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Semantics-aware Test-time Adaptation for 3D Human Pose Estimation | Accept (poster) | Summary: This paper presents a 3D human pose estimation that supports test-time optimization with semantics. The authors leverage video understanding and a well-structured motion-text space to adapt the model motion predictions. In addition, they incorporate a missing 2D pose completion with the motion-text similarity.... | Rebuttal 1:
Rebuttal: We express our sincere appreciation for the helpful reviews and tackle the concerns below:
**[Weakness 1] Failure cases from the VLM**
**R:** Thank you for your suggestions. We manually examined 5,000 frames from the 3DPW dataset, where 96.4% is accurate, as the actions are relatively simple and... | Summary: The paper introduces a TTA HMR method from videos using semantic information to address challenges caused by a lack of information when a large portion of the body is occluded. The paper suggests using MotionCLIP to align predicted motions with the CLIP representation of the textual description of the actions ... | Rebuttal 1:
Rebuttal: We express our sincere appreciation for the helpful reviews and tackle the concerns below:
**[Method 1] Fairness in Table 1**
**R:** Our paper highlights the problem of motion semantics and proposes a method to incorporate semantics as the core contribution. No existing works use semantics, so ... | Summary: This paper proposes a novel semantics-aware test-time adaptation (TTA) framework for 3D human pose estimation, addressing the issue of overly smoothed or unguided predictions, especially under occlusion or truncation. The key innovation is integrating motion semantics into the TTA process by leveraging MotionC... | Rebuttal 1:
Rebuttal: We express our sincere appreciation for the helpful reviews and welcome further discussion!
---
Rebuttal Comment 1.1:
Comment: There are no further issues from my side. I will keep my score at 4: Accept.
---
Reply to Comment 1.1.1:
Comment: Thank you for your time and effort in reviewing our p... | null | null | null | null | null | null | null | null |
☕ Decaf: A Deconfounding Causal Generative Model | Reject | Summary: The paper introduces Decaf, a normalizing-flow based causal generative model that can sample interventional and counterfactual data when given the graph and trained on observational data. Importantly, as opposed to many prior works, Decaf does not assume causal sufficiency (i.e., unobserved confounding may be ... | Rebuttal 1:
Rebuttal: We appreciate all the feedback and references. Due to the space limit, we only respond to the most critical questions below.
> issues I have with the claims
Claim 1:
We thank the reviewers for the references. We will better position our contributions with the related work and relax our claim ... | Summary: To identify interventional and counterfactual queries in the presence of hidden confounders, this work proposes Decaf, an encoder-decoder architecture that combines causal normalizing flow (CNF) (Javaloy et al., 2023) as the decoder with conditional normalizing flow (CdNF) as the encoder. The key idea is to tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough comments. We will revise our work to include the clarifications below and improve its clarity. We believe that the necessary (and already-implemented) changes are not substantial and thus hope the reviewer will reconsider their assessment.
> clause (iii) inc... | Summary: This paper proposes a Causal Generative Model (CGM) that can identify all causal queries under certain conditions. The architecture of the model is an encoder-decoder network where both encoder and decoder are conditional normalizing flows, which is constrained by an assumed causal graph. The authors then info... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and appreciation of our paper, especially regarding the quality of our proofs. In the following, we clarify the points raised by the reviewer, which are of great help to further improve the presentation of our contributions.
> I believe some of the... | Summary: The paper proposes a method called Decaf that learns a causal generative model in presence of unobserved confounders. After training the model, the model can perform interventional and counterfactual estimation. Finally, the authors showed empirical evaluation on the Ecoli70 dataset.
Claims And Evidence: * Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our extensive experiments and questions, whose clarification will help to further improve our paper.
> Its not clear how the authors are doing the abducting step for u.
Note that, given $z$, the generative network becomes a regular CNF and ... | null | null | null | null | null | null |
Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation | Accept (poster) | Summary: This paper introduces Outcome-Refining Process Supervision (ORPS), a method unifying outcome supervision and process supervision in large language models (LLMs) for code generation tasks. The authors propose a tree-structured, inference-only, search framework using a combination of execution-based feedback, se... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your concerns point by point:
## Metrics
Unlike methods that report Pass@k which attempts k solutions on testset (e.g. LDB and Self-Repair use ground-truth test cases which can be considered contamination), **our Pass@1 measures attempting only 1 solution on t... | Summary: The paper proposes outcome refining process supervision (ORPS), a unified framework to bridge the gap between process supervision and outcome supervision through structured reasoning and execution-based feedback. The experiment results show that concrete feedback signals are pivotal for solving complex program... | Rebuttal 1:
Rebuttal: Thank you for your positive review and recognition of our work's effectiveness. We appreciate your thoughtful questions and address them below.
> **Q1:** Does the proposed approach composed of components which already exists in the literature? If yes, how do the authors see the novelty of the wo... | Summary: - The paper proposes an LLM-based algorithm (outcome-refining process supervision, ORPS) for code generation. The primary contributions are algorithmic and empirical. ORPS leverages LLMs abilities significantly more than prior work in this area. Specifically, this includes self-reflection, self-critique and pr... | Rebuttal 1:
Rebuttal: Thank you sincerely for your thoughtful review and constructive feedback. We deeply appreciate your recognition of our work and we address your questions and concerns point by point:
> **Q1:** What initial LLM inputs (system prompts) used in each call to the LLM, if any. If system prompts exist, ... | Summary: This paper introduces ORPS, a novel framework that unifies outcome and process supervision to address complex code problems. Notably, this approach does not require training PRMs. ORPS demonstrates significant improvements when utilizing ground truth test cases.
Claims And Evidence: Overall, most of the claim... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
## Answers to Questions
**Q1**: Currently, there's an assumption in process supervision and test-time scaling research (e.g. OmegaPRM, Math-Shepherd, Let's Verify Step by Step, Deepseek-Math, etc.) that a specially trained Process Reward Model is required to... | null | null | null | null | null | null |
Robust Reward Alignment via Hypothesis Space Batch Cutting | Accept (poster) | Summary: The paper introduces a novel method called Hypothesis Space Batch Cutting (HSBC), which iteratively refines a space of potential reward functions by using batches of human preferences to make "cuts" based on a voting function. To handle potentially erroneous human feedback, HSBC employs a conservative cutting ... | Rebuttal 1:
Rebuttal: # Response to reviewer #GLNZ
We sincerely thank you for your comments. Below, we address each of your comments in detail. All our responses will be incorporated into the final paper.
### 1. There are multiple ways... What kind of errors can it handle more easily?
Thanks for your question. For dif... | Summary: This study addresses the challenge of reward design in reinforcement learning and proposes a robust and efficient preference-based reward alignment method, particularly for noisy human feedback. The method introduces a novel framework called "hypothesis space batched cutting," which iteratively refines the rew... | Rebuttal 1:
Rebuttal: # Response to Reviewer #sofd
We sincerely appreciate your thoughtful feedback and constructive comments on our paper. Below, we address each of your concerns in detail. All our responses will be incorporated into the final paper.
### 1. Does gamma really play an adaptive role?
Thank you for the ... | Summary: The paper introduces Hypothesis Space Batch Cutting (HSBC), a framework for robust reward alignment in reinforcement learning (RL). HSBC addresses the challenge of learning reward functions from human preferences, particularly in the presence of false or noisy feedback. The core idea is to iteratively refine a... | Rebuttal 1:
Rebuttal: # Response to Reviewer #e9yZ
We sincerely appreciate your thoughtful feedback and comments on our paper. Below, we address each of your concerns in detail. All our responses will be incorporated into the final paper.
### 1. Comparisons with other robust PbRL methods?
Thank you for highlighting t... | null | null | null | null | null | null | null | null |
On the Guidance of Flow Matching | Accept (spotlight poster) | Summary: In this paper, the authors provide a general flow matching framework for conditional generation based on an energy function. The authors propose a unifying framework that constructs guidance for arbitrary source distributions and couplings. They derive various guidance methods based on MC estimation, which th... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for acknowledging the contribution and potential impact of our work. We will address your concerns in the following:
> Q1: Misleading expression of "guidance"
We apologize for the confusion. We will switch to 'energy guidance' to further enhance clarity.
>... | Summary: This paper proposes a general framework for guidance in continuous flow matching that includes arbitrary source distributions, conditional paths, and coupling (to some extent). Guidance is viewed as tackling energy-based sampling or posterior sampling given an existing flow matching model. This general theor... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the theoretical and empirical soundness, the presentation, and ample details of our work, as well as the contributions and potential benefits for future works in the field. We would like to address your concerns in the following:
> Q1: The correctness of guidance VF as... | Summary: This paper provides a unified perspective on the guidance of flow matching and proposes a bunch of guidance methods to make it more general. The result shows the relevance between the tasks and guidance methods.
Claims And Evidence: All the claims are clear and convicing.
Methods And Evaluation Criteria: Thi... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contribution in the theoretical framework, the novelty of guidance methods, the soundness of our empirical validation, and the clarity of presentation. We will respond to your questions below:
> Q1: How to further address the scenarios where the $\mathcal{P}\approx... | Summary: The paper introduces a framework for guiding flow-matching models, which are advanced generative models. It extends the concept of guidance from diffusion models to a more general form. The framework includes training-free, asymptotically exact guidance using Monte Carlo methods, new training-based guidance lo... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the theoretical and empirical soundness of our work, as well as the potential impact in addressing the challenges in the generative modeling field. We answer your questions in the following:
> Q1: How can the MC sampling efficiency be improved?
Many existing techniques... | null | null | null | null | null | null |
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction | Accept (poster) | Summary: Other reviews checked. I keep my score. Thanks for the rebuttal.
This paper investigates two types of robust Markov decision processes (RMDPs) in tabular reinforcement learning: CRMDP and RRMDP.
It introduces hard instances and visitation ratios and establishes both regret lower and upper bounds.
The autho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on our work. We hope our response will fully address all of the reviewer's questions.
---
### 1. Discussion on possible relaxation of Assumption 4.5
We appreciate the reviewer's insightful question and agree that Assumption 4.5 might be relaxed t... | Summary: The paper considers two types of robust MDP formulations - one with constraints and one with regularization.
A new value update is proposed for both which can be shown to guarantee for a tabular case some regret bounds.
Lower and upper regret bounds are given as a function of the supremal visitation ratio w... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of your questions.
---
### 1. Explanation about the bounds in 3.2 and 3.3
Sections 3.2 and 3.3 present update formulas for different types of robust sets (constrai... | Summary: This paper explores robust Markov decision processes (RMDPs) in the context of off-dynamics reinforcement learning, where distribution shifts occur between training and deployment environments. The authors investigate two variants: constrained RMDPs (CRMDPs) and regularized RMDPs (RRMDPs). They propose computa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on our work. We hope our response will fully address all of the reviewer's questions.
---
### 1. The key insights from Figure 2
The primary purpose of Figure 2 is to show that our algorithm converges stably by the end of training in a fixed envir... | Summary: This paper considers distributionally robust RL with online access to the nominal model, including the constrained MDP (CMDP) and the regularized robust MDP (RRMDP) frameworks. They propose *supremal visitation ratio* $C_{vr}$ as a hardness measure and show that this measure is unavoidable in the regret lower ... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of your questions.
---
### 1. The definitions of $P_h^\pi$ and $d_h^\pi$ in Assumption 4.5
The definitions are provided in Definition 4.3, where $P_h^\pi$ represen... | null | null | null | null | null | null |
Federated Learning for Feature Generalization with Convex Constraints | Accept (poster) | Summary: For generalization in FL framework, this paper proposes FedCONST which impose more updates with larger probabilities to under-learned features by centralization and orthogonal constraints at clients. Various FL algorithms could get performance improvement with the proposed constraints.
## update after rebutta... | Rebuttal 1:
Rebuttal: ## Generalization Area
We appreciate your insightful concern regarding the relationship between the convex constraint region and the generalization area. You raised an important point: if the generalization area is narrower than the constraint region, then the aggregated model could potentially d... | Summary: This paper introduces FedCONST, a novel federated learning (FL) algorithm that addresses the challenges of generalization and overfitting in FL environments with heterogeneous data. By employing convex constraints based on the global model's parameter strengths, FedCONST adaptively modulates update magnitudes ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive feedback and thoughtful suggestions.
## Theoretical Overhead:
The proposed constraints—centralization and orthogonal projection—are implemented using simple linear operations with negligible computational overhead. Specifically, for a grad... | Summary: This paper targets at addressing the generalization challenges in federated learning (FL). The authors propose FedCONST, an approach that adaptively adjusts update magnitudes based on the global model's parameter strength, preventing overemphasis on well-learned parameters and reinforcing underdeveloped ones. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. Your comments have helped us refine our intuition and better communicate the contributions of this work.
## Justification on Conjecture
We appreciate your comment regarding the lack of direct evidence supporting *Conjecture 1*. In our paper, w... | Summary: This paper proposes FedCONST, a federated learning (FL) framework, to boost generalization under heterogeneous client data distributions. Specifically, the authors adaptively modulate the magnitudes of updates based on the global model's parameter strength by applying convex constraints during client training.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and valuable suggestions. Below, we address each point in detail:
---
## Dataset diversity
We appreciate your suggestion. While we agree that evaluating on larger datasets such as Tiny-ImageNet would further strengthen the experimen... | null | null | null | null | null | null |
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning | Accept (poster) | Summary: This paper introduces a pre-training strategy for MEG recordings, which consists in neuroscientifically-grounded pretext tasks.
It shows scaling laws on two downstream tasks on two different datasets.
## update after rebuttal
Since the authors did not provide additional results or modifications along my sugge... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide a review. We are glad that you found the paper easy to follow and that our experiments were sound and well-executed. Please find below, our responses to your question and concerns:
> authors do not report anywhere [...] results obtained when finetuning the... | Summary: This paper presents a unified solution through data-efficient, self-supervised pretext tasks to improve the speech detection and voicing Classification tasks. The experiments demonstrate significant gains from self-supervised pre-training. The method surpassed the baselines and is comparable to the model train... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our work. We are glad that you found the paper well written and organized, and that the experiments validated our hypothesis. Below, we have provided our responses:
> The designed pre-text tasks might be suitable for simple tasks only. The gain might be lim... | Summary: This paper proposes a framework about how to leverage self-supervised learning (SSL) to improve the decoding of speech from brain activity. The authors propose a approach that utilizes large-scale unlabeled MEG data to train models, thereby addressing challenges posed by individual differences and dataset hete... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide a detailed review. We are glad you found the paper easy to follow. Please find our responses below:
> significant progress has already been made in Brain-to-Text tasks by numerous studies [1][2][3]
While [1] and [2] are impressive, they address a differen... | Summary: Current speech decoders are generally trained individually per subject and only on task-specific data. The authors propose an MEG-specific self-supervised learning objective to build representations from a vast quantity of unlabeled MEG data from several subjects and tasks. They then built decoders that used t... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide a review. We are glad you found the paper to be well-written and the experiments and comparisons to be sound. Please find below, our responses to your questions and concerns:
> Most tables mention a single ROC AUC score [...] I am assuming that all scores... | null | null | null | null | null | null |
$S^2$FGL: Spatial Spectral Federated Graph Learning | Accept (poster) | Summary: This paper identifies and defines two significant limitations in FGL: label signal disruption and spectral client drifts. The proposed framework, S2S^2S2FGL, addresses these issues by consolidating globally accessible semantic knowledge and aligning the high- and low-frequency spectral domains. Abundant experi... | Rebuttal 1:
Rebuttal: Dear Reviewer uVfb:
Thank you for your encouraging comments on our work. We hope that our responses below will address your concerns and reinforce your positive evaluation.
## Weakness
**W1: A more detailed analysis of existing methods and their limitations
is required, particularly for the FG... | Summary: The authors propose S2FGL, a framework that simultaneously addresses the spatial and spectral challenges in federated graph learning. Instead of focusing on static graph structures, it provides solutions through the lens of graph signal propagation.
Claims And Evidence: Given the inherent interconnection betw... | Rebuttal 1:
Rebuttal: Dear Reviewer yo13:
Thank you for your thoughtful review and for acknowledging the value of our work. We hope the responses provided below will clarify your concerns and contribute to a more favorable evaluation.
## Weakness
**W1: The authors should conduct a hyperparameter study for the NLIR a... | Summary: This paper investigates graph signal propagation in federated graph learning through both the spatial and spectral domains, highlighting the issues of label signal disruption and spectral client drift. In response, it proposes two methods: Node Label Information Reinforcement and Frequency-aware Graph Modeling... | Rebuttal 1:
Rebuttal: Dear Reviewer yM48:
We sincerely appreciate the time and effort you have invested in reviewing our paper, as well as your favorable assessment of the motivation and design of our method. We hope that our rebuttal has effectively addressed your concerns.
## Weakness
**W1: The authors should con... | Summary: The paper presents a novel framework called S2FGL (Spatial Spectral Federated Graph Learning) to address two key challenges in subgraph federated learning (FGL): Label Signal Disruption (LSD) and spectral client drifts. LSD occurs when subgraphs lose critical label signals due to edge losses between clients, w... | Rebuttal 1:
Rebuttal: Dear Reviewer svqb:
We sincerely appreciate your time and effort and hope that our responses will address your concerns and lead to an updated score.
## Experimental Designs Or Analyses
**There are only experiments with ACM-GCN.**
A1: $S^2FGL$ is a backbone-agnostic framework designed to addre... | null | null | null | null | null | null |
Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms | Accept (poster) | Summary: The paper presents a higher-order version of the WL test for hypergraphs. It is a conservative extension of the well-known higher-order WL for graphs, in the sense that when run over graphs, both tests have the same expressive power. A hypergraph-GNN architecture is then designed in terms of this higher-order ... | Rebuttal 1:
Rebuttal: >As mentioned before, I feel lukewarm about this paper. While it is a solid piece of work and may interest some researchers in the community, it lacks truly innovative contributions. Perhaps its main strength lies in the clear presentation of the extension of k-WL from graphs to hypergraphs, but I... | Summary: The paper defines a k-WL variant for hypergraphs which did not exist so far. The test relies on a new structure that only relies on the nodes of the hypergraph and follows the idea of k-FWL or k-WL (the oblivious variant) which differ in the order of aggregation. They additionally come up with a GNN variant of... | Rebuttal 1:
Rebuttal: >As mentioned before, I was struggling with lacking formality around sets, multisets, and tuples. Misleading examples.
Injectivity and resulting equality in expressivity should only hold when its about tuples.
Thank you for the valuable comment. We should formally define tuples, multisets, and se... | Summary: The paper introduces the k-dimensional Generalized Weisfeiler-Leman (k-GWL) algorithm, an extension of the classical Weisfeiler-Leman (WL) test to hypergraphs. The primary contribution is the formulation of k-GWL, which generalizes k-WL from graphs to hypergraphs, providing a unified theoretical framework for ... | Rebuttal 1:
Rebuttal: >The underperformance on IMDB-Wri-Genre suggests that certain hypergraph structures may not benefit from k-GWL, requiring further investigation.
Thank you very much for your comment. Following the suggestion of Reviewer yrE6, we have run experiments on k-HNNs for $k=3$ based on the vertex sampli... | Summary: This work generalize high-order weisfeiler-Lehman test and high-order GNNs on graph to hypergraph. Furthermore, it build expressivity hierarchy among different orders of WL test on hypergraph. Using it instead of hyperGNNs corresponding to 1-WL leading to performance increase on real-world datasets.
Claims An... | Rebuttal 1:
Rebuttal: >The number of parameters is not compared between baselines and the proposed models, which may lead to unfair comparison.
Thank you for the valuable comment. We have collected the number of parameters in all tested models and reported them in the table below. It can be observed that our k-HNN mod... | null | null | null | null | null | null |
DataDecide: How to Predict Best Pretraining Data with Small Experiments | Accept (poster) | Summary: This paper presents DataDos, a suite of experiments to examine the comparison of 25 variously processed corpora across scales by pretraining them with up to 1B models and 100B tokens. It finds that 150M models trained with < 2% compute of 1B targets correctly predict 80% of comparisons; spending compute toward... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed clarifying questions and are happy they found our work has “comprehensive experiments and detailed analyses.”
**Expanded results**
We appreciate the reviewer’s encouragement to polish our paper. Since submission we have fully revised our paper and extende... | Summary: The paper "Data Differences over Scale (DataDos) Suite: How to Predict Best Pretraining Data with Small Experiments" presents an empirical study on the predictability of pretraining data decisions at large scales using small-scale experiments. The authors conduct controlled pretraining experiments across 25 co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their clear and helpful feedback and supporting that “using 1B as the target model is sufficient for generalization purpose given the compute budget.”
**Expanded results**
Since our submission we have enriched our analysis and extended our suite with more small runs and... | Summary: The paper introduces the DATADOS Suite, an extensive experimental framework designed to guide pretraining data decisions for large language models using small-scale experiments. By systematically exploring 25 data recipes (varying in sources, deduplication, filtering, and mixing) across 5 model scales with a f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and are glad they found our “paper is well motivated and the results are very helpful.”
The reviewer writes that “the main contribution of this paper is that small-scale pretraining experiments (e.g., using 150M–parameter models) can reliably p... | null | null | null | null | null | null | null | null |
Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity | Accept (poster) | Summary: The paper proposes a two-stage algorithm to obtain the principal component of the single-spiked covariance model (Sparse PCA). The first stage, called the thresholding-based algorithm, obtains a first estimation of the principal component and, most importantly, accomplishes reduced computational cost compared ... | Rebuttal 1:
Rebuttal: > 1. You mention that "operating under a specific condition on $\lambda$", which is the implication of this restriction? It would be interesting to include it this discussion in the paper, either theoretically or experimentally.
**Reply:** **Please refer to the response to the first question for ... | Summary: This paper presents their algorithms for Sparse Principal Component Analysis (Sparse PCA) under the setting of the Single-Spiked Covariance Model. With the assumption of signal strength, the authors introduce a thresholding-based algorithm with better (big-Omega) sample complexity and show it merges the gap of... | Rebuttal 1:
Rebuttal: > **1. The strength and necessity of the additional assumption on $\lambda$**
**Reply:** The single-spiked covariance model is a well-studied framework in high-dimensional statistics. Nevertheless, there remains a substantial gap between the information-theoretic sample complexity, $\Omega(k \log... | Summary: The paper presents efficient algorithms for solving the sparse Principal Component Analysis (PCA) problem, it is one of fundamental problems in machine learning. The proposed algorithm significantly reduces the required sample complexity compared to previous polynomial-time methods. Under typical sparse condit... | Rebuttal 1:
Rebuttal: > 1. Was Algorithm 1 used in Section 4?
**Reply:** Thank you for your comment. In Section 4, all experiments are conducted using Algorithm 2. This is because Algorithm 2 is our full two-stage procedure, which first employs Algorithm 1 for initialization and then refines the estimate via truncated... | Summary: This paper looks at sparse PCA with few samples in the spiked Gaussian
model, under the assumption that the largest single coordinate of the
spike has pretty high variance.
## update after rebuttal
I remain unenthusiastic about this paper, but I don't strongly object to it. The main contribution is the prop... | Rebuttal 1:
Rebuttal: > 1. Under the assumption given, the result seems like it should be pretty straightforward to achieve. Why not just take $(\hat{\Sigma} e _{j _0}) _{\hat{S}}$ as your estimate $v^0$? The scaling $(\lambda v_j) > 1$ only helps; ignoring it, we have $v_j < 1$ so the error is $|v^0 _j - v_j| \leq c \... | null | null | null | null | null | null |
Reflection-Window Decoding: Text Generation with Selective Refinement | Accept (poster) | Summary: This paper introduces a built-in mechanism for refinement and correction during LLM generation. The authors provide theoretical analysis characterizing the sub-optimality of purely autoregressive decoding and propose a "reflection-window" decoding approach that allows for selective refinement during generation... | Rebuttal 1:
Rebuttal: # Response to Reviewer ZxMG
Thank reviewer for the comments and questions, as well as the time devoted! Please kindly notice that there might be some **potential misunderstandings**. Below please see our point-by-point responses:
---
### **C1:** "In Figure 5, the authors compare the win rate be... | Summary: This proposes Reflection-Window decoding, an attempt at addressing the limitations of autoregressive text generation in large language models (LLMs), which lack built-in mechanisms for refining or correcting generated content. The authors analyze how sequential token-by-token optimization can deviate from a gl... | Rebuttal 1:
Rebuttal: # Response to Reviewer Fghj
We are very grateful for the thoughtful comments, as well as the time and effort devoted! Below please see our point-by-point responses:
---
### **C1:** "There are a few more recent works/commercial practices (such as OpenAI O1/3 and DeepSeek R1) that uses a combinat... | Summary: The paper makes the observation that, given an autoregressive language model $p_{LM}$, the token sequences $\mathbf{\hat{x}}$ generated via greedy decoding does not always correspond to the MAP state $\mathbf{x}^* = argmax_{\mathbf{x}} p\_{LM}(\mathbf{x})$. Theoretical analysis shows that under mild assumption... | Rebuttal 1:
Rebuttal: # Response to Reviewer Lq2Q
Thanks for the thoughtful and detailed comments, as well as the time and effort devoted! Below please see our responses to specific comments and questions:
---
### **Q1:** "[Theorem 3.6] what does the ratio $\epsilon_L$ mean or how does it correlate with the discrepa... | Summary: The authors describe, theoretically and empirically, how greedy sampling is suboptimal for generating the sentence with maximum likelihood.
They then propose an alternative algorithm, which pauses the generation when a specific criterion is triggered, and regenerates a small portion of the text.
The proposed m... | Rebuttal 1:
Rebuttal: # Response to Reviewer KVvm
We are very grateful for the insightful questions and constructive comments! Below please see our point-by-point response:
---
### **Q1:** "the criterion requires the entropy of all past $d$ tokens to be above the threshold [..., but] would not trigger if the LLM is ... | null | null | null | null | null | null |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective | Accept (poster) | Summary: This paper explores a different scheme for rehearsal training in continual learning. It also provides theoretical framework for forgetting and generalization error of concurrent and sequential rehearsal. And the authors show under conditions where the difference between the sequential tasks is large, sequentia... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the valuable review. Please note that all our new experiment results (i.e., the tables we quote below) and our codes can be accessed via the link https://anonymous.4open.science/r/repo-c14014
**Q:** Are there any risks of overfitting in sequential rehearsal?
... | Summary: In the context of Continual Learning, the authors propose a new rehearsal method, which is sequential. Then the authors present a theoretical analysis of both sequential and concurrent rehearsal methods. The authors derive a closed form expression of generalisation and forgetting for both methods. The main tak... | Rebuttal 1:
Rebuttal: Please note that all our new experiment results (i.e., the tables we quote below) and our codes can be accessed via the link
https://anonymous.4open.science/r/repo-c14014
**==Questions about Experiments==**
**Effectiveness of the method under label corruption:** We conduct experiments under lab... | Summary: This paper theoretically and numerically investigates the effects of concurrent and sequential rehearsal in the context of continual learning. The authors analytically derive that, in a linear regression model, sequential rehearsal leads to better performance than concurrent rehearsal when tasks are more dissi... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the valuable review. Please note that all our new experiment results (i.e., the tables we quote below) and our codes (including code for Fig. 2) can be accessed via the link
https://anonymous.4open.science/r/repo-c14014
**Q:** Clarify the choice of $\mathcal{... | Summary: The paper studies rehearsal in continual learning for overparameterized linear models. Next to concurrent rehearsal, which is the commonly used setting, they also look into sequential rehearsal, where different task data is revisited sequentially. From the theoretical analysis, they conclude that for highly di... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing the valuable review. Please note that all our new experiment results (i.e., the tables we quote below) and our codes can be accessed via the link
https://anonymous.4open.science/r/repo-c14014
**Q1:** Results with more / larger datasets (e.g. (mini)ImageNet), ... | null | null | null | null | null | null |
Stealix: Model Stealing via Prompt Evolution | Accept (poster) | Summary: This paper introduces a method for model stealing attacks that do not require manually crafted prompts. Unlike prior approaches, which rely on predefined class names or expert knowledge to generate synthetic data, Stealix employs a genetic algorithm to iteratively refine prompts based on a victim model’s respo... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the realism of our threat model, the scalibility of our approach, and the effectiveness of our proposed proxy metric. We aim to address their concerns below.
> W1. While Stealix is tested on various victim model architectures (e.g., ResNet, VGG, MobileNet), t... | Summary: This paper proposes a model stealing attack method, named Stealix, to steal the functionality of an image classification victim model. Stealix generates synthetic images through a diffusion model, and fine-tunes the image-generation prompt based on victim model's responses. An iterative prompt refinement and r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of this direction of soliciting queries as promising, and we're glad that the completeness and thoroughness of our experiments came through clearly. We aim to answer the questions below.
> Q1. Why the comparison with PEZ method is deferred to appendix and ... | Summary: This paper introduces Stealix, a new model stealing method that leverages images synthesized from diffusion models to steal victim models. Compared with existing diffusion model-based model stealing attacks, the key improvement is that Stealix can automatically construct attack prompts for the stealing-image g... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our strict black-box threat model and recognizing the novelty of our automatic prompt construction approach for model stealing. We answer their questions below.
> Q1. In Algorithm 1, are you performing Stealix with only a single class? Is that really effecti... | Summary: The paper proposes a new method for model stealing attacks for computer vision classification models. Specifically, they find that prior work uses a pretrained text to image generator model to synthesize images similar to the victim data. However, this step requires an attacker to have knowledge to craft usefu... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contribution in addressing a key limitation of prior work, and for their appreciation of our method's clarity, effectiveness, and thorough evaluation. We answer the questions below. The reviewer can try the provided prompts at [Stable Diffusion 2.1 demo](h... | null | null | null | null | null | null |
Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs | Accept (poster) | Summary: The paper introduces Grammar Forced Translation (GraFT), a framework for translating natural language into temporal logic. GraFT simplifies the translation process by restricting the output tokens to a limited set, using the unique properties of each task and exploiting the known grammar of temporal logic duri... | Rebuttal 1:
Rebuttal: **Remarks for all reviewers:** We would like to thank all reviewers for their time and detailed feedback. With two weak accepts and two weak rejects (with one weak reject indicating that they would consider raising their score if provided additional experimental results), the paper is on the borde... | Summary: This paper introduces Grammar-Forced Translation (GraFT), a framework to translate natural language (NL) into temporal logic (TL) using large language models (LLMs).
Claims And Evidence: The claims regarding the performance improvements and complexity reduction of GraFT are convincingly supported by empirical... | Rebuttal 1:
Rebuttal: **Before proceeding, please read our response addressed to all reviewers found in our rebuttal for reviewer JKCj.**
Q1: I think some implementation details (e.g., hyperparameter selection, number of runs per experiment) could be better documented to ensure reproducibility.
A1: We have included t... | Summary: The authors propose GraFT, an innovative framework that employs masks to ensure the syntax correctness of generated LTL programs. The approach first utilizes BERT to extract atomic propositions (APs) and map them into a predefined set of equivalent classes for co-references. Then, it leverages T5 to learn the ... | Rebuttal 1:
Rebuttal: **Before proceeding, please read our response addressed to all reviewers found in our rebuttal for reviewer JKCj.**
Q1. Why is the vocabulary size for atomic propositions (APs) limited to only 6? If the narration spans an entire movie lasting over two hours, wouldn’t it require significantly more... | Summary: This paper introduces a framework for translating natural language to temporal logic by restricting output token space during both grounding and translation phases. The framework employs a masked language model for atomic propositions grounding and a fine-tuned sequence-to-sequence model for translation. Using... | Rebuttal 1:
Rebuttal: **Before proceeding, please read our response addressed to all reviewers found in our rebuttal for reviewer JKCj.**
Q1: The method for obtaining grounded NL sequences for T5 training lacks clear explanation. The paper fails to justify whether these grounded NL sequences are suitable as learning o... | null | null | null | null | null | null |
A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language? | Accept (poster) | Summary: This article examines whether LLMs exhibit long memory under different conditions. It reports that temperature settings and prompting methods may disrupt long memory.
Claims And Evidence: The authors claim that temperature settings and prompting methods could destroy long memory, and this finding is robust to... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for taking the time to review our paper and sharing your concerns. While we wish to clarify certain aspects, we have taken your points seriously and conducted additional experiments to address them. We believe these new results significantly strengthen the paper, demonst... | Summary: The paper examines whether large language models (LLMs) replicate the fractal characteristics of natural language. Using a dataset of 240,000 LLM-generated articles, the authors analyze fractal parameters (Hölder and Hurst exponents) across three models (Gemini 1.0 Pro, Mistral-7B, Gemma-2B), decoding temperat... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your detailed review and constructive feedback. We are pleased that you have found our experiments thorough, claims well-supported, the overall study well-structured, and the findings valuable and insightful. As you stated, GAGLE includes various prompting strateg... | Summary: This paper investigates whether LLMs can replicate the fractal complexity found in natural language. The authors use the Hölder exponent (S) to examine self-similarity and Hurst exponent (H) for long range dependence. Authors have explored a large range of models, sampling temperature and prompts etc. Through ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We thank you for your detailed review and constructive feedback. We are pleased that you have found our work comprehensive, the evaluation valid and thorough, the use of fractals novel, and the paper well-organized and easy to read.
Your primary concerns regarding the generalizab... | Summary: This study constructs a dataset named GAGLE, comprising 240,000 AI and human-generated language instances. It employs fractal parameters, including Self-Similarity and Long-Range Dependence (LRD), to examine the differences between various language model sizes and architectures compared to human texts.The rese... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive feedback. We are pleased that you have found our work innovative and valuable.
We have carefully considered your concerns and have conducted new experiments to address them, particularly regarding the diversity of models and datasets. We believe these... | null | null | null | null | null | null |
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML | Accept (poster) | Summary:
The authors introduce a multi-agent LLM framework for full pipeline AutoML. The authors perform an extensive empirical study against relevant baselines, demonstrating that their AutoML-Agent outperforms both frontier models and partial pipeline language agents. The authors' experiments are informative,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive review and appreciate your recognition of our paper’s strengths. Below, we address your specific comments.
> Relationships to Magentic-One [7] and Agent K [2]
**R1** Thank you for pointing out these valuable concurrent papers that we had pr... | Summary: The paper introduces AutoML-Agent, a novel multi-agent framework leveraging large language models (LLMs) to automate the entire AutoML pipeline, including data retrieval, preprocessing, model selection, hyperparameter optimization, and deployment. The proposed framework employs a retrieval-augmented planning s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and encouraging review. We greatly appreciate your positive assessment of our paper’s novelty, empirical rigor, and technical soundness. Below, we address your thoughtful concerns.
> High computational cost
**R1** We would like to clarify that, apart from... | Summary: The paper introduces AutoML-Agent, a multi-agent LLM framework that automates the full AutoML pipeline from data retrieval to model deployment. Unlike prior approaches that focus on specific pipeline components (e.g., hyperparameter optimization or feature engineering), AutoML-Agent leverages retrieval-augment... | Rebuttal 1:
Rebuttal: Thank you for recognizing our contributions and for your thoughtful, detailed feedback. Below, we respond to your specific concerns.
> Scalability concerns
**R1** This is an important point. A key motivation behind AutoML-Agent is to reduce the computational overhead of the search process. Our f... | Summary: In this paper, the authors propose a novel multi-agent framework tailored for full-pipeline AutoML, including initialization, planning, and execution, incorporating 5 types of agents, such as Agent Manager, Prompt Agent, Data Agent, Model Agent, and Operation Agent. Results on 14 tasks and 5 baselines demonst... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on the novelty, motivation, and empirical rigor of our work. We are glad to address your concerns below.
> Method complexity
**R1** Thank you for pointing this out. In the final version, which permits an extra page, we will **move key details curre... | null | null | null | null | null | null |
Patch-wise Structural Loss for Time Series Forecasting | Accept (poster) | Summary: Traditional loss functions, such as Mean Squared Error, often miss structural dependencies in time series forecasting. This paper proposes a Patch-wise Structural Loss to improve accuracy by focusing on patch-level structural alignment. It uses Fourier-based Adaptive Patching to divide the series and incorpora... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. Below are our responses to your concerns and suggestions.
# [W1] Additional quantitative metrics for evaluating PS loss performance
To provide a more comprehensive evaluation, we incorporated additional metrics: **Dynamic Time Warping (DTW)**, **Time... | Summary: This paper proposes the Patch-wise Structural (PS) loss function for time series forecasting. The PS loss improves the alignment of local statistical properties (correlation, variance, and mean), addressing the limitations of traditional point-wise loss functions like MSE. By incorporating patch-level analysis... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. Below are our responses to your concerns and suggestions.
# [E1] Comparison between GDW and grid-search
To evaluate GDW against a traditional grid search for selecting loss weights, we conducted experiments on the ETTh1 dataset using iTransformer. B... | Summary: Most previous time series forecasting models use MSE as the loss function, which treats each time step independently and neglect the structural dependency among steps. To fill the gap, this work proposes Patch-wise Structural (PS) Loss. PS Loss first splits target series into patches with patch size determined... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. Below are our responses to your concerns and suggestions.
# [M1] PS loss performance on non-periodic targets
When there is no clear periodic pattern in the target series, the dominant frequency—i.e., the one with the highest amplitude in the FFT spec... | Summary: The authors propose a novel patch-wise structural (ps) loss, which is designed to enhance structural alignment by comparing time series at the patch level. By leveraging local statistical properties, e.g., correlation, variance, and mean, PS loss captures nuanced structural discrepancies overlooked by traditio... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. Below are our responses to your concerns and suggestions.
# [Q1] PS loss on ultra-long-term and short-term forecasting
We evaluated PS loss on **ultra-long-term (T = {1080, 1440, 1800, 2160})** and **short-term (T = {12, 24, 48})** forecasting tasks... | null | null | null | null | null | null |
FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting | Accept (poster) | Summary: The paper presents FSTLLM, a novel Spatio-Temporal Large Language Model (LLM) framework designed for few-shot time series forecasting. The model effectively integrates domain knowledge through a fine-tuned LLM and a graph-based learning approach to capture spatial-temporal correlations. Experimental results on... | Rebuttal 1:
Rebuttal: Response to Reviewer
We thank the reviewer for the thoughtful comments. We address each point below and will incorporate the corresponding revisions into the manuscript.
**Q1: Node Description, Pattern Analysis, and Case Study**
We used ChatGPT-4o to generate both node descriptions and pattern ... | Summary: This work introduces a framework called FSTLLM. This framework provides enhanced few-shot time series forecasting performance by integrating LLMs with the STGNN backbone. Specifically, it leverages LLMs for spatial correlation modeling, an STGNN network for spatio-temporal pattern modeling, and a domain knowle... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely thank the reviewer for the insightful and constructive feedback. Below, we address each point in detail and outline the corresponding changes we will make in the revised manuscript.
**Q1: Missing References — DUET and Time-MoE**
We appreciate the reviewer’s suggesti... | Summary: Considering the heavy time cost to collect data for training deep learning time series forecasting models, this work focuses on enhancing forecasting performance with limited training data. This paper propose a graph construction module to ensure stable graph construction used in Spatio-Temporal Graph Neural N... | Rebuttal 1:
Rebuttal: Dear Reviewer
We sincerely thank the reviewer for the constructive feedback. We address each of the comments below and will update the manuscript accordingly.
**Q1: Discussion of AutoTimes**
Thank you for the suggestion. We will add the following discussion to Section 2.3 (Related Work):
>"Aut... | Summary: This paper proposes a time-series prediction framework that leverages the prior knowledge of LLMs. Based on Author’s introduction about the framework, the framework can be flexibly applied to any advanced time-series prediction model (such as the STGNNs mentioned in the related works). The experiments were con... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely thank you for your valuable feedback. We address your comments in detail below and will revise the manuscript accordingly:
**Q1. Citation of In-Context Learning**
We appreciate your suggestion regarding the citation of in-context learning. We will revise the manuscri... | null | null | null | null | null | null |
Fast and Low-Cost Genomic Foundation Models via Outlier Removal | Accept (poster) | Summary: The paper "Making Genomic Foundation Models more Foundational Requires Outlier Removal: A Case Study on DNABERT-2" introduces GERM, an outlier-free genomic foundation model (GFM) designed to improve quantization robustness and low-rank adaptation efficiency. The authors argue that eliminating outliers in atten... | Rebuttal 1:
Rebuttal: The updated manuscript can be accessed anonymously at [link](https://www.dropbox.com/scl/fi/itpm5n21pfu3at01bofab/germ_icml2025.pdf?rlkey=zl8noukikpz1s4493b752s9uj&e=1&st=qma9ihc9&dl=0).
> **Reviewer's Comment**: a detailed comparison to ...
**Response**: We thank the reviewer for the feedback an... | Summary: This article introduces the outlier-free Hopfield layer into the Genomic foundation model to achieve a better trade-off between performance and efficiency. They also propose a continued training approach to avoid the additional cost of training from scratch. Comprehensive experimental results demonstrate that ... | Rebuttal 1:
Rebuttal: The updated manuscript can be accessed anonymously at [link](https://www.dropbox.com/scl/fi/itpm5n21pfu3at01bofab/germ_icml2025.pdf?rlkey=zl8noukikpz1s4493b752s9uj&e=1&st=qma9ihc9&dl=0).
> **Reviewer's Comment**: does not clearly explain what an outlier is or how to quantify it...
**Response**: W... | Summary: This paper describes an adaptation of the DNABERT genomic foundation model to reduce the impact of outliers in attention mechanism. The outlier phenomenom was first observed in large language models, where it was shown attention mechanisms can learn to pay large attention to irrelevant tokens, like the [SEP] t... | Rebuttal 1:
Rebuttal: The updated manuscript can be accessed anonymously at [link](https://www.dropbox.com/scl/fi/itpm5n21pfu3at01bofab/germ_icml2025.pdf?rlkey=zl8noukikpz1s4493b752s9uj&e=1&st=qma9ihc9&dl=0).
> **Reviewer's Comment**: what would lead to an outlier in a GFM...
**Response**: Thank you for your insightf... | Summary: This paper addresses the limitations of current GFMs, particularly DNABERT-2, when applying low-bit quantization and parameter-efficient fine-tuning methods like LoRA. The authors attribute performance degradation to outliers in attention mechanisms and propose GERM, a variant using an outlier-free attention m... | Rebuttal 1:
Rebuttal: The updated manuscript can be accessed anonymously at [link](https://www.dropbox.com/scl/fi/itpm5n21pfu3at01bofab/germ_icml2025.pdf?rlkey=zl8noukikpz1s4493b752s9uj&e=1&st=qma9ihc9&dl=0).
> **Reviewer's Comment**: However, the motivation ….
**Response**: We thank the reviewer for suggesting a d... | null | null | null | null | null | null |
Separating Knowledge and Perception with Procedural Data | Accept (poster) | Summary: The paper introduces a novel method to fully compartmentalize visual memory by training representation models exclusively with procedural data, thus eliminating the risks associated with privacy and bias inherent in real-world data. The main findings include achieving near state-of-the-art performance on stand... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions, and for agreeing that our approach is novel and shows strong abilities with perfect data unlearning. Below, we clarify how to run our baselines. We will also add a new figure to the camera ready that makes the connection between the limitat... | Summary: This paper introduces a memory-based approach to visual perception by training an embedding model solely on procedurally generated data, then using real data embeddings in a separate memory for classification and segmentation tasks. The authors emphasize advantages in unlearning and privacy, aiming to decouple... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and for agreeing we provide empirical demonstrations on the benefits of our approach. In particular, we appreciate their identification of areas which could be strengthened, such as clarifying the benefits of our approach and additional baselines... | Summary: This paper introduces a novel process for training neural networks using procedural data: Shaders KML and Shaders KML Mixup. Despite relying on simple programs and lacking any direct resemblance to the target distribution, the proposed method achieves impressive results in K-NN-based classification. The paper’... | Rebuttal 1:
Rebuttal: We thank the reviewer for agreeing that the approach is novel and has broad utility, as well as their insightful comments, questions, and suggestions. We answer the questions and follow up on the suggestions below
## 1 Benefits of KML over Mixup
We agree the benefits of KML may initially appear m... | null | null | null | null | null | null | null | null |
Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation | Accept (poster) | Summary: The paper **"Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation"** introduces **Adapting While Learning (AWL)**, a two-component fine-tuning approach that enables LLMs to intelligently decide when to rely on internal reasoning or external tools for solving scientific pro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address your concerns below:
---
## Information on the custom-created datasets
> "...The descriptions, and construction method should be clearly stated in the main text. However, I can not find this...."
> "Could you provide comprehensive information o... | Summary: This paper presents Adapting While Learning i.e AWL, a novel fine-tuning method to improve the performance of LLMs on scientific problems by adaptively using external tools based on question complexity. It tackles the issue of either hallucinations obtained thorugh fine tuning or excessive reliance on tools by... | Rebuttal 1:
Rebuttal: Thank you for your invaluable feedback. We address your suggestions below:
---
## Variations in loss functions
> "May be they could have considered more variations in loss functions and also showed some result with some experiments why is the proposed loss function better than the existing one ... | Summary: This paper proposes a new fine-tuning method called “Adapting While Learning (AWL)” that addresses the challenges of using large language models (LLMs) to solve scientific problems. LLM is effective for simple scientific problems, but can hallucinate on complex problems, and while integration with external too... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive suggestions. We address your concerns below.
---
## Validation on Larger Models
> ...limited to models other than Llama and large-scale models(10B+)
We additionally included Qwen2.5-14B-Instruct and trained it with our method. We conducted ex... | Summary: The paper introduces a two-component fine-tuning approach that first trains a model on direct reasoning (WKL) and then selectively incorporates tool usage based on the assessed complexity of the scientific problem. The method is empirically validated across six scientific benchmark datasets, demonstrating impr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address your concerns below:
---
## Novelty and Methodological Depth
> "The claim of novelty and methodological depth ... existing fine-tuning technologies."
We are not claiming novelty in fine-tuning technologies. Rather, our innovation primarily lies ... | null | null | null | null | null | null |
Reward-free World Models for Online Imitation Learning | Accept (poster) | Summary: This paper presents an approach called IQ-MPC for online imitation learning using reward-free world models. It can alleviate some issues in IL, such as handling complex, high-dimensional inputs and intricate environmental dynamics without explicit reward modeling. Key ideas include leveraging decoder-free worl... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and your recognition of its strengths, including its clear positioning within the literature, effective extensions of prior works, and practical advantages in complex tasks. Below, we provide detailed responses to your comments.
### 1: Lack of Comparison wit... | Summary: This paper gives an approach for online imitation learning with reward free world models that learn dynamics efficiently in latent space. The method is evaluated in DMControl, MyoSuite, and ManiSkill2 against several baselines.
Claims And Evidence: Several key claims are made in this paper.
1. The author cla... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and your recognition of its strengths, including our novel approach, theoretical support, strong performance, and clear presentation. Below, we provide detailed responses to your comments.
### 1: Harder Evaluation Tasks in ManiSkill2
The experiments could b... | Summary: The paper proposes a world-model-based approach for online imitation learning. The method aims to address limitations in current imitation learning techniques by leveraging world models to improve training stability and performance. The authors demonstrate that their approach achieves better training efficienc... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and your recognition of its strengths, including its clarity, the simplicity and effectiveness of our approach, and the solid theoretical grounding with comprehensive empirical validation. Below, we provide detailed responses to your comments.
### 1: Rationa... | Summary: The paper proposes a reward-free world model approach, IQ-MPC, for online imitation learning, addressing challenges in high-dimensional and complex tasks. The method integrates decoder-free latent dynamics models with inverse soft-Q learning, eliminating explicit reward modeling. By reformulating the optimizat... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review and your recognition of its strengths, including our novel approach, comprehensive evaluation, and clear presentation. Below, we provide detailed responses to your comments.
### 1: Limited Scope on Visual Tasks and Noisy Dynamics Test
The evaluated scope on... | null | null | null | null | null | null |
Generation from Noisy Examples | Accept (poster) | Summary: This paper studies the model of language generation in the limit, first introduced by Kleinberg and Mulainathan at NeurIPS 2024 and later extended by Lee et al. It builds on seminal work by Gould and Angwin from the 1960s, which has had a profound influence on learning theory. The study this language generatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will make sure to address all typos (i.e., typos 1-7) and suggestions (i.e., suggestions 1-5) in the camera-ready version.
> Could you please confirm if the issue I mentioned in the proof of Theorem 3.1 is correct, and could you check whether it could... | Summary: The paper studies the problem of language generation in the limit in a noisy setting where an adversary inserts a finite number of negative examples. The paper provides necessary and sufficient conditions for when a binary hypothesis class can be noisily generatable. The paper examines various definitions of n... | Summary: The paper extends prior work on language generation by studying the generation of new, unseen positive examples even when the example stream is adversarially contaminated with a finite number of noisy (negative) examples.
It introduces new notions, namely noisy uniform generatability, noisy non-uniform generat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We agree with all the suggestions made by the reviewer and will make sure to incorporate these along with fixing the typos in the camera-ready version. Below, we address some questions and concerns.
> More precisely, there is a wrong choi... | Summary: This paper proposes an extension of the theoretical model of language generation introduced by Kleinberg & Mullainathan (2024), exploring the impact of noisy example streams on generatability. The authors introduce the concepts of "noisy uniform generatability," "noisy non-uniform generatability," and "noisy g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address their questions and concerns below.
> However, what are the technical difficulties and contributions, especially given that noise has already been extensively studied in non-generative contexts?
As the reviewer noted, noise has been extensive... | null | null | null | null | null | null | |
Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective | Accept (spotlight poster) | Summary: This paper determines the sparsity rate of each layer for LLMs from a theoretical perspective, proposes that there is a "reconstruction error explosion" problem in sparse LLMs, and proposes to use a monotonically increasing arithmetic progression to determine the sparsity rate of each layer, thereby alleviatin... | Rebuttal 1:
Rebuttal: **Thanks for your careful review and comments!**
> Weaknesses (Smaller improvements for larger models.)
We have observed that larger models show less performance improvement with ATP. This observation aligns with common patterns in model pruning and compression, where the returns tend to diminis... | Summary: This paper identifies the issue of "reconstruction error explosion" in existing LLMs sparsification methods. Through theoretical analysis, it derives a layer-wise sparsity allocation method based on a monotonically increasing arithmetic progression to alleviate the above issue. Both theoretical analysis and ex... | Rebuttal 1:
Rebuttal: **Thanks for your careful review and comments!**
> Weakness 1 (Smaller improvements for lower sparsity.)
As sparsity decreases, the returns on performance improvements tend to diminish, which is consistent with common patterns in model pruning and compression. However, we believe that our ATP met... | Summary: This work proposes a relatively simple monotonically increasing layer-wise sparsity schedule for LLMs where the layers near the head are more sparse than earlier layers. The method is motivated by a detailed analysis of the effect of increasing sparsity on layerwise reconstruction errors and the propagation of... | Rebuttal 1:
Rebuttal: **Thanks for your careful review and comments!**
> Weakness 1 (Missing sparsity preserving fine-tuning results.)
Thank you for your suggestions. The results of fine-tuning 70% sparse LLaMA2-7B obtained by Wanda using LoSA [1], LoRS [2] and SPP [3] are below.
|Method|Fine-tuning|Wikitext2 PPL|He... | Summary: The paper establishes a relationship between sparsity and reconstruction error in pruning LLMs, demonstrating that increased sparsity leads to higher reconstruction error, which propagates through linear layers. The authors support this claim through empirical evaluation on transforms and theoretical analyses ... | Rebuttal 1:
Rebuttal: **Thanks for your careful review and comments!**
> Theoretical Claims (Any assumptions should be included in the text of Theorem, not just appear in the proof.)
We will restate Theorem 3.1 as: *When the input is the same, increasing the sparsity of the weights in the $i$-th layer will lead to an ... | null | null | null | null | null | null |
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models | Accept (poster) | Summary: The paper introduces **ProLoRA**, a parameter-efficient method for model adaptation and cross-domain knowledge transfer by decomposing pre-trained weight matrices via Singular Value Decomposition (SVD). ProLoRA splits a weight matrix W into a dominant subspace and a null-space. Key contributions include a low-... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and comments. Below, we provide detailed responses.
[C1] However, I still .... further justify the motivation.
[R1] We agree that visualizing the subspace and nullspace's roles would strengthen our justification. We will include: (a) Semantic Ablation... | Summary: This paper proposes ProLoRA, a zero-shot method for transferring pre-trained LoRA adapters between different text-to-image diffusion models without requiring retraining or access to original training data. The key motivation is that traditional LoRA adapters are tied to specific base models, making them diffic... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and comments.
[C1] The majority of comparisons … strengthen paper’s claims.
[R1] "No LoRA" can degrade performance (e.g., produce blurry outputs in Table 4). We are currently running experiments for Tables 1. We will add results of Tables 6 & 7 in the ... | Summary: This paper introduces ProLoRA, a method for zero-shot transfer of LoRAs between source and target diffusion models. It features a projection technique that transfers both subspace and nullspace components of source LoRAs to target models while preserving generation performance. The method works by identifying ... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and comments. Below, we provide detailed responses.
[C1] The method still requires computing SVD … for very large models.
[R1] We address this point in our response to Reviewer DiKB's comment [C5].
[C2] The performance varies …. limitations in general... | Summary: The paper proposes ProLoRA, which can transfer the pre-trained LoRA to another target model without training. This addresses a key constraint in existing methods where LoRA adapters are trained to specific models, requiring complete retraining to a new model. ProLoRA projects source to target weight space by u... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and comments. Below, we provide detailed responses.
[C0] Its contribution could be extended to other areas other than image generation.
[R0] Please refer [C3] of Reviewer eBaK
[C1] The authors provide theoretical insights ... why standard LoRAs affec... | null | null | null | null | null | null |
R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts | Accept (poster) | Summary: The paper introduces R2-T2, a method designed to optimize routing weights in multimodal Mixture-of-Experts (MoE) models during test time. R2-T2 maintains a reference set comprising samples for which the MoE model's outputs are either correct or preferred for each task. When presented with a sample from a new t... | Rebuttal 1:
Rebuttal: # Response to Reviewer 8cuF
Thank you for your detailed feedback! We address your comments below.
>**Q1: Concern about reference set construction: Academic benchmarks use predefined sample types, but real-world scenarios may not allow task type selection before inference. It might become necessa... | Summary: This paper proposes R2-T2, a test-time re-routing method designed to enhance multimodal mixture-of-experts (MoE) models without retraining. It addresses the limitation of suboptimal routing weights produced by pretrained routers, which often fail on complex or out-of-distribution tasks. R2-T2 introduces three ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 1y8A
Thank you for your detailed feedback! We address your comments below.
> **Q1: Generalization to OOD samples—The method relies on a reference set with similar questions. How does it perform on truly out-of-distribution (OOD) cases?**
Thank you for your question. Our ... | Summary: The paper introduces R2-T2, a test-time re-routing method for multimodal Mixture-of-Experts (MoE) models. The core idea is to optimize routing weights during inference by leveraging reference samples with correct predictions, addressing suboptimal routing in pretrained MoE models. The method is training-free a... | Rebuttal 1:
Rebuttal: # Response to Reviewer bdGh
Thank you for your detailed feedback! We address your comments below.
> **Q1: Possible data contamination—subsampled reference datasets (e.g., VQA-V2, MathVista) may overlap with evaluation benchmarks (e.g., MMBench, TextVQA), potentially inflating results. How was th... | Summary: The authors introduce test-time re-routing (R2-T2) for vision-language MoEs. R2-T2 adapts the routing weights for each test sample based on similar samples in a reference set. In particular, three various strategies with different optimization objectives and neighbor-search spaces have been proposed, with "Nei... | Rebuttal 1:
Rebuttal: # Response to Reviewer HT7U
Thank you for your detailed feedback! We address your comments below.
> **Q1: Significant increase in computational and memory costs.**
R2-T2 does not require significant increase in computations and memory because it only optimizes a low-dimensional routing weight ... | null | null | null | null | null | null |
Accelerating Large Language Model Reasoning via Speculative Search | Accept (poster) | Summary: **I am not very familiar with the subarea of this paper, so I am not highly confident in my review. I expect my review not to play a significant role in the decision.**
This paper introduces SpecSearch, a method that enables a small model to collaborate with a large model at both the thought and token levels... | Rebuttal 1:
Rebuttal: # Response to Reviewer zUgA
We sincerely thank the reviewer for the valuable comments! We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us ... | Summary: This paper introduces a new LLM reasoning method via speculative Tree-Search-Base reasoning. It involved a quality-preserving rejection mechanism and also has theoretical properties that means it can maintain reasoning quality compared to the large model. Experiments on math problems show up to 2–3× speedup ov... | Rebuttal 1:
Rebuttal: # Response to Reviewer EXez
We sincerely thank the reviewer for the thoughtful and encouraging feedback! We hope our response has addressed your concerns. If so, we would be truly grateful if you would consider raising your score. If not, we welcome any further comments and will continue working ... | Summary: This paper proposes SpecSearch to optimize thought generation through strategic collaboration between a small model and a large model at both thought and token levels. This approach efficiently produces high-quality reasoning thoughts. A key feature of SpecSearch is its quality-preserving rejection mechanism, ... | Rebuttal 1:
Rebuttal: # Response to Reviewer uX9o
We sincerely thank the reviewer for the insightful and valuable feedback. We genuinely hope our response has addressed your concerns. If it has, we would be truly grateful if you would consider raising your score. If not, we warmly welcome any further suggestions and w... | Summary: This paper proposes a speculative search framework, that extends speculative decoding framework to reasoning chains. SpecSearch framework works by rejecting and selecting both at thought and token levels. The authors show both performance and speedup improvements.
Claims And Evidence: The claim is that SpecSe... | Rebuttal 1:
Rebuttal: # Response to Reviewer cNpL
We sincerely thank the reviewer for the insightful, valuable, and positive comments. We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns. If so, we would deeply appreciate it if you could raise your sco... | null | null | null | null | null | null |
Equivariant Neural Tangent Kernels | Accept (poster) | Summary: This paper derives neural tangent kernels (infinite width) for group convolutional networks, and prove a equivalence between data augmentation and equivariance. They provide experimental validations for the finite-width framework. They also test group-equivariant kernels and compare them to non-equivariant one... | Rebuttal 1:
Rebuttal: ## Expectation and “off-manifold”
We are thankful for pointing out this confusion. The mentioned ensembles are a collection of independently initialized NNs and the average is understood over this family. For considerations about single networks, see heading “Ensembles” in the rebuttal for review... | Summary: This paper studies the training dynamics of equivariant neural networks via neural tangent kernels. The authors derive NTKs for group convolutions and nonlinearities, and also consider group convolutions for SO(3) in the Fourier domain (similar to spherical CNNs and G-steerable CNNs). The authors show that non... | Rebuttal 1:
Rebuttal: ## Ensembles
Thank you for this important question. Although Theorem 5.1 is formulated in terms of ensembles, the statement in fact also holds for individual models at finite width if the infinite-width NTKs in (32) are replaced by the empirical NTKs
$$
\Theta(x,x')=\left(\frac{\partial\mathcal{... | Summary: In this work, the authors propose a way to understand the training dynamics of equivariant models by deriving neural tangent kernels for a broad class of equivariant architectures based on group convolutions. For rototranslations in 2D and 3D, the authors show that equivariant NTKs outperform their non-equiva... | Rebuttal 1:
Rebuttal: ## Approximate results at finite width
Our theoretical claims hold for infinitely wide networks and in the ensemble mean, i.e. for infinitely large ensembles (for comments on extensions to single networks see rebuttal to reviewer hpPR, heading “Ensembles”). In this case, they predict exact agreem... | null | null | null | null | null | null | null | null |
Concentration Distribution Learning from Label Distributions | Accept (poster) | Summary: This paper proposes Concentration Distribution Learning (CDL), a new variant of Label Distribution Learning (LDL). In CDL, in addition to the common label distribution that represents the descriptions of each class, there is a dimension that represents the background information. Based on an assumption about t... | Rebuttal 1:
Rebuttal: Thank you for your time and effort on reviewing our paper. In what follows, we will address your **questions** in detail.
**Experimental Designs Or Analyses 1: The traditional LDL experimental datasets may differ from real-world scenarios because the last dimension may not serve as the background... | Summary: The paper proposes a new label distribution learning method based on a novel hypothesis. Hypothesis: In existing label distribution learning, there is an issue where the current labels within the label set cannot adequately describe the samples. Based on this hypothesis, the authors suggest that concentration ... | Rebuttal 1:
Rebuttal: Thank you for your precious suggestions. After careful consideration, our responses to the **questions** you mentioned are listed as follows.
**Claims And Evidence: The authors believe that in the existing label distribution learning, the labels in the label space are insufficient to describe the... | Summary: The paper introduces a novel concept called concentration distribution learning (CDL), which extends traditional label distribution learning (LDL) by incorporating a background concentration term. This term represents the absolute description degree of labels not present in the existing label space. The author... | Rebuttal 1:
Rebuttal: Thanks for your valuable reviews, and your suggestions will effectively help us improve our work. Below are our responses to your **questions**.
**Claims And Evidence: The authors claim that "excavating the background concentration makes full use of the information in the datasets and benefits th... | Summary: This paper proposes Concentration Distribution Learning (CDL), which introduces background concentration to address the limitation of Label Distribution Learning (LDL) in capturing hidden information. The authors designed the CDL-LD model based on the Dirichlet distribution, combining confidence and background... | Rebuttal 1:
Rebuttal: Thanks for your valuable reviews, and your suggestions will effectively help us improve our work. Below are our responses to your **questions**.
**Claims And Evidence 1 \& 2: Why does the paper assume that each $alpha\_i$ is composed of the sum of the dataset $e\_i$ and the background concentrati... | null | null | null | null | null | null |
Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation | Accept (poster) | Summary: This paper proposes a method called Fine-Grained Guidance, FGG, to improve precision and controllability in diffusion-based symbolic music generation, especially in the domain of tonal music where out-of-key notes can be perceived as mistakes.
## update after rebuttal
**I raised the score to 4 as the rebutta... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's valuable comments. Please allow us to provide responses as follows:
**1. Using this FGG is of course beneficial for most of the cases, but it "eliminates" the generation of such tension-imposed music. How can the authors justify this?**
We agree that some comp... | Summary: This paper presents a fine-grained guidance (FGG) mechanism for improving symbolic music generation using diffusion models.
The proposed approach incorporates strict harmonic control by integrating domain knowledge into the generative process, ensuring that generated musical sequences adhere to predefined chor... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's suggestions on revising! Due to this year's policy, it is not allowed to upload a revised paper, external links can only contain figures/tables, and rebuttal has a 5000 length requirement. Please allow us to describe a revision plan in the follows and promise to... | Summary: This paper introduces a Fine-Grained Guidance (FGG) approach for diffusion-based symbolic music generation, addressing precision and controllability challenges.
FGG incorporates harmonic and rhythmic constraints during both training and sampling, ensuring generated music aligns with user intent.
Theoretica... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s recognition of our work's theory, experiments, and the practicality shown by the interactive demo.
Regarding OOD datasets, we unfortunately have not yet found a high-quality dataset other than the one we used. Alternatively, in the numerical experiments, the s... | Summary: The two main motivators of this work are (1) the importance of providing user control for symbolic music generation, and (2) specifically the importance of *exact* pitch control, and the particular challenge of achieving this when using an image-based representation (i.e. piano roll). The authors solve this by... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's suggestions on revising! Due to this year's policy, it is not allowed to upload a revised paper, external links can only contain figures/tables, and rebuttal has a 5000 length requirement. Please allow us to describe a revision plan in the follows and promise to... | null | null | null | null | null | null |
Empowering World Models with Reflection for Embodied Video Prediction | Accept (poster) | Summary: This paper proposes a world model based on video prediction. The world model is specifically designed for embodied AI (manipulation, more specifically). The authors design a novel strategy, termed Reflection of Generation (RoG), to leverage VLM and video generation models to serve as a world model. Besides, th... | Rebuttal 1:
Rebuttal: # Response to Reviewer cisM
We thank the reviewer for recognizing the novelty of RoG, the value of our benchmark, and the thoroughness of our experimental results. Your constructive feedback helps us further improve the clarity and rigor of the paper.
---
## 1. Paper Organization and Benchmark Pr... | Summary: In this work, the authors propose a Reflection of Generation (RoG) solution to enhance video prediction in embodied scenarios. To achieve it, they further introduce an Embodied Video Anticipation Benchmark (EVA-Bench) and an Embodied Video Anticipator (EVA) model.
Claims And Evidence: Most claims are support... | Rebuttal 1:
Rebuttal: # Rebuttal to Reviewer aEuy
We sincerely thank the reviewer for the insightful comments and for highlighting the relevance of Pandora. We also appreciate your recognition of the strengths of our work, such as “most claims are supported by clear evidence” and “the methods and evaluation basically m... | Summary: This paper introduces Reflection of Generation, a set of reasoning strategies aimed at improving video generation models for multi-step predictions and Out-of-Distribution scenarios. To support RoG, the authors propose the Embodied Video Anticipation Benchmark to evaluate world models across diverse tasks, an... | Rebuttal 1:
Rebuttal: # Rebuttal to Reviewer wGih
We sincerely thank the reviewer for the thoughtful feedback and recognition of our contributions. Below, we address the key concerns raised:
## 1: Accumulated Errors in Long Video Generation
We appreciate the concern regarding error accumulation in long-horizon video g... | Summary: This paper introduces Reflection of Generation (RoG), an intermediate reasoning strategy that enhances video prediction by combining pre-trained vision-language and video generation models as a world model. We introduce Embodied Video Anticipation Benchmark (EVA-Bench) to evaluate these models across diverse t... | Rebuttal 1:
Rebuttal: # Response to Reviewer 3Yfm
We thank the reviewer for their thoughtful and encouraging feedback. We are glad that the reviewer appreciated the novelty of our Reflection-of-Generation (RoG) mechanism, the well-structured EVA-Bench, and the demonstrated real-world applicability of our EVA model. Be... | null | null | null | null | null | null |
Enhancing Pruned Models by Input Compensation | Reject | Summary: This paper proposes a method called input compensation (IC) for enhancing pruned models by adjusting the input to compensate for the removed weights. IC is designed in the input space and is orthogonal to existing pruning methods designed in the parameter space. Emprically, IC can be combined with existing pru... | Rebuttal 1:
Rebuttal: Dear Reviewer QATu,
We sincerely thank you for your positive rating, thoughtful review, and valuable suggestions that have helped us improve our paper.
We have carefully addressed your concerns as follows.
If you have any other concerns or questions, please let us know. We are more than happy t... | Summary: The paper proposes an input compensation approach for pruning, which reformulates weight tuning as adaptive input modifications. Specifically, the method begins with the dual problem of weight compensation and approximates input compensation using a pre-trained encoder and attention-based computations. Experim... | Rebuttal 1:
Rebuttal: Dear Reviewer aNPP,
Thank you for your time and effort in reviewing our paper.
We have carefully addressed your concerns and hope you are satisfied with our responses.
**If you have any further questions/concerns, please let us know** and we are more than happy to address them.
Best,
Authors... | Summary: The work, Enhancing Pruned Models by Input Compensation, proposes a new fine-tuning method where, instead of compensating the retained parameters in compressed neural network models, the work introduces input compensation to adjust inputs to compensate the removed parameters and fine-tune the pruned models for... | Rebuttal 1:
Rebuttal: Dear Reviewer MNqZ,
We sincerely thank you for your positive rating, thoughtful review, and valuable suggestions that have helped us improve our paper.
We have carefully addressed your concerns as follows.
If you have any other concerns or questions, please let us know. We are more than happy t... | Summary: The paper introduces a novel post-pruning algorithm that enhances pruned models by leveraging input compensation (IC) instead of traditional weight updates. This approach is compatible with any pruning method. Through extensive experiments on ViT, LLaMa, and DDPM, the study demonstrates that the proposed atten... | Rebuttal 1:
Rebuttal: Dear Reviewer Wf6n,
We sincerely thank you for your thoughtful review and valuable suggestions that enhanced our paper.
We have carefully addressed your concerns as follows.
**If you have any other concerns or questions, please let us know.** We are more than happy to address them and further i... | null | null | null | null | null | null |
LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection | Accept (poster) | Summary: The paper introduces LensLLM, a novel framework for selecting Large Language Models (LLMs) by analyzing their fine-tuning dynamics. The authors propose a Hessian-based PAC-Bayes generalization bound to model the transition phases in fine-tuning, aiming to improve the efficiency and accuracy of model selection.... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. The followings are our answers to your concerns.
Q1: "The analysis of the paper heavily depends on Theorem 1, which is a Generalization bound of LLM’s finetuning. I am not quite sure whether the bound is tight enough to claim that model A is better than mo... | Summary: The paper proposes a framework, called **LensLLM**, for predicting and selecting the best large language model (LLM) to fine-tune under computationally constrained scenarios. It introduces a theoretical foundation using a Hessian-based PAC-Bayes generalization bound to illustrate how fine-tuning progresses thr... | Rebuttal 1:
Rebuttal: Thank you for the insightful questions. The followings are our answers to your concerns.
Q1: How robust is the identified transition point between pre-power and power phases to hyperparameter choices (e.g., learning rate, batch size, sequence length)? Do small changes in these settings significa... | Summary: The paper first derives a Hessian-based PAC-Bayes generalization bound that provides deep insight into the fine-tuning dynamics of large language models. It then introduces LENSLLM—a Rectified Scaling Model based on the Neural Tangent Kernel (NTK)—which demonstrates impressive accuracy in predicting performanc... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. The followings are our answers to your concerns.
Q1: Could you please clarify the architecture of your regression model?
A1: As illustrated in Section 3.2, our regression model is constructed based on the NTK matrix as follows:
$$
L(D) = \frac{B}{F(\Thet... | Summary: LensLLM introduces a novel theoretical framework that addresses the fundamental challenge of efficient Large Language Model selection through the lens of fine-tuning dynamics. The paper develops a rigorous Hessian-based PAC-Bayes generalization bound that characterizes two distinct phases in LLM fine-tuning: a... | Rebuttal 1:
Rebuttal: Q1: How sensitive is the method to hyperparameter choices, particularly in the algorithm's stopping criteria?
A1: Thank you for your question.
a. We performed ablation studies to assess the sensitivity of our method to the stopping criteria—specifically, the regression threshold ($\gamma$) and ... | null | null | null | null | null | null |
Privacy-Shielded Image Compression: Defending Against Exploitation from Vision-Language Pretrained Models | Accept (poster) | Summary: This paper proposes a novel Privacy-Shielded Image Compression (PSIC) method aimed at protecting images from being exploited by Vision-Language Pretrained (VLP) models. The PSIC framework integrates an adaptive multi-objective optimization strategy that balances perceptual quality and encryption effectiveness.... | Rebuttal 1:
Rebuttal: First of all, we greatly appreciate your thorough review and helpful suggestions. Below, we address your concerns one by one, and we hope our responses fully clarify each point. If any misunderstanding remains, we sincerely welcome further clarification or suggestions.
Q.1 Incorporate more qualit... | Summary: The paper presents a novel approach for privacy protection in image compression, termed Privacy-Shielded Image Compression (PSIC), aimed at defending against exploitation by Vision-Language Pretrained (VLP) models. The method leverages a flexible compression scheme that creates bitstreams with multiple decodin... | Rebuttal 1:
Rebuttal: First of all, we deeply appreciate your kind suggestions and positive feedback on our work, which have greatly encouraged us. We hope our responses below adequately address your concerns.
Q.1: Discuss the challenges of extending to other data types.
Ans.: Thanks for your insightful comments. Bas... | Summary: This paper proposes Privacy-Shielded Image Compression (PSIC), a learned image compression framework that prevents Vision-Language Pretrained models from extracting semantic information while preserving perceptual quality. PSIC enables a single bitstream to be decoded into an encrypted version or a full versio... | Rebuttal 1:
Rebuttal: First of all, we would like to express our sincere gratitude for your responsible and constructive comments, which have been very helpful in further improving the quality of our manuscript. Below, we summarize your concerns into five key points and address them one by one. We hope our responses sa... | null | null | null | null | null | null | null | null |
POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval | Accept (poster) | Summary: The paper introduces POQD, a framework for optimizing multi-vector retrieval (MVR) for retrieval-augmented generation (RAG) systems. The key idea is to improve retrieval performance by decomposing a query into sub-queries. POQD uses an LLM in two roles: one acts as a Query Decomposer that splits the input quer... | Rebuttal 1:
Rebuttal: Thank for your comments. Our responses are below:
+ About the additional training data:
Indeed, in our experiments, we did not include additional training data to train POQD throughout the paper. During the training process, we train POQD using the same set of training samples as those baseline ... | Summary: The paper proposes an approach to decompose a query into sub-queries, and the decomposition is optimized to obtain better performance in the end task. The decomposition is performed by using meta-prompting to an LLM. It is expected that the LLM is able to iteratively generate better prompts when the performanc... | Rebuttal 1:
Rebuttal: We would thank your comments. Our responses are below:
+ About qualitative examples:
Thanks for pointing out this issue. Indeed, in figure 1, we leverage one example to show the differences between ColBERT, ICL-QD and POQD. We further expand it by reporting the decomposed sub-queries (both before... | Summary: The paper presents Performance-Oriented Query Decomposer (POQD), a framework for optimizing query decomposition in retrieval-augmented generation (RAG) tasks, particularly in multi-vector retrieval (MVR). POQD leverages an LLM-driven iterative optimization strategy to generate sub-queries that enhance downstre... | Rebuttal 1:
Rebuttal: Thanks for your comments. Our responses are below:
+ About the contribution of the paper:
While we don't propose a novel LLM-based optimization method, our key contribution is recognizing the need to optimize query decomposition for multi-vector retrieval—a critical factor in improving retrieval... | Summary: The paper tackles an important problem of jointly optimizing the query decomposition and retreival model for downstream generation task. The query decomposition and embedding model are trained alternatively.
Given an embedding model, the query decomposition is performed using a LLM with the optimization spac... | Rebuttal 1:
Rebuttal: We would thank your comments. You can find our responses to your comments below:
+ Regarding the retrieval results in Table 1:
We admit that these results look confusing. Indeed, the discrepancy between Table 1 and Table 2 can arise from the fact that we report Top-20 and Top-100 retrieval accur... | null | null | null | null | null | null |
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis | Accept (poster) | Summary: This paper proposes a DMOSpeech speech synthesis method. It utilizes Connectionist Temporal Classification (CTC) loss and Speaker Verification (SV) loss to realize the direct optimization of diffusion-based models. It was evaluated using subjective and objective tests. It outperforms the previous methods in mo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper. We appreciate your feedback and address your concerns below:
**Correlation Between Subjective and Objective Metrics**
We have indeed analyzed the correlation between subjective and objective metrics in our paper. As shown in Figure 3 and Figure ... | Summary: This paper introduces DMOSpeech, a distilled diffusion-based text-to-speech (TTS) model that achieves faster inference and superior performance compared to its teacher model. It has two advantages: (1) reducing sampling steps from 128 to 4 via distribution matching distillation, and (2) providing direct gradie... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback, but we believe there is a fundamental misunderstanding about our paper's contribution. Our work presents several key innovations that have not been explored in prior research, including FlashSpeech:
**Clarification on Direct Metric Optimization vs. Adversari... | Summary: This paper presents DMOSpeech, a distilled diffusion-based speech synthesis model that achieves true end-to-end optimization of perceptual metrics, specifically through CTC loss for intelligibility and SV loss for voice similarity. The authors integrate these loss functions into a distilled student model train... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation and constructive feedback. Below, we address each point raised:
**StyleTTS-ZS Comparison**
The performance of StyleTTS-ZS in our evaluation aligns with what was reported in their original paper. The fundamental architectural differenc... | Summary: Diffusion models have shown strong potential in speech synthesis tasks such as text-to-speech (TTS) and voice cloning. However, their iterative denoising process is computationally expensive, and previous distillation methods have led to quality degradation. Existing TTS approaches also suffer from non-differe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive recommendation for our paper. We appreciate the recognition of our model's ability to enable direct gradient pathways for end-to-end optimization and the acknowledgment of our efficiency improvements. While we agree with many points raised in the ... | null | null | null | null | null | null |
On Temperature Scaling and Conformal Prediction of Deep Classifiers | Accept (poster) | Summary: This paper focuses on a popular calibration technique known as temperature scaling (TS) and investigates its effect on major conformal prediction (CP) methods (LAC, APS, RAPS). They show that TS improves class-conditional coverage of adaptive CP but increases prediction set sizes; the effect on LAC is negligib... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We appreciate your recognition of our extensive empirical study and clear experimental design.
Below, we address your comments on the theoretical analysis point by point.
***
> Theory for conditional coverage.
***
Our work develops a comprehensive mathe... | Summary: In this work, the authors studied the effect of the widely used temperature scaling calibration on the performance of conformal prediction techniques for deep neural network classifiers. A wide range of experiments are conducted and a theoretical framework is proposed to explain the effects.
Claims And Eviden... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable suggestions. We appreciate your recognition of our comprehensive experiments and clear presentation.
Below, we provide a point-by-point response to your comments.
***
> Improving readability of the figures.
***
Following this comment, in the r... | Summary: Calibration and Conformal Prediction (CP) are two popular approaches to solving the overconfidence problem in modern DNN classifiers. In this paper, the authors studied the effects of temperature scaling (TS), which is effective for calibration, on the efficiency of CP. The authors first designed extensive exp... | Rebuttal 1:
Rebuttal: We are grateful for your constructive and thorough review of our theoretical work. We appreciate your acknowledgment of our novel analysis, supported by extensive empirical studies and theoretical investigation. We have carefully addressed your comments and suggestions.
Below, we provide a point... | Summary: The paper aims to study the interplay between conformal prediction (CP) and temperature scaling (TS) calibration. They study the effect of TS on conformal prediction using extensive empirical evaluation with three different CP methods. They present a theoretical analysis to explain the effect of TS on APS and ... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review. We are pleased that you recognized the extensive empirical evaluation of our experimental design.
Below, we carefully respond to all of your comments.
> Regarding the technical assumption
The core of this technical assumption is the strong sim... | null | null | null | null | null | null |
Potemkin Understanding in Large Language Models | Accept (poster) | Summary: The authors discussed the phenomenon of $\textbf{potemkins}$ in large language models, referring to the cases where a model’s misunderstanding of a concept does not align with the way humans would have misconceived it. The authors proposed a framework to formally define what Potemkin is and designed a benchmar... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper. We're glad that you appreciated our paper and findings. We respond to your comments and describe new results; to summarize the main changes, we've added:
- An expansion of the coherence analysis to include 9 models
- Unaggregated results for table 2... | Summary: This paper introduces the idea of Potemkin understandings, which is defined as differences in how human and large language models understand concepts. The main contribution of this paper is the design of a benchmark that tests the discrepancy in the model's ability to claim a definition of a concept and its ab... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We respond to your comments and describe new results; to summarize the main changes, we've added:
- An expansion of the coherence analysis to include 9 models
- A visualization of our mathematical framework
- A rewritten and simplified framework
- Analysis of ... | Summary: This paper investigates Potemkin understanding in language models. To assess model behavior, the authors design evaluation datasets across diverse domains, including literary techniques, game theory, and psychological biases. The findings indicate that while language models are good at explaining concepts, the... | Rebuttal 1:
Rebuttal: Thank you for your careful and insightful review of our paper. We respond to your comments and describe new results below; to summarize the main changes, we've added:
- A rewritten and simplified framework
- A visualization of keystone questions and potemkins
- Analysis of the role of question com... | Summary: This paper introduces and systematically investigates a novel failure mode in large language models (LLMs), termed Potemkin Understanding. This phenomenon refers to the model's ability to correctly interpret a concept while demonstrating inconsistent or incorrect behavior in practical applications, akin to cre... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We’re glad you found our work novel, rigorous, and empirically interesting. We respond to your comments and describe new results; to summarize the main changes, we've added:
- An expansion of the coherence analysis to include 9 models
- Analysis of difficulty
-... | null | null | null | null | null | null |
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization | Accept (poster) | Summary: The paper focus on bilevel optimization and incorporates curvature information into the approximation of hypergradients in bilevel optimization. The authors propose a Newton-based framework (NBO) that solves lower-level problems with computing Hessian inverse-vector products. They establish the convergence r... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We will address each point in detail. The figures and the tables mentioned below are in this anonymous link: https://drive.google.com/file/d/1lCY1UF3isNnoujM8AGCPIdJlRqHctj-b/view?usp=sharing
## Essential References Not Discussed:
* **Comparison with fully f... | Summary: This paper consider the bilevel problem $\min_x \Phi(x) = f(x, y^*(x))$ where $y^*(x) = \arg\min_y g(x, y)$ where the inner function $g$ is strongly convex w.r.t. the inner variable $y$. The paper proposes a new AID-based method where the inner variable $y$ and the linear system variable $u$ are updated by an ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We will address each point in detail below. The figures mentioned below are in this anonymous link: https://drive.google.com/file/d/1v6ftNYExUb_ClkgoS7b9wU2Q3nNY1IsP/view?usp=sharing
## Other Strengths And Weaknesses:
* **Concern about ba... | Summary: This paper proposes a novel method for bilevel optimization, focusing on improving hypergradient estimation by incorporating curvature information. The key contributions include: (1) New Algorithmic Framework: An enhanced algorithm using an inexact Newton method, with improved computational complexity and conv... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We will address each point in detail below. The referenced figures are compiled in a single-page PDF (containing only figures) available at the anonymous link:
https://drive.google.com/file/d/15xqtvUMRk7Ah7Gi5hnvBQWyuZ6W15zX8/view?usp=sharing ... | Summary: This paper introduces a Newton-based approach to efficiently compute hypergradients in bilevel optimization. Instead of directly inverting the Hessian, which is costly, the method approximates Hessian-inverse-vector products (HVPs) to improve computational efficiency. The proposed Newton-based bilevel optimize... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We will address each point in detail below. The referenced figures are compiled in a single-page PDF (containing only figures) available at the anonymous link: https://drive.google.com/file/d/1ZEzn2mKcwrPzlBeziFpC1mKGWDeyq6-C/view?usp=sharing
... | null | null | null | null | null | null |
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting | Accept (poster) | Summary: This paper proposes a novel method，named LaGA and it tackles the challenge of view-dependent semantics in language-driven open-vocabulary 3D scene understanding.
LaGa decomposes the 3D scene into distinct objects and then builds view-aggregated semantic representations by clustering semantic descriptors and ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We hope our response can help address your concern.
## Weaknesses
>W1: The claimed innovation in 3D Scene Decomposition shows notable similarities with the previous work on openGaussian. I recommend that the authors explicitly discuss these ... | Summary: The paper addresses the challenge of view-dependent semantics in 3D Gaussian Splatting for language-driven open-vocabulary scene understanding. The authors propose LaGa (Language Gaussians), a method that decomposes 3D scenes into objects and constructs view-aggregated semantic representations by clustering an... | Rebuttal 1:
Rebuttal: We thank you for your effort in reviewing our paper. We hope the following response would address your concerns.
## Weaknesses:
>W1: ... does not support multi-scale segmentation like LangSplat ...
Thank you for the comment. LaGa supports multi-scale segmentation, though not sufficiently emphasi... | Summary: The paper proposes to perform open-vocabulary semantic segmentation of 3DGS scenes that respect view-dependent and view-independent semantics. Using SAM masks, per-gaussian contrastive features are learned to learn 3D object clusters. The method is evaluated on LERF OVS and ScanNet datasets showcasing competit... | Rebuttal 1:
Rebuttal: Thanks for careful evaluation.
## Weaknesses
> W1: Core part similar to SAGA, GARField ... seem crude and not impactful...
This is a misunderstanding about our method's core. Rather than decomposition, its core lies in the view-aggregated representation, which we believe is not crude.
It consis... | Summary: This paper proposes LaGa, which explores open-vocabulary 3D Gaussian understanding by decomposing the 3D scene into objects and then establishing view-dependent semantic connections. The proposed approach does not rely on aligning 3D Gaussian semantic features with 2D semantic priors and therefore is simple bu... | Rebuttal 1:
Rebuttal: We sincerely thank you for instructive comments. We hope our response can help clear your concerns.
## Weaknesses
> W1: ... failure cases and analysis.
Thanks for the suggestion. We summarize key failure cases and provide representative examples:
1. Bag-of-Words Effect in CLIP:
When prompted w... | Summary: The paper introduces LaGa, a novel method designed to improve open-vocabulary 3D scene understanding by tackling view-dependent semantics in 3D Gaussian Splatting. LaGa works by breaking down the scene into 3D objects using multi-view 2D masks. It then aggregates the semantics of these objects through a combin... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort dedicated to evaluating our work. We greatly appreciate your recognition of our motivation, methodological design, and the clarity of our writing. We find the remaining concerns mainly locates in the 'Experimental Designs Or Analyses' section. We hop... | null | null | null | null |
ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference | Accept (spotlight poster) | Summary: This paper presents ShadowKV, a system for long-context LLM inference that optimizes memory usage and throughput with negligible impact on output quality.
ShadowKV consists of two key techniques:
1. In GPU memory, it only stores the SVD decomposition of pre-ROPE key caches to reduce memory usage per request. ... | Rebuttal 1:
Rebuttal: Thank you for the supportive comments and for recognizing the novelty of our method and the thorough evaluations. We hope our detailed clarifications below address the remaining concerns.
---
### **Q1: Missing clarification on which dataset was used for observations**
We appreciate the reviewer... | Summary: This paper presents several interesting findings, including the observation that pre-ROPE keys exhibit low-rank properties, and that post-ROPE keys show high similarity with neighboring tokens.
Based on these insights, the authors propose two main techniques: Low-Rank Keys and Offloaded Values for Storage, an... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and thorough review. We truly appreciate your recognition of our findings and experiments. We have thoroughly addressed each of your questions and hope our responses will lead you to consider raising your score.
---
### **Q1: Concern about unaccounted latency from ke... | Summary: This paper finds a novel method of KV cache management that takes advantage of partial offloading to CPU and matrix decomposition to obtain impressive KV reduction without affecting accuracy and reducing latency significantly. The paper finds two important properties of LLMs that it takes advantage of: (i) the... | Rebuttal 1:
Rebuttal: Thank you for detailed review and valuable feedback. We appreciate the reviewer's recognition of the novelty and effectiveness of our method. Below, we address the raised concerns below and will incorporate clarifications into the revised version. We hope the reviewer can consider raising your sco... | Summary: Large language models (LLMs) excel at handling extended contexts but face challenges with key-value (KV) cache scaling, increasing memory usage and reducing inference throughput. Existing methods like KV eviction and sparse attention either degrade accuracy or inadequately optimize memory use and latency. This... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and your positive assessment of our work. We appreciate your recognition of the novelty of ShadowKV, particularly its system-level insights and the strength of accuracy evaluations. We address your concerns with additional clarifications and experiments as detai... | null | null | null | null | null | null |
LoRA-Gen: Specializing Large Language Model via Online LoRA Generation | Accept (poster) | Summary: The paper proposes LoRA-Gen, a framework for specializing language models (LMs) on edge devices by generating task-specific LoRA parameters using a cloud-side model. The core idea involves leveraging a large cloud-based LM to generate meta tokens from task descriptions, which dynamically assemble LoRA paramete... | Rebuttal 1:
Rebuttal: **Q1: The training-free generalization claim requires broader validation across diverse tasks.**
**Ans:**
As shown in Table 2, Table 3 in the manuscript and Table 10 in the response to reviewer HcLz, we have mainly validated our method in the fields of mathematics, commonsense reasoning, science... | Summary: The authors automate the creation of task specific LoRAs, by leveraging a larger scale LLM finetuned to generate LoRA parameters. The larger-scale llm is prompted with a system prompt specifying the task. The LoRa parameters are applied to a smaller scale edge model. The authors find these generated lora param... | Rebuttal 1:
Rebuttal: **Q1: It's not clear to me that context is needed for trained (non-base models), especially on seen tasks.**
**Ans:**
Sincerely sorry for the confusion. We aim to utilize the online large language model and the online LoRA Expert Pool to convert user instructions, system prompts, or contextual c... | Summary: This paper introduces LoRA-Gen, a framework that enhances domain-specific task performance for small edge-side models by leveraging a large cloud-side model to generate LoRA parameters based on task descriptions. Utilizing reparameterization, LoRA-Gen merges these parameters into edge-side models, enabling fle... | Rebuttal 1:
Rebuttal: **Q1: Do not include reasoning-specific datasets such as mathematical reasoning.**
**Ans:**
Thanks for the great advice, we further evaluate our method on the mathematical reasoning dataset GSM8K. As shown in Table 10, LoRA-Gen brings gains. Due to time constraints, we will collect more types of... | null | null | null | null | null | null | null | null |
Unifying Specialized Visual Encoders for Video Language Models | Accept (poster) | Summary: The paper presents a VideoLLM that aligns and unifies the outputs of four different visual encoders to improve its video understanding capabilities. Specifically, the authors use a spatial encoder (DINOv2), a temporal encoder (ViViT), an image-language encoder (SigLIP), and a video-language encoder (LanguageBi... | Rebuttal 1:
Rebuttal: Dear Reviewer jZRP, we thank you for spending your time and effort on reviewing our work. We appreciate your recognition of our thorough experimental setup and ablations that sufficiently backs up our claim, as well as its relevant and timely contribution towards a broader collection of works rega... | Summary: The authors propose to use multiple video encoders (instead of a single encoder) for visual feature extraction in the context of video-LLMs. They propose a simple, straightforward technique to ensemble multiple encoders with a clever feature fusion strategy leading to lesser inference-time FLOPs than any of th... | Rebuttal 1:
Rebuttal: Dear Reviewer 44Cd, thank you for your time and effort on reviewing our work. We are grateful for the insights that you have provided us. Specifically, we appreciate that you have seen our work to have **novel and clever strategy** with **less inference-time and FLOPs**, providing **interesting in... | Summary: Traditional VideoLLMs typically rely on a single vision encoder, which restricts the model's ability to leverage the diverse strengths of different visual encoders. To overcome this limitation, this paper propose a novel framework that integrates multiple specialized vision encoders into a unified video repres... | Rebuttal 1:
Rebuttal: Dear Reviewer 1K5Z, we thank you for spending your time and effort on reviewing our work. We appreciate your recognition of the motivation, the novelty of our framework, extensive experiments, insights we provided to practitioners, and high reproducibility of our work. Your concerns seem to be wit... | Summary: This paper tackles the problem of video-language understanding by introducing a multi-encoder strategy for constructing a comprehensive representation for videos. The authors claim that existing single-encoder methods can merely obtain a limited amount and type of visual information. Therefore, they proposed t... | Rebuttal 1:
Rebuttal: Dear Reviewer ukCZ, we thank you for spending your time and effort on reviewing our work. We appreciate your positive comments on our practical design choices that can enhance the capabilities of existing Multimodal LLMs (MLLMs). Your concerns are with a) the **insights** gained from our paper, an... | null | null | null | null | null | null |
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network | Accept (spotlight poster) | Summary: This paper presents a multimodal asynchronous hybrid network for real-time anomaly detection in autonomous driving scenarios by combining event-camera streams with RGB-camera data. The method uses an asynchronous Graph Neural Network for event-stream processing and a CNN for spatial feature extraction from RGB... | Rebuttal 1:
Rebuttal: Thank you for your inspiring review and actionable suggestions. Below, you will find our detailed responses to your questions.
> **Q1**: Real-time Anomaly Detection Methods
We did omit some real-time anomaly detection papers, but these methods are not applicable to the traffic domain. For exampl... | Summary: The paper presents a real-time anomaly detection framework for autonomous driving through a novel multimodal asynchronous hybrid network. The method integrates high-temporal resolution event data from event cameras with spatially rich RGB images processed by a CNN, combined with an asynchronous GNN. Temporal d... | Rebuttal 1:
Rebuttal: > **Claims 3**: V2E Transformation
Please refer to the response to Weakness in Reviewer rGL6 and Q1 in Reviewer WT2W.
> **Exp 3**: Inference Speed
We evaluated inference speed on different platforms: RTX3090, RTX4090, and A100-80G. The RTX4090 achieved the fastest speed at 603 FPS, followed by ... | Summary: This paper introduces a multimodal asynchronous hybrid network designed for real-time anomaly detection in autonomous driving. The main contribution lies in combining high-temporal-resolution event stream data captured via event cameras processed asynchronously using GNN and spatial information extracted from ... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback and practical recommendations. Detailed responses to your questions are listed as follows.
> **Methods And Evaluation Criteria 3**: Two-stage Contribution
(1)Our two-stage model first performs object detection to generate bounding boxes, providing p... | Summary: This paper focuses on real-time anomaly detection tasks in autonomous driving, aiming to balance detection accuracy and response time. The core algorithm is a multimodal asynchronous hybrid network that integrates event streams from event cameras with RGB camera image data. Process event streams through asynch... | Rebuttal 1:
Rebuttal: Thank you for your motivating review and concrete suggestions. Detailed responses to your questions are listed as follows.
> **Weakness**: V2E Transformation vs. Real Event Stream
(1)The current traffic anomaly detection dataset does not have a real Event mode. Our subsequent work is to use even... | null | null | null | null | null | null |
On the Private Estimation of Smooth Transport Maps | Accept (poster) | Summary: This paper leverages Differential Privacy (DP) to address concerns about privacy leakage when estimating the transport map between two distributions derived from user data. The authors propose a DP transport map estimator and establish its statistical guarantees in terms of an upper bound and a minimax lower b... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate the time and effort you have taken to review our paper. Below, we provide detailed responses to each of your comments.
- **Comment 1:** *The authors establish statistical guarantees for the proposed private transport map estimator. However, there is a lack ... | Summary: This paper presents a differentiably private (DP) estimator for continuous $W_2$ transport maps, obtained from discrete samples from source and target distributions. It builds on top of the framework established by [Hutter & Rigollet 21] for estimating the Brenier potential ($\nabla f$ returns the transport ma... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate the time and effort you have taken to review our paper. Below, we provide detailed responses to each of your comments.
- **Comment 1:** *I could add that it would be nice to see which components of the bounds seem to dominate in which regimes over particula... | Summary: This paper considers the problem of learning the optimal transport map from $P$ to $Q$, given samples from each, under the requirement of (pure) differential privacy. They impose assumptions on smoothness of the optimal map which are standard in the map estimation literature. They obtain an upper bound using a... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate the time and effort you have taken to review our paper. Below, we provide detailed responses to each of your comments.
- **Comment 1:** *These works look at the problem of private distribution estimation under Wp and were not included. I think they have tig... | Summary: The paper studies the private estimation of smooth optimal transport maps using a semi-dual formulation of optimal transport, where the transport map is obtained as the gradient of a Brenier potential. To make the problem tractable, they restrict the function space using standard nonparametric estimation techn... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate the time and effort you have taken to review our paper. Below, we provide detailed responses to each of your comments.
- **Comment 1:** *Only toy experiments are presented to illustrate how the proposed transport maps are close to the optimal ones. Unfortun... | null | null | null | null | null | null |
When do neural networks learn world models? | Accept (poster) | Summary: The paper explores whether neural networks can develop internal world models akin to those humans create to understand the underlying data generation processes. The research presents the first theoretical results in this area, demonstrating that in a multi-task setting, models with low-degree bias can recover ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and your approval of our work. We provide point-by-point responses to your questions in the following:
**Synthetic experiments:** Thank you for these questions; we believe that they greatly help to improve our work. Inspired by them, we conducted additional synt... | Summary: this paper aims to answer the question "when do neural nets learn world models". This is an ambitious goal that has been studied in many papers. the paper proposes a theoretical results for this problem. then the paper also shows the algorithmic implications of the results.
Claims And Evidence: yes
Methods A... | Rebuttal 1:
Rebuttal: Thank you for your review. We are happy to engage in further discussion if you have additional questions or concerns in the author-reviewer discussion stage.
On the point of causality, we agree that causality is also an important aspect of learning world models. Moreover, we would like to emphasi... | Summary: This paper provides a theoretical framework to reveal whether neural networks are capable of learning world models that capture the underlying data generation process. The framework connects the latent variable models with the world model learning. The theoretical results provide insights for future research.
... | Rebuttal 1:
Rebuttal: Thank you for your review and your approval of our work. The following are our responses to your questions.
**Applications:** Thank you for your questions on potential algorithmic applications of our results. Yes, we believe that our results may inspire future algorithmic research in the followin... | Summary: The authors explore the theoretical and empirical conditions under which neural networks could learn world models. To do so, they abstract the possible task that a model would need to learn as a set of Boolean functions which can be approximated using polynomials over binary inputs. The authors claim that this... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We provide point-by-point responses to your concerns in the following.
**Definition of the world model:** Our abstraction of the world model is motivated by a line of research in the machine learning literature. In brief, the fundamental aspect of our world m... | null | null | null | null | null | null |
Differentiable Solver Search for Fast Diffusion Sampling | Accept (poster) | Summary: This paper proposes a novel solver search algorithm for fast sampling of diffusion models, which optimizes both timesteps and solver coefficients. The key idea is to treat the solver design as a learning problem, optimizing solver parameters to minimize the numerical error and improve image quality. Experiment... | Rebuttal 1:
Rebuttal: We would like to express our heartfelt gratitude for the valuable feedback you've provided on our manuscript. Your in-depth analysis and suggestions are of great significance to us, and we are committed to using them to enhance the quality of our work.
**Q.1 Writing typos and inconsistent presen... | Summary: The paper aims to accelerate reverse diffusion by integrating a novel differentiable solver search algorithm for better diffusion solvers.
The paper demonstrates that a data-driven approach in the post-training scenario can also enable fast sampling.
Using a compact search space related to the timesteps and so... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback on our manuscript. Your insights are extremely helpful and have provided us with clear directions for improvement.
**Q.1 Quality comparison**
We plan to expand the quality comparison by including more models, such as SD3, Pixart-$\alpha$-R512, and ... | Summary: This paper proposes a differentiable solver search algorithm to find an optimal ODE solver for reverse-diffusion solving of pre-trained diffusion models. The authors use gradient-based optimization to identify solver parameters that lead to improved sample quality with very few function evaluations. The approa... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback on our manuscript.
**Q.1 Total burden of searching**
Searching one solver step with 50,000 samples using FlowDCN-B/2 requires approximately 30 minutes on 8 × H20 computation cards.
**Q.2 More Quality comparison**
We plan to expand the quality comparison by in... | null | null | null | null | null | null | null | null |
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble | Accept (poster) | Summary: FedBEns proposes a one-shot federated learning utilizing a Bayesian ensemble approach. Unlike standard FL methods that simply rely on averaging, FedBEns combine client models using a mixture of Laplace approximations to model multimodal local posteriors. Empirically, FEDBEns demonstrates superior performance o... | Rebuttal 1:
Rebuttal: **1) Computational/Communication overhead quantification:** See answer 1) to reviewer wHXT.
**2) Computational/Communication trade-offs and practical applicability:**
The proposed method is inherently more computationally intensive, as it is based on an ensemble of M models. Its applicability de... | Summary: This paper focuses on one-shot Federated Learning, where the model is aggregated in a single communication round. The authors provide an analysis through the lens of Bayesian inference and then propose a method to leverage the inherent multimodality of local loss functions to find better global models.
Claims... | Rebuttal 1:
Rebuttal: **1) Additional server-side computation:**
We appreciate the reviewer’s observation—this is indeed an important point. Our method does entail increased server-side computation as the number of mixture components grows. However, we would also like to clarify that some of the baselines considered ca... | Summary: The paper introduces FedBEns, a one-shot federated learning (FL) algorithm using Bayesian inference to address multimodal local loss functions. It approximates local posteriors with a mixture of Laplace approximations (GMM) and aggregates them to estimate the global posterior. The server identifies global mode... | Rebuttal 1:
Rebuttal: We are happy the reviewer appreciated the paper.
**1) Runtime/communication costs quantification:**
We observe that the FedBEns per-client computational and communication costs are both linear in the number of mixtures M. On the contrary, in our implementation, the computation time at the server... | null | null | null | null | null | null | null | null |
A Classification View on Meta Learning Bandits | Accept (poster) | Summary: This paper studies a meta-learning approach to multi-armed bandits (MAB), where multiple bandit instances (tasks) are drawn from an unknown prior distribution. The key contribution is formulating meta-learning bandits as a classification problem, leveraging a novel complexity measure called the classification ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We provide below detailed replies.
**Questions for authors**
1. Our analysis and experiments consider overlapping tasks [for “overlap”, we mean the bandits may have the same, or similar, reward distribution for some of the arms/contexts. If... | Summary: The authors address a meta-linear latent contextual bandit setting, where there are M total possible bandit settings, and where there is separation between these M settings. Authors thus propose a classification view that leverages this separation: first, classify the test task as one of M settings; then, perf... | Rebuttal 1:
Rebuttal: We are glad to hear that the reviewer appreciated our work. We thank them for their thoughtful comments, useful suggestions, and for pointing out typos. We will make use of them to improve the manuscript. We address their questions below.
**Value of $N_{cls}$ in the experiments**
We used $N_{cls... | Summary: This paper presents a novel classification - based approach to meta - learning bandits. Contextual multi - armed bandits are widely used for sequential decision - making, but common bandit algorithms have issues like high regret and lack of interpretability.
The authors consider a meta - learning setting of l... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their feedback. We are replying to their comments below.
**Claims and evidence**
Interpretability is a crucial motivation of our paper and we want to make sure we are on the same page with the reviewer on this. Especially:
- **Interpretability of bandit algori... | null | null | null | null | null | null | null | null |
Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion | Accept (poster) | Summary: The paper introduces WASP, an approach for generating differentially private synthetic data by leveraging multiple pre-trained language models (PLMs) in a collaborative manner. They tackle (1) limited private samples, (2) noisy synthetic data, and (3) risky PLM selection. They employ a Top-Q voting mechanism ... | Rebuttal 1:
Rebuttal: We appreciate our reviewer's insightful comments that help us improve our work.
**Q: Complexity of combining multile PLMs and computational cost & complexity comparison.**
First of all, as stated in Lines 105~107 on the left in our paper, no additional queries are made to PLMs under the same num... | Summary: The paper proposes a novel framework called WASP, designed to generate synthetic data that mimics real private datasets while ensuring DP. WASP addresses three key challenges in existing methods: limited private samples, noisy synthetic data, and the risk of selecting the wrong PLM. It uses a Top-Q voting mech... | Rebuttal 1:
Rebuttal: Thanks very much!
**Q: Fair comparison with equal API budget.**
We emphasis that, as stated in Lines 105~107 on the left in our paper, no extra query to the PLMs is incurred by WASP compared to single-PLM PE baselines, when they are compared under the same number of required synthetic samples $N... | Summary: This paper studies differentially private generation of synthetic data using LLM APIs. The general idea is based a lines of prior works Private Evolution, which generate samples tailored for synthetic data by resampling from the ones that is closer to the private dataset. This work improves upon existing works... | Rebuttal 1:
Rebuttal: **Q: Lemma issue and results.**
Thank you for pointing this out. Lemma D.3 should be added with "with $\delta_{total}$ increased to larger than $T\times\delta$".
Denote the $\delta_{total},\delta_{iter}$ as the final $\delta$ and $\delta$ for each iteration respectively. Therefore, as we perform... | Summary: Two of the main strategies for generating private synthetic text or image data are private fine-tuning and private evolution. Private fine-tuning can work well but this requires access to the parameters of a generative model (many of which are closed-source) and the computational resources to fine-tune the mod... | Rebuttal 1:
Rebuttal: We appreciate these insightful comments from our reviewer which help us improve our work.
**Q: Different noise scale for different function sensitivity considering contrastive prompting.**
You are absolutely right, and we indeed have applied different noise scales according to different sentitiv... | null | null | null | null | null | null |
Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects | Accept (poster) | Summary: The paper introduces a novel two-stage design for randomized controlled trials (RCTs) that aims to improve efficiency compared to traditional single-stage designs. In the first stage, all treatment arms are explored uniformly, and a data-driven screening procedure prunes those with low estimated effect. In the... | Rebuttal 1:
Rebuttal: Dear Reviewer hoJN
We thank the reviewer for their kind words and enthusiasm for our paper. We address your concerns below.
### **Claims and evidence/Questions For Authors:**
>I have reservations about the experimental results. The authors claim that two-stage designs outperform single-stage ... | Summary: The paper studies a setup where one is trying to optimize the design of a randomized controlled trial to identify effective treatments more efficiently. Specifically, they describe a two-stage RCT design/algos to that end.
### update after rebuttal
I thank the authors for their response, which helped me unde... | Rebuttal 1:
Rebuttal: Dear Reviewer S1mJ,
We thank the reviewer for their kind words. We address your concerns below.
### **Questions/Weaknesses**
>Theoretical claims made in the paper are supported. They are mainly adaptations of existing results from bandit literature and not substantial.
We believe there are s... | Summary: The paper introduces a two-stage randomized controlled trial design to enhance the best possible treatment effect guarantee while reducing wasted resources on sub-optimal arms. In the first stage, a data-driven screening process eliminates low-impact treatments, and in the second stage, the focus shifts to est... | Rebuttal 1:
Rebuttal: Dear Reviewer HuaZ,
We thank the reviewer for their kind words and enthusiasm for our paper! We address your questions below.
### **Questions/Weaknesses**
>The use of "treatment effect" in this paper is a bit confusing to me. Typically in RCT, there will be one control arm and many other trea... | Summary: This study proposes a two-stage RCT design aimed at improving efficiency in treatment effect estimation by reducing unnecessary resource allocation to sub-optimal treatments. The idea is pretty straightforward: use top-K policy to screen the inferior arms, then put more resources on the better arms. The author... | Rebuttal 1:
Rebuttal: Dear Reviewer Kupv,
We thank the reviewer for their kind words and enthusiasm for our paper. We address your concerns below.
### **Questions/Weaknesses**
> In Alg 1, there is data splitting procedure. How much is this impacting the efficiency of the algorithm? Is it possible to do a cross fit... | null | null | null | null | null | null |
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra | Accept (poster) | Summary: This paper proposed WASP for phenotype prediction based on the genomic data, which utilizes an iterative algorithm and an approximation inverse to optimize the goal efficiently. Experimental results on GWAS show its superiority over LD Score Regression.
Claims And Evidence: Not very clear
1. The introduction... | Rebuttal 1:
Rebuttal: Thank you for your review. In our paper, we train the first large scale models on recently released huge genomics and functional datasets, enabled by modern linear algebra techniques. Below we address your points on additional baselines and downstream uses of our model.
**On weak baselines**. LD ... | Summary: The paper addresses the challenge of predicting how genetic variants affect phenotypes from large datasets. LD score regression, make simplifying assumptions to avoid computationally expensive linear algebra across genomic metrics. Their method leverages preconditioned linear algebra and GPU acceleration for i... | Rebuttal 1:
Rebuttal: Thank you for your review! In our paper, we train the first large scale models on recently released huge genomics and functional datasets. Below we address your points on additional baselines and extensions of our model.
**On additional baselines.** Note there are no methods that have been able t... | Summary: This paper introduces WASP, a method leveraging accelerated linear algebra to train flexible neural network models for predicting genetic variant effects from functional annotations. By employing banded LD matrix approximations and a structured preconditioner, WASP efficiently handles large-scale genomic data ... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful review. We ran your suggested experiments which have significantly strengthened our paper.
**On semi-synthetic simulations.** You raised a fair point that having a randomly initialized Enformer as a ground-truth model might benefit WASP. Thus, we re-ran... | Summary: The paper introduces WASP, a method for training large-scale neural network models to predict the effects of genetic variants from functional annotations. The paper begins by introducing linear models for variant effects, the need for good priors, and how they can be fit using anonymous summary statistics. Thi... | Rebuttal 1:
Rebuttal: Thank you for your support and thoughtful review! Below we discuss new experiments that address the sensitivity to the LD window distance cutoff, larger model sizes and clarify the purpose of Section 4.3.
**On distance sensitivity and assumptions.** We make two distance-based approximations: (1) ... | null | null | null | null | null | null |
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol | Reject | Summary: The paper proposes a new method for model selection in the offline policy evaluation (OPE) setting. That is, given multiple policies from an offline RL algorithm, the question is which model (either a value function or simulator MDP) among many is best for evaluating these policies? The authors propose new c... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the contributions of our work and the helpful comments for improvement.
---
**Why is picking a single model a good idea? ensemble?**
First, it is not clear what "ensemble" of models means in the context of OPE: do we simply average the final predi... | Summary: This paper studies the problem of model selection for OPE, where you have one evaluation policy and several candidate OPE estimates, and the goal is to find the best OPE estimate. The paper presents new OPE selection procedure for both model-free and model-based OPE methods, leveraging some new theoretical ins... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We do not find major concerns in the review - can you let us know what are the main factors that lead to your "weak reject"?
---
**Importance sampling (IS)**
We do not consider IS-based OPE. Vanilla IS does not need any function approximation, whi... | Summary: The work proposes two new model selection algorithms for off-policy evaluation. These methods are inspired by "batch value function approximation" (BVFT) and its shortcomings. The authors propose to use LSTDQ as model/Q-function selector when following the steps of BVFT and dub the resultiung algorithm "LSTD-T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. The main criticisms arise from technical misunderstandings, which we clarify first.
---
## Major Technical Misunderstandings
**Comparing ...MF.G (g=-30, $\sigma$=100.0) and MF.N (g=-30, $\sigma$=100.0) give vastly different results even though the basic ... | Summary: The paper tackles the setting of off-policy evaluation (OPE). It analyzes different method for OPE, model-free as well as model-based. The paper introduces the general setting with a short overview over related work and lists its contribution. The paper presents a short overview of preliminary theory. The pape... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
---
**The paper presents relevant and interesting bits of information.**
**In general such a demonstration seems fitting to the presented claims.**
We are glad that the reviewer finds new insights in our paper and that they are supported with ev... | null | null | null | null | null | null |
Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback | Accept (poster) | Summary: This paper studies the setting of tabular MDP, with trajectory feedback, i.e., every round after executing a policy the learner obtains the entire trajectory with the total reward along the trajectory. The per-step reward is not given to the learner. This paper provided an algorithm for this setting and also p... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's detailed comments and constructive suggestions. Below we present our response.
**Regarding significance:** We would like to stress that even for RL in the standard setting, minimax optimal regret bounds have not been obtained until recent few years, and the bu... | Summary: This paper investigates reinforcement learning with trajectory feedback, where the agent receives only the cumulative reward for an entire trajectory rather than individual state-action rewards, while still observing all visited state-action pairs. The authors establish the first asymptotically nearly optimal ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's detailed reviews and valuable suggestions. Below we present our response.
**Regarding Lemma B.1:** In Lemma B.1, $\bar{\pi}$ represents a mixed policy. That is, by following $\bar{\pi}$, the learner takes each deterministic policy $\pi\in \Pi$ with a certain pr... | Summary: This work considers the problem of online learning in a tabular finite horizon MDP with stochastic rewards and aggregate bandit feedback, where agent observes only the sum of rewards she collected after each episode.
An algorithm based on policy elimination is proposed, which builds on the linear bandits pers... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough evaluation and constructive feedback. Below we present our response.
**Regarding the computational issue:**
We admit that the current algorithm is computationally inefficient, and indeed, devising algorithm with minimax optimal regret bound and pol... | Summary: This work studies Reinforcement Learning with only Trajectory Feedback, where the agent does not observe the reward for each individual step separately. Under this setting, the author proposes a novel algorithm based on the arm-elimination method over all possible deterministic policies and achieves a near-opt... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's detailed assessment and helpful suggestions. Below we present our response.
**Regarding your concerns about correctness:**
1. In the first $K_0=O(\sqrt{K})$ episodes (other factors ignored), the main target is to identify the infrequent state-action-state tripl... | null | null | null | null | null | null |
Volume Optimality in Conformal Prediction with Structured Prediction Sets | Accept (poster) | Summary: The submission studies volume minimization in conformal prediction. It proposes a dynamic programming algorithm to construct prediction sets with marginal (and approximate conditional) coverage. The sets returned by the method have provably optimal volume within the class of union of k intervals. Experiments o... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed suggestions and providing more related works. We will ensure to make these changes to improve our paper and include discussions to related works.
- Experiments on real-world datasets would strengthen the utility of the proposed method
We implement our method a... | Summary: This paper studies the problem of providing guarantees regarding the efficiency (ie, small size/volume and thereby informativeness) of conformal prediction (CP) sets. The paper first presents an impossibility result for volume optimality where any CP method can only find a trivial solution. Then, the paper int... | Rebuttal 1:
Rebuttal: We sincerely thank you for the detailed and constructive feedback and the additional references that we missed. We will implement these changes in the revision of the paper.
We evaluated our method and different baseline methods on several real-world datasets in both unsupervised and supervised s... | Summary: Conformal prediction is a framework to construct label sets such that the marginal probability of coverage is guaranteed to be above a desired level. This paper studies the conformal label sets for unidimensional regression problems, where the conformal label sets are restricted to be a union of $k$ intervals.... | Rebuttal 1:
Rebuttal: Thank the reviewer for constructive suggestions and providing comprehensive references. We will make sure to implement these changes and include all related works in the revision.
- It would help to see a similar analysis done on real-world data.
We evaluated our experiments on various real-wor... | Summary: Conformal prediction is a technique that produces prediction sets with marginal $(1-\alpha)$-coverage guarantees; in general there are many subsets of the label space that may satisfy this coverage guarantee, and conformal methods do not necessarily produce the smallest such set (by measure) that satisfies thi... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions and insightful questions.
- Since the guarantees are in expectation over the calibration set and the data is being simulated anyway, it could be good to get also the results averaged over multiple draws of the calibration data.
We appreciate this sugge... | null | null | null | null | null | null |
Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions | Accept (poster) | Summary: The authors study repeated uniform price auctions with respect to bidder behavior. They consider value maximizing buyer that has return of investment (RoI) constraint. The paper proposes safe bidding strategies that allow bidder to be sure that RoI will not be violated in the future rounds. The main contributi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and are glad the theory and structure were clear and well-received. We believe the changes suggested in response to your thoughtful comments can be incorporated into the camera-ready version using the extra page.
**Re the use of Unif[0,1]:** We t... | Summary: This paper introduces the notion of safe bidding strategies for value-maximizing buyers in uniform price multi-unit auctions, ensuring return-on-investment (RoI) constraints are met. A value-maximizing buyer aims to maximize the received value, while only factors in the payment in the RoI constraint. In a unif... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We're glad the theoretical depth and effort came through. The changes suggested by the reviewer are easily manageable with the additional page allowed for the camera-ready version, and we believe they will improve the organization of the manuscrip... | Summary: This paper focus on one buyer’s bidding strategy in repeated uniform price multi-unit auctions. The buyer aims to maximize value under RoI constraints in each round. The authors restrict the buyers to adopt an $m$-uniform bidding format and introduce the notion of safe bidding strategies, which ensure that RoI... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback. We're glad the reduction to the maximum weight path in the directed acyclic graph and regret guarantees were clear and appreciated.
**Regarding the comment that the paper might only interest a small group of researchers:** We believe that the ... | null | null | null | null | null | null | null | null |
Epsilon-VAE: Denoising as Visual Decoding | Accept (poster) | Summary: - The paper proposes an alternative autoencoder that itself uses a (conditional) diffusion / rectified flow model as decoder replacing the standard VAE for latent diffusion models (LDMs).
- For that, the paper experimentally explores the design space in terms of decoder / denoiser architecture, the implementat... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We will rephrase our claims and reduce the redundancy as suggested, and include suggested related work in the revision. Below we provide a point-by-point response to all of your questions. Please let us know if you have any further questions.
**Q1: Becaus... | Summary: In this work, the authors propose using denoising diffusion model as the decoder in autoencoder for image reconstruction and generation. $\epsilon$-VAE develops denoising decoder conditioned on the learnable latents. The work includes solid experiments in validating the design choices for image reconstruction.... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Below we provide a point-by-point response to all of your questions. Please let us know if you have any further questions.
**Q1: The reported performance in Table 3 is worse than numbers reported in the original DiT paper.**
We did the following modification... | Summary: The author propose a new autoencoder paradigm, in which decoder is in the form of a diffusion model. The advantage of this is that it has better reconstruction quality compared with standard VAE. The proposed architecture is straight-forward, by directly upsampling encoded latents and then run diffusion at the... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below we provide a point-by-point response to your questions. Please let us know if you have any further questions.
**Q1: Clarification on high-level insights.**
Thank you for raising this concern. Rather than opposing latent diffusion, we offer a complementary... | Summary: This paper proposes to use a diffusion decoder in an autoencoder training for image generation. The autoencoder is trained with a diffusion loss, together with a LPIPS loss and a GAN loss defined on the one-step generation. The authors show that the proposed method outperforms prior state-of-the-art autoencode... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback. Below we provide a point-by-point response to your questions. Please let us know if you have any further questions.
**Q1: The LPIPS and GAN loss are applied on the estimated one-step sample, which may not be accurate and may cause objective bias in theory. Altho... | null | null | null | null | null | null |
Stable Fair Graph Representation Learning with Lipschitz Constraint | Accept (poster) | Summary: This paper proposes a Stable Fair Graph Neural Network (SFG) to address training instability in fairness-aware graph representation learning by introducing a Lipschitz constraint for stability and employing a stochastic optimization algorithm. Extensive experiments demonstrate that SFG outperforms existing met... | Rebuttal 1:
Rebuttal: > Q. The reason for significant accuracy fluctuations observed during the optimization process in Figure 1
Thank you for your insightful comment. The significant accuracy fluctuations(even though we have used a small learning rate) observed in Figure 1 are caused by **the addition** of weight fl... | Summary: The paper introduces a tight upper bound and distributionally robust optimization to address the challenges of training instability that have been ignored by most previous methods for fair graph representation learning. SFG’s novel upper bound is tight and considers the changes of masks, enhancing training sta... | Rebuttal 1:
Rebuttal: > W1/Q1
> The update of the weight in Eq (9) has not been explained, and the Multi-convex component in Figure 1 is unclear.
Thank you for your valuable comment. We provide the following clear description for the weight update:
We update the weights layer by layer, and $B_{new}^{(i,t)}$ alw... | Summary: This paper focuses on addressing the challenge of training instability in adversarial-based fair GNN models. To mitigate this issue, it establishes a tight upper Lipschitz bound to regulate stability and leverages Distributionally Robust Optimization (DRO) to improve the encoder’s robustness across different f... | Rebuttal 1:
Rebuttal: > W1/Q1
> The training process could be explained more clearly.
Thank you for your valuable suggestion. We provide the following clear description of the training process:
We train SFG in an alternating manner throughout the entire process. In each epoch, we first train discriminator(i.e., $... | Summary: This paper presents a novel approach "Stable Fair Graph Neural Network (SFG)" that addresses the issue of instability in adversarial-based fair graph representation learning. The main contributions and findings are as follows (1) The authors derive a tight, easy-to-compute upper Lipschitz bound for the composi... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestion and insightful comment.
> CE. The indicator of weight fluctuation
> the absolute of weight changes would be more suitable.
We list **the absolute weight changes(FairVGNN: 0.079, SFG:0.018)** between two epochs in following table(both are uniformly rescal... | null | null | null | null | null | null |
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification | Accept (poster) | Summary: The paper introduces a Physics-Informed Deep Operator Network (DeepONet) approach for solving the drift-diffusion equation on metric graphs. The authors decompose the graph into different types of edge domains, each represented by a pre-trained DeepONet sub-model. These sub-models are assembled using physical ... | Rebuttal 1:
Rebuttal: **Quantitative comparison of computational efficiency against traditional numerical methods**
For the pure simulation task, the FVM solver is typically faster than our method. This is a caveat of most physics-informed neural network and operator network approaches. However, our methodology shines... | Summary: ## Summary of paper
This paper discusses how functionals can be flowed on a metric graph by learning surrogates of drift-diffusion equations. The method applies DeepONet backbone as the physical-informed dynamics surrogate model to learn how observations at inflow vertices can be pushed to the outflow vertice... | Rebuttal 1:
Rebuttal: **Comparison to alternative operator learning frameworks**
Thank you for this remark. In fact, the DeepONet shares a lot of similarity with the FNO approach as pointed out in *Kovachki, Nikola, et al. "Neural operator: Learning maps between function spaces with applications to PDEs." Journal of Ma... | Summary: The paper builds a physics-informed DeepONet setup for solving drift-diffusion PDEs on metric graphs. They train separate models for inflow, inner, and outflow edges, then stitch them together using a domain decomposition trick. Once trained, these models can be reused on any graph—kind of like Lego blocks. It... | Rebuttal 1:
Rebuttal: **Experiments on larger graphs**
We built larger networks with more edges using a directed network construction of varying sizes (102, 306, 1034 edges) and apply our methodology to this. This is easily done by providing the adjacency structure and the inflow and outflow nodes. We also included **m... | null | null | null | null | null | null | null | null |
Empirical Privacy Variance | Accept (poster) | Summary: For a variety of memorization based privacy attacks on language modeling, this paper studies how the average attack success can vary across hyperparameters that are meant to give the same DP guarantees. This phenomenon is observed across model sizes, architectures, datasets, and DP guarantees. Several correlat... | Rebuttal 1:
Rebuttal: We thank the reviewer for initiating the interesting discussions.
Let us begin by explaining 1) what we mean by “empirical privacy”, 2) our empirical privacy measures, and 3) the choice of computing the average score.
1) As noted in the introduction, we take a practical perspective on empirica... | Summary: This paper uses the concept of empirical privacy variance to show that models trained with DP-SGD under the same $(\epsilon, \delta)$ guarantee but using different hyperparameter settings can yield varying empirical privacy. Empirical variance metrics are defined that quantify how much a model memorizes inform... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our claims and methods as clear, sound and relevant, and our contributions to be of broad interest to the community. We will take the reviewer’s feedback and incorporate a more detailed related work section into the main body, incorporating discussions in our ... | Summary: The paper empirically estimates the privacy loss of language models fine-tuned with DP-SGD in many different configurations, including different hyperparameters, model sizes, and dataset characteristics. The paper finds that models calibrated to the same DP guarantee can have very different empirical privacy l... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising the subtlety between implementing DP-SGD with shuffled batches, but performing privacy accounting as if Poisson subsampling was used.
In the camera-ready version, we are happy to expand the discussion and move it to the main paper under a “limitation” section. Ad... | Summary: The paper investigates privacy implications when fine-tuning language models using DP-SGD. Importantly, when using DP-SGD, the same (ε, δ)-DP guarantee can be achieved through multiple hyperparameter configurations (batch size, number of iterations, learning rate, etc.).
The authors' key finding is that despit... | Rebuttal 1:
Rebuttal: Thanks for the review.
> Summay of our key points
- We measure empirical privacy in controlled settings
- We conduct extensive experiments (> 10,000 H100 GPU hours) for robust findings
- Our contributions go well beyond “showing the existing variance”
- We recommend empirical privacy as an addit... | null | null | null | null | null | null |
Rethinking Point Cloud Data Augmentation: Topologically Consistent Deformation | Accept (poster) | Summary: This paper proposes a novel data augmentation method, SinPoint, for the 3d point cloud, leveraging the topological consistency deformation technique. It utilizes a sine-based mapping function for deformation under the Markov process. The approach has demonstrated the effectiveness of data augmentation by theor... | Rebuttal 1:
Rebuttal: Dear Reviewer Agtr:
**Thanks for your time and insightful reviews. We appreciate your recognition of our work. We responded in detail as follows:**
**Q1:** Concepts of homeomorphism need further discussion. From mathematical aspects, the 3D surfaces in the mentioned datasets (e.g., ModelNet40) a... | Summary: The paper proposes to use sine functions to augment the point cloud for the point cloud classification and segmentation tasks, with a Markov chain augmentation process to further improve the performance. The method achieves SOTA on different tasks with various backbones.
Claims And Evidence: Although the meth... | Rebuttal 1:
Rebuttal: Dear Reviewer 8z5w:
**Thanks for your time and insightful reviews. We responded in detail as follows:**
**Q1:** ...for example [1]. Despite this claim, the paper’s analysis ... appears trivial, and it lacks an in-depth discussion on how increasing variance—specifically through the proposed augme... | Summary: This paper presents "SinPoint," a new data augmentation method for point clouds. The main idea is to deform point clouds in a way that's supposed to preserve their overall structure, using sine functions to create the deformations. The authors argue, using the concept of homeomorphism, that these deformations ... | Rebuttal 1:
Rebuttal: Dear Reviewer SdQc:
**Thanks for your time and insightful reviews. We responded in detail as follows:**
**Q1:** It's possible that large deformations, even if theoretically homeomorphic, could still distort the shape in a way that effectively changes the topology.
**A1:** Thank you for agreeing... | Summary: The paper introduces SinPoint, a novel data augmentation technique for point clouds that employs homomorphism-based sine transformations to increase geometric diversity while preserving topological consistency. SinPoint has two variants: SinPoint-SSF, which uses a single sine function anchored at the origin, a... | Rebuttal 1:
Rebuttal: Dear Reviewer MuAY:
**Thanks for your time and insightful reviews. We responded in detail as follows:**
**Q1:** ...such transformations may generate unrealistic data.
**A1:** Your opinion is very professional. Indeed, when the deformation parameter is too large, it will produce unrealistic data... | null | null | null | null | null | null |
Minimalist Concept Erasure in Generative Models | Accept (poster) | Summary: This paper introduces "Minimalist Concept Erasure," a framework designed to remove unwanted concepts from generative models with minimal performance degradation. The core algorithmic idea involves learning a binary mask that selectively prunes neuron connections, guided by an end-to-end optimization process ai... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of our work. We’re glad you found the minimalist objective conceptually appealing, and we appreciate your acknowledgement of the practicality and scalability of our framework. We reply to your concerns below:
> “While metrics such as FID and SSIM scores are... | Summary: The paper introduces a concept erasure method that is minimal in design, requiring only the final output of the diffusion model, rather than access to intermediate timesteps. In addition, the authors propose a neuron masking technique as an efficient alternative to traditional fine-tuning. Both approaches demo... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and thorough review. We greatly appreciate your acknowledgment that our claim is well-supported by the evaluation with conventional settings as well as adversarial settings. We also appreciate your review of our theoretical derivations and acknowledgement of their soun... | Summary: This paper studies minimalist concept erasure, which aims to remove inappropriate content from a generative model with minimal modification to the original model, specifically in diffusion/flow matching models. Unlike previous approaches that operate on each step of the sampling chain, the training objective i... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and thorough review. We’re glad you found our approach to minimalist concept erasure well-motivated and effective across benchmarks. We appreciate your feedback and the recognition of our theoretical and empirical contributions.
Regarding your concern, we acknowledge ... | Summary: The paper presents a technique to unlearn concepts from generative models. Unlike existing concept erasure techniques, the proposed technique unlearns concept based only on distributional distances of the final generation outcomes.
Claims And Evidence: - The authors claim that “our method adopts a connectioni... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and thorough review. We address your concern below:
#### **Claims**:
1. As stated in L201, prior masking-based unlearning methods achieve accurate and robust results, which we build on due to their strong performance. Regarding neuron masking for strongly correlated c... | null | null | null | null | null | null |
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space | Accept (poster) | Summary: The paper introduces Holistic Physics Mixer (HPM), a neural operator framework that integrates spectral-based and attention-based PDE solvers in a unified space. The authors claim that HPM inherits the generalization ability of spectral methods while maintaining the local adaptability of attention mechanisms, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful questions. They have significantly improved this work. We respond to them below.
`"#1-#4"` provide complements for several important concerns. `"#5-#12"` provide "point-by-point responses" exactly aligned with your review.
**#1 Statistical Signi... | Summary: Holistic Physics Mixer (HPM) unifies attention-based and spectral methods for PDE solving, combining point-level adaptability with spectral continuity constraints. This integration enables strong generalization and flexibility, surpassing existing methods in accuracy, efficiency, and zero-shot performance acro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful questions. They have significantly improved this work. We respond to them below.
**#1 Handling triangular mesh data**
> I do have one question to ask, is the method applicable to triangular mesh data? Since FNO is only applicable to structured g... | Summary: The paper introduces the Holistic Physics Mixer (HPM), a unified framework that leverages a holistic spectral feature space to integrate domain-level structures with point-wise physical states. The author claims that HPM achieves strong performance in scarce-data scenarios, offers resolution generalizability, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful questions. They have significantly improved this work. We respond to them below.
**#1 Discussion about other generalization scenarios**
> Question 1: Beyond resolution generalization, would the design of HPM still be beneficial in other generali... | Summary: The paper introduces Holistic Physics Mixer (HPM), a framework that integrates spectral transformation and data-dependent modulation (i.e. attention). HPM employs a learnable coupling mechanism that enables adaptive modulation of spectral components while preserving the advantages of spectral transformation. T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful questions. They have significantly improved this work. We respond to them below.
**#1 Computational cost of LBO**
> What is the computational cost of precomputing the LBO?
The LBO eigenfunctions is computed efficiently using the robust-laplaci... | null | null | null | null | null | null |
Wasserstein Policy Optimization | Accept (poster) | Summary: The paper applies Wasserstein gradient flows to reduce the parameter space, and this way obtain a closed-form update rule. It lifts the necessity of using the reparameterization trick in stochastic policy learning. The method merges the strengths of the policy gradient approaches that work on sampled evaluatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and hope to convince them of the merits of our approach.
1. With regards to the performance of WPO relative to other baseline methods: while Figs. 3 and 6 show that WPO is competitive over the DM control suite, our main performance claims are highlighted in... | Summary: This paper naturally and clearly derives a natural gradient version of the stochastic policy extension of deterministic policy gradient from the perspective of Wasserstein gradient flow, proposes a practical implementation method, and conducts detailed tests on DeepMind Control Suite and scientific control tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and helpful suggestions.
1. With regards to the state-occupancy term $d^\pi(s)$ in the definition of the gradient of the value function, we agree that including a correction in the off-policy case could potentially improve performance, but want to emphasize t... | Summary: This paper introduces Wasserstein Policy Optimization (WPO) for continuous-action reinforcement learning. By viewing policy optimization as a Wasserstein gradient flow in the space of distributions, the authors derive a closed-form update that: i) Uses gradients of action values w.r.t. actions (like determinis... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments and favorable review. The reviewers made a few suggestions for improving the paper which we will address:
1. We have re-run experiments on the high-variance environments with more seeds to smooth out the learning curves and will add... | Summary: The paper introduces a novel policy gradient update using Wasserstein Gradient Flows called Wasserstein Policy Optimization. While classical policy gradient update works with stochastic policy, it does not take gradient through the action-value space, while deterministic policy gradients are able to take gradi... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments. We appreciate the overall positive evaluation of the paper, and wanted to address the two points raised in “Claims and Evidence”:
1. To go from the idealized WPO update to the practical algorithm presented in the paper, two approximations were made: the Fisher... | null | null | null | null | null | null |
LoRA Training Provably Converges to a Low-Rank Global Minimum Or It Fails Loudly (But it Probably Won't Fail) | Accept (oral) | Summary: The authors investigate the landscape of LoRA fine-tuning under assumptions of restricted strong convexity and smoothness. In particular, the authors prove a characterization of second-order stationary points for problems with regularization, showing that spurious high-rank local minima are bounded away from t... | Rebuttal 1:
Rebuttal: ### **Common Response (Repeated in all responses)**
First of all, we thank the reviewers for their positive and constructive feedback. We are excited to see that the reviewers are appreciative of our theoretical contributions. Below, we address each of the reviewers' comments individually.
### *... | Summary: This paper provides a theoretical analysis of Low-Rank Adaptation (LoRA) loss landscape (near the global or local min).
The main contributions are as follows:
(1) The authors identify two regimes—special (well conditioned) and generic (more realistic). In the generic regime, second-order stationary points (... | Rebuttal 1:
Rebuttal: ### **Common Response (Repeated in all responses)**
First of all, we thank the reviewers for their positive and constructive feedback. We are excited to see that the reviewers are appreciative of our theoretical contributions. Below, we address each of the reviewers' comments individually.
### *... | Summary: This paper provides a theoretical understanding of the training dynamics of LoRA (i.e. Low-Rank Adaptation) of transformers. The authors first establish the equivalence between the low-rank form of loss and the rank-constrained optimization problem. Then the authors state their main results that the LoRA resul... | Rebuttal 1:
Rebuttal: ### **Common Response (Repeated in all responses)**
First of all, we thank the reviewers for their positive and constructive feedback. We are excited to see that the reviewers are appreciative of our theoretical contributions. Below, we address each of the reviewers' comments individually.
### *... | Summary: The authors have shown that a low rank and low magnitude initialisation in LoRA models results in convergence towards a global minima. Conversely, larger rank models with larger initialisation variance results in convergence towards spurious local minima with high probability. Whilst this is a result observed ... | Rebuttal 1:
Rebuttal: ### **Common Response (Repeated in all responses)**
First of all, we thank the reviewers for their positive and constructive feedback. We are excited to see that the reviewers are appreciative of our theoretical contributions. Below, we address each of the reviewers' comments individually.
### *... | null | null | null | null | null | null |
HyperIV: Real-time Implied Volatility Smoothing | Accept (poster) | Summary: The paper studies the problem of fitting the implied volatility surface. They consider a challenging setting when the time interval is reduced to one minute. This would mean the sample size is much smaller, therefore making the problem challenging. Toward this goal, they use hyper-network, the practice of usin... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
* Data allocation between 1-day and 1-minute intervals
To clarify the data usage: while the 1-day dataset covers more *assets* (8 vs 2), the 1-minute data constitutes the majority (about 87%) of surfaces analyzed (~130,000 out of ~150,000 total, see Table ... | Summary: This paper introduces a new method called HyperIV, designed to quickly construct accurate and arbitrage-free implied volatility surfaces using minimal market data.
Main findings and results include:
1. HyperIV generates high-quality implied volatility surfaces in real-time—approximately within just 2 milliseco... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
* Dividend modelling
The method itself does not rely on specific dividend assumptions like proportional dividends. It uses log forward moneyness ($k = \log(K/F)$), where the forward price $F$ (taken from data vendors in our study) already incorporates the ... | Summary: This paper presents a framework based on hypernetwork to perform the implied volatility smoothing with very few reference samples and small computational cost. The robustness and reliability of the proposed approach is evaluated under a special circumstance, where the smoothing needs to be completed within mil... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
* Justification of real use cases.
The implied volatility surface is a starting point for option trading and hedging. HyperIV's ability to generate an arbitrage-free surface in ~2 ms from sparse data (9 contracts) is useful for intra-day option traders. Sp... | null | null | null | null | null | null | null | null |
Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example | Accept (poster) | Summary: The authors use a the procustes method to compare how well do recurrent neural networks trained with eligibility propagation compare with networks trained with backpropagation through time on matching data from neural recordings on monkeys.
As secondary results, the authors add a theoretical point suggesting ... | Rebuttal 1:
Rebuttal: **A more focused title/claim and why mainly one bio-plausible rule**. We thank the reviewer for this thoughtful and detailed feedback. We agree that our study does not aim to characterize all biologically plausible learning rules; rather, our goal is to show that at least one such rule—e-prop—can ... | Summary: This paper examines the properties of recurrent neural networks trained on experimentally motivated tasks using both Backpropagation Through Time (BPTT) and biologically plausible learning rules, in particular e-prop, a truncated approxiamtion of BPTT which uses only local information for weight updates. They... | Rebuttal 1:
Rebuttal: **Theoretical claims**. We appreciate the reviewer’s constructive feedback and acknowledge that the main text did not clearly convey the limited scope of our theoretical result. We agree that the theorem is restrictive in two key ways: (1) it applies only to a 1D setting, and (2) W* can be the sol... | Summary: This paper investigates whether biologically plausible learning rules (e-prop) in RNNs can achieve neural activity similarity to biological data comparable to models trained with BPTT. Using primate datasets and Procrustes distance as a similarity metric, the authors demonstrate that e-prop-trained models matc... | Rebuttal 1:
Rebuttal: **Alternative metrics**. We thank this reviewer for their constructive comments and now add additional metrics to the manuscript:
**Table 3:** Additional measures.
| Rule | CCA | CKA | Duong et al., 2023 |
|---------|----------------|----------------|-------------------... | Summary: This paper compares the representations learned by BackProp Through Time (BPTT), truncated BPTT (tBPTT), and e-prop–a “biologically plausible” learning rule designed as a model of neural plasticity–to those learned in the brains of monkeys performing several tasks. Specifically, the distance between neural rep... | Rebuttal 1:
Rebuttal: **Tabular results and statistical test for Fig 2**. We appreciate this reviewer's concrete feedback and tabulate the results:
**Table 1**: Results from Fig2. *Noise ceiling corresponds to untrained models.
| rule | gain=0.0 | gain=0.5 | gain=1.0 | gain=1.5
| -------------|... | null | null | null | null | null | null |
Dimension-Independent Rates for Structured Neural Density Estimation | Accept (poster) | Summary: The authors study density estimation for the setting of Markov Random Field (MRF) data where we only have local correlations (cliques). Under these assumptions they study the convergence rate of Neural Networks with their bounds only depend on the range of the correlations. Morally the dimension in convergence... | Rebuttal 1:
Rebuttal: __“The main result (Theorem 4.2) is quite abstract in the sense, that it only makes a qualitative statement about the existence of neural networks achieving the bound. This in combination without sufficient practical experiments makes it difficult to judge how impactful this result is.”__
We appr... | Summary: This paper claims to propose a novel theoretical framework that exploits the data structure using Markov Random Fields (MRFs) to provide a dimension independent converge rate for structured density estimation. It shows that using Markov Random Fields allows capturing local dependency between pixels while consi... | Rebuttal 1:
Rebuttal: __“It would be useful to provide more explanation to the unfamiliar reader for the four examples of section E.”__
We will extend this discussion. In our response to Reviewer ZT7H, we included a more technical explanation of how the manifold hypothesis connects to dependence. We can incorporate a... | Summary: This paper analyzes density estimation using a neural network under a conditional independence (MRF) assumption and shows that density estimation is possible with a dimension-independent rate under this assumption.
Claims And Evidence: I don't think there's enough evidence here to support the claim that image... | Rebuttal 1:
Rebuttal: __"There are no real experiments here."__
__"No real experiments"__
We appreciate the reviewer’s concern about the limited empirical validation. While we acknowledge this limitation, we note that:
* The paper's primary contribution is theoretical: Proving rigorously that neural networks can achi... | Summary: Estimating probability densities is a recurring task in machine learning and statistics. However, this becomes challenging in high-dimensional settings due to the curse of dimensionality, where the number of required samples grows exponentially with the dimension.
This paper addresses the challenge of high-... | Rebuttal 1:
Rebuttal: __"A mild criticism is that while the theoretical results establish improved convergence rates when the probability distribution follows a known clique structure, they do not address how to infer this structure from data."__
__"The theoretical results assume that the clique structure of the proba... | null | null | null | null | null | null |
Investigating the Overlooked Hessian Structure: From CNNs to LLMs | Accept (poster) | Summary: In this work, the authors report a previously overlooked power-law Hessian structure in well-trained deep neural networks, which includes CNNs and LLMs. The authors show that both the top few thousand eigenvalues as well as the eigengaps follow approximately a power-law $p(\lambda) = Z_c^{-1} \lambda^{-\beta}$... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback. Below, we duly address your concerns through additional experiments and careful responses.
Q1: Is it possible to reproduce some of the results for instance on ResNet18 trained on Cifar10?
A1. Yes, it is possible. The Kolmogorov-Smirnov distance decreases f... | Summary: This paper investigates the power-law structure of the Hessian matrix in deep neural networks, including Convolutional Neural Networks (CNNs) and Large Language Models (LLMs). Key contributions include: A maximum-entropy principle from statistical physics is proposed to explain the emergence of the power-law s... | Rebuttal 1:
Rebuttal: Thank you for your support of our work and constructive suggestions. We try our best to structure your concerns and duly address them as follows:
Q1: The author cannot provide a rigorous explanation of the maximum entropy principle. However, this article still provides a new perspective on how to... | Summary: This paper investigates the Hessian structure in CNNs and LLMs from the point of view of the power laws in the Hessian spectrum. It is shown that across a range of settings the power law like trend holds for the Hessian eigenvalues of a trained network, which however is not the case at random initialization. T... | Rebuttal 1:
Rebuttal: Thanks for your support of our work. We try best to structure your concerns and address them as follows:
Q1: Normalization by the Hessian trace in Eqn.2 may not be suitable when Hessian is indefinite.
A1: Eqn.2 attempts to describe the power-law from a frequency perspective, and we reported true... | null | null | null | null | null | null | null | null |
Dataflow-Guided Neuro-Symbolic Language Models for Type Inference | Accept (poster) | Summary: The authors present a framework for enhancing language models’ ability to accurately infer types from code. This is achieved by decomposing the input into a high-level program composed of evaluations and type analyses using LMs. This high level program can then be deterministically executed to perform type inf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
* * *
**Q1:** How much more expensive is Nester inference in terms of time and energy over the naive CL and L3 models?
**A1:** We conduct additional experiments to evaluate Nester's computational cost in terms of inference time and energy consumpt... | Summary: The paper presents Nester, a novel neuro-symbolic technique for type inference. Nester decomposes the type inference process into sub-tasks that are aligned with the data and control flows of the input code, encapsulating these into a modular high-level program. This program executes multi-step actions, such a... | Rebuttal 1:
Rebuttal: **Q1:** The key differences between Nester and TypeGen.
**A1:** Please see **Q2** of **Reviewer Q6Mu** for a discussion.
* * *
**Q2:** Could the author please provide a detailed formalization of high-level programs?
**A2:** This is provided in Appendix D.1. We will better highlight.
* * *
**Q... | Summary: The paper introduces a neuro-symbolic approach named NESTER, which integrates language models (LMs) with program analysis for type inference in dynamically typed languages like Python. With the help of LMs, NESTER translates target code into a high-level program composed of predefined API-driven analysis units... | Rebuttal 1:
Rebuttal: **Q1:** How does the proposed method perform for complex parametric types and user-defined types?
**A1:** In response to your concern, we will provide Nester's performance on **complex parametric types** (depth = 0, e.g., list; depth = 1, e.g., list[str]; depth ≥ 2, e.g., Dict[str, List[int]]) an... | Summary: This paper proposes Nester, a neurosymbolic tool for performing type inference with LLMs. It uses LLMs to generate high-level versions of the code in question, and determines the return type of a function by analyzing its data and control flow. It outperforms existing SOTA type inference tools for simple as we... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
* * *
**Q1:** What is the fundamental reason why Nester couldn't just be run with a larger LLM?
**A1:** Nester can work with LLMs of various sizes. In this work, we use a modest-size LLM as we target scenarios where users prefer running models loc... | null | null | null | null | null | null |
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model | Accept (poster) | Summary: The paper challenges the recent paradigm of self-supervised genomic language models (gLMs), which are trained on the DNA sequence alone, and proposes supervised genomic profile prediction (GPP) task as a more effective pre-training method. The motivation stems from the number and complexity of different factor... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. Additional experiments are presented in https://anonymous.4open.science/r/charts-CDE6. We respond your concerns below.
## Q1:Performance on Splicing Tasks and GUE Dataset
We clarify two key points:
* SPACE outperforms DNABERT2 across all splicing benchmarks (... | Summary: This paper introduces a novel approach using a genomic profile prediction models (GPPMs) as a foundation model in biological sequence modeling. The authors propose SPAC which leverages species Mixture-of-Experts (MoE) and an enhanced category operator to improve predictive performance. They conduct ablation st... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for highlighting areas where our manuscript can be improved. We supplemented with additional experiments which are presented in https://anonymous.4open.science/r/charts-CDE6. Below, we address your concerns and outline revisions to strengthen the paper.
#... | Summary: The paper claims that self pretraining alone in DNA is not a good prior to later generalize for downtream tasks. Instead this paper revisits Genomic Profile Prediction Models (GPPMs) such as Enformer the are trained to directly to predict genome profiles. The paper proposear a further refinement on Enformer mo... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. We appreciate your acknowledgment of our work’s potential contributions to multi-genome foundation models. We supplemented with additional experiments which are presented in https://anonymous.4open.science/r/charts-CDE6. Below, we address your points to cla... | Summary: The paper introduces SPACE, a supervised DNA foundation model that predicts genomic profiles (e.g., chromatin accessibility) to learn effective DNA sequence representations. The authors argue that unsupervised DNA foundation models (DFMs) lack biological context, leading to suboptimal generalization. SPACE add... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback. We supplemented with additional experiments which are presented in https://anonymous.4open.science/r/charts-CDE6. We address each key issue and outline manuscript improvements below.
## Q1:Generalization Across Species and Tasks
We concur that eva... | null | null | null | null | null | null |
Geometry-Informed Neural Networks | Accept (poster) | Summary: The paper proposes geometry-informed neural network (GINN) a general framework that allows for generating a diverse set of implicit shapes all satisfying a set of constraints while minimizing an objective function. The paper formulates this problem as a probabilistic generative problem with a novel diversity l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We would merely like to answer the question concerning the extension of the method to constraints that require running a physics solver. Conceptually, this is straightforward by adopting a solver-in-the-loop [1], which we already do with the persi... | Summary: This paper studies a novel and interesting problem of learning a generative model of shapes under certain geometric constraints. It tackles this question in a simple yet effective manner: perform constrained optimization with a diversity penalty term. Some applications are included, and some analyses are condu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and balanced review. In the following, we address the main questions.
## Q1
The reviewer writes that while the experiments look promising, their scope is insufficient and, in particular, only two studied problems are relevant to the core research question. W... | Summary: Paper introduces geometry-informed neural networks — a gradient-based way to optimize neural implicit function (SDF) without data based on local and global geometric constraints. Authors propose a set of practically relevant constraints (particular design region, particular interface, connectedness, smoothness... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. In the following, we will address the questions.
## Sharp features
The method is not limited to smooth surfaces. The smoothness of the surface is inherited from the NN used to represent the shapes. The smoothness properties of MLPs are governed by ... | Summary: The paper introduces Geometry-Informed Neural Networks (GINNs), a framework that trains shape-generative neural fields without relying on data. Instead, the method leverages user-specified design constraints (e.g. connectivity, smoothness, and topology) to drive the generation of feasible shapes. A key novelty... | Rebuttal 1:
Rebuttal: We thank the reviewer for their considerate review. In the following, we address the three weaknesses.
## Task diversity
While we agree that more experiments are almost always better, we would like to present a matrix of problems and constraints that illustrates the variety of considered settings... | null | null | null | null | null | null |
Protein Structure Tokenization: Benchmarking and New Recipe | Accept (poster) | Summary: This study considers the problem of protein structure tokenization, which it defines as the problem of distilling protein 3D structure into discrete or continuous representations. The authors introduce two main contributions: 1) a set of benchmark datasets for evaluating protein structure tokenization models a... | Rebuttal 1:
Rebuttal: Thanks for your positive review and insightful comments! We respond to your concerns as below:
> R1: Discrepancy observation that FoldSeek, which has the worst curve shape for the distinctiveness evaluation, has much better performance on efficiency benchmark. However, the distinctiveness and eff... | Summary: The paper presents a new framework, StructTokenBench, for evaluating protein structure tokenization (PST) methods, which break down protein 3D structures into discrete or continuous representations. This framework is critical because existing methods for protein structure tokenization (PST) lacked a unified ev... | Rebuttal 1:
Rebuttal: Thanks for invaluable suggestions. We respond to your questions below
>R1: AminoAseed doesn’t perform well on binding site prediction in Fig. 4
We'd like to clarify our observation:
1. **Fig. 4 shows that when using continuous structural representations**, AminoAseed beats ESM3 on all three bind... | Summary: In order to fully evaluate the performance of existing PST methods, the authors constructed a comprehensive evaluation benchmark called StructTokenBench. AminoAseed, a new improvement scheme, is proposed to address the problem of “codebook collapse” in the traditional VQ-VAE method, whose main innovations incl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments
>R1: The claim “adding “a MLP” to reparametrize the codebook can alleviate codebook collapse” needs theoretical analysis and ablation experiments
1. We'd like to clarify that in Sec. 4.2 (L246), we **add a simple linear layer** (instead of a MLP) ... | Summary: This paper presents a benchmark for comparing methods of tokenizing proteins. They divide structure tokenization methods into two categories: those which hand-design structure based tokens, and those which learn the tokenization. Of the learned methods, they distinguish between those that produce learned codeb... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We address concerns below
>R1: Explain “StructTokenBench focuses on local(per-residue) over global(per-protein) structures”
PSTs tokenize per-residue substructures, matching StructTokenBench: **supervised tasks and “Sensitivity” are at residue level**. Current b... | null | null | null | null | null | null |
Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources | Accept (poster) | Summary: This paper investigates the phenomenon of model competition across multiple data sources. The authors propose heterogeneous data game, where each of the model providers decide to deploy a single model, aiming to win the choose from data sources as much as possible. The model is characterized by its parameters ... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts and constructive comments.
> ***Linear Model Assumption***
1. Despite the prevalence of deep learning, **linear models remain important due to their interpretability—an essential requirement in high-stakes domains** such as healthcare and the judicial system ... | Summary: The paper explores the HDG problem, aiming to identify the Pure Nash Equilibrium (PNE) in distributing data resources among machine learning (ML) model producers. Each producer provides parameters, influencing resource allocation based on both their own and the ML parameters. The analysis covers conditions for... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts and constructive comments.
> ***It is unclear if Nash equilibrium is good solution concept here. What is the role of pure Nash equilibria in this scenario? How to use it in the real world?***
1. **Nash equilibrium is a well-established and widely used solutio... | Summary: This work analyze competition through Nash equilibria between multiple ML model providers across heterogeneous data sources. The game is characterized under two different data source choice models and provides conditions for each type of equilibrium. Synthetic experiments are conducted.
Claims And Evidence: C... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts and constructive comments.
> ***Focus on Linear Models, IID Assumptions, and Lack of Empirical Validation***
1. **Linear Models**: Despite the rise of deep learning, linear models remain widely used for their interpretability, particularly in high-stakes doma... | Summary: This work considers a game between model providers who choose which data sources to include in their models training. The approach is one like facility location games: the data sources are also the customers, and so e.g. a model provider could prioritize one data source to ensure that they win business from th... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts and constructive comments.
> ***Comparison with Competitive Location Models***
**1. Differences in Setting**
Our model captures two key features of ML markets often missing in prior work: (1) **Source-specific distance metrics** from distribution shifts, an... | null | null | null | null | null | null |
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects | Accept (poster) | Summary: The paper aims to generate motions of different rigs from text inputs. The paper witness the problem in the current 3D content creation community, that it lacks high-quality motion dataset with annotations, and it lacks methods handling hetergeneous skeleton templates. Therefore, the paper present a high-quali... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s detailed review and thoughtful insights.
### Q1: Physical plausibility of the augmented rigs.
As noted in our response to Q5 of Reviewer 2SeY, we carefully designed the augmentation pipeline and visually inspected ~10K augmented motions, which we found sufficie... | Summary: This paper proposes a novel problem, text-driven motion synthesis of different skeletal structures, constructs a dataset, and develops a new model structure. The key innovation is the explicit incorporation of skeletal configuration information through Tree Positional Encoding (TreePE) and Rest Pose Encoding (... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback and questions.
### Q1: Typo & Link for the demo.
We will correct the typo. For the demo, please visit: t2m4lvo.github.io
### Q2: Human study.
Please refer to our response to Q2 of Reviewer qPq2, where we address a similar point.
### Q3: ... | Summary: This work presents a unified framework for motion synthesis across a diverse range of objects with varying skeletal structures and rest poses. To generate training data, the authors augment the Truebones Zoo dataset by modifying skeletal structures and rest poses , and providing textual descriptions at multipl... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for raising important points and providing constructive feedback.
### Q1: Why do the simple retargeting method perform better than other baselines?
Data-driven learning-based methods each have limitations in generalization or controllability:
- GPT-Caption is trained o... | Summary: This work presents a major advancement in text-driven motion synthesis for large-vocabulary objects with heterogeneous skeletal structures. By augmenting datasets, introducing novel rig adaptation techniques, and extending motion diffusion models, the authors enable realistic motion synthesis for both seen and... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s valuable time and feedback.
### Q1: Comparisons with SOTA models like OmniMotion-GPT or SinMDM.
We agree that comparisons to SOTA models are valuable. However, OmniMotion-GPT and SinMDM address different goals and operate under different assumptions, making... | null | null | null | null | null | null |
Near Optimal Non-asymptotic Sample Complexity of 1-Identification | Accept (poster) | Summary: This paper studies fixed-confidence $1$-identification for sub-Gaussian distributions, where the learner should output one arm whose mean exceed a given threshold if it exists, and output none otherwise. This pure exploration problem with multiple correct answers has been considered under different names in th... | Rebuttal 1:
Rebuttal: Thank you very much for your suggestions, and we will correct the typos in the revision. Due to length limit, we can only answer part of the questions in comments. We are looking forward to discuss more in the next iterations.
1. "no non-asymptotic lower bound when the instance has multiple corre... | Summary: This paper studies the 1-identification problem, a multi-armed bandit exploration problem with the goal of identifying an arm whose mean exceeds a given threshold. The paper introduces a new algorithm that achieves near-optimal non-asymptotic sample complexity. Theoretical guarantees establish its efficiency i... | Rebuttal 1:
Rebuttal: Our primary contributions lie in the theory towards optimal performance on the 1 identification problem, and there could be misunderstanding in the numerical performance of AP-GAI. We are thankful for the perspective that motivates us to clarify more, and we look forward to discuss more in the nex... | Summary: This paper addresses the problem of 1-identification in stochastic multi armed bandits. In particular, given a reward threshold $\mu_{0}$ an algorithm solving the 1-identification problem has to return an arm whose associated expected reward is greater than $\mu_{0}$ whether it exists. The authors propose the ... | Rebuttal 1:
Rebuttal: Thank you very much for your suggestions. We will correct the typos, and we look forward to discuss more in the next iteration. We will also remove part of the sketch proof to allow space for algorithm description.
1. "the reported plots do not show the confidence intervals"
In fact, all fig... | Summary: In this paper, the authors consider the identification problem a pure exploration problem with bandit feedback, where the objective is to determine where there exists an arm whose mean reward is at least a known threshold or to output None if it believes such an arm does not exist. They proposed an algorithm t... | Rebuttal 1:
Rebuttal: Thank you very much for your suggestions. Let us respond below to the points raised, and we look forward to discuss more in the next iteration.
1. "If $\mu_1>\mu_0\geq\mu_2\geq...\geq\mu_K$, does the lower bound work for all permutation of [K]"
We cannot expect the current lower bound holds... | null | null | null | null | null | null |
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization | Accept (poster) | Summary: This paper introduces DynAMO - a method for offline model-based optimization whose objective is to produce a diverse distribution of various designs. The objective is clearly formulated, the method is carefully derived, and empirically evaluated. The empirical evaluation is very extensive and it is certainly i... | Rebuttal 1:
Rebuttal: We thank Reviewer sKFn for their helpful and constructive feedback on our manuscript, and appreciate their efforts in helping us improve our work. Please find our responses below - we would be happy to discuss further and answer any follow-up questions as necessary. Thank you!
1. **(Assumption in... | Summary: This paper proposes a distributed matching based adversarial optimization framework (DynAMO) aimed at addressing the issue of insufficient design diversity in offline model optimization (MBO) tasks. By explicitly modeling diversity objectives as a matching problem between generative design and offline dataset ... | Rebuttal 1:
Rebuttal: We thank Reviewer DFtV for their thoughtful feedback and careful consideration of our work. Please find our responses to your comments below.
1. **(Combination of KL Divergence and Adversarial Constraints)** Thank you for this comment - indeed, we believe that it is a *strength* of our method tha... | Summary: This paper presents a novel approach to incorporating design diversity as an explicit objective in the offline model-based optimization problem. Specifically, the original optimization objective is modified to enhance the diversity of generated samples using a distribution matching technique inspired by recent... | Rebuttal 1:
Rebuttal: We appreciate Reviewer scBu for their insightful feedback and thorough evaluation of our work. We have provided our responses to your comments below and would be happy to answer any follow-up questions. Thank you!
1. **(Presentation of Results)** To improve the legibility of our results, we summa... | Summary: The paper introduces Diversity in Adversarial Model-based Optimization (DynAMO), a new approach for offline model-based optimization (MBO) that aims to generate diverse and high-quality design candidates. The core idea is to frame diversity as a distribution matching problem by optimizing a KL-divergence-based... | Rebuttal 1:
Rebuttal: We thank Reviewer kDhU for their thoughtful comments and feedback on our proposed work, which we believe have substantially improved the quality of our manuscript. Please find our responses to your comments below.
1. **(Performance According to the Median@128 Metric)** Thank you for this comment.... | null | null | null | null | null | null |
Sample Complexity of Correlation Detection in the Gaussian Wigner Model | Accept (poster) | Summary: The paper focuses on detecting correlations between a pair of random graphs, using the Gaussian Wigner model where edge weights are drawn from a Gaussian distribution. This is framed as hypothesis testing, determining whether the graphs are independent (null hypothesis) or edge-correlated (alternative hypothes... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
**Q1: The correlation analysis between random graphs and its relation with modern machine learning.**
There are many papers on top machine learning conferences and journals addressing related problems.
Our hypothesis testing problem is also known... | Summary: This paper addresses the problem of detecting correlation between two random graphs generated from the Gaussian Wigner model. In particular, tha authors focus on the scenario where two induced subgraphs are sampled. It establishes the optimal sample complexity for correlation detection and proposes two test st... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We will add an overview of the paper and highlight the contributions in the introduction. | Summary: This paper studies the problem of correlation detection in the Gaussian Wigner Model, formulating it as a hypothesis testing problem. The authors analyze the sample complexity required for correlation detection when only two induced subgraphs are observed. The main theoretical contribution is the derivation of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
**Q1: Graph topology and structural noise.**
Thank you for the question. The graph topology and structural noise can be incorporated into our analytical framework. Particularly, most results in this paper can be extended to the Erdős–Rényi model. ... | Summary: The paper studies the detection of correlations in pairs of graphs generated by the Gaussian Wigner model when only induced subgraphs are observed. It establishes nearly sharp sample complexity thresholds for successful detection via both possibility and impossibility results and introduces two estimators (one... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
**Q1: The impact of the parameters on runtime and performance.**
Thanks for the suggestion. We will add more discussions on this point after Algorithm 1. The time complexity is $O(N_1\cdot s^{K_1}+N_2^{K_2})$ (see line 368, column 1). Hence, the r... | null | null | null | null | null | null |
Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models | Accept (poster) | Summary: The manuscript addresses federated LLM fine-tuning, highlighting how existing methods often lead to catastrophic forgetting, diminishing the global model's generality, and failing to properly balance model updates across clients with different downstream task datasets. The authors propose the FedICU method, wh... | Rebuttal 1:
Rebuttal: Dear Reviewer CCAN:
Thank you very much for your recognition of our work. Below I will provide detailed responses to your questions. Thank you for taking your valuable time to offer suggestions for our work.
**Q1:Is the proposed framework orthogonal to other LoRA training methods?** (Questions F... | Summary: This paper proposes FedICU to address client heterogeneity in FL. FedICU consists of two key components:
1. **Consensus-Divergence Splitting**: This method decomposes client updates into magnitude and direction, treating magnitude as consensus and direction as divergence. The two components are then aggrega... | Rebuttal 1:
Rebuttal: Dear Reviewer UVcT:
Thank you very much for your time and review suggestions on our paper. We hope the following responses can address your concerns.
**Q1：The claim regarding the relationship between magnitude-direction and consensus-divergence does not appear to be particularly strong.** (Claim... | Summary: The paper proposes FedICU, a novel federated learning framework for large language models (LLMs) in heterogeneous settings. It decomposes client updates into consensus and divergence components. In the global aggregation phase, it balances these components based on their contribution to the global model perfor... | Rebuttal 1:
Rebuttal: Dear Reviewer UEdC:
Thank you very much for your recognition of our paper and detailed suggestions. We will answer and explain the issues in detail below.
**Q1: The method relies on LoRA, which may not achieve the same performance as full parameter fine-tuning.** (Other Strengths And Weaknesses)... | Summary: This paper introduces FedICU, a framework designed to enhance fine-tuning of Large Language Models in Heterogeneous Federated Learning.
The paper presents two core innovations:
- **Consensus-Divergence Splitting**: A technique that decomposes client updates into **consensus (common capabilities)** and **dive... | Rebuttal 1:
Rebuttal: Dear Reviewer fzFX:
Thank you for your valuable review of our paper. We hope our responses will address your concerns and improve our score.
**Q1: Clarify how inactive parameter contribute to catastrophic forgetting.** (Claims And Evidence)
**A1:** We apologize for the confusion in our original... | null | null | null | null | null | null |
Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective | Accept (poster) | Summary: The paper explores the impact of sparsity in preference learning, establishing a minimax lower bound on empirical error under sparse RUM and deriving upper bounds for two sparsity-regularized estimators. The experiments, conducted on both a synthetic dataset and an LLM alignment setting, validate the theoretic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We are glad that the reviewer found the theoretical contributions comprehensive, the experiments well-designed, and the writing clear in presenting the connections between sparsity and preference learning. Below, we respond to eac... | Summary: This paper proposes a sparse setting for preference learning, the authors state that human preferences are driven by some critial factors, of which the dimension is generally low.
Therefore, the authors study the preference learning problem in the sparse setting from a theoretical perspective, deriving bounds ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. We greatly appreciate the recognition of our theoretical contributions and the time taken to carefully examine the proofs of our four main theorems. Below, we address the comments and questions in detail.
**(1) Evidence for k-spa... | Summary: The authors analyze the sample complexity of RUMs where utilities are an inner product between $d$-dimensional item features $x$ and preference parameters $\theta$, and where $\theta$ is $k$-sparse. Whereas existing results on the sample complexity of learning $\theta$ find that error decays as $\Theta(d / n)$... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of the clarity of our presentation, the significance of the theoretical contributions, and the relevance of our work to both RLHF and choice modeling. We also thank the reviewer for taking the time to examine the proofs, and are glad that the experimental r... | Summary: The paper investigates leveraging sparsity in preference learning to achieve improved sample efficiency. Under the sparse random utility model (RUM), the authors derive minimax optimal estimation rates, emphasizing the theoretical benchmark provided by an $l_0$-constrained estimator. However, recognizing that ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and the recognition of our theoretical contributions, the clarity of our motivation, the novelty of applying sparsity to preference learning, and the solid experimental setup. Below, we address the concerns and questions:
**(1) On the empirical ga... | null | null | null | null | null | null |
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning | Accept (poster) | Summary: This paper introduces a method, MARASON, for predicting a mass spectrum from molecular graphs. MARASON extends an existing deep learning framework (ICEBERG) by integrating retrieval with neural graph matching. The model retrieves reference molecules with known spectra, then aligns fragments between the target ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the state-of-the-art accuracy of MARASON and the organization of this paper. We believe there are several misunderstandings and we clarify them as follows.
> The paper does not describe a generation process and does not utilize RAG.
The problem MARASON aims ... | Summary: This paper proposes a modification of a method for generating mass spectra from molecular structures. Inspired by recent successes in retrieval augmented generation (RAG), the authors decided to apply this technique for querying similar molecules from the training set and to use them as references for generati... | Rebuttal 1:
Rebuttal: We truly appreciate your recognition of our contribution to the mass spectrometry field and our technical novelty of introducing neural graph matching. We conduct statistical tests and perform preliminary MassSpecGym experiments following your suggestions. We will work actively to complete the new... | Summary: This study introduces MARASON, an advanced computational framework that enhances RAG in mass spectrum prediction through neural graph matching. It evolves from the ICEBERG framework through a synergistic integration of graph-based neural architectures and spectral alignment mechanisms.
Claims And Evidence: Th... | Rebuttal 1:
Rebuttal: Thank you for recognizing our state-of-the-art performance and our technical soundness. We update with experiments on the recently developed MassSpecGym benchmark, evaluation with non-similar reference structures, and elaboration on the ablation study to address your concerns. We are more than hap... | Summary: Authors present MARASON, which augments a previously-developed framework ICEBERG by retrieving the most similar molecules in a database to a target structure based on Tanimoto similarity. Both target and reference structures are fragmented using ICEBERG; a GNN is used to construct a matching matrix to predict ... | Rebuttal 1:
Rebuttal: Thank you for agreeing with the novelty and technical solidity of our paper. We provide more random seeds, new MassSpecGym results, and MassFormer baseline with collision energy as per your comments. Please find our reply to your questions as follows.
> Why does the scaffold split experiment have... | null | null | null | null | null | null |
Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging | Accept (poster) | Summary: This is an experimental paper. This paper enhances VLMs with reasoning capabilities using Reasoning LLMs. Specifically, this paper integrates reasoning capability of Reasoning LLMs into VLMs through linear merging. Extensive experiments demonstrates the effectiveness of this simple model merging method on math... | Rebuttal 1:
Rebuttal: Thanks for the comments! We address the concerns below.
W1:
>The workload of experimental analysis and discovery in this paper is not enough…For examples…why not try a dynamic model merging method considering this finding?
We sincerely appreciate your feedback on our work. But we respectfully d... | Summary: This paper proposes merging models across modalities to enable the incorporation of the reasoning capabilities of LLMs into VLMs. Effectiveness of this training-free recipe is verified via extensive experiments. It also evaluates VLMs in different sizes to verify the generalization ability of merging. Addition... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and detailed comments! Your suggestions will be helpful in improving the paper.
Q1:
> Lack of baselines of other merging methods. Only TIES is provided in the appendix.
We appreciate the suggestion and have expanded our experiments to evaluate DARE merging [3]... | Summary: This paper investigates the impact of integrating math-specific LLMs into VLMs through model merging. The experimental results demonstrate that this approach effectively transfers reasoning abilities from math-specific LLMs to VLMs in a training-free manner. Furthermore, the authors conduct extensive experimen... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and detailed comments! We address your questions below.
Q1:
>The authors assert that "after merging, we observe that all layers..whereas the distribution of perception abilities across layers remains largely unchanged." I could not find any text discussing this... | null | null | null | null | null | null | null | null |
TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation | Accept (poster) | Summary: This paper proposes a layer-wise method with Dirchlet energy imitation for node-level knowledge distillation from GNN teacher to MLP student, to reduce the latency of inference for time-sensitive applications.
Claims And Evidence: The problem is well-defined, and the claims in introduction section is clear, a... | Rebuttal 1:
Rebuttal: ### Thank you for appreciating the strengths of our work. Below are our responses to address your important comments.
>**C1.** The justification of using Dirchlet energy ratio as the quantity for knowledge transfer, and the reason why it is more effective than previous SOTA methods.
**Response:... | Summary: This paper addresses the GNN2MLP distillation task, which aims to transfer knowledge from computationally expensive Graph Neural Networks (GNNs) to more efficient Multilayer Perceptrons (MLPs) for faster inference on graph-structured data.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Overall, th... | Rebuttal 1:
Rebuttal: ### Thank you for acknowledging the strengths of our work. Below are our responses addressing your important comments.
> **C1.** The hyperparameter search space.
**Response:** We clarify that we mainly adopt the conventional hyperparameter search space for the teacher model and the student mod... | Summary: This paper introduces TINED, a method for distilling Graph Neural Networks (GNNs) into Multi-Layer Perceptrons (MLPs) via layer-wise Teacher Injection and Dirichlet Energy Distillation (DED). The key idea is to directly inject parameters from GNN feature transformation layers into MLP layers and use DED to pre... | Rebuttal 1:
Rebuttal: ### Thank you for recognizing the strengths of our work. Here are our responses to your important comments.
> **C1.** Elaboration on deployment with and without graph dependency (line 185-190)
**Response:** When performing inference on a new node with limited connections to a graph, such as a ne... | Summary: This work proposes TINED, a method to distill knowledge from teacher GNNs into student MLPs. Extensive experiments show its good performance.
Claims And Evidence: No
Methods And Evaluation Criteria: Yes
Theoretical Claims: No
Experimental Designs Or Analyses: No, but the author seems to provide the code. I... | Rebuttal 1:
Rebuttal: ### We appreciate your effort in reviewing our paper. We have conducted extensive experiments to address your key comments. Thank you for your consideration.
> **C1.** Could the author provide the sensitivity analyses on the datasets mentioned in this paper?
**Response:** In our method, the pa... | null | null | null | null | null | null |
Online Learning with Unknown Constraints | Accept (poster) | Summary: The paper provides new insights into the problem of online learning with unknown constraints. Lower and upper bounds that connect the difficulty of the problem with Eluder dimension are derived.
Claims And Evidence: Yes
Methods And Evaluation Criteria: See Questions for Authors
Theoretical Claims: No
Exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their questions. If the reviewer wishes, we are more than happy to engage in follow-up discussions through OpenReview!
We would like to highlight the fact that our paper is theoretical in nature, and our main focus is to establish information theoretically when safe lear... | Summary: The paper addresses the problem of online learning with unknown safety constraints, where constraint satisfactions is required per round. The authors provide a general meta-algorithm based on the Online Learning oracle for online learning strategy with known constraints, and Online Regression oracle for constr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and helpful suggestions, which we will be sure to incorporate into our final version. If the reviewer wishes, we are more than happy to engage in follow-up discussions through OpenReview!
> In particular, the novel complexity measure proposed is recalled as ... | Summary: This paper studies the online adversarial learning problem of achieving no-regret guarantees while playing actions subject to an unknown constraint at each time-step. At each round $t$, the set of safe actions is a function of the adversarially chosen context and an unknown safety constraint $f*$; after playin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their review and questions. If the reviewer wishes, we are more than happy to engage in follow-up discussions through OpenReview!
> In Section 4.4, the authors say that Assumption 5.1 is necessary for safe learning in the given setting, but this seems misl... | Summary: This paper is on contextual online learning with unknown constraints that are stochastic and roundwise. Let $\ell$ be a given loss function, of an action $a \in \mathcal{A},$ a context $x \in \mathcal{X}$ and an "outcome" $y \in \mathcal{Y}$; and let $f_* \in \mathcal{F}$ an unknown constraint function paramet... | Rebuttal 1:
Rebuttal: Thank you for the careful read and very helpful suggestions, which we will be sure to incorporate into our final version. If the reviewer wishes, we are more than happy to engage in follow-up discussions through OpenReview!
> The obvious issue here is that this is strongly trajectory-dependent. N... | Summary: This paper studies the problem of bandit with constraints. Specifically, the forecaster wants to minimize the regret, while keeping the safe constraints satisfied with high probability. To resolve this question, the authors propose a new complexity measure, and they provide upper bounds and lower bounds to the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their review and questions. If the reviewer wishes, we are more than happy to engage in follow-up discussions through OpenReview!
> "The upper and lower bounds do not match" and "Can you prove a lower bound stating that the regret is lower bounded by the s... | null | null | null | null |
Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration | Accept (oral) | Summary: This paper presents Memory-driven Intrinsic Cost Estimation (MICE), an algorithm for reducing constraint violations in constrained RL throughout training, rather than just at the end of training. It does this using a memory buffer that stores a representation previously seen constraint-violating states, which ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The followings are detailed responses to the points raised by Reviewer kehG.
>When using a random network to project into latent space, my understanding is that distance in random latent space is mostly a "these two states ... | Summary: The paper proposed MICE, which introduces the concept of intrinsic cost to combat the issue of underestimation bias in cost-value function present in many safe RL algorithms. The paper discusses their flashbulb memory design, which attaches additional intrinsic cost to previously visited risk regions, and show... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer rbBW.
>The paper might benefit from investigating how this embedding function interact with the effect of KNN choice.
**Response:**
We appreciate th... | Summary: The paper tackles a important issue in CRL: the underestimation bias in the cost value function, arising from the functio approximation error, which often results in unsafe exploration and frequent constraint violations. The paper proposes the MICE algorithm to address this issue. It uses a flashbulb memory me... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer 9DRS.
>The assumptions (e.g., finite MDP, extensive sampling) might restrict practical applicability in more complex or continuous environments.
**Re... | null | null | null | null | null | null | null | null |
Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data | Accept (poster) | Summary: This paper introduces a latent score-based reweighting framework for improving classification robustness on imbalanced tabular datasets. The approach leverages score-based generative models (diffusion models) to estimate the joint distribution P(X,Y), identifying underrepresented data regions and upweighting s... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and valuable feedback. Below is our concise response:
**[A1]** The explanation is as follows:
- Your interpretation of our method is correct. We first learn score to model the complex data distribution. A subsequent score-based reweighting ensures balanced represe... | Summary: This paper introduces a score-based approach to address data distribution imbalance. First, a variational autoencoder (VAE) is used to transform raw data into a latent representation space, where a diffusion model is applied to learn the joint data distribution. The proposed method estimates relative density u... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed reviews. Below are our responses to your concerns (**R**) and questions (**A**):
**[R1]** Due to readability considerations, we report standard deviations in Appendix A.3. For detailed dataset statistics, please refer to Appendix A.5, which lists the attribut... | Summary: The paper proposes a latent score-based reweighting framework to improve robustness in machine learning models on tabular data, addressing biases from imbalanced distributions. Unlike existing methods that require prior group labels or focus only on P(Y|X), the approach leverages score-based (diffusion) models... | Rebuttal 1:
Rebuttal: We would like to thank you for providing helpful comments and positive feedbacks. Below are our responses to your concerns.
> Lack of details about noise preconditioning factor $\sigma$ and hyper-parameter $\tau$
**[A1]** We appreciate this observation. Regarding the network preconditioning fact... | null | null | null | null | null | null | null | null |
ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Low-Perplexity Toxic Prompts | Reject | Summary: This paper proposes ASTPrompter, an approach to automating the red-teaming of LLMs by generating harmful yet fluent (i.e., low-perplexity) prompts. While the underlying motivation is not new, and the vulnerability of LLMs to such attacks is well known in the community, our main contribution lies in modifying t... | Rebuttal 1:
Rebuttal: ## Ours vs. Robey, et al., 2023 vs. GCG
Thank you for your feedback! [1] and our work have significant differences. First, we note that they are fundamentally different in their goals and methods. [1] is a defense method against attacks. We are an attack method.
Concerning attacks mentioned in [1]... | Summary: This paper introduces ASTPrompter, a Reinforcement Learning (RL) based approach for automated red-teaming of Large Language Models (LLMs). The method is designed to identify prompts that elicit toxic outputs from a defender LLM while also maintaining low perplexity, ensuring the generated prompts are likely to... | Rebuttal 1:
Rebuttal: ## Other Models
In addition to GPT 2 and GPT2-XL, we already report results using Llama-8b models and TinyLlama in the article (Table 1), demonstrating successful attacks with low perplexity prompts.
Furthermore, we include here **black-box attack results against Claude 3.5 Sonnet (cutoff 202410... | Summary: The paper presents a reinforcement learning-based red-teaming method to identify prompts that elicit toxic outputs from language models while maintaining fluency. The approach uses Adaptive Stress Testing (AST) and Identity Preference Optimization (IPO) to generate prompts with high likelihood and increased to... | Rebuttal 1:
Rebuttal: Thank you for your review! With respect to IPO vs. DPO vs. RLHF-PPO type methods, we do not compare our method to DPO, since we use a multi-objective reward function and DPO assumes rationally ranked responses. Since the LM perplexity/toxicity evaluations may not be exactly rationally ranked, the ... | Summary: The paper introduces ASTPrompter, a reinforcement learning-based red-teaming approach that uses Adaptive Stress Testing and online weakly supervised Identity Preference Optimization to find toxic prompts. The method outperforms baselines by eliciting 2-23X more toxicity while maintaining fluency, and works in ... | Rebuttal 1:
Rebuttal: ## Fluency and Naturalness
We do not directly test the relationship between low perplexity and naturalness, as the goal of our paper is to identify low perplexity prompts, independent of their naturalness. However, we do qualitatively observe a relationship, as shown in the rollouts in Appendices... | null | null | null | null | null | null |
Determinant Estimation under Memory Constraints and Neural Scaling Laws | Accept (poster) | Summary: This paper proposes a scalable way to compute Neural Tangent Kernel log-determinants of dense matrices which may arise in training deep neural networks. The empirical Neural Tangent Kernel has been shown as the effective tool to study the behavior of neural networks during both training and inference. In parti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our paper and positive feedback on our work. We acknowledge that some points need clarification and aim to do so below.
Regarding the implicit value of $m=10$: this is the number of classes used in all the classification datasets we used in experi... | Summary: The paper designs two algorithms for computing log-determinant of large PSD matrices under memory constraints. The first algorithm is named MEMDET, designed based on block LDL decomposition. The other one is named FLODANCE, designed based on neural scaling law assumptions. Empirical results show that the propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and positive review of our work. The reviewer has raised a few points that deserve addressing, and we believe that doing so will improve our work.
The first of these is our dependence on the scaling law assumption. In the submitted version of ... | Summary: This work addresses the memory and computation bottlenecks in estimating log-determinants of large matrices such as the Neural Tangent Kernel (NTK) for large models and datasets. The paper introduces MEMDET, a memory-constrained algorithm for exact log-determinant calculations for matrices too large to fit in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and positive review of our work. This work is indeed part of a broader active research program aimed at providing computational tools for estimating linear algebraic quantities at scale. We will make this more clear in the final version of our ... | Summary: This work proposes a method to scale the calculation of matrix determinant to extremely large matrices, especially in context of ill-conditioned matrices such as the empirical NTK matrix. They first develop a memory-constrained algorithm which computes determinant exactly and can serve as a baseline for their ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper, and for their constructive feedback.
Regarding general applicability of our methods: we stress that MEMDET computes the exact (64-bit) determinant, and is a general method that works for arbitrary matrices.
On the other ... | null | null | null | null | null | null |
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution | Accept (poster) | Summary: The paper explores feature attribution by determining the importance of individual wavelets in prediction tasks. Unlike traditional vision-based attribution methods that assess pixel importance, this approach evaluates how wavelets contribute to model predictions. The key idea is to compute gradients with resp... | Rebuttal 1:
Rebuttal: We first would like to thank reviewer UvKx for the reviews on our work and underlining the potential of our idea, regarded as innovative. We would like to address the comments raised by the reviewer on our work. These comments will be taken into account in our work and will help us improve the qua... | Summary: This paper proposes an explanation method for DNN by using wavelet coefficients as features for attribution instead of image features. The proposed WAM can be adopted across diverse modalities, including audio, images, and volumes. It unifies and extends existing methods, SmoothGrad and Integrated Gradients, w... | Rebuttal 1:
Rebuttal: We thank reviewer EDJ8 for the review and the comments on our work. We also thank the reviewer for pointing the duplicate reference, which we have corrected.
### Regarding the evaluation criteria
The Reviewer said to include localization evaluation, Pointing Game, and visual explanation accurac... | Summary: Presents a feature attribution method that performs attributions on wavelets derived from input domain. This helps to naturally extend explanations that are outside the image domain, such as an audio input domain. The method essentially constructs a wavelet transform of the input, then applies standard gradien... | Rebuttal 1:
Rebuttal: We first would like to thank reviewer KCeN for reviewing our manuscript and for the comments on our work. The points raised by the reviewers will help us improve the quality of our work.
**Question** *Pg 4, line 209, left: [...] Please expound / I can see how wavelets is another way to attribute... | null | null | null | null | null | null | null | null |
An Efficient Pruner for Large Language Model with Theoretical Guarantee | Accept (poster) | Summary: The paper proposes an proximal-operator based approach that allows to prune linear layers in large language models. The paper provides a solid theoretical analysis of the method as well as experiments on a Llama 7B, where it (marginally) improves over previous methods.
The proximal operator is (known) the ele... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. Below, we provide our responses and clarifications.
**Can you get results on a LLaMA 3.1 model and also on a 70B model?**
We appreciate the reviewer's valuable suggestion. Due to resource constraints, we have conducted additional experiments on ... | Summary: This paper addresses the challenge of pruning Large Language Models (LLMs) to reduce computational and storage costs without retraining. The authors reformulate the pruning process as an $\ell_0$-penalized optimization problem and propose a “monotone accelerated Iterative Hard Thresholding” (mAIHT) method. The... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. Below are our responses and clarifications for the various concerns raised.
**High-Sparsity Regime: Have you tested or do you plan to test the method at extremely high sparsities (e.g., 80-90%)?**
**Reply**: Thank you for the question. We found ... | Summary: $\ell_0$ regularization and propose to use proximal gradient descent to solve it. The authors also provide theoretical analysis for their method, but the review who is very bad at math is unable to justify them. Empirical evaluations show that the proposed method can alightly outperform existing pruning metho... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments. Below, we address the concerns and provide clarifications.
**Reply to the concerns in Methods and Evaluation Criteria**
Our method can be directly extended to n:m sparsity by modifying the term $\lambda\Vert W\Vert_0$ in problem (2) to $I_{S}(W)$... | Summary: The paper introduces monotone accelerated Iterative Hard Thresholding (mAIHT), designed to improve the efficiency and theoretical soundness of pruning large language models (LLMs). The authors reformulated the pruning problem as L0-penality optimization problem, addressing the limitations of heuristic and retr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback. Below, we provide our responses and clarifications.
**How well does mAIHT scale to larger models? What are the computational trade-offs?**
**Reply**:
We thank you and Reviewer PbeQ for the comments. In response, we have added pruning experiment... | null | null | null | null | null | null |
Larger or Smaller Reward Margins to Select Preferences for LLM Alignment? | Accept (poster) | Summary: This paper identifies that existing metrics for selecting preference data, which rely on either explicit reward margins or implicit reward margins, often yield contradictory evaluations for the same dataset. To address this issue, the authors propose a new metric called the alignment potential ($M_{AP}$), whic... | Rebuttal 1:
Rebuttal: ## Contradictions
We have conducted an analysis to measure the contradictions between explicit and implicit margin metrics ($M_r$ and $M_\pi$) by comparing the Jaccard similarity of subsets selected by each metric:
- **Subset selection**: Using $M_r$ and $M_\pi$, we select the top-rated k% subsets... | Summary: This paper proposes the new "alignment potential metric" to evaluate the quality of (and select) data for offline preference optimization. This metric quantifies the gap between the model's implicit reward margin and the target explicit reward margin, and thus aims to estimate the model's potential to align on... | Rebuttal 1:
Rebuttal: Due to space limits, we put all tables in this anonymous link: https://anonymous.4open.science/r/tables-11915/ZHMc.md.
## Large/Small Margins
To reiterate, the primary claim of our paper is **not** that "large explicit reward margin" and "small implicit reward margin" **individually** imply high-... | Summary: This paper examines how reward margins influence preference data selection in LLM alignment. It introduces a novel metric, Alignment Potential (AP), which integrates both explicit reward margins (provided by the reward model) and implicit reward margins (derived from the policy and reference models). Furthermo... | Rebuttal 1:
Rebuttal: ## Additional Alignment Approaches
Thanks for your suggestion!
Aside from the DPO and SimPO methods, we conduct additional experiments using the IPO method for alignment training.
Like the setting in Section 3.2, we utilize different metrics to select top-40% subsets from existing preference da... | Summary: This paper investigates the techniques of preference data selection, particularly between the explicit reward margin by reward models and the implicit reward margin by the SFT models. This paper proposes a new preference pair quality metric (MAP) based on the discussion between the above two reward margins, en... | Rebuttal 1:
Rebuttal: ## The $M_+$ Metric
The $M_+$ metric is a modified version of the proposed $M_1$ metric designed to adapt to the **unidirectional update nature of existing preference optimization methods** (e.g. DPO, SimPO).
As described in section 3.1, we first quantify the discrepancy between the current mode... | Summary: The submission addresses the challenge of selecting high-quality preference data for aligning large language models with human values. It introduces the metric of alignment potential. This metric quantifies the gap between a model’s current implicit reward margin and the target explicit reward margin to identi... | Rebuttal 1:
Rebuttal: ## Suggestions & Questions Regarding Figure 1
Thanks for your valuable suggestion!
In Figure 1, we present two examples to compare the existing metrics:
- **Explicit reward margin**: $M_r = |r(x,y_w) - r(x,y_l)|$, which quantifies **how much $y_w$ is more preferable than $y_l$**.
- **Implicit r... | Summary: - This paper proposes a new way to select good preference data jointly using two different reward signals which are captured through the external reward model and training model’s implicit DPO reward, respectively. Start from the mathematical derivation of DPO, the authors suggest the revised score function to... | Rebuttal 1:
Rebuttal: ## Ablation I: absolute values
Although Eq.11 incorporates two additional absolute values compared with Eq.10: $|r(x,y_w) - r(x,y_l)|$ and $|\hat r_\theta(x,y_w) - \hat r_\theta(x,y_l)|$, only the latter absolute values (on implicit rewards) actually changes the metric's value.
The reason is that... | null | null |
Concept Reachability in Diffusion Models: Beyond Dataset Constraints | Accept (poster) | Summary: This paper compares the effects of prompting vs. steering on diffusion models, and in particular how well these approaches allow for the control of specific concepts in the generated output. To provide control in the experiments, a synthetic dataset of overlapping shapes is created, and the concepts of intere... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We are very happy to read your positive review! We have read through the comments and suggestions you made and addressed them below:
___
- **Does the underlying accuracy of the classifier not need to serve as the baseline for the reachability measu... | Summary: This paper studies the concept reachability in diffusion models and focuses on the effects of three common constraints in datasets: scarcity of concepts, underspecification of captions, and biases. The work shows that although some concepts are reachable for the model, prompting fails to provide sufficient inf... | Rebuttal 1:
Rebuttal: Thank you for reading through our paper! We really appreciate your positive feedback, and have addressed the questions/comments you made below:
___
- **How is the efficiency of steering in terms of application?**
Our current work did not focus on analysing the computational efficiency of steering... | Summary: This paper explores the influence of three core dataset issues on concept reachability in text-to-image diffusion models. Through a synthetic setup, the paper constructs dataset variations corresponding to the three dataset issues and test concept reachability by evaluating the concepts in generated images aft... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful comments! We address your questions below:
___
- **Evidence behind the claim that overly detailed prompts may not help with reachability**
The starting prompt y_s used to implement the steering is as described in Section 3.5, however the accuracy ... | Summary: This paper focuses on the limitations of prompting for model control. It shows how steering is a more robust mechanism to enhance concept reachability. The authors study three common dataset issues: concept scarcity, underspecification of captions, and biased co-occurrence of concepts. Experiments are evaluate... | Rebuttal 1:
Rebuttal: Thank you for reading through our work and providing your feedback! We have read through your comments and address your questions below:
___
- **Notation of steering vectors**
We noticed that the notation $\mathbf{s}$ may also lead to confusion due to the labels $s_1$ and $s_2$ for the factors in... | null | null | null | null | null | null |
Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization | Reject | Summary: This article presents two online bilevel optimization algorithms SOGD and ZO-SOGD. Among them, SOGD achieves the sota local regret bound without using window-smoothed functions. ZO-SOGO provides a fully-zero-order approach and achieves the hypergradient estimation only with function values. The authors present... | Rebuttal 1:
Rebuttal: **Response:** Thank you for your review and suggestions. We would like to clarify that the runtime plots presented in both Figure 2 and Figure 3 already show wall-clock time measurements in seconds. Please refer to the left subplot in Figures 2 and 3, where we directly report time elapsed. Speci... | Summary: This paper proposes a novel approach to online bilevel optimization (OBO) that achieves sublinear stochastic bilevel regret without window smoothing, addressing limitations in existing methods under dynamic conditions. By introducing a new search direction, it improves efficiency through reduced oracle depende... | Rebuttal 1:
Rebuttal: **Response to W1:** Thank you for the helpful comment. We chose to use a hyper-gradient-based approach due to its robustness and strong theoretical guarantees in the context of **stochastic** OBO problems. In contrast, alternative approaches such as value function methods [1] and asynchronous bil... | Summary: This paper introduces a novel framework for stochastic online bilevel optimization (OBO) that addresses limitations of existing methods by achieving sublinear stochastic bilevel regret without relying on window smoothing. The authors propose a new search direction and develop two algorithms, Simultaneous Onlin... | Rebuttal 1:
Rebuttal: > **W1:** While the experimental evaluations support the claims ... generalizability of the proposed algorithms. A suggestion to explore the sensitivity of … $\rho$ and ablation studies on the momentum components ... .
**Response:** We appreciate the reviewer’s feedback. To address the genera... | Summary: This paper introduces new first-order and zeroth-order algorithms for Online Bilevel Optimization (OBO) that achieve sublinear stochastic bilevel regret without using window smoothing. The authors propose a new search direction and develop methods that work with limited feedback, including function value oracl... | Rebuttal 1:
Rebuttal: > **W1:**... experiments on larger datasets ... .
**Response:** We appreciate the reviewer’s feedback regarding evaluation on additional datasets. To address this, we conducted additional experiments on CIFAR10 to demonstrate the scalability of our approach beyond MNIST. Results are provided in o... | null | null | null | null | null | null |
Uncertainty Quantification for LLM-Based Survey Simulations | Accept (poster) | Summary: This study investigates the reliable use of simulated survey responses generated by large language models from the perspective of uncertainty quantification. The proposed approach transforms synthetic data into confidence intervals for human response group parameters, addressing distributional shifts between ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and the encouraging score. Below are our responses.
**Weaknesses**
> *1. I extremely expected to see some discussions on the nature and characteristics of language models for simulation, leading to methodological design and theoretical analysis bas... | Summary: This study proposed a method to convert LLM simulated results into confidence sets for population parameters of human responses. It provides an estimate for the optimal number of simulations with theoretical proofs.
Claims And Evidence: Claims:
(1) A general mathematical formulation of uncertainty quantific... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. Below are our responses. We hope they address your questions and concerns.
**Claims And Evidence**
> *the current study does not provide results for other confidence set construction.*
We clarify that the coverage guarantee (Theorem 3.3) of our g... | Summary: The paper proposes a novel framework for converting synthetic survey responses generated by large language models (LLMs) into statistically valid confidence intervals for population parameters. By focusing on uncertainty quantification, the authors develop a principled method to adaptively select the simulatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. Below are our responses. We hope they address your questions and concerns.
**Claims And Evidence**
>*... the discussions focus on multiple-choice survey responses... which raises questions about its generality.*
Our motivating example in Section ... | Summary: **Survey Simulation Problem**: This paper proposes using LLMs to estimate the statistics for each survey question without asking humans to fill out the survey. For example, in an educational test scenario, if we aim to estimate the probability $\mu$ of students correctly answering a test question, we can empl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and the encouraging score. Below are our responses.
**Experimental Designs Or Analyses**
> *... most of the experimental results are put in the Appendix and some details are not shown...*
Thanks for raising this point. We will add more experiment d... | null | null | null | null | null | null |
Rethink GraphODE Generalization within Coupled Dynamical System | Accept (spotlight poster) | Summary: The paper investigates the generalization challenges of GraphODE models when used for coupled dynamical systems. It shows that mixing static attributes with dynamic states during initialization, along with an over-reliance on context-specific coupling patterns, can hurt the model’s performance in new settings.... | Rebuttal 1:
Rebuttal: # Response to Reviewer CHTQ
Thank you for your positive review and insightful questions. We are grateful for your recognition of our work's novelty and contributions. We address your comments below:
> `Weakness 1`: Need more parameter sensitivity analysis.
We appreciate this valuable feedback. ... | Summary: This paper presents the GREAT framework to improve GraphODE models' generalization in coupled dynamical systems. It tackles two key issues: the entanglement of static and dynamic information during initialization and the reliance on environment-specific coupling patterns. The framework introduces two modules: ... | Rebuttal 1:
Rebuttal: # Response to Reviewer XMqH
Thank you for your thorough review and encouraging feedback. We are grateful for your positive assessment about novelty and importance of our work. We address your questions below:
> `Weakness`: Limited exploration of approach limitations.
We fully agree that a more ... | Summary: The paper proposes a new graphODE-based method to process the compiled dynamical system. The trajectory is decomposed into dynamic and static parts in the latent space. The experiments demonstrate the effectiveness of the proposed method.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoret... | Rebuttal 1:
Rebuttal: # Response to Reviewer qXoi
Thank you for your constructive comments and positive assessment of our work. We address your concerns below and hope our responses will help update your score:
> `Weakness 1`: Limited background on coupling dynamics.
We sincerely appreciate this valuable suggestion.... | Summary: This paper introduces a new framework named GREAT (Generalizable GraphODE with disEntanglement And regularizaTion) to enhance the generalization capabilities of Graph Ordinary Differential Equations (GraphODE) models for coupled dynamical systems. The key contributions and findings include identifying generali... | Rebuttal 1:
Rebuttal: # Response to Reviewer LVHe
Thank you for your thorough review and constructive feedback. We address your concerns below and hope these clarifications will help you re-evaluate and update the score:
> `Weakness 1`: Systems too simple with low dimensionality (≤5).
Thank you for this observation. ... | null | null | null | null | null | null |
EFDTR: Learnable Elliptical Fourier Descriptor Transformer for Instance Segmentation | Accept (poster) | Summary: The paper presents EFDTR as an innovative solution for instance segmentation, combining the strengths of polygon-based representations with advanced deep learning techniques. The proposed framework not only enhances segmentation accuracy but also paves the way for future advancements in contour learning and ap... | Rebuttal 1:
Rebuttal: ## Response to Reviewer jXM9
Thank you for your constructive comments and insightful suggestions, which have helped us improve the quality and clarity of our manuscript. We address your points in detail below.
### Response to Hole Case
The COCO dataset primarily uses polygon annotations for in... | Summary: This paper proposes EFDTR, an instance segmentation framework that leverages Elliptical Fourier Descriptors (EFDs) to represent object contours. The approach employs a two-stage Transformer decoder: the first stage predicts low-order (particularly first-order) elliptical Fourier coefficients to capture global ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer GvJY
Thank you very much for your valuable and constructive feedback. We truly appreciate your time and effort. Below, we provide point-by-point responses to the concerns and suggestions you raised.
### Response to More Dataset
We have conducted additional experiments o... | Summary: This paper devises a method for regressing vertex positions using Elliptic Fourier Descriptors (EFDs). Furthermore, it proposes a learnable transformer architecture to incorporate these EFDs. The transformer pipeline consists of two stages: 1) a transformer predicting EFDs to get coarse instance regions and 2)... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 3kZq
Thanks a lot for the time and effort you invested in providing the detailed reviews. Regarding the current weaknesses you pointed out, we are glad to give our responses.
### Summary and Strengths
We are pleased that you found our method well motivated, the design so... | Summary: The paper proposes an instance segmentation method for images, based on the prediction of a polygon as a series of connected points instead of a more commonly used pixel mask. The contour of a polygon capturing an instance is decomposed with a Fourier decomposition. The authors propose a transformer-like archi... | Rebuttal 1:
Rebuttal: ## Response to Reviewer tj3E
We sincerely appreciate your time and effort in reviewing our paper and are glad to provide detailed responses to your insightful questions and suggestions.
### Clarification on Method
While higher-order EFDs can offer finer contour approximations, the concern regard... | null | null | null | null | null | null |
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations | Accept (poster) | Summary: The paper tackles the issue of computing verifiably sufficient input-level
explanations of neural network predictions. The proposed algorithm speeds up
the computation, which involves several invokations to an exact solver, by
constructing a smaller-scale abstraction of the target neural network. The
abstract... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and their acknowledgment of the significance of our work.
**Enumeration of minimal sufficient explanations**
We thank the reviewer for the insightful comment and agree that this is a highly relevant direction, particularly in the context of form... | Summary: This paper aims to improve the scalability of verification algorithms for computing minimal sufficient explanations of neural network predictions. \
The traditional approach iteratively removes input features while preserving the invariance property that the retained set must remain a sufficient explanation. H... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and their acknowledgment of the significance of our work.
**Expanding the scope**
We thank the reviewer for bringing up this point and will improve its discussion in the final version. We note that while our primary experiments focus on vision c... | Summary: This paper introduces a novel abstraction-refinement technique to efficiently compute provably sufficient explanations of neural network predictions, defined as a subset of input features that are sufficient to determine that the prediction remains the same. The method constructs an abstract neural network, wh... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and their acknowledgment of the significance of our work.
**Extension to different $\epsilon_p$ perturbations**
We thank the reviewer for raising this important point. As correctly noted, larger $ϵ_p$ perturbations yield stronger sufficiency gua... | Summary: The paper seeks to use an abstraction-refinement approach to generate "provably sufficient" explanations for neural networks.
==
The rebuttal has satisfactorily addressed my major concerns.
Claims And Evidence: 1. The paper is motivated by the need for proofs in high-assurance systems. However, the investiga... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable and constructive feedback.
**Improving the motivation behind the work**
We agree that the motivation for obtaining explanations with provable guarantees, as well as the importance of the specific guarantees we provide, can be better articulated. We will ... | null | null | null | null | null | null |
You Always Recognize Me (YARM): Robust Texture Synthesis Against Multi-View Corruption | Accept (poster) | Summary: This paper addresses real-world unknown image degradation that affects deep learning model performance. The authors propose a novel data-centric approach that optimizes the textures of 3D objects to enhance their robustness against corruption. The methodology is based on 3D voxel grid representations reconstru... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ thoughtful feedback and insightful suggestions. We have carefully reviewed all the comments and provide our detailed responses below.
**A1.Regarding writing issues:**
Thank you for pointing out these issues! We will correct these errors in the revised version ... | Summary: This paper proposes a data-centric approach to enhance the performance of deep learning models in the presence of image degradation by utilizing multi-view 3D reconstruction and optimizing for a robust texture. They have shown that a generalized robust texture exists that can transfer across objects of the sam... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ thoughtful feedback and provide our detailed responses below.
**A1.Regarding the specific differences from prior methods:**
Thanks for your question! We acknowledge that our work is inspired by Unadv; however, there are clear differences that distinguish our approach ... | Summary: The paper focuses on the reconstruction of a 3D object from a set of low-quality 2D images. The method applies 15 different corruption techniques during training to the respective images just like a data augmentation technique to make the classifier strong enough independent of the noise introduced. Furthermor... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ thoughtful feedback and insightful suggestions. We have carefully reviewed all the comments and provide our detailed responses below.
**A1.Regarding experiments on ModelNet40 datasets:** Thanks for your question! Please refer to ***A3 to Reviewer eS8T***.
**A... | null | null | null | null | null | null | null | null |
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