfactual explanations to interpret predictions made by Temporal Graph Neural Networks (TGNNs) on continuous-time dynamic graphs (CTDGs). Existing explanation methods focus on static graphs or factual explana... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and insightful feedback on our paper. Please find our rebuttal to the weaknesses below:
**W1: Potential bias in selection policies**
The selection policies are indeed designed as heuristics to introduce a bias, guiding the search efficiently within the vast c... | Summary: The paper introduces CoDy, a method for generating counterfactual explanations for Temporal Graph Neural Networks (TGNNs). Unlike existing methods that focus on factual explanations, CoDy explores how minimal modifications to a dynamic graph can alter predictions (counterfactuals).
CoDy employs Monte Carlo T... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and valuable feedback.
**1.** CoDy strictly removes events from the input graph reflecting the causal relationship between those events and the prediction; i.e., if events $\\{e_i,...\\}$ would not have happened, the prediction would be different. The count... | Summary: This paper proposes CoDy and GreeDy, two algorithms that can generate counterfactual explanations for continuous-time dynamic graphs (CTDGs). The main component of the algorithm is a Monte-Carlo-Tree Search to find good edge combinations for removal. The method is experimentally evaluated on three dynamic grap... | Rebuttal 1:
Rebuttal: We sincerely thank you for the detailed and insightful review.
**Runtime vs. Performance Trade-off**
The noted potential 10-fold increase in model calls for CoDy is largely a configurable design choice, not an inherent limitation of CoDy. CoDy, in our experiments, continues searching after findi... | null | null | null | null | null | null | null | null |
Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs | Accept (poster) | Summary: This work proposes a synthetic data generation method with a differential privacy guarantee. It leverages a lightweight data generator and a topic model for topic clustering. This method tackles the limitation of the existing DP fine-tuning method, which is expensive and relies on large LLM, and the prompt-bas... | Rebuttal 1:
Rebuttal: We appreciate your encouraging remarks that our method is reasonable, our experimental design is both proper and comprehensive, and that our analyses are well-supported with most claims being appropriately justified.
### Privacy Budget Allocation
Our budget allocation follows [1], our "Post-Gener... | Summary: This paper presents CTCL, a framework that synthesizes privacy-preserving data by combining a lightweight 140M parameter generator with a clustering-based topic model. The generator is differentially privately fine-tuned on private data, while the topic model produces a DP topic histogram to capture high-level... | Rebuttal 1:
Rebuttal: Thank you for your supportive and detailed comments! We are encouraged by your feedback that our approach is novel, our experiments and analyses are comprehensive, and that our method makes significant advancements in privacy-preserving data synthesis.
### Privacy Attacks
According to the post-... | Summary: This paper introduces CTCL (Data Synthesis with Controllability and Clustering), a framework to generate privacy-preserving synthetic text data without fine-tuning LLMs or extensive domain-specific prompt engineering. The CTCL framework consists of two primary components: a lightweight 140M-parameter condition... | Rebuttal 1:
Rebuttal: Thank you for your encouraging remarks that our claims are well-supported by clear experiments and detailed analyses, and our proposed method is reasonable.
### Generator design choices
We use a seq2seq generator because its encoder is well-suited for understanding the conditions. For size, the 1... | Summary: The paper proposes a novel framework for generating privacy-preserving synthetic text data called CTCL. The authors claim that previous works mainly utilized fine-tuning the LLMs with differential privacy, which is computationally expensive or relies on extensive prompt engineering, which is time-consuming and... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the adequate discussion in our paper writing, the accurate identification of limitations in prior approaches, the efficiency of our lightweight final model, and our efforts to reduce reliance on prompt engineering.
### Public Data capturing private doma... | Summary: This paper introduces a new method CTCL to enable efficient synthetic data generation through privately fine-tuning a controlled generation LM. CTCL has two stages, first learning the general topics of private data through DP histogram learning and then privately fine-tunes a language model given the topics as... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and encouraging feedback that the problem we investigate is important, that our method is well-motivated and matching real-world applications, that our experiments are thorough, and that our paper is well-written!
### Pretraining
We would like to clarify tha... | null | null | null | null |
Idiosyncrasies in Large Language Models | Accept (poster) | Summary: This paper explores how distinguishable the outputs of different LLMs are from each other. The main results demonstrate that the LLM to produce a given output can easily be predicted by a trained classifier. Further experiments show commonly-used phrases and other idiosyncrasies that differentiate LLMs from on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We are happy to address your concerns.
- **Different prompt datasets**
In our experiments, we primarily use the UltraChat dialogue dataset to generate responses from instruction LLMs and Chat APIs, and the high-quality pretraining dataset Fi... | Summary: This paper studies the question of how idiosyncratic the responses of different LLMs are. The authors frame this as a classification task where models are trained to predict which LLM (among a fixed set) generated a particular output. The experimental results show that these classifiers have high accuracy, >95... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We are happy to address your concerns.
- **Contrived setting**
We choose this setting for several reasons:
1. We are motivated by prior works [1,2] showing the bias in computer vision datasets and thus adopt a standard classification setup to s... | Summary: This paper shows that deployed large language models have idiosyncracies in their output that makes it possible to distinguish which model generated a particular piece of text. These idiosyncracies seem to transcend the surface structure of the output, persisting after text is shuffled in various ways and even... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and recognition of the novelty of the question our paper seeks to answer. We are happy to address your concerns.
- **The claims are primarily focused on being able to classify the output well, and the models perform far better than chance although no ... | Summary: This paper studies the samples generated by various LLMs. Specifically, they show that it is possible to effectively determine from which LLM a piece of text was sampled. Furthermore, they connect this predictability to "idiosyncrasies" in the word-level patterns, which persist even when the text has been tran... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper and the constructive comments. We are happy to address your concerns.
- **Broader impact may be limited**
In our submission, we have discussed the broader impact of our results mainly in the first paragraph of the introduction and ... | null | null | null | null | null | null |
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models | Accept (poster) | Summary: CASE-Bench evaluates whether LLMs can make safety judgements based on contexts that align well with human judgments. The paper uses contextual integrity theory to formulate prompts from SORRY bench, introducing additional parameters such as the sender, the recipient, and the transmission principle. Whether a q... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer dcxK for recognizing the contributions of our research and for the insightful questions. We address them as follows:
1. __Extra Experiments on Reasoning Models__:
- We provide results on DeepSeek-R1 as follows.
| LLM |Method| Accuracy | R(Safe/Unsafe) |
| ----... | Summary: This paper introduces CASE-Bench, a new LLM safety benchmark with a special focus on the context-aware safety. The authors employ the contextual integrity theory when generating safe and unsafe contexts for different queries. Large-scale experiments have been conducted to assess the safety of different LLMs un... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance of incorporating context in safety evaluations, and we would like to address the concerns as follows.
1. __Regarding Jailbreaking__:
- Context manipulation aims to bypass the safety mechanism and make LLM respond with harmful content, which i... | Summary: The paper extends the Sorry-Bench dataset by context, introducing CASE-Bench. The benchmark is designed to evaluate how well LLM can judge the safety of a query depending on the context (e.g. applications). The constructed dataset comprises 900 query-context pairs. The contexts are automatically generated by G... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer P2mw for the detailed and constructive suggestions. We would like to address the following concerns:
1. __Regarding experiments with LlamaGuard__:
- Following the reviewer’s suggestion, and as an example, we have also evaluated LlamaGuard using our benchmark. We will a... | Summary: The paper introduces CASE-Bench, a novel Context-Aware Safety Benchmark for assessing large language models (LLMs). The benchmark integrates contextual information into safety evaluations by pairing 450 controversial queries with two types of contexts—safe and unsafe—in a total of 900 query‐context pairs. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer's acknowledgement of our work, and would like to address the following weakness raised in the review:
1. __Regarding Dataset Bias__:
- We chose to base our benchmark on SORRY-Bench because it addresses a key issue in prior datasets—imbalance and over-representation of certai... | null | null | null | null | null | null |
Graph4MM: Weaving Multimodal Learning with Structural Information | Accept (poster) | Summary: This paper introduces Graph4MM, a novel framework for multimodal learning that leverages graph structures to model complex relationships between text and images. The framework can be divided into two parts: Hop-Diffused Attention and MM-QFormer. By giving both theoretical and empirical analysis, the authors de... | Rebuttal 1:
Rebuttal: Thanks very much for your valuable questions, each of them are quite actionable. We provide our response in the form of Q&A as follows.
> **Claims And Evidence (CE) 1 & CE4**: Graph as a standalone modality in discriminative setting; empirical validation of Proposition 4.1.
**A1**: We conducted ... | Summary: The paper Graph4MM introduces a graph-based multimodal learning framework that integrates structural relationships into foundation models to improve multimodal understanding. Unlike previous methods that treat graphs as standalone modalities, Graph4MM incorporates Hop-Diffused Attention to model multi-hop conn... | Rebuttal 1:
Rebuttal: Thanks very much for your constructive questions, each of them are quite actionable. We provide our response in the form of Q&A as follows.
> **Weakness (W) 1**: Should cite relevant papers working on the multimodal knowledge graphs and talking about the relationship between them and Graph4MM.
*... | Summary: The paper proposes Graph4MM, a graph based multimodal learning framework. It integrates multi hop structural information into foundation models and fuses modality specific information. The main algorithmic ideas include Hop Diffused Attention and MM QFormer. Experiments show that Graph4MM outperforms larger VL... | Rebuttal 1:
Rebuttal: Thanks very much for your constructive questions, each of them are quite actionable. We take them quite seriously and prepare the following QA formatted response.
>**Weakness (W) 1**: A weakness could be that the framework's complexity might limit its scalability.
**A1**: We analyze the complexi... | Summary: The paper presents Graph4MM, for modelling multi-hop relationship within and between texts and images, modelled as an undirected graph. This approach enables the foundation model to be aware of the structural information (through the graph topology). They introduce Hop-Diffused MM-QFormer for incorporating mul... | Rebuttal 1:
Rebuttal: Thanks very much for your constructive questions. We provide our response in the form of Q&A.
> **Theoretical Claim (TC) 1**: Softmax after masking.
In our implementation, we do apply row-wise softmax after causal masking as $A_{i:}= \text{Softmax}(M_{i:} \odot A'_{i:})$. This ensures that $A$ i... | null | null | null | null | null | null |
Is Complex Query Answering Really Complex? | Accept (spotlight poster) | Summary: Authors carefully analysed limitations of benchmark datasets used in current SOTA neural KG queries, showing that they reflect only the performance of predicting answers where only one link is truly missing and that FB15k237 and NELL995 are not suitable to precisely assess performances of QA systems. Authors p... | Rebuttal 1:
Rebuttal: We thank the reviewer for considering our paper a piece of beautiful work and considering our experiment design easy to understand. We proceed by answering their questions.
> authors do not explain at methodological level why these SOTA CQA systems fail for complex queries.
In Table 2 we showed... | Summary: This paper investigates the problems in current benchmarks for complex query answering over knowledge graphs. The main points are: 1. Previous benchmarks failed to effectively evaluate the capabilities of CQA methods because most test queries can be reduced to simpler queries, 2. Using the proposed benchmark t... | Rebuttal 1:
Rebuttal: We believe the main concerns of the reviewer can be fully addressed, as discussed in the comments below. We hope the score can be raised.
>The authors “balance each query type a QA pair can be reduced to”, which is confusing to me,
We acknowledge that the usage of the term ''balance'' can lead... | Summary: This paper critically examines the validity of current benchmarks for complex query answering (CQA) on knowledge graphs (KGs). The authors argue that existing benchmarks (e.g., FB15k237, NELL995) overrepresent "easy" queries that reduce to simpler tasks like link prediction (1p), distorting perceived progress ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a precise summary of the paper and for considering our analysis rigorous. We proceed by answering their questions, believing all can be easily addressed.
> no discussion of whether the new distributions reflect real-world query patterns (e.g., frequency of 4p/4... | null | null | null | null | null | null | null | null |
How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation | Accept (poster) | Summary: This paper presents Image-Assists-LiDAR (IAL), a new panoptic 3D multi-modal segmentation method combining camera images with LiDAR point clouds. The key contributions are PieAug, multi-modal data augmentation with aligned data, Geometric-guided Token Fusion (GTF) for effective cross-modal fusion of features, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and valuable feedback.
### C1: Contribution of performance improvement
As in Table 8 of our supplementary material, using only texture-prior and no-prior queries (row 3) performs worse than the combination of all three query types (last row). Th... | Summary: Aiming at 3D panoptic segmentation, the paper proposes using a transformer decoder to directly predict class labels and mask outputs. The authors further introduce a Geometric-guided Token Fusion (GTF) module and a Prior-based Query Generation (PQG) module to obtain effective queries and fuse tokens as input. ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and constructive suggestions.
### C1: Suggestion for adding additional references.
Thank you for pointing out these two papers. We will add them in the final version and provide the following discussions:
1. **UniSeg (ICCV’23)** proposes... | Summary: This work proposed a novel framework for multi-model 3D panoptic segmentation. By leveraging the proposed modality-synchronized data augmentation (PieAug) with geometric-guided token fusion (GTF) and prior-based query generation (PQG), this work achieves new state-of-the-art (SOTA) performance on two challengi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback and positive comments on our contributions.
### C1: Fig. 3 is too far from the Sec. 3.1
Thank you for the suggestion. We will update the placement of Fig. 3 in the final version to ensure that PieAug is introduced and illustrated in... | Summary: This paper proposes a new multi-modal framework for multi-modal 3D panoptic segmentation. First, the authors adopt a PieAug strategy to ensure consistency across LiDAR and image data during data augmentation. Then, they use a Geometric-Guided Token Fusion mechanism to fuse LiDAR and image tokens. Finally, seve... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on the voxel representation, the model efficiency, and the design of query initiation. Regarding your questions, we provide the following detailed explanation.
### Q1: Justification of the adoption of cylindrical representation in the LiDAR branch.
While Carte... | null | null | null | null | null | null |
Eigen Analysis of Conjugate Kernel and Neural Tangent Kernel | Accept (poster) | Summary: This paper analyzes the spectral properties of deep feedforward neural networks with random weights, focusing on Gaussian mixture model inputs. It rigorously examines isolated eigenvalues in the conjugate and neural tangent kernels, showing how they capture group features and evolve through hidden layers, infl... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their kind and encouraging feedback on our paper: "The work is highly thorough, and the theoretical contribution is solid. The precise characterization of the isolated eigenvalues is very interesting". This positive recognition motivates us greatly. Below, we pr... | Summary: The paper explores the eigenvalue spectrum of the conjugate kernel (CK) and the neural tangent kernel (NTK) with random weights. The authors demonstrate the existence of isolated eigenvalues and present a theoretical approach to identifying where they lie and their possible impact on the model.
## Update afte... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive questions. In the following, we provide point-by-point responses (marked with $\Huge{\cdot}$).
> The paper doesn't explain why these results are novel or relevant. The results appear well validated and perhaps interesting. However, how can a mac... | Summary: This paper analyzes the eigenvalues and eigenvectors of the Conjugate Kernel (CK) and Neural Tangent Kernel (NTK) for deep feedforward networks with random weights, in a high-dimensional setting where the input data come from a Gaussian Mixture Model (GMM). The authors show that, under certain assumptions, “sp... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for his/her encouraging feedback. The recognition of our work's novelty, technical rigor, and clarity is highly motivating. We also greatly appreciate the insightful comments provided. In the following, we provide point-by-point responses (marked with $\Huge{\cdot}$... | Summary: This paper studies outlier eigenvalues and eigenvectors of conjugate and neural tangent kernels for multi-layer fully connected neural networks at random initialization. The dataset can be a general Gaussian mixture model. This result shows how the information of the group features in the dataset propagates th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our paper as well-organized and clearly presented, and we appreciate the constructive comments. Below, we provide point-by-point responses (marked with $\Huge{\cdot}$). The statements of weaknesses have been condensed to comply with character limit... | null | null | null | null | null | null |
Smoothed Normalization for Efficient Distributed Private Optimization | Reject | Summary: This work focuses on differential privacy in the federated learning. It mentions clipping-based DP-FL optimization like DP-SGD is hard to converge due to clipping bias, especially for non-convex, smooth problems. Instead of (adaptive) clipping, this work chooses smoothed normalization to tackle the problem by ... | Rebuttal 1:
Rebuttal: Dear reviewer LMxv,
We appreciate your time, effort, and thoughtful feedback.
We thank you for your appreciation towards the contributions of our work on leveraging smoothed normalization and error feedback to design distributed algorithms with the first provable convergence under the privacy b... | Summary: This paper proposes a distributed optimization algorithm (called α-NormEC). It uses smoothed normalization with error feedback to solve non-convex, smooth optimization problems in both non-private and differentially private settings. The method claims to achieve provable convergence guarantees without requirin... | Rebuttal 1:
Rebuttal: Dear reviewer yzkj,
We appreciate your time, effort, and thoughtful feedback.
> The proposed algo provides convergence guarantees (theoretically), but the baseline assumption is the objective functions' smoothness and bounded from below.
Our assumptions are standard for analyzing distributed... | Summary: This paper studies federated learning with gradient clipping in the non-private and private settings. In the non-private setting, their algorithm matches existing results for clipped methods. In the private setting, to my knowledge, their convergence results are new.
Claims And Evidence: Their authors claim t... | Rebuttal 1:
Rebuttal: Dear reviewer BGLy,
We appreciate your time, effort, and thoughtful feedback.
> At every iteration, each client is computing a gradient at the same globally known point, x^k.
Could you please specify the federated methods? Because federated learning algorithms exchange the local model updates... | Summary: Clipping the gradients is a common practice in differentially private training with DP SGD and a common technique used to analyze the privacy-utility trade-off of DP-SGD. However, as the authors correctly point out, most theoretical works ignore the effect clipping can have on convergence by assuming bounded g... | Rebuttal 1:
Rebuttal: Dear reviewer zAsL,
We appreciate your time, effort, and thoughtful feedback.
> Q1. It is not obvious why it is possible to pick and in Corollary 1 to ensure that is very small.
We would like to clarify your misunderstanding. We can initialize $x^0,g_i^0 \in \mathbb{R}^d$ to ensure that $\||... | null | null | null | null | null | null |
Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning | Accept (poster) | Summary: This paper proposes a Causally Invariance-aware Augmentation for brain Graph Contrastive Learning（CIA-GCL）which addresses distribution shifts in brain graph datasets by using causal decoupling with invariant learning to identify the invariant subgraphs and designing an invariance-aware augmentation strategy fo... | Rebuttal 1:
Rebuttal: We are deeply grateful for your insightful and constructive comments. We have carefully addressed each of them as follows.
**[Weekness]**
**W1:** Thank you for pointing this out, and we sincerely apologize for the formatting issues in the original submission. We have carefully **revised the manu... | Summary: This paper proposes a causally invariance-aware augmentation for brain graph contrastive learning, utilizing a learnable brain invariant subgraph, which is identified based on a causal decoupling approach to capture the maximum label-related invariant information with invariant learning. The paper is well-orga... | Rebuttal 1:
Rebuttal: We are deeply grateful for your insightful and constructive comments. We have carefully addressed each of them as follows.
**[Claims]**
Thank you for raising this important point. We acknowledge that [1] also incorporates invariant learning by using an information bottleneck-based loss to extra... | Summary: This work proposed a Causally Invariance-aware Augmentation for brain Graph Contrastive Learning to address the challenges of
data shift in multi-site brain data. Outstanding performance by their method has been shown in the experiments on ABIDE-I, -II, and ADHD200. While the method presentation is not easy to... | Rebuttal 1:
Rebuttal: **[Claims]**
**C1**.We have **added references to better support** the two challenges
- Low SNR: [1，2] highlight the noise in fMRI data-posing challenges for analysis
- Distribution shift: [3，4] show that site and individual variability introduce distributional heterogeneity in brain graphs
[1]... | Summary: The paper "Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning" (CIA-GCL) introduces a method to enhance generalization and interpretability in brain graph classification by leveraging causal invariance-aware learning within graph contrastive learning. The proposed approach aims to addres... | Rebuttal 1:
Rebuttal: We are deeply grateful for your insightful and constructive comments. We have carefully addressed each of them as follows.
**[Claims]**
**C1**. **statistical significance tests** : We sincerely thank the reviewer for the valuable suggestion regarding enhancing the experimental validation. We h... | null | null | null | null | null | null |
A Theoretical Justification for Asymmetric Actor-Critic Algorithms | Accept (poster) | Summary: This paper investigates reinforcement learning in partially observable environments. The authors present finite sample analysis for neural actor-critic methods, comparing both asymmetric and symmetric critics. Their findings elucidate the relationship between the use of an asymmetric critic and the reduction o... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your review and for hihglighting that we attain state-of-the-art convergence rate. Below, we answer to your remarks.
**Environment and agent states.** We agree that some other key concepts could be better introduced in the background. The environment state is typical... | Summary: This paper analyzes two different versions of a natural actor-critic method for learning policies in finite POMDPs. The first version is "symmetric": both actor and critic depend only on the internal agent state. The second version is "asymmetric". Here, the critic depends also on the underlying environment st... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for you review. We are happy that you found the paper convincing and accessible. Please find our answers below.
**Minor remarks.** All your remarks and typos were valid. The set needs to be closed, we can assume it strictly convex to ensure the uniqueness of the projecti... | Summary: This paper provides a theoretical justification for the empirical success of asymmetric actor-critic algorithms in partially observable environments. Using finite-time convergence analysis, the authors prove that asymmetric critic methods (which leverage additional state information during training) eliminate ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your review. We are happy to read that you appreciated our manuscript and that you considered that we addressed an important gap in the theoretical understanding of these methods. Please find our answers below.
**Toy experiment.** We agree that an empirical validatio... | Summary: This paper provides a finite-time analysis of asymmetric AC approach in POMDPs, showing that leveraging privileged state information during training eliminates uncertainty errors inherent in symmetric critics. Theoretically, it compares convergence bounds for asymmetric/symmetric critics, proving that asymmetr... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your review. We are glad that you found the results interesting and the paper clear. Please find our answers below.
**Aliasing.** We acknowledge a confusion in our paper about the definition of aliasing. We agree that aliasing could refer to the fact that an history ... | null | null | null | null | null | null |
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models | Accept (spotlight poster) | Summary: This paper mainly consists of two parts: discovering that intermediate image features help jailbreak VLLMs and proposing "Clipped-PPO" for jailbreak defense. Experimental results prove the existence of ICET and the effectiveness of alignment.
Claims And Evidence: It is clear that layer-wise ASR on aligned LLa... | Rebuttal 1:
Rebuttal: We are glad you found our work insightful. Please see our responses below.
***Note: References are added in our response to reviewer WDjm to optimize space.***
---
### Q1) Clarifying the Threat Model and Motivation Behind the ICET Vulnerability
Thank you for raising this point. Recent studies i... | Summary: This paper studies the safety alignment of Vision-Language Models (VLMs) by examining what happens when early existing from intermediate layers of the image encoder, rather than using the final-layer embeddings for inference. The authors show that models often produce harmful content when these intermediate em... | Rebuttal 1:
Rebuttal: We're glad you found our work interesting and impactful. Please see our responses below.
***Note: References are added in our response to reviewer WDjm to optimize space.***
---
### Q1) Clarifying the Seriousness of the Threat and Broader Implications of the ICET Vulnerability
Thank you for rai... | Summary: This paper uncovers an ICET vulnerability in Vision-Language Models (VLMs), where utilizing intermediate layer embeddings from the image encoder can compromise the VLM's safety alignment even with safe input images. The authors propose a modification to the Clipped-Proximal Policy Optimization (Clip-PPO) algor... | Rebuttal 1:
Rebuttal: We thank you for your suggestion and questions. Please see our responses below.
***Note: References are added in our response to reviewer WDjm to optimize space.***
---
### Q1) Improving the Theoretical Foundations of ICET and L-PPO to Strengthen the Paper
As discussed in the paper, we attribute... | Summary: The paper researches the safety alignment of Vision-Language Models (VLMs) and identifies a vulnerability called "Image enCoder Early-exiT" (ICET), where using intermediate layers of the image encoder increases the risk of harmful outputs. The paper reveals that skipping certain layers and performing early exi... | Rebuttal 1:
Rebuttal: We’re happy to hear that you found our work interesting and novel, the paper well written, and our proposed L-PPO method effective in addressing the identified ICET vulnerability. We address your questions below:
---
### Q1) Effect of KL Constraint (η) and Value Loss Coefficient (c₁)
Thanks for ... | null | null | null | null | null | null |
Ranked from Within: Ranking Large Multimodal Models Without Labels | Accept (poster) | Summary: This paper addresses the challenge of ranking large multimodal models (LMMs) without access to labeled data. Specifically, the authors propose uncertainty-based ranking methods that utilize softmax probabilities, self-consistency, and labeled proxy sets to estimate model performance. They evaluate 45 LMMs on e... | Rebuttal 1:
Rebuttal: > Q1. The novelty of this paper seems limited. The authors only test many existing ranking methods (NLL loss, Entropy, BLEU, and BERTScores) without providing new insights.
**A**: While NLL, Entropy, BLEU, and BERTScore have been widely used for uncertainty quantification in LLMs, to the best of ... | Summary: This paper presents a study on ranking methods for evaluating large language models (LLMs) without accessing labels. The authors main findings are that Accuracy-on-the-Line is unreliable for ranking LLMs in new domains, the choice of token significantly impacts output probability-based ranking, and the negativ... | Rebuttal 1:
Rebuttal: > Q1. In the section "Related Work: Evaluation & Benchmarking LMMs," the paper mentions RealWorldQA and several other benchmarks for evaluation purposes. However, it fails to address some commonly used benchmarks for LLMs, such as CV-benchmarks (e.g., GQA, MMVP, OCRBench) and math-benchmarks (e.g.... | Summary: The paper investigates alternative ways to rank large multimodal models on new domains in absence of ground truth annotations. The authors compare 3 types of approaches: (1) labeled proxy datasets (AoL: where the performance on N-1 datasets is used to predict the performance on the Nth dataset; ATC: where prox... | Rebuttal 1:
Rebuttal: > Q1. These works should be discussed as well: (1) The Internal State of an LLM Knows When It’s Lying (2) Estimating Knowledge in Large Language Models Without Generating a Single Token
**A**: Thank you for your valuable suggestions. We will include these two studies, along with other relevant w... | Summary: This paper explores how to effectively evaluate the performance of LMMs without requiring task-specific labels, and systematically validates three types of model uncertainty-based approaches, including softmax probabilities, self-consistency, and labeled proxy sets.
Comprehensive experiments across various LM... | Rebuttal 1:
Rebuttal: > Q1. Authors did not propose new methods or metrics based on these findings for selecting the optimal LMM for a given task in an unlabeled scenario.
**A**: We acknowledge that our work does not introduce new methods or metrics. However, it addresses a previously under-explored yet practically im... | null | null | null | null | null | null |
Identifying Causal Direction via Variational Bayesian Compression | Accept (spotlight poster) | Summary: This work proposes using Bayesian neural networks with variational inference (I will refer to this as variational BNNs) for bivariate causal discovery via the MDL principle (COMIC). The causal direction is determined by the direction that best trades off model complexity with model fit under the factorization ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 3osf's comments to further elucidate and enhance our work. The following responses address the major points that you raised.
**1. Relation to Appendix B.2 of [1]**
Our work originates from the causal discovery via MDL using neural networks, with variational Bayesian codele... | Summary: The paper introduces COMIC (Causal direction identification via Bayesian COMpression), a novel method for determining causal relationships between pairs of variables using variational Bayesian compression with neural networks. This approach improves upon existing compression-based methods by balancing model fi... | Rebuttal 1:
Rebuttal: We thank Reviewer YARz for your novelty and favorable qualities of our work. Our responses below aim to rectify the issues that you mentioned.
**1. Formulating the capability of the neural networks in Prop. 4.2**
Thank you for your recommendation, we will specifically formalize the function of t... | Summary: This work does not focus on traditional or recently popular functional class-based methods. Instead, it aims to study more general models, where the asymmetry in determining causal direction is assumed based on the Kolmogorov complexity. However, due to the incomputability of Kolmogorov complexity, the Minimum... | Rebuttal 1:
Rebuttal: We thank Reviewer 3KpH for your valuable insights. We provide our responses below to clarify and resolve your concerns.
**1. Kolmogorov complexity and MDL**
There are two problems [1] when directly applying the Kolmogorov criterion in Eq. (4):
1. We do not have access to the true generating mod... | Summary: The paper proposes a new method, called ‘COMIC’, for bivariate causal direction identification under the causal sufficiency assumption. It is based on the familiar ICM + MDL principles, utilising a variational Bayesian learning of the complexity of neural network approximations to the marginal/conditionals imp... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer NJ5G for recognizing the positive aspects of our work. We expect the following rebuttal will address your concerns.
**1. Underlying model assumptions**
We summarize three key definitions related to the identifiability of our Bayesian causal models (BCMs), introduc... | null | null | null | null | null | null |
In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval | Accept (oral) | Summary: ## Update After Rebuttal
## I maintain my score. Please see below for my reasonings in my response. Overall, I think it is a great paper despite some of my comments.
-----
This work introduces a concept, in-context denoising, which is a task that refines the connection between attention-based architectures and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment and the high score (4/5) provided. We concur with their observation that, collectively, we still don't know why the transformer block works so well. This shared curiosity drives our work to build theoretical foundations for understanding the mech... | Summary: This paper studies the in-context unsupervised denoising of data points in transformers. They show that single layer transformers with a single attention head are sufficient to learn this task and that standard training procedures from random initialization can converge to Bayes optimal solutions. Lastly, they... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful review and their high evaluation of our paper (4/5). We agree that this is an important problem and are thus grateful for their positive assessment of our theoretical findings and empirical validations.
**Re: Weaknesses**
The reviewer notes that our paper... | Summary: The paper explores a link between one-layer transformer attention and associative memory retrieval.
It frames an in-context denoising setting with three synthetic data scenarios: linear subspaces, points on a sphere, and Gaussian mixture clusters.
It derives Bayes-optimal predictors in each setting and shows t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful engagement with our work, particularly their recognition of our work's **originality** in studying in-context denoising, its **significance** in demonstrating transformers performing optimal denoising updates beyond exact pattern retrieval, and its **clarity... | Summary: The paper considers in-context denoising as a fundamental task of attention (when applied in a prompt-conditioned, auto-regressive manner). When interpreted this way, there are clear connections to Dense Associative Memories (DAMs), and a model trained on a final-token denoising task can approach the optimal B... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful review describing our paper as 'a pleasure to read' with 'clear exposition and motivations.' We're encouraged by the recognition of our 'strongly defended connection of ICL to Dense AM' and we are particularly thankful that the reviewer found the p... | null | null | null | null | null | null |
Demeaned Sparse: Efficient Anomaly Detection by Residual Estimate | Accept (poster) | Summary: This paper proposes a novel test for detecting anomalies in structural images using a discrete Fourier transform (DFT) under a factor model framework, which enables interpretable and effective reconstruction-based anomaly detection.
Claims And Evidence: To my understanding, the claims are articulated clearly ... | Rebuttal 1:
Rebuttal: Dear Reviewer 2ntV:
Firstly, we express our gratitude for your thorough review and insightful comments on our paper. Your recognition of the novelty and contribution of our work is greatly appreciated. We also greatly appreciate your rigorous and professional feedback. Your suggestions are highly... | Summary: This paper proposes a reconstruction-based method for anomaly detection, by using the construction of a mask in the Fourier domain to sparsify the information by reducing the number of estimated common factors of the input images. Then a U-Net is used to reconstruct the images, using the reconstruction error a... | Rebuttal 1:
Rebuttal: Dear Reviewer a5G4:
We appreciate your review of our paper and the insightful comments you provided. We are glad to hear that you find the method we proposed to be both simple and effective. We have addressed the issues you raised as follows:
>W1:” Lacking important reviews in the literature”
A... | Summary: The paper "Demeaned Sparse: Efficient Anomaly Detection by Residual Estimate" proposes a novel approach for unsupervised anomaly detection in structural images using a factor model framework combined with Discrete Fourier Transform (DFT). The authors introduce a test to detect anomalies by analyzing weighted r... | Rebuttal 1:
Rebuttal: Dear Reviewer LTm8,
Firstly, we express our gratitude for your thorough review and insightful comments on our paper. Your recognition of the rigor and contribution of our work is greatly appreciated. We also greatly appreciate your rigorous and professional feedback. Your suggestions are highly m... | null | null | null | null | null | null | null | null |
Benchmarking Quantum Reinforcement Learning | Accept (poster) | Summary: This paper addresses the issue of valid performance comparisons between classic RL and QRL. The authors propose a benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. In addition, a novel RL benchmark is established on which ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the assessment of our paper. We appreciate the constructive comments of the reviewer and are confident that addressing them has further strengthened the quality of our work.
- Regarding **Methods And Evaluation Criteria**:
1. An argument for why the BeamManagement6G env... | Summary: The paper introduces a standard benchmarking methodology for quantum reinforcement learning (QRL) algorithms, executed with high statistical rigor. The proposed methodology emphasizes sample complexity as a key metric and introduces a statistical estimator for empirical sample complexity. Additionally, the aut... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the positive assessment of our paper. We are sure that we can address the comments sufficiently in a camera-ready version of the paper:
- Regarding **Theoretical Claims**: You advocated for a more formal proof of the estimator's properties. We have derived key properties... | Summary: This paper analyzes quantum reinforcement learning to provide a more nuanced evaluation on the potential for quantum advantage. They introduce a sample complexity metric that aims to tackle the evaluation issues, and perform a number of empirical simulations on a new RL environment to evaluate the potential fo... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the assessment of our paper and are pleased to read that the reviewer thinks that `This paper does a lot of things right, and I think it serves an important role in the QRL literature.` We appreciate the constructive comments and are confident that addressing them has fur... | Summary: The paper presents a benchmarking methodology for quantum reinforcement learning (QRL) by introducing a statistical estimator for sample complexity and a robust definition of statistical outperformance. Through experiments in a novel benchmarking environment inspired by wireless communication tasks, the study ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the assessment of our paper. We appreciate the constructive comments made by the reviewer and believe that addressing them has further strengthened our work.
- Regarding **Claims and Evidence**:
1. We agree on the concern, that the claim of *definitive quantum advantage... | null | null | null | null | null | null |
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