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Aligning LLMs by Predicting Preferences from User Writing Samples | Accept (poster) | Summary: This paper introduces PROSE, a method for inferring precise and personalized user preferences to enhance LLM-based writing agents. The approach employs iterative refinement and cross-sample verification to generate more accurate preference descriptions compared to existing techniques. Alongside PROSE, the auth... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions. We appreciate that you find PLUME and our comprehensive evaluation to be valuable contributions along with our learning about the importance of iterative refinement.
**Response to questions**
*Re your question about sorting preference components:* We a... | Summary: The paper introduces PROSE (Preference Reasoning by Observing and Synthesizing Examples), a method for aligning large language models (LLMs) with user preferences inferred from writing samples. It improves upon previous sota/baselines on various aspects, proposes new metrics and preference frameworks, and prov... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions. We appreciate that you find the problem important, our evaluation thorough, and both our algorithm and benchmark improvements to be meaningful contributions.
**Response to questions/”weaknesses”**
**(1a)** Thank you for letting us know the table is con... | Summary: This paper is an extension work following Gao, 2024. Agent alignment is achieved by conditioning on an inferred description of user preferences. Yet, existing methods often lead to generic descriptions that fail to capture the unique, individualized aspects of human preferences. To address this limitation, thi... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions. We appreciate you finding PLUME to be effective and PROSE to be efficient and effective.
**Response to “weaknesses”**
(1) Our contributions are spread across our algorithmic developments in PROSE and our benchmark improvements in PLUME.
In addition ... | null | null | null | null | null | null | null | null |
QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal | Accept (poster) | Summary: The paper proposes Quasi-Potential Reinforcement Learning to tackle environments with asymmetric traversal cost.
Claims And Evidence: The paper claims several contributions:
1. The decomposed asymmetric costs enable Lyapunov-stable policy optimization.
2. Theoretically, it proves QPRL has better sample compl... | Rebuttal 1:
Rebuttal: > In Fig 5, what do the authors mean by "learning curves"? ... is the std or confidence interval used?
The learning curves represent the mean success rate measured over 5 independent runs (each with a different random seed) as a function of environment interactions. The shaded regions (or error b... | Summary: This paper proposes Quasi-Potential Reinforcement Learning (QPRL), a framework that decomposes asymmetric costs into pathindependent potentials and path-dependent residuals, enabling Lyapunov-stable policy optimization. The performance of the proposed QPRL has been validated in some customized classic RL envir... | Rebuttal 1:
Rebuttal: >What's the main different between QRL and QPRL? Just additional potential function s.t. provide Lyapunov stability guarantee? It seems to me that the asymmetric transition is more important, which is the same as QRL
Our work indeed builds directly on the insight from Quasimetric RL (QRL) that ma... | Summary: This paper proposes a new RL algorithm designed to effectively deal with asymmetric traversal costs, for example, when transitions are irreversible or incur different costs in forward and backward directions. The main idea is to decompose asymmetric costs into path-independent potentials and path-dependent res... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful questions. We address your concerns in detail below.
> “The paper lacks clarity on its motivation.”
We appreciate this critique and will revise the introduction to emphasize more real-world scenarios where cost asymmetry is central—**e.g.,** mo... | null | null | null | null | null | null | null | null |
Equivariant Polynomial Functional Networks | Accept (poster) | Summary: A neural functional network takes (the weights and biases of) a neural net as input and predicts some related quantity, such as its expected performance. A central problem is that standard architectures exhibit a variety of symmetries, such as permuting the neurons in fully connected in each layer, or scaling ... | Rebuttal 1:
Rebuttal: **Q1: If you refer to the hyperbolic tangent as "tanh" I suggest you refer to "sine" as you "sin"**
**Q3: I think that in lines 194 and 222 in the right hand column "with" should be "which"**
**Q4: The sentence on line 118 in the right hand column seems a bit garbled**
**Answer Q1+Q3+Q4**. We a... | Summary: This article proposes the design of specific equivariant polynomial functional network.
The authors introduce their notations and some definitions about polynomial terms, that are transformations of the input weights. Their main result states that for some choice of this polynomial map, it is $G$-invariant whe... | Rebuttal 1:
Rebuttal: **W1: The authors present intermediate claims on stability ... Functional Neural Networks thereof.**
**Answer W1.** We kindly refer the Reviewer to our response to **W1+W3** in Reviewer FUc7’s review.
**W2: Also, the notation is particularly heavy, ... to clarify this.**
**Answer W2.** We kindl... | Summary: This paper introduces MAGEP-NFN (Monomial mAtrix Group Equivariant Polynomial Neural Functional Network), a novel neural functional network (NFN) designed to process neural networks as input data. Existing NFNs with permutation and scaling equivariance typically rely on either graph-based message-passing or pa... | Rebuttal 1:
Rebuttal: **W1: The paper could benefit from a more detailed discussion of how their use of stable polynomials ... the broader scientific context.**
**W3: In summary, the paper makes a meaningful contribution to the literature on equivariant neural networks ... would enhance the discussion of its relation ... | Summary: The paper is an extension to Tran et al 2024, by presenting a neural functional network based on stable polynomials. The input to the network are weights of other networks, and the constructed network is equivariant to permutations of its neurons and scaling of the weights. The construction is based on definin... | Rebuttal 1:
Rebuttal: **Q1. I think the main issue is that this paper is written as a follow-up ...**
**Answer Q1.** We appreciate the suggestion from the Reviewer and will include explanations of relevant concepts from prior work in the revised Appendix. Below, we address each part of the question.
**a), b)** In th... | null | null | null | null | null | null |
Differentially Private Boxplots | Accept (poster) | Summary: This paper constructs an algorithm for (pure) differentially private box plots by combining two somewhat recent works on private multiple quantiles (for the box), private extreme quantiles (whiskers), and Laplace noise (# outliers). Its theoretical contributions are a few further results about algorithms from ... | Rebuttal 1:
Rebuttal: Thank you for your careful review! We think most of your concern comes from a miscommunication on our part, see point 2 in the following theoretical claims.
**Theoretical Claims:**
1. The PrivateQuantile algorithm only generates one quantile. So, for PrivateQuantile, we would be applying it $m$ ... | Summary: The paper proposes a new private data visualization in the form of differentially private (DP) boxplots. DP boxplots accomplish this by utilizing three differential privacy mechanisms to privatize the various components of the boxplots – JointExp is used to combute the median and inner quantiles, the unbounded... | Rebuttal 1:
Rebuttal: Thank you for your careful review.
**Gaps and unexplained notions:**
1. We use weak consistency, otherwise known as convergence in probability. We are happy to clarify this in the paper. This means that if $X_n$ converges in probability to $X$ then for all $t>0$, $Pr(|X_n-X|>t)\to 0$ as $n\to\i... | Summary: This paper introduces a differentially private algorithm for creating boxplots. The method specializes in the specific quantiles required for a boxplot (median, quartiles, and extremes for whiskers) rather than treating them as a generic sequence of quantiles, as previous differentially private algorithms have... | Rebuttal 1:
Rebuttal: Thank you for your careful review!
We are happy to cite the papers on medians and range estimation.
**Weaknesses:**
1. DP data visualization poses unique challenges that are not fully addressed by the current literature on DP statistics. For instance, traditional DP statistics focus on numeric... | null | null | null | null | null | null | null | null |
Function-Space Learning Rates | Accept (poster) | Summary: This paper introduces FLeRM, an optimization algorithm that trains a smaller model, records function-space learning rates, and uses those recorded learning rate in the training process of a larger model.
The authors perform experiments on various datasets such as CIFAR-10 and Wikitext-103. They use residual M... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful review!
> ...the proposed algorithm needs to record and store the model parameters at every iteration. This would require double the amount of memory to maintain this buffer...
We agree that if we needed to store an extra copy of the weights at every s... | Summary: This paper provides a novel method to transfer learning rates across model sizes. The approach is very flexible as it leverages monte-carlo estimation of the changes in model outputs under a proposed change in one of the weight matrices. The authors show that their approach can enable consistent optimal learni... | Rebuttal 1:
Rebuttal: Thank you for your positive review noting that we introduce "a novel and flexible approach that would be easy and cheap to implement for the practitioner."
> One potential drawback is the need to train a side by side base model...
This is unavoidable whenever doing any form of hyperparameter tra... | Summary: The paper defines function space learning rate as the rate of change of a neural network's outputs per training iteration. Then, the method FLeRM is introduced for either estimating the per-layer function space learning rate of a model over training, or setting these learning rates (LRs) to fit an arbitrary sc... | Rebuttal 1:
Rebuttal: Thank you for your review, stating that "The paper is really well presented and the experiments are thorough. I am particularly looking forward to the possibility of empirically measuring output dynamics and relating them to various theoretical predictions." (We're looking forward to that too!)
... | Summary: This paper introduces the concept of function-space learning rates, which measure the magnitude of changes in a neural network's output function in response to updates in parameter space. The authors propose an efficient Monte-Carlo-based method to estimate these function-space learning rates and introduce FLe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review with many excellent suggestions, acknowledging that "3. The paper introduces FLeRM, a robust solution for hyperparameter transfer that can be used with any network architecture and at any point during training. 4. The paper extensively evaluates FLeRM in multip... | null | null | null | null | null | null |
An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective | Accept (poster) | Summary: Focusing on federated multi-view learning, this paper presents a novel method to alleviate the dilemma between privacy protection and multi-view clustering performance improvement. The authors conduct both theoretical analysis and empirical evaluations, demonstrating superior performance over baseline methods ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions.
**Q1: In order to analyze their impact to the final performance, could the author provide the hyperparameter experiment about $\mathcal{L}^m$ and $\mathcal{L}_a^m$ of Equation 12? Even if the author do not tune hyperparameter in their experim... | Summary: The paper proposes ESFMC which aims to address the privacy concerns and performance trade-offs in federated learning for multi-view clustering. The main idea is to allow all clients to do collaborative clustering without leaking sensitive data, and they follow a privacy-preserving strategy based on information... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and suggestions that have greatly improved our paper.
**Q1: While the paper focuses on information-theoretic privacy preservation, it does not provide a detailed comparison with other commonly used privacy-preserving techniques in federated learning.**
... | Summary: This paper introduces an effective and secure federated multi-view clustering method from an information-theoretic perspective. The proposed approach preserves privacy while effectively mining complementary global clustering structures. Additionally, the paper provides theoretical analyses of its generalizatio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for these valuable comments.
**Q1: The paper lacks results on large-scale datasets.**
**A1:** We conduct further experiments on the large-scale YoutubeVideo dataset, which contains 101,499 samples across 31 classes, where each sample has three views of cuboids his... | Summary: The paper proposes a novel federated multi-view clustering (FedMVC) method, Effective and Secure Federated Multi-View Clustering (ESFMC), which aims to address the privacy-performance trade-off in federated learning settings. The key contribution of this work is an information-theoretic feature-splitting mecha... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments and suggestions.
**Q1: Have you considered evaluating ESFMC on real-world federated datasets,to better assess its applicability in practical scenarios?**
**A1:** Thank you for your suggestion. We have considered evaluating ESFMC on real-world d... | null | null | null | null | null | null |
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning | Accept (poster) | Summary: This paper tackles ICRL, adding safety constraints in addition to classical IRL.
It claims to stand out by handling unknown environments, unlike many recent ICRL studies that assume known conditions.
The paper focuses on balancing expert imitation with exploration in Inverse Constrained Reinforcement Learning... | Rebuttal 1:
Rebuttal: Dear Reviewer p7jo, we sincerely appreciate your constructive feedback and thank you for recognizing the significance of our work. We have carefully considered your suggestions and hope the following response can address your concerns.
> *Q1. Empirical results ..., showing PCSE outperforming base... | Summary: The paper presents a new exploration approach for inverse constrained reinforcement learning (ICRL). in ICRL, the goal is to identify (safety) constraints and a well-performing policy from expert demonstrations resp. an interactive environment. The paper proposes a theoretically motivated way for efficient exp... | Rebuttal 1:
Rebuttal: Dear Reviewer fGXy, we sincerely appreciate your valuable and constructive comments. We have carefully considered your comments and hope the following responses address your concerns satisfactorily.
> *Q1. ...PCSE (red line) converges much faster and is therefore better than the baselines, but th... | Summary: The authors propose a pair of elegant exploration methods for a variant of the inverse constrained RL problem where one wants to recover the entire set of feasible constraints and provide corresponding sample complexity benefits.
## Update After Rebuttal
The authors added in a discussion of some of the point... | Rebuttal 1:
Rebuttal: Dear Reviewer SXpM, we sincerely value your time and effort in evaluating our work. We appreciate your recognition and have prepared comprehensive responses and clarifications to address each point you raised. We hope these responses can resolve your concerns.
>*Q1. Prior work in ICRL recovers a ... | null | null | null | null | null | null | null | null |
Binary Hypothesis Testing for Softmax Models and Leverage Score Models | Accept (poster) | Summary: The paper addresses the problem of binary hypothesis testing in the context of two important probabilistic models: softmax models and leverage score models. The main contributions and findings of the paper are as follows:
- **Binary Hypothesis Testing for Softmax Models**: The authors study the fundamental pr... | Rebuttal 1:
Rebuttal: We express our deepest gratitude to the reviewer for the time and effort in reviewing our work. Below, we want to respond to the weaknesses and questions.
**Concern 1**: The primary issue lies in the disconnect between the stated motivation—understanding large language models (LLMs) through the s... | Summary: This paper studies binary hypothesis testing in the setting of softmax models and leverage score models. That is, quantifying the number of queries needed to identify an unknown distribution given two possible candidates. Some theoretical analysis shows the lower and upper bound for such problem.
Claims And E... | Rebuttal 1:
Rebuttal: We express our deepest gratitude to the reviewer for the time and effort in reviewing our work. Below, we want to respond to the weaknesses and questions.
**Concern 1**: However, in the latter sections of the paper, the contents are not cycling back to the theme.
**Answer**: We thank you very mu... | Summary: The paper derived orderwise tight upper and lower bounds on the sample complexity of hypothesis testing for softmax distributions (capturing the last layer output) and the leverage score distribution.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: Looks correct.
Experimen... | Rebuttal 1:
Rebuttal: We express our deepest gratitude to the reviewer for the time and effort in reviewing our work. Below, we want to respond to the weaknesses and questions.
**Response to Weakness 1**: We thank the reviewer for raising this important point. Our work intentionally focuses on single-layer softmax and... | null | null | null | null | null | null | null | null |
PAC-Bayes Analysis for Recalibration in Classification | Accept (poster) | Summary: In this paper, the PAC-Bayesian framework is used to analyze recalibration in a multiclass classification setting. Specifically, the bias of estimators of the calibration error is bounded, taking both binning and statistical effects into account. The resulting bounds are used as the basis for new recalibration... | Rebuttal 1:
Rebuttal: We sincerely thank you for your feedback.
## Experimental Designs Or Analyses
### Q.1: Regarding the Gibbs error
Step 5 of Algorithm 1 shows that we take the average over $J$ posterior samples. In contrast, the theory defines bias as the expectation over the hypothesis distribution outside the ... | Summary: The paper presents the PAC-Bayes based analysis of generalisation in recalibration of predictors. Recalibration of predictors to minimise calibration errors is a common task, however to the best of my knowledge, I haven't seen formal PAC-Bayes results on the generalisation aspect of it. Some arguments that I h... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestions. All proposed changes have been incorporated into the main text. Due to the word limit, we cannot provide all revision details here. Please feel free to contact us during the discussion period if you'd like more information.
## Claim and Evidence
... | Summary: This paper provides a PAC-Bayesian analysis of recalibration in multiclass classification, particularly focusing on evaluating and controlling the bias and generalization error of the expected calibration error (ECE) viewed as an estimator of the top-label calibration error (TCE; infeasible to evaluate); see S... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestions. All proposed changes have been incorporated into the main text. Due to the word limit, we cannot provide all revision details here. Please feel free to contact us during the discussion period if you'd like more information.
## Theoretical Claims ... | Summary: Existing recalibration methods either lack theoretical analysis or are limited to binary classification. To address this, the authors first analyze the generalization error and estimation bias of the ECE in multiclass classification, deriving non-asymptotic bounds and identifying the practical optimal bin size... | Rebuttal 1:
Rebuttal: We sincerely thank you for your feedback.
### Q.1: Regarding the key technical innovations of this paper relative to Futami and Fujisawa (2024)
The key difference from Futami and Fujisawa (2024) lies in extending the analysis from binary to multi-class classification. In the binary setting, the ... | null | null | null | null | null | null |
QMamba: On First Exploration of Vision Mamba for Image Quality Assessment | Accept (poster) | Summary: According to this work, it is the first to introduce the Mamba architecture into IQA and proposes the StylePrompt Tuning Mechanism to enhance transfer capability. The proposed method achieves better results with lower FLOPS across multiple datasets.
Claims And Evidence: The proposed method does not achieve le... | Rebuttal 1:
Rebuttal: **Q1: Some clarification about the suboptimal performance on some small datasets, i.e., LIVE, CSIQ, LIVEFB.**
**A1:** Thanks for your great suggestions. We will clarify the reasons for the suboptimal performance of our method on some classical datasets, e.g., LIVE, CSIQ, and LIVEFB with similar p... | Summary: This paper introduces QMamba and LQMamba, a new network architecture based on Mamba for image quality assessment.
QMamba operates through a global scanning approach while LQMamba operates through a local scanning approach.
In addition, a style prompt injector is proposed to adjust mean and variance of the feat... | Rebuttal 1:
Rebuttal: **Q1: Key difference between LQMamba and LocalMamba.**
**A1:** Thank you for pointing this out. We want to clarify that while **LQMamba** is inspired by **LocalMamba**, it differs in *design motivation* and *technical implementation*, specifically with a **hierarchical structure** for image quali... | Summary: In this paper, an algorithm named QMamba is proposed for NR-IQA. QMamba is based on Mamba, but it employs style prompt tuning method to boost its performances with small learnable parameters. Specifically, style prompt tuning consists of two steps: SPG and SPI. In SPG, it generates the style prompt from input ... | Rebuttal 1:
Rebuttal: **Q1: About the reason for relatively lower performance on LIVE and CSIQ Datasets**
**A1:** Thanks for your positive and constructive comments. We will provide a more thorough explanation for this result in the revision from two perspectives:
**(i) Limited Dataset Scale and Diversity in LIVE an... | Summary: This paper proposes a no-reference image quality measure, and specifically it is the first work to explore vision mamba for blind IQA.
Experimental results on task-specific, universal, and transferable IQA tasks demonstrate the advantages of the proposed method.
The whole work is interesting and may be useful... | Rebuttal 1:
Rebuttal: **Q1: About the suggestion to develop an all-in-one model that performs well on all databases.**
**A1:** Thanks for your great questions and impressive suggestions. We have conducted a thorough analysis of the reasons why the differences occur and have raised some proposals for how to design an a... | null | null | null | null | null | null |
From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers | Accept (poster) | Summary: This paper proposes a new graph transformer model using Green and Martin kernels. Specifically, GKSE and MKSE are defined as the structural encoding (SE) using Finite-Step Green Kernel and approximated finite-step Martin Kernel, respectively. They are used as the graph transformer's attention mechanism.
Theor... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive and encouraging feedback. We are glad that the mathematical rigor, clarity, and structure of our paper were positively noted. Below, we address each comment in detail.
---
### 1. Motivation and Problem Statement
We appreciate the suggestion t... | Summary: This paper builds on the framework introduced in GRIT, where a carefully chosen set of relative positional/structural embedding (SE) is defined at the input of the model, which is then updated every layer. Specifically, the paper introduces two random-walk based SEs - GKSE and MKSE, which are well motivated wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our theoretical development, writing quality, and comprehensive benchmarking. We also appreciate the thoughtful concerns raised regarding statistical significance and generalization. Below, we respond to each point in detail.
---
### 1. Clarific... | Summary: This paper proposes two new structural encodings for graph transformers, Green and Martin kernels. These two kernels are some extension of the random walk kind. This paper also demonstrates that the proposed two kernels outperform the existing one.
Claims And Evidence: The theoretical claims are interesting. ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed feedback and critical questions, which allow us to clarify both the motivation and contributions of our work. Below, we address the main concerns.
---
### 1. Limitations of Existing Random Walk-Based SEs
We agree that random walk (RW)-based SEs, such as RRW... | Summary: This paper proposes to utilize green and martin kernels to build new SEs for better graph transformers. The proposed method extends previous RW-based SE such as RRWP in GRIT, demonstrating great empirical accuracy across multiple datasets.
Claims And Evidence: - Both theoretical and empirical analyses of the ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed and thoughtful feedback. Below, we clarify the key motivations, contributions, and empirical implications of our work, while responding to specific concerns regarding novelty, positioning, and evaluation scope.
---
## 1. Novelty Beyond RRWP and Va... | null | null | null | null | null | null |
Fisher Divergence for Attribution through Stochastic Differential Equations | Reject | Summary: The paper introduces a feature attribution framework for deep neural networks using Stochastic Differential Equations (SDEs) and Fisher Divergence. The method models continuous perturbations to explore input spaces and quantifies feature importance by linking Fisher Divergence to the time derivative of Kullbac... | Rebuttal 1:
Rebuttal: Below we address your concerns one by one:
**Experimental Designs Or Analyses:**
Our experiments are conducted on ImageNet (3*224*224). We also provide the detail optimization algorithm in the third reviewer's rebuttal section and the score network training algorithm at the end of this rebuttal.
... | Summary: The paper considers perturbation-based methods for feature attribution. In order to employ a large perturbation space, an SDE is defined. The paper derives a connection between Fisher divergence and the KL divergence, and proposes utilizing the information bottleneck principle for optimization.The paper provid... | Rebuttal 1:
Rebuttal: Below, we address your specific questions and concerns:
**Theoretical Claims:**
Please see in 'Claims And Evidence' part of in the first reviewer’s rebuttal section.
**Experimental Designs Or Analyses:**
Our experiments are conducted on ImageNet, as it is a standard dataset frequently employed i... | Summary: The paper studies the dynamics of the mutual information through SDE with the fisher divergence and the dynamics KL divergence. The computation process is proposed by discretization and the numerical studies apply the proposed framework in the feature attribution in the explainability of neural network.
Claim... | Rebuttal 1:
Rebuttal: Below are our responses to the specific concerns:
**Other Strengths And Weaknesses:**
1. Technical Contribution Clarification:
While it is true that the mutual information dynamics under perturbations build on existing ideas, our work significantly extends these concepts by analyzing general ... | Summary: This paper proposes a perturbation-based feature attribution method, where the input features are perturbed based on a stochastic differential equation (SDE). The proposed framework optimizes an input such that the input has small mutual information with the unperturbed input, and large mutual information with... | Rebuttal 1:
Rebuttal: Below, we address each of your concerns in detail.
**Claims And Evidence**:
Comparison with Theorem 1 in Lyu et al. (2012):
Theorem 1 in Lyu et al. (2012) considers the simple case of the SDE
$
\mathrm{d}Y_t = \mathrm{d}W_t,
$
where the drift is \(\mu(t)=0\) and the diffusion coefficien... | null | null | null | null | null | null |
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs | Accept (poster) | Summary: The paper introduces a novel cryptographic framework aimed at providing verifiable explanations for machine learning models while ensuring model confidentiality. The central idea is to leverage ZKPs and cryptographic commitments to guarantee the correctness of explanations in adversarial settings, where partie... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and the time taken by the reviewer to review our paper. We are glad that the reviewer enjoyed reading the first two sections of the paper clearly presenting the motivation and overall narrative.
Major:
- **Missing References**: Thank you for sending these in... | Summary: The paper proposes to compute model explanations, in particular LIME, using Zero Knowledge Proofs (ZKP). This allows consumers (users) of a service to receive verifiable explanations for their predictions, without the service having to reveal their model and thus preserving their IP. The paper theoretically co... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and the time taken by the reviewer to review our paper. We are glad that the reviewer finds our paper well-written, easy to follow, thinks the proposed problem in our paper is interesting and novel and finds our cryptographic solution theoretically sound with ... | Summary: This paper proposes a solution for operationalizing explanations in adversarial contexts where the involved parties have misaligned interests. The authors focus on LIME and propose a method called ExpProof, which integrates ZKPs to ensure that explanations remain trustworthy while maintaining model confidentia... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and the time taken by the reviewer to review our paper. We are very glad that the reviewer finds our paper well-written, easy to follow, accessible and could learn a lot from it – this is very rewarding!
Next we address your concerns and questions.
Major:
-... | Summary: The paper proposes a protocol with a zero-knowledge proof to ensure that a provided explanation is correct while maintaining confidentiality of the model parameters. In particular, the paper focuses on LIME and standard ZKP libraries. This involves modifying the pipeline so that the protocol is computationally... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and the time taken by the reviewer to review our paper.
Below we address your concerns.
- **Commitment phase** : As mentioned in our paper, commitments are a standard procedure in cryptography and it is well known how to implement this in practice and preexi... | Summary: The paper introduces ExpProof, a system that produces model predictions, explanations for the prediction and proof that the explanations are correct without revealing the model's weights. The key idea is to use cryptographic commitments and zero-knowledge proofs to ensure that a model owner cannot cheat when p... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and the time taken by the reviewer to review our paper. We are glad that the reviewer thinks our paper introduces a new paradigm for explanations, acknowledges that we are the first to integrate ZKPs with an explanation algorithm and finds our paper an interes... | null | null | null | null |
TS-SNN: Temporal Shift Module for Spiking Neural Networks | Accept (poster) | Summary: The paper introduces the Temporal Shift module for Spiking Neural Networks, by utilizing a Temporal Shift operation, the model integrates past, present, and future spike features within a single timestep, aiming to improve the temporal dynamics of SNNs. The key advantage of TS-SNN lies in its ability to model ... | Rebuttal 1:
Rebuttal: ## Questions for Authors
**Performance Discrepancy for ResNet-19 on CIFAR-100 (Timestep=1)**
Thank you for pointing out this discrepancy. As you correctly observed, the performance of our TS-SNN with ResNet-19 on CIFAR-100 under a single timestep lags slightly behind MPBN, whereas it outperforms... | Summary: This paper introduce a Temporal Shift module for SNN called TS-SNN, which enhances the ability of SNNs for temporal information. The TS-SNN consists of two parts. The first part, Temporal Shift module (TS), divides the spike output matrix into C_k groups in the channel dimension, and divides each group into th... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We address your points as follows:
---
## Questions for Authors
**How TS operation when timestep = 1**
As you correctly observed, the performance of our TS-SNN with ResNet-19 on CIFAR-100 under a single timestep lags slightly behind ... | Summary: Research related to spiking neural networks (SNNs) has received increasing attention, but it is still a challenge to strike a balance between time steps and low energy consumption. In this paper, we introduce the Time Shift Module for Spiking Neural Networks (TS-SNN), which integrates past, present, and future... | Rebuttal 1:
Rebuttal: Thank you for this insightful question. Indeed, one of the advantages of the Temporal Shift module is its lightweight and general design, making it applicable beyond classification.
While our current work focuses on image classification due to space constraints and the need for standardized compa... | null | null | null | null | null | null | null | null |
Wyckoff Transformer: Generation of Symmetric Crystals | Accept (poster) | Summary: This paper proposes WyFormer, a novel generative model for materials design that leverages Wyckoff positions to encode space group symmetry. The authors argue that symmetry rules are crucial for determining material properties, and that traditional material discovery approaches are limited by the vastness of t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful feedback. We appreciate their recognition of the **novelty of WyFormer's symmetry–focused and efficient tokenization approach**, **comprehensive evaluation across various metrics and datasets**, and the **explainability** provided by the use of Wy... | Summary: The author uses Wyckoff positions as the basis for structure representation and develops a permutation-invariant autoregressive model based on the Transformer architecture, with the absence of positional encoding.
## update after rebuttal
My concern has been addressed.
Claims And Evidence: Claims made in th... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive review of our submission. We greatly appreciate the time and effort you dedicated to evaluating our work. We are particularly encouraged by your positive feedback on the key aspects of our paper: **reasonable experimental results leading to clear and c... | Summary: The paper proposes a transformer-based model called WyFormer to learn the representation of crystal structures considering their symmetry information. The model represents crystals as discrete tokens encoding space group, chemical elements, and Wyckoff positions rather than using 3D coordinates. Through their ... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and recognition of our main claim that **WyFormer addresses the limitation in existing generative models and achieves better symmetry in generated crystals with competitive stability**, and its support by **reasonably designed experiments**. We are especially ... | Summary: This paper introduces WyFormer, a Transformer-based architecture to generate Wyckoff sites of crystal structures. The key idea is the tokenization approach to convert the structure into a sequence of the space group and Wyckoff sites, and a permutation-invariant auto-regressive transformer for sequence generat... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and for recognizing the strengths of our paper, particularly the **importance of generating high–symmetry crystal structures,** the **well–designed experiments, and the clarity of evidence** they produce!
We appreciate your detailed comments aimed at bettering... | null | null | null | null | null | null |
Language Models over Canonical Byte-Pair Encodings | Accept (poster) | Summary: The submission discusses issues arising from tokenization wherein language models place positive probability mass on sequences unobservable during training. The submission presents approaches for both test time and train time for lessening the severity of this issue.
Claims And Evidence: > Are the claims made... | Rebuttal 1:
Rebuttal: ### General Response
**Clarity and notation improvements**: Several reviewers pointed out that our mathematical presentation, although precise, could be more reader-friendly in various places. In the next revision, we will include more reminders about notation and add several clarifying remarks w... | Summary: This paper considers an issue with language models trained on BPE-tokenized sequences, where they assign positive probability to so-called non-canonical sequences that could not result from the BPE encoding procedure. They give efficient membership tests for canonicality, and several methods for enforcing the ... | Rebuttal 1:
Rebuttal: **Clarity and notation improvements**: Several reviewers pointed out that our mathematical presentation, although precise, could be more reader-friendly in various places. In the next revision, we will include more reminders about notation and add several clarifying remarks where beneficial.
**Ex... | Summary: This paper addresses the problem of non-canonical tokenization, which arises when there are multiple tokenizations that decode to the same sequence of input characters (all of which are assigned some probability by a language model), but only one is ever produced by the deterministic tokenizer. The paper asser... | Rebuttal 1:
Rebuttal: ### General Response
**Clarity and notation improvements**: Several reviewers pointed out that our mathematical presentation, although precise, could be more reader-friendly in various places. In the next revision, we will include more reminders about notation and add several clarifying remarks wh... | Summary: This paper examines a key limitation of current language models: the fact that they allocate probability mass to token sequences that are impossible given their tokenizer. For example, for GPT4's tokenizer, the token sequence __t_, _he_ will never occur (since "the" is tokenized as __the_), yet GPT4 assigns a ... | Rebuttal 1:
Rebuttal: ### General Response
**Clarity and notation improvements**: Several reviewers pointed out that our mathematical presentation, although precise, could be more reader-friendly in various places. In the next revision, we will include more reminders about notation and add several clarifying remarks wh... | null | null | null | null | null | null |
Unified Analysis of Continuous Weak Features Learning with Applications to Learning from Missing Data | Accept (poster) | Summary: This paper proposes a unified theoretical framework for Continuous Weak Feature Learning (or continuous WFL), addressing scenarios where input features are low-quality due to missing data, measurement errors, or ambiguous observations. The authors introduce a novel risk-based formulation specifically tailored ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you dedicated to evaluating our work and are grateful for your insightful feedback and constructive suggestions.
**Practical Implications and Robustness of Mathematical Assumptions:**
We think that our assumptions, such as Lipschitz continuity and bou... | Summary: This paper proposes a unified analysis framework of weak features learning, where part of the features are inaccurate. The paper analyzes generalization performance of a class of learning algorithms, in which a feature predictor $g_j$ is learned for all dimension of "weak feature", and a classifier $f$ is lear... | Rebuttal 1:
Rebuttal: We are thankful for your careful examination of our paper and for their helpful suggestions to improve the clarity and depth of our research.
**Clarification of the Theoretical Contribution of Continuous WFL:**
Thank you for your suggestion. Our main contribution is a unified framework for conti... | Summary: This paper aims to provide a systemical theoretical framework for continuous weak feature learning (WFL). Previous studies focus on discrete WFL while neglecting the continuous weak features. Moreover, they have not addressed the fundamental questions such as the influence of feature estimation and label predi... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and the detailed feedback, which will greatly help us enhance the quality of our paper.
**The importance of continuous WFL:**
Existing research on specific cases of WFL, such as ItR and CFL, has established them as a recognized research area. The discrete WFL... | null | null | null | null | null | null | null | null |
Safety Alignment Can Be Not Superficial With Explicit Safety Signals | Accept (poster) | Summary: This paper studies the problem of safety alignment. Different from previous works that alleviate the problem of superficial safety alignment by data augmentation, this paper proposes a new paradigm with explicit [CLS] safety signals in pretraining and SFT phases. With thorough experiments and analysis, the enh... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and questions. Below, we have summarized the major points raised and our responses:
---
**Q1. The claim that the method “does not increase computation time” would be stronger if training time was reported.**
> We would like to clarif... | Summary: This paper proposes to address the superficiality of safety alignment with explicit safety signals provided by a special token [CLS]. A safety-related binary classification task is integrated into the pre-training and supervised fine-tuning phases, so that the hidden state and the prediction of [CLS] can offer... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments. Below, we have summarized the major points raised and our responses:
---
**Q1. No clear explanation for why a single additional token can improve the safety decision boundary.**
> Thank you for your thoughtful question. Since the ge... | Summary: The authors propose integrating an explicit safety-related binary classification task into the model training process by introducing a [CLS] token at the beginning of each input sequence. This token enables the model to assess both input queries and generated content for safety concerns. The approach leverage... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and questions. Below, we have summarized the major points raised and our responses:
---
**Q1 & Q2: 1) Table 3 bolding is incorrect, as Llama2-7B-Chat performs better than the bolded model. 2) The claim that Mistral-7B-Instruct-v0.2-CL... | Summary: 1. This paper emphasizes, based on previous work, that existing safety alignment is superficial, which causes the model to be vulnerable to adversarial attacks.
2. The paper claims that the reason for the superficiality of current alignment methods is the typical assumption that the model can implicitly learn ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments and questions. Below, we have summarized the major points raised and our responses:
---
**Q1. Table 1 result highlighting is inconsistent with the numerical values**
> We bolded Llama2-7B-CLS (0.3% ± 0%) because a lower ASR indicates ... | null | null | null | null | null | null |
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization | Accept (poster) | Summary: This paper introduces MMedPO, a clinical-aware multimodal preference optimization approach for aligning Medical Large Vision-Language Models (Med-LVLMs). The authors leverage sentence corruption techniques from GPT-4o and a noise interaction process to generate rejected samples. They then use specialized tools... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback to help us improve our paper. **All tables and images referenced in this rebuttal can be found in** https://anonymous.4open.science/r/ICML_rebuttal-0304/README.md
>**Q1**: More diverse clinical datasets could strengthen the evaluation.
**A1**: We conducted e... | Summary: The paper proposed a novel medical large visual language model (LVLM) preference optimization alignment method called MMedPO. To solve the hallucination issue in the regular preference data curation process, MMedPO uses existing VLM and LLM to create depreferred data with better clinical correspondence. It fur... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and suggestions. **All tables and images referenced in this rebuttal can be found in** https://anonymous.4open.science/r/ICML_rebuttal-0304/README.md
****
>**Q1**: The performance improvement with SFT is much smaller.
**A1**: As clarified in Section 4.2, w... | Summary: This work proposes MMedPO, a DPO-based Preference Alignment paradigm that aims to let LVLMs provide more accurate and expertise textual responses to X-ray/medical images. The authors design a way to curate preferred-and-dispreferred responses using hallucination-inducing noisy images, and proposes a quantified... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for your valuable feedback. Below, we address your concerns point by point. We would appreciate it if you could let us know whether your concerns are addressed by our response. **All tables referenced in this rebuttal can be found in** https://anonymous.4open.... | Summary: This paper focuses on aligning Medical Vision-Language Models (Med-LVLMs) with clinical-aware multimodal preference optimization to improve factual accuracy and reduce hallucinations. The authors identify modality misalignment as a major issue, where models prioritize textual knowledge over visual input, leadi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback to help us improve our paper. We detail our response below and please kindly let us know if our response addresses your concerns. **All tables referenced in this rebuttal can be found in** https://anonymous.4open.science/r/ICML_rebuttal-0304/README.md
****
>**... | null | null | null | null | null | null |
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability | Accept (poster) | Summary: This paper studies online decision-making under information asymmetry and knowledge transportation. They formulate this problem using strategic MDP where an principle interacts with a sequence of myopic agents whose can impact the reward functions and transition kernels. The goal is for the principal to design... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions!
**Regarding the technical novelty compared to [1]**: In contrast to Yu et al. [1], this work studies the **online** strategic interaction model. Below, we briefly highlight the key technical novelties, with further details provided i... | Summary: This work considers a principal-agent RL framework, where the reward and transitions also depend on the unobserved action.
In this framework, the authors propose an algorithm that will learn the principal rewards, but also --- and this is the challenging part because of the fact that only partial, cofounding ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions!
**Regarding the limitations of standard online RL algorithms in our framework**: The core reason standard online RL algorithms cannot be directly applied is that the online strategic interaction model under the source agent distribut... | Summary: This submission investigates online strategic decision-making in multi-agent environments characterized by information asymmetry and knowledge transportability. Specifically, it addresses the challenge of learning optimal decision policies when agents have private information that introduces confounding factor... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and feedback!
**Regarding the Markov policy class**: We focus on the Markov policy class because any online strategic interaction model has an **optimal Markov policy**. This follows from the fact that an online strategic interaction model is equivalent to a... | Summary: The authors consider a principal-agent problem where the principal interacts with a sequence of strategic agents with private types drawn from a different distribution than one the principal has information about. The principal's reward depends on unobserved confounders, so the authors propose using an instrum... | Rebuttal 1:
Rebuttal: Thanks for your useful comments and suggestions!
**Regarding the minimax estimators for IV**:
We employ F-R duality to formulate a minimax estimator instead of using DeepIV or DFIV for the following reasons:
1. Our primary objective is to design **provably sample-efficient exploration algorith... | null | null | null | null | null | null |
Learning with Exact Invariances in Polynomial Time | Accept (spotlight poster) | Summary: Building from results on Riemannian manifolds, group theory, and the spectral properties of the Laplace-Beltrami operator, the authors propose a learning algorithm to minimize the population risk in the class of Sobolev space (s times differentiable for s >= 2d where d is the dimension of the input space, here... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of our work’s merits and their valuable comments.
> The claim that the proposed algorithm produce a predictor that achieves exact invariance in time logarithmic in the size of the symmetry group is supported by two results: a nice property of groups (provi... | Summary: The paper addresses the challenge of learning with exact invariances (symmetries) in kernel regression. Traditional methods either fail to provide polynomial-time solutions or are not applicable in the kernel setting. The authors propose a polynomial-time algorithm that achieves exact invariances using oracle ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging the merits of our work and for raising interesting questions.
> The algorithm relies on oracle access to the geometric properties of the input space, which may not always be available in practical applications.
**Answer:** Thanks for mentioning this... | Summary: The paper suggested that, in Kernel Ridge Regression (KRR) under certain assumptions including the kernel space, achieving exact invariance of the kernel function through group averaging is feasible in polynomial time. This is done by using a finite number of bases derived from the constrained spectral method ... | Rebuttal 1:
Rebuttal: Thanks a lot for your valuable feedback and constructive comments regarding additional experiments. While we mention that we are committed to including additional detailed experiments in the camera-ready version of the paper, we would like to emphasize that the main focus of this work is **theoret... | Summary: This paper investigates the statistical-computational trade-offs involved in learning with invariances, particularly in the context of kernel regression. While the Kernel Ridge Regression (KRR) estimator can be applied to this problem, it lacks invariance unless combined with group averaging, which is computat... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive feedback. We’ve done our best to address your concerns in detail below, and we hope this will support a more favorable evaluation and score of our work.
> Conducting more numerical experiments could help demonstrate the proposed algorithm's ... | Summary: This work addresses the problem of learning exactly invariant models in the kernel regression setting. Given an assumption on the smoothness of the target function, they propose an algorithm for computing an invariant estimator in polynomial time with respect to the number of samples and polylogarithmic with r... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive and constructive feedbacks.
> The paper only provides a simple experimental evaluation, which, as stated above, focuses on a single comparison with KRR. A possible interesting addition would be to provide comparisons with other invariant methods such as group avera... | null | null | null | null |
Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach | Accept (poster) | Summary: This paper presents a new framework for contextual optimization problems where predictive models may not perfectly capture the true underlying relationships. Unlike traditional methods that assume the model is well-specified, this approach introduces a new surrogate loss function designed to ensure that even w... | Rebuttal 1:
Rebuttal: We thank the reviewer for these thoughtful questions. First, we believe that the paper the reviewer mentioned (Adam N Elmachtoub, Henry Lam, Haixiang Lan, and Haofeng Zhang. Dissecting the impact of model misspecification in data-driven optimization) is indeed relevant to our work, and aim to ment... | Summary: The paper addresses the case of hypothesis class misspecification and proposes a new contextual optimization framework that ensures both tractability (via regularizing) and generalizability.
post-rebuttal: I thank the authors for their response and explanation on generalizability of their ideas. I am keeping ... | Rebuttal 1:
Rebuttal: We thank the reviewer for this thoughtful question. While we focus on a linear hypothesis class $\mathcal{H}$ in the later part of Section~3.3 for clarity and analytical tractability, the core ideas and surrogate loss formulation extend well beyond the linear setting.
In particular, suppose $ \Ph... | Summary: This paper considers misspecification in the contextual optimization problem, or the predict-then-optimize problem. The authors use a toy example to illustrate the failure of some existing approaches, such as SPO+ and SLO, when the hypothesis class for the prediction part is misspecified. Then, to address this... | Rebuttal 1:
Rebuttal: We thank the reviewer bringing up the work of Sun et al. [2023], which proposes a novel approach for learning a cost predictor by maximizing the optimality margin—ensuring that the reduced cost of the predicted solution is positive in the ground-truth optimal basis.
This method can lead to robust... | Summary: The paper proposes a new optimization surrogate for contextual linear optimization which is a hard problem due to the nonconvexity of the loss function. The newly proposed surrogate is also non convex but is a difference of convex functions. They prove generalization bounds for their surrogate relative to the ... | Rebuttal 1:
Rebuttal: Replies to initial remarks: we thank the reviewer for their remarks.
- About Proposition 2.1: In binary classification, we aim to maximize the cost. Hence, if we make a cost prediction $\hat{c}$, then we solve $\min -\hat{c}^\top w $ to make a decision. Consequently, in the minimization setting,... | Summary: In contextual optimization, real-world settings often suffer from model misspecification, meaning the chosen predictor family does not include the true cost function. While existing contextual optimization literature largely focused on well-specified models, this paper tackles that gap by introducing a surroga... | Rebuttal 1:
Rebuttal: Thank you for there insightful questions. Here are our answers in order.
1. We thank the reviewer for this thoughtful question. While we focus on a linear hypothesis class $\mathcal{H}$ in the later part of Section~3.3 for clarity and analytical tractability, the core ideas and surrogate loss for... | null | null | null | null |
Temporal Query Network for Efficient Multivariate Time Series Forecasting | Accept (poster) | Summary: The paper presents a new model to do point-forecasting of multivariate time series: TQNet.
TQNet combines a single attention layer to handle multi-time-steps interactions and an MLP to handle multi-channel interactions. The main novel contribution of TQNet is that the attention layer doesn't use the input to ... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review! We must apologize for the concise response earlier due to length limits.**
> Summary: TQNet combines a single attention layer to handle multi-time-steps interactions and an MLP to handle multi-channel interactions.
Sorry for any unclear point... | Summary: This paper introduces the Temporal Query Network (TQNet) to address multivariate time series forecasting (MTSF) tasks. At its core, it employs periodically shifted learnable parameters to model more stable inter-variable correlations adaptively. Extensive experiments are conducted to demonstrate the effectiven... | Rebuttal 1:
Rebuttal: **Thank you very much for your detailed review!**
> W1: The paper only provides results for multivariate-to-multivariate forecasting.
We have further supplemented the comparison between TQNet and baseline models in the multivariate-to-univariate forecasting scenario. **The results show that TQNe... | Summary: This paper proposed Temporal Query technique for multivariate time series forecasting framework, which is aiming at capturing optimal representations of inter-variable relationships. The lightweight improvement show advanced performance on real-world datasets and can be integrated easily to existing models.
C... | Rebuttal 1:
Rebuttal: **Thank you for your insightful review!**
> Claims And Evidence and Essential References Not Discussed.
Thanks for pointing this out. Indeed, changes in time scales can cause variations in inter-variable correlations, and in fact, Figure 1 of TQNet also demonstrates this. The key differences ar... | Summary: This paper proposes Temporal Query Network (TQNet), a new approach for multivariate time series forecasting (MTSF). The key idea is the Temporal Query (TQ) technique, where periodically shifted learnable vectors serve as the query in a single-layer multi-head attention (MHA) module. TQ provides one vector per ... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comments!**
> C1: TQ is fixed across different samples, whereas per-example correlations differ from global correlations.
This concern is valid in general, but it is effectively addressed within the TQ-MHA mechanism. In TQ-MHA, **the learnable shifted TQ serves *on... | null | null | null | null | null | null |
Neural Solver Selection for Combinatorial Optimization | Accept (poster) | Summary: This paper proposes an ensemble framework to select appropriate neural solvers from the solver pool for each instance to solver. This framework includes a feature extraction step to extract instance-level features. Based on the features, a selection model alongside several selection strategies is proposed to s... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We sincerely appreciate your agreement on the significance of our neural solver selection framework. Here are the detailed responses to your questions.
1. In Other Strengths And Weaknesses, weakness 1, **“the number of collected models are not strictly consiste... | Summary: The authors propose to train a neural network that selects the most appropriate solver to use on a given instance. They tested their method on TSP and CVRP problems using a pool of state-of-the-art neural solvers. This solver selection is effective and allows a better tradeoff between computation-time and opti... | Rebuttal 1:
Rebuttal: Thank you for your positive review and constructive comments. Below please find our responses. **Corresponding experimental results** can be found at [link](https://anonymous.4open.science/api/repo/9356_rebuttal-D4F5/file/addtional_results_9356.pdf).
**R1: How the model selection behaves on large... | Summary: The paper proposes a framework to coordinate neural solvers for combinatorial optimization problems (COPs), addressing the complementary performance of individual solvers across instances. It introduces a three-component framework: (1) feature extraction using graph attention networks or hierarchical encoders,... | Rebuttal 1:
Rebuttal: Thank you for your valuable and encouraging comments. We sincerely appreciate your agreement on the effectiveness of the neural solver selection framework in our paper, which, we believe, has the potential to be a new branch of techniques for the application of NCO solvers. We summarize the concer... | Summary: This submission introduces a framework for intelligently coordinating multiple neural solvers to tackle combinatorial optimization problems (COPs). The core idea involves feature extraction from problem instances, training a selection model to identify the most suitable solver, and employing robust selection ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We are delighted to learn that you generally appreciate the core idea of our work, and we sincerely value your insightful comments. However, we believe there may be some misunderstandings regarding certain aspects of the paper, which we would like... | null | null | null | null | null | null |
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain | Accept (poster) | Summary: This paper presents an active learning methods to select the most informative examples in the training samples within a fixed budget, with the idea of consider greedily optimise the fisher information over the Hessian matrix with respect to the LLMs. To solve the issue under high-dimensional and large sample s... | Rebuttal 1:
Rebuttal: We wanted to thank the reviewer for positive evaluation and appreciating that we bring classic ideas from statistical learning theory to LLMs. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional ... | Summary: The paper presents a statistical approach to enhancing supervised fine-tuning efficiency through strategic training example selection, which stands in contrast to conventional random sampling methods. By conceptualizing the example selection problem as an optimal design task that maximizes the Hessian of the L... | Rebuttal 1:
Rebuttal: We wanted to thank the reviewer for positive evaluation, recognizing our contributions, and bringing up the prompt bias issue to our attention. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional... | Summary: The paper studies the problem of data selection for the training (in particular, fine-tuning) of autoregressive language model. The data selection regime is based on an efficient estimation of a lower bound of the empirical fisher matrix. This estimation is then combined with a greedy optimal design algorithm ... | Rebuttal 1:
Rebuttal: We wanted to thank the reviewer for positive evaluation, and recognizing the practicality of our solution as well as the importance of the solved problem. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have... | Summary: This paper casts the problem of data pruning for fine-tuning an LLM via SFT as one of optimal experiment design. In optimal experiment design, one wants to "probe" a system in a way that the combination of the probes you use are most effective in allowing you to extract the designed information from the system... | Rebuttal 1:
Rebuttal: We wanted to thank the reviewer for positive evaluation and clearly summarizing the main technical contributions of our work. Our rebuttal is below. We focus on major issues and will incorporate all comments of the reviewer in the next version of our paper. If you have additional concerns, please ... | null | null | null | null | null | null |
Masked Autoencoders Are Effective Tokenizers for Diffusion Models | Accept (spotlight poster) | Summary: This paper proposes to use masked autoencoder for reconstruction and verify it works better for diffusion model generation compared to AE and VAE.
Claims And Evidence: It's a bit contradictive between figure 2 and 4, from Figure 4, AE seems to have fewer GMM mode, i.e. it's more concentrated on one mode.
Met... | Rebuttal 1:
Rebuttal: We thanks for your time reviewing this paper and suggestions on additional ablation studies and comparison results.
---
> It's a bit contradictive between figure 2 and 4.
Thanks for your question.
- **Figure 4 and Figure 2 are aligned**. Figure 4 shows that the latent space of AE is more co... | Summary: This paper analyzes how to develop a good image tokenizer. The authors bridge the GMM model and the quality of the latent space for generation and provide interesting discussion. Based on their investigation, they introduce MAETok to regularize the latent space with mask modeling when training tokenizer and ac... | Rebuttal 1:
Rebuttal: Thanks for your time and efforts reviewing our paper. We now address the raised questions as follows.
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> Does the evaluation in Figure 2 perform at the same latent size? The authors should provide more detailed experimental settings about this experiment.
Sorry for the confusion. In Figure... | Summary: In this work, the authors find that the latent space with fewer modes and more discriminative features are better for training latent diffusion models. They propose a masked autoencoder for learning the latent space, where the decoder is later finetuned with encoder frozen, and achieve state-of-the-art FID.
C... | Rebuttal 1:
Rebuttal: Thanks for your suggestions on reviewing our paper. We now address the questions raised as follows.
---
> While I wonder that, a more discriminative latent leads a lower FID, would this be related to how FID is computed? If a latent contains more discriminative information, it may also be refl... | Summary: This paper studies the properties of latent space for diffusion models, and claims that a more discriminative latent space (fewer Gaussian mixture modes) enable more effective diffusion learning and generation quality. Specifically:
1. The paper conducts both empirical and theoretical analysis to show that the... | Rebuttal 1:
Rebuttal: Thanks for your time and efforts reviewing our paper. We now address the raised questions as follows.
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> Why would it be more difficult to apply linear CFG scheme on semantically rich latent features?
Thanks for this interesting question.
We believe the limitation here is to apply the li... | null | null | null | null | null | null |
How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective | Accept (poster) | Summary: The authors explore the theoretical performance of distributed diffusion models, particularly in environments where computational resources and data availability vary across workers. The authors establish a generation error bound for distributed diffusion models under resource constraints, demonstrating a line... | Rebuttal 1:
Rebuttal: We thank the Reviewer YCna for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to “Theoretical Claims”:** We agree that supplementing technical proofs with intuitive explanations greatly enhances accessibility. And we have added some explanati... | Summary: This paper presents a theoretical analysis of distributed diffusion model training in scenarios where computational resources and data availability vary across workers. Traditional single-worker diffusion models assume uniform resources and centralized data, which are impractical in distributed settings. To ad... | Rebuttal 1:
Rebuttal: We thank the Reviewer ySb9 for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to “Essential References Not Discussed”:**
We appreciate the reviewer’s insightful suggestion. While our work primarily focuses on theoretical contributions, we agre... | Summary: In this work, the authors investigate the impact of distributed collaboration on diffusion model training in environments with heterogeneous computational resources and data availability. It establishes the first theoretical generation error bound for distributed diffusion models, demonstrating a linear relati... | Rebuttal 1:
Rebuttal: We thank the Reviewer grcd for the time and valuable feedback! We would try our best to address the comments one by one.
**Response to “Other Weaknesses 1” ＆ “Other Comments Or Suggestions 1”:**
We thank the reviewer for this constructive feedback. We agree that the proofs in Lemma 4.6 and Theore... | Summary: This theoretical paper analyzes the possibilities of distributed training of diffusion models The authors propose a new, privacy-preserving approach to distributed training of diffusion models and present a proof of the error bound. They further analyze hyperparameter adjustments to improve performance in this... | Rebuttal 1:
Rebuttal: We thank the Reviewer hsmD for the time and valuable feedback! We would try our best to address the comments one by one.
**In response to the concern about the outputs of diffusion models**, we have provided additional Figures 4-6 in Appendix E.3, which can also be found at the anonymous link: ht... | null | null | null | null | null | null |
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts | Accept (poster) | Summary: The paper introduces the Dual-balance Collaborative Experts (DCE) framework to address imbalanced domain-incremental learning. The key challenges tackled are intra-domain class imbalance and cross-domain class distribution shifts. DCE employs two main components: (1) frequency-aware experts trained with speci... | Rebuttal 1:
Rebuttal: **[Reviewer xasy (Claims1-3), N4e7 (Q4), udEf (W1, Q2)]: Questions on many/medium/few-shot class division**
Thank you for your questions. Reviewer xasy and N4e7 raised similar concerns, which might be due to our explanation of dataset division being placed in Appendix E.2. The division of many/me... | Summary: This paper introduces a practical task, Imbalanced Domain-Incremental Learning, which involves both intra-domain class imbalance and cross-domain class distribution shifts. To address this task, the authors propose the Dual-Balance Collaborative Experts (DCE) framework, which leverages a multi-expert collabora... | Rebuttal 1:
Rebuttal: Thanks for your suggestions.
**[Reviewer xasy(Q), N4e7(E1)] Scalability**
We analyze scalability from two aspects: memory consumption and computational efficiency.
- Memory Consumption:
In incremental learning, it is common to retain certain past task information to mitigate forgetting. Am... | Summary: This paper addressed the problem of imbalanced domain-incremental learning, where the imbalance includes intra-domain class imbalance and cross-domain class distribution shifts. A Dual-Balance Collaborative Experts (DCE) framework is proposed, which first trains frequency-aware expert networks separately to mi... | Rebuttal 1:
Rebuttal: Thank you for your suggestions to help us improve the paper.
**W2**: Our paper does not claim that "integrating new patterns for the new domains will not influence the learned patterns in previous domains." Instead, our goal in imbalanced DIL is to strike a balance between the two.
As detailed i... | Summary: The paper introduces Dual-Balance Collaborative Experts (DCE), a novel framework designed to address two key challenges in domain-incremental learning (DIL) under class-imbalanced conditions:
1. Intra-domain class imbalance, where some classes have significantly fewer samples than others within the same domai... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their suggestions, which enhanced our paper.
**[Reviewer xasy(Claims1-1,Claims1-2), eXBQ(E3)] Effectiveness of multiple experts.**
In Section 5.3, we discussed the effectiveness of multiple experts. To address the reviewers' concerns more thoroughly, we conduc... | null | null | null | null | null | null |
GraphFLEx: Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s | Reject | Summary: This paper proposes a graph structure learning framework GraphFLEx for large and expanding graphs, which consists of three modules: graph clustering, graph coarsening and graph learning. By leveraging clustering and coarsening, it improves the efficiency by restricting possible connection to only relevant node... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful feedback and for recognizing the potential of our work. We appreciate the reviewer’s positive response and for highlighting areas where the manuscript can be further improved.
**W1 and Q1)** We would like to clarify that along with *K-means* an... | Summary: This paper addresses graph structure learning, a critical challenge in graph machine learning. In contrast to standard strategies, the proposed solution, GraphFLEx, is particularly effective in large, expanding graph scenarios. By leveraging clustering and coarsening techniques, GraphFLEx significantly reduces... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and for recognizing the potential of our work. We appreciate the detailed insights and suggestions for improvement.
**Missing references:** Thankyou for pointing this out. We have added Table1 comparing vanilla SUBLINE[2] vs. GFLEx, where GFLEx si... | Summary: The article proposes a new framework for graph structure learning in large and expanding graphs. The key challenges addressed include the high computational costs and memory demands of existing methods, especially when dealing with dynamically growing graphs.
Claims And Evidence: A formal complexity analysis ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and for recognizing the potential of our work. We appreciate the detailed insights and suggestions for improvement.
**Complexity**: We clarify that Sec3.6 and Table2 breaks down the complexity for both (a)best & (b)worst scenarios, highlighting ea... | null | null | null | null | null | null | null | null |
Modularized Self-Reflected Video Reasoner for Multimodal LLM with Application to Video Question Answering | Accept (poster) | Summary: This paper enhances the interpretability and reasoning capabilities of Multimodal Large Language Models (MLLMs) in video question answering.
The authors propose a modular system that constructs explicit reasoning paths and extracts precise spatial-temporal information.
The framework is further optimized using ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking time to review our paper and providing insightful feedback and suggestions. We address the weaknesses and questions as follows:
### **Methods And Evaluation Criteria**
#### **Q1 Lack of Novelty**
We highlight the contributions of our paper as follows: ... | Summary: The paper addresses the interpretability problem in VideoQA by introducing the Modularized Self-Reflected Video Reasoner (MSR-ViR) framework, which decomposes complex questions into smaller parts through its Modularized SpatialTemporal Grounding (MoST-Grounding) module and employs a reinforcement learning-base... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking time to review our paper and providing insightful feedback and suggestions. We address the questions as follows:
### **Supplementary Material**
We will open-source our code after the review and provide detailed documentation to facilitate the reproducti... | Summary: This paper introduces MSR-ViR, a novel framework designed for interpretability of video question answering. Multiple modular networks are integrated wth a multimodal large language model in the proposed method.
To refine its reasoning, the framework utilizes an Alternate Self-reflection Training Strategy, op... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking time to review our paper and providing insightful feedback and suggestions. We address the weaknesses and questions as follows:
### **Claims And Evidence**
1.The Question Parser's prompt is meticulously crafted. It details each module's function and inc... | Summary: The paper introduces a framework MSR-ViR designed to improve interpretability of multimodal LLMs in VideoQA. Unlike traditional end-to-end multimodal LLMs that function as black boxes, MSR-ViR integrates modular networks to provide explicit reasoning paths. MSR-ViR serially combines (1) a question decomposer t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking time to review our paper and providing insightful feedback and suggestions. We address the weaknesses and questions as follows:
### **Claims And Evidence**
Providing interpretable reasoning paths for black-box Multimodal LLMs in the scenario of VideoQA ... | null | null | null | null | null | null |
Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective | Accept (poster) | Summary: This paper proposes a new way to solve the ARC tasks. It first employs a depth-first search algorithm to generate diverse, high-probability candidate solutions for the ARC tasks, then applies an LLM to not only act as a generator but also as a scorer, using its output probabilities to select the most promising... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and review, and we address each of the points raised below and improve the clarity of the manuscript in the next revision.
Regarding why the autoregressive nature leads to the mentioned problem: (from the paper: “However [...] the highest probability solution ... | Summary: This paper presents a novel approach to solving the Abstraction and Reasoning Corpus (ARC-AGI) challenge, which tests abstract reasoning abilities in AI systems. The authors achieve SOTA performance for open-source models with a 71.6% accuracy rate (286.5/400 solved tasks) on the public ARC-AGI evaluation set.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review.
**Regarding DFS vs BFS**:
The output of our generation process would be the same if we use DFS or BFS, as only the paths with sufficiently high sampling probability are kept. However, using BFS would make our optimizations harder. During generat... | Summary: The paper describes a system for the ARC challenge, based on using data augmentations.
The augmentations are basic transformations of the images (rotation, reflection, shuffling of the example order and permutation of colors) that can be applied to both the input problem and the solution, such that the transfo... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their careful evaluation, interesting questions and positive feedback. We appreciate the detailed questions and suggestions, which will help us improve both the clarity and presentation of our work. Below, we address each of the reviewer’s points:
Bayesian Framework: We ... | Summary: Authors propose a new approach to solve ARC-AGI challenge. In particular, authors train an LLMs to generate diverse, high probability solutions using augmented training data. Authors define an augmentation transformation for ARC-AGI dataset, which include rotations
and reflections of each task, shuffling of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable feedback. We appreciate the detailed comments, and we address each of the points raised below.
First of all, we appreciate the reviewer’s careful observations regarding formatting issues. We will revise the manuscript to address inconsistencies su... | null | null | null | null | null | null |
Preserving AUC Fairness in Learning with Noisy Protected Groups | Accept (poster) | Summary: UPDATE AFTER REBUTTAL:
Thanks for the rebuttal. Due to the additional experiments a) better visualizing the fairness/accuracy tradeoffs and b) baselining against CLIP labelling, I'm increasing my score from 2 to 3.
########
This paper proposes an approach to learning models that improve on a fair-AUC metric ... | Rebuttal 1:
Rebuttal: **Claims And Evidence**.
1) **Fairness accuracy trade-off**. The formulation Eq. (7) introduces no explicit hyperparameter balancing fairness and utility. Instead, Eq. (9) uses Lagrangian multipliers $\lambda_{z,z'}$ learned **automatically via our minimax optimization**, eliminating the need for ... | Summary: The authors consider fair AUC optimization problem. They consider a problem where the sensitive attributes are noisy, which is quantified by a distribution shift. They employ robust optimization technique to account for possible shift in the distribution.
The optimization method is rather involved and utilise... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for providing valuable input. We are glad to answer them below.
**Cons**
1) **Formulation**. Our method models the **feature distribution conditional on group**, i.e., $X|Z$, which is standard in group fairness literature (e.g., Hardt et al.... | Summary: This paper proposes a robust AUC fairness approach under noisy protected group with fairness theoretical guarantees using distributionally robust optimization. Also, experiments have been implemented on tabular and image datasets.
Claims And Evidence: No. The submission claims that their approach has fairness... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for providing valuable input. We are glad to answer them below.
**Claims And Evidence**. We would like to clarify that our paper **does not aim to provide convergence guarantees** for Algorithm 1. The goal of Section 4.4 is to describe the *... | Summary: The paper addresses the critical problem of preserving AUC fairness in machine learning models when protected group labels are noisy. The authors propose a novel distributionally robust optimization (DRO) approach with theoretical fairness guarantees, which bounds the Total Variation (TV) distance between clea... | Rebuttal 1:
Rebuttal: We thank you for taking the time to read our paper and for providing valuable input. We are glad to answer them below.
**Other Strengths And Weaknesses**. Thank you for your suggestion. We will add a discussion about the potential extensions of our methods to multi-class classification settings.... | null | null | null | null | null | null |
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning | Accept (poster) | Summary: This paper focuses on the failure of model-based reinforcement learning (MBRL) in sparse-reward and long-horizon environments, emphasizing that the key to addressing this issue lies in generating data that incorporates temporal information. To tackle this challenge, this paper introduces a novel MBRL framework... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews and insightful feedback about this work. Below, we describe how we have revised the paper to address the reviewer's concerns and questions.
---
# Experimental coverage of baseline algorithms
We thank the reviewer for pointing this out. In our work, w... | Summary: This paper proposes TempDATA, a new offline model-based reinforcement learning (MBRL) method that learns a temporal-distance-aware autoencoder model, a latent dynamic model, and an offline policy. Specifically, TempDATA first trains the autoencoder and latent dynamic model to possess the temporal-distance attr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort. Here are our answers to the reviewer's comments.
---
# Performance according to dataset
We agree with the concern raised by the reviewer. To address the concern, we ran additional experiments by gradually reducing the offline dataset coverage for the ... | Summary: This paper addresses the challenges of offline model-based reinforcement learning, particularly in sparse reward and long-horizon environments. The authors propose Temporal Distance-Aware Transition Augmentation (TempDATA), a novel method that generates additional transitions in a geometrically structured repr... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's thorough review and valuable suggestions about this work. Below, we outline how we have revised the paper to address the reviewer's concerns and questions.
-----
# Generalizability
We acknowledge the importance of demonstrating applicability beyond long-horizon ... | Summary: This paper presents an offline model-based reinforcement learning algorithm called TempDATA. The algorithm aims to tackle goal-conditioned tasks with long horizon and sparse task-completion reward.
----
The main idea is to learn an embedding space which that enables the computation of a temporal distance meas... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback.
---
# Main contribution and reward shaping
We agree with the reviewer’s observation that TempDATA involves multiple components and that it is important to delineate our main contribution clearly. Our main contribution lies in temporal-distance-aware... | null | null | null | null | null | null |
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs | Accept (poster) | Summary: The paper presents an approach that utilizes a fine-tuned LLM to generate adversarial suffixes for adversarial prompting of another LLM. The suffixes are interpretable by humans and are appended to the harmful prompts. In practice this makes it often possible to successfully attack the target LLM, which then d... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review. We address your concerns below.
---
> *There is not convincing evidence to say the method is state of the art in terms of the attack success rate. There is the BEAST method (ICML’24)....*
While we understand your concerns regarding the ASR perf... | Summary: This paper introduces AdvPrompter, a learning-based method for efficient jailbreak prompting. Unlike search-based attacks, it trains a model to generate adversarial suffixes directly, improving speed and transferability. Experiments on AdvBench and HarmBench show competitive ASR, low perplexity, and strong bla... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for highlighting both the strengths and potential areas of improvement in our work. Below, we address the specific concerns related to baselines, robustness comparison, training setting, and evaluation methodology.
---
> *Missing baseline: Previous work [... | Summary: This paper proposes a method for quickly generating adversarial prompts for large language models. Their method relies on a language model which they pre-train to effectively generate adversarial prompts for other target models using tokens that appear natural (i.e. low perplexity). They train AdvPrompter by o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. Below, we respond to the questions regarding perplexity baselines, efficiency comparisons, and readability.
---
> *"However, they do not present baseline comparisons to un-attacked text, which would demonstrate what effect the att... | Summary: This paper proposes AdvPrompter, a jailbreak prompt generation method that creates adversarial suffixes using another LLM. AdvPrompter uses an iterative approach consisting of AdvPrompterOpt, a method that generates adversarial suffixes, which are then used to conduct supervised fine-tuning. The authors state ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive evaluation. Below, we address the questions regarding model choice and the safety-finetuning setup.
---
> *"Why is Vicuna used as the transfer model for blackbox attacks? How do results look like when other models are used?"*
Than... | null | null | null | null | null | null |
Circumventing Backdoor Space via Weight Symmetry | Accept (poster) | Summary: The paper highlights the vulnerability of deep neural networks to backdoor attacks, which can compromise model integrity and lead to unauthorized access or malfunction. The proposed method TSC leverages the concept of weight symmetry to purify models. It trains a quadratic Bezier curve in the parameter space, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments! We address your concerns as below.
**W1: Adaptive Attack Design**
Due to space limitations, the initial version includes implementation details and the design of the adaptive attack in **Appendix F**, with the attack process outlined in **Algorit... | Summary: This paper introduces a backdoor purification method called Two-stage Symmetry Connectivity (TSC). The approach is devided into two stages, aiming to use permutation invariance and mode connectivity to circumvent backdoor spacenwhile maintaining clean accuracy. The method is designed to be applicable beyond su... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments! We address your concerns as below.
**W1.Assumption of TSC and Advanced attacks**
The assumption that "benign and backdoor samples occupy significantly different loss landscape basins" does not fully correspond to our method. Instead, the underlyi... | Summary: The authors proposed an extension to Mode Connectivity Repair (MCR) [Zhao et al. (2020)]. The task is to purify a backdoored model using a small number of clean samples. MCR uses the poisoned model and a fine-tuned model to find an intermediate model that can lower the attack effectiveness.
The proposed model... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments! We address your concerns as below.
**Theoretical Claims: Lipschitz Condition**
The Lipschitz condition required in Theorem 4.2 and Corollary 4.3 specifically applies to the loss and activation functions, which is realistic and commonly satisfied ... | Summary: This paper proposes a new method for removing backdoor attacks from trained models post-training. Specifically, it maps the network to a different basin resulting in an functionally equivalent model and then shows that the bezier curve that cnnects the original and new model greatly reduces the adversarial sam... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments! We address your concerns as below.
**W1: Applicability of the Proposed Setting (Threat Model)**
As clarified in Section 3.4, we specifically consider two practical scenarios where defenders either face partial data poisoning or do not control the... | null | null | null | null | null | null |
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning | Accept (poster) | Summary: This paper introduces RealRAG, a retrieval-augmented generation (RAG) framework that enhances text-to-image models by retrieving real-world images to improve realism, accuracy, and faithfulness to fine-grained and unseen objects. Unlike conventional text-to-image models that suffer from hallucinations due to t... | Rebuttal 1:
Rebuttal: **We sincerely thank *Reviewer HZx9* for the constructive comments and insightful suggestions.**
# Evaluation on More General Datasets
Thanks for the reviewer's suggestion. We add the experiments with the ImageNet dataset, please check the results in the response for ***Reviewer 5Kbn***. The re... | Summary: The paper introduces a novel retrieval-augmented generation (RAG) framework aimed at improving text-to-image generative models. Traditional generative models suffer from hallucinations and distortions when generating fine-grained or novel real-world objects due to their fixed training datasets. RealRAG overcom... | Rebuttal 1:
Rebuttal: **We sincerely thank *Reviewer 5Kbn* for the constructive comments on our work. We are very grateful to the reviewer for recognising the novelty of our idea and the richness and rationality of our experiments.**
# About the Database
***We are sorry for the misunderstanding***. For the fine-grain... | Summary: The paper introduces RealRAG, a retrieval-augmented generation framework designed to enhance text-to-image models by addressing their inherent knowledge limitations. The main idea is to retrieve and integrate real-world images to supplement the generator's missing knowledge. The key innovation of RealRAG lies ... | Rebuttal 1:
Rebuttal: **We sincerely thank *Reviewer vQw4* for the constructive comments on our work. We promise to revise the paper based on the comments. We will cite and discuss all the papers in the "Essential References Not Discussed" in the final version**
# About the "first work" claim and the novelty.
The ref... | null | null | null | null | null | null | null | null |
Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models | Accept (poster) | Summary: The paper introduces ModCirc, a framework for global-level mechanistic interpretability of LLMs by discovering modular circuits -- task-agnostic functional units that enable cross-task interpretability while reducing computational costs. It defines the MC vocabulary discovery problem with five evaluation crite... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you've dedicated to reviewing and providing invaluable feedback. We provide a point-to-point reply below for the mentioned concerns and questions. We use the [anonymous repository](https://anonymous.4open.science/r/ModCirc-4887/README.md) (termed "the li... | Summary: This paper proposes a novel formulation of the circuit discovery problem, which is a mechanistic interpretability task concerned with identifying a small subset of an LLM’s components responsible for a specific task. The authors propose a variant of this problem that involves identifying multiple subsets of th... | Rebuttal 1:
Rebuttal: We sincerely thank you for providing invaluable feedback. We provide a point-to-point reply below to address the concerns and questions. We use the [anonymous repository](https://anonymous.4open.science/r/ModCirc-4887/README.md) (termed "the link") to store supplementary results.
> **Reviewer**: ... | Summary: This work aims to address key challenges in the current state of mechanistic interpretability literature, namely: (1) the limited generalization of results from task-specific circuit analysis and (2) the high human effort required to determine the functional interpretation of each computational node. To tackle... | Rebuttal 1:
Rebuttal: We sincerely appreciate your dedicated time and effort in reviewing and providing invaluable feedback. We also thank you for recognizing the novelty and the significance of our contributions. We provide a point-to-point reply below for the mentioned concerns and questions. The [anonymous repo](htt... | Summary: The authors address two key limitations in current MI research: (1) task-specificity of circuit identification and (2) high computational costs for interpreting new tasks. Their solution introduces a modular circuits (MC) vocabulary - a collection of task-agnostic functional units, each consisting of a computa... | Rebuttal 1:
Rebuttal: We sincerely thank you for the invaluable feedback. Here, we reply to the mentioned concerns and questions. The [anonymous repo](https://anonymous.4open.science/r/ModCirc-4887/README.md) ("the link") stores supplementary results.
> **Reviewer**: For a new task, verify whether the functional inter... | null | null | null | null | null | null |
Online Differentially Private Conformal Prediction for Uncertainty Quantification | Accept (poster) | Summary: This paper proposes a framework for differentially private conformal prediction in an online setting.
Claims And Evidence: Yes -- claims are supported by evidence.
Methods And Evaluation Criteria: The proposed methods are reasonable.
Theoretical Claims: 1. Theorem 4.4 is supposed to be the privacy guarantee... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. We address each point below.
- **Privacy Guarantees of Theorem 4.4**. Our core contribution is Algorithm 2's online private quantile construction, which enables Algorithm 1 to generate online private prediction sets via DP's post-processing property. Whil... | Summary: In this paper, the authors propose a method for returning private conformal prediction sets in an online framework. They theoretically prove that their method guarantees long-term coverage control at a nominal confidence level while returning a private set. Finally, they empirically evaluate the method on synt... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. We address each point below.
- **Definition of μ-GDP.** We will revise the paper to include a formal definition of μ-GDP(Dong et al., 2022).
- **Clarification of Line 150.** We will revise Line 150 to include the private quantile online update rule and c... | Summary: The submitted paper presents an online differentially private conformal prediction (ODPCP) framework that generates private prediction sets in real time using pre-trained models. The key idea is to compute differentially private quantile thresholds in a one-pass online manner without re-accessing historical da... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. We address each point below
- **Finite sample converge rates.** Theorem 4.5 shows that ODPCP achieves the desired long-run coverage. Although it does not provide explicit finite-sample convergence rates, our empirical results indicate rapid convergence in p... | null | null | null | null | null | null | null | null |
Graph-Based Algorithms for Diverse Similarity Search | Accept (poster) | Summary: This paper studies the problem of diversifying the results of approximate nearest search (ANNS) for vectors. It adapts the state-of-the-art proximity graph algorithm for ANNS and modifies both the graph construction and query processing algorithm to consider diversity. It also conducts theoretical analysis to ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
**Response for W1/Q1:** Thanks for your suggestion! For the colorful NN definition, the current algorithm 2 always optimizes the furthest point, which only provides approximation guarantees on the maximal distance but not the total distance. For the total (av... | Summary: This paper considers the nearest-neighbor search problem with diversity constraints. It builds on existing graph-based algorithms for similarity search and proposes a new indexing algorithm which can more efficiently answer queries with diversity constraints.
Through experimentation on several large datasets,... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
> W1: In my view, the experimental section could be more extensive by comparing against additional baseline methods for ANN and post-processing, such as HNSW.
**Response:** It is indeed possible that using different graph-based methods could improve the perf... | Summary: In this paper, the authors present provably efficient algorithms for approximate nearest neighbor search with diversity constraints. They propose several problem formulations, such as Colorful NN and k'-Colorful NN, and further generalize them into more general problems. To solve these problems, the authors pr... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
**Response to W1:** Since we cite HNSW, NGT and DiskANN earlier in the introduction, we did not want to repeat the discussion later.. However, given the feedback, we will expand the discussion and provide a comprehensive overview of graph-based NN algorithms.... | Summary: This paper addresses an important problem of graph-based nearest neighbor search (NNS) with diversity constraints. The new algorithm is proposed that is supported by theoretical analysis and also shows promising experimental results.
## update after rebuttal
I appreciate the authors' rebuttal and will keep m... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
Response for W1: Thank you for the suggestions. We will include preliminaries on the DiskANN algorithm in the appendix. We have included intuition on the new algorithms, but for completeness we will add informal descriptions of them too. We will also include... | null | null | null | null | null | null |
Physics-Informed Weakly Supervised Learning For Interatomic Potentials | Accept (poster) | Summary: The paper proposes a new method for training machine learning interatomic potentials (MLIPs) using loss functions based on the Taylor expansion of the potential energy and the notion of conservative forces. The papers starts by describing ab-initio computational chemistry simulation methods and motivates the n... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive and encouraging feedback. We have carefully considered your comments and will incorporate the necessary revisions in the camera-ready version. Below, we provide detailed responses to each of your encouraging proposals and questions. Please let us kno... | Summary: The paper proposes a physics-informed weakly supervised learning (PIWSL) framework to improve the accuracy and robustness of machine-learned interatomic potentials (MLIPs). PIWSL incorporates two new loss functions: Physics-Informed Taylor-Expansion-Based Consistency (PITC) loss and Physics-Informed Spatial Co... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We have carefully considered your comments and will incorporate the necessary revisions in the camera-ready version. Below, we provide detailed responses to each point.
$\\textbf{W1}$:
A detailed discussion regarding the computational tim... | Summary: In this paper, the authors propose two auxiliary loss functions to improve the generalization of machine learning interatomic potentials (MLIP). Using molecular and crystal datasets, they demonstrate that the proposed method enhances the accuracy of energy and force predictions across multiple MLIPs.
## updat... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We have carefully considered your comments and will incorporate the necessary revisions in the camera-ready version. Below, we provide detailed responses to each point.
$\\textbf{Q1}$. The iterations in Table A2 apply to both the baseline ... | null | null | null | null | null | null | null | null |
General agents need world models | Accept (poster) | Summary: - This paper proves a bound on an agent's ability to achieve zero-shot generalization.
- It studies a full-observable controlled Markov process, with standard simplifying environment assumptions.
- They find a bound on the regret of an agent with a key term being an L1 distance between true and estimated tran... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review and helpful comments. We hope to address your main concerns about the core claims of our paper, which we believe stem from a misunderstanding of theorem 1, and have implemented your suggestions for improving the paper.
**What do we actually show... | Summary: This paper shows the insight that any agent capable of performing zero-shot generalization must have learned an accurate generative model as a world model of its environment. This paper provides a comprehensive theoretical analysis to support the claims.
## update after rebuttal
Thanks to the authors for prov... | Rebuttal 1:
Rebuttal: Thank you for you helpful comments. As noted by **Reviewer Xhe1**, our paper does propose a new method for eliciting world models from agents. However, this was quite unclear in the submitted draft, and we have included an explicit algorithm (below) in the manuscript to clarify this.
Following y... | Summary: The authors establish that an agent capable of generalizing across a sufficiently large number of goal-conditioned tasks within an environment must have learned an accurate approximation of the environment’s transition model. As a consequence of this result, their proof provides a method for extracting the tra... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our paper and for your helpful comments. In particular, the following comment on the need to clarify the size of the `universal’ set of goals, compared to the set of finite horizon trajectories.
**Reviewer:** _It not obvious that the set of 'universal' goal-d... | null | null | null | null | null | null | null | null |
StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models | Accept (poster) | Summary: This paper proposes a multi-bit stealthy (a.k.a. unbiased) LLM watermark. The method is based on partitioning the text into different intervals, increasing the probabilities for some parts while decreasing the others, and keeping the overall distribution unchanged. The evaluation shows that the method can inde... | Rebuttal 1:
Rebuttal: # "Methods And Evaluation Criteria" and "Relation To Broader Scientific Literature" and "Questions For Authors":
We appreciate the reviewer’s insightful observation. We fully agree that normal utility metrics (e.g., BLEU, BERTScore, PPL) alone are not sufficient to evaluate stealthiness, as they ... | Summary: The paper introduces StealthInk, a watermarking scheme for LLMs that embeds multi-bit information into AI-generated text without disrupting the original text distribution. Unlike previous methods that either altered text outputs or limited watermarks to simple detection, StealthInk preserves the generative qua... | Rebuttal 1:
Rebuttal: # "Methods And Evaluation Criteria" and "Relation To Broader Scientific Literature":
We thank the reviewer for the thoughtful comments. We clarify several key points regarding the originality of our work relative to DiPmark.
Although we set $m=1$ in our main experiments, StealthInk is fundamenta... | Summary: This paper introduces a novel watermarking scheme that allows for the stealthy embedding of multi-bit information within generated text. This method aims to enhance the traceability of AI-generated content while preserving the original text quality and ensuring robustness against various attacks.
Claims And E... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive evaluation of our work. We are glad to hear that our contributions were well received, including the theoretical foundation for multi-bit watermarking, the efficient and accurate decoding scheme, and the comprehensive evaluation.
We particularly ap... | Summary: The paper proposes a novel multi-bit watermarking scheme, StealthInk, for large language models (LLMs). It discusses both the embedding and detection of watermarks, with theoretical and experimental support.
Claims And Evidence: The main challenge addressed is multi-bit watermarking and authors' main claim is... | Rebuttal 1:
Rebuttal: # Theoretical claims:
We correct eq. (4) as
$$
F_{k}(\theta, M, P_{O}) =
\begin{cases}
(X_k - \beta)^+ + (X_k - \bar{\beta})^+ - (X_k - \alpha)^- - (X_k - \bar{\alpha})^+, & \text{Case 1 or 3} \\\\
(X_k - \beta)^+ + (X_k - \bar{\beta})^- - (X_k - \alpha)^- - (X_k - \bar{\alpha})^-, & \text{Case ... | null | null | null | null | null | null |
Exploiting Curvature in Online Convex Optimization with Delayed Feedback | Accept (poster) | Summary: This paper studies online learning with delayed feedback under curved loss functions. Specifically, for strongly convex functions, the proposed FTRL method achieves a regret bound of $O(\min\{\sigma\_{\max} \ln T, \sqrt{d}\_{\text{tot}}\})$, where $\sigma\_{\max}$ denotes the maximum number of missing data. Th... | Rebuttal 1:
Rebuttal: **Q:** Missing results of BOLD-OGD for strongly convex functions in Figures (a) and (b).
**A:** To address the request of a more comprehensive empirical comparison, we will also include the performance of BOLD-OGD in the strongly convex setting. We originally omitted BOLD-OGD because it is a typi... | Summary: This paper investigates various types of loss functions in the context of Online Convex Optimization (OCO) with delayed feedback and proposes a variant of Follow-The-Regularized-Leader (FTRL) to improve upon previous results. Firstly, it slightly enhances the existing regret bound for strongly convex loss func... | Rebuttal 1:
Rebuttal: **Q:** Definition of delays and presentation of results.
**A:** In our introduction, we mainly focus on the delay-dependent terms in the bounds for conciseness, but we will clarify the presentation to avoid any confusion. While we also appreciate the suggestion on the definition of delays, we rem... | Summary: In this paper, the authors consider online convex optimization with delayed feedback, and aim to exploit the curvature property of loss functions, i.e., strong convexity and exp-concavity, to improve the regret bound. Specifically, for strongly convex functions, the authors show that a delayed variant of follo... | Rebuttal 1:
Rebuttal: **Q:** $T=1000$ is too small in experiments.
**A:** We extended our experiments to have $T=10000$ and plan to include the new plots in our next revision. The new plots essentially show the same behavior with an extended time horizon. You can find these extra plots in the "Synthetic Data" folder a... | Summary: The authors present a FTRL-based algorithm that achieves logarithmic regret for strongly convex loss functions. More importantly it depends on $\min(\sigma_{max} \log T, \sqrt{d_{tot}})$ which improves the previous results $O(\sqrt{d_{max}} \log T)$. The key idea is to have a regularizer that uses all the prev... | Rebuttal 1:
Rebuttal: **Q:** Can you run the experiments on more adversarial settings?
**A:** Following your suggestion, we run our benchmark algorithms on the following more adversarial (i.e., non-stationary) environment. Specifically, in this environment, we set the time horizon as $T = 10000$ (as to also address a ... | null | null | null | null | null | null |
Neural Event-Triggered Control with Optimal Scheduling | Accept (poster) | Summary: This paper considers designing feedback controllers for continuous-time nonlinear systems, and the controller is updated only at certain chosen times, ensuring stability and using as few updates as possible. Experiments over three examples are provided to compare with several existing periodical control and ev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall valuable comments and respond to the reviewer's major concerns one by one.
```
Q1: The authors claimed in the Related Work that "we are the first to study the optimization scheduling problem of ETC in the continuous dynamics". Nonlinear continuous systems hav... | Summary: This paper presents a novel approach to learning event-triggered controllers with maximum inter-event times using neural networks. Compared to related works, the key innovation is that the entire framework is developed for continuous dynamics and continuous triggering times. The authors propose two approaches:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive feedback and the valuable comments. For the major comments, we are going to respond to them one by one.
```
Q1: Please explain and analyze the outstanding behaviour of Neural-ETC MC in detail.
```
**Response**: Many thanks for your valuable comment. T... | Summary: This study proposes a neural-based learning method for optimal scheduling in event-triggered control problems. The proposed method formulates an optimization problem to optimize the triggering rule in control problems. Then, it demonstrates how to solve this problem using neural networks. Finally, theoretical ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments and valuable suggestions. We address the major concerns of the reviewer one by one.
```
Q1: I am not sure whether the values of the controller in Theorem 4.1 are continuous at the origin. This type of controller does not necessarily guarantee co... | null | null | null | null | null | null | null | null |
Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response | Accept (poster) | Summary: This paper studies the problem of learning Bayesian Nash Equilibrium (BNE) in auction games as the number of bidders grows. The authors propose the Approximate Best Response Gradient method, including an analytic solution for gradient estimation to avoid the biased utility, and the Best Response Distance objec... | Rebuttal 1:
Rebuttal: > There have been several works proposing gradient-based approach to solve NE of some classes of games, and the proposed metric, Best Response Distance, is commonly used. Nevertheless, the gradient estimation is novel for auction games.
Thank you for your positive feedback and for highlighting th... | Summary: This paper investigates the problem of learning approximate ex-ante BNE in auction games under a publicly known prior distribution of bidder values. It proposes three new algorithms:
1. **Utility Grad**, which computes the gradient of bidders' utilities analytically using the CDF and PDF of the value distribu... | Rebuttal 1:
Rebuttal: ## More Settings with Unknown BNE
We acknowledge that the experimental evaluation in our paper primarily focuses on auctions with known BNEs. This choice was made deliberately to allow for a clear and precise assessment of the learned strategies by comparing them against analytically derived solut... | Summary: This paper presents the Approximate Best Response Gradient method for learning Bayesian Nash Equilibrium (BNE) in auction games. Auction plays a crucial role in many modern trading environments, including online advertising and public resource allocation, but computing BNE is computationally hard. Existing met... | Rebuttal 1:
Rebuttal: ## Theoretical Assumptions
Thanks for highlighting this point!
First, we acknowledge that achieving **convergence in learning algorithms under general game settings is a challenging problem**, which is further compounded by unknown BNE solutions for such settings.
**The focus of this work is on a... | Summary: This paper introduces the Approximate Best Response Gradient method to efficiently learn Bayesian Nash Equilibrium (BNE) in auction games. It addresses the challenges of gradient computation and slow convergence in existing methods by using an analytic gradient solution and a novel Best Response Distance objec... | Rebuttal 1:
Rebuttal: ## General Settings with Unknown BNEs
Thank you for your valuable feedback.
Indeed, the experimental evaluation in our paper primarily focuses on auctions with known BNEs. This choice was made deliberately to **allow for a clear and precise assessment of the learned strategies** by comparing the... | null | null | null | null | null | null |
Identifiable Object Representations under Spatial Ambiguities | Accept (poster) | Summary: The paper presents a multi-view probabilistic approach aimed at learning modular object-centric representations that are essential for human-like reasoning. This paper introduces View-Invariant Slot Attention (VISA), which addresses spatial ambiguities caused by occlusions and view ambiguities. This method agg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are glad that the reviewer found our experiments to be extensive and method to be robust.
The primary focus of the work is that it provides theoretical guarantees for identifiability in multi-view scenarios, and requires no viewpoint annotations, whic... | Summary: The paper aims to learn identifiable object representations even under spatial ambiguities, i.e., occlusions and view ambiguities.
The authors propose View-Invariant Slot Attention (VISA), a probabilistic slot attention variant to learn such representations.
Theoretic analysis is provided to prove identifiable... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and are glad that the reviewer finds our paper well written, clear, and with reasonable claims and proofs. We are also glad that intuitions aided in the understanding of theorems and our claims.
> As mentioned in the weakness section on page 8,... | Summary: This paper focuses on object-centric learning and proposes View-Invariant Slot Attention (VISA). It extends the probabilistic slot attention (PSA) into multi-view scenarios. It introduces a content descriptor, learns identifiable object-centric representations from multi-view observations and accounts for occl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and are glad to see that the reviewer acknowledges our superior performance and believes the paper to address novel and important topic.
> (W1)It is difficult to parse the curves in Figure 5 and 6. …
We do agree with complications in analysing... | Summary: The paper introduces View-Invariant Slot Attention (VISA), a probabilistic object-centric learning model designed to achieve identifiable object representations from multi-view images without explicit viewpoint annotations. VISA overcomes limitations of single-view methods by resolving spatial ambiguities like... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and are glad that the reviewer found our claims were supported by clear and concise evidence with correct proofs and sound experiments.
> more extensive details on computational complexity, model training time, parameter counts
**VISA complexity... | null | null | null | null | null | null |
Testing Conditional Mean Independence Using Generative Neural Networks | Accept (poster) | Summary: The paper introduces a new nonparametric test for conditional mean independence (CMI) that leverages deep generative neural networks to estimate conditional mean embeddings. The proposed method uses a novel population measure based on RKHS embeddings and constructs a test statistic in a multiplicative form tha... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable comments, which have helped lead to a much-improved manuscript. In the following, we present our point-by-point responses to your questions and will take into account all your suggestions in a revised version of our manuscript.
**Generality and practical impli... | Summary: This paper proposes a novel statistical method to conditional mean independence (CMI) testing. First, the authors introduce a new population-level CMI measure and develop a bootstrap-based hypothesis testing framework that employs generative neural networks to approximate conditional mean functions. Its test s... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable comments, which have helped lead to a much-improved manuscript. In the following, we present our point-by-point responses to your questions and will take into account all your suggestions in a revised version of our manuscript.
**Computational cost.** Due to t... | Summary: This develops a novel method to test for conditional mean independence that works well in high-dimensions, gives asymptotic size control, and has nontrivial power against local alternatives. This depends on using deep learning to learn g_y and g_x using a bootstrap sample. They then use the test statistic in t... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable comments, which have significantly contributed to improving the quality of our manuscript. Below, we provide point-by-point responses to your questions and will incorporate all of your suggestions in the revised version of our manuscript.
**Sensitivity to netwo... | null | null | null | null | null | null | null | null |
Discovering Symbolic Cognitive Models from Human and Animal Behavior | Accept (spotlight poster) | Summary: This paper presents a new method, CogFunSearch, that automatically discovers symbolic models for a given dataset. Their approach builds on FunSearch [1], an LLM-driven evolutionary algorithm that searches over the program’s structure, by adding an inner level of optimization that fits model parameters to the d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission, their useful comments, and are glad they found our approach “novel” and yielding “new behavioral insights”.
## “missing reference to [1].”
Thank you for pointing out this relevant work! We will cite it and include it in our discu... | Summary: This paper proposes to extend FunSearch (Romera-Paredes et al., 2024) to symbolic cognitive modeling, namely CogFunSearch, an LLM-based evolutionary program synthesis framework.
Experimental results have strongly supported the value of CogFunSearch in discovering high-quality symbolic programs on human, rat, a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission, their useful comments, and are glad they found it “exceptionally well-written and (surprisingly) easy to follow”, and are thrilled (and grateful) they “enjoyed reviewing the paper and learnt a lot”.
Below, we address the main issues... | Summary: This paper extends the FunSearch evolutionary algorithm to autonomously uncover symbolic cognitive models that effectively represent human and animal behavior. The authors compare the top-discovered program with an RNN trained on data from all subjects collectively, showcasing the efficacy of CogFunSearch. Add... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission, their useful comments, & are glad they found our proposed methodology “particularly impressive”.
## RNN trained on data from all subjects…rather than…separate RNNs…
To clarify: This concern applies only to the human bandit dataset... | Summary: This paper introduces CogFunSearch, an automated approach to discovering symbolic cognitive models that accurately describe human and animal behavior. The method builds on FunSearch, a program synthesis tool powered by Large Language Models (LLMs) and an evolutionary algorithm, to systematically explore and op... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission and their useful feedback. We are glad the reviewer found the submission “well-structured, comprehensive, and methodologically rigorous”. Below we provide responses to some of the main concerns raised.
## “Can researchers leverage t... | null | null | null | null | null | null |
Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models | Accept (poster) | Summary: This paper introduces LLM-BP, a framework for zero-shot inference on text-attributed graphs using large language models. The framework requires no training and generalizes well across homophilic and heterophilic graphs. Experiments show that LLM-BP outperforms existing methods.
Claims And Evidence: Yes.
Meth... | Rebuttal 1:
Rebuttal: We thank *reviewer gfhg*’s time in reviewing our paper and their constructive comments. Below we try to address the concerns.
**1. [Convergence of BP]**
We acknowledge that there is no general convergence guarantee for BP on graphs with loops. However, LLM-BP does not require BP or its approxim... | Summary: The paper proposes a new method to enhance LLM's ability on graph learning tasks. It first proposes to incorporate task and class information into the node embedding generated by the language model, it then proposes to use belief propagation on pseudo-labels of the nodes to enhance prediction. Experiments show... | Rebuttal 1:
Rebuttal: We sincerely thank the time and efforts *reviewer JRhf* took to review our paper. *Reviewer JRhf* provides some insightful questions and constructive suggestions to further improve the paper’s quality. Below we try our best to address the concerns:
**1. [Connections with other works]**
We agree... | Summary: This paper tackles node classification on text-attributed graphs (TAGs) -- graphs where each node has a textual description but labelled examples are scarce. It identifies two major challenges of existing approaches that utilise LLMs for this task: *(i)* LLMs have limited context length, making it hard to incl... | Rebuttal 1:
Rebuttal: We thank *reviewer ppW1*’s time and effort for reviewing the manuscript and their constructive comments. Below, we respond to the three concerns raised by the reviewer:
**1. [Average Ranking]**
We respectfully offer a different perspective on this comment. First, it seems that one of our key c... | Summary: This paper explores zero-shot generalization in graph problems on Text-Attributed Graphs (TAGs) using a pure LLM-based approach. The authors propose two key principles for model design:
- Task-Adaptive Embeddings – An LLM-based encoder processes raw node text along with a prompt, allowing node embeddings to d... | Rebuttal 1:
Rebuttal: We sincerely thank *reviewer JW3s*’s time and effort in reviewing the paper. We are also thankful for the constructive suggestions. Below we try to address the concerns from *reviewer JW3s*:
**1. [New Class with limited data]**
LLM-BP does not require abundant data from new classes and can gene... | null | null | null | null | null | null |
Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models | Accept (poster) | Summary: This paper addresses the computational challenges of using Gaussian process (GP) priors in Variational Autoencoders (VAEs) for high-dimensional time series analysis. While GP-based VAEs effectively capture temporal dependencies, their cubic time complexity limits scalability. To overcome this limitation, the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
**Missing relevant models.** Our experiments include previous GP-VAE methods, as our main goal is to improve these models. We also evaluate an RNN-based method (BRITS) and a latent neural ODE (L-NODE). We also compare against SGP-BAE suggested by reviewer... | Summary: The authors present an approach for performing efficient variational inference in an additive GP prior VAE (additive GP prior with an MLP parameterised likelihood). The approach uses standard low-rank Hilbert space approximations for the GP kernels, approximating the additive GP prior as an additive linear mod... | Rebuttal 1:
Rebuttal: **Lacking novelty.** We kindly ask the reviewer to find what we contribute to the literature by referring to our response "Contributions" for the reviewer VgcQ.
**Generalization for new instances.** Thank you for giving us the opportunity to clarify this important aspect of our model. We agree wi... | Summary: This paper proposes a generative model based on Variational Autoencoders (VAE) where latent variables are assigned a GP prior.
The main contribution is to approximate the GP prior with random features so that the model can be optimized through mini-batching and linearly in the number of data.
Claims And Evide... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and the opportunity to clarify our contributions. Before addressing specific comments, we emphasize that, unlike traditional methods that use random feature approximations, our approach leverages the Hilbert space approximation of the GP prior.
**Interpretati... | Summary: This work presents a scalable basis function-based approximation for Gaussian Process prior Variational Auto-Encoders (GP-VAEs), to overcome the cubic time-complexity (without resorting to inducing-point GP variational inference techniques) and to accomodate shared and individual-specific correlations across t... | Rebuttal 1:
Rebuttal: **Independence across latent dims and additive GP prior** Correlated GP priors are typically formulated via the linear model of co-regionalization (LMC), which multiplies independent GPs by a factor loading matrix to introduce correlations across latent dimensions. In GP prior VAE models, a neural... | null | null | null | null | null | null |
FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification | Accept (poster) | Summary: This article innovatively introduces the concept of combined modality pedestrian re-identification. Compared with traditional cross-modal identification, it demonstrates greater flexibility in dealing with complex scenarios. Centered around this concept, the article constructs the FlexiReID framework, which in... | Rebuttal 1:
Rebuttal: > In the related work section, the author could include a review of the literature on feature fusion.
>
A1: Thank you for the suggestion. We appreciate your insight and will include a review of relevant literature on feature fusion in the related work section in the revised version.
> The autho... | Summary: This paper presents the FlexiReID framework, which addresses the issue of modality combination retrieval that has been largely overlooked in the current cross-modal person re-identification (ReID) field. Specifically, traditional approaches in cross-modal ReID typically use a single modality as the query to ma... | Rebuttal 1:
Rebuttal: > The ablation experiment section lacks a comparison between the adaptive routing mechanism and the Top-K routing mechanism. It would be helpful to include this comparison.
>
A1: In fact, we have already compared a method using the Top-K mechanism in our ablation study, specifically in Row No.1 ... | Summary: This paper firstly introduces the concept of flexible retrieval in the field of person re-identification and propose a corresponding method FlexiReID which supports flexible retrieval with arbitrary modality combinations. The authors also constructed a unified dataset by existing ReID datasets.
Claims And Ev... | Rebuttal 1:
Rebuttal: > I am curious about the comparative experiment. This paper only provides the comparison with SOTA methods on Text-to-RGB task. However, there exists previous methods for other dual modalies retrieval. But this paper do not provide the comparison results.
>
A1: Thank you for the valuable questio... | Summary: The paper proposes the FlexiReID framework to support person retrieval across seven different modality combinations (such as text, sketches, infrared images, RGB images, and their combinations). The framework comprises an AEA-MoE mechanism for dynamically selecting varying numbers of expert networks according ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback. We'll address each of your concerns in detail.
> No. The paper misses model comparison experiments for multi-modal retrieval on benchmark datasets.
>
> In Table 1, except for T-R task, there are few comparison results for other task, which cannot demonstrate... | Summary: FlexiReID is a novel framework for multimodal person re-identification that enables flexible retrieval across various single or combined modalities—including text, sketches, RGB, and infrared images—thereby addressing the limitations of existing methods that focus on only one or two modality pairs. By introduc... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We will explain your concerns point by point.
> Table1 illustrates FlexiReID achieves promising accuracy in the T+S+IR→R situations. However these sketches are generated from RGB images, while in real-world scenarios, sketches is a front-facing portr... | null | null | null | null |
TRUST-VLM: Thorough Red-Teaming for Uncovering Safety Threats in Vision-Language Models | Accept (poster) | Summary: This paper presents a framework named TRUST-VLM for automatic red-teaming vision-language models. The framework mainly involves three stages of test-case generation, execution and evaluation, and test-case refinement, by incorporating a large language model and a text-to-image model. It is shown to be more eff... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and suggestions.
>**Q1: Comparison with automatic red-teaming methods like HADES.**
R1: Thank you for your thoughtful suggestion. We have conducted comparisons with both HADES and another recent jailbreak-based method. Besides, we also conducted comparisons ... | Summary: The paper presents an automate, iterative mechanism to red team vision language multimodal models. The approach consists of three parts: (1) text case generation, (2) attacking the VLM, and classifying the responses, and (3) refining the test cases using the mdoeration feedback. The first and the final step in... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback and supportive comments.
>**Q1: Restricting the number of ICL examples / Tips would be a good test of the refinement process.**
R1: Thank you for the insightful suggestion. We conducted additional experiments as per your recommendation. As shown in the table ... | Summary: This paper introduces TRUST-VLM, a novel multi-modal automatic red-teaming approach that leverages in-context learning and target model feedback to enhance attack success rates and test case diversity. Experimental results show that TRUST-VLM surpasses traditional methods, offering actionable insights for impr... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and insightful suggestions.
>**Q1: Limited novelty of LLM-based automation.**
R1: We acknowledge that various red-teaming methods have emerged recently, targeting different models, such as LLMs [1] and text-to-image models [2]. However, these existi... | Summary: This paper proposes a novel red-teaming framework (TRUST-VLM) to systematically uncover safety vulnerabilities in VLMs with black-box access. The proposed method improves both the fault detection rate and the diversity of generated test cases.
Extensive experiments show that TRUST-VLM not only outperforms tra... | Rebuttal 1:
Rebuttal: Thanks for your careful review and thoughtful comments.
>**Q1: Distinction from existing adversarial methods.**
R1: We apologize for any confusion caused. Our red-teaming method differs significantly from adversarial attacks such as jailbreak. As detailed in Related Works (Section 2.3), the prim... | null | null | null | null | null | null |
Beyond KL-Regularization: Achieving Unbiased Direct Alignment through Diffusion $f_{\chi^n}$-Preference Optimization | Reject | Summary: The paper presents Diffusion-$\chi^n$PO, a novel method for aligning diffusion models with human preferences in text-to-image generation. It introduces an $f_{\chi^n}$-regularization technique to refine the gradient ratio of the objective function, balancing optimization between preferred and non-preferred sam... | Rebuttal 1:
Rebuttal: > **C1. It would be helpful if the authors could provide either a reference to relevant literature or an intuitive explanation for this phenomenon.**
Contrastive Nature of the DPO Loss: The occurrence of the same token in both the selected and rejected responses induces contradictory learning ob... | Summary: This paper proposes XPO, a framework to align T2I diffusion models with human preferences. XPO introduces novel regularization techniques to smooth the training process. The authors show that XPO is more resident to conflicting samples in the training data from a theoretical perspective, and provided empirica... | Rebuttal 1:
Rebuttal: > For example, in table 3, all win rates above 50 are bolded, including ones that are only marginally above 50%. It's hard to judge the significance of these results. The authors are suggested to conduct a thorough statistic analysis on the significance of these results. Doe they actually show th... | Summary: The authors extend chi-square preference optimization to text-to-image tasks using diffusion models. To encompass a broader class of probability divergences, they generalize chi-square divergence to the chi-n function for positive integers n>1 and analyze the gradient of the proposed chi-n preference optimizat... | Rebuttal 1:
Rebuttal: > **W1 . In Section 4.3 (lines 255–259), the authors state that a larger n can prevent the z value from being excessively amplified. However, Figure 1 (left) shows that increasing n causes y to grow rapidly, whereas DPO exhibits the smoothest curve.**
As the alignment process progresses, the valu... | Summary: This paper introduces Diffusion-$\chi^n$PO, a method to align text-to-image (T2I) diffusion models with human preferences. The core idea is based on generalized preference optimization with $\chi^2$ divergence, where the author generalizes to $\chi^n$ to control the regularization for over-optimization issues ... | Rebuttal 1:
Rebuttal: >**W1. additional testing on broader datasets or with human evaluation would improve the significance of the findings.**
We used 8,667 high-quality prompts from the [open-image-preferences-v1-binarizeds dataset](https://huggingface.co/blog/image-preferences) to generate images. We report both th... | null | null | null | null | null | null |
Interaction-Aware Gaussian Weighting for Clustered Federated Learning | Accept (poster) | Summary: This paper proposes a novel federated learning (FL) method called FedGWC (Federated Gaussian Weighting Clustering), which aims to mitigate the challenges of data heterogeneity and class imbalance in FL by clustering clients based on their data distributions. This method allows for the creation of more homogene... | Summary: This paper introduces FedGWC (Federated Gaussian Weighting Clustering), a clustered federated learning (CFL) framework designed to address data heterogeneity and class imbalance. The key idea behind FedGWC is to group clients into homogeneous clusters based on their data distributions, enabling personalized mo... | Summary: This paper focus on the clustered FL method to mitigate the non-iid problem in FL. FedGWC groups clients based on the data distribution. Gaussian reward mechanism is used to form homogeneous clusters. Comprehensive experiments demonstrate this method achieve better performance.
Claims And Evidence: See weakne... | Summary: This paper proposes FedGWC, a new clustered FL algorithm to tacke data heterogeneity and class imbalance among clients. FedGWC clusters clients based on their empirical losses, using a Gaussian reward mechanism. They also propose a new clustering metric, Wasserstein Adjusted Score, to evaluate cluster cohesion... | null | null | null | null | null | null | ||||
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training | Accept (poster) | Summary: This paper conducts a comparative study of SFT and RL for post-training on GeneralPoints, an arithmetic reasoning card game, and also considers V-IRL, a real-world navigation environment. Experimental results show RL leads to model generalizing better in OOD cases while models trained with SFT hardly generaliz... | Rebuttal 1:
Rebuttal: ## General Response
Dear reviewer KPrc,
We sincerely thank you for your valuable feedback. We especially appreciate your advice on making the claim more rigorous. To best of our effort in the rebuttal period, we conduct the following experiments to strengthen our evidence:
- Experiments on Qwen-2... | Summary: This paper studies the generalization of RL and SFT.
It uses two visual-language reasoning tasks, and shows that RL has better generalization and SFT mainly memorizes the training samples and struggles with the OOD samples.
Further analysis shows that RL can also improve the model's underlying visual recogni... | Rebuttal 1:
Rebuttal: ## General Response
Dear reviewer NgMz,
Thank you for your appreciation of our work, especially the importance of our studied problem. We also acknowledge your constructive feedback on improving the simplicity of our works. Here is our feedback:
> Q1. Do RL and SFT start from different checkpoin... | Summary: This paper compares supervised fine-tuning (SFT) and reinforcement learning (RL) on both textual and visual reasoning tasks. The authors introduce GeneralPoints, an arithmetic reasoning card game, and V-IRL, a real-world navigation environment, to evaluate model generalization to unseen variants involving nove... | Rebuttal 1:
Rebuttal: ## General Response
Dear reviewer Dv4H,
Thank you for your appreciation of our work. We are delighted to hear that you find our research original, significant, and clear. We provide the following feedback and additional experiments for your concerns:
> Q1. Regarding Verifier design and PPO config... | Summary: This study compares the effects of supervised fine-tuning (SFT) and reinforcement learning (RL) on the post-training of foundation models, particularly in terms of generalization and memorization. It introduces two tasks -- GeneralPoints and V-IRL -- to evaluate how these techniques influence model performance... | Rebuttal 1:
Rebuttal: ## General Response
Dear reviewer rx4i,
Thank you for your appreciation of our work. We are glad that you find our work comprehensive, well-designed, and recognize our SOTA results on V-IRL.
Regarding your concerns, we provide the following feedback.
> Q1. Suggestion on more diverse experiments f... | null | null | null | null | null | null |
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions | Reject | Summary: First of all, the format of the official review in ICML 2025 is very uncomfortable and would fragment the reviewer's thoughts. My review comments will be summarized in the first block.
This paper theoretically presents the upper bound of the minimax risk of physics-informed linear regressors. The theoretical ... | Rebuttal 1:
Rebuttal: > **Q1.** This paper only takes linear regressors on at most two input dimensions.
**A1.** Our input setting is not limited to two dimensions. As noted in lines 151–168, our analysis handles general $m$-dimensional input, i.e., $x\_i \in \Omega \subset \mathbb{R}^m$.
---
> **Q2.** What does t... | Summary: This paper focuses on analyzing the generalization ability of physics-informed machine learning models. It shows that for linear regressors with differential equation structures, the generalization performance is determined by the dimension of the associated affine variety instead of the number of parameters. ... | Rebuttal 1:
Rebuttal: > Q1. The bound given by Theorem 3.2 is trivial and can be derived without the analyzing tools used. Could you give a tighter bound on the minimax risk?
Our generalization bound cannot be derived without the analyzing tools we introduced, particularly the covering number of the affine variety. Ou... | Summary: This paper provides an analytical framework for the generalization of linear regressors that incorporate differential equation structures. The authors demonstrate that the generalization bound depends on the dimension of the associated affine variety rather than the number of parameters. Additionally, they sho... | Rebuttal 1:
Rebuttal: > Q1. Assumption 2 requires positive min. singular value of X, which typically holds only if n ≥ d...
A1. Since $\hat{\beta} - \beta^* \in \mathbb{B}_2(2R)$, Eq. (12) still holds under a weaker condition. For $\kappa > 0$,
$$
\frac{1}{\sqrt{n}}\\|\Phi\beta\\| \geq \sqrt{\kappa} \\|\beta\\|_2,\qua... | null | null | null | null | null | null | null | null |
Spatial Reasoning with Denoising Models | Accept (poster) | Summary: The authors investigate the application of diffusion models as solvers of probabilistic inference over continuous variables, which accommodates various problems in spatial reasoning. A key consideration is the various possible decompositions of the joint distribution of unobserved variables, and how some deco... | Rebuttal 1:
Rebuttal: We thank you very much for your time and positive feedback. We are happy to see that you agree with us that reasoning over complex domains is certainly relevant in science.
> Missing Proposition, Lemma, or Theorem in Appendix A
Thank you for pointing out this lack of clarity. On a high level, th... | Summary: This paper introduces “Spatial Reasoning Models” (SRMs), a framework for performing high-level reasoning across sets of continuous variables in diffusion/flow-based generative models. By allowing each spatial variable (e.g., an image patch) to have its own noise level, SRMs can systematically add or remove noi... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We address your concerns as follows:
> Justification for uniform distribution of mean noise level during training
1. We would like to refer you to Fig. 8 of our paper’s Appendix. It shows that for the two extreme cases of parallel and autoregressive generatio... | Summary: This paper introduces Spatial Reasoning Models (SRMs), a framework for performing reasoning over sets of continuous variables using denoising generative models. The authors observe that standard diffusion/flow models often collapse to hallucination when handling complex distributions. The key innovations inclu... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive comments and are happy to see that you value the *originality* of our *novel ideas* achieving *significant improvements [...] highlighting the effectiveness of the proposed approach for spatial reasoning tasks*.
> W1. Real-world applicability
We agree ... | Summary: This paper studies how diffusion models perform on higher-level reasoning tasks, such as the Sudoku game. The authors introduce a novel SRM framework to integrate several key improvements for semanticalization in generation, the associated order, and the sampling strategies. The experimental results are encour... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback. To address your concerns, we present a point-to-point response in the following:
> Why not use the latest VLMs [...] to address this symbolic visual reasoning task?
Thank you for this important question. Our response is threefold:
1. The goal o... | null | null | null | null | null | null |
Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation | Accept (poster) | Summary: This work observes that distillation-based training of diffusion models may result in a mismatch of local minima between the student and teacher models. Additionally, it demonstrates that employing a standalone GAN objective, without a distillation objective, is sufficient to transform diffusion models into ef... | Rebuttal 1:
Rebuttal: Thanks for the comments. However, the reviewer may have overlooked some important content of our paper. Below is the point-by-point response.
**Summary:**
Our work is not merely about finding "different local minima" or "distilling diffusion using only a GAN," as the reviewer stated, which only... | Summary: This work proposes a method called D2O that fine-tunes a pretrained diffusion model for one-step generation with GAN loss. Pretrained VGG-16 is used as discriminator and the specific GAN loss objective used for the discriminator is Projected GAN (Sauer et al. 2022). In addition, they use other techniques like ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. Here, we provide a point-by-point rebuttal, which we hope helps to clarify the confusion:
**Claims And Evidence:**
1. "Writing and overall structure…"
- "Consider the following two statements about the use of augmentation…"
... | Summary: The paper proposes a novel approach, D2O (Diffusion to One-Step), which uses a GAN objective to convert diffusion models (DMs) into efficient one-step generators. It identifies a key issue in previous distillation methods: the teacher and student models' distinct local minima, which enables effective knowledge... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive feedback. We acknowledge that our current work is primarily empirical. Nevertheless, we believe that our work provides important insights for the community, particularly regarding how to take advantage of the capabilities within pre-trained diffusion models... | null | null | null | null | null | null | null | null |
Solving Zero-Sum Convex Markov Games | Accept (poster) | Summary: In this paper, the authors provide two (policy gradients-like) algorithms which learns $\epsilon$-Nash equilibria in convex Markov games. The authors provide bounds to the number of iterations required to compute the approximate ($\epsilon$) Nash. In order to do so, the authors leverage properties of hidden co... | Rebuttal 1:
Rebuttal: ## Dependence on $\min_s 1/\rho(s)$.
This quantity is merely a variant of the dependence on the *mismatch coefficient* [1, page 6 in the arxiv version][2]. The quantity $\min_s 1/\rho(s)$ upper bounds the mismatch coefficient when $\rho(s)>0\forall s$. The single-agent policy gradient convex MDP ... | Summary: The paper studies two-player zero-sum convex Markov games and considers a regularization-based policy gradient approach for finding the Nash equilibrium. Two algorithms are proposed, and their complexity are provided.
Claims And Evidence: I did not carefully review the paper due to a serious ethical concern. ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your time and effort in reviewing the paper and pointing out this mistake in our bibliography. We apologize for our negligent mistake, thankfully, the wrong LLM-generated bib items are limited to the related work sec of Appendix B. As we clarified to the AC, all hallu... | Summary: This paper addresses global convergence to Nash equilibria in two-player zero-sum convex Markov games (cMGs)—a recently introduced framework generalizing Markov decision processes by allowing convex utilities over occupancy measures. The main contribution is proving that independent policy gradient algorithms,... | Rebuttal 1:
Rebuttal: Thank you for your positive reception of the paper. We are glad that you recognize our technical contributions that we deem important in broadening our understanding of multi-agent RL and nonconvex optimization.
Regarding your question **''Do you anticipate straightforward extensions to general-... | Summary: The paper studies convergence of policy gradient methods in zero-sum convex Markov games, giving the first convergence result to $\epsilon$-Nash equilibria for such games. The approach uses the inherent hidden convex-concave structure present with respect to the occupancy measures. This structure is formalized... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive reception of our work and recognizing the technicality and contributions of our paper. Allow us to address your concerns.
## Policy gradient terminology
* Given that literature labels as "policy gradient methods" most algorithms that use a gradient wrt t... | null | null | null | null | null | null |
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads | Accept (poster) | Summary: This paper introduce Orthus, which is a unified multimodal LLM for generating interleaved images and
text from mixed-modality inputs by simultaneously handling discrete text tokens and continuous image features under the AR modeling
principle.
## update after rebuttal
After considering the authors' response,... | Rebuttal 1:
Rebuttal: Thank you for your attentive comments! We are glad you thought our paper was well-written and easy to follow. We address your concerns point by point below.
**W1:** Orthus does not perform exceptionally well in multimodal comprehension, which may be limited by its relatively weak vision embeddin... | Summary: The paper introduces Orthus, a unified multimodal model that autoregressively generates interleaved images and text. The key idea is to handle discrete text tokens and continuous image features within a single transformer framework by employing modality-specific heads. One head is dedicated to language modelin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time to read our paper. We address your feedback point by point below.
**W1:** Overclaim of lossless.
By "lossless", we primarily emphasize the continuous treatment of visual signals compared to discrete tokens. We will clarify this in the revised version ... | Summary: This paper introduces an architecture to conduct unified multimodal understanding and generation. Specifically, it introduces a dedicated diffusion head to generate continuous visual token during image generation. By doing so, the proposed approach can enable native image generation with LLM at a low training ... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and useful suggestions! We are glad you thought our proposed design achieved impressive results. We address your concerns point by point below.
**W1&Q1:** Numerical comparisons on the inference latencies of the proposed approach and other baseline methods.
Tha... | Summary: This paper proposes Orthus, an interleaved image-text generation model with modality-specific heads. Orthus shows that language model heads for discrete tokens and diffusion heads for continuous image generation can work together. By fine-tuning from Chameleon, Orthus obtain good performance on both image unde... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and useful suggestions! We address your concerns point by point below.
**Weakness:** The idea is relatively not novel enough, just combining diffusion head and lm head, which shows limited insight into unified image understanding and generation.
- We would li... | null | null | null | null | null | null |
Understanding Synthetic Context Extension via Retrieval Heads | Accept (poster) | Summary: The paper demonstrates that synthetic context extension can partially emulate real data’s effects on LLMs but falls short due to less effective training of retrieval heads. It provides a framework for understanding this gap through retrieval heads, offering both a diagnostic tool and a path toward improving sy... | Rebuttal 1:
Rebuttal: Thank you for your review!
> The paper heavily attributes the performance gap to retrieval heads, potentially underplaying the role of other transformer components like MLPs, which are known to handle parametric knowledge. Although the authors note in a footnote that similar conclusions hold when... | Summary: This work aims to answer an important research question in the field of long-context modeling: how could the training on synthetic long-context data improve LLMs.
The authors present a novel investigation into the fine-tuning of LLMs using synthetically-generated long-context data.
One of the key contributions... | Rebuttal 1:
Rebuttal: Thank you for your review!
> For constructing synthetic long-context data, there has been some existing work that proposed general principles for creating synthetic training data beyond dataset-specific constructions, such as [1,2]. Although these work did not focus on the principles proposed in ... | Summary: The paper explores the impact of fine-tuning large language models (LLMs) with synthetic data for long-context tasks, particularly in retrieval and reasoning. The study evaluates different methods of synthetic data generation, varying both the realism of the "needle" (key concept) and the diversity of the "hay... | Rebuttal 1:
Rebuttal: Thank you for the review!
> It is suggested to discuss its position in the context of general-purpose data synthesis for LLMs [1,2]; context compression for long-context LLMs [3,4]; and analysis of synthetic data biases and their effects on downstream performance [5,6].
Thank you for the suggest... | Summary: This paper examines how synthetic data affects the performance of long-context language models (LLMs) on retrieval-based tasks. The authors find that while models fine-tuned on synthetic data generally underperform compared to those trained on real data, careful construction of synthetic datasets can partially... | Rebuttal 1:
Rebuttal: Thank you for the review!
> Statistical analysis such as confidence intervals, p-values are missing.
To address this, we will make the following additions to Tables 1, 3 and 11 to show when a performance gain is significant:
**Table 1**: $\dagger$ indicates that a model trained on the **bold** ... | null | null | null | null | null | null |
Towards flexible perception with visual memory | Accept (poster) | Summary: This paper introduces a retrieval-based visual memory framework, challenging the traditional paradigm of deep learning models storing knowledge in static ("stone") weights.
Instead, it separates representation (via pre-trained embeddings) from memory (through fast nearest-neighbor search), enabling a dynamica... | Rebuttal 1:
Rebuttal: Dear Reviewer xHyy,
Thanks for your review, we’re humbled to hear you appreciated the **“extremely well done experiments”** and found the work **“highly relevant with enormous potential for future extensions”**, & **“one of the most exciting papers I’ve seen this year in this space”** that **“cou... | Summary: The authors tackle the question whether splitting the ‘knowledge’ a neural model has to acquire into 1) a (small) set of learnt parameters (encoder) combined with 2) a non-parametric memory can prove superior to the classic “only-learnt” parameter approach – somewhat akin to what has been successfully pursued ... | Rebuttal 1:
Rebuttal: Dear Reviewer 4C32,
Thank you for your helpful comments. We’re happy to hear you found our work **“well justified”**, appreciated the **“insightful experiments & valuable insights”** (even though some of them were admittedly a bit buried in the appendix), and described it as **“a timely analysis ... | Summary: The authors observe that it is hard to edit knowledge acquired by deep models during training, because this knowledge is encoded in a vast number of interconnected weights. To address this issue, they suggest keeping a pre-trained model frozen, and enhancing it with a visual memory and a simple KNN algorithm t... | Rebuttal 1:
Rebuttal: Dear Reviewer Em8N,
Thank you very much for your detailed review. We’re glad to hear you appreciated the **“well-designed experiments”**, **“very well organized presentation / clarity”** and **“very well written manuscript”**.
*Abstract: “nearly impossible” is pretty strong*
Noted - we’ll chan... | null | null | null | null | null | null | null | null |
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL | Accept (poster) | Summary: This paper proposes selective state-adaptive regularization method for offline RL, addressing the limitations of fixed-strength regularization. It involves state-adaptive regularization coefficients through learning and regularization on a subset of high-quality data. Experiments on the D4RL benchmark show som... | Rebuttal 1:
Rebuttal: Thanks for your thorough review and positive recognition of our work. We are glad that you consider our work "well-supported, simple yet effective, logically rigorous and coherently structured." We are glad to answer all your questions.
**Q1:More comparative results**
**A1:** We compare our app... | Summary: This paper introduces an offline RL method that balances regularization strength conditioned on the provided state. This state-adaptive form of regularization is applied to CQL and explicit policy constraint methods demonstrating improved performance in both offline and offline-to-online settings.
Claims And ... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and positive recognition of our paper. We appreciate the questions you raised and are committed to delivering a comprehensive response to address the issues.
**Q1: Questions about claims**
**A1:** Our main claim is derived from Proposition 3.1 and can be succin... | Summary: The paper introduces a selective state-adaptive regularization method for offline RL to address the challenge of extrapolation errors caused by varying data quality. Unlike existing methods that apply uniform regularization across all states, the proposed approach learns adaptive regularization coefficients an... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive recognition of our work. We appreciate your thoughtful feedback and are pleased that you found our paper to be "well-written, methodologically sound, and of practical value." We also appreciate the questions you raised and are committed to delivering... | Summary: This paper proposes to learn a state-adaptive function to dynamically judge how reliable the Bellman update is. This method starts with CQL and transforms the hyperparameter used to modulate how much the value regularizer (i.e. the term responsible for making the value function more conservative) into a parame... | Rebuttal 1:
Rebuttal: Thanks you for the high praise and the comprehensive review of our paper. We appreciate the questions you raised and are committed to delivering a comprehensive response to address the issues.
**Q1: Lack of statistical significance**
**A1:** To ensure statistical rigor, we have now included 95%... | null | null | null | null | null | null |
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts | Accept (poster) | Summary: The paper tackles the problem of robust federated evaluation, in which a server aims to evaluate a model $h$ on the private federated data while taking into account the scenario in which the model could be used on a different distribution in deployment. The paper provides theoretical bounds to characterize the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful analysis and insightful comments on our paper. Below, we provide point-by-point responses to each concern.
--------------------------
**Claims and Evidence**
- **"Remark 7.3 is not an actual certificate."**
The reviewer is absolutely cor... | Summary: The paper proposes algorithms to robustly estimate a model performance under a ball of f-divergence or Wasserstain distance, and provide the generalization bounds of the proposed methods.
Claims And Evidence: The paper claims the proposed methods can estimate model performance robustly, though it is mainly a ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful and positive comments on our paper. Please let us know if you have any further questions or concerns—we would be happy to address them and clarify any points that might help you further increase your score. | Summary: This paper introduces a robust optimization framework for evaluating machine learning models in federated settings with non-IID client data, where the data distributions are governed by an unknown meta-distribution. The goal is to assess model performance not only on a given client network (standard A/B testin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments. Below, we provide point-by-point responses to each concern.
-----------------------------
**Questions**
1) **"The theorems analyze bounds under different distributions. Can the authors please highlight the key challenges of this... | Summary: The paper provides an analysis of model evaluation under tighter risk assessment conditions compared to previous works on federated evaluation. The main contribution includes a novel extension of the Dvoretzky–Kiefer–Wolfowitz (DKW) inequality adapted for federated data distributions. The authors claim improve... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful comments. Here, we give point-to-point responses to each concern or question.
---------------------------------
**Main Comments**
The reviewer’s main concerns are:
i) Justifications for our claim that previous methods either ignore the i... | null | null | null | null | null | null |
Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation | Accept (poster) | Summary: The paper introduces PhyGenBench, a benchmark designed to evaluate whether Text-to-Video models accurately adhere to fundamental physical laws. The study aims to assess how well these models can simulate intuitive physics, which is considered essential for developing a general world simulator. To systematicall... | Rebuttal 1:
Rebuttal: We appreciate your suggestions, which are essential for enhancing the paper. We address all questions sequentially and will incorporate those details in the revisions.
Q1:Comparing generated videos with real physics to eval.
A1:We considered using real videos as references to eval but encountere... | Summary: The paper introduces a benchmark designed to assess the extent to which generative video models internalize physical laws. The authors construct a dataset comprising 160 prompts that incorporate 27 physical phenomena. Additionally, they propose a method leveraging large vision-language models to automatically ... | Rebuttal 1:
Rebuttal: Many thanks for your feedback, which is vital to improving our paper's standard. We respond to all inquiries in sequence and will incorporate those details in the revision.
Q1: The evaluation is done using only 160 which might not be significant enough...
A1: Following your suggestion, we random... | Summary: The paper introduces PhyGenBench, a benchmark specifically designed to assess text-to-video (T2V) models on their ability to generate physically plausible videos grounded in intuitive physics. It comprises 160 carefully constructed prompts covering 27 distinct physical laws across four fundamental domains: mec... | Rebuttal 1:
Rebuttal: We're grateful for your suggestions, which plays a critical role in elevating our paper's quality. We tackled all questions one by one and will incorporate those details in the revision.
Q1: Can the PhyGenEval framework be easily adapted to new physical phenomena
A1: We sampled 50 prompts from W... | Summary: This paper introduces PhyGenBench, a benchmark assessing physical commonsense correctness in Text-to-Video (T2V) models, and PhyGenEval, an automated evaluation framework. PhyGenBench includes 160 prompts covering 27 physical laws across mechanics, optics, thermal, and material properties, ensuring a comprehen... | Rebuttal 1:
Rebuttal: Thank you for your valuable insights, which are fundamental to strengthening our paper. We handle all questions in their given order and will incorporate those details in the revision.
Q1: more ablation studies on different training techniques.
A1: We randomly sampled 1200 Video-Prompt pairs fro... | null | null | null | null | null | null |
Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM | Accept (poster) | Summary: This paper focuses on the computational redundancy of visual tokens in large multimodal models (LMMs). It is found that there is computational-level redundancy in visual tokens within LMMs, and different LLMs exhibit varying degrees of redundancy. The ProxyV algorithm is proposed. By introducing proxy visual t... | Rebuttal 1:
Rebuttal: We appreciate your constructive and insightful feedback. We carefully considered your reviews and revised paper accordingly. Below, we directly address each point you raised:
- *Incomplete evidence for the existence of computational redundancy*
Thanks for your suggestion to validate our observ... | Summary: This paper focuses on the token acceleration of LMM. It understands the hierarchical redundancy on visual tokens through validation experiments, and finds that visual tokens from LMM visual encoders do not necessarily require all heavy operations in the decoder-only LMM. ProxyV is designed to reduce computatio... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful feedback. We have carefully considered your comments and made detailed revisions to address each point as follows:
- *Generalizability of Claims*
The exploratory experiments presented in Section 2 are designed to identify computational redundancy patterns ... | Summary: Summary:
This paper explores the computational redundancy inherent in vision tokens within multimodal large language models. This paper reveals significant computational redundancy exists in vision token processing, particularly in the middle and later layers of such models. To address this inefficiency, this ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have reviewed your suggestions thoroughly and made corresponding revisions to address each of your concerns as outlined below.
- *Paper Organization and Readability*
We appreciate your suggestion on improving readability. We have modified our figures, a... | null | null | null | null | null | null | null | null |
Online Episodic Convex Reinforcement Learning | Accept (poster) | Summary: This paper studies online learning in episodic finite-horizon Markov Decision Processes (MDPs) with convex objective functions, known as the concave utility reinforcement learning (CURL) problem. CURL generalizes RL by applying convex losses to state-action distributions induced by policies, rather than just l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their long and detailed review. We address below the raised concerns.
- **References:**
- **Rosenberg and Mansour (2019):** Their setting differs from ours; in fact, our setting generalizes theirs. In our notation, they consider a sequence of adversarial losses $(\ell... | Summary: This paper addresses online convex RL, a generalization of RL in which the loss is a convex/concave function of the state-action occupancy, with adversarial losses and unknown transitions. In the setting with full feedback, i.e., the loss function is revealed to the agent at the end of the episode, the paper p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We address the raised concerns below.
- **Claims (papers):** We discuss Zahavy et al. (2021) as an offline approach, as they consider a fixed and known loss function aiming to minimize the optimization error (see their Eq. 5). In the final version... | Summary: This paper studies the setting of online RL with concave utility function. This paper analyze two settings:
1. When the learner receive the full information of the utility function at each step.
2. When the learner only receive bandit feedback at each step.
At the first setting, the learner propose an FTRL-ty... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We address the raised concerns below.
- **Tabular RL:** We agree with the reviewer that both the linear MDP case and the case with function approximation are interesting directions for future work. However, since the adversarial convex reinforcem... | Summary: The paper proposes a mirror descent algorithm (with exploration bonuses) for achieving sub-linear regret in concave utility RL in an online episodic and adversarial setting. The authors first propose an algorithm designed for full-feedback over adversarial losses, achieving sub-linear regret. Then, they propos... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We address the raised concerns below.
- **Regret:** In our experiments, we compare our approach with the oracle optimal policy, which can be well approximated when the dynamics are fully known. We will specify it in the final version of the paper... | null | null | null | null | null | null |
BAnG: Bidirectional Anchored Generation for Conditional RNA Design | Accept (poster) | Summary: This paper proposes a deep learning-based model, RNA-BAnG, for designing RNA sequences that interact with specific proteins without requiring extensive experimental data or structural knowledge. Its core innovation, Bidirectional Anchored Generation, exploits the presence of functional binding motifs within br... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful comments and the ideas provided for additional experiments.
We understand that the **synthetic task** serves as a controlled environment to test the model’s performance, and we agree that the simplifications involved may not fully reflect the com... | Summary: The paper proposes a generative model which takes as input a protein structure and generates a potential RNA binder to that protein. The main methods contribution is a modification to usual left to right autoregressive generation which better fits the given task.
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# Post-rebuttal, based on authors' last c... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for bringing attention to key aspects of our study. Below, we address your concerns and clarify key points regarding our methodology and evaluation.
**RNA structural information** from PDB was used only during data preprocessing to identify interacting nucleotides;... | Summary: This manuscript presents RNA-BAnG, a deep learning model for generating RNA sequences that bind to specific proteins. The method involves a novel Bidirectional Anchored Generation (BAnG) technique, which generates RNA sequences by conditioning on protein sequence and structure. RNA-BAnG utilizes geometric atte... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. We appreciate your suggestions and have made adjustments accordingly. Below, we address the points you raised regarding our methods and analysis. Additionally, we are exploring ways to provide a more detailed **analysis of the binding motifs**, as suggested, to... | Summary: This paper proposes RNA-BAnG, a deep learning framework for conditional RNA design, focusing on generating RNA sequences that can bind to specific proteins. The core contribution is the introduction of the Bidirectional Anchored Generation (BAnG) method, which generates RNA sequences bidirectionally starting f... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments. Let us now address the specific questions and concerns raised in the review regarding our evaluation choices and methodological decisions.
**Expanding the comparison** to additional baseline models is challenging because most existing models (cite... | Summary: The paper introduces RNA-BAnG, a model for designing RNA sequences that bind to specific proteins, which does not require to be trained on extensive experimental data of RNA sequences known to interact with target proteins or detailed RNA structural information. The model combines a novel generative method. BA... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and suggestions. We will add the necessary citations and work on further clarifying our methods and biological explanations to ensure the content is more accessible.
In response to the reviewer’s request, we have conducted **ablation studies** to a... | null | null | null | null |
Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks | Accept (poster) | Summary: The authors propose a principled approach using Bayesian optimization to optimize stabilizing actions to efficiently stabilize a tokamak (a type of nuclear fusion reactor). The author’s proposed approach integrates both more-reliable observed data from the tokamak when specific actions are taken, with less-rel... | Rebuttal 1:
Rebuttal: Thank you kindly for your detailed review. We have corrected all the typos you pointed out. Here are the answers to your questions:
**Lessons:** During the experiments, we realized the importance of having a compressed representation of the actuator space. The high data efficiency of our approach... | Summary: This paper introduces a multi-scale Bayesian optimization approach, termed DynaBO, specifically designed to control tearing instabilities in tokamaks. The approach integrates a high-frequency Recurrent Probabilistic Neural Network (RPNN) with a low-frequency Gaussian Process (GP), allowing rapid adaptation bet... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. If you feel that our responses to other reviewers improve the quality of the paper, we would be very thankful if you would consider changing your score accordingly. | Summary: - Machine learning algorithms face difficulties in controlling complex real-world systems, i.e., nuclear fusion.
- Existing methods like reinforcement learning and Bayesian optimization fall short of fully addressing these challenges.
- A new approach integrates a high-frequency data-driven model with a low-fr... | Rebuttal 1:
Rebuttal: Thank you kindly for your review. We have addressed your questions and comments in detail below.
**Definition of frequencies:** By frequencies, we mean the resolution of the input signal. The RPNN takes as input the full step-by-step actuator signals, which have a frequency of 50Hz, and makes pre... | null | null | null | null | null | null | null | null |
SkipGPT: Each Token is One of a Kind | Accept (poster) | Summary: The paper focuses on speeding up inference of large language models (LLMs) by skipping different layers, at the granularity of attention or feed forward network (FFN) layers, for different tokens. It does this by introducing a router (a simple MLP layer with Gumble-Softmax) for each attention and FFN layer. It... | Rebuttal 1:
Rebuttal: **Q1**: The reviewer suggests including experiments on LLaMA3, which was released in April 2024.
**Response**: To address the reviewer’s suggestion, we conducted additional experiments on LLaMA3-8B, evaluating SkipGPT under both 25% and 40% sparsity settings. Due to space constraints, we compar... | Summary: The authors propose SkipGPT by addressing the challenges of existing dynamic pruning methods, namely horizontal dynamics, vertical dynamics, and the training paradigm. In other words, they redefine sparsity, separate MLP and self-attention within layers, and train routing and LoRA in a two-stage process.
Clai... | Rebuttal 1:
Rebuttal: **Q1**: The phrase "novel sparsity concept" seems exaggerated.
**Response**: We appreciate the reviewer’s feedback regarding the “novel sparsity concept.” Our intention was not to overstate novelty in this regard. Rather, we aimed to highlight that SkipGPT enables fully dynamic layer skipping, wh... | Summary: The paper proposes a method to prune LLMs in horizontal (per token processing) and vertical (layer-wise) dimensions. It also provides a two-stage pruning pipeline in which first it trains a router given a fixed pre-trained LLM and then fine-tunes the model with LoRA adapters to recover the performance. Experim... | Rebuttal 1:
Rebuttal: **Q1:** Is the definition of sparsity in the paper appropriate and reflective of practical inference efficiency?
**Response:** Our sparsity metric, based on skipped modules, is a simple and consistent proxy for dynamic pruning, though it doesn't directly reflect FLOPs. While actual efficiency dep... | null | null | null | null | null | null | null | null |
ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces | Accept (poster) | Summary: This paper proposes a collection of quantization techniques to reduce memory usage of extreme classification (where the number of classes is large). Proposed techniques include stochastic rounding, Kahan summation, FP8 weight, FP16 gradient, chunking, etc, leading to several folds of memory usage reduction com... | Rebuttal 1:
Rebuttal: > Novelty: the paper is largely applying a collection of existing techniques to a specific problem. Key techniques such as stochastic rounding and chunking are quite standard. This paper looks more like an engineering optimization rather than research.
Please check our response to reviewer M72L o... | Summary: This paper considers the problem of extreme classification where given an input, it is to be categorized into a few categories among a large set of possible categories. Full one-vs-all classifier training is an expensive approach to solve this problem, but has been shown to give best results and be possible to... | Rebuttal 1:
Rebuttal: > Scope is limited to extreme classifier based approach which is not very broadly applicable.
Beyond the immediate applications in tagging, search and recommendation systems, large output spaces are becoming largely prevalent in modern LLMs [1,2,3] with increasing vocabulary sizes such as 256K to... | Summary: This paper introduces ELMO, a low-precision training framework designed to optimize memory and computation for Extreme Multi-label Classification (XMC), where the classification layer dominates memory and compute costs. Key results include: 1) 6× memory reduction compared to Renee (previous SOTA) for 3M-label ... | Rebuttal 1:
Rebuttal: > Fused update has already been proposed in early literature
Thank you for pointing out these references. While related in motivation, our approach differs in key aspects:
- LOMO: LOMO performs layer-wise fused updates by materializing gradients, applying optimizer steps, and releasing memory (ht... | Summary: This paper presents ELMO, an efficient training method designed for solving extreme multilabel classification (XMC) problems by leveraging low-precision computation. The key techniques that ELMO leverages are pure 16-bit training, classifier parameter chunking, and 8-bit training to reduce memory usage and imp... | Rebuttal 1:
Rebuttal: > It seems that using the ELMO method inevitably causes a precision drop compared to FP32 methods, which can potentially hurt the method's practicality for precision-sensitive applications.
On 5 out of 7 datasets in the paper, pure FP8/FP16 training with ELMO already achieves similar prediction p... | null | null | null | null | null | null |
TabFlex: Scaling Tabular Learning to Millions with Linear Attention | Accept (spotlight poster) | Summary: This paper explores the use of linear attention for TabPFN, to overcome its limitations in terms of scalability. Indeed, TabPFN is limited by the quadratic complexity of self-attention, making it inefficient for datasets with more than a few 1,000s samples.
The authors:
- Demonstrate experimentally that linear... | Rebuttal 1:
Rebuttal: We thank Reviewer 1peU for the insightful feedback and constructive suggestions. We are very excited that you truly enjoyed reading our work and finds that (i) our claims are well supported by clear and convincing evidence on well-chosen evaluation criteria, providing interesting finding on perfor... | Summary: The paper proposes an in-context learning architecture for tabular learning. The overall method is incremental to TabPFN.
Claims And Evidence: - **TabFlex improves scalability over TABPFN by using linear attention instead of quadratic attention.**: This claim is well supported by their theoretical analysis al... | Rebuttal 1:
Rebuttal: We thank Reviewer K5HQ for the detailed review and constructive suggestions.
We greatly appreciate that Reviewer K5HQ found that (i) our main claim (on improving scalability over TABPFN by linear attention) is well-supported by both theoretical analysis and corresponding empirical evaluation, (ii... | Summary: This paper evaluates scaling the tabular in-context model TabPFN to larger dataset sizes by using linear attention to circumvent the quadratic memory complexity of regular attention. They first compare to state-space-models (SSMs) like MAMBA, finding SSMs to underperform, attributed to their causal nature (in... | Rebuttal 1:
Rebuttal: We thank Reviewer qdGi for the constructive feedback and suggestions. We are encouraged that Reviewer qdGi found that our main claim (on linear attention and its performance) is well-supported by a sensible experiment design on the well-established benchmark. Please find our answers to your commen... | null | null | null | null | null | null | null | null |
Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity | Accept (poster) | Summary: The paper studies the problem of ranking a set of $N$ items using $M$ ranking oracles under weak or strong stochastic transitivity conditions. Each oracle $u$, when queried with a pair of items $i$ and $j$, independently returns an indicator of its preference, represented by a probability $p_{i,j}^u$. The obje... | Rebuttal 1:
Rebuttal: Dear reviewer thanks for your time and effort for the review and suggestions, we address your concern below:
**Q1**: Line 104–105, $\Delta := | p_{i,j} - \frac12 | $. Something may be missing here, e.g., min or sum.
**A1**: thanks for spotting this, the correct definition should be $\Delta := \... | Summary: This paper considers the problem of estimating the ranking of $N$ items using pairwise comparisons. . Two different assumptions are considered: weak stochastic transitivity (if $p_{ij} \ge 1/2$ and $p_{jk} \ge 1/2$, then $p_{ik} \ge 1/2$) and strong stochastic transitivity ($p_{ik} \ge \max (p_{ij}, p_{ik})$),... | Rebuttal 1:
Rebuttal: Dear reviewer thanks for your time and effort for the review and suggestions, we address your concern below:
**Q1**: Since the proposed method is a combination of high-level ranking algorithm and a low-level oracle selection algorithm, it is possible to consider benchmarks based on existing metho... | Summary: This paper studies the problem of ranking items using noisy pairwise comparisons from multiple oracles under both the weak stochastic transitivity (WST) and strong stochastic transitivity (SST) conditions. While previous work has explored ranking under SST with single and multiple oracles and WST for a single ... | Rebuttal 1:
Rebuttal: Dear reviewer, thanks for your positive comment. We address your question raised below:
**Q1**: ``medium'' in Algorithm 4 should be defined in the text.
**A1**: medium follows the definition in conventional statistics, where it’s the number(item) that is ranked in the middle of all numbers in t... | Summary: The paper studies the problem of identifying the ranking of a set of items by querying pairwise preferences from several oracles. In particular, they study two settings: (1) weak stochastic transitivity (WST), where there exists a ranking (permutation) of items such that items ranked higher has higher probabil... | Rebuttal 1:
Rebuttal: Dear reviewer, thanks for your positive comment. We address your question raised below:
**Q1**: Some assumptions might not hold in practical real-world applications. For instance, different people might rank different LLMs differently.
**A1**: It is possible to consider more general cases. Wit... | Summary: This paper addresses the problem of efficiently aggregating preferences from multiple oracles to determine rankings under different stochastic transitivity conditions. The authors propose two main algorithms: RMO-WST for the Weak Stochastic Transitivity (WST) setting, and RMO-SST for the Strong Stochastic Tran... | Rebuttal 1:
Rebuttal: Dear reviewer, thanks for your positive and constructive comments. We address your questions below:
**Q1**: The consistency assumption (Assumption 3.1) seems quite strong in practical settings. Have you considered a relaxed version where oracles can disagree on some small fraction of pairs? How ... | null | null | null | null |
Grokking at the Edge of Linear Separability | Accept (poster) | Summary: The paper studies the grokking phenomenon and points out that grokking occurs near the critical point where data separability transitions. Specifically, the authors consider a simple logistic regression problem in the limit as the number of data points and the dimension of the model go to infinity. They show t... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s positive evaluation and are pleased that they found our perspective on grokking to offer interesting insights. We have thoroughly addressed their concerns below. If they find our responses satisfactory, we hope they will be able to raise their confidence in acc... | Summary: The paper develops a minimal setup under the binary logistic classification task to theoretically characterize when and how grokking occurs. They provide both empirical and analytical insights into the mechanism of grokking. The theory utilizes past work on the implicit bias of gradient descent. The paper demo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and valuable feedback.
The reviewer's main concern is the simplicity of the setup and how our results could be generalized to other models (a point also raised by some of the other reviewers). However, the reviewer also noted that *“Nonetheless, I believe... | Summary: This paper considers Grokking phenomenon on a simple binary logistic regression model. The authors consider gradient descent (GD) for solving a binary logistic regression problem and analyze the relationship between separability, generalization and overfitting.
In particular, they consider the case when the ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s thoughtful feedback and positive appraisal of our work. We hope that by addressing the reviewer’s main concerns, they will be able to raise their confidence in accepting our work.
**Weaknesses:**
1. *Limitations of simple models* - The reviewer's main concern... | Summary: This work investigates the grokking phenomenon where an increase in test performance is significantly delayed behind achieving perfect training performance. It primarily considered the highly simplified case of a linear model with constant labels, which is analyzed theoretically. In particular, the grokking ef... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and useful feedback, and are glad that the reviewer tends towards accepting our paper.
The reviewer's primary concern is regarding the applicability of our setting to other existing models of Grokking in the literature, which we address below, along with ... | null | null | null | null | null | null |
Sanity Checking Causal Representation Learning on a Simple Real-World System | Accept (oral) | Summary: This work evaluates causal representation learning (CRL) methods on a simple, real-world optical system designed as a sanity check for CRL assumptions. The authors argue that while many CRL methods show theoretical promise, they fail when applied to this controlled real-world system due to critical issues such... | Rebuttal 1:
Rebuttal: Thank you for your review. We respond to your points below.
**Motivation & differences to other benchmarks:** As all other reviewers highlight and we stress in the paper, the primary distinction of our sanity check is that it stems from a real, physical system to evaluate the practical validity o... | Summary: The authors test three representative causal representation learning (CRL) algorithms on real-world data generated from Causal Chambers, which is a small light tunnel that takes in 5 controllable inputs (factors) and outputs numerical sensor and imaging data. The authors treat these measurement processes as a ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive feedback.
**Re: Gamella et al., 2025:** Thank you for raising this point. As other reviewers (KaPR, uuRR) also pointed this out, we will add a paragraph text in section 2 to clearly separate our contributions from Gamella et al., 2025. Please see... | Summary: The paper benchmarks 3 representative causal representation learning methods on real data produced by a simple controlled physical system with known ground truth. CRL models have underlying causal factors that are "mixed" into observed variables. In this benchmark, the underlying causal model is simulated (Lin... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review! We answer your points below.
**Re: bayesian:** We have fixed this in an updated version of the manuscript, thank you.
**Re: Contributions w.r.t.. Gamella et al., 2025:** Indeed, Gamella et al., 2025 introduce the light tunnel. In our work we leverage... | Summary: The authors evaluate existing methods for causal representation learning (CRL) on a real-world system that appears to meet the assumptions of such methods, and yet they show that the methods almost entirely fail to recover a valid causal model of the system. Specifically, they test methods under assumptions ab... | Rebuttal 1:
Rebuttal: Thank you for your positive review!
We wanted to be cautious about making unsubstantiated claims regarding the failure points of the algorithms. Due to their complexity (especially Multiview CRL and CITRIS), understanding the exact source of failure may be very difficult, and it may not be possib... | null | null | null | null | null | null |
Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set | Accept (poster) | Summary: This paper challenges the widely held belief that linear Q-learning can diverge and instead proves that linear Q-learning converges to a bounded set. Unlike previous studies that required algorithmic modifications (e.g., target networks, experience replay), this work establishes the first L2 convergence rate f... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and perfect score for our manuscript. We appreciate the opportunity to clarify the comparison of our tabular $Q$-learning convergence rate with existing literature:
> Comparing the convergence rate for the tabular case with existing rates in the literature wo... | Summary: This paper provides the first L² convergence rate analysis for linear Q-learning with no algorithmic modifications. The authors show that linear Q-learning converges to a bounded set without requiring target networks, weight projection, experience replay, or regularization techniques that are typically employe... | Rebuttal 1:
Rebuttal: We're grateful for your thoughtful inquiry and positive assessment. We address your points below:
> Could empirical evidence be provided ...?
This paper studies the same algorithm as Meyn (2024), which already provides extensive empirical results on its behavior. So we feel there is no need to ... | Summary: The paper provides a theoretical analysis of Q-learning with linear approximation and a tabular setting.
Claims And Evidence: The claims made in the manuscript are clear and well-written.
Methods And Evaluation Criteria: No numerical experiments.
Theoretical Claims: Please see questions.
Experimental Desig... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and careful reading of our manuscript, which has improved our manuscript. We will incorporate your suggestions into the revised version. Below are our responses:
> Assumption A1 seemed non-trivial, and the citation to Zhang (2021) Lemma 9 appears incorrect.... | Summary: The authors propose the analysis of Q-learning with linear functional approximation that identifies under an assumption of $\varepsilon$-softmax parametrization with an adaptive temperature that keeps the norm of the logits fixed, the rate of convergence to a bounded region. Additionally, the authors provided... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and positive evaluation. We appreciate the opportunity to address the specific questions and comments.
> Hardness results in Liu (et al., 2023).
Thanks for the excellent suggestion. The hardness results indeed provide key context and may suggest that convergen... | null | null | null | null | null | null |
Meta-Learning in Self-Play Regret Minimization | Reject | Summary: Traditional self-play methods are often used to compute equilibria in large, extensive-form, two-player, zero-sum games. This submission studies meta-learning in games in the self-play setting, motivated by the observation that many real real world decision making problems involves a distribution of related-bu... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewers for their time spent to help improve our work.
We appreciate all the comments and will integrate them into a revised version of the paper.
Let us address the questions and comments raised.
We would like to politely disagree with your point about th... | Summary: This paper extends meta-learning (i.e., learning a regret minimizer algorithm over a sequence of games drawn from a distribution) to the self-play setting. In particular, it derives a new meta loss for training a regret minimizer. The performances of this procedure are evaluated on two-player normal form and e... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewers for their time spent to help improve our work.
We appreciate all the comments and will integrate them into a revised version of the paper.
Let us address the questions and comments raised.
We don't want to claim the cross-infostate communication gu... | Summary: The authors extend Neural Online Algorithm (NOA) and Neural Predictive Regret Matching (NPRM) from Sychrovský et al., 2024 to the self-play setting, creating a meta-learned self-play regret minimizer. This is done by modeling the computational graphs for both players instead of just one. In two-player zero-sum... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewers for their time spent to help improve our work.
We appreciate all the comments and will integrate them into a revised version of the paper.
Let us address the questions and comments raised.
While the normal-form games we evaluate our algorithms can ... | null | null | null | null | null | null | null | null |
Revisiting Non-Acyclic GFlowNets in Discrete Environments | Accept (poster) | Summary: The paper introduces a new theory of Non-Acyclic GFlowNet. The main intuition is that in the non-acyclic case, the visiting probability of an edge doesn't follow the flow matching constraint, because it doesn't take into account the cycles. Rather, the expected number of visits satisfies the FM, making it the ... | Rebuttal 1:
Rebuttal: We highly appreciate the feedback from the reviewer and are happy to answer their questions. We are pleased that the reviewer acknowledged the contributions of our paper and found it very well written.
Firstly, we would like to note that we actually implement the permutation task with a more comp... | Summary: This paper offers a new perspective on the theory of GFlowNets in the cas where the state space is no longer acyclic. The author analyzed an existing theory of non-acyclic GFlowNets and provide insights as to when their proposed approach is necessary, and show that under an appropriate regularization, all exis... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their constructive feedback and valuable suggestions, and are happy to provide further details.
Firstly, we thank the reviewer for the provided reference to [1]. While Proposition 4.1.3 does not state minimality of the expected trajectory length of the solution... | Summary: This paper revisits the theoretical framework of Generative Flow Networks (GFlowNets) in non-acyclic discrete environments, where the traditional assumption of acyclicity is relaxed. The authors propose a simplified and more intuitive formulation of non-acyclic GFlowNets that extends prior work by Brunswic et ... | Rebuttal 1:
Rebuttal: We want to express our gratitude to the reviewer for the very detailed review and valuable suggestions. We are pleased that the reviewer acknowledged the contributions of our paper, novelty and soundness of our theoretical results, as well as the strengths of our experimental design.
Regarding th... | null | null | null | null | null | null | null | null |
LEAD: Large Foundation Model for EEG-Based Alzheimer’s Disease Detection | Reject | Summary: This paper introduces LEAD, the first large foundation model for EEG-based Alzheimer’s disease detection, which overcomes challenges related to small dataset sizes and inter-subject variability by curating the largest EEG-AD corpus to date with 813 subjects from nine datasets. The proposed pipeline features ro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our work! We appreciate your careful reading and endorsement. Below, we address each of your questions and concerns:
---
**Q1**: How manage dataset quality and potential demographic bias?
**A1**:
**Data Quality**
For data quality, we initially surve... | Summary: This paper proposes a foundational model called LEAD for the early diagnosis of AD using EEG. The authors constructed a large EEG-AD dataset comprising data from 813 subjects and utilized 11 EEG datasets (4 AD and 7 non-AD) to perform pre-training via self-supervised contrastive learning. Subsequently, the mod... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and endorsement of our work! Below are our responses to each of your points:
---
**Q1**: The effect of pre-training with other EEG data types (motor imagery, sleep, epilepsy, etc.).
**A1**: Thank you for this suggestion. In the original paper, we did include... | Summary: This paper presents LEAD, a large foundation model for EEG-based Alzheimer’s Disease (AD) detection. The authors curate one of the largest EEG-AD datasets, comprising 813 subjects, and propose a comprehensive pipeline including data preprocessing, self-supervised contrastive pretraining, and unified fine-tunin... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our work! Below are our responses to each of your concerns. If you still feel we have not adequately justified a higher score, please let us know how we can further improve.
---
**Q1**: Using both sample-level and subject-level modules doesn’t always impro... | Summary: In the proposed manuscript, the authors present LEAD, a large foundational model trained in contrastive learning framework, for the classification of Alzheimer's disease.
From the provided comparisons, the proposed approach outperforms the current state of the art models in two-class Alzherimer's disease detec... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper! We respond to each of your questions with new experiments, more references, and detailed elaborations. If you do not feel we have sufficiently justified a higher score, please let us know where we can further improve our work. Due to the space limitatio... | null | null | null | null | null | null |
CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning | Accept (poster) | Summary: The paper presents CERTAIN, a novel framework designed to address challenges in context-based offline meta-reinforcement learning (COMRL), particularly context ambiguity and out-of-distribution (OOD) issues, in one-shot adaptation settings. The authors propose leveraging heteroscedastic uncertainty in task rep... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ constructive feedback. Below, we respond to each concern point by point. We will incorporate all the reviewers’ suggestions in the final version of the paper.
> Reviewer:
>
> Weaknesses
>
> 1.While the paper provides strong empirical results ...
>
> 2.Wh... | Summary: This paper presents the CERTAIN method for Offline Meta Reinforcement Learning. The CERTAIN method models uncertainty for each transition sample explicitly, and performs task representation by selecting samples with lower uncertainty, thereby achieving more accurate task identification. Experimental results in... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ constructive feedback. Below, we respond to each concern point by point. We will incorporate all the reviewers’ suggestions in the final version of the paper.
> Reviewer: Uncertainty Estimation: The paper uses the Heteroscedastic Uncertainty loss function fr... | Summary: This paper studies the task representation problem in context-based offline meta-reinforcement learning (COMRL). It first identifies the problem of task uncertainty in a context, including task ambiguity and out-of-distribution problems. Then, the paper proposes a training method that learns both context repre... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ constructive feedback. Below, we respond to each concern point by point. We will incorporate all the reviewers’ suggestions in the final version of the paper.
> Reviewer:
> - (line 40, right) ... However, CORRO [1] focuses on addressing OOD contexts in OMR... | Summary: This paper deals with the context ambiguity problem in context-based offline meta-reinforcement learning. The authors propose an uncertainty-aware context-collection algorithm to produce in-distribution, unambiguous contexts using heteroscedastic uncertainty estimates as rewards. Experiments are conducted in s... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ constructive feedback. Below, we respond to each concern point by point. We will incorporate all the reviewers’ suggestions in the final version of the paper.
> Reviewer: Definitions 3.1-3.3 are not very clear ... How is $\sigma$ defined and computed?
Resp... | null | null | null | null | null | null |
Faster Approximation Algorithms for k-Center via Data Reduction | Accept (poster) | Summary: This paper presents fast algorithms for approximate $k$-center in Euclidean spaces. In general metric spaces, it is not possible to be faster than $n\cdot k$ for any bounded approximation. A folklore result in Euclidean spaces yields a $O(n\cdot(k + d)\cdot\varepsilon^{-2}\log n)$ for a $2+\varepsilon$ approxi... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments. We address the main concerns as follows.
> As a alternative for deriving the bound, one could also first construct a spanner for high dimensional Euclidean spaces by Sariel Har-Peled, Piotr Indyk, Sidiropoulos (SODA'13) and then run an algorithm for... | Summary: This paper studies fast algorithms for the k-Center problem, focusing on the regime of large k. It presents a novel approach based on the corsets that achieves better running time.
Claims And Evidence: The claims are supported by the evidence.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments. We address the main concerns as follows.
> The algorithm achieves a constant factor approximation but not 2.
We agree that achieving a factor of $2$ in near-linear time when $k = n^{1 - \epsilon}$ is an ideal goal and is certainly an interesting ope... | Summary: The paper proposes speeding up existing algorithms for the $k$- center problem by using coresets. An $\alpha$-coreset for $k$- center problem is a subset of data such that if you have a $\beta$ approximation for the $k$- center problem on the subset then you have an $(\alpha + \beta) $ approximation for $k$-ce... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments. We address the main concerns as follows.
> In terms of methods, in coreset literature, people typically obtain centers from the coreset and then report the loss on full data using those centers and compare it with the cost on full data when the probl... | Summary: The paper "Faster Approximation Algorithms for k-Center via Data Reduction" presents a study on efficient algorithms for solving the Euclidean k-Center problem, focusing particularly on large values of k. The main contribution of the paper is the development of approximation algorithms using a data reduction a... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments. We address the main concerns as follows.
> General Applicability of Coresets: ... The paper focuses on the Euclidean k-Center problem, so it’s unclear how easily these methods could extend to non-Euclidean metrics or more complex clustering structur... | null | null | null | null | null | null |
Tuning LLM Judge Design Decisions for 1/1000 of the Cost | Accept (poster) | Summary: This paper aims to broadly profile the various factors impacting judge LLM performance and results, systematically analyzing the impact of factors related to prompt, hyperparameter selection, answer extraction method, and model design. It adopts a cost-effective approach to minimize search overhead while prese... | Rebuttal 1:
Rebuttal: Thank you for your review and for reviewing all appendices. We are delighted that you found the analyses relevant and insightful across all settings.
Please find our answer for the two points raised in your review.
**Experiments could consider finer gradations of inference temperature.**
Indeed... | Summary: This paper proposes an efficient way to finetune LLM judges via a multi-objective multi-fidelity approach.
Claims And Evidence: The claim is supported by experiments on three models and three datasets. Therefore the result is convincing.
Methods And Evaluation Criteria: No significant flaws in method and eva... | Rebuttal 1:
Rebuttal: Thank you for your review. We are delighted to hear that you found the results convincing.
Please find our answers below to the three points raised in your review.
**The number 1/1000 in the title does not have clear support in the main text. Therefore, it should be replaced with non-quantitati... | Summary: This paper is more like an extensive experimental report. The authors systematically analyze the hyperparameters of LLM judges, including the choice of model, inference parameters, and prompt hyperparameters (e.g., output format, provide answer or other information, JSON formatting). Overall, our search space ... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable feedback. We answer here the main three points raised by your review.
**Comparison with other search approaches. The methodology relies on brute-force search.**
We believe the characterization of brute-force may be a bit strong since we are using an efficie... | Summary: This paper proposes a cost-effective approach to systematically tune hyperparameters of Large Language Model (LLM)-based judges for evaluating other LLMs, significantly reducing the required resources. The authors leverage a multi-objective, multi-fidelity optimization framework to efficiently search through 4... | Rebuttal 1:
Rebuttal: Thank you for your feedback and thorough review. We are delighted to hear that you found the approach sound and well-executed.
Your point on potential bias is indeed very relevant. One concern one could have is that the selection could on one hand improve human agreement but, on the other hand wo... | null | null | null | null | null | null |
Sample Efficient Demonstration Selection for In-Context Learning | Accept (poster) | Summary: The paper presents a novel and efficient method for selecting demonstration examples in In-Context Learning (ICL). The proposed method, CASE, is shown to outperform existing exemplar selection techniques by significantly reducing the number of LLM calls, improving efficiency, and maintaining task performance.
... | Rebuttal 1:
Rebuttal: Dear Reviewer xSam,
Thank you very much for providing us with valuable feedback. We appreciate the detailed comments. Below, we have provided responses to each of your comments.
### Other Strengths And Weaknesses
***Can the method be applied to machine translation in ICL? How to select suitable... | Summary: This paper studies the in-context sampler selection problem using MAB.
The proposed method can work in isolation or combined with existing variants.
Results look promising.
Claims And Evidence: I have questions regarding this part.
Can the authors elaborate more on the train-validation-test data split process... | Rebuttal 1:
Rebuttal: Dear Reviewer FjhN,
Thank you very much for providing us with valuable feedback. Below, we have provided responses to queries raised in the review.
***Train-Validation-Test data split***
> The train-test split is provided in **Appendix C** and **Table 3**. For subset selection runs, we select $2... | Summary: The paper introduces a sample-efficient method for exemplar selection in ICL with LLMs. It formulates the selection of high-scoring exemplar sets as a top-$m$ best arms identification problem in stochastic linear bandits with a crafted linear reward model based on sentence similarity between exemplars and vali... | Rebuttal 1:
Rebuttal: Dear Reviewer j7VF,
Thank you very much for providing us with valuable feedback. We appreciate the detailed comments. Below, we have provided responses to queries raised in the review.
### Questions
***How can the proposed method adapt to the tasks unseen in the validation tasks?***
> Our goal ... | Summary: This paper investigates efficient example selection for ICL. It formulates the selection of exemplars as a top-m best arms identification problem. To address the challenge that the space of possible subsets (arms) is combinatorially large, the authors propose the sampling-based CASE method that maintains a sho... | Rebuttal 1:
Rebuttal: Dear Reviewer 946f,
Thank you very much for providing us with valuable feedback. We appreciate the detailed comments. Below, we have provided responses to queries raised in the review.
### Methods And Evaluation Criteria:
***Reward for an arm can be modeled as a linear function of its features.... | null | null | null | null | null | null |
InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory | Accept (poster) | Summary: The author proposes a novel InfoCons framework based on the principle of information theory. The framework divides point clouds into different 3D concepts with different influences by using the mutual information principle. It also learns meaningful concept structures by combining learnable prior knowledge. Th... | Rebuttal 1:
Rebuttal: **Dear Reviewer LVUY,**
We sincerely thank you for your thorough review and valuable comments on our paper. We have summarized your concerns into three parts and provided our responses as follows:
### 1: **Balancing Fidelity and Conceptual Coherence**
Thank you for raising an important question... | Summary: This paper mainly focuses on how to extract interpretable key concepts in point cloud models to enhance the interpretability of the models. This work addresses the issue that existing methods often fail to simultaneously meet the two criteria of "faithfulness" and "conceptual cohesion" when providing interpret... | Rebuttal 1:
Rebuttal: **Dear Reviewer vGFq,**
We sincerely thank you for your thorough review and detailed comments on our paper.
In response to your concerns regarding the statement on “Good Explanations” and our qualitative comparison, we provide the following clarifications.
### 1: **Clarification on “Good Explan... | Summary: This paper addresses the problem of explaining decisions made by point cloud classification models, which is particularly important in applications such as autonomous vehicles. Current methods mainly focus on mathematical values like gradients or neuron activations. However, the authors break down the 3D point... | Rebuttal 1:
Rebuttal: **Dear Reviewer h8Li,**
We sincerely appreciate your thorough review and your positive assessment of our work. Based on your feedback, we will refine the writing and presentation to enhance readability as follows:
### **1: Expanding the Related Work Section and Adjusting Paper Structure**
In the... | null | null | null | null | null | null | null | null |
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes | Accept (poster) | Summary: The paper investigates the convergence of gradient-based methods with large and adaptive step sizes on logistic regression with linearly separable data. The main result establishes that GD can achieve arbitrarily fast convergence rates by using an adaptive step-size schedule.
Furthermore, the authors prove a l... | Rebuttal 1:
Rebuttal: Thank you for your comments and pointing out missing references. We will cite and discuss them in the revision. We address your questions below.
---
Q1: “The arbitrarily fast convergence rate is trivial in this setting…..”
A1: We respectfully disagree. Note that the algorithm you proposed is m... | Summary: This paper considers using GD to optimize linear classification losses, primarily the exp and logistic losses, but also extended to certain qualitatively similar losses. They show that GD using a particular adaptive stepsize schedule which is roughly proportional to the reciprocal of the loss value (for the lo... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! You are correct that there is a typo in the statement of Theorem 5.2. We will make sure to fix it (and all other typos) in the revision. We address your other questions as follows.
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Q1: “...Is this type of adaptive stepsize useful for non-separable linear c... | Summary: The paper shows that in logistic regression with linearly separable data, gradient descent can achieve arbitrarily fast convergence through large and adaptive stepsizes for exponential and logistic loss. This occurs in the edge of stability regime and does not require monotonic risk decrease to occur. Addition... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We will make sure to correct all the typos in the revision. | Summary: The authors analyze the gradient descent optimization procedure for logistic regression in the large-stepsize regime. Their upper bounds lead to a "soft-perceptron" view of logistic regression, which extends to two-layer leaky ReLU networks and other loss functions with regularity properties similar to exponen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for pointing out the typos. We will make sure to fix all typos in the revision. We address your questions as follows.
---
Q1: “It is widely accepted that the average-iterate convergence and the last-iterate convergence are not directly comparable in ge... | null | null | null | null | null | null |
Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching | Accept (poster) | Summary: The paper introduces a framework for asynchronous stochastic optimization that leverages quantile delays instead of traditional average delay measures. It presents a black-box conversion that transforms any standard stochastic first-order method into an asynchronous version with only simple analyses of classic... | Rebuttal 1:
Rebuttal: Thanks for the feedback—please see below our responses to the main points you raised.
> “This work is entirely theoretical; the lack of empirical evaluation limits the practical effectiveness and robustness of Algorithms 1 and 2 in real-world scenarios.”
Our work is primarily theoretical, focusi... | Summary: This paper studies the convergence of asynchronous stochastic methods, and the key differences with the literature include 1) a relatively general algorithm framework; 2) a new model/characterization of the delays, which, compared to delay models like maximum/average delay, can characterize the delays better. ... | Rebuttal 1:
Rebuttal: Thanks for the review and strong support! Please see below our responses to the main points you raised.
> “I would expect a theoretical bound $\bar{\tau}_q$ on $\tau_q$. This is because that $\bar{\tau}_q$ is used to determine $B$ in Algorithm 1, while $B$ may affect $\tau_q$. Therefore, without ... | Summary: N/A
Claims And Evidence: N/A
Methods And Evaluation Criteria: N/A
Theoretical Claims: N/A
Experimental Designs Or Analyses: N/A
Supplementary Material: N/A
Relation To Broader Scientific Literature: N/A
Essential References Not Discussed: N/A
Other Strengths And Weaknesses: I do not believe I am suffic... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for your transparency regarding your familiarity with the field in relation to the review.
> “The algorithms are poorly written, to the point of being nearly incomprehensible.”
The algorithms aggregate gradients for mini-batching while filtering stale ones based o... | Summary: The authors consider the problem of stochastic optimization with delays. Concretely, they minimize an objective function $f:\mathcal{W} \to\mathbb{R}$ for a convex set $\mathcal{W} \subseteq \mathbb{R}^d$. They consider access to a stochastic unbiased gradient oracle with variance $\sigma^2$. Additionally, at ... | Rebuttal 1:
Rebuttal: Thanks for the feedback—please see below our responses to the main points you raised.
> “The authors provided **no experiments**. As the goal of this paper is to propose an algorithm, I request the authors to provide some experiments for their algorithm in practice.”
Our work is primarily theore... | Summary: This paper proposes an asynchronous mini-batch black-box algorithm that aggregates asynchronously computed stochastic gradients as input to any stochastic-gradient-type optimization algorithm. In contrast to performing biased update using stale stochastic gradients, the proposed algorithm adaptively aggregates... | Rebuttal 1:
Rebuttal: Thanks for the feedback. Due to space constraints, we provide our responses to the main points below.
> “What does “Play $w_t=\tilde{w}_k$” means?”
In the asynchronous paradigm we consider, at each step $t$ the algorithm plays a point $w_t$ (essentially the current model at time $t$). This line ... | null | null | null | null |
On the Provable Separation of Scales in Maximal Update Parameterization | Accept (poster) | Summary: This paper provides a theoretical framework for analyzing the separation of “macro” and “micro” scales in large-width neural networks trained under the Maximal Update Parameterization (μP). Building on techniques from stochastic differential equations (SDEs) and tools in wide-network theory, the authors prove ... | Rebuttal 1:
Rebuttal: 1. Comparison with Earlier Wide-Network Theory Frameworks:
We appreciate the suggestion to clarify how our work provides a novel perspective complementing earlier wide‐network theories like the NTK and mean-field SDE frameworks. In our paper, we emphasize an explicit separation of scales that dis... | Summary: The authors try to propose a theoretical framework for hyperparameter transfer in neural networks under maximal update parameterization (µP) by trying to demonstrate a separation of scales between macro-variables (such as loss landscapes, activation norms, and gradient statistics) that converge at an $O(1/n)$ ... | Rebuttal 1:
Rebuttal: 1. Def. of “Separation”:
Thanks for suggesting the formal use of “separation” in learning theory. Here, we use “separation of scales” to describe the difference in convergence rates between macro- and micro-variables under μP. We show that loss landscape descriptors converge at rate O(1/n), while ... | Summary: The authors explained why hyperparameter tuning can be done effectively at early stages of training or narrower networks under the Maximal Update Parameterization scheme. They defined the seperation of scales for μP between macro-variable (loss, activation variance, gradient norms etc) and micro-variable (weig... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and thoughtful comments. Below we address each point raised:
(1) Lemma 4.7 and the asymptotic notation: The reviewer correctly identified that if L is treated as a constant independent of n, this would typically yield O(n^(3/2)). However, in our an... | Summary: The paper provides a theoretical framework to understand Maximal Update Parameterization ($\mu$P). It introduces a decomposition of variables into macro-level descriptors (e.g., gradient norms, loss landscapes) and micro-level variables (e.g., individual weights). Via the formulation, the analysis shows why hy... | Rebuttal 1:
Rebuttal: 1. Width-Dominance
Our analysis explicitly relies on the width-dominance regime (Assumption 3.2) ensuring as width grows, certain terms dominate the learning dynamics. We will revise to explicitly mention it in the abs/intro. Note that it is a standard condition formalizing the intuition that we... | null | null | null | null | null | null |
Reinforcement Learning with Random Time Horizons | Accept (poster) | Summary: The paper derives the policy gradient theorem for the setting where the MDP horizon is random (and typically policy dependent). Algorithmically, the "corrected" PG boils down to the standard PG with a multiplicative factor correction for the expected horizon length.
Numerical experiments are carried in two en... | Rebuttal 1:
Rebuttal: Dear Reviewer WigC. We thank you very much for your careful review and are happy that you - similar to the other reviewers - think our "corrected" policy gradient in principle "has merit" and adds a meaningful contribution to the reinforcement learning community. We want to highlight that - compar... | Summary: In this paper, the authors consider the problem of undiscounted, random horizon RL. In this setting, a learner is attempting to optimize the cumulative reward of a policy interacting with an MDP such that the horizon, $N$, is a possibly random stopping time adapted to the filtration of the episode thus far. ... | Rebuttal 1:
Rebuttal: Dear Reviewer TpCt. Thank you very much for your educated review. We are happy that you conclude that our contribution is closing an important gap in reinforcement learning and we appreciate that you value our numerical benchmarks as appropriate.
Thank you also for raising the aspect of time-in... | Summary: This paper considers a more realistic setting of reinforcement learning when the time horizon is random rather than fixed finite or infinite. The authors extend the RL to incorporate random time horizons and present the expected returns under the random time horizon from both trajectory-based and state space b... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We are happy that you value our "clear theoretical advances", addressing "the gap between the standard formulation and the real application", thus leading to a "valuable contribution to the field of reinforcement learning".
Thanks for asking for more comprehen... | null | null | null | null | null | null | null | null |
Chip Placement with Diffusion Models | Accept (poster) | Summary: This paper proposes to address the challenges faced in RL-based placement methods, including 1) scalability to larger circuits, and 2) the trajectory cannot be reversed in RL-based methods. A method to synthesize placement data, and a diffusion-based method to tackle the placement task are proposed.
## update... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback. Our response is as follows:
**EfficientPlace:** Although EfficientPlace uses tree search to address the shortcomings of RL, it still requires significant training on every new circuit to perform well. We present additional experiments on the IBM ... | Summary: The authors propose a new diffusion-based method to address chip placement. Compared to existing RL approaches, pre-trained diffusion models can obtain the placement results on new circuits within minutes, which are much more efficient. After global placement, users can fix the positions of macros and optimize... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. Our response is as follows:
**Choice of dataset:** We choose the IBM dataset for several reasons. First, it contains more circuits - 18, compared to 8 for ISPD2005. Second, the IBM dataset allows for easier comparison with other macro placement... | Summary: The authors proposed a diffusion model-based chip placement strategy. They also developed a novel data generation algorithm and a synthetic dataset, training the model to enable zero-shot transfer to real circuits. Additionally, they introduced a neural network model that demonstrates strong performance and sc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback. Our response is as follows:
**Additional benchmarks:** We have included experiments on the ISPD2005 benchmark, which we show in the table below. To facilitate comparison with baselines, we follow the suggestion of reviewer m7a3 and present HPWL an... | Summary: This paper applies diffusion models to macro placement. The motivation is that existing RL-based methods for macro placement are slow and lack flexibility. To provide more data for training, this paper generates synthetic data by randomly placing objects, sampling pins, and creating edges based on a distance-d... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and suggestions. We hope the following can address your concerns.
**PPA evaluation:** We agree that PPA evaluation and optimization is important. As a step towards analyzing and optimizing downstream objectives, we also evaluated congestion of our macro pla... | null | null | null | null | null | null |
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks | Accept (poster) | Summary: The paper investigates how scaling parallel data collection (i.e., the product of the number of parallel environments
$N_\text{envs}$ and rollout length ($N_\text{RO}$) affects the performance and representation quality of deep RL agents, focusing primarily on PPO (and briefly on a value-based variant, PQN).
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, useful comments, and address their concerns below.
## The claim is too general and suggests that there should be a trend (potentially a scaling law)
We ran extra experiments varying the number of environments, rollout lengths, and across different domai... | Summary: This paper focuses on the problem of reinforcement learning with multiple environments, which has gained increasing interest over the past years due to GPU utilization. Through empirical analysis of the effect of the number of environments and the length of rollouts, the authors provide recommendations (e.g., ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! We are glad that the reviewer finds that “the study is well-timed”, and “nice to have a paper dedicated to studying this setting and finding best practices”.
We address their main concerns below.
## two variables using two data points, which is unsubstan... | Summary: The paper claims that larger batch sizes in deep reinforcement learning obtained by parallelized data collectors help mitigate several optimization challenges, listed below, and give recommendations on the scaling of the dimensions of the batch size (num envs vs rollout size).
1) Performance gains: Increasing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! We are happy that the reviewer found that “the claims made in the paper are interesting and give important insights to RL researchers and practitioners” and that “the claims are backed by sufficient empirical evidence”.
We respond to their main concerns b... | null | null | null | null | null | null | null | null |
Unsupervised Learning for Class Distribution Mismatch | Accept (poster) | Summary: This paper proposes an Unsupervised Learning for Class Distribution Mismatch (UCDM), which constructs positive-negative pairs from unlabeled data for classifier training. The method randomly samples images and uses a diffusion model to add or erase semantic classes, synthesizing diverse training pairs. Extensi... | Rebuttal 1:
Rebuttal: > Q1: For small Weaknesses, "Imagenet: A large-scale hierarchical image database." appears twice in the references.
Thank you for your careful review. We will correct this in the final version.
> Q2: How can authors effectively ensure that the generated images are truly positive or negative sam... | Summary: The paper deals with learning with synthetic data from a stable diffusion model with labels that are obtained from prompting the model. The generated labels are only partially available to the model, i.e. the data is split in three subsets "known", "unknown" and "knew", where the known label categories are ava... | Rebuttal 1:
Rebuttal: > Q1:Unvalidated assumptions of noise estimators.
Thanks for your question.
The assumption **follows existing works[1,2,3]**. We further **verify its validity** as follows.
+ **Same assumption in DDIM and DDIM inversion.** DDIM[1] solves diffusion ODEs via **forward Euler, where $\epsilon(\math... | Summary: The paper addresses the problem of class distribution mismatch (CDM), where training and target task class distributions differ.
Previous methods rely on labeled data in semi-supervised settings, limiting applicability.
The authors propose Unsupervised Learning for CDM (UCDM), which uses a diffusion model to ... | Rebuttal 1:
Rebuttal: > Q1: Rationale behind the proposed motivation: While positioned as an unsupervised method, the use of a conditional diffusion model to generate positive-negative instance pairs appears to implicitly incorporate label information through the class-conditional generation process. This creates poten... | null | null | null | null | null | null | null | null |
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach | Accept (poster) | Summary: This paper proposes a novel adversarial training algorithm to improve the robustness of MoE models based on pilot studies on MoE attacks. They further interpolate the robust MoE and the unrobust MoE by linear interpolation to balance clean accuracy and robust accuracy.
Claims And Evidence: I am concerned abou... | Rebuttal 1:
Rebuttal: Thank you for raising the seven insightful questions. We appreciate your thorough review and address each concern in detail below.
1. ## Why Attack is not Weak?
We compared our self-implemented AutoAttack with the official library. The perturbations generated by both methods are identical, ... | Summary: The paper proposes:
1. A loss function that specifically enhance the robustness of experts in MoE architecture;
2. A dual-model strategy for robustness-accuracy trade-off;
3. A joint-training strategy for dual-model.
to enhance the adversarial robustness of MoE model.
Experiments are conducted on CIFAR10 a... | Rebuttal 1:
Rebuttal: Thank you for your comments.
## Contributions of Our Method
We appreciate the reviewer's comment. We would like to point out that aligning outputs with KL-loss and mixing clean and adversarial outputs are classical loss design techniques used in most of the adversarial training papers. We would ... | Summary: The paper studies the adversarial robustness of mixture-of-experts (MoE) models in detail, investigating the susceptibility to adversarial attacks to both the router and the experts modules. Under some assumptions, the paper proofs that the perturbation on the entire model can be decomposed as the sum of the p... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback and the opportunity to clarify and strengthen our submission. Below, we respond to each of your concerns in detail.
## A concern regarding assumption 5.3 not holding in real practice
We would like to clarify that Assumption 5.3 is reasonable and holds in se... | null | null | null | null | null | null | null | null |
Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching | Accept (poster) | Summary: The paper describes a technique for matching dataset schemas. They use a compositional language model for this. They benchmark their solution against multiple competing works and usually achieve superior performance.
Claims And Evidence: I have not found unsupported claims. However, I think the impact of the... | Rebuttal 1:
Rebuttal: Dear ``R-pQV8.``
Thank you for your thoughtful and insightful comments! We provide answers to each of the following in turn.
---
### **(A) Related work - Magneto**
Thank you for pointing out this work (Magneto), we will incorporate a discussion of it in the related work of the camera-ready. ... | Summary: The authors introduce Matchmaker, a self-improving compositional LLM program, where multi-stage LLM calls are involved for candidate generation, refinement, and confidence scoring for the task of schema matching, which the authors formulate in the context of information retrieval. Its self-improving aspect com... | Rebuttal 1:
Rebuttal: Dear ``R-15kv.``
Thank you for your thoughtful and insightful comments! We provide answers to each of the following in turn.
---
### **(A) Clarifications and questions on the dynamic nature of the algorithm**
---
### *Q1: Clarifying the dynamic nature of the algorithm - is it only generatin... | Summary: This paper introduces Matchmaker, a self-improving compositional language model (LLM) program designed for schema matching, a critical task in data integration and interoperability. Schema matching involves finding correspondences between attributes across disparate data sources with different schemas and hier... | Rebuttal 1:
Rebuttal: Dear ``R-mppD``,
Thank you for your insightful comments.
In *Part 1* we address points **already** addressed in our paper (responses A-E), then in *Part 2* we respond to additional points (F-J).
---
## **PART 1 - Points **already** addressed in our paper (A-E)**
---
### **(A) Scalability and... | Summary: This paper presents Matchmaker for schema matching problem, the task of finding matches between attributes across disparate data sources with different tables and hierarchies. Matchmaker has 3 main stages: candidate generation, refinement and confidence scoring. The authors also propose a synthetic data-based ... | Rebuttal 1:
Rebuttal: Dear ``R-9YfQ.``
Thank you for your thoughtful and insightful comments. We provide answers to each of the following in turn.
---
### **(A) Clarifying and motivating paper area as healthcare**
We agree with the reviewer that Matchmaker is generally applicable outside of healthcare. However, we ... | null | null | null | null | null | null |
Investigating Non-Transitivity in LLM-as-a-Judge | Accept (spotlight poster) | Summary: The authors argue that the existing automated (using LLM-as-a-Judge) LLM ranking algorithms are unreliable and not aligned with human judgement. The authors propose judgement transitivity as a metric of self-consistency to estimate the quality of judgement. In fact, the authors propose two judgement self-consi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful and positive comments, which significantly enhance the clarity and impact of our manuscript. Please see below for our detailed response.
> It is good to see the results for gpt-3.5-turbo, but its low performance is expected and does not carry ... | Summary: The paper shows that LLM judges have non-transitive preference in pairwise comparison, which is not only caused by position bias. Furthermore, non-transitivity can be mitigated by round-robin tournaments combined with the Bradley-Terry model. The efficiency can be further improved by Swiss-Wise Iterative Match... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback! To our understanding, the main concerns of the reviewer lie in 1) the limited evaluation on only the AlpacaEval datasets, and 2) the claim that round-robin tournaments reduce non-transitivity – we address both in our response. We hope that the... | Summary: This paper explores an issue in comparison-based evaluation: non-transitivity, meaning that in evaluations based on a baseline, if A > B and B > C, it does not necessarily follow that A > C.
The paper first defines how to measure this non-transitivity and establishes a framework for evaluating model perform... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and constructive feedback, which greatly helps us strengthen our manuscript. Below, we provide detailed responses addressing each of the reviewer’s comments:
> …since the authors only select GPT-4 and GPT-3.5 as judges, the robustness of the concl... | Summary: This paper investigates the assumption of transitive preferences in LLM-based evaluation frameworks. The authors highlight that non-transitivity exists in LLM judgments, leading to inconsistencies in model rankings depending on the choice of baseline. The authors propose using round-robin tournaments combined ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive and insightful comments. These suggestions have significantly enhanced the clarity and depth of our manuscript. Below, we address each concern raised in detail.
> The study does not include explicit human verification of non-transitive cases. Whi... | Summary: The paper investigates whether LLM exhibit non-transitive preferences when comparing model outputs. Typically, people use pairwise comparisons against a single baseline model, implicitly assuming transitive preferences. However, the authors find that such judgments can violate transitivity and that rankings ca... | null | null | null | null | |
On the Alignment between Fairness and Accuracy: from the Perspective of Adversarial Robustness | Accept (poster) | Summary: This work theoretically discusses adversarial attacks against fairness, include the connection between adversarial attacks on fairness and those on accuracy, the connection between accuracy adversarial robustness and fairness adversarial robustness.
Claims And Evidence: The work makes several claims, but the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments.
[Theoretical Contribution and Insights]
Our discussion is not focused on designing novel attack schemes, as this has been extensively covered in the existing literature. Instead, our goal is to identify efficient defense strategies against fairnes... | Summary: The paper "On the Alignment between Fairness and Accuracy: from the Perspective of Adversarial Robustness" explores the connections between adversarial training for fairness and accuracy objectives. The authors demonstrate the potential synergy between these two robustness goals. After a theoretical analysis o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments.
[W1: Extension to Non-Binary Classification]
Our method can be readily extended to multi-class scenarios by simply replacing $L_{\text{CE}}$ and the fairness constraint $L$ with their respective multi-class formulations. We show results in the fol... | Summary: The authors introduce a cohesive framework for adversarial training that can be adapted to multiple definitions of group fairness. The general idea is to formulate a certain objective function that captures the loss and then to perturb the input in a direction given by the gradient so as to increase the loss.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We will carefully refine the writing and include tables of notations in the revised paper to enhance readability. We include a short list of notations in the following table:
|Notation | Meaning|
| --------- | ------------|
|$x_{\text{sub},a}^{t,... | Summary: The paper analyzes adversarial attacks and robustness with respect to both fairness and accuracy. The authors prove theoretically the equivalence of adversarial attacks against different fairness notions, like DP and EOD. The theoretical analysis also shows the connections between attacks targeting accuracy an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments.
[W1: Clarification of Threat Model]
In our threat model, we assume that the adversarial has full access to the parameters of the target model. The adversarial manipulation is performed at the input level, subject to a maximum perturbation level $... | null | null | null | null | null | null |
NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders | Accept (poster) | Summary: The paper introduces Neurotree, a graph convolutional network that employs ordinary differential equations (ODEs) to model neural dynamics and learns a tree topology using contrastive loss to identify functional connectivity (FC) pathways. The model is evaluated on two datasets, achieving state-of-the-art perf... | Rebuttal 1:
Rebuttal: > **C1. Cannabis addicts have stronger FC compared to schizophrenia patients.**
We appreciate the reviewer's careful inquiry about this claim! Study [1] found cannabis users show higher baseline functional connectivity in reward circuits than schizophrenia patients. Our `Fig 4. (a-2) and (a-3)` r... | Summary: This paper proposes NeuroTree as a framework for feature learning from functional connectivity for brain disease characterization. NeuroTree integrates standard graph convolutional network with neural ordinary differential equations.
Claims And Evidence: I find various claims and evidence in this paper proble... | Rebuttal 1:
Rebuttal: **We thank the reviewer for valuable suggestions and insightful comments, and we have clarified a few things about accuray and brain age so that our contribution can be better understood.**
> **Q1. About the terminology of 'interpretability' and 'causal' modeling.**
`1.) The three meanings of in... | Summary: This paper introduces NEUROTREE, a novel framework for analyzing functional brain networks derived from fMRI data. The framework integrates k-hop Graph Convolutional Networks (GCNs) with neural Ordinary Differential Equations (ODEs) to enhance the learning of dynamic functional connectivity (FC) features and ... | Rebuttal 1:
Rebuttal: > **M1 \& W2:** **The study's evaluation is limited by using only two datasets, including disorder representation, dataset heterogeneity, and demographic diversity.**
We thank the reviewer concern for data diversity!
Due to data privacy concerns and the limited availability of public fMRI data f... | null | null | null | null | null | null | null | null |
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks | Accept (poster) | Summary: This research introduces an interpretable threat model for assessing the vulnerability of LLMs to jailbreaking attacks. The paper proposes using N-gram language model perplexity as a unified, LLM-agnostic metric to evaluate the fluency and likelihood of attacks. It demonstrates that many existing attacks rely ... | Rebuttal 1:
Rebuttal: Dear reviewer zGpd,
Thank you for your questions and reviewing our paper! We address all of them below.
------------
**Q: “Is it a defense or an attack paper?”**
**A:** Our paper is neither solely an attack nor a defense paper. We propose a principled framework for comparing attacks by introd... | Summary: This paper presents a fundamental formulation of jailbreaking attacks, i.e. a unified threat model for them. Leveraging the N-gram language model theory, the proposed technique successfully constructs a threat model and demonstrates a successful defense against multiple attacks.
## update after rebuttal
My c... | Rebuttal 1:
Rebuttal: Dear reviewer 6bX4,
------------
Thank you for your review and for the high assessment of our work. We would be happy to answer your question regarding the In-Context Attacks (ICA).
The best-performing attack, PRS [1a], builds upon and cites [1]. As [1], it relies on an in-context template that... | Summary: This paper proposes an interpretable threat model for evaluating jailbreak attacks on large language models by leveraging N-gram perplexity as a measure of text fluency. By constructing a lightweight N-gram language model on a trillion-token subset of the Dolma dataset, the approach enables LLM-agnostic and co... | Rebuttal 1:
Rebuttal: Dear reviewer FYyk,
Thank you for your review and the interest in our work. Below, we answer your questions.
-----------
**Q: “Supplementary Material”**
**A:** Our code is available here: https://anonymous.4open.science/r/llm-threat-model-57C3/README.md
Furthermore, we believe that there mig... | Summary: This paper introduces an interpretable threat model for evaluating jailbreak attacks on Large Language Models (LLMs), using N-gram language model perplexity as a unified fluency metric. Specificailly, a lightweight LLM-agnostic bigram model has been built for providing interpretability, computational efficienc... | Rebuttal 1:
Rebuttal: Dear Reviewer X1bx,
Thank you for your review and the positive assessment of our work. We would like to address the questions you raised.
-------
**Q: “Table 2 suggests that Gemma-7b is less robust than some Llama models…”**
**A:** Thank you for pointing this out, we are happy to provide clari... | null | null | null | null | null | null |
EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration | Accept (poster) | Summary: This paper studies the problem of in-context exploration, where an LLM interacts with a bandit environment and decides its next action based on the given context. The authors propose a framework called BanditBench, which includes both multi-armed bandit and contextual bandit instances, and suggest two methods ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review and thank you for highlighting our contributions.
> Some LLMs for exploration, although not under bandit tasks, should be discussed to explain the differences in exploration from a contextual perspective.
Here we provide some comments on these two recent work... | Summary: This study investigates how the LLMs explore in context. The paper used Gemma/Gemini family of models of varying sizes, and bandit tasks (i.e., multi-armed bandit task and contextual bandit task) to evaluate the model's exploration behavior by in-context learning. Results show that all LLMs deviate from the op... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful reviews.
> the comparison between metrics has not been tested by statistical approaches, which should be supplemented.
The win-rate we calculated is actually after the Student’s t-test. We report this in Section 6.1 Metrics (Page 6). For each model on a given task, s... | Summary: This paper examines the ability of large language models (LLMs) to perform decision-making tasks, focusing on Multi-Armed Bandit (MAB) and Contextual Bandit (CB) problems. The authors introduce BanditBench, a benchmark suite for evaluating LLM decision-making capabilities in bandit environments. Additionally, ... | Rebuttal 1:
Rebuttal: Thank you for the review and your willingness to support our work!
We appreciate the feedback and we are happy to answer questions if they come up! | Summary: The paper introduces BanditBench, a benchmark for in-context exploration using LLMs, and empirically investigates the ability of LLMs to explore using this benchmark. The paper also investigates ways to improve models ability to explore, using either in-context support from a bandit algorithm, or algorithm dis... | Rebuttal 1:
Rebuttal: Thank you for the review! We appreciate your effort!
> It might be interesting to see how far one can get by training a randomly initialized small model with algorithm distillation. Is pre-training doing a lot of work?
We agree with your intuition. Pre-training indeed provides the right amount o... | null | null | null | null | null | null |
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games | Accept (poster) | Summary: This paper develops a value-incentivized model-based method for computing epsilon-optimal NE and CCE in matrix games and Markov games. The key idea of value-incentivized model-based method is that, conditioned on any policy at certain iterate, it finds the most adversarial game model that both fits well with t... | Rebuttal 1:
Rebuttal: # Response to Reviewer n2KT
Thank you for your valuable feedback. If the clarifications below address your primary concerns, we'd appreciate your consideration of increasing your score. Certainly, please don't hesitate to request any further clarification.
> **relationship with reward-biased MLE... | Summary: The authors propose a novel algorithm for solving online general-sum $n$-player Markov games in finite and linear mixture MDPs. The proposed algorithm incentivizes exploration without introducing bonuses or constrained optimization to achieve optimism. Instead, it carefully applies regularization to the main o... | Rebuttal 1:
Rebuttal: # Response to Reviewer 9p5x
Thank you very much for your positive feedback and for carefully reviewing our proofs! Below we answer your questions.
> **Step 1 in the proof of Lemma B.9 is a well-known performance difference lemma (up to adaptation to a regularized and finite-horizon case), I thin... | Summary: This paper propose the strategy of value-incentivized exploration for online Markov games. Specifically, this method use a regularizer term to incentive the players to deviate from their current policy, resulting in exploration. Theoretical analysis shows that the proposed algorithms achieve a near-optimal ... | Rebuttal 1:
Rebuttal: # Response to Reviewer NUsj
Thank you for your insightful comments! Since the references in your review are not specified, we addressed some questions through best guesses -- we are happy to provide further answers if there are gaps in our interpretation. If these clarifications address your conc... | Summary: This paper introduces VMG, a model-based MARL algorithm that balances exploration and exploitation without requiring explicit uncertainty quantification. By biasing model estimation toward higher collective best-response values, VMG enables simultaneous and uncoupled policy updates while achieving near-optimal... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4hiw
Thank you for your feedback. If our responses resolve your questions, we'd appreciate your consideration in raising the score.
> **missing the reference [Xiong et al, 2024] and overclaim of contribution**
Thank you for bringing up this highly relevant work! We will a... | null | null | null | null | null | null |
Peri-LN: Revisiting Normalization Layer in the Transformer Architecture | Accept (poster) | Summary: Extending existing works on the placement of layer normalization in transformers, such as Pre-LN and Post-LN, this study proposes specific positions to apply layer normalizations, called Peri-LN configurations. The authors claim improved theoretical properties such as variance accumulation and gradient behavio... | Rebuttal 1:
Rebuttal: ### **1) “$o$ seems intermediate; softmax is applied in an unnatural way.”**
We believe there may be a misunderstanding regarding the reviewer’s concern that $o$ is an intermediate representation and that softmax is applied in an unnatural manner. As noted in Section 3.4 and Section D, our theoret... | Summary: This paper focuses on how different LN strategies influence on training dynamics
in transformer architectures training and present a LN strategy called Peri-LN ,
which applies LN around the sub-layer.
By theoretical analysis and experiments, the authors suggest that Peri-LN can
not only improves gradient sta... | Rebuttal 1:
Rebuttal: ### **1) Extending Analysis to Other Layers**
Thank you for highlighting this. As noted in Section 3.4 and Section D, our analysis focuses on the final layer. Following Theorem 1 in [1], we analyze the last layer because its gradients are often the largest in magnitude. We chose $W^{(2)}$ (the ... | Summary: This paper investigates the effectiveness of position where layer normalization (LN) (mainly its reduced version RMSNorm) is placed in the Transformer architecture. It also provides analyses from the perspective of activation/gradient propagation in the network to explain why a position LN placed usually w... | Rebuttal 1:
Rebuttal: ### **1) Considering $W^{(2)}$ in the MLP**
Following Theorem 1 in [1], we analyze the last layer, as the gradient norm at the final layer is empirically known to be the most unstable (see Figure 1 in the paper). We choose $W^{(2)}$ (the final linear projection in the MLP) as a representative exam... | Summary: This paper examines a layernorm "layout" in the transformer architecture called PeriLN. The PeriLN combines Prelayernorm with a module out layernorm (similar to post layernorm but before the residual stream). The authors provide intuition for how this addresses weaknesses in the Post layernorm and prelayernorm... | Rebuttal 1:
Rebuttal: ### **1) Confirming That Pre + Post LayerNorm is Worse Than Peri-LN**
>*“It seems that pre + post layernorm should be worse than periLN, perhaps that can be confirmed experimentally?”*
In response to the reviewer's comment, we additionally conduct further experiments on LN placements to compare... | null | null | null | null | null | null |
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